diff --git a/Eugene Berson Project 11.27.2016.pdf b/Eugene Berson Project 11.27.2016.pdf
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diff --git a/Eugene project ideas.md b/Eugene project ideas.md
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+++ b/Eugene project ideas.md	
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+Idea #1 Slack integrations
+Parameters:
+1) Uses a slash command?
+2) Is a bot?
+3) Is listed in App Directory?
+4) Featured in the app directory (at any point in time)?
+
+Trying to predict
+1) Is it used by paid team or a free team?
+
+
+Idea #2 Strava segment data
+Parameters:
+1) Number of times riding per week
+2) Average distance ridden per week
+3) Longest ride ever
+
+Trying to predict
+1) Rider's percentile on the leader board for most popular segment in their area
+
+
+Idea #3: Google Finance
+Parameters:
+1) P/E ratio average LTM
+2) P/E ratio % change LTM
+3) Industry sector
+
+Trying to predict
+1) LTM change in stock price
+
+
+Idea #4: Slack credit card payments data from Stripe
+Parameters:
+1) Country of credit card issue
+2) Type of credit card (Visa, MC, etc.)
+3) Issuing bank (Chase vs Wells etc)
+4) Transaction size (bucketed by size)
+
+Trying to predict:
+1) Will the transaction attempt be successful or will it fail?
\ No newline at end of file
diff --git a/Misc/.ipynb_checkpoints/Misc-checkpoint.ipynb b/Misc/.ipynb_checkpoints/Misc-checkpoint.ipynb
new file mode 100644
index 0000000..0428e8e
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@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Skipping line 104: expected 1 fields, saw 3\n",
+      "Skipping line 114: expected 1 fields, saw 3\n",
+      "Skipping line 116: expected 1 fields, saw 3\n",
+      "Skipping line 118: expected 1 fields, saw 3\n",
+      "Skipping line 120: expected 1 fields, saw 3\n",
+      "Skipping line 125: expected 1 fields, saw 3\n",
+      "Skipping line 126: expected 1 fields, saw 3\n",
+      "Skipping line 377: expected 1 fields, saw 2\n",
+      "Skipping line 839: expected 1 fields, saw 3\n",
+      "Skipping line 840: expected 1 fields, saw 3\n",
+      "Skipping line 841: expected 1 fields, saw 3\n",
+      "Skipping line 842: expected 1 fields, saw 3\n",
+      "Skipping line 843: expected 1 fields, saw 3\n",
+      "Skipping line 844: expected 1 fields, saw 3\n",
+      "Skipping line 852: expected 1 fields, saw 2\n",
+      "Skipping line 853: expected 1 fields, saw 3\n",
+      "Skipping line 854: expected 1 fields, saw 3\n",
+      "Skipping line 855: expected 1 fields, saw 3\n",
+      "Skipping line 856: expected 1 fields, saw 3\n",
+      "Skipping line 857: expected 1 fields, saw 3\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>&lt;!DOCTYPE html&gt;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;html lang=\"en\" class=\"\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;head prefix=\"og: http://ogp.me/ns# fb: http...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;meta charset='utf-8'&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>&lt;meta http-equiv=\"X-UA-Compatible\" content...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>&lt;meta http-equiv=\"Content-Language\" conten...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>&lt;meta name=\"viewport\" content=\"width=devic...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>&lt;title&gt;DataQuest/crime_rates.csv at master...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>&lt;link rel=\"search\" type=\"application/opens...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>&lt;link rel=\"fluid-icon\" href=\"https://githu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" href=\"/apple-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"57x57\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"60x60\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"72x72\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"76x76\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"114x11...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"120x12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"144x14...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"152x15...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"180x18...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>&lt;meta property=\"fb:app_id\" content=\"140148...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>&lt;meta content=\"https://avatars2.githubus...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>&lt;meta content=\"https://avatars2.githubus...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>&lt;meta name=\"browser-stats-url\" content=\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>&lt;meta name=\"browser-errors-url\" content=\"h...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>&lt;link rel=\"assets\" href=\"https://assets-cd...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>&lt;meta name=\"pjax-timeout\" content=\"1000\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>&lt;meta name=\"request-id\" content=\"C04DEFE9:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>&lt;ul class=\"site-footer-links\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>&lt;/ul&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>&lt;div id=\"ajax-error-message\" class=\"ajax-e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>&lt;button type=\"button\" class=\"flash-close...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>&lt;/button&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>You can't perform that action at this time.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>&lt;script crossorigin=\"anonymous\" src=\"htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>&lt;script crossorigin=\"anonymous\" src=\"htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>&lt;script async=\"async\" crossorigin=\"anony...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>&lt;div class=\"js-stale-session-flash stale-s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>&lt;span class=\"signed-in-tab-flash\"&gt;You si...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>&lt;span class=\"signed-out-tab-flash\"&gt;You s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>&lt;div class=\"facebox\" id=\"facebox\" style=\"d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>&lt;div class=\"facebox-popup\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>&lt;div class=\"facebox-content\" role=\"dialog\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>&lt;button type=\"button\" class=\"facebox-close...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>&lt;/button&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>&lt;/body&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>&lt;/html&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>742 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       <!DOCTYPE html>\n",
+       "0                            <html lang=\"en\" class=\"\">\n",
+       "1      <head prefix=\"og: http://ogp.me/ns# fb: http...\n",
+       "2                               <meta charset='utf-8'>\n",
+       "3        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "4        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "5        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "6        <meta http-equiv=\"X-UA-Compatible\" content...\n",
+       "7        <meta http-equiv=\"Content-Language\" conten...\n",
+       "8        <meta name=\"viewport\" content=\"width=devic...\n",
+       "9        <title>DataQuest/crime_rates.csv at master...\n",
+       "10       <link rel=\"search\" type=\"application/opens...\n",
+       "11       <link rel=\"fluid-icon\" href=\"https://githu...\n",
+       "12       <link rel=\"apple-touch-icon\" href=\"/apple-...\n",
+       "13       <link rel=\"apple-touch-icon\" sizes=\"57x57\"...\n",
+       "14       <link rel=\"apple-touch-icon\" sizes=\"60x60\"...\n",
+       "15       <link rel=\"apple-touch-icon\" sizes=\"72x72\"...\n",
+       "16       <link rel=\"apple-touch-icon\" sizes=\"76x76\"...\n",
+       "17       <link rel=\"apple-touch-icon\" sizes=\"114x11...\n",
+       "18       <link rel=\"apple-touch-icon\" sizes=\"120x12...\n",
+       "19       <link rel=\"apple-touch-icon\" sizes=\"144x14...\n",
+       "20       <link rel=\"apple-touch-icon\" sizes=\"152x15...\n",
+       "21       <link rel=\"apple-touch-icon\" sizes=\"180x18...\n",
+       "22       <meta property=\"fb:app_id\" content=\"140148...\n",
+       "23         <meta content=\"https://avatars2.githubus...\n",
+       "24         <meta content=\"https://avatars2.githubus...\n",
+       "25         <meta name=\"browser-stats-url\" content=\"...\n",
+       "26       <meta name=\"browser-errors-url\" content=\"h...\n",
+       "27       <link rel=\"assets\" href=\"https://assets-cd...\n",
+       "28           <meta name=\"pjax-timeout\" content=\"1000\">\n",
+       "29       <meta name=\"request-id\" content=\"C04DEFE9:...\n",
+       "..                                                 ...\n",
+       "712                     <ul class=\"site-footer-links\">\n",
+       "713                                              </ul>\n",
+       "714                                             </div>\n",
+       "715                                             </div>\n",
+       "716      <div id=\"ajax-error-message\" class=\"ajax-e...\n",
+       "717        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "718        <button type=\"button\" class=\"flash-close...\n",
+       "719          <svg aria-hidden=\"true\" class=\"octicon...\n",
+       "720                                          </button>\n",
+       "721        You can't perform that action at this time.\n",
+       "722                                             </div>\n",
+       "723        <script crossorigin=\"anonymous\" src=\"htt...\n",
+       "724        <script crossorigin=\"anonymous\" src=\"htt...\n",
+       "725        <script async=\"async\" crossorigin=\"anony...\n",
+       "726      <div class=\"js-stale-session-flash stale-s...\n",
+       "727        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "728        <span class=\"signed-in-tab-flash\">You si...\n",
+       "729        <span class=\"signed-out-tab-flash\">You s...\n",
+       "730                                             </div>\n",
+       "731      <div class=\"facebox\" id=\"facebox\" style=\"d...\n",
+       "732                        <div class=\"facebox-popup\">\n",
+       "733      <div class=\"facebox-content\" role=\"dialog\"...\n",
+       "734                                             </div>\n",
+       "735      <button type=\"button\" class=\"facebox-close...\n",
+       "736        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "737                                          </button>\n",
+       "738                                             </div>\n",
+       "739                                             </div>\n",
+       "740                                            </body>\n",
+       "741                                            </html>\n",
+       "\n",
+       "[742 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = 'https://github.com/fatmazaman/DataQuest/blob/master/crime_rates.csv'\n",
+    "df = pd.read_csv(url,error_bad_lines=False)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Misc/DSOnboardingTask 4.26.16.pdf b/Misc/DSOnboardingTask 4.26.16.pdf
new file mode 100644
index 0000000..0d8042f
Binary files /dev/null and b/Misc/DSOnboardingTask 4.26.16.pdf differ
diff --git a/Misc/Misc.ipynb b/Misc/Misc.ipynb
new file mode 100644
index 0000000..0428e8e
--- /dev/null
+++ b/Misc/Misc.ipynb
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Skipping line 104: expected 1 fields, saw 3\n",
+      "Skipping line 114: expected 1 fields, saw 3\n",
+      "Skipping line 116: expected 1 fields, saw 3\n",
+      "Skipping line 118: expected 1 fields, saw 3\n",
+      "Skipping line 120: expected 1 fields, saw 3\n",
+      "Skipping line 125: expected 1 fields, saw 3\n",
+      "Skipping line 126: expected 1 fields, saw 3\n",
+      "Skipping line 377: expected 1 fields, saw 2\n",
+      "Skipping line 839: expected 1 fields, saw 3\n",
+      "Skipping line 840: expected 1 fields, saw 3\n",
+      "Skipping line 841: expected 1 fields, saw 3\n",
+      "Skipping line 842: expected 1 fields, saw 3\n",
+      "Skipping line 843: expected 1 fields, saw 3\n",
+      "Skipping line 844: expected 1 fields, saw 3\n",
+      "Skipping line 852: expected 1 fields, saw 2\n",
+      "Skipping line 853: expected 1 fields, saw 3\n",
+      "Skipping line 854: expected 1 fields, saw 3\n",
+      "Skipping line 855: expected 1 fields, saw 3\n",
+      "Skipping line 856: expected 1 fields, saw 3\n",
+      "Skipping line 857: expected 1 fields, saw 3\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>&lt;!DOCTYPE html&gt;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;html lang=\"en\" class=\"\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;head prefix=\"og: http://ogp.me/ns# fb: http...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;meta charset='utf-8'&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>&lt;link crossorigin=\"anonymous\" href=\"https:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>&lt;meta http-equiv=\"X-UA-Compatible\" content...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>&lt;meta http-equiv=\"Content-Language\" conten...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>&lt;meta name=\"viewport\" content=\"width=devic...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>&lt;title&gt;DataQuest/crime_rates.csv at master...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>&lt;link rel=\"search\" type=\"application/opens...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>&lt;link rel=\"fluid-icon\" href=\"https://githu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" href=\"/apple-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"57x57\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"60x60\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"72x72\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"76x76\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"114x11...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"120x12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"144x14...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"152x15...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>&lt;link rel=\"apple-touch-icon\" sizes=\"180x18...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>&lt;meta property=\"fb:app_id\" content=\"140148...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>&lt;meta content=\"https://avatars2.githubus...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>&lt;meta content=\"https://avatars2.githubus...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>&lt;meta name=\"browser-stats-url\" content=\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>&lt;meta name=\"browser-errors-url\" content=\"h...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>&lt;link rel=\"assets\" href=\"https://assets-cd...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>&lt;meta name=\"pjax-timeout\" content=\"1000\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>&lt;meta name=\"request-id\" content=\"C04DEFE9:...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>&lt;ul class=\"site-footer-links\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>&lt;/ul&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>&lt;div id=\"ajax-error-message\" class=\"ajax-e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>&lt;button type=\"button\" class=\"flash-close...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>&lt;/button&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>You can't perform that action at this time.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>&lt;script crossorigin=\"anonymous\" src=\"htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>&lt;script crossorigin=\"anonymous\" src=\"htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>&lt;script async=\"async\" crossorigin=\"anony...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>&lt;div class=\"js-stale-session-flash stale-s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>&lt;span class=\"signed-in-tab-flash\"&gt;You si...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>&lt;span class=\"signed-out-tab-flash\"&gt;You s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>&lt;div class=\"facebox\" id=\"facebox\" style=\"d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>&lt;div class=\"facebox-popup\"&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>&lt;div class=\"facebox-content\" role=\"dialog\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>&lt;button type=\"button\" class=\"facebox-close...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>&lt;svg aria-hidden=\"true\" class=\"octicon o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>&lt;/button&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>&lt;/div&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>&lt;/body&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>&lt;/html&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>742 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       <!DOCTYPE html>\n",
+       "0                            <html lang=\"en\" class=\"\">\n",
+       "1      <head prefix=\"og: http://ogp.me/ns# fb: http...\n",
+       "2                               <meta charset='utf-8'>\n",
+       "3        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "4        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "5        <link crossorigin=\"anonymous\" href=\"https:...\n",
+       "6        <meta http-equiv=\"X-UA-Compatible\" content...\n",
+       "7        <meta http-equiv=\"Content-Language\" conten...\n",
+       "8        <meta name=\"viewport\" content=\"width=devic...\n",
+       "9        <title>DataQuest/crime_rates.csv at master...\n",
+       "10       <link rel=\"search\" type=\"application/opens...\n",
+       "11       <link rel=\"fluid-icon\" href=\"https://githu...\n",
+       "12       <link rel=\"apple-touch-icon\" href=\"/apple-...\n",
+       "13       <link rel=\"apple-touch-icon\" sizes=\"57x57\"...\n",
+       "14       <link rel=\"apple-touch-icon\" sizes=\"60x60\"...\n",
+       "15       <link rel=\"apple-touch-icon\" sizes=\"72x72\"...\n",
+       "16       <link rel=\"apple-touch-icon\" sizes=\"76x76\"...\n",
+       "17       <link rel=\"apple-touch-icon\" sizes=\"114x11...\n",
+       "18       <link rel=\"apple-touch-icon\" sizes=\"120x12...\n",
+       "19       <link rel=\"apple-touch-icon\" sizes=\"144x14...\n",
+       "20       <link rel=\"apple-touch-icon\" sizes=\"152x15...\n",
+       "21       <link rel=\"apple-touch-icon\" sizes=\"180x18...\n",
+       "22       <meta property=\"fb:app_id\" content=\"140148...\n",
+       "23         <meta content=\"https://avatars2.githubus...\n",
+       "24         <meta content=\"https://avatars2.githubus...\n",
+       "25         <meta name=\"browser-stats-url\" content=\"...\n",
+       "26       <meta name=\"browser-errors-url\" content=\"h...\n",
+       "27       <link rel=\"assets\" href=\"https://assets-cd...\n",
+       "28           <meta name=\"pjax-timeout\" content=\"1000\">\n",
+       "29       <meta name=\"request-id\" content=\"C04DEFE9:...\n",
+       "..                                                 ...\n",
+       "712                     <ul class=\"site-footer-links\">\n",
+       "713                                              </ul>\n",
+       "714                                             </div>\n",
+       "715                                             </div>\n",
+       "716      <div id=\"ajax-error-message\" class=\"ajax-e...\n",
+       "717        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "718        <button type=\"button\" class=\"flash-close...\n",
+       "719          <svg aria-hidden=\"true\" class=\"octicon...\n",
+       "720                                          </button>\n",
+       "721        You can't perform that action at this time.\n",
+       "722                                             </div>\n",
+       "723        <script crossorigin=\"anonymous\" src=\"htt...\n",
+       "724        <script crossorigin=\"anonymous\" src=\"htt...\n",
+       "725        <script async=\"async\" crossorigin=\"anony...\n",
+       "726      <div class=\"js-stale-session-flash stale-s...\n",
+       "727        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "728        <span class=\"signed-in-tab-flash\">You si...\n",
+       "729        <span class=\"signed-out-tab-flash\">You s...\n",
+       "730                                             </div>\n",
+       "731      <div class=\"facebox\" id=\"facebox\" style=\"d...\n",
+       "732                        <div class=\"facebox-popup\">\n",
+       "733      <div class=\"facebox-content\" role=\"dialog\"...\n",
+       "734                                             </div>\n",
+       "735      <button type=\"button\" class=\"facebox-close...\n",
+       "736        <svg aria-hidden=\"true\" class=\"octicon o...\n",
+       "737                                          </button>\n",
+       "738                                             </div>\n",
+       "739                                             </div>\n",
+       "740                                            </body>\n",
+       "741                                            </html>\n",
+       "\n",
+       "[742 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = 'https://github.com/fatmazaman/DataQuest/blob/master/crime_rates.csv'\n",
+    "df = pd.read_csv(url,error_bad_lines=False)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Misc/crime_rates.csv b/Misc/crime_rates.csv
new file mode 100644
index 0000000..fe7a731
--- /dev/null
+++ b/Misc/crime_rates.csv
@@ -0,0 +1,73 @@
+Albuquerque,749
+Anaheim,371
+Anchorage,828
+Arlington,503
+Atlanta,1379
+Aurora,425
+Austin,408
+Bakersfield,542
+Baltimore,1405
+Boston,835
+Buffalo,1288
+Charlotte-Mecklenburg,647
+Cincinnati,974
+Cleveland,1383
+Colorado Springs,455
+Corpus Christi,658
+Dallas,675
+Denver,615
+Detroit,2122
+El Paso,423
+Fort Wayne,362
+Fort Worth,587
+Fresno,543
+Greensboro,563
+Henderson,168
+Houston,992
+Indianapolis,1185
+Jacksonville,617
+Jersey City,734
+Kansas City,1263
+Las Vegas,784
+Lexington,352
+Lincoln,397
+Long Beach,575
+Los Angeles,481
+Louisville Metro,598
+Memphis,1750
+Mesa,399
+Miami,1172
+Milwaukee,1294
+Minneapolis,992
+Mobile,522
+Nashville,1216
+New Orleans,815
+New York,639
+Newark,1154
+Oakland,1993
+Oklahoma City,919
+Omaha,594
+Philadelphia,1160
+Phoenix,636
+Pittsburgh,752
+Plano,130
+Portland,517
+Raleigh,423
+Riverside,443
+Sacramento,738
+San Antonio,503
+San Diego,413
+San Francisco,704
+San Jose,363
+Santa Ana,401
+Seattle,597
+St. Louis,1776
+St. Paul,722
+Stockton,1548
+Tampa,616
+Toledo,1171
+Tucson,724
+Tulsa,990
+Virginia Beach,169
+Washington,1177
+Wichita,742
\ No newline at end of file
diff --git a/Yelp_Eugene.ipynb b/Yelp_Eugene.ipynb
new file mode 100644
index 0000000..7ab0ba3
--- /dev/null
+++ b/Yelp_Eugene.ipynb
@@ -0,0 +1,861 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>My wife took me here on my birthday for breakf...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>I have no idea why some people give bad review...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
+       "      <td>2012-06-14</td>\n",
+       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
+       "      <td>4</td>\n",
+       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
+       "      <td>5</td>\n",
+       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              business_id        date               review_id  stars  \\\n",
+       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
+       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
+       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
+       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
+       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
+       "\n",
+       "                                                text    type  \\\n",
+       "0  My wife took me here on my birthday for breakf...  review   \n",
+       "1  I have no idea why some people give bad review...  review   \n",
+       "2  love the gyro plate. Rice is so good and I als...  review   \n",
+       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
+       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
+       "\n",
+       "                  user_id  cool  useful  funny  \n",
+       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
+       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
+       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
+       "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
+       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn import metrics\n",
+    "import statsmodels.formula.api as smf\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "# visualization\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data = pd.read_csv('http://localhost:8888/files/data/yelp.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x12e573c8>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DOoxpmppPZ+UPRmUYnbUL1+qWfvjWnH789rwalt00Pj4U09kzk7p9grLDdnEc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5+f5u1TE6Rzjcntz2+k+PxGip39QY4Nb2NXPLTlv0xxulw4l0wNTU3M4XYalCpB7JqBw9H+lVO3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\np7P8wfdG6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Zlkgf5O36ap8tnqC/K2Ap2jlekcrU6rnicVJVhZq1T5nE0V6QrfrvKZypYJ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12e57048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data, x_vars=['cool','useful','funny'], y_vars='stars', size=6, aspect=0.7, kind='reg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x125f4748>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7vKR0MqrwpXmFTFOGEVAmW1Sx7GhqLquhZEwHh2LqMa4cmxsz7WZ9s5qtR1Jb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\niagiYVO5fElL2WX1RntkGAGVy44uz+W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yf4x2j2+pCl1nJfufK7vHJ9k/RuIrHSv+Nsqw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S7g103TfCab2xQRERERERHJhmwm4EPA7xmG8TzQBBwwDGPY6o2maS54HnDDMD4K3El68/YngB8D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\nbJx7vOHSyOZ7drZw/PzQnGtkz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C4NTaZciAF8/SmTbZv8rK92Rkc4VyKRKJFI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6dGCncrhZHbqo/TxhrjetA5vZ4qLKSXRMaFh/zcppeztSHDvcfSXwTK7MU7NfTy1qOwZqD/i2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\niIhIAv8PHDnCfQbnykwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125f4550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stars</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>stars</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.052555</td>\n",
+       "      <td>-0.023479</td>\n",
+       "      <td>-0.061306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cool</th>\n",
+       "      <td>0.052555</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.887102</td>\n",
+       "      <td>0.764342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>useful</th>\n",
+       "      <td>-0.023479</td>\n",
+       "      <td>0.887102</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.723406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>funny</th>\n",
+       "      <td>-0.061306</td>\n",
+       "      <td>0.764342</td>\n",
+       "      <td>0.723406</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           stars      cool    useful     funny\n",
+       "stars   1.000000  0.052555 -0.023479 -0.061306\n",
+       "cool    0.052555  1.000000  0.887102  0.764342\n",
+       "useful -0.023479  0.887102  1.000000  0.723406\n",
+       "funny  -0.061306  0.764342  0.723406  1.000000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x148448d0>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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urfa2K7OyliSpj06y3b+z9K4b7Xl9PbD9dMVispYktcbYzPSCN47JWpLUGuMzdM26aUzW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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1629dbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(data.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.750433\n",
+       "cool         0.030870\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ cool', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stars</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.777500</td>\n",
+       "      <td>0.876800</td>\n",
+       "      <td>1.409300</td>\n",
+       "      <td>0.701300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.214636</td>\n",
+       "      <td>2.067861</td>\n",
+       "      <td>2.336647</td>\n",
+       "      <td>1.907942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              stars          cool        useful         funny\n",
+       "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
+       "mean       3.777500      0.876800      1.409300      0.701300\n",
+       "std        1.214636      2.067861      2.336647      1.907942\n",
+       "min        1.000000      0.000000      0.000000      0.000000\n",
+       "25%        3.000000      0.000000      0.000000      0.000000\n",
+       "50%        4.000000      0.000000      1.000000      0.000000\n",
+       "75%        5.000000      1.000000      2.000000      1.000000\n",
+       "max        5.000000     77.000000     76.000000     57.000000"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 10)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.794700\n",
+       "useful      -0.012205\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ useful', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.804871\n",
+       "funny       -0.039029\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ funny', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 5, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 1, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "features = np.array([data.cool, data.useful, data.funny]).T\n",
+    "features  # 2D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5],\n",
+       "       [5],\n",
+       "       [4],\n",
+       "       ..., \n",
+       "       [4],\n",
+       "       [2],\n",
+       "       [5]], dtype=int64)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = np.array([data.stars]).T \n",
+    "response  # 1D array\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# step 1: split data into training set and test set\n",
+    "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n",
+    "# the random_state allows us all to get the same random numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 6, 4],\n",
+       "       [1, 2, 1],\n",
+       "       [0, 1, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [2, 3, 2],\n",
+       "       [0, 2, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 3, 1],\n",
+       "       [0, 1, 0],\n",
+       "       [0, 0, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3],\n",
+       "       [5],\n",
+       "       [5],\n",
+       "       ..., \n",
+       "       [4],\n",
+       "       [2],\n",
+       "       [1]], dtype=int64)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5],\n",
+       "       [5],\n",
+       "       [5],\n",
+       "       ..., \n",
+       "       [3],\n",
+       "       [4],\n",
+       "       [3]], dtype=int64)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.35320000000000001"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(features_train, response_train)      # Note that I fit to the training\n",
+    "knn.score(features_test, response_test)      # and scored on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = data[['cool', 'useful', 'funny']]\n",
+    "y = data['stars']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import cross_val_score\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.28657014,  0.24787606,  0.22011006,  0.17558779,  0.24274274])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/data/App Weekly Review.csv b/data/App Weekly Review.csv
new file mode 100644
index 0000000..f75d6c4
--- /dev/null
+++ b/data/App Weekly Review.csv	
@@ -0,0 +1,212 @@
+ID,Featured,Pct_of_Active_(7d)_Teams_with_App_Installed,Active_(7d)_Installed_Teams,Pct_of_Installed_Teams_Using_App,App_Active_(7d)_Teams,App_Active_(7d)_Teams_Last_Week,Net_Growth,Pct_Growth,New,Pct_New,Resurrected,Retained,Pct_Retention,Churned,Pct_Churn,Stale
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+9,yes,0.08,49277,0.41,20335,20118,217,0.01,3217,0.16,2332,14786,0.74,5332,0.27,34376
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+26,yes,0.03,18095,0.32,5862,5796,66,0.01,183,0.03,277,5402,0.93,394,0.07,5010
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+33,yes,0.02,10851,0.10,1122,1027,95,0.09,188,0.18,318,616,0.60,411,0.40,5758
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+39,yes,0.01,8359,1.02,8523,8511,12,0.00,251,0.03,360,7912,0.93,599,0.07,4320
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diff --git a/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb b/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb
index 7d018b1..8f26202 100644
--- a/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb
+++ b/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 121,
    "metadata": {
     "collapsed": true
    },
@@ -27,36 +27,262 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>city</th>\n",
+       "      <th>postal_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>phone_number</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>application_date</th>\n",
+       "      <th>owner_name</th>\n",
+       "      <th>owner_address</th>\n",
+       "      <th>owner_city</th>\n",
+       "      <th>owner_state</th>\n",
+       "      <th>owner_zip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id              name        address           city postal_code  \\\n",
+       "0           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "\n",
+       "    latitude   longitude phone_number TaxCode  business_certificate  \\\n",
+       "0  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "\n",
+       "  application_date    owner_name owner_address     owner_city owner_state  \\\n",
+       "0              NaN  Tiramisu LLC  33 Belden St  San Francisco          CA   \n",
+       "\n",
+       "  owner_zip  \n",
+       "0     94104  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "businesses = pd.read_csv('./data/businesses_plus.csv', parse_dates=True, dtype={'phone_number': str})\n",
-    "businesses.head()\n",
+    "businesses.head(1)\n",
     "# dtype casts the column as a specific data type"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140124</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20141110</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id  Score      date                   type  inspections_id\n",
+       "0           10    NaN  20140807  Reinspection/Followup               0\n",
+       "1           10   94.0  20140729  Routine - Unscheduled               1\n",
+       "2           10    NaN  20140124  Reinspection/Followup               2\n",
+       "3           10   92.0  20140114  Routine - Unscheduled               3\n",
+       "4           19   94.0  20141110  Routine - Unscheduled               4"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "inspections = pd.read_csv('./data/inspections_plus.csv', parse_dates=True)\n",
+    "inspections.head()\n",
+    "inspections['inspections_id']=inspections.index\n",
     "inspections.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103154</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103119</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103145</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103129</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103144</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id      date  ViolationTypeID  risk_category  \\\n",
+       "0           10  20140114           103154       Low Risk   \n",
+       "1           10  20140114           103119  Moderate Risk   \n",
+       "2           10  20140114           103145       Low Risk   \n",
+       "3           10  20140729           103129  Moderate Risk   \n",
+       "4           10  20140729           103144       Low Risk   \n",
+       "\n",
+       "                                         description  \n",
+       "0       Unclean or degraded floors walls or ceilings  \n",
+       "1  Inadequate and inaccessible handwashing facili...  \n",
+       "2   Improper storage of equipment utensils or linens  \n",
+       "3            Insufficient hot water or running water  \n",
+       "4   Unapproved or unmaintained equipment or utensils  "
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "violations = pd.read_csv('./data/violations_plus.csv', parse_dates=True)\n",
     "violations.head()"
@@ -64,25 +290,508 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>...</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>phone_number</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>application_date</th>\n",
+       "      <th>owner_name</th>\n",
+       "      <th>owner_address</th>\n",
+       "      <th>owner_city</th>\n",
+       "      <th>owner_state</th>\n",
+       "      <th>owner_zip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103154.0</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103119.0</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103145.0</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140124</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>1</td>\n",
+       "      <td>103129.0</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id  Score      date                   type  inspections_id  \\\n",
+       "0           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "1           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "2           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "3           10    NaN  20140124  Reinspection/Followup               2   \n",
+       "4           10   94.0  20140729  Routine - Unscheduled               1   \n",
+       "\n",
+       "   ViolationTypeID  risk_category  \\\n",
+       "0         103154.0       Low Risk   \n",
+       "1         103119.0  Moderate Risk   \n",
+       "2         103145.0       Low Risk   \n",
+       "3              NaN            NaN   \n",
+       "4         103129.0  Moderate Risk   \n",
+       "\n",
+       "                                         description              name  \\\n",
+       "0       Unclean or degraded floors walls or ceilings  Tiramisu Kitchen   \n",
+       "1  Inadequate and inaccessible handwashing facili...  Tiramisu Kitchen   \n",
+       "2   Improper storage of equipment utensils or linens  Tiramisu Kitchen   \n",
+       "3                                                NaN  Tiramisu Kitchen   \n",
+       "4            Insufficient hot water or running water  Tiramisu Kitchen   \n",
+       "\n",
+       "         address    ...      longitude phone_number  TaxCode  \\\n",
+       "0  033 Belden Pl    ...    -122.403816          NaN      H24   \n",
+       "1  033 Belden Pl    ...    -122.403816          NaN      H24   \n",
+       "2  033 Belden Pl    ...    -122.403816          NaN      H24   \n",
+       "3  033 Belden Pl    ...    -122.403816          NaN      H24   \n",
+       "4  033 Belden Pl    ...    -122.403816          NaN      H24   \n",
+       "\n",
+       "   business_certificate application_date    owner_name  owner_address  \\\n",
+       "0              779059.0              NaN  Tiramisu LLC   33 Belden St   \n",
+       "1              779059.0              NaN  Tiramisu LLC   33 Belden St   \n",
+       "2              779059.0              NaN  Tiramisu LLC   33 Belden St   \n",
+       "3              779059.0              NaN  Tiramisu LLC   33 Belden St   \n",
+       "4              779059.0              NaN  Tiramisu LLC   33 Belden St   \n",
+       "\n",
+       "      owner_city owner_state owner_zip  \n",
+       "0  San Francisco          CA     94104  \n",
+       "1  San Francisco          CA     94104  \n",
+       "2  San Francisco          CA     94104  \n",
+       "3  San Francisco          CA     94104  \n",
+       "4  San Francisco          CA     94104  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# 1 Combine the three dataframes into one data frame called restaurant_scores\n",
-    "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html"
+    "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html\n",
+    "\n",
+    "restaurant_scores = pd.merge(businesses, inspections, on='business_id', how='inner')\n",
+    "restaurant_scores = pd.merge(restaurant_scores, violations, on='business_id', how='inner')\n",
+    "restaurant_scores.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(restaurant_scores.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>application_date</th>\n",
+       "      <th>owner_name</th>\n",
+       "      <th>owner_address</th>\n",
+       "      <th>owner_city</th>\n",
+       "      <th>owner_state</th>\n",
+       "      <th>owner_zip</th>\n",
+       "      <th>new_date</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103154.0</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>2014-01-14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103119.0</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>2014-01-14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>103145.0</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>2014-01-14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140124</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>2014-01-24</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>1</td>\n",
+       "      <td>103129.0</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>...</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>2014-07-29</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id  Score      date                   type  inspections_id  \\\n",
+       "0           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "1           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "2           10   92.0  20140114  Routine - Unscheduled               3   \n",
+       "3           10    NaN  20140124  Reinspection/Followup               2   \n",
+       "4           10   94.0  20140729  Routine - Unscheduled               1   \n",
+       "\n",
+       "   ViolationTypeID  risk_category  \\\n",
+       "0         103154.0       Low Risk   \n",
+       "1         103119.0  Moderate Risk   \n",
+       "2         103145.0       Low Risk   \n",
+       "3              NaN            NaN   \n",
+       "4         103129.0  Moderate Risk   \n",
+       "\n",
+       "                                         description              name  \\\n",
+       "0       Unclean or degraded floors walls or ceilings  Tiramisu Kitchen   \n",
+       "1  Inadequate and inaccessible handwashing facili...  Tiramisu Kitchen   \n",
+       "2   Improper storage of equipment utensils or linens  Tiramisu Kitchen   \n",
+       "3                                                NaN  Tiramisu Kitchen   \n",
+       "4            Insufficient hot water or running water  Tiramisu Kitchen   \n",
+       "\n",
+       "         address  ...  TaxCode business_certificate  application_date  \\\n",
+       "0  033 Belden Pl  ...      H24             779059.0               NaN   \n",
+       "1  033 Belden Pl  ...      H24             779059.0               NaN   \n",
+       "2  033 Belden Pl  ...      H24             779059.0               NaN   \n",
+       "3  033 Belden Pl  ...      H24             779059.0               NaN   \n",
+       "4  033 Belden Pl  ...      H24             779059.0               NaN   \n",
+       "\n",
+       "     owner_name owner_address     owner_city  owner_state owner_zip  \\\n",
+       "0  Tiramisu LLC  33 Belden St  San Francisco           CA     94104   \n",
+       "1  Tiramisu LLC  33 Belden St  San Francisco           CA     94104   \n",
+       "2  Tiramisu LLC  33 Belden St  San Francisco           CA     94104   \n",
+       "3  Tiramisu LLC  33 Belden St  San Francisco           CA     94104   \n",
+       "4  Tiramisu LLC  33 Belden St  San Francisco           CA     94104   \n",
+       "\n",
+       "    new_date month  \n",
+       "0 2014-01-14     1  \n",
+       "1 2014-01-14     1  \n",
+       "2 2014-01-14     1  \n",
+       "3 2014-01-24     1  \n",
+       "4 2014-07-29     7  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restaurant_scores['new_date']=pd.to_datetime(restaurant_scores.date,format='%Y%m%d')\n",
+    "restaurant_scores['month'] = restaurant_scores.new_date.apply(lambda x:x.month)\n",
+    "restaurant_scores.head()\n",
+    "# restaurant_scores.groupby(['date'])\n",
+    "# freq = restaurant_scores.date_y.apply(lambda x:str(x)[:6])\n",
+    "# freq.value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xa7f1b00>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x96305f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 2 Which ten business have had the most inspections?"
+    "restaurant_scores.groupby('month').inspections_id.nunique().plot(kind='bar')\n",
+    "\n",
+    "# df = pd.DataFrame(freq)\n",
+    "# # df.reset_index(inplace=True)\n",
+    "# df.head()"
    ]
   },
   {
@@ -93,7 +802,107 @@
    },
    "outputs": [],
    "source": [
-    "# 3 Group and count the inspections by type"
+    "# which 10 businesses have the most inspections\n",
+    "\n",
+    "newframe=pd.DataFrame(restaurant_scores.business_id.value_counts(ascending=False)).reset_index()\n",
+    "newframe.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Length of values does not match length of index",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-90-22eabc44a323>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2355\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2356\u001b[0m             \u001b[1;31m# set column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2357\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2359\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2423\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2424\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2577\u001b[0m             \u001b[1;31m# turn me into an ndarray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2578\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2579\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2580\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m_sanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m   2768\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2769\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2770\u001b[0;31m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Length of values does not match length of '\u001b[0m \u001b[1;34m'index'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2772\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPeriodIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
+     ]
+    }
+   ],
+   "source": [
+    "df['date']=freq.value_counts().index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>insp_type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>128015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>67793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Complaint</td>\n",
+       "      <td>18157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>New Ownership</td>\n",
+       "      <td>8646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Non-inspection site visit</td>\n",
+       "      <td>7683</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   insp_type   count\n",
+       "0      Routine - Unscheduled  128015\n",
+       "1      Reinspection/Followup   67793\n",
+       "2                  Complaint   18157\n",
+       "3              New Ownership    8646\n",
+       "4  Non-inspection site visit    7683"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 3 Group and count the inspections by type\n",
+    "newframe1 = pd.DataFrame(restaurant_scores.type.value_counts(ascending=False)).reset_index()\n",
+    "newframe1.rename(columns={'index': 'insp_type','type':'count'}, inplace=True)\n",
+    "newframe1.head()"
    ]
   },
   {
@@ -103,10 +912,37 @@
     "collapsed": false
    },
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'value_counts'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-96-99a710b881a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'scatter'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2670\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2671\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2672\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2673\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'value_counts'"
+     ]
+    }
+   ],
    "source": [
     "# 4 Create a plot that shows number of inspections per month\n",
     "# Bonus for creating a heatmap\n",
-    "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap"
+    "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "df.value_counts().plot(kind='scatter')"
    ]
   },
   {
@@ -535,9 +1371,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/hw/hw1.ipynb b/hw/hw1.ipynb
index 7d018b1..cc26a4c 100644
--- a/hw/hw1.ipynb
+++ b/hw/hw1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -27,36 +27,262 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>city</th>\n",
+       "      <th>postal_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>phone_number</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>application_date</th>\n",
+       "      <th>owner_name</th>\n",
+       "      <th>owner_address</th>\n",
+       "      <th>owner_city</th>\n",
+       "      <th>owner_state</th>\n",
+       "      <th>owner_zip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tiramisu LLC</td>\n",
+       "      <td>33 Belden St</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id              name        address           city postal_code  \\\n",
+       "0           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "\n",
+       "    latitude   longitude phone_number TaxCode  business_certificate  \\\n",
+       "0  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "\n",
+       "  application_date    owner_name owner_address     owner_city owner_state  \\\n",
+       "0              NaN  Tiramisu LLC  33 Belden St  San Francisco          CA   \n",
+       "\n",
+       "  owner_zip  \n",
+       "0     94104  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "businesses = pd.read_csv('./data/businesses_plus.csv', parse_dates=True, dtype={'phone_number': str})\n",
-    "businesses.head()\n",
+    "businesses.head(1)\n",
     "# dtype casts the column as a specific data type"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140124</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>20141110</td>\n",
+       "      <td>Routine - Unscheduled</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id  Score      date                   type  inspections_id\n",
+       "0           10    NaN  20140807  Reinspection/Followup               0\n",
+       "1           10   94.0  20140729  Routine - Unscheduled               1\n",
+       "2           10    NaN  20140124  Reinspection/Followup               2\n",
+       "3           10   92.0  20140114  Routine - Unscheduled               3\n",
+       "4           19   94.0  20141110  Routine - Unscheduled               4"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "inspections = pd.read_csv('./data/inspections_plus.csv', parse_dates=True)\n",
+    "inspections.head()\n",
+    "inspections['inspections_id']=inspections.index\n",
     "inspections.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103154</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103119</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103145</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103129</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103144</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id      date  ViolationTypeID  risk_category  \\\n",
+       "0           10  20140114           103154       Low Risk   \n",
+       "1           10  20140114           103119  Moderate Risk   \n",
+       "2           10  20140114           103145       Low Risk   \n",
+       "3           10  20140729           103129  Moderate Risk   \n",
+       "4           10  20140729           103144       Low Risk   \n",
+       "\n",
+       "                                         description  \n",
+       "0       Unclean or degraded floors walls or ceilings  \n",
+       "1  Inadequate and inaccessible handwashing facili...  \n",
+       "2   Improper storage of equipment utensils or linens  \n",
+       "3            Insufficient hot water or running water  \n",
+       "4   Unapproved or unmaintained equipment or utensils  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "violations = pd.read_csv('./data/violations_plus.csv', parse_dates=True)\n",
     "violations.head()"
@@ -64,14 +290,533 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>city</th>\n",
+       "      <th>postal_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>phone_number</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>owner_state</th>\n",
+       "      <th>owner_zip</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date_x</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "      <th>date_y</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103154</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103119</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103145</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103129</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103144</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id              name        address           city postal_code  \\\n",
+       "0           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "1           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "2           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "3           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "4           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "\n",
+       "    latitude   longitude phone_number TaxCode  business_certificate  \\\n",
+       "0  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "1  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "2  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "3  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "4  37.791116 -122.403816          NaN     H24              779059.0   \n",
+       "\n",
+       "                         ...                         owner_state owner_zip  \\\n",
+       "0                        ...                                  CA     94104   \n",
+       "1                        ...                                  CA     94104   \n",
+       "2                        ...                                  CA     94104   \n",
+       "3                        ...                                  CA     94104   \n",
+       "4                        ...                                  CA     94104   \n",
+       "\n",
+       "  Score    date_x                   type inspections_id    date_y  \\\n",
+       "0   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "1   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "2   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "3   NaN  20140807  Reinspection/Followup              0  20140729   \n",
+       "4   NaN  20140807  Reinspection/Followup              0  20140729   \n",
+       "\n",
+       "   ViolationTypeID  risk_category  \\\n",
+       "0           103154       Low Risk   \n",
+       "1           103119  Moderate Risk   \n",
+       "2           103145       Low Risk   \n",
+       "3           103129  Moderate Risk   \n",
+       "4           103144       Low Risk   \n",
+       "\n",
+       "                                         description  \n",
+       "0       Unclean or degraded floors walls or ceilings  \n",
+       "1  Inadequate and inaccessible handwashing facili...  \n",
+       "2   Improper storage of equipment utensils or linens  \n",
+       "3            Insufficient hot water or running water  \n",
+       "4   Unapproved or unmaintained equipment or utensils  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# 1 Combine the three dataframes into one data frame called restaurant_scores\n",
-    "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html"
+    "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html\n",
+    "\n",
+    "restaurant_scores = pd.merge(businesses, inspections, on='business_id', how='inner')\n",
+    "restaurant_scores = pd.merge(restaurant_scores, violations, on='business_id', how='inner')\n",
+    "restaurant_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(restaurant_scores.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>address</th>\n",
+       "      <th>city</th>\n",
+       "      <th>postal_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>phone_number</th>\n",
+       "      <th>TaxCode</th>\n",
+       "      <th>business_certificate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>date_x</th>\n",
+       "      <th>type</th>\n",
+       "      <th>inspections_id</th>\n",
+       "      <th>date_y</th>\n",
+       "      <th>ViolationTypeID</th>\n",
+       "      <th>risk_category</th>\n",
+       "      <th>description</th>\n",
+       "      <th>new_date</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103154</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103119</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140114</td>\n",
+       "      <td>103145</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Improper storage of equipment utensils or linens</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103129</td>\n",
+       "      <td>Moderate Risk</td>\n",
+       "      <td>Insufficient hot water or running water</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Tiramisu Kitchen</td>\n",
+       "      <td>033 Belden Pl</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>94104</td>\n",
+       "      <td>37.791116</td>\n",
+       "      <td>-122.403816</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>H24</td>\n",
+       "      <td>779059.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20140807</td>\n",
+       "      <td>Reinspection/Followup</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20140729</td>\n",
+       "      <td>103144</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   business_id              name        address           city postal_code  \\\n",
+       "0           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "1           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "2           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "3           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "4           10  Tiramisu Kitchen  033 Belden Pl  San Francisco       94104   \n",
+       "\n",
+       "    latitude   longitude phone_number TaxCode  business_certificate  ...    \\\n",
+       "0  37.791116 -122.403816          NaN     H24              779059.0  ...     \n",
+       "1  37.791116 -122.403816          NaN     H24              779059.0  ...     \n",
+       "2  37.791116 -122.403816          NaN     H24              779059.0  ...     \n",
+       "3  37.791116 -122.403816          NaN     H24              779059.0  ...     \n",
+       "4  37.791116 -122.403816          NaN     H24              779059.0  ...     \n",
+       "\n",
+       "  Score    date_x                   type inspections_id    date_y  \\\n",
+       "0   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "1   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "2   NaN  20140807  Reinspection/Followup              0  20140114   \n",
+       "3   NaN  20140807  Reinspection/Followup              0  20140729   \n",
+       "4   NaN  20140807  Reinspection/Followup              0  20140729   \n",
+       "\n",
+       "  ViolationTypeID  risk_category  \\\n",
+       "0          103154       Low Risk   \n",
+       "1          103119  Moderate Risk   \n",
+       "2          103145       Low Risk   \n",
+       "3          103129  Moderate Risk   \n",
+       "4          103144       Low Risk   \n",
+       "\n",
+       "                                         description   new_date  month  \n",
+       "0       Unclean or degraded floors walls or ceilings 2014-08-07      8  \n",
+       "1  Inadequate and inaccessible handwashing facili... 2014-08-07      8  \n",
+       "2   Improper storage of equipment utensils or linens 2014-08-07      8  \n",
+       "3            Insufficient hot water or running water 2014-08-07      8  \n",
+       "4   Unapproved or unmaintained equipment or utensils 2014-08-07      8  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restaurant_scores['new_date']=pd.to_datetime(restaurant_scores.date_x, format='%Y%m%d')\n",
+    "restaurant_scores['month'] = restaurant_scores.new_date.apply(lambda x:x.month)\n",
+    "restaurant_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'month'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-32-eababacfb3c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrestaurant_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'month'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minspections_id\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;31m# df = pd.DataFrame(freq)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;31m# # df.reset_index(inplace=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;31m# df.head()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[1;32m   3776\u001b[0m         return groupby(self, by=by, axis=axis, level=level, as_index=as_index,\n\u001b[1;32m   3777\u001b[0m                        \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3778\u001b[0;31m                        **kwargs)\n\u001b[0m\u001b[1;32m   3779\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3780\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0masfreq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(obj, by, **kwds)\u001b[0m\n\u001b[1;32m   1425\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'invalid type: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1427\u001b[0;31m     \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[1;32m    352\u001b[0m                                                     \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m                                                     \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m                                                     mutated=self.mutated)\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(obj, key, axis, level, sort, mutated)\u001b[0m\n\u001b[1;32m   2381\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2382\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_in_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# df.groupby('name')\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2383\u001b[0;31m             \u001b[0min_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2384\u001b[0m             \u001b[0mexclusions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1995\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1999\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2002\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2003\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3289\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3290\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3291\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3292\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1945\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1949\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4154)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4018)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12368)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12322)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'month'"
+     ]
+    }
+   ],
+   "source": [
+    "restaurant_scores.groupby('month').inspections_id.nunique().plot(kind='bar')\n",
+    "\n",
+    "# df = pd.DataFrame(freq)\n",
+    "# # df.reset_index(inplace=True)\n",
+    "# df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'count'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-26-23e1dbbc7b89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;31m# newframe.head()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrestaurant_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'business_id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;31m# newframe2 = pd.merge(newframe1, restaurant_scores, on='business_id', how='inner')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'count'"
+     ]
+    }
+   ],
+   "source": [
+    "# which 10 businesses have the most inspections\n",
+    "\n",
+    "# newframe=pd.DataFrame(restaurant_scores.business_id.value_counts(ascending=False)).reset_index()\n",
+    "# newframe.head()\n",
+    "\n",
+    "restaurant_scores.groupby(['business_id','name']).name.count()\n",
+    "\n",
+    "# newframe2 = pd.merge(newframe1, restaurant_scores, on='business_id', how='inner')\n",
+    "# newframe2"
    ]
   },
   {
@@ -82,18 +827,21 @@
    },
    "outputs": [],
    "source": [
-    "# 2 Which ten business have had the most inspections?"
+    "df['date']=freq.value_counts().index"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# 3 Group and count the inspections by type"
+    "# 3 Group and count the inspections by type\n",
+    "newframe1 = pd.DataFrame(restaurant_scores.type.value_counts(ascending=False)).reset_index()\n",
+    "newframe1.rename(columns={'index': 'insp_type','type':'count'}, inplace=True)\n",
+    "newframe1.head()"
    ]
   },
   {
@@ -103,10 +851,37 @@
     "collapsed": false
    },
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'value_counts'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-96-99a710b881a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'scatter'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32mC:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2670\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2671\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2672\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2673\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'value_counts'"
+     ]
+    }
+   ],
    "source": [
     "# 4 Create a plot that shows number of inspections per month\n",
     "# Bonus for creating a heatmap\n",
-    "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap"
+    "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "df.value_counts().plot(kind='scatter')"
    ]
   },
   {
@@ -535,9 +1310,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb b/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb
new file mode 100644
index 0000000..2c0764f
--- /dev/null
+++ b/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "titanic = pd.read_csv('../data/titanic.csv')\n",
+    "# 1. Read `titanic.csv` into a DataFrame.\n",
+    "titanic.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.616162\n",
+       "1    0.383838\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2. What is the null accuracy rate for predicting survival? (This means the probability of choosing the largest unique category, either survived or not)\n",
+    "titanic['Survived'].value_counts() / titanic.shape[0]\n",
+    "# 0.616162"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# 3. Can you think of some variables that are in the dataset that might contribute to predicting survival of the crash?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# 4. Define Pclass and Parch as the features, and Survived as the response.\n",
+    "feature_cols = ['Pclass', 'Parch']\n",
+    "X = titanic[feature_cols]\n",
+    "y = titanic.Survived"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "466    0\n",
+       "707    1\n",
+       "613    0\n",
+       "739    0\n",
+       "512    1\n",
+       "Name: Survived, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 5. Split the data into training and testing sets. (Hint: use the train test split modules from sklearn)\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
+    "train_X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pclass</td>\n",
+       "      <td>-0.815161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Parch</td>\n",
+       "      <td>0.243198</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        0         1\n",
+       "0  Pclass -0.815161\n",
+       "1   Parch  0.243198"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 6. Fit a logistic regression model and examine the coefficients to confirm that they make intuitive sense.\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)\n",
+    "pd.DataFrame(zip(X.columns, logreg.coef_[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.69506726457399104"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 7. Make predictions on the testing set and calculate the accuracy.\n",
+    "logreg.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb
new file mode 100644
index 0000000..0ad6fa6
--- /dev/null
+++ b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb
@@ -0,0 +1,1713 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn import metrics\n",
+    "import statsmodels.formula.api as smf\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "# visualization\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data = pd.read_csv('http://localhost:8888/files/data/titanic.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.616162\n",
+       "1    0.383838\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Survived.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>455.366120</td>\n",
+       "      <td>0.672131</td>\n",
+       "      <td>1.191257</td>\n",
+       "      <td>35.674426</td>\n",
+       "      <td>0.464481</td>\n",
+       "      <td>0.475410</td>\n",
+       "      <td>78.682469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>247.052476</td>\n",
+       "      <td>0.470725</td>\n",
+       "      <td>0.515187</td>\n",
+       "      <td>15.643866</td>\n",
+       "      <td>0.644159</td>\n",
+       "      <td>0.754617</td>\n",
+       "      <td>76.347843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.920000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>263.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>29.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>457.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>676.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>47.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>890.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   183.000000  183.000000  183.000000  183.000000  183.000000   \n",
+       "mean    455.366120    0.672131    1.191257   35.674426    0.464481   \n",
+       "std     247.052476    0.470725    0.515187   15.643866    0.644159   \n",
+       "min       2.000000    0.000000    1.000000    0.920000    0.000000   \n",
+       "25%     263.500000    0.000000    1.000000   24.000000    0.000000   \n",
+       "50%     457.000000    1.000000    1.000000   36.000000    0.000000   \n",
+       "75%     676.000000    1.000000    1.000000   47.500000    1.000000   \n",
+       "max     890.000000    1.000000    3.000000   80.000000    3.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  183.000000  183.000000  \n",
+       "mean     0.475410   78.682469  \n",
+       "std      0.754617   76.347843  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000   29.700000  \n",
+       "50%      0.000000   57.000000  \n",
+       "75%      1.000000   90.000000  \n",
+       "max      4.000000  512.329200  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dropna().describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0xecc42e8>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EREREpE4ymSzheErNDhpIAUhEREREpA5i8VlSuZKaHTSYApCIiIiISA1ZlkUwFGEOJx6v\nv9HltD0FIBERERGRGimXy0wFI9hdflwOR6PLERSARERERERqolAoMDEdxePrxGazNbocuUABSERE\nRESkypLJFNOhhJodNCEFIBERERGRKorEYthdXjz+Dsplq9HlyCJqPC4iIiIiUgWWZTE9EyZbtOP1\n+RpdjixDR4BERERERFapVCoxGYzg8gRwudTsoJkpAImIiIiIrEI2lyMUTWq9T4tQABIRERERWaHZ\nRIJEqqjhpi1EAUhEREREZAWC4SiFsg2Pv6PRpch1UAASEREREbkOlUqF6WAYy+HD7dbb6VajZ0xE\nRERE5BoVi0WmQjENN21hCkAiIiIiItcgnc4QmU2r2UGLUwASEREREXkDsfgsyVxJzQ7WgJYIQIZh\neIDPAh8AssB/N03zj5bZ9qeA/wxsAo4Av2Ga5pFrva9SqbT6gkVERERkTbAsi2AowhxOvF5/o8uR\nKrA3uoBr9N+A24D7gE8A/49hGB9YvJFhGKPA3zAfgG4CjgLfNAzDe613FAzHOD8ZpFwuV6NuERER\nEWlR5XKZ89MhynYvLpen0eVIlTR9ADIMww/8IvDrpmkeNU3za8B/BT65xObvBF42TfNvTNM8A/zf\nwAgweq33Z7PZsHs6mJiOkE5nqrAHIiIiItJq8vk8E9NhnJ4ADoej0eVIFTV9AAJuZv5UvacXXPYE\ncHCJbaPAjYZh3G0Yhg34KJAAXr2eO7TZbHj9nURTeUKRGJZlrbB0EREREWk1qVSKmUgSr79Lnd7W\noFYIQOuAiGmaCxfnBAGvYRj9i7b9IvAt5gNSkfkjRR80TTOxkjv2eHzMWS7Gp4IUi8WV3ISIiIiI\ntJBINEYsVcDrDzS6FKmRVmiC4AcKiy67+PXikzH7mT/l7RPAIeDjwF8ZhnGraZqRa71Dh8MOVC78\n24nb3U0wmqS3y0NPd/cKdqE5ze/n5f+vZe20r6D9XcvaaV+hOfezGWtaSiu+VlRz7bVavVC/mi3L\nYjoYoWxz4+9Y3VvkK2uuVKG62mu1mlfzemiFAJTn9UHn4tfZRZf/F+Al0zQ/D2AYxseA48BHgD+8\n1jsMBF7fM6G720+xWCCTS7N+ZBC7vXV+cbyRri5fo0uom3baV9D+rmXttK/NptUe+1arF1RzPbRa\nvVDbmkulEhOTIbr6+qr6Hm+p95TNrhVrvl6tEIAmgQHDMOymaV6MoyNAzjTN2UXb7gc+ffEL0zQt\nwzCOAluu5w7T6Tzl8tLJN1OxCL9yhqH+bvy+1vvlsZDDYaery0cymVt2f9eKdtpX0P6uZe20r3B5\nf5tJqzz2rfhaUc2112r1Qu1rzuZyBCOJ+fk+c/mq3KbDYScQ8F71PWWzabWa1/oRoBeBOeBO4KkL\nl70JeHaJbad4fcc3Azh8PXdYLlcol5drfGDD6Q4wFUrR6cvQ39d3PTfdlMrlCqVS87/Qq6Gd9hW0\nv2tZO+1rs2m1x77V6gXVXA+tVi/UpubZRIJEqojHH7jKe7+VmK/z6u8pm02r1bzy10LTByDTNHOG\nYTwMfN4wjI8CG4F/C/wCgGEYw0DCNM088D+BhwzDeI75rnG/DGwGvlDturw+P7m5OSamgqwb6sfp\nbPqHUkREREQuCIaiFCo2PP6ORpciddYqC1l+C3geeBz4DPC7F+YBAUwDHwIwTfMfmJ8P9O+AF4C7\ngJ+4ngYI18PpcuHydjIZjJFIJmtxFyIiIiJSRZVKhfPTQeZw4Xav/fUu8notcdjCNM0c840MPrLE\ndfZFXz8EPFSn0gDw+AIksnmy2TDDQ/1rqkGCiIiIyFpRLBaZCkbxaL5PW9M79Spxu73g8jM+FSad\nzjS6HBERERFZIJ3OMB2exdvRrfDT5hSAqshms+H1dxJN5pgJRbCsVlhAJiIiIrK2xeKzRJI5PD4N\nNxUFoJrweP1U7F7Gp0Jkc7lGlyMiIiLSlizLYiYYJlOw8Hr9jS5HmoQCUI3Y7XY8vk5CsQzhSExH\ng0RERETqqFwuc346RNnuxeX2NLocaSIKQDXm9fkpVJxMTAUpFAqNLkdERERkzcvn80xMh3F6Ajgc\njkaXI01GAagOnE4nbl8X0+EEsfhso8sRERERWbNSqRQzkSRedXqTZSgA1ZHXHyBbgImpIKVSqdHl\niIiIiKwpkWiMWKqA169mB7I8BaA6c7rdOD0Bzs9ENTxVREREpAosy2J6Jky+5MCjZgfyBhSAGuBi\nu+xEtsTUTJhKpdLokkRERERaUqlUYnwqiOX04XS5Gl2OtAAFoAZyu73YLgxPzWSyjS5HREREpKVk\ncznOz0Tx+Lqw2/W2Vq6NXikNdvFoUDiRJRyJNbocERERkZYwm0gQjqbx+jsbXYq0GAWgJuH1Xm6X\nrQYJIiIiIssLhqOkcmU8/o5GlyItSAGoiTidzksNElLpdKPLEREREWkqlUqF89NB5iwXLre30eVI\ni1IAajIXT4mLJfOEIjEsy2p0SSIiIiINVywWGZ8K4XAHcDqdjS5HWpgC0CL5YrnRJQDg8fopVpyc\nnw7plDgRERFpa+lMlslgXMNNpSoUgBb5N//jEH/3vVOkssVGl4LT6cTl7dQpcSIiItK2YvFZQtG0\nhptK1SgALVKx4MVTEf7oi0d5+pUZKpXGn4KmU+JERESk3ViWxUwwTCZv4fFpuKlUjwLQMgpzZb7+\n5Fk+97WXOR9u/NGXi6fEqUuciIiIrHXlcpnz0yHKdi9Ot7vR5cgaowC0iN/juOLryXCGz331ZR59\n4gz5YmODh9PpxO3rmj8lLpVqaC0iIiIitVAoFJiYDuP0BHA4HG/8DSLXSQFokf/4i7dy266BKy6z\ngGfGgvzRF49y9HSk4aehef2dxNNFgqFow2sRERERqZZUKsV0OKFmB1JTCkCLdHW4+Rdv28kvvW8P\ngz1X9pdP5+b44uOn+d/fOk5kNtegCue5PT5KNjcTUyHy+XxDaxERERFZrWgsRixdVLMDqTkFoGVs\nX9/Nr/30Tbzzjk04HVd+AvHqZJJPf/klvvfcBHOlSoMqBIfDgdvXyUw0TVgNEkRERKQFWZbF9EyY\nXNGOx+NrdDnSBhSArsLpsHPfrRv4zZ+5GWNTzxXXlSsWj78wyae/fJSTE7MNqnCe1+enUHEyMR2i\nUCg0tBYRERGRa1UqlRifCmI5fWp2IHWjAHQN+rq8fPjdBj/3jl10d1z5wxlLFvirb5/gb793kkSm\ncbODnE4nbm8nM+Ek0VisYXWIiIiIXItsLsf5mSgeXxd2u96SSv3o1XaNbDYbN27r4zc/dDP33rQO\n+6J1eS+/FuNP/uEoTx6bptzA2UEefwe5kpPxyRkdDRIREZGmlEgmCUfTeP2djS5F2pAC0HXyuBzc\nf+cWfvUD+9g8fOUivcJcmW8+fY7PfvUYE6HGtam+2C57OpxgNpFoWB0iIiIii4UiMRKZOTz+jkaX\nIm1KAWiF1vV38K8fuJEPvHk7Po/ziuumo1k+/8grPPLj18gVGjc7yOsPkMpXmJ4JU6k0rlmDiIiI\nSKVSYXI6yFzFiVvNDmSFKpaFOR7n4e+cWPFtON94E1mO3Wbj9t1D7Nnay3eeGef5k+FL11nA4eMh\nXjkT4z13buHWnQMN6WfvcnmoVFyMT4UYHujB5/W+8TeJiIiIVFGxWGQqFMPtDWi9j6xIOjfH82aI\nw8dDxFOrW+ahAFQFHV4XP33fDezfPcgjPz5DKH55RlAmX+LLP3iV580QD9y7jeFef93rs9vteP1d\nhKJpOv15+np73vibRERERKogk8kSjqfw+rsaXYq0GMuyOBdMcWgsyMuvxaq2zl4BqIq2jnTxaz+9\njydfmuH7L5y/YkbQmekUf/qPx7j3pnX8xG0bcDsdda/P4/OTLRTJz4QZGepHZ0CKiIhILcXisySz\nJTU7kOuSL5Y4cirC4bEgwQUHFqpFAajKHHY7b75lPftu6OcbT53l+Ln4pevKFYsfvjjFS69Gef89\nW9m9ubfu9TndbizLxcRUiPUjffSiBYgiIiJSXZZlEQxHmbMceH31P/tFWtNUJMOhsSBHT0colpZe\nv+6w2xjd2sfd+4b5/c+tbB2QAlCN9HZ6+Pl3GRw/F+frT55hNn15RlA8VeDh75iMbu3lfXdvpSfg\nqWttNpsNj7+LmUgKj8eO3abBYyIiIlId5XKZqWAEu8uPqwFnvEhrmStVOPZalENjQSZC6WW36wm4\nObBnmP3GIJ1+Nw7HytfWKwDV2J4tvdywvovHX5jkiZemqViXz10cOxvn9PkEb9u/kbv3jeCo86JA\nr89PMlshmwoz0NerRYkiIiKyKoVCgelwHI+vsyHNn6R1RBI5Do+FeP5keNmuyTZg1+YeDo4Os2tj\nD/bFgzhXSAGoDtwuB+8+uJlbdw7wtSfPcHb68oygYqnCtw+Nc+RUhAfv3caWkfqeI+v2eMhk55i4\n0CXOqy5xIiIisgKpdJrYbE7NDmRZ5UqF4+dmOTwW5PTk8rMqO3wubjcGObBniN7O6r83VQCqo+E+\nP7/8vlGOnIrwrWfOkc1fTrszsSx//ugr3G4M8u6Dm/F7XXWry263XzglLklPZ4Ge7u663beIiIi0\nvmgsRrpgabipLCmRLvDsiRDPnQiRzM4tu93WdZ0c3DPMjdv6cDpqd2aSAlCd2Ww2bts1yO7NvXz3\n8DjPnghdcf1zZpixs3HefXAztxmD2Ot4+Hh+cGqBXC7M8FC/TokTERGRq7Isi5lghDIuPB6tKZbL\nKpbF6fMJDh8PcuJcnOU6WHtcDm7dNcDBPcMM99WnYYYCUIP4vU5+6s3b2W/Mzw6aiWUvXZctlPjK\nj17jeTPMg2/axkidXgxweXDqxFSIwf5u/D5NahYREZHXK5VKTAUjONwdOB1qdiDzMvk5njfDHD4e\nJJZcfmDp+n4/B0eHuWnHAB5XfV8/CkANtnm4k1/9wD6efnmG7z03cUXLv3PBFH/6jy9xz751vG3/\nRtx1enFcPCUuFMvQ4ckxONBXl/sVERGR1pDL5wlGEmp2IMD8kcDxYHp+YOmZKKXy0od7nA4bN90w\nwMHRITYOBhr22lEAagIOu417b1rHvu19fOPpc7xyJnbpuooFP35p+tLsoNGt9QsjXp+fQqnE+OQM\nI4N9uN06tC0iItLuEskks8mChpsKhWKZF09HODQWvOJspsUGur0c2DPMbbsG8XsbHz8aX4Fc0h3w\n8HPv2IU5HufRJ88ST10+bJjIFPnrx06ye3Mv779nS006YizF6XSCs4up0CxdHW76envqcr8iIiLS\nfEKRGPk5NTtodzOxLIfGghw5FaY4t/TAUrvNxujWXg6ODrN9fVdTHSlUAGpCxuZefnN9Nz84MsmP\njk5RXrBq7MR4nFcnE7x1/wbu2beuph0yFvL6A2SLc2SmgowM9uFy1a9LnYiIiDRWpVJhOhjGsntx\ne/QeoB3NlSq8cibGobEg54KpZbfr7nBzx54hbt89RJe/Oc8eUgBqUi6nnXfcsYmbdw7w6BNneG0q\neem6uXKF7x6e4MipCA/cs43t6+vTb9/pcoHLxWQwTk+nR+2yRURE2kCxWGQ6FMPlDdR9aLs0XjSZ\n59njQZ4zw1eMcFls58ZuDo4OY2zuxVGlgaW1ogDU5IZ6fPzie/dw9HSUbz5zjkzucu/0UDzHX35j\njFt3DvCeO7cQ8NXnExmvP0A6VySdDTIy2D9/mpyIiIisOZlMlnA8peGmbaZcsTDH4xwaC3Lq/PID\nS/1eJ7cbg9yxZ5j+rvosz6gGvXNtATabjVt2DmBs7uGxZyc4PBZkYW+NI6cinBiP864Dm7l991Bd\nZgc53W4sy8VkMEZPp5fuLv1iFBERWUtmEwlm03NqdtBGZlMFvv/cBIfHQiQyxWW32zLcycHRYfZu\nr+3A0lpRAGohPo+TB+/dxm27BvnaE2eYimQuXZcrlHnkx2d44WSYB+/dxrr+2i9OtNlseHwBkrkC\nmWyI4cF+HJoDICIi0tIsyyIUjlGo2PD66jeLUBrDsixenUxy+HiQsbNxKtbSLaw9Lge37Bzg4Ohw\nXWdU1oIC0CJ2u425uSJ2e/M+NJuGAnziJ/fyzFiQf3p2gsJc+dJ148E0f/aVY9x14whvv30THnft\nA4nL5cFE3dkaAAAgAElEQVSy3ExMR+jv9tPZqU+KREREWlG5XGYqGMHm9OF2N+97IVm9bL7ECyfD\nHDoeJJrIL7vdun4/B/YMc8uOgbq8r6wHvbIXGRkaIJedIjabwu7yNe36Frvdxt17R9i7rY9vPn2O\nY69FL11XseDJl2c49lqU9969lb3b+mreetBms+H1dxLP5ElnwgwP9WPXQkkREZGWUSgUmAnHcWu4\n6ZplWRbnw/MDS1969eoDS/dt7+fg6DCbhho3sLRWmvPdfYP1dHfT4Q8Qn02QzKRwefxNe2pXV4eb\nn337Tm4/P8ijT5wlmryc4JPZOf7ue6fYtamb99+zrS6L09xu7/w04Kkw/T0ddAYCNb9PERERWZ10\nOkNkNqNmB2tUca7M0QsDS6eiyw8sHezxcceeIW7dOUCHd+22O1cAWobNZqOvt4feHotYPE4qm8Xt\n7Wjaoxo7N/bw6x+8iR++OMkPX7xydtDJiQSf/tJR7rt1A2++eX3NF6tdOhqUypFO53Q0SEREpInF\n4rOkciW8fn1oudYEY1kOHQ9y5GTkiiUTC9lssGdLL3ftHWH/jetIJXOUlzkytFYoAL0Bm81Gf18f\nvT0VIrFZstkSHl9HUx4KdDntvP32Tdyyc4BHnzjL6cnLbQtLZYvvPXeeF09FePDebdywofYzfNwe\nH5VKhYnpMAM9nXR0tPaCORERkbUmGIpStOx4vPobvVaUyhcGlh4PcnZ6+YGlnX4Xd+we4o7dQ3QH\nPDgctrp0Em4GCkDXyG63MzTQR7lcJhyNky9WmjYIDXT7+Mj9uzn2WpRvPn2OVPby7KBIIs//+uZx\nbt7Rz/vv2Up3d21/4dntdjy+TiKJLOlMjqHB2q9HEhERkaurVCpMTgexHD5cLr0dXAviqTyHj4d4\n7kSIzFUGlu7Y0M2B0WH2bOlp28G2esVfJ4fDwcjQAKVSiXAkTr7cnC0ibTYbN90wwK5NPfzTc+d5\n5pUZFnY1PHo6ijk+y0/et4Obt/XWvB6P10+5UmF8Mshgfzd+n6/m9ykiIiKvVyqVmJgKYXN1tO0b\n4LWiUrE4OTHLobEgJydmWe7ENZ/Hwf5dQxzYM8RAj96DKQCtkNPpZN3IIIVCgUg8QanSnIePvW4n\n77976/zsoB+/xvnw5dlB+WKZv3/M5ImhDh68ZxsbBmt77q/dbsfj7yIUz+BLZxka0NEgERGResrm\ncsSSCdy+zjW/zmMtS2WLPG+GOXw8yGx6+YGlm4YCHBwdZt/2flxOhd2LFIBWyePxsGFkaP4XymyK\nis2J2137bmvXa8NAB7/y4F4OHw/y2LMT5IuXF8KdD2X47CMvc+foCO+4YyPeGvf993r9lMplJqZC\nDPZ34/M23+MlIiKy1iSSSVLZOYZGBigklu8EJs3JsizOTCc5NBbklTPLDyx1O+3cvGN+YOn6gY46\nV9kaFICqxO/z4ff5SKczxBIp7A4PTre70WVdwW63ceeNI9y4rY9vPzPOi6cjl66zLHj6lRlefi3K\n/Xdt4aYb+mt6dMbhcODwdRKMpunwZBno79XRIBERkRoJR2Lk5ix8/uY7W0WuLlcoceRUmENjIcKz\nuWW3G+71cXB0mFt2DtT8w+xWp0enygKBDgKBDmYTCRKpJA63v+mGqXb63XzorTu4Y88Qjz55lmDs\n8qdAqdwcX3z8NM+bYR64dysD3bU9T9Tr81MolRifCjIy0IvH46np/YmIiLSTSqXCdDCCZffg9qzd\nuS5r0WQ4zTNjQV46HWWuXFlyG4fdxt7tfRwcHWbLsAbYXqvmeme+hvR0d9Pd1dXUw1R3bOzmdz56\nkG/86DTff/78FdOAT08m+PSXXuItt6znLbdsqOl5o06nE6ezi5lwkoDfQX9fX83uS0REpF0Ui0Wm\nQzFc3oCaHbSIYqnMS6ejHDoeZHLBuu3Fejs9HNgzxH5jiIBPwfZ6KQDV0MJhqtFYnHQTDlN1Oe28\ndf9G9m3v5+tPnsWcmL10Xbli8fgLk7x4OsID92xj16aemtbi8XeQK5UYn5xhWEeDREREViyVShFN\n5PD6uxpdilyDUDx3YWBp+Ip12gvZbLB7cy8HR4fZsbG7bWb21IICUB3YbDYG+vvoLZeJRGfJ5ctN\nN0Oor8vLh99t8MqZGN94+hzJzOWOIrFkgb/69gn2be/jvXdtpaujdmubnE4nOLuYDifo6nDT11vb\n0CUiIrKWWJZFKBKjUAKvv7bdXWV1SuUKY2fjHBoLcmY6uex2nT4Xt+8e4o49Q/QE9OFwNSgA1ZHD\n4WB4qJ9SqUQkOkt+zsLrb57uHDabjb3b+9m5sYfvP3+ep16eprKgwcix12KcnEjwjjs2cnB0BIe9\ndgHO6w+QLc6RmQqybqi/6dZRiYiINJtyucxUMILN6cPt0d/NZhVPFXj2xPzA0nRubtnttq/v4uDo\nMKNbe3UKY5Xpp6MBnE4nI8MDFAoFovEExYodbxPNEPK4Hdx/1xZu3TXAIz8+w0Qofem6wlyZbzx1\njhfMMA++aTubhmr36ZLT5QKXi/MzMXq7vHR36TC+iIjIUgqFAjPhOG6fFsI3o0rF4tT5WQ6NhTAn\n4izTwRqv28FtuwY5MDrMkAaW1owCUAN5PB7WjwyRy+eJxpOUceDxNM+LfV1/Bx978EaePxHiO4fH\nyRUun5M6Fc3y+Ude5o49Q7zrwGZ8NfykyesPkMjmyeUjDA/Wtj23iIhIq0ml00Rns1rv04TSuTkO\njwU5fDxEPFVYdruNgx3zA0tv6MftbK6mWWtRSwQgwzA8wGeBDwBZ4L+bpvlHy2y778K2+4FTwG+Y\npvmDOpW6Ij6vl43rvPMzhJIpbHY3LndznONpt9m4Y88we7b28Z1D47xwMnzpOgs4fDzEK2fj3H/n\nZm7ZMVCzcOJ2eymXy4xPBVk32Ie7yWYsiYiI1JtlWYSjcXJzltb7NBHLsjgzleL5H77GCydClCtL\nH+5xOezcvKOfg6PDbBjU81dPLRGAgP8G3AbcB2wFHjYM46xpml9ZuJFhGF3AY8AjwC8AHwa+ahjG\nTtM0IzS5izOEkqkk8WQKu9OLy9UcrQ0DPhcfvO8G9huDfO2JM4TilwdxZXJzfOmfX70wO2hbzQ7Z\nzg9P7WIqNEun30lfr4aniohIeyqVSkwFI9hdfjxa79MU8sUSR05FODwWJBhffmDpYI+Xg6PD3Lpz\nsKZn0Mjymv5RNwzDD/wi8C7TNI8CRw3D+K/AJ4GvLNr8XwEp0zQ/fuHr/9cwjPcAtwPfqVPJq9bV\n2UVXZ9f8MNV0CofL1zRNALat6+KTH9jHk8emefz5ySsGc702leQzX36JN920jvtu21CzQ7hef4Bc\nqcTEVIiBvi78vuY5bVBERKTWUqkUsWQOj0+nvDWDqUiGQ2NBjp6OUCwtP7B0dOv8wNJt67ROq9Ga\n41311d3MfJ1PL7jsCeDfLbHtW4CvLbzANM2DtSutthYOU01lmycIOR123nLLBm66YYBvPHWW4+fi\nl64rVyx+8OIUR1+N8sA9WzE299amBqcTnJ2E4xk8yQxDg31NNV9JRESk2izLIhiOUizb8Ph0ylQj\nzZUqHHstyqGx4BXNohbr7fRwx+4h9huDdPp1+n6zaPy76Te2DoiYpllacFkQ8BqG0W+aZnTB5duB\nw4Zh/DnwAHAG+D9N03yqfuVW18JhqolkkmQ6hd3hwdkEa2B6Oz38/LsMjp+N8fWnzjKbvjw7KJ4q\n8IXvmNy4tY/33b2F7hr1rfd4/VQqFcanwgz0BAgEmqetuIiISLVc7PLm9HTg9miRfKNEEjkOj4V4\n/mSYXKG05DY2wNjcw9sObGFjv2/Zjm/SOK0QgPzA4rYZF79e/K46APw28Gng3cDPAo8ZhmGYpjlZ\n0yprzGaz0dPdTU93N8lUkkQ6TaVJusbt2drHDRu6efyFSZ54aZrKgp/0V87GOHV+lrffvom79tZm\ndpDdbsfr7ySWypFKZxke6tfRIBERWTNmEwlmUwV1eWuQcqXC8XOzHB4Lcnoysex2HT4XtxuDHNgz\nxECPj+5uP4lElnJZCajZtEIAyvP6oHPx6+yiy0vAEdM0/+OFr48ahvFO4OeBP7jWO3Q4mvvNc19v\nD329kMlmmU1kKJYtvL7rP/JxcT/n/7/0OavXyudw8t67t7B/9yCP/Og1zkynLl1XLFX41jPnOHIq\nzE++eTtbRzpXdV/L1uCfPxo0HYowMtiLx3P5ZXPlvq592t+1q532FZpzP5uxpqW04mtFNV+pUqkw\nE4pStpx0dFbnb2c1//bXS6NqTqQLHD4e4vDxIMnM8gNLt63r5M4bR9i7vQ/noteDHufaWc3PXCsE\noElgwDAMu2maF5+NESBnmubsom2ngROLLjsJbLqeO+zqavxRlWvR29vBxg2DFAoFIrEE+UIFj7/j\nuhfWBQLeqtXU3e3n/9raz9PHpvnKP5++YsLxdDTL5776MvfcvJ6fum8HAV+tOtwFyGTSeH1Oerqv\n/LSsVZ7batH+rl3ttK/NptUe+1arF1QzQD6fZ2omQVdfbda4VvNvf73Uo+aKZXHibIwfvjDJsdOR\nK85qWcjrcXDnjet4860bWH+VFtZ6nJtTKwSgF4E54E7g4lqeNwHPLrHtM8CbF122G/ib67nDZDJH\nudz8yXchv7cDj6tMNB4nmyvh9PhwOK5+jrDDYScQ8JJO56u+vzdu6WHrv7yZbz8zzrPHQ1dc9+TR\nKV40Q9x/1xb2G4M16oRi59xkgumZOMOD/TidDrq6fC353K6Ew2HX/q5R7bSvcHl/m0mrPPat+FpR\nzfNmE0niyTxefwf5VL4qt3lRLf/210o9as7k53juRJhDY0GiieUf8/UDHdx54zC37BzA45p/n5VI\nLD4hSY9zPazpI0CmaeYMw3gY+LxhGB8FNgL/lvk5PxiGMQwkTNPMA58HPmkYxn9gPvT8ArAN+Ovr\nuc9yuUJpmTaGzc1GX08vvd3WfOe4dOYNOsfN72O5XKnJ+alel5OfetN2bts5PztoJnb5F0QmX+JL\n//wqzx4P8eC92xju81f9/u0ON4VymdfGp9myYQjwtfBzuzLa37Wrnfa12bTaY99q9UL71mxZFqFw\njELFhtvjr9Hakdr+7a+N2tRsWRYToTSHxoIcey1KaZnbdjps3HTDAAdHh9g4GLj0we3Va9HjXHsr\n/3lr+gB0wW8BnwUeBxLA75qmebHd9TTz838eNk1z3DCMdwGfAT4FHAfuN01zuv4lN85SneNsDRyq\numWkk1/9wD6efnmG7z03cUWP/LMzKT7zj8e496YR3nrbRtyu6na2uTg8dWI6itfrYL43i4iISHMp\nl8tMBSPYnD7c7lZ5e9aaCsUyL56OcPh4kOno64/eXDTQ7eXAnmFu2zWI36vnZC1piWfTNM0c8JEL\n/y2+zr7o66eZH3za9i52juvu6rrcQrtBs4Qcdhv33rSOvdv7+OZT53jlbOzSdRXL4kdHp3np1Sjv\nu3sro1v7qn7/Xn+A8GyOuXyOvp7azCYSERFZiUwmSziewuMLaEBmDc3EshwaC/LiqQiFufKS29ht\n891tD44Oc8P6Lj0fa1RLBCBZnYVBaDaRIJmZH6rqcNT/iFBPwMPPvXMX5nicR588Szx1ucP5bLrI\nXz92kt2be3n/PVvp7azu7CCP10c6M8fEVJD1wwNvuEZKRESk1sKRGJliBa+/Nh1S291cqcLLZ+YH\nlo4Hlx9Y2t3h5vbdQ9yxe4iujsbPWpTaUgBqIzabjd6eHnourBHK5tKUazSg9I0Ym3v5jfVd/ODI\nFD8+OkW5cvlc0xPjcV6dTPDW/Ru4Z9+6Sy0lq2H+6FeAiekIQ31d+P3NtbhaRETaw8JT3rw6varq\nosk8h8eCPG+GyS4zsBRg58ZuDo4OY2zurcmsQmlO+olrQxfXCA06bFSsIpFsCrvzjbvGVZvb6eCd\nd2zilh0DfO2JM5yZTl66bq5c4buHJzhyKsKD925j27rqDX+z2Wx4/Z2E4hk68zn6+6p/yp2IiMhy\n5ubmmApGcfs6dYpVFZUrFuZ4nENjQU6dX35gqd/jZL8xyIHRYfq71n7LZ3k9BaA2ZrPZGOjrw25z\nEwpHSWWzuL0dNZk3cDVDvT5+6X17ePF0hG89M05mweygUDzH//z6GLftGuTdBzdXdXaQ1+cnNzfH\n5HSQkSGdEiciIrVXKBSYDs/i9Vfvg712l8wUec4M8ezxEIlMcdnttgx3cmB0iL3b+nE5W2fYrlSf\nApBgs9no7+ujt6dCLD5LOjtX9yBks9m4decguzf38t3D87ODFjZgfOFkmOPn4rz7wCb27x7CXqVP\nzJwuF5blZHwqzPBAN36fTokTEZHayGZzhGIprfepAsuyeHUqyaGxIMfPxpcdWOp22bl15yAH9gyx\nrr+jzlVKs7rmAGQYxuIBo8syTfNHKytHGslutzPQ30dfpUIkNksuV8Ll8dc1CPk8Tn7yTdvZbwzy\ntR+fYWpBe8pcocRXf3yG50+GefDebVX7RWaz2fB1dBGKZ/ClsgwN9umUBBERqapEMslssoDXH2h0\nKS0tmy/xwskwh48HiVxlYOlIn5+Do8PcsmMAj1tneMiVrucI0A8Ai/lBKgtj9sV3igsv0yuthdnt\ndoYG+iiXy0RjCbLZEh5fR11DwaahTj7+U/s4NDbDPz17/op2lePBNH/2lWPctXeEt+/fVLVfbF6v\nn1K5zPhkkMF+HQ0SEZHqiMZiZAoWHr+OQKyEZVmcmUrwvUPnOHo6ctWBpXu39XNwdJjNw2opLsu7\nngC0bcG/3wb8LvCbwFPAHHAH8CfAf6laddJQDoeDocH5IBSJzpItlvDWcUaBw27j7r3r2Lutn28+\nfZZjry2cHQRPHpvh2Gsx3nfXFm7cVp2jNg6HA4dfR4NERGT1LMtiJhihjAu3R62Vr1dxrszR0xEO\nHQ8xFcksu11fl4eDe4a5zRikw9uYoe/SWq45AJmmee7ivw3D+BTwS6Zpfn/BJv9kGMYngC8AD1ev\nRGk0h8PB8FA/5XKZcDROvlip6xGhrg43P/v2XeyfmOXRJ88QS16eHZTMFPnb751i16YeHrhnK31V\n6uZy6WjQVIjBvi4dDRIRketSqVSYCoaxOf041WTnugTj8wNLj5xcfmCpzQZ7tvTODyzd0F21tcHS\nHlbaBGE9MLnE5XFAPYXXKIfDwcjQAKVSiUh0lvxcfYPQrk09/MYHb+aHL07ywxevnB10cmKWP/nS\nUe67dQNvvnl9VWYHORwOHL5OQrEMfk+Owf5eHQ0SEZE3VCqVmAxGcHkCde+s2qpK5QqvnIlx6HiQ\ns9OpZbfr8rsuDSztbtAsQ2l9Kw1Ah4DfMwzjX5mmmQYwDKMP+EPgh9UqTpqT0+lkZHhhELLw1um8\nZpfTzttvn58d9OiTZzk9ebnPf6ls8b3nzvPiqQgPvmkbN6zvrsp9en1+iqUSE1NBRgb7cLt1GoOI\niCxtvs11HI9m/FyTeCrP4eMhnjPDV4zBWGz31j7uMAbYtakHh0KlrNJKA9CvA98HpgzDOAnYgV1A\nEHhrlWqTJncxCBWLRSKxBMWyDa/PX5f7Hujx8ZH7d/PSq1G+9fQ5Ugt+aUYSef7XN45zy44B3nPn\nZjr9qw8sTqcTnF1MhWbp7fLS3aX5DSIicqVEMkk8mdeMnzdQqVicnJjl0FiQkxOzLN3SAHweB/t3\nDXHX3mFu2NJPIpGlvEwDBJHrsaIAZJrmK4Zh7AJ+FtjLfAe4PwX+3jTN7FW/WdYct9vN+pFBCoUC\n0XiybkHIZrNx844BjM09PPbsBIfGgiwcA/Di6QgnxuO888Am7rpxpCr36fUHSGTzZLNhhof6dWqD\niIjMr/eZCZMv2dTm+ipS2SLPm/MtrGfTyw8s3TQU4ODoMPu2zw8sdTh0JE2qa8WDUE3TTBqG8RDz\n3eFeu3DZ8scuZc3zeDwLglCCYsWO11v7IOR1O3ngnm3s3zXII0+cYTJ8uVNMvljm0SfO8sLJMB++\nf5Ru3+pn/7rdXizLYnwqzEBPgEBAbU1FRNpVJpMllkyA04dbn4m9jmVZnJmeH1j6ypmrDCx12rl5\nxwAHR4dZP6C/q1JbK3o3aBiGDfh95k+FczN/+tt/NgwjA3xcQai9zQehIfL5PNHZJKWKHU8dgtCG\nwQAff3Avh48H+e7hiSs6x5wPZfj9Lzx7YXbQRrzu1QUhm82G199JNJklnc0xPNivc71FRNpIpVIh\nFI5Rws7QcD+FuSwsezJX+8kVShw5FebQWIjwbG7Z7YZ6fRwcHebWnQOr/tsscq1W+kr7NeDngU8A\nf3bhskeAzzK/Dujfr740aXVer5cNI16yuRyx2RQVHLg9tW0nbbfbuPPGEW7c1se3njnH0dPRS9dZ\nFjx1bIZjp6O89+4t7Nu++tDi8fqpVCoanioi0kZSqRTRRBaPL4DHqcM+C50Ppzk0FuSl01HmypUl\nt3HYbezd3sfB0WG2DKtZhNTfSgPQx4BPmqb5VcMwPgNgmuYXDcMoAn+MApAs4Pf58Pt8ZLJZ4ok0\nFZsTt7s683qW0+l38y/eupP9xhCPPnGGSCJ/6bpUbo6///5pnjsR5oF7tzLQvbrQYrfb8VwYnurP\nqF22iMhadXGw6RwOvP7ORpfTNIqlMi+djnLoePCK09AX6+30cGDPEPuNIQI+DSyVxllpANoGHFni\n8qNAdVaby5rT4ffT4feTTmeIJ1Ngd+Ny17aH/44N3fz6B2/iiWPTPP78JKUFn0adnkzwP778Em+5\nZX52kGuVn+J5vX7mymXGp4KMDPTi8Wg+gYjIWlEsFpkOxXB6OnBrsCkAodkch8eCvHAyTL64/MBS\nY1MvB0eH2LmpRwNLpSmsNACdBe648P+F3sOFhggiywkEOggEOkil0sSTKewOD84aztZxOuy8bf9G\n3nTrRv6/bx3n5MTspetKZYvvPz8/O+iBe7eyc2PPqu5rfnhqFzPhJAG/g/4+zQUWEWl186e85dTe\nmvmBpWNn4xwaC3JmOrnsdgHf/MDSA3uG6NHAUmkyKw1Afwh81jCMdczPAHqbYRj/mvmmCL9VreJk\nbevsDNDZGSCRTDKbTGJ3+XC5andIfLDXz0ffu5ujp6N886mzJLOXe3VEk3ke+tYJbrqhn/vv2kLX\nKmcHefwd5ObmmJgKMjzQq+GpIiItKhyJkS1W2r699Wy6MD+w9ESI9FUGlm5f38XB0WFGt/ZqYKk0\nrZXOAXrIMAwX8DuAD/hzIAz8jmman69ifdIGuru66OrsZDaRIJFO4fL4cdTo9AKbzca+7f3s2tjD\n956f4KmXZ66YHfTSq1HM8Vnecccm7hwdxm5f+aF6p8sFLhdToVl6Oj30dHdXYQ9ERKQeLMtiOhih\nYnPj8dZ23WqzqlgWp88nODQW5MR4nGU6WON1O7ht1yAHRocZ6lEzIGl+K22DHTBN8y+AvzAMYwCw\nm6YZqm5p0k5sNhu9PT30dFvE4nFS2Sxub0fNBo163A7ee9dWbt05yNeeOMNEKH3pusJcmW88NT87\n6Cfv3cbGodV96uf1B0jniqSzQdYNDdQs3ImISHWUSiUmgxFcngDONjyKkc7N8bwZ4vDxEPFUYdnt\nNg52zA8svaEft1N/26R1rPQUuBnDMP4R+CvTNP+5mgVJe7PZbPT39dHbUyESmyWbLeHxddSsq9r6\ngQ4+9uCNPHcixHcPj5MrXF7EORXJ8LlHXubA6DDvvGMTPs/K5xM43W4sy8XEdITeLh/dXTqPXESk\nGeXzeWYiibZb72NZFmdnUhcGlsYoV5Y+3ONy2Ll5Rz8HRofZONjepwVK61rpO7pPAD8HPGYYxiTw\nBeALpmmqAYJUhd1uZ2igj3K5TDgaJ1+s1CwI2W02DuwZZnRrH985dI4XTkYuXWfBpT8G99+5hZt3\nrHx20MXhqclsgXQ2xPBAH06nhr6JiDSLdDpDZDbTVi2u88USR05FODwWJBhffmDpYI/3wsDSwVV9\nICjSDFa6Buhh4GHDMIaB/+PCf79jGMaTwEOmaT5UxRqljTkcDkaGBiiVSkRis+SLFdxef01OjQv4\nXHzwvh3sN4b42hNnCC34Q5DOzfEP/3ya58wQD9y7bVXnOLvcHizLzWQwRndAa4NERJrBbCLBbHqu\nbZodTEUyPHVshqOnIxRLyw8sHd06P7B02zoNLJW1Y1UR3jTNIPDHhmH8KfDLwO8DfwkoAElVOZ1O\nRoYGKJfLRGOJmp4at21dF5/8wD6evDA7aOEk69emknzmyy/xppvX8xO3bljx7CCbzYbHN782KJVR\npzgRkUapVCoEQ9H54aY+f6PLqam5UoUXT8d49kSIM1PLt7DuCbg5sGeY/cYgnavsiirSjFYVgAzD\nuJf5U+F+5sJtfQmFH6khh8PB0GAfpVKJUCROoWLD663+Hyynw85bbtnATTf08/Unz3Ji/PLsoHLF\n4gdHJjl6OsID92zF2Ny78vtxuwE3U6FZujrc9PX+/+zdeXQcd3bY+28t3V29A41GNwASIMGtSUik\nVpJaZ9VImk2SZ/PEiY/j2JPYk3FsJ+e9k5M8P+fk5Dy/l5PY42XsceLYnomdZUazSBprZM2MZtFK\nUhLFDWRxJ0hiawCNRjd6r6r3RwMgQBEiutEAsdzPOTwiu6qrfiWC3XXrd3/3Lq4PkRBCiIWbnMwx\nMp7B5fHjXsPFDkbSeQ72DvPW6ST5YuWG+yjA9s4m7uuJs6OzaVFVUIVY6eqtAvd7wOeBTuCnwG8D\nT5umOX/yqBANpOs6HW2tFAoFRlIT2IqO2934MqXNQYNffCzByUspnnv1IunJ0sy2VKbI114wua07\nwifu30R4EY3eDF+AXKlM9uog8WgzHo80jRNCiKU0PN3fx7s21/tYts3JS+Mc7B3i7NX0vPv5DZ17\nd8bYuzNGJLQ+y32L9afeGaDPUZ3p+ZppmpcaOB4hamIYBhvbDbLZScbSGVTdqPbfaSBFqeZAb90Q\n5onbo4cAACAASURBVKW3rvDqsQFmF8c5cWGMM1fGeeSeTu6/vQ2tzqdm032DBpMTBHwakeZmybcW\nQogGsyyLgeER0LwYxtpbzJ/OFjl0qtqwdHbD7+t1twfZtyvObd0RdG3tzn4JcSP1FkHY2uiBCLEY\ngYCfQMBfXcSaWZpmqh6Xxkfv28RdO1p55uULXBrKzGwrlW2ef+MSh88kefKhbrri9T9R9Pj85CsV\n+vqHaJPZICGEaJhqietxPN61taDfdhzOXZ1qWHopxTwVrPG4NO5JtPLI/k343SqWNc+OQqxxCw6A\nEonES8CnTNMcn/r9vEzT/NCiRyZEHZrCYULB4FQPofySFEpoi/j4whM9vG0meeFAH7lZ+dQDozm+\n+swJ9u6M8di+Lnx1Pl3UdR1dD83MBrVEIo0avhBCrEuZbJbR8dya6u+TK5R5y0xy8OQwoxOFeffr\naPGxvyfOnm1RfIZOOOwjnc4t40iFWFlquTu7BEx3ieyj2iJFiBVnuodQpVJhZHScQtnBH2xsWVNV\nUbh3Z4yezc28cKCPN83knO2HTg1z4mK1d9Bd26N1B2Ezs0GyNkgIIeqWnphgPFtaEyWuHcfh8nCW\nA71DHDs/SmWeWRxdU9izNcr+nhgbWwNrasZLiMVacABkmuYvz/rjl0zTzC7BeIRoGF3XaYtHKZVK\npNIZCjmHap2bxvEZLj71/q3ck4jx3ZfPz2kilytUePon53jTHObJh7qJN9dXrU7XddBDDCTTBH26\nzAYJIUQNxlLjZAoWniWoGLqciiWLd86OcPDkEAOj88/eRMMG+3bFuXtHa91ZCEKsdfX+yxhMJBLf\nAv7aNM0fN3JAQjSa2+1mQ3srPp/OmXMDFIo2hs/f0HNsagvypU/v5rVjg/zorStzmspdHMjwx08f\n4+E72vng3Rtw6/WtTTJ8AfLlMpf7pW+QEEIsxOjYGLkSeDz1N6++1QbHchzoHeKdMyMUy9YN91EV\nhZ7NzezribO1IySzPULcRL0B0Bep9v95MZFIXAW+RrUi3PmGjUyIBvN4PGxobyWXKzCWmqBQthu6\nRkhTVR6+o4PdW1v43msX6b2YmtlmOw4/faefI2dH+OSD3ezaVF/voOlKcdI3SAgh5uc4DoNDI1i4\ncK3Ch0Xlis2JC2Mc6B2aU3DnemG/m727YtybiBHyr77rFOJWqbcK3NeBrycSiTjwC1O//q9EIvEq\n8FemaUozVLFiud1u2uLRWWuEGhsINQU8/KNHE5y6lOK51y6SyhRnto1nS/z3vzfZtamZTz64maY6\newcZvgC5YoncwBDtsWjDK94JIcRqValU6B8aQXP70VfZZ+PoRIFDJ4d400ySK9y4YSnA9o1h9vfE\nSXQ11916QYj1bFHJoaZpDgF/kEgk/gT4AvB7wF9Q7REkxIo2vUaoUqkwMjZOoWQ3dIHszk3NbNkQ\n4idvX+XlowNYs+qSnryU4uzVNB++eyMP7mlDq6MDue524zguLg+MEAl7CQXXTmUjIYSoRy6fZ3h0\nYlWVubZsB7MvxYHeIc5cmb9hqc+jc0+ilX09cVqkYakQi7KoACiRSDxENRXus1PH+iYS/IhVRtd1\n2mJRyuUyydEUZVtt2GJZt67x6L4u7tzeyjOvnOfCwLVUhnLF5oWDfdXeQQ93s7mt9gBGURQMX5B0\nrkh2cphYNFItmiCEEOvMWGqciVwFw1d/H7blNDFZ4k1zmEMnh0lPlubdryseYH9PnNu7W3Dp0rBU\niEao604pkUj8HvB5oBP4KfDbwNOmaebf841CrGAul4uOthiTuRyj4xkU1Y3L3Ziy07FmL7/6iR7e\nOTPC829cYnJWasNQKs9/ebaXe3a08vh9XfgNVx1j9+A4bq4OjREOeIi21LfGSAghVhvbthkYSuIo\nHgzvyq705jgO5/onONA7xMmLKWznxiWs3S6Vu7a3sm9XjPaWxhbtEULUPwP0OaozPV8zTfNSA8cj\nxC3n9/nw+3xkMhlSExkUrTGBkKIo3LWjlURXMy8e6uPQyeE5zbTeOp2k91KKx/d3cU+iFbXG9A1F\nUfB4A2TzJQpXhwgGuxY9ZiGEWMmy2UlG01ncxsruc5MrVHj7dJKDJ4cYSc/fsLQtUm1Yeue2KB73\n6lq/JMRqUm8AdAz4pgQ/Yi0LBoMEg8FqIJTJgOrC7V583rXP0Hnq4S3cvaOVZ165MKefQ75Y4Ts/\nO89b5jBPPbyFtkjtTzN1txtVdXPpahJdUfB55emhEGJtsSyL4eQYJVvB412ZKW+O43AlOcmB3iGO\nnht5z4alt3e3sL8nTld8ZQdyQqwV9QZAHwDm78IlxBoyHQhls5OMZ7JUbAWjAUFFVzzIF39uN2+c\nGOQHb16mVL7WO6hvKMuffOsoD+xu58P3bMTjqu1J4PTaoJFkinQ6STzWglpHoQUhhFhp0hMTpCby\neLwBPCswWCiVLY6cG+VA7xD9I5Pz7hcJedi/K87dida6Up+FEPWrNwD6a+A/JhKJfw+cNU2zeJP9\nhVj1AgE/gYCfQqHA2PgEpYqCx+tb1NM6TVV4cHc7t29p4e9ev8jx82Mz22wHXjk6wLFzo3z8gc3c\ntrm55nO53B7KZZ3L/cO0toTxeVdvM0AhxPqWLxQYGUuD6l6RhQ6GxnK8fnyIw2eSFEo3bliqKLBr\nUzP7e+Js3RCuOdVZCNEY9QZAHwe2Ap8BSCQSczaapimJq2LNMgyDjjaDcrnM6Fi6IX2Ewn43v/DI\nDk5fHufZVy8wNnHtmUJ6ssT/+MFpEp1NfPLBzURqLH+qqioeX4jhsUmC3jwtkUjd4xRCiOU2ne5W\ntBUMY2UFPhXL5tj5FG+aSc5cHp93v5DPxb07Y+zdGSNcZ/83IUTj1BsA/YeGjkKIVcjlctEWj2JZ\nFsmRFIUKi65AtKOzid/8zB389J2r/PSd/jm9g8zL45z75hE+dPdGHtrTjq7VltJmeH3ky2Uu9w8R\njzbjXoXd0YUQ64fjOKTG00xMlvB4/RgraLYklSlw8OQwb5pJJvPleffbtiHMvp44uzY11dXvTQix\nNOoKgEzT/FqjByLEaqVpGm3xKMVikeRYGlvRF1UswaWrPHJvJ3dui/LMqxc4d3ViZlvFcnjx0GUO\nn0nyxEPdbO0I13Rs3eUCl4v+4XGCPl1mg4QQK1Iun2dweBzV5W1og+rFsG2H05fHOdA7xOnL49y4\npAF4PRr37IixrydGNCxpx0KsRPX2Afq/32u7aZr/vr7hCLF6eTweNrbHpsqyZtBc3kU1JY02efkn\nH9vF0XOjPP/6JTKznjImxwv8t++d5M5tUT56XxdBX22zOYYvQL5S4dLVQeItTRiGdBUXQtx6juMw\nMJhkeGwS9wqp7pbJlXjLrJawHs++d8PSfbvi7N4iDUuFWOnqvTv75RscJw6UgVcXNSIhVrlAwI/f\n75tK3cjg8dZf1lRRFO7YFiXR1cSLhy5z4MTQnKeO75wd4VRfisf2dbF3V6ymBbW6rqPrIQZHs3hd\nk8RaI1J+VQhxy+TyecbSGVpao3gMB2uestHLwXEcLgxUG5aeuPAeDUt1lTt3RPnI/s2EDO2WjlkI\nsXD1psB1X/9aIpEIAf8NeG2xgxJitVMUhUhzE+HQ1PqgsoPhq790tuHWeeLB7mrvoJcvcHVWadVC\nyeKZVy7M9A7qiNZ2HsPrw7Jt+vqHaQkHCASkb5AQYvk4jkNyNEWuZOP3B9G0W1dHKV+scPhMkgO9\nwyTH8/PuF2v2sr8nzl3bo/i9LsJhH+m0dAcRYrWoPz/nOqZpTiQSid8FXgT+oFHHFWI1m70+aCSV\npuJoeDz154RvbA3w60/dzoGTQ7x48DLF8rVSq1eSk3zlO8e477Y2Ht/fSS2rg1RVxeMNMpbJk53M\nE2uNSN8gIcSSy+XzJEfT6B4/hnHrAp8ryWy1YenZUcqWfcN9NFXhtu4I+3vibG4Lyoy5EKtYwwKg\nKWGgqcHHFGLV83g8bGiLMTmZYzwzieWouOsMhFRV4f7b2ritO8Lzr1/i6LnRmW2OA68fH+T4+VF+\n/iMJtrXXtnjY7fFiT80GtTaH8PsXV9VOCCFuxLZtkiMp8hUHwxe6JWMoVSyOnh3lwMkhribnb1ja\nHPSwb1eMexIxAl5pWCrEWtDIIggh4OeBlxY1IiHWML/fh9/vq+a6j2ewHBWPUV+QEfK5+fyHt3Nv\nIsazr15gJF2Y2ZbJlfmLZ46zozPMJx/opiW88CIHqqpi+EKMTOTJTOaIRWU2SAjROJlMhrGJHC6P\nH8NY/s+W4fE8B3uHePv0ezcsTXQ2s78nxvbOJmlYKsQKYds2lmVh2xaq4jCRvFBXLNOoIggAJeBH\nwL+p85hCrBs+rxef10uhUGB0fIKyrWLUGQht2xjmX3xmDz870s9PDl+lMmsR7unLaf7w6SO8/84N\nvP/Ojpp6B3lmZoOStDT5CQZWRilaIcTqVKlUGB5JUUHDs8wV3izbpvdiigO9Q5zvn5h3v4C32rB0\n364YTdKwVIgl5TjOTDBj2xbYDo5jo6oKqlJdT60qSvW/KqiKgkdX0T06LpcHj8fF8R/9+VH4as3n\nXnQRhEQi0Qq8Dxg0TVMqwAlRA8Mw2NBmUCwWGUtlKFr1NVPVNZUP3b2RO7ZFee7VC5y+nJ7ZVrEc\nfvTWFd45O8KTD3azbePCVwdVZ4Oqa4My2TyxaPOiSnsLIdanfKHAYHIcwxfEvYyzKePZIodODvPm\nqeE5rQSut6UjxP6eOD2bm6VhqRB1cBwH27axpwIax3FwHAsFpgKa6WBmKrBRFTRVweXV0DUPuq6j\naVpNRVB0XcVxnBsv2rvZe2vZOZFI/A7wm8B9pmmeTSQS9wPfB4JT218CnjBNc/7SKUKId/F4PLS3\neSgWi4ym0pTqnBFqCRn80uM76b2U4nuvXSKdLc5sG00X+MvnT7Jnawsfu38ToRp6B3k8XhzH4crg\nGEGfTqS5WRYACyEWJJPNMjqew+tfnrU+tuNw9kqaA71DnOpLMU8Fawy3xt07WtnXEyfWJA1LhZjN\nsqx5gxlFUVBn/15R0DTQ3Bq67sKle2eCmZV6r7DgACiRSPxT4N9SrfA2PPXyXwE54AEgDXwL+NfA\n7zZ2mEKsDx6Ph4622ExqnIWO211bk1JFUdiztYV7b2vnWz86zavHBubcABw9N4rZN86jezvZ3xNH\nVRf24aQoCoYvQMGy6Ls6REtTUEpmCyHeU3piglSmhOFb+hTabL7MW+YwB08Ok8oU591vY6uf/T1x\ndm9twa3fuspzQiyX2etmHNuqVkzCmZmJmQlmUNFsFZ0CLre6aoKZetQyA/SrwL8yTfMrAIlE4l5g\nB/BvTdPsnXrtPwD/GQmAhFiU6dS4bHaS0XQGzeWtOfXM69H55IObuXNblGdeucDl4ezMtmLZ4rnX\nLvL26SRPPtzNxtaF35xomobmCzGWyTORzRFvjdzSvh1CiJWnUqkwMjpOyVbqSutdKMdxuDSU4UDv\nEMfPj2HZN57ucWkqd2xrYV9PvKbPOyFWIsdxqFQq2FYF27ZRcFAU5gYzs1LOZq+bmQ5mblTcSNdV\nmpv9eNyTVCp1ZZatGrXcUe2i2uNn2ocAB3h+1msngE0NGJcQAggE/Pj9PlLjaSYms3i8/pqfwHRE\n/fyzJ2/j0Mlh/v5g35yqR1dHJvmz7xxnf0+cj+ztxOtZ+EeCeyot7vLACOGAh+YmqYAvxHpnWRbJ\n0RSFko3H61+y9T6FUoXDZ0Y42DvEUGr+rPvWJmOqYWlrTZ9vQtwqlmVhWdXgxnHs6toZVUGbKgag\nqQqaphDwuXC7jTU5O7Mcavk0UKgGPNPeB4yZpnlk1mshqilxQogGURSFSHMToWCFweQojuLG5a6t\nOpGqKDMLfF840MfhMyMz2xzgjd4hjl8Y42P3beKObS0L/iCtpsUFmSyUyPYPEY8243YvfG2REGJt\ncByHkdEUk4UKHq8fw7c0N2P9I5Mc6B3iyNkRSvM8oVYVhdu6m9nfE6e7PSQ3hmJFsG2bSqU8N7BR\nmAlwposCeD0aLt2DyxVA13X5+V0itQRAx4AHgbOJRKIJ+CDw3ev2+ezUfkKIBtN1nY3tccbTacYz\nGTzeQM0fjEGfm89+cBv3JGI888oFkuPXnpxm82W+8eOzvGkO8+RD3bTWsChYd7sBN/3D41IkQYh1\nZiIzQWoij+72YfhqW7O4EOWKzbHzoxzoHZqTynu9poCbvTvj3LuzlWANRV6EWIyZdDTbwrFtHNtC\nVRXcLg27DIqVR7WraWgenwQ2K0UtAdCfAF9NJBJ3Ui164AH+ECCRSHQA/xD4P4BfafQghRDXNIXD\nBAOBmTSTehYXb+kI8Ruf3s0rRwf48dtXKVvXnqSe75/gj54+yvvu6OADd23ApS+8JKzhC5CvVLg8\nMExrJIzXaPzNkBBiZZhu6OworiXp6zOSznOwd5i3TifJFys33EcBtnc2sb8nTqKzacFFXcT6Nqdk\ns2PjOHa1MIAD4OBQ/f10yeaZ/8INSzkHfC5crmulnBVFmVlPk0qt/fU0q9GCAyDTNP82kUh4gF8H\nbODnTdM8OLX53wBfAP4/0zT/pvHDFELMpmkabbEo5XKZ5PRC4xrLZuuaygfu2sCerS1877WLnOob\nn9lm2Q4/PnyVI2dHeOKhbnZ0Lnx9j67roAcZGs3ic+dojcpskBBrSalUYmQsPfW509iCApZlc9gc\n5keH+jh7JT3vfn5D596dMfbujBEJyYMW8W7X1tJY4FhomoqmVNfPuDQVbarKmT5VEKAa0KhTAY8i\n31trXE0rAk3T/EvgL2+w6feA3zVNc7QhoxJCLIjL5aKjrZVcPs9IagJVM9BdrpqOEQkZ/OJjCU5e\nSvHcqxdJT5Zmto1livz1909xe3eEjz+wmbB/4WklhtdHWUpmC7FmTOZyTEzkKFUcPD4/jQw70pMl\nDp0c4k1zmInJ+RuWbm4Psn9XnNu6I+iaNCxdL6bTzErFIuVKBWynuo5Gra6juVbxTEFVq7M0HpeK\nx+/B7XZLA2/xLg35iTBN82ojjjOfqZmnPwU+RbXIwn82TfP3b/KezVTXI33cNM2fLeX4hLjVfF4v\nXV4vqfFx0tkMvkBt6SiKotCzOcLWDWFeeusKrx4bYHY12eMXxjh9ZZxH7unk/tvb0BaYZjJTMjtb\nIJ2dpDXSJEUShFhFbNsmNT7OZKECqo7b7cPToH/CtuNw7upUw9JLKeapYI3HpXHXjij7d8WJR5au\npLZYWtVmmtVfM6ln1/ekmSeYcesaTT4vum0A6kzlMyHqtVpC4v8E3A18ANgMfD2RSFw0TfPb7/Ge\nPwPkk1KsK81NTYSCFmPjaQo5qGbIL5zHpfHR+zZx145Wnnn5ApeGMjPbSmWb59+4xOEzSZ58qJuu\n+MKDrOlmrv3D4/gMndYWSYsTYiWbDnyy+Qq624u7gev5coUyb5lJDp4cZnSiMO9+HS0+9vfE2bMt\nisclN7srkWVZ1WDGtqaCG+td1c00VUVVpwIbTZlaH1NNPXuvnjSz6bpKOOzHtmU9jWiMFR8AJRIJ\nH9XCCo9Nldw+kkgk/iPwJeCGAVAikfiHgHQ6E+uSpmm0x6N4vRqnz/ZjOa6pKm0L1xbx8YUnenjb\nTPL9A31zFiAPjOb482dOcO/OGI/t68JnLPxjxPAFJC1OiBWsWCySnpgkVyzj8vjweBsT+DiOw+Xh\nLAd6hzh2fpSKdePpHl1T2NvTxt3bW+hoqb3vmaiP4zhYVrWK2exgRmGqueZ0AYBZDTY1DQy3hq67\ncOle6UcjVpUVHwABd1Ad5+uzXnuFauGFd0kkEi3A/ws8SrUxqxDrkmEYdG6IMzKaYjyTweXx1ZQy\noCoK9+6MsWuqd9BbZnJmmwMcOjVM78Wx6ozR9uiCv/Rm0uIyeTLZHLHWiKQyCHGLOI5DoVAgM5mn\nUKpgOwoeo3HlrItli3fOjHDw5BADo/O3CWwJG+zfFWfvrlba42HS6RzWPEGSmJ9t29i2XQ1kZlLM\nmFkvoyjg0jWsEjiVPFjOTCUz3VDRNRe6BDNiHVgNAVA7MGKa5uwamEOAkUgkWm5QeOH3gb82TfNk\nIpFYtkEKsVI1hcOEQyGSoylyuTweb21PVf2Gi0+/fyv3JFr57ssXGJ7VdX2yUOHpn5zjLXOYJx7q\nJt688KxTt8dbfSo8MEJzyEs4FKrpuoQQtatUKmSyWYoli7JlU7FsNM2Fy+1paJrb4FiOA71DvHNm\nhGLZuuE+qgK7NkfY3xNna0e1YammyQ33e7Ftm3KpiG1X0FQFbapymaZW18touoqmqajqtRQzVVVn\nqpsBUp5ZCFZHAOQDite9Nv1nz+wXE4nEI1R7FH1hMSfU1kllmenrXA/Xu56uFW58vR1tUSqVCoPJ\nMWx0XG7PfG+/oa0bwvzWZ/fwytEBfvDmFcqzvjgvDGT4428d4313tPPhezbiXnC+voIeDJErlsgn\nR2hrjdRVrWc9/f2up2uFlXmdK3FMNzI9znwhz3h6knLZxgJcbgPN46HR867lis3x86O8cWKIi4OZ\nefcL+d3s74mzb1eM0HWVJef+fK+Om/N6x2xZFpVKGRxnqg9NtQONwrXVm9MpZ9MBjsfQ8TYF8Xg8\ndc/OrMbPEBnz8lhtY17MOFdDAFTgukBn1p9n5tMTiYQBfBX4ddM0SyxCKORdzNtXnfV0vevpWuHG\n19vaGmZiIkMylcFtBG66+PR6T3xgOw/d1cn//uFpjpy5lhZn2w4/OdzP0XNjfP7RBHu2RWs4qg/H\ncZjITdIc8hJpDtc0pmnr6e93PV3rSrPS/987jkMmO0kmm2EkNY6iuQm3tCzZ+ZKpHC+/089rR/vJ\n5ucvYd3THeF9d21k97YWtJt87gQCq6+3z+wxT6+pqVQqYFs4jo0yVRhA01RcuorH7cbnbZ6ZoZn+\ntVxW+s/xjciYl8dqHHOtFMdZ2Tm2iUTifuCngGGapj312geA75mmGZi13/uAHwOTXHt44gfywNdM\n0/ziAk/pTEzksazV8eRpMTRNJRTysh6udz1dKyzsem3bZnhkjHzJxvDWV4yg9+IYz75ykVTm+kla\nuK27mU8+2E1zsLaZpnK5DFaBttbIgktmr6e/3/V0rTBzvSspL2pFfUc4jkOpVKJQLFIslSmVbcoV\nB1V34fUaBAIG2Wyh4eO1bIdTl1K8cWKQ05fnb1jqM3T27oyxvydOS/jmQY2mqUs25qVQqVRw7Ap+\nn4tCvoQCaBpTQY6OZ6oHzUpaT7MaP0NkzMtjtY15Md8Pq2EG6B2gDNwHvDb12sPAoev2OwBsv+61\ns1QryP2wlhNalr2u8mLX0/Wup2uFm19vNBKhUCiQHEujqJ6aq8UlOpv5zc+E+PHhq7x8ZAB71gOV\nExdSnLmc5sP3bOSB3W03feI7TVV1UAP09Y8R9OlEmhdeMns9/f2up2tdaW71//tCocBENkexXKFi\nOSiqhq670XUPaDCdgTp9A2NZdsMKCkxMlnjTHObQyeE5TZOv1xUPsL8nzu3dLbh0dWocCxlD48fc\nCJVKhUqlBLaFpqnoanUmx+/RCfiDxGJNjI/n5v25qF7LyrkeuPU/x/WQMS+P1TjmWq34AMg0zXwi\nkfg68NVEIvFPgI3AvwJ+CSCRSMSBtGmaBeD87PdOFUHoN01zZHlHLcTqYRgGnR0G4+k045kMbsNf\nUxqG26Xx2L4u7twW5ZlXL3BxYFbvoIrN9w/08fbpJE89vIVNbQvvHWT4AuQrFS73D9PaEsbbwAXa\nQqwmlmWRncySy5UpWTaoGm63gctj4FqG8zuOw7n+CQ70DnHyYmrOg47Z3C6Vu7a3sm9XjPaW1Vfi\nvlwuY1llHLvay0abqo6maSpBQ8NrhHC5XO96IKPr6oqZ3RFCLMyKD4Cm/EvgT4GXgDTwO6ZpPjO1\nbQD4x8DXb/C+lfW4RYgVrCkcJhQMMjI2zmSujOEN1PSlHo/4+MInejh8ZoTn37hErnCtcONQKs+f\nP3uCexOtPL6/C5+xsNs2XddBDzI0msXrmiTWGpEbDbFmTaez5fJ5SmUL23ao2A6W5aC7DXS3710L\nYpdSvljh7dNJDvQOMZKev2FpW6TasPTObVE87pVf0t62bSrlErZVQVXBpVUrpzX53Hg8XnRdX9a1\nOEKI5bcqAiDTNPPAL0/9un7bvJ9Spmmu/E9iIVYQVVWJRSNUKhVGRscpVMDwLry0taIo3L2jlZ1d\nzfz9wT4OnRqes/1NM0nvxRSP7+/i7kQr6gKDGcPro2JZ9PUP0xSUktli9XIcp3oDXqmQLxQolS0s\ny8Gy7Wo6m6bhcnlQNTeKBi5Yllme2eO7kpzkQO8QR8+NvGfD0tu7W9jfE6crXtvDkuUyu2S0ripo\nqoqmKXhdGobfi8fjkUBHiHVqVQRAQojlpes6bfEohUKBkdQEtqLjdi88Bc1n6Pzc+7ZwT6KVZ165\nMKcBYq5Y4ds/O89bZpInH+6mLbKwAEvTNDRvkIlckczkMNHmEIakxYkVaLr6V75QoFSyqFg2lm1j\n2Q4OoKCiqCq6y42muVDU6pfxrfxCLpUtjpwb5UDvEP0jk/PuFwl52L8rzt2JVvwLnMldDpVKBasy\nlb42NavjcWm0tARwu90rMkATQtw6EgAJIeZlGAYb2w0ymQxj6Qk0t6+mPj1d8SBf/LndvH58kB++\ndZlS+dqiyktDGf7kW0d5cHc7H7pnI54F9g6q9i/yMDiVFtcabQbkKa5YfpVKhXy+QL5QomLZVGwb\n23bgugBH1ao/oSsnXLhmKJXjYO8wh88kKZRu3LBUUWDXpuZqw9IN4QXP3C4Fx3Eol4pYVhl9qhiB\nrqn4vDoedwCXyyWzOkKIm5IASAhxU8FgkEAgwFgqRSZXwOP1L/iJqqYqPLSnnd1bIvzd65c4fmFs\nZpvtwMtHBzh6bpRPPLCZns0Lr/hmeH1Ytk1ff5JYS4Dm5tW36FqsPhcvD5JOV9fooGhouguXT5Uw\n/QAAIABJREFUy0DVoLYairdOxbI5cWGMAyeH5hQtuV7I5+LenTH27owRDizn6qMqx3Eol0vYlTKa\nCrqm4napNDV5MYyFf1YIIcT1JAASQiyIoii0RCKEQxWGR1KUHRW3Z+HN0sIBD7/wkR2YfSmee/Ui\nY7N6B6UnS/ztD06T6Grikw9sJhJaWGqbqqoYviDj2RLKlUHcevWJuxBLxXI0XIYf1bX6auykMgUO\nnhzmTTPJ5Hs0LN22Icy+nji7NjUtuHx9I5RKRVTHouy2oVLErUA4ZGAYYZnVEUI0lARAQoia6LpO\nR1sr2ewko+MT6B4/mrbweiOJrma2dIT5yeGr/OxIP5Z97UbS7Bvn/NWjfPDuDTy0px1dW9hNj8vt\nQfP46B8YwaU6tEabaxqTEGuVbTucvJji9eODnL48Pm9pVK9H454dMfb1xIiGl74L/HQqm22Vcesa\nuq4SCXoIBf20tARJpSbXfB8SIcStIwGQEKIugYAfv9/HyGiKyVwewxdY8HtduspH9nZy5/Yoz7xy\ngfP9EzPbypbNi4cuc/jMCE8+1M2WjoVXfDN8PioVmyuDowR9bpqbwpImI9alTK7E26eTHDqVZGxi\n/hLWnbFqw9LdW641LF0q5VIJq1LCpSm4XNoNU9lkpkcIsRwkABJC1E1RFFqjEcKlEsmxcSxqqxbX\n2uTlVz6+iyPnRnn+9UtkZ6XlJMfz/MX3erlre5SP3reJgHdhqW2KouDxBsiVK2T7h4lGQvi8S/9E\nW4hbzXEcLgxUG5b2XkzNmV2dzaWr3Lktyv6eOB3RpVs75zgOxUIOBRuPrtHkN/D7QxLkCCFuOQmA\nhBCL5na72dAWm6kWV0tanKIo3LktSqKziRcPXeZg79CcNJ3DZ0Y41Zfi0b1d7N0VW3AFqukmqsOp\nSYyJSUmLE2tWvljh8JkkB3qHSY7n590v1uxlf0+cu7ZHMdxL8/VfLpexykVcuoLh1mhpDeN2r5by\nEEKI9UICICFEw0xXi6snLc7r0XnyoW7u2dHKd1+5MKcXSb5o8cwrF3j7dJInH+qu6am1YfhwHIfL\nAyOEAx6am5pquiYhVqqrySwHTg5z5OwI5XnWy2iqwu4tEfbuirO5LdjwlFDLsigVC2iqg1tTafIZ\n+P1RmeURQqxoEgAJIRpqdlrc0EgKVPdU756F2RgL8MWnbudA7xAvHrpMsXytN8nl4Sxf+c4xHrit\njUfu7cTjXvgsk+ELMlksk+kfItosaXFidSpVLI6eHeXAySGuJudvWNoc9LC/J86H9m3CqVSwrMZU\nrXMch1KpALaFW1PwGS7izc019QcTQohbTT6xhBBLwu1209kRJz0xQWoig8cbWPDTZ1VVuP/2Nm7b\nEuH51y9x9NzozDbHgVePD3Ls/Cgff2Azt3dHgAWmxblc4HLNpMXFWiPypFqsCsPjeQ72DvH26fdu\nWJrobGZ/T4ztG5twuVRCfjfpdGXR57csi3Ixj+FSiTcHMIyFr/UTQoiVRgIgIcSSCodCBAMBhpNj\nFC3wGL4Fvzfkc/P5D2/nnkQrz75ykdFZ1awmcmX+5w/PsKMzzFMPbyEcXvhxp9Pi+vqTNAUNmsLh\nmq5JiOVg2Ta9F1Mc6B2aUynxegFvtWHpvl0xmhrcsNSyLCqlHAGvi/YOSW0TQqwNEgAJIZacqqq0\nxaNMTuYYGc+gu301FSTYvrGJf/GZPfzsSD8/fecqlVnpPKcvp/n9//0OH32gm/t2taIscDZoOi0u\nmy8xMSlpcWLlGM8WOXRymDdPDZN5j4alWzpC7O+J07O5eUkalhZzk3gNjY6OmJSTF0KsKRIACSGW\njd/vw+fzMjqWIltH76AP37ORO7a18NyrFzlzJT2zrWI5PPfyeV4/2s8TD3WzbcPCZ3R0txtwk0xN\n4kpniUVlPYNYfrbjcPZKmgO9Q5zqS+HMs2THcGvcvaOVfT1xYk1LE7BXSiUcu0hbaxMeT2NnlIQQ\nYiWQb3khxLJSFIVoS4RQnUUSomEv//ijOzl2foy/e/0imdy1J+Qj6QJ/+Xcn2bO1hY/fv4mgb+Hl\ndz1TaXFXBsfwujVao82S7iOWXDZf5i1zmIMnh0llivPut7HVX21YurUFt7405dxt26ZUmJxKC40v\nyTmEEGIlkABICHFLTBdJmMhMMD6RQXV5FzzzoigKe7a2sKMzzA/evMIbJwbnPDE/em4Us2+cR/d1\nsn9XHFWtJS0ugGXb9PUnCfpcRJqbJf1HNJTjOFwaynCgd4jj58fmb1iqqdyxrYV9PXE2ti58trSe\n8RTzk/gMnbaOVgn8hRBrngRAQohbKhQMEQwEGUulyOTyNVWLM9w6n3xgM3t3tvLsq5e4OHBtoXix\nbPHcqxdnegfVcgOpqiqGL0jBsujrHyYckEIJYvEKpQqHz4xwsHeIodT8DUtbm4yphqWteD1L+zVd\nyE1iuBQ6Yk3SsFQIsW5IACSEuOUURaElEqEpbJEcTVEoORi+hTc73dAa4P/8xXv5wRsX+P4bfXPK\nBF9NTvJn3znO/p44j+7rxHAv/GNP0zQ071ShhOwQkXCAQGDh4xICoH9kkgO9Qxw5O0JpnoalqqJw\nW3cz+3vidLeHlnzWsVDI4VYdOmJhCXyEEOuOBEBCiBVD0zTaYlGKxSIjqTQWOm73wvqNqKrCfbe1\nsbOrme+/0cc7Z0dmtjnAG71DnLgwxsfu38SerS013WBOF0oYyxZIZydpjcjTcvHeyhWbY+dHOdA7\nxOXh7Lz7NQXc7N0Z596drTWtWatXdZ1PllhLWKoeCiHWLQmAhBArjsfjYUNbjEwmw9hEBpfHv+B1\nCUGfm899aBv37Gzl2VcukBy/1jsoky/zv186y5vmME8+2E20xipa08HYwHAaw61KoQTxLiPpPAd7\nh3nrdJJ88cYNSBVge2cT+3viJDqbFrxGbbFKpQIuLLo6YvJzK4RY1yQAEkKsWMFgkEAgQHIkRS5v\nY3gX3ux0a0eY3/j0Hl4+MsCPD1+Z0zvo3NUJ/vDpo7z/zg7ef+cGXHptN4Men18KJYgZlu1w8lKK\ng71DnL2annc/v6Fz784Ye3fGiIQWNrPZKIVcluaQQTgUWdbzCiHESiQBkBBiRVMUhVhrhEKhQHIs\njaJ6plLSbk7XVD549wbu2NbCs69e5PTl8Zltlu3w0ttXeefsCE8+1M32jU01jWt2oYRLV4cJ+txE\nmpskEFpH0pMlDp0c4s1Tw0zk5m9YurktyP6eOLd1R9C15Z15qVQqlAsZKXIghBCzSAAkhFgVDMOg\ns8NgPJ1mPJOpqVpcJGTwS48nOHFhjO+9fomJydLMtrGJIn/1/Cl2b4nw8fs3E/LXdpOoaRrarEAo\nHPDQFA5LILRG2Y7D6cvjvH58kFOXUsxTwRqPS+Ou7VH29cRpiyx85rKRioUcLWGNzo44ljXPQIUQ\nYh2SAEgIsao0hcOEgkGGR8YoVMAwFnZzqSgKt29pYfvGJn701hVeOz4w5+b12PkxTl9O88i9G7nv\ntja0GtdlTAdCk6UKmf5hwkEv4VCopmOIle//+dphRtKFebd3tPjY3xNnz7YoHtfSNCy9mVKpgOpU\niEXDxFtbSKUmqZYCEUIIARIACSFWIVVVaYtFyU+lxbk8C3/C7nFrfOz+Tdy1I8ozr1ygb+haha5i\n2eLvXr/E4dNJnny4m85YsOax6boOepBMrlo6u6U5JNW21pAbBT+6Vm3Mu3+qYemtmv2rlMvYlcJU\nufYIeo1r24QQYr2QAEgIsWp5DYOuDoNMdoL8ZAbHWfgNX3uLn3/6xG28ZSZ54UDfnIpd/aM5vvrd\nE+zdFeOxfV11NaOcLp2dTE3iSmeJRZurwZFYM6Jhg3274ty9oxWfcWv/bouFHAFDpSUev6XjEEKI\n1UC+jYUQq15zUxOhkMGp05cplhw8C0yLUxWFvTtj7NrUzN8f6OOt08mZbQ5w8OQwJy6m+Nj+Lu7c\nHq3ryb7H8OE4DlcGxwh4dVoiUjFuNVMV6OmOsG9XnK0dS9+w9GYcx6GQyxCLhPD7b81aIyGEWG0k\nABJCrAmaptHR1spEZpKRsQlUl3fBMy4Br4tPf2ArdydaeeaVCwyn8jPbJvNlvvmTc7xpJnnyoW5i\nzbWnsymKguELULQs+q4O0dIUJBDw13wccev9u1+5F4/LtSKKClQqFexyjs72qMwuCiFEDSRBWAix\npvi8Xro2xPG5bIr5LI6z8BvV7vYQv/Hp3Ty+r+tdvYEuDEzwx986yt8f7KNUseoam6ZpeHwhxrJF\n+geHqVRu3ChTrFy1VglcCtVZnyw+l01nR1yCHyGEqJEEQEKINSnS3MTGthYo5ygWcgt+n6aqvO/O\nDn7rs3ewa1PznG2W7fDTd/r5w28e5VRfqu6xud0GisvPlcExRsfGagrSxPpWKuZxyjk2tkWk75QQ\nQtRJAiAhxJqlaRrtba1Ewz6K+UxNMy7NQQ+/+FiCX3x0B02BuU/9U5kiX3/B5G9eNBnPFusa23Ra\nXL6i09c/TCaTvfmbxLplWRbF3ASRoEFHW6vM+gghxCLIJ6gQYs3z+334fF7GUikyuQKGL7Dg9+7a\nHGHrhjAvvX2FV44OYs+arem9mOLslTQfvmcjD+xuQ1Nrf6ak6zq6HiQ1WSCdHaalOYTXMGo+jli7\nCrksAa9Ox4a4zPgIIUQDyAyQEGJdUBSFlkiEjW0RKsUMlVJpwe91uzQe37+JL316N5vb5vYGKlVs\nvn+gj698+ziXBjN1j8/tNtA9AYbGsvQPDlMozN9sU6wPlUqFciHDhngz0ZaIBD9CCNEgEgAJIdYV\nXdfZ2B4n4FUo5GpLO2uL+PjCJ3v49Pu3vKvvy+BYjj9/9gTf/uk5coVy3eMzDB+qO8DgWJarEgit\nW4VCDo9aobMjjsvlutXDEUKINUVS4IQQ61JTOEzAX2FgeBRF9Uw1Lr05RVG4J1HtHfTCwcu8eWp4\nzvY3zSS9F1M8vr+LuxOtqHU+tTemehkNjmVxqxlppLpOOI5DMZ+ltTkofX2EEGKJyAyQEGLd0nWd\nzo7qbFCtJbN9hotPvW8Lv/bkbbRF5t6o5ooVvv2z8/zX53oZHFt4BbobMQwfisvHlcExhpNj2La9\nqOOJlcuqVKgUs3S2RyX4EUKIJSQBkBBi3WsKh9kQj+CUJymXa6vq1hUP8s8/tZuP3bcJ93W9gy4N\nZviTbx3j+29colSur3cQXKsYV1Hc9F1NMjqWktLZa0yxmMc9lfKmadqtHo4QQqxpEgAJIQTV2aCO\nthghr0Yhl6kpwNBUhYf2tPPbn7uD27ojc7bZjsPLRwf48jeP0HtxbFFjVFUVjy9AtqjQd3WITKb+\nogti5SjkMrQEDVqjkZvvLIQQYtEkABJCiFnCoRCd7VGsUramSnEA4YCHf/iRHfzS4wmag54528az\nJf7mxdN8/QWTVKa+3kHTdF3H7Q0ynqtwuX+IXD6/qOOJW8OyLEr5CTa2tRAI+G/1cIQQYt2QAEgI\nIa6jadpMpbha1wYBJLqa+c3P7uEDd21AU+cWQTjVl+LL3zjCT9+5SsVa3Hoel8uDywiSTE1KxbhV\nplQqoDlFOjviUtxCCCGWmQRAQggxj8WsDXLrGo/u7eQ3PrOH7vbQnG1ly+bvD17mT759jAsDE4se\np8fwobkDDI5WewgVi4ubYRJLq5DLEvbptMWi0ttHCCFuAQmAhBDiPcxeG1TPbFCsycuvfmIXn/3g\nVvzeuf1chlN5/utzvTz9k7Nk8/X3DppmeKs9hAZGMgwMJimXF39M0TiWZVHMT9ARayIcCt38DUII\nIZaEzLsLIcQChEMhAn4/Q8kxKoqOy+W5+ZumKIrCXdtb2dnVzIuHLnOwd4jZYdTbp0c4eSnFY/u6\nuHdnrO7eQdMMrw/HcegfGsfjUoi2NEma1S1WLObxuhQ6OuIy6yOEELeYzAAJIcQCaZpGR1trXZXi\nALwenScf6ubXnrqdjujcRe/5osV3X77Anz9zgoHRyUWPVVEUPD4/ju7lyuAYg8MjWFb9pbhFfRzH\nIT85QUvQIBaNSPAjhBArgARAQghRo3AoxMa2FpzyJKVS7YUHOmMBvvjU7Xzigc14XHN7vlwezvKV\nbx/j716/SLG0+IBluoeQo3m5PDDK8Ig0U10ulanGpl0drVLlTQghVhAJgIQQog7Ta4Oa/S4KuUzN\nQYWqKjxwexu//bk72L2lZc4224FXjw3yB988wvHzow1pejrTTBU3ff1JRkYlEFpKhUIOjzQ2FUKI\nFUkCICGEWIRgMEhXRyuqXaBYrL0fT8jv5h88sp1f/thOIqG564omJkv8jx+e4WsvmIxNNKbEtaqq\nGL4gRdvF5YERRsfGGhJgiWsKuQzRkFcamwohxAolAZAQQiySqqq0xaJEQ14KuYm6Zla2b2ziNz9z\nBx+6+929g05fHufL3zzCS29fWXTvoGmapuHxBihYLi5dHSY1Pi6B0CJNV3nbEI9IypsQQqxgEgAJ\nIUSD+P0+ujpi1dmgQq7m97t0lUfu7eQ3P7uHbRvCc7ZVLIcfvnmFL3/jCKcujjVqyGiahuELMllS\nudw/THpi8X2J1qNSqYBqFejqiONyuW7+BiGEELeMBEBCCNFA07NBLSFvXZXiAKJhL7/8sZ18/sPb\nCPrm3kwnxwt8+X8d5n/98AyZXKlRw0bXddzeIJmcTd/VITKZTMOOvdZNNzZtb2uVKm9CCLEKSGMI\nIYRYAoGAH6/XqPYNQsflXnjfIKgWLdizNcqOziZ+8OYV3jgxyOxY6vCZau+gR/d2sm9XHFVtzI23\n7nYDbsYni6QmhmgO+QgGgw059lpjWRaV0iQdsQhut/tWD0cIIcQCyQyQEEIskem+QUGvWvdskOHW\n+eQDm/niz+1mY+vcdSWFksWzr17kq88c52oy26hhA+Bye3B7g4xPVujrH2JysvaUvrWsVCqgOyW6\nOuIS/AghxCojAZAQQiyxpnCYzvYoSiVPoY61QQAbon5+7cnbeerhbryeuZP3V5KT/Ol3j/Pcqxcp\nlCqNGPIMl9uD2wgyOpHn6uAwxWKxocdfjQr5HGGfTjzWIilvQgixCkkAJIQQy0DTNNriUeKRAOVC\nhnK5XPMxVFXh/tvb+HdfuI+7tkfnbHMceP3EIH/wjSMcPTfS8Ipubo8XzR1gYCTDwGCSSqWxgdZq\nUSpO0hYNEg6FbvVQhBBC1EkCICGEWEZew6CzI47PZVHI1Ze2Fg54+Pwj2/mVj+8iGjbmbMvkyvyv\nH53lr54/xUi69r5EN2N4feDycWVwjKHh0XXXTHXHlk68hnHzHYUQQqxYEgAJIcQt0BKJ0N4appif\nqHs2ZeuGMP/iM3t45N6N6NrcVKyzV9P80dNH+eGblylXGhukKIqC4QtgqR76+pOMpdZPDyFN0271\nEIQQQiySBEBCCHGLeDweujriGFqFQr6+tUG6pvKhuzfyW5+9gx2dTXO2VSyHl96+yh89fZQzV8Yb\nMeQ5VFXF8AXJlVX6+ofJZicbfg4hhBCi0SQAEkKIW0hRFKItEWIRP8XcRN0pZZGQwS89nuAXHtlO\nyD+3KtnoRIG/ev4U//OHZ5iYbFzvoGm6ruPxBhnLFrkyIIUShBBCrGzSB0gIIVYAn9dLZ4eHoeFR\nSmi43bWvM1EUhdu3tLB9YxM/fOsyrx8fxJ6VmXbs/CinL4/zkb0bua+nrWG9g6ZNj3kwOYHhVmmN\nNqOq8pxNCCHEyiLfTEIIsUKoqkp7WyvNflfdfYMAPG6Nj9+/mX/+qd10xgJzthXLFt977RJ/+t3j\nXB5ubO+gmfP7/DPrg0bHUutmfZAQQojVQQIgIYRYYYLBIJ3tUZxyjnK5/nSy9hY//+zJ2/i5h7vx\neuYu3u8fmeSr3z3OM69cIF9sfEnr6fVBBUunr3+Y1Pj6KZQghBBiZZMASAghViBN0+hoayXk1RY1\nG6QqCnt3xfntz93J3Tuu6x0EHOgd4ve/cYTDZ5JLEqBomobHGyRbVOi7OsR4Ot3wcwghhBC1WBVr\ngBKJhAf4U+BTQA74z6Zp/v48+34c+A/ANuAc8DumaT63XGMVQohGCodC+H0+BpOjoHjQvJ66jhPw\nuvjMB7ZxTyLGM69cYDh1rUfQZL7MN398jrfMJE881E2syduo4c9wuVzgcpHNl0hnh2gOeQkFpZmo\nEEKI5bdaZoD+E3A38AHgi8DvJhKJT12/UyKR2AN8C/gL4A7gvwBPJxKJ3cs3VCGEaCxd19nYHifg\nVSjkFldqurs9xJc+tZvH9nXi0uZ+BZzvn+CPnz7Kiwf7KFWsRZ1nPrrbjccbZGLSpq9/SEpnCyGE\nWHYrfgYokUj4gF8BHjNN8whwJJFI/EfgS8C3r9v9HwA/Mk3zK1N//tNEIvEE8Dng2HKNWQghlkJT\nOEwoWCGXm6BSAUWprymnrqm8/84N7Nka5XuvXeTkpdTMNst2+Mk7/Rw5N8oTD24m0dXcqOHPHYPb\nDbgZyxZITWSJNAXx+3xLci4hhBBittUwA3QH1UDt9VmvvQLsv8G+fw386xu8Hm78sIQQYvm53W42\nd7Xj0SoUCvU1T53WHPTwi48l+EeP7iB8Xe+gVKbI114w+dsXT5POLl1fH7fbwGUEGU3nuTooPYSE\nEEIsvRU/AwS0AyOmac4uUzQEGIlEosU0zdHpF03TNGe/MZFI3AZ8mOr6ISGEWBMURSEWbcGTzpJM\nZfB4AyhK/T19ejZH2LYhzEtvX+GVo4PYs4ohnLg4xpkr4zxybyf3396G1uDeQdPcnuq6o4GRDB5t\ngtZoM7q+Gr6ihBBCrDarYQbIB1z/SHD6z/OuBk4kElGq64FeNk3z2SUamxBC3DJ+v4+ujtZFl8sG\ncLs0Ht+/iS99ejeb2oJztpUqNs+/cYmvfPsYlwYzizrPzRheH7h8XBkcYzg5hm3bS3o+IYQQ689q\neLxW4N2BzvSfb5j/kUgk4sAPqFZ5/WytJ9S01RAXLt70da6H611P1wpyvWvZu69VpWtjnPF0mtRE\nDsPnX9TxN7T6+bWnbuMtM8nzr18iV7g2+T44luPPnz3B3l0xPnpfF37DtahzzU9BDwaxbZsrg6Ps\nfuCjLf3mK6M3f9/yWC0/Z6vx34WMeemttvGCjHm5rLYxL2acykpvTJdIJO4HfgoYpmnaU699APie\naZqBG+y/AXgJsIAPmqY5VOMpV/b/ECGEmEelUuHKQBJUDy63++ZvuIlsvsx3fnyWV4/2v2tbwOvi\nUx/cxv272xeVfrcQj37ut7af+PFfnF3SkyycfEcIIcTKUdcX0GqYAXoHKAP3Aa9NvfYwcOj6Hacq\nxr0wtf8HTdNM1nPCiYk8lrX20y40TSUU8q6L611P1wpyvWvZza41HAgxnk4zNJpe9GwQwBMPbmLP\nlma+87MLDI5dm3TP5st8/fmTvHz4Ck+9bwttkaWp4LYSn0Sulp+z1fjvQsa89FbbeEHGvFxW25in\nx1uPFR8AmaaZTyQSXwe+mkgk/gmwEfhXwC/BTLpb2jTNAvBvgW6q/YLUqW0AedM0JxZ6TsuyqVRW\n/l98o6yn611P1wpyvWvZe11rwB/E8HgZGB5F0Qx01+JS1TpjQf75p27nteOD/OjNK5RmnffCQIY/\n/MZRHtrTxofu3ojbVV9p7vmtvL/P1fZzttrGCzLm5bDaxgsy5uWyGsdcq5X3aO3G/iXwFtXUtj8G\nfsc0zWemtg1Q7fMD8CnACxwA+mf9+vKyjlYIIW4xXdfp7IjjdVkU8osrlw2gqSoP7+ngtz53Bz2b\n5/YGsh2Hnx0Z4MvfPMLJi2OLPpcQQgixlFb8DBBUZ4GAX576df02ddbvdy3nuIQQYqVriUTwFQoM\nj6Zxefyo6uKeezUFPPyjRxOc6kvx3KsXSWWuVZ8bz5b47y+eZtemZj754GaaAvMW6hRCCCFumVUR\nAAkhhKif1zDo6vAwlBylWAKPsfj1Oju7mtnSEeInb1/l5aMDWPa12gAnL6U4ezXNh+/eyIN72tAW\nGXQJIYQQjSTfSkIIsQ4oikJbLEpLyEshl6ERFUDdusaj+7r4jU/vobs9NGdbuWLzwsE+/vhbx7gw\nsOAlmEIIIcSSkwBICCHWkUDAT1dHK5RzFIv5hhwz1uzlVz+xi89+YCt+Y25iwXAqz399rpenf3KO\nbL7ckPMJIYQQiyEBkBBCrDOqqtLe1kok4G7YbJCiKNy1o5V/+fN3sm9X7F2NGd4+neQPvnGEQ6eG\nsVd4/zkhhBBrmwRAQgixTgWDwZnZoFKp0JBjej06Tz28hV976jY6WuauNcoXK3znZ+f5L8+eYGB0\nsiHnE0IIIWolAZAQQqxj07NBzX4XhVymYcftjAX59Z/bzSce2ITnut5AfUNZvvLtYzz/+iWKJath\n5xRCCCEWQgIgIYQQBINBNra1UC5kqJQbs1ZHUxUeuL2d3/7cHezeEpmzzXbglWMD/ME3j3D8/GhD\n0vCEEEKIhZAASPz/7d15mFx1ne/xd6ezNpCNkIVNVPSLgIJcFR3GQdEZRK/oRWZAvdcFHFTGBx2c\nK6CAiMw4IC4DKqgj+OC+jQOCOC64gwyMCoPgV1G4QQjRkJAgnaWT9P3jnMZK01sl1V2n6rxfz5Mn\nXad+VfX99a/q/PpTZ5MkoPUXTx0yd6eZvPz5T+Q1R+3HwrnbXhto3cOb+Oy3f80V30hWr2vNbniS\nJI3FACRJ2sauCxeyeOFObOhf19ItM0/caz5vPvYgjjhkD3qnbXuahLznQT74pVv47k/vZfOWrS17\nTUmShjMASZIepW/OHPbefTEM9DMwsLFlzztj+jSe/7S9ePOxT2HfPeZtc9/mLYN86+Z7uOjLt/Kb\n+9a27DUlSWpkAJIkjWjoBAlz5/Sycf0fW7o1aNH8Obz2hftx3BH7ssucGdvct2rtBj5x9R188bo7\neah/U8teU5IkgOnjN5Ek1dm8uXPZqa+PFb9/gJ5ps5g+c2ZLnrenp4eD9l1E7D2fb96O+s3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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xecc4320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data, x_vars=['Pclass','Parch'], y_vars='Survived', size=6, aspect=0.7, kind='reg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 3],\n",
+       "       [0, 1],\n",
+       "       [0, 3],\n",
+       "       ..., \n",
+       "       [2, 3],\n",
+       "       [0, 1],\n",
+       "       [0, 3]], dtype=int64)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "features = np.array([data.Parch, data.Pclass]).T\n",
+    "features  # 2D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [0],\n",
+       "       [1],\n",
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+       "       [0],\n",
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+       "       [1],\n",
+       "       [1],\n",
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+       "       [0],\n",
+       "       [0],\n",
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+       "       [1],\n",
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+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
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+       "       [0],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [1],\n",
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+       "       [0],\n",
+       "       [1],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = np.array([data.Survived]).T \n",
+    "response  # 1D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# step 1: split data into training set and test set\n",
+    "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n",
+    "# the random_state allows us all to get the same random numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 2],\n",
+       "       [0, 3],\n",
+       "       [0, 3],\n",
+       "       ..., \n",
+       "       [0, 2],\n",
+       "       [0, 1],\n",
+       "       [0, 2]], dtype=int64)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.63228699551569512"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(features_train, response_train)      # Note that I fit to the training\n",
+    "knn.score(features_test, response_test)      # and scored on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = data[['Parch', 'Pclass']]\n",
+    "y = data['Survived']\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.61452514,  0.68156425,  0.70786517,  0.75280899,  0.70621469])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "linreg.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.820560041245\n",
+      "[ 0.05306367 -0.19793906]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print linreg.intercept_\n",
+    "print linreg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10dd2ba8>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1BofMHI2IK4BVEXEisDdwJnA8QETsATycmZuAf5v83IgAuCcz76+zJkmSVJ9unP1xBrAG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\nSVIxg4MkSSpmcJAkScX+PxNq2W1eCu2LAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d925c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data.Parch,data.Survived)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_cols = ['al']\n",
+    "X = glass[feature_cols]\n",
+    "y = glass.assorted\n",
+    "linreg.fit(X, y)\n",
+    "assorted_pred = linreg.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/labs/06_titanic_lab_solutions.ipynb b/labs/06_titanic_lab_solutions.ipynb
index e8e27d6..e17f53d 100644
--- a/labs/06_titanic_lab_solutions.ipynb
+++ b/labs/06_titanic_lab_solutions.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -132,7 +132,7 @@
        "4      0            373450   8.0500   NaN        S  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,10 +429,11 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/labs/Titanic_Eugene.ipynb b/labs/Titanic_Eugene.ipynb
new file mode 100644
index 0000000..8cbf3ab
--- /dev/null
+++ b/labs/Titanic_Eugene.ipynb
@@ -0,0 +1,1752 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn import metrics\n",
+    "import statsmodels.formula.api as smf\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "# visualization\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data = pd.read_csv('http://localhost:8888/files/data/titanic.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
+      "  RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['male', 'female'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Sex.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.616162\n",
+       "1    0.383838\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Survived.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>455.366120</td>\n",
+       "      <td>0.672131</td>\n",
+       "      <td>1.191257</td>\n",
+       "      <td>35.674426</td>\n",
+       "      <td>0.464481</td>\n",
+       "      <td>0.475410</td>\n",
+       "      <td>78.682469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>247.052476</td>\n",
+       "      <td>0.470725</td>\n",
+       "      <td>0.515187</td>\n",
+       "      <td>15.643866</td>\n",
+       "      <td>0.644159</td>\n",
+       "      <td>0.754617</td>\n",
+       "      <td>76.347843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.920000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>263.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>29.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>457.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>676.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>47.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>890.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   183.000000  183.000000  183.000000  183.000000  183.000000   \n",
+       "mean    455.366120    0.672131    1.191257   35.674426    0.464481   \n",
+       "std     247.052476    0.470725    0.515187   15.643866    0.644159   \n",
+       "min       2.000000    0.000000    1.000000    0.920000    0.000000   \n",
+       "25%     263.500000    0.000000    1.000000   24.000000    0.000000   \n",
+       "50%     457.000000    1.000000    1.000000   36.000000    0.000000   \n",
+       "75%     676.000000    1.000000    1.000000   47.500000    1.000000   \n",
+       "max     890.000000    1.000000    3.000000   80.000000    3.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  183.000000  183.000000  \n",
+       "mean     0.475410   78.682469  \n",
+       "std      0.754617   76.347843  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000   29.700000  \n",
+       "50%      0.000000   57.000000  \n",
+       "75%      1.000000   90.000000  \n",
+       "max      4.000000  512.329200  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dropna().describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0xecc42e8>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EREREpE4ymSzheErNDhpIAUhEREREpA5i8VlSuZKaHTSYApCIiIiISA1ZlkUwFGEOJx6v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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xecc4320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data, x_vars=['Pclass','Parch'], y_vars='Survived', size=6, aspect=0.7, kind='reg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 3],\n",
+       "       [0, 1],\n",
+       "       [0, 3],\n",
+       "       ..., \n",
+       "       [2, 3],\n",
+       "       [0, 1],\n",
+       "       [0, 3]], dtype=int64)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "features = np.array([data.Parch, data.Pclass]).T\n",
+    "features  # 2D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [1],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [0],\n",
+       "       [1],\n",
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+       "       [0],\n",
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+       "       [1],\n",
+       "       [1],\n",
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+       "       [0],\n",
+       "       [0],\n",
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+       "       [1],\n",
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+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
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+       "       [0],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
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+       "       [1],\n",
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+       "       [0],\n",
+       "       [1],\n",
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+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0],\n",
+       "       [1],\n",
+       "       [0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = np.array([data.Survived]).T \n",
+    "response  # 1D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# step 1: split data into training set and test set\n",
+    "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n",
+    "# the random_state allows us all to get the same random numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 2],\n",
+       "       [0, 3],\n",
+       "       [0, 3],\n",
+       "       ..., \n",
+       "       [0, 2],\n",
+       "       [0, 1],\n",
+       "       [0, 2]], dtype=int64)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.63228699551569512"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(features_train, response_train)      # Note that I fit to the training\n",
+    "knn.score(features_test, response_test)      # and scored on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = data[['Parch', 'Pclass']]\n",
+    "y = data['Survived']\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.61452514,  0.68156425,  0.70786517,  0.75280899,  0.70621469])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "linreg.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.820560041245\n",
+      "[ 0.05306367 -0.19793906]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print linreg.intercept_\n",
+    "print linreg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10dd2ba8>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1BofMHI2IK4BVEXEisDdwJnA8QETsATycmZuAf5v83IgAuCcz76+zJkmSVJ9unP1xBrAG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\nSVIxg4MkSSpmcJAkScX+PxNq2W1eCu2LAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d925c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data.Parch,data.Survived)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_cols = ['al']\n",
+    "X = glass[feature_cols]\n",
+    "y = glass.assorted\n",
+    "linreg.fit(X, y)\n",
+    "assorted_pred = linreg.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/labs/untitled b/labs/untitled
new file mode 100644
index 0000000..e69de29
diff --git a/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb
new file mode 100644
index 0000000..b826e3f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb
@@ -0,0 +1,7213 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nCLASS: Pandas for Data Exploration, Analysis, and Visualization\\n\\nWHO alcohol consumption data:\\n    article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/    \\n    original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\\n    files: drinks.csv (with additional 'continent' column)\\n\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "CLASS: Pandas for Data Exploration, Analysis, and Visualization\n",
+    "\n",
+    "WHO alcohol consumption data:\n",
+    "    article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/    \n",
+    "    original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\n",
+    "    files: drinks.csv (with additional 'continent' column)\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# imports\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Pandas Basics: Reading Files, Summarizing, Handling Missing Values, Filtering, Sorting\n",
+    "'''\n",
+    "\n",
+    "# read in the CSV file from a URL\n",
+    "drinks = pd.read_csv('../data/drinks.csv')\n",
+    "\n",
+    "type(drinks)            # Use the type method to check python type\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Antigua &amp; Barbuda</td>\n",
+       "      <td>102</td>\n",
+       "      <td>128</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>193</td>\n",
+       "      <td>25</td>\n",
+       "      <td>221</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Armenia</td>\n",
+       "      <td>21</td>\n",
+       "      <td>179</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>261</td>\n",
+       "      <td>72</td>\n",
+       "      <td>212</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Austria</td>\n",
+       "      <td>279</td>\n",
+       "      <td>75</td>\n",
+       "      <td>191</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Azerbaijan</td>\n",
+       "      <td>21</td>\n",
+       "      <td>46</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bahamas</td>\n",
+       "      <td>122</td>\n",
+       "      <td>176</td>\n",
+       "      <td>51</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Bahrain</td>\n",
+       "      <td>42</td>\n",
+       "      <td>63</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Bangladesh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Barbados</td>\n",
+       "      <td>143</td>\n",
+       "      <td>173</td>\n",
+       "      <td>36</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>142</td>\n",
+       "      <td>373</td>\n",
+       "      <td>42</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Belgium</td>\n",
+       "      <td>295</td>\n",
+       "      <td>84</td>\n",
+       "      <td>212</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Belize</td>\n",
+       "      <td>263</td>\n",
+       "      <td>114</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Benin</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Bhutan</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>167</td>\n",
+       "      <td>41</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Bosnia-Herzegovina</td>\n",
+       "      <td>76</td>\n",
+       "      <td>173</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>173</td>\n",
+       "      <td>35</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>245</td>\n",
+       "      <td>145</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Brunei</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Bulgaria</td>\n",
+       "      <td>231</td>\n",
+       "      <td>252</td>\n",
+       "      <td>94</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Burkina Faso</td>\n",
+       "      <td>25</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Burundi</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Cote d'Ivoire</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Cabo Verde</td>\n",
+       "      <td>144</td>\n",
+       "      <td>56</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>Suriname</td>\n",
+       "      <td>128</td>\n",
+       "      <td>178</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>Swaziland</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>152</td>\n",
+       "      <td>60</td>\n",
+       "      <td>186</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>185</td>\n",
+       "      <td>100</td>\n",
+       "      <td>280</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Syria</td>\n",
+       "      <td>5</td>\n",
+       "      <td>35</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>Tajikistan</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>99</td>\n",
+       "      <td>258</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Macedonia</td>\n",
+       "      <td>106</td>\n",
+       "      <td>27</td>\n",
+       "      <td>86</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>Timor-Leste</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>Togo</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>Tonga</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Trinidad &amp; Tobago</td>\n",
+       "      <td>197</td>\n",
+       "      <td>156</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Tunisia</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>Turkey</td>\n",
+       "      <td>51</td>\n",
+       "      <td>22</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>Turkmenistan</td>\n",
+       "      <td>19</td>\n",
+       "      <td>71</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Tuvalu</td>\n",
+       "      <td>6</td>\n",
+       "      <td>41</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>Uganda</td>\n",
+       "      <td>45</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>206</td>\n",
+       "      <td>237</td>\n",
+       "      <td>45</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>United Arab Emirates</td>\n",
+       "      <td>16</td>\n",
+       "      <td>135</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>219</td>\n",
+       "      <td>126</td>\n",
+       "      <td>195</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Tanzania</td>\n",
+       "      <td>36</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>249</td>\n",
+       "      <td>158</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>Uruguay</td>\n",
+       "      <td>115</td>\n",
+       "      <td>35</td>\n",
+       "      <td>220</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>Uzbekistan</td>\n",
+       "      <td>25</td>\n",
+       "      <td>101</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>Vanuatu</td>\n",
+       "      <td>21</td>\n",
+       "      <td>18</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>Venezuela</td>\n",
+       "      <td>333</td>\n",
+       "      <td>100</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>111</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>32</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>64</td>\n",
+       "      <td>18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>193 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "0             Afghanistan              0                0              0   \n",
+       "1                 Albania             89              132             54   \n",
+       "2                 Algeria             25                0             14   \n",
+       "3                 Andorra            245              138            312   \n",
+       "4                  Angola            217               57             45   \n",
+       "5       Antigua & Barbuda            102              128             45   \n",
+       "6               Argentina            193               25            221   \n",
+       "7                 Armenia             21              179             11   \n",
+       "8               Australia            261               72            212   \n",
+       "9                 Austria            279               75            191   \n",
+       "10             Azerbaijan             21               46              5   \n",
+       "11                Bahamas            122              176             51   \n",
+       "12                Bahrain             42               63              7   \n",
+       "13             Bangladesh              0                0              0   \n",
+       "14               Barbados            143              173             36   \n",
+       "15                Belarus            142              373             42   \n",
+       "16                Belgium            295               84            212   \n",
+       "17                 Belize            263              114              8   \n",
+       "18                  Benin             34                4             13   \n",
+       "19                 Bhutan             23                0              0   \n",
+       "20                Bolivia            167               41              8   \n",
+       "21     Bosnia-Herzegovina             76              173              8   \n",
+       "22               Botswana            173               35             35   \n",
+       "23                 Brazil            245              145             16   \n",
+       "24                 Brunei             31                2              1   \n",
+       "25               Bulgaria            231              252             94   \n",
+       "26           Burkina Faso             25                7              7   \n",
+       "27                Burundi             88                0              0   \n",
+       "28          Cote d'Ivoire             37                1              7   \n",
+       "29             Cabo Verde            144               56             16   \n",
+       "..                    ...            ...              ...            ...   \n",
+       "163              Suriname            128              178              7   \n",
+       "164             Swaziland             90                2              2   \n",
+       "165                Sweden            152               60            186   \n",
+       "166           Switzerland            185              100            280   \n",
+       "167                 Syria              5               35             16   \n",
+       "168            Tajikistan              2               15              0   \n",
+       "169              Thailand             99              258              1   \n",
+       "170             Macedonia            106               27             86   \n",
+       "171           Timor-Leste              1                1              4   \n",
+       "172                  Togo             36                2             19   \n",
+       "173                 Tonga             36               21              5   \n",
+       "174     Trinidad & Tobago            197              156              7   \n",
+       "175               Tunisia             51                3             20   \n",
+       "176                Turkey             51               22              7   \n",
+       "177          Turkmenistan             19               71             32   \n",
+       "178                Tuvalu              6               41              9   \n",
+       "179                Uganda             45                9              0   \n",
+       "180               Ukraine            206              237             45   \n",
+       "181  United Arab Emirates             16              135              5   \n",
+       "182        United Kingdom            219              126            195   \n",
+       "183              Tanzania             36                6              1   \n",
+       "184                   USA            249              158             84   \n",
+       "185               Uruguay            115               35            220   \n",
+       "186            Uzbekistan             25              101              8   \n",
+       "187               Vanuatu             21               18             11   \n",
+       "188             Venezuela            333              100              3   \n",
+       "189               Vietnam            111                2              1   \n",
+       "190                 Yemen              6                0              0   \n",
+       "191                Zambia             32               19              4   \n",
+       "192              Zimbabwe             64               18              4   \n",
+       "\n",
+       "     total_litres_of_pure_alcohol continent  \n",
+       "0                             0.0        AS  \n",
+       "1                             4.9        EU  \n",
+       "2                             0.7        AF  \n",
+       "3                            12.4        EU  \n",
+       "4                             5.9        AF  \n",
+       "5                             4.9       NaN  \n",
+       "6                             8.3        SA  \n",
+       "7                             3.8        EU  \n",
+       "8                            10.4        OC  \n",
+       "9                             9.7        EU  \n",
+       "10                            1.3        EU  \n",
+       "11                            6.3       NaN  \n",
+       "12                            2.0        AS  \n",
+       "13                            0.0        AS  \n",
+       "14                            6.3       NaN  \n",
+       "15                           14.4        EU  \n",
+       "16                           10.5        EU  \n",
+       "17                            6.8       NaN  \n",
+       "18                            1.1        AF  \n",
+       "19                            0.4        AS  \n",
+       "20                            3.8        SA  \n",
+       "21                            4.6        EU  \n",
+       "22                            5.4        AF  \n",
+       "23                            7.2        SA  \n",
+       "24                            0.6        AS  \n",
+       "25                           10.3        EU  \n",
+       "26                            4.3        AF  \n",
+       "27                            6.3        AF  \n",
+       "28                            4.0        AF  \n",
+       "29                            4.0        AF  \n",
+       "..                            ...       ...  \n",
+       "163                           5.6        SA  \n",
+       "164                           4.7        AF  \n",
+       "165                           7.2        EU  \n",
+       "166                          10.2        EU  \n",
+       "167                           1.0        AS  \n",
+       "168                           0.3        AS  \n",
+       "169                           6.4        AS  \n",
+       "170                           3.9        EU  \n",
+       "171                           0.1        AS  \n",
+       "172                           1.3        AF  \n",
+       "173                           1.1        OC  \n",
+       "174                           6.4       NaN  \n",
+       "175                           1.3        AF  \n",
+       "176                           1.4        AS  \n",
+       "177                           2.2        AS  \n",
+       "178                           1.0        OC  \n",
+       "179                           8.3        AF  \n",
+       "180                           8.9        EU  \n",
+       "181                           2.8        AS  \n",
+       "182                          10.4        EU  \n",
+       "183                           5.7        AF  \n",
+       "184                           8.7       NaN  \n",
+       "185                           6.6        SA  \n",
+       "186                           2.4        AS  \n",
+       "187                           0.9        OC  \n",
+       "188                           7.7        SA  \n",
+       "189                           2.0        AS  \n",
+       "190                           0.1        AS  \n",
+       "191                           2.5        AF  \n",
+       "192                           4.7        AF  \n",
+       "\n",
+       "[193 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the data\n",
+    "drinks                  # print the first 30 and last 30 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "0  Afghanistan              0                0              0   \n",
+       "1      Albania             89              132             54   \n",
+       "2      Algeria             25                0             14   \n",
+       "3      Andorra            245              138            312   \n",
+       "4       Angola            217               57             45   \n",
+       "\n",
+       "   total_litres_of_pure_alcohol continent  \n",
+       "0                           0.0        AS  \n",
+       "1                           4.9        EU  \n",
+       "2                           0.7        AF  \n",
+       "3                          12.4        EU  \n",
+       "4                           5.9        AF  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.head()           # print the first 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>Venezuela</td>\n",
+       "      <td>333</td>\n",
+       "      <td>100</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>111</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>32</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>64</td>\n",
+       "      <td>18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "188  Venezuela            333              100              3   \n",
+       "189    Vietnam            111                2              1   \n",
+       "190      Yemen              6                0              0   \n",
+       "191     Zambia             32               19              4   \n",
+       "192   Zimbabwe             64               18              4   \n",
+       "\n",
+       "     total_litres_of_pure_alcohol continent  \n",
+       "188                           7.7        SA  \n",
+       "189                           2.0        AS  \n",
+       "190                           0.1        AS  \n",
+       "191                           2.5        AF  \n",
+       "192                           4.7        AF  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.tail()           # print the last 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>193.000000</td>\n",
+       "      <td>193.000000</td>\n",
+       "      <td>193.000000</td>\n",
+       "      <td>193.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>106.160622</td>\n",
+       "      <td>80.994819</td>\n",
+       "      <td>49.450777</td>\n",
+       "      <td>4.717098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>101.143103</td>\n",
+       "      <td>88.284312</td>\n",
+       "      <td>79.697598</td>\n",
+       "      <td>3.773298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>56.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>4.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>188.000000</td>\n",
+       "      <td>128.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>7.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>376.000000</td>\n",
+       "      <td>438.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>14.400000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       beer_servings  spirit_servings  wine_servings  \\\n",
+       "count     193.000000       193.000000     193.000000   \n",
+       "mean      106.160622        80.994819      49.450777   \n",
+       "std       101.143103        88.284312      79.697598   \n",
+       "min         0.000000         0.000000       0.000000   \n",
+       "25%        20.000000         4.000000       1.000000   \n",
+       "50%        76.000000        56.000000       8.000000   \n",
+       "75%       188.000000       128.000000      59.000000   \n",
+       "max       376.000000       438.000000     370.000000   \n",
+       "\n",
+       "       total_litres_of_pure_alcohol  \n",
+       "count                    193.000000  \n",
+       "mean                       4.717098  \n",
+       "std                        3.773298  \n",
+       "min                        0.000000  \n",
+       "25%                        1.300000  \n",
+       "50%                        4.200000  \n",
+       "75%                        7.200000  \n",
+       "max                       14.400000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.describe()       # describe any numeric columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'country', u'beer_servings', u'spirit_servings', u'wine_servings',\n",
+       "       u'total_litres_of_pure_alcohol', u'continent'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.columns          # get series of column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  1  3\n",
+       "1  2  4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Note: the dataframe is like a more complicated dictionary where the \n",
+    "# column names are the keys and the column itself is the value\n",
+    "\n",
+    "d = {'a':[1,2], 'b':[3,4]}\n",
+    "pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(193, 6)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.shape            # tuple of (#rows, #cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country                          0\n",
+       "beer_servings                    0\n",
+       "spirit_servings                  0\n",
+       "wine_servings                    0\n",
+       "total_litres_of_pure_alcohol     0\n",
+       "continent                       23\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# find missing values in a DataFrame\n",
+    "drinks.isnull()         # DataFrame of booleans\n",
+    "drinks.isnull().sum()   # convert booleans to integers and add"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Antigua &amp; Barbuda</td>\n",
+       "      <td>102</td>\n",
+       "      <td>128</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bahamas</td>\n",
+       "      <td>122</td>\n",
+       "      <td>176</td>\n",
+       "      <td>51</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Barbados</td>\n",
+       "      <td>143</td>\n",
+       "      <td>173</td>\n",
+       "      <td>36</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Belize</td>\n",
+       "      <td>263</td>\n",
+       "      <td>114</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>240</td>\n",
+       "      <td>122</td>\n",
+       "      <td>100</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Costa Rica</td>\n",
+       "      <td>149</td>\n",
+       "      <td>87</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>93</td>\n",
+       "      <td>137</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Dominica</td>\n",
+       "      <td>52</td>\n",
+       "      <td>286</td>\n",
+       "      <td>26</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Dominican Republic</td>\n",
+       "      <td>193</td>\n",
+       "      <td>147</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>El Salvador</td>\n",
+       "      <td>52</td>\n",
+       "      <td>69</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Grenada</td>\n",
+       "      <td>199</td>\n",
+       "      <td>438</td>\n",
+       "      <td>28</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>53</td>\n",
+       "      <td>69</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Haiti</td>\n",
+       "      <td>1</td>\n",
+       "      <td>326</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Honduras</td>\n",
+       "      <td>69</td>\n",
+       "      <td>98</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>Jamaica</td>\n",
+       "      <td>82</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>238</td>\n",
+       "      <td>68</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>Nicaragua</td>\n",
+       "      <td>78</td>\n",
+       "      <td>118</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>Panama</td>\n",
+       "      <td>285</td>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>St. Kitts &amp; Nevis</td>\n",
+       "      <td>194</td>\n",
+       "      <td>205</td>\n",
+       "      <td>32</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>St. Lucia</td>\n",
+       "      <td>171</td>\n",
+       "      <td>315</td>\n",
+       "      <td>71</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>St. Vincent &amp; the Grenadines</td>\n",
+       "      <td>120</td>\n",
+       "      <td>221</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Trinidad &amp; Tobago</td>\n",
+       "      <td>197</td>\n",
+       "      <td>156</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>249</td>\n",
+       "      <td>158</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          country  beer_servings  spirit_servings  \\\n",
+       "5               Antigua & Barbuda            102              128   \n",
+       "11                        Bahamas            122              176   \n",
+       "14                       Barbados            143              173   \n",
+       "17                         Belize            263              114   \n",
+       "32                         Canada            240              122   \n",
+       "41                     Costa Rica            149               87   \n",
+       "43                           Cuba             93              137   \n",
+       "50                       Dominica             52              286   \n",
+       "51             Dominican Republic            193              147   \n",
+       "54                    El Salvador             52               69   \n",
+       "68                        Grenada            199              438   \n",
+       "69                      Guatemala             53               69   \n",
+       "73                          Haiti              1              326   \n",
+       "74                       Honduras             69               98   \n",
+       "84                        Jamaica             82               97   \n",
+       "109                        Mexico            238               68   \n",
+       "122                     Nicaragua             78              118   \n",
+       "130                        Panama            285              104   \n",
+       "143             St. Kitts & Nevis            194              205   \n",
+       "144                     St. Lucia            171              315   \n",
+       "145  St. Vincent & the Grenadines            120              221   \n",
+       "174             Trinidad & Tobago            197              156   \n",
+       "184                           USA            249              158   \n",
+       "\n",
+       "     wine_servings  total_litres_of_pure_alcohol continent  \n",
+       "5               45                           4.9       NaN  \n",
+       "11              51                           6.3       NaN  \n",
+       "14              36                           6.3       NaN  \n",
+       "17               8                           6.8       NaN  \n",
+       "32             100                           8.2       NaN  \n",
+       "41              11                           4.4       NaN  \n",
+       "43               5                           4.2       NaN  \n",
+       "50              26                           6.6       NaN  \n",
+       "51               9                           6.2       NaN  \n",
+       "54               2                           2.2       NaN  \n",
+       "68              28                          11.9       NaN  \n",
+       "69               2                           2.2       NaN  \n",
+       "73               1                           5.9       NaN  \n",
+       "74               2                           3.0       NaN  \n",
+       "84               9                           3.4       NaN  \n",
+       "109              5                           5.5       NaN  \n",
+       "122              1                           3.5       NaN  \n",
+       "130             18                           7.2       NaN  \n",
+       "143             32                           7.7       NaN  \n",
+       "144             71                          10.1       NaN  \n",
+       "145             11                           6.3       NaN  \n",
+       "174              7                           6.4       NaN  \n",
+       "184             84                           8.7       NaN  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# So we have 23 missing continent values, that's a no bueno situation\n",
+    "\n",
+    "''' SELECT * from drinks where continent is NULL '''\n",
+    "drinks[drinks['continent'].isnull()]         # DataFrame of values with null continent value\n",
+    "\n",
+    "# Anyone have any thoughts?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Antigua &amp; Barbuda</td>\n",
+       "      <td>102</td>\n",
+       "      <td>128</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>193</td>\n",
+       "      <td>25</td>\n",
+       "      <td>221</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Armenia</td>\n",
+       "      <td>21</td>\n",
+       "      <td>179</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>261</td>\n",
+       "      <td>72</td>\n",
+       "      <td>212</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Austria</td>\n",
+       "      <td>279</td>\n",
+       "      <td>75</td>\n",
+       "      <td>191</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Azerbaijan</td>\n",
+       "      <td>21</td>\n",
+       "      <td>46</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bahamas</td>\n",
+       "      <td>122</td>\n",
+       "      <td>176</td>\n",
+       "      <td>51</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Bahrain</td>\n",
+       "      <td>42</td>\n",
+       "      <td>63</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Bangladesh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Barbados</td>\n",
+       "      <td>143</td>\n",
+       "      <td>173</td>\n",
+       "      <td>36</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>142</td>\n",
+       "      <td>373</td>\n",
+       "      <td>42</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Belgium</td>\n",
+       "      <td>295</td>\n",
+       "      <td>84</td>\n",
+       "      <td>212</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Belize</td>\n",
+       "      <td>263</td>\n",
+       "      <td>114</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Benin</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Bhutan</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>167</td>\n",
+       "      <td>41</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Bosnia-Herzegovina</td>\n",
+       "      <td>76</td>\n",
+       "      <td>173</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>173</td>\n",
+       "      <td>35</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>245</td>\n",
+       "      <td>145</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Brunei</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Bulgaria</td>\n",
+       "      <td>231</td>\n",
+       "      <td>252</td>\n",
+       "      <td>94</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Burkina Faso</td>\n",
+       "      <td>25</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Burundi</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Cote d'Ivoire</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Cabo Verde</td>\n",
+       "      <td>144</td>\n",
+       "      <td>56</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>Suriname</td>\n",
+       "      <td>128</td>\n",
+       "      <td>178</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>Swaziland</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>152</td>\n",
+       "      <td>60</td>\n",
+       "      <td>186</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>185</td>\n",
+       "      <td>100</td>\n",
+       "      <td>280</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Syria</td>\n",
+       "      <td>5</td>\n",
+       "      <td>35</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>Tajikistan</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>99</td>\n",
+       "      <td>258</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Macedonia</td>\n",
+       "      <td>106</td>\n",
+       "      <td>27</td>\n",
+       "      <td>86</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>Timor-Leste</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>Togo</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>Tonga</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Trinidad &amp; Tobago</td>\n",
+       "      <td>197</td>\n",
+       "      <td>156</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Tunisia</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>Turkey</td>\n",
+       "      <td>51</td>\n",
+       "      <td>22</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>Turkmenistan</td>\n",
+       "      <td>19</td>\n",
+       "      <td>71</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Tuvalu</td>\n",
+       "      <td>6</td>\n",
+       "      <td>41</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>Uganda</td>\n",
+       "      <td>45</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>206</td>\n",
+       "      <td>237</td>\n",
+       "      <td>45</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>United Arab Emirates</td>\n",
+       "      <td>16</td>\n",
+       "      <td>135</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>219</td>\n",
+       "      <td>126</td>\n",
+       "      <td>195</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Tanzania</td>\n",
+       "      <td>36</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>249</td>\n",
+       "      <td>158</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>Uruguay</td>\n",
+       "      <td>115</td>\n",
+       "      <td>35</td>\n",
+       "      <td>220</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>Uzbekistan</td>\n",
+       "      <td>25</td>\n",
+       "      <td>101</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>Vanuatu</td>\n",
+       "      <td>21</td>\n",
+       "      <td>18</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>OC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>Venezuela</td>\n",
+       "      <td>333</td>\n",
+       "      <td>100</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>111</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>32</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>64</td>\n",
+       "      <td>18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>AF</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>193 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "0             Afghanistan              0                0              0   \n",
+       "1                 Albania             89              132             54   \n",
+       "2                 Algeria             25                0             14   \n",
+       "3                 Andorra            245              138            312   \n",
+       "4                  Angola            217               57             45   \n",
+       "5       Antigua & Barbuda            102              128             45   \n",
+       "6               Argentina            193               25            221   \n",
+       "7                 Armenia             21              179             11   \n",
+       "8               Australia            261               72            212   \n",
+       "9                 Austria            279               75            191   \n",
+       "10             Azerbaijan             21               46              5   \n",
+       "11                Bahamas            122              176             51   \n",
+       "12                Bahrain             42               63              7   \n",
+       "13             Bangladesh              0                0              0   \n",
+       "14               Barbados            143              173             36   \n",
+       "15                Belarus            142              373             42   \n",
+       "16                Belgium            295               84            212   \n",
+       "17                 Belize            263              114              8   \n",
+       "18                  Benin             34                4             13   \n",
+       "19                 Bhutan             23                0              0   \n",
+       "20                Bolivia            167               41              8   \n",
+       "21     Bosnia-Herzegovina             76              173              8   \n",
+       "22               Botswana            173               35             35   \n",
+       "23                 Brazil            245              145             16   \n",
+       "24                 Brunei             31                2              1   \n",
+       "25               Bulgaria            231              252             94   \n",
+       "26           Burkina Faso             25                7              7   \n",
+       "27                Burundi             88                0              0   \n",
+       "28          Cote d'Ivoire             37                1              7   \n",
+       "29             Cabo Verde            144               56             16   \n",
+       "..                    ...            ...              ...            ...   \n",
+       "163              Suriname            128              178              7   \n",
+       "164             Swaziland             90                2              2   \n",
+       "165                Sweden            152               60            186   \n",
+       "166           Switzerland            185              100            280   \n",
+       "167                 Syria              5               35             16   \n",
+       "168            Tajikistan              2               15              0   \n",
+       "169              Thailand             99              258              1   \n",
+       "170             Macedonia            106               27             86   \n",
+       "171           Timor-Leste              1                1              4   \n",
+       "172                  Togo             36                2             19   \n",
+       "173                 Tonga             36               21              5   \n",
+       "174     Trinidad & Tobago            197              156              7   \n",
+       "175               Tunisia             51                3             20   \n",
+       "176                Turkey             51               22              7   \n",
+       "177          Turkmenistan             19               71             32   \n",
+       "178                Tuvalu              6               41              9   \n",
+       "179                Uganda             45                9              0   \n",
+       "180               Ukraine            206              237             45   \n",
+       "181  United Arab Emirates             16              135              5   \n",
+       "182        United Kingdom            219              126            195   \n",
+       "183              Tanzania             36                6              1   \n",
+       "184                   USA            249              158             84   \n",
+       "185               Uruguay            115               35            220   \n",
+       "186            Uzbekistan             25              101              8   \n",
+       "187               Vanuatu             21               18             11   \n",
+       "188             Venezuela            333              100              3   \n",
+       "189               Vietnam            111                2              1   \n",
+       "190                 Yemen              6                0              0   \n",
+       "191                Zambia             32               19              4   \n",
+       "192              Zimbabwe             64               18              4   \n",
+       "\n",
+       "     total_litres_of_pure_alcohol continent  \n",
+       "0                             0.0        AS  \n",
+       "1                             4.9        EU  \n",
+       "2                             0.7        AF  \n",
+       "3                            12.4        EU  \n",
+       "4                             5.9        AF  \n",
+       "5                             4.9        NA  \n",
+       "6                             8.3        SA  \n",
+       "7                             3.8        EU  \n",
+       "8                            10.4        OC  \n",
+       "9                             9.7        EU  \n",
+       "10                            1.3        EU  \n",
+       "11                            6.3        NA  \n",
+       "12                            2.0        AS  \n",
+       "13                            0.0        AS  \n",
+       "14                            6.3        NA  \n",
+       "15                           14.4        EU  \n",
+       "16                           10.5        EU  \n",
+       "17                            6.8        NA  \n",
+       "18                            1.1        AF  \n",
+       "19                            0.4        AS  \n",
+       "20                            3.8        SA  \n",
+       "21                            4.6        EU  \n",
+       "22                            5.4        AF  \n",
+       "23                            7.2        SA  \n",
+       "24                            0.6        AS  \n",
+       "25                           10.3        EU  \n",
+       "26                            4.3        AF  \n",
+       "27                            6.3        AF  \n",
+       "28                            4.0        AF  \n",
+       "29                            4.0        AF  \n",
+       "..                            ...       ...  \n",
+       "163                           5.6        SA  \n",
+       "164                           4.7        AF  \n",
+       "165                           7.2        EU  \n",
+       "166                          10.2        EU  \n",
+       "167                           1.0        AS  \n",
+       "168                           0.3        AS  \n",
+       "169                           6.4        AS  \n",
+       "170                           3.9        EU  \n",
+       "171                           0.1        AS  \n",
+       "172                           1.3        AF  \n",
+       "173                           1.1        OC  \n",
+       "174                           6.4        NA  \n",
+       "175                           1.3        AF  \n",
+       "176                           1.4        AS  \n",
+       "177                           2.2        AS  \n",
+       "178                           1.0        OC  \n",
+       "179                           8.3        AF  \n",
+       "180                           8.9        EU  \n",
+       "181                           2.8        AS  \n",
+       "182                          10.4        EU  \n",
+       "183                           5.7        AF  \n",
+       "184                           8.7        NA  \n",
+       "185                           6.6        SA  \n",
+       "186                           2.4        AS  \n",
+       "187                           0.9        OC  \n",
+       "188                           7.7        SA  \n",
+       "189                           2.0        AS  \n",
+       "190                           0.1        AS  \n",
+       "191                           2.5        AF  \n",
+       "192                           4.7        AF  \n",
+       "\n",
+       "[193 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pandas isn't brilliant, it saw the \"NA\" string and assumed it meant a null value\n",
+    "\n",
+    "# handling missing values\n",
+    "drinks.dropna()             # drop a row if ANY values are missing\n",
+    "drinks.fillna(value='NA')   # fill in missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country                         0\n",
+       "beer_servings                   0\n",
+       "spirit_servings                 0\n",
+       "wine_servings                   0\n",
+       "total_litres_of_pure_alcohol    0\n",
+       "continent                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fix the original import, this time using a live url!\n",
+    "drinks = pd.read_csv('https://raw.githubusercontent.com/sinanuozdemir/sfdat28/master/data/drinks.csv', na_filter=False)\n",
+    "drinks.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      AS\n",
+       "1      EU\n",
+       "2      AF\n",
+       "3      EU\n",
+       "4      AF\n",
+       "5      NA\n",
+       "6      SA\n",
+       "7      EU\n",
+       "8      OC\n",
+       "9      EU\n",
+       "10     EU\n",
+       "11     NA\n",
+       "12     AS\n",
+       "13     AS\n",
+       "14     NA\n",
+       "15     EU\n",
+       "16     EU\n",
+       "17     NA\n",
+       "18     AF\n",
+       "19     AS\n",
+       "20     SA\n",
+       "21     EU\n",
+       "22     AF\n",
+       "23     SA\n",
+       "24     AS\n",
+       "25     EU\n",
+       "26     AF\n",
+       "27     AF\n",
+       "28     AF\n",
+       "29     AF\n",
+       "       ..\n",
+       "163    SA\n",
+       "164    AF\n",
+       "165    EU\n",
+       "166    EU\n",
+       "167    AS\n",
+       "168    AS\n",
+       "169    AS\n",
+       "170    EU\n",
+       "171    AS\n",
+       "172    AF\n",
+       "173    OC\n",
+       "174    NA\n",
+       "175    AF\n",
+       "176    AS\n",
+       "177    AS\n",
+       "178    OC\n",
+       "179    AF\n",
+       "180    EU\n",
+       "181    AS\n",
+       "182    EU\n",
+       "183    AF\n",
+       "184    NA\n",
+       "185    SA\n",
+       "186    AS\n",
+       "187    OC\n",
+       "188    SA\n",
+       "189    AS\n",
+       "190    AS\n",
+       "191    AF\n",
+       "192    AF\n",
+       "Name: continent, dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# selecting a column ('Series')\n",
+    "drinks['continent']\n",
+    "drinks.continent            # equivalent\n",
+    "\n",
+    "# Note the dictionary like selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(drinks.continent)      # Series if pandas equivalent to list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     193\n",
+       "unique      6\n",
+       "top        AF\n",
+       "freq       53\n",
+       "Name: continent, dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarizing a non-numeric column\n",
+    "drinks.continent.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AF    53\n",
+       "EU    45\n",
+       "AS    44\n",
+       "NA    23\n",
+       "OC    16\n",
+       "SA    12\n",
+       "Name: continent, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drinks.continent.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings\n",
+       "0  Afghanistan              0\n",
+       "1      Albania             89\n",
+       "2      Algeria             25\n",
+       "3      Andorra            245\n",
+       "4       Angola            217"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "'''\n",
+    "note the double square bracket\n",
+    "the outer pair is used like in a python dictionary\n",
+    "    to select\n",
+    "the inner pair is a list!\n",
+    "\n",
+    "so in all, the double use of square brackets is telling\n",
+    "the dataframe to select a list!\n",
+    "'''\n",
+    "# selecting multiple columns\n",
+    "''' SELECT country,beer_servings from drinks '''\n",
+    "drinks[['country', 'beer_servings']].head()\n",
+    "\n",
+    "\n",
+    "# remember the .head() just shows us the first 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings\n",
+       "0  Afghanistan              0\n",
+       "1      Albania             89\n",
+       "2      Algeria             25\n",
+       "3      Andorra            245\n",
+       "4       Angola            217"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_cols = ['country', 'beer_servings']\n",
+    "drinks[my_cols].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>AF</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>217</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>AF</td>\n",
+       "      <td>319</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "0  Afghanistan              0                0              0   \n",
+       "1      Albania             89              132             54   \n",
+       "2      Algeria             25                0             14   \n",
+       "3      Andorra            245              138            312   \n",
+       "4       Angola            217               57             45   \n",
+       "\n",
+       "   total_litres_of_pure_alcohol continent  total_servings  \n",
+       "0                           0.0        AS               0  \n",
+       "1                           4.9        EU             275  \n",
+       "2                           0.7        AF              39  \n",
+       "3                          12.4        EU             695  \n",
+       "4                           5.9        AF             319  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add a new column as a function of existing columns\n",
+    "drinks['total_servings'] = drinks.beer_servings + drinks.spirit_servings + drinks.wine_servings\n",
+    "drinks.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Armenia</td>\n",
+       "      <td>21</td>\n",
+       "      <td>179</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Austria</td>\n",
+       "      <td>279</td>\n",
+       "      <td>75</td>\n",
+       "      <td>191</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Azerbaijan</td>\n",
+       "      <td>21</td>\n",
+       "      <td>46</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "1      Albania             89              132             54   \n",
+       "3      Andorra            245              138            312   \n",
+       "7      Armenia             21              179             11   \n",
+       "9      Austria            279               75            191   \n",
+       "10  Azerbaijan             21               46              5   \n",
+       "\n",
+       "    total_litres_of_pure_alcohol continent  total_servings  \n",
+       "1                            4.9        EU             275  \n",
+       "3                           12.4        EU             695  \n",
+       "7                            3.8        EU             211  \n",
+       "9                            9.7        EU             545  \n",
+       "10                           1.3        EU              72  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# logical filtering and sorting\n",
+    "\n",
+    "'''\n",
+    "How it works:\n",
+    "    drinks.continent=='EU' by itself returns a bunch\n",
+    "        of Trues and Falses\n",
+    "        \n",
+    "drinks.continent=='EU'\n",
+    "\n",
+    "See?\n",
+    "\n",
+    "\n",
+    "when you wrap drinks around it with square brackets\n",
+    "you're telling the drinks dataframe to select\n",
+    "only those that are True, and not the False ones\n",
+    "\n",
+    "drinks[drinks.continent=='EU']\n",
+    "'''\n",
+    "drinks.continent=='EU' # this is a series of T and F\n",
+    "\n",
+    "# we put tht series of Trues and Falses directly into the square brackets of justice\n",
+    "drinks[drinks.continent=='EU'] .head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Antigua &amp; Barbuda</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bahamas</td>\n",
+       "      <td>349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Barbados</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Belize</td>\n",
+       "      <td>385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Costa Rica</td>\n",
+       "      <td>247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Dominica</td>\n",
+       "      <td>364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Dominican Republic</td>\n",
+       "      <td>349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>El Salvador</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Grenada</td>\n",
+       "      <td>665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Haiti</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Honduras</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>Jamaica</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>Nicaragua</td>\n",
+       "      <td>197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>Panama</td>\n",
+       "      <td>407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>St. Kitts &amp; Nevis</td>\n",
+       "      <td>431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>St. Lucia</td>\n",
+       "      <td>557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>St. Vincent &amp; the Grenadines</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Trinidad &amp; Tobago</td>\n",
+       "      <td>360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>491</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          country  total_servings\n",
+       "5               Antigua & Barbuda             275\n",
+       "11                        Bahamas             349\n",
+       "14                       Barbados             352\n",
+       "17                         Belize             385\n",
+       "32                         Canada             462\n",
+       "41                     Costa Rica             247\n",
+       "43                           Cuba             235\n",
+       "50                       Dominica             364\n",
+       "51             Dominican Republic             349\n",
+       "54                    El Salvador             123\n",
+       "68                        Grenada             665\n",
+       "69                      Guatemala             124\n",
+       "73                          Haiti             328\n",
+       "74                       Honduras             169\n",
+       "84                        Jamaica             188\n",
+       "109                        Mexico             311\n",
+       "122                     Nicaragua             197\n",
+       "130                        Panama             407\n",
+       "143             St. Kitts & Nevis             431\n",
+       "144                     St. Lucia             557\n",
+       "145  St. Vincent & the Grenadines             352\n",
+       "174             Trinidad & Tobago             360\n",
+       "184                           USA             491"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# North American countries with total servings\n",
+    "# drinks[['country', 'total_servings']][drinks.continent=='NA']\n",
+    "\n",
+    "new_df = drinks[['country', 'total_servings']] # selection of two columns\n",
+    "new_df[drinks.continent=='NA']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>El Salvador</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Honduras</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>Jamaica</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>Nicaragua</td>\n",
+       "      <td>197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Costa Rica</td>\n",
+       "      <td>247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Antigua &amp; Barbuda</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>Mexico</td>\n",
+       "      <td>311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Haiti</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Dominican Republic</td>\n",
+       "      <td>349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bahamas</td>\n",
+       "      <td>349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Barbados</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>St. Vincent &amp; the Grenadines</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Trinidad &amp; Tobago</td>\n",
+       "      <td>360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Dominica</td>\n",
+       "      <td>364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Belize</td>\n",
+       "      <td>385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>Panama</td>\n",
+       "      <td>407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>St. Kitts &amp; Nevis</td>\n",
+       "      <td>431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>St. Lucia</td>\n",
+       "      <td>557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Grenada</td>\n",
+       "      <td>665</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          country  total_servings\n",
+       "54                    El Salvador             123\n",
+       "69                      Guatemala             124\n",
+       "74                       Honduras             169\n",
+       "84                        Jamaica             188\n",
+       "122                     Nicaragua             197\n",
+       "43                           Cuba             235\n",
+       "41                     Costa Rica             247\n",
+       "5               Antigua & Barbuda             275\n",
+       "109                        Mexico             311\n",
+       "73                          Haiti             328\n",
+       "51             Dominican Republic             349\n",
+       "11                        Bahamas             349\n",
+       "14                       Barbados             352\n",
+       "145  St. Vincent & the Grenadines             352\n",
+       "174             Trinidad & Tobago             360\n",
+       "50                       Dominica             364\n",
+       "17                         Belize             385\n",
+       "130                        Panama             407\n",
+       "143             St. Kitts & Nevis             431\n",
+       "32                         Canada             462\n",
+       "184                           USA             491\n",
+       "144                     St. Lucia             557\n",
+       "68                        Grenada             665"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# same thing, sorted by total_servings\n",
+    "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>68 </th>\n",
+       "      <td>                      Grenada</td>\n",
+       "      <td> 665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>                    St. Lucia</td>\n",
+       "      <td> 557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>                          USA</td>\n",
+       "      <td> 491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32 </th>\n",
+       "      <td>                       Canada</td>\n",
+       "      <td> 462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>            St. Kitts &amp; Nevis</td>\n",
+       "      <td> 431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>                       Panama</td>\n",
+       "      <td> 407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17 </th>\n",
+       "      <td>                       Belize</td>\n",
+       "      <td> 385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 </th>\n",
+       "      <td>                     Dominica</td>\n",
+       "      <td> 364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>            Trinidad &amp; Tobago</td>\n",
+       "      <td> 360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14 </th>\n",
+       "      <td>                     Barbados</td>\n",
+       "      <td> 352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td> St. Vincent &amp; the Grenadines</td>\n",
+       "      <td> 352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51 </th>\n",
+       "      <td>           Dominican Republic</td>\n",
+       "      <td> 349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11 </th>\n",
+       "      <td>                      Bahamas</td>\n",
+       "      <td> 349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73 </th>\n",
+       "      <td>                        Haiti</td>\n",
+       "      <td> 328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>                       Mexico</td>\n",
+       "      <td> 311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5  </th>\n",
+       "      <td>            Antigua &amp; Barbuda</td>\n",
+       "      <td> 275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41 </th>\n",
+       "      <td>                   Costa Rica</td>\n",
+       "      <td> 247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43 </th>\n",
+       "      <td>                         Cuba</td>\n",
+       "      <td> 235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>                    Nicaragua</td>\n",
+       "      <td> 197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84 </th>\n",
+       "      <td>                      Jamaica</td>\n",
+       "      <td> 188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74 </th>\n",
+       "      <td>                     Honduras</td>\n",
+       "      <td> 169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69 </th>\n",
+       "      <td>                    Guatemala</td>\n",
+       "      <td> 124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54 </th>\n",
+       "      <td>                  El Salvador</td>\n",
+       "      <td> 123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          country  total_servings\n",
+       "68                        Grenada             665\n",
+       "144                     St. Lucia             557\n",
+       "184                           USA             491\n",
+       "32                         Canada             462\n",
+       "143             St. Kitts & Nevis             431\n",
+       "130                        Panama             407\n",
+       "17                         Belize             385\n",
+       "50                       Dominica             364\n",
+       "174             Trinidad & Tobago             360\n",
+       "14                       Barbados             352\n",
+       "145  St. Vincent & the Grenadines             352\n",
+       "51             Dominican Republic             349\n",
+       "11                        Bahamas             349\n",
+       "73                          Haiti             328\n",
+       "109                        Mexico             311\n",
+       "5               Antigua & Barbuda             275\n",
+       "41                     Costa Rica             247\n",
+       "43                           Cuba             235\n",
+       "122                     Nicaragua             197\n",
+       "84                        Jamaica             188\n",
+       "74                       Honduras             169\n",
+       "69                      Guatemala             124\n",
+       "54                    El Salvador             123"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sorted in reverse order\n",
+    "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings', ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "3  Andorra            245              138            312   \n",
+       "\n",
+       "   total_litres_of_pure_alcohol continent  total_servings  \n",
+       "3                          12.4        EU             695  "
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# contries with wine servings over 300 and total liters over 12\n",
+    "drinks[drinks.wine_servings > 300][drinks.total_litres_of_pure_alcohol > 12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "3  Andorra            245              138            312   \n",
+       "\n",
+       "   total_litres_of_pure_alcohol continent  total_servings  \n",
+       "3                          12.4        EU             695  "
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Same result but using python logic operators\n",
+    "# Note the paranthesis around each filter when using logical operators\n",
+    "drinks[(drinks.wine_servings > 300) & (drinks.total_litres_of_pure_alcohol > 12)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>245</td>\n",
+       "      <td>138</td>\n",
+       "      <td>312</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>193</td>\n",
+       "      <td>25</td>\n",
+       "      <td>221</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Chile</td>\n",
+       "      <td>130</td>\n",
+       "      <td>124</td>\n",
+       "      <td>172</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Cook Islands</td>\n",
+       "      <td>0</td>\n",
+       "      <td>254</td>\n",
+       "      <td>74</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>OC</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Croatia</td>\n",
+       "      <td>230</td>\n",
+       "      <td>87</td>\n",
+       "      <td>254</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>224</td>\n",
+       "      <td>81</td>\n",
+       "      <td>278</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Equatorial Guinea</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>233</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>AF</td>\n",
+       "      <td>325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>France</td>\n",
+       "      <td>127</td>\n",
+       "      <td>151</td>\n",
+       "      <td>370</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>Georgia</td>\n",
+       "      <td>52</td>\n",
+       "      <td>100</td>\n",
+       "      <td>149</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Greece</td>\n",
+       "      <td>133</td>\n",
+       "      <td>112</td>\n",
+       "      <td>218</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>Italy</td>\n",
+       "      <td>85</td>\n",
+       "      <td>42</td>\n",
+       "      <td>237</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>Laos</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>20</td>\n",
+       "      <td>55</td>\n",
+       "      <td>31</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>Luxembourg</td>\n",
+       "      <td>236</td>\n",
+       "      <td>133</td>\n",
+       "      <td>271</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>Montenegro</td>\n",
+       "      <td>31</td>\n",
+       "      <td>114</td>\n",
+       "      <td>128</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>Portugal</td>\n",
+       "      <td>194</td>\n",
+       "      <td>67</td>\n",
+       "      <td>339</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>Qatar</td>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>Sao Tome &amp; Principe</td>\n",
+       "      <td>56</td>\n",
+       "      <td>38</td>\n",
+       "      <td>140</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>AF</td>\n",
+       "      <td>234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Slovenia</td>\n",
+       "      <td>270</td>\n",
+       "      <td>51</td>\n",
+       "      <td>276</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>152</td>\n",
+       "      <td>60</td>\n",
+       "      <td>186</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>185</td>\n",
+       "      <td>100</td>\n",
+       "      <td>280</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>EU</td>\n",
+       "      <td>565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Syria</td>\n",
+       "      <td>5</td>\n",
+       "      <td>35</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>Timor-Leste</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>Turkmenistan</td>\n",
+       "      <td>19</td>\n",
+       "      <td>71</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Tuvalu</td>\n",
+       "      <td>6</td>\n",
+       "      <td>41</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>OC</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>Uruguay</td>\n",
+       "      <td>115</td>\n",
+       "      <td>35</td>\n",
+       "      <td>220</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>370</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "3                Andorra            245              138            312   \n",
+       "6              Argentina            193               25            221   \n",
+       "35                 Chile            130              124            172   \n",
+       "40          Cook Islands              0              254             74   \n",
+       "42               Croatia            230               87            254   \n",
+       "48               Denmark            224               81            278   \n",
+       "55     Equatorial Guinea             92                0            233   \n",
+       "61                France            127              151            370   \n",
+       "64               Georgia             52              100            149   \n",
+       "67                Greece            133              112            218   \n",
+       "83                 Italy             85               42            237   \n",
+       "92                  Laos             62                0            123   \n",
+       "94               Lebanon             20               55             31   \n",
+       "99            Luxembourg            236              133            271   \n",
+       "113           Montenegro             31              114            128   \n",
+       "136             Portugal            194               67            339   \n",
+       "137                Qatar              1               42              7   \n",
+       "148  Sao Tome & Principe             56               38            140   \n",
+       "156             Slovenia            270               51            276   \n",
+       "165               Sweden            152               60            186   \n",
+       "166          Switzerland            185              100            280   \n",
+       "167                Syria              5               35             16   \n",
+       "171          Timor-Leste              1                1              4   \n",
+       "177         Turkmenistan             19               71             32   \n",
+       "178               Tuvalu              6               41              9   \n",
+       "185              Uruguay            115               35            220   \n",
+       "\n",
+       "     total_litres_of_pure_alcohol continent  total_servings  \n",
+       "3                            12.4        EU             695  \n",
+       "6                             8.3        SA             439  \n",
+       "35                            7.6        SA             426  \n",
+       "40                            5.9        OC             328  \n",
+       "42                           10.2        EU             571  \n",
+       "48                           10.4        EU             583  \n",
+       "55                            5.8        AF             325  \n",
+       "61                           11.8        EU             648  \n",
+       "64                            5.4        EU             301  \n",
+       "67                            8.3        EU             463  \n",
+       "83                            6.5        EU             364  \n",
+       "92                            6.2        AS             185  \n",
+       "94                            1.9        AS             106  \n",
+       "99                           11.4        EU             640  \n",
+       "113                           4.9        EU             273  \n",
+       "136                          11.0        EU             600  \n",
+       "137                           0.9        AS              50  \n",
+       "148                           4.2        AF             234  \n",
+       "156                          10.6        EU             597  \n",
+       "165                           7.2        EU             398  \n",
+       "166                          10.2        EU             565  \n",
+       "167                           1.0        AS              56  \n",
+       "171                           0.1        AS               6  \n",
+       "177                           2.2        AS             122  \n",
+       "178                           1.0        OC              56  \n",
+       "185                           6.6        SA             370  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# contries with more wine servings than beer servings\n",
+    "drinks[drinks.wine_servings > drinks.beer_servings]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      False\n",
+       "1      False\n",
+       "2      False\n",
+       "3       True\n",
+       "4      False\n",
+       "5      False\n",
+       "6       True\n",
+       "7      False\n",
+       "8      False\n",
+       "9      False\n",
+       "10     False\n",
+       "11     False\n",
+       "12     False\n",
+       "13     False\n",
+       "14     False\n",
+       "15     False\n",
+       "16     False\n",
+       "17     False\n",
+       "18     False\n",
+       "19     False\n",
+       "20     False\n",
+       "21     False\n",
+       "22     False\n",
+       "23     False\n",
+       "24     False\n",
+       "25     False\n",
+       "26     False\n",
+       "27     False\n",
+       "28     False\n",
+       "29     False\n",
+       "       ...  \n",
+       "163    False\n",
+       "164    False\n",
+       "165     True\n",
+       "166     True\n",
+       "167     True\n",
+       "168    False\n",
+       "169    False\n",
+       "170    False\n",
+       "171     True\n",
+       "172    False\n",
+       "173    False\n",
+       "174    False\n",
+       "175    False\n",
+       "176    False\n",
+       "177     True\n",
+       "178     True\n",
+       "179    False\n",
+       "180    False\n",
+       "181    False\n",
+       "182    False\n",
+       "183    False\n",
+       "184    False\n",
+       "185     True\n",
+       "186    False\n",
+       "187    False\n",
+       "188    False\n",
+       "189    False\n",
+       "190    False\n",
+       "191    False\n",
+       "192    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# to reiterate how the filter works:\n",
+    "drinks.wine_servings > drinks.beer_servings # is a series of T and F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "      <th>spirit_servings</th>\n",
+       "      <th>wine_servings</th>\n",
+       "      <th>total_litres_of_pure_alcohol</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>total_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>98 </th>\n",
+       "      <td>      Lithuania</td>\n",
+       "      <td> 343</td>\n",
+       "      <td> 244</td>\n",
+       "      <td>  56</td>\n",
+       "      <td> 12.9</td>\n",
+       "      <td> EU</td>\n",
+       "      <td> 643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>         Poland</td>\n",
+       "      <td> 343</td>\n",
+       "      <td> 215</td>\n",
+       "      <td>  56</td>\n",
+       "      <td> 10.9</td>\n",
+       "      <td> EU</td>\n",
+       "      <td> 614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 </th>\n",
+       "      <td>        Germany</td>\n",
+       "      <td> 346</td>\n",
+       "      <td> 117</td>\n",
+       "      <td> 175</td>\n",
+       "      <td> 11.3</td>\n",
+       "      <td> EU</td>\n",
+       "      <td> 638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62 </th>\n",
+       "      <td>          Gabon</td>\n",
+       "      <td> 347</td>\n",
+       "      <td>  98</td>\n",
+       "      <td>  59</td>\n",
+       "      <td>  8.9</td>\n",
+       "      <td> AF</td>\n",
+       "      <td> 504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45 </th>\n",
+       "      <td> Czech Republic</td>\n",
+       "      <td> 361</td>\n",
+       "      <td> 170</td>\n",
+       "      <td> 134</td>\n",
+       "      <td> 11.8</td>\n",
+       "      <td> EU</td>\n",
+       "      <td> 665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>        Namibia</td>\n",
+       "      <td> 376</td>\n",
+       "      <td>   3</td>\n",
+       "      <td>   1</td>\n",
+       "      <td>  6.8</td>\n",
+       "      <td> AF</td>\n",
+       "      <td> 380</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            country  beer_servings  spirit_servings  wine_servings  \\\n",
+       "98        Lithuania            343              244             56   \n",
+       "135          Poland            343              215             56   \n",
+       "65          Germany            346              117            175   \n",
+       "62            Gabon            347               98             59   \n",
+       "45   Czech Republic            361              170            134   \n",
+       "117         Namibia            376                3              1   \n",
+       "\n",
+       "     total_litres_of_pure_alcohol continent  total_servings  \n",
+       "98                           12.9        EU             643  \n",
+       "135                          10.9        EU             614  \n",
+       "65                           11.3        EU             638  \n",
+       "62                            8.9        AF             504  \n",
+       "45                           11.8        EU             665  \n",
+       "117                           6.8        AF             380  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# last 5 elements of the dataframe sorted by beer servings\n",
+    "drinks.sort_index(by='beer_servings').tail(6)\n",
+    "# note tail took in an optional input of 6, showing us the last 6 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Quiz\n",
+    "\n",
+    "# 1. List countries that drink more spirits than beer on average\n",
+    "\n",
+    "# 2. What are the top three beer drinking countries?\n",
+    "\n",
+    "# 3. What are the top three beer drinking countries in Europe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Answers below don't peak!\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1                           Albania\n",
+       "5                 Antigua & Barbuda\n",
+       "7                           Armenia\n",
+       "10                       Azerbaijan\n",
+       "11                          Bahamas\n",
+       "12                          Bahrain\n",
+       "14                         Barbados\n",
+       "15                          Belarus\n",
+       "21               Bosnia-Herzegovina\n",
+       "25                         Bulgaria\n",
+       "30                         Cambodia\n",
+       "36                            China\n",
+       "38                          Comoros\n",
+       "40                     Cook Islands\n",
+       "43                             Cuba\n",
+       "49                         Djibouti\n",
+       "50                         Dominica\n",
+       "54                      El Salvador\n",
+       "61                           France\n",
+       "64                          Georgia\n",
+       "68                          Grenada\n",
+       "69                        Guatemala\n",
+       "71                    Guinea-Bissau\n",
+       "72                           Guyana\n",
+       "73                            Haiti\n",
+       "74                         Honduras\n",
+       "77                            India\n",
+       "82                           Israel\n",
+       "84                          Jamaica\n",
+       "85                            Japan\n",
+       "                   ...             \n",
+       "89                         Kiribati\n",
+       "91                       Kyrgyzstan\n",
+       "94                          Lebanon\n",
+       "96                          Liberia\n",
+       "101                          Malawi\n",
+       "112                        Mongolia\n",
+       "113                      Montenegro\n",
+       "119                           Nepal\n",
+       "122                       Nicaragua\n",
+       "125                            Niue\n",
+       "134                     Philippines\n",
+       "137                           Qatar\n",
+       "139                         Moldova\n",
+       "141              Russian Federation\n",
+       "143               St. Kitts & Nevis\n",
+       "144                       St. Lucia\n",
+       "145    St. Vincent & the Grenadines\n",
+       "149                    Saudi Arabia\n",
+       "155                        Slovakia\n",
+       "161                       Sri Lanka\n",
+       "162                           Sudan\n",
+       "163                        Suriname\n",
+       "167                           Syria\n",
+       "168                      Tajikistan\n",
+       "169                        Thailand\n",
+       "177                    Turkmenistan\n",
+       "178                          Tuvalu\n",
+       "180                         Ukraine\n",
+       "181            United Arab Emirates\n",
+       "186                      Uzbekistan\n",
+       "Name: country, dtype: object"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1. \n",
+    "drinks['country'][drinks.spirit_servings>drinks.beer_servings]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Gabon</td>\n",
+       "      <td>347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Czech Republic</td>\n",
+       "      <td>361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>Namibia</td>\n",
+       "      <td>376</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            country  beer_servings\n",
+       "62            Gabon            347\n",
+       "45   Czech Republic            361\n",
+       "117         Namibia            376"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2. \n",
+    "drinks[['country', 'beer_servings']].sort_index(by='beer_servings').tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>beer_servings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Czech Republic</td>\n",
+       "      <td>361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>Poland</td>\n",
+       "      <td>343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            country  beer_servings\n",
+       "45   Czech Republic            361\n",
+       "65          Germany            346\n",
+       "135          Poland            343"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 3. \n",
+    "drinks[drinks.continent=='EU'][['country', 'beer_servings']].sort_index(by='beer_servings', ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.434782609\n"
+     ]
+    }
+   ],
+   "source": [
+    "# average North American beer consumption\n",
+    "'''\n",
+    "Note the procedure:\n",
+    "drinks                                          Dataframe\n",
+    "drinks.beer_servings                            one column (Series)\n",
+    "drinks.beer_servings[drinks.continent=='NA']    logical filtering\n",
+    "drinks.beer_servings[drinks.continent=='NA'].mean() mean of that filtered column\n",
+    "'''\n",
+    "\n",
+    "print drinks.beer_servings[drinks.continent=='NA'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# average European beer consumption\n",
+    "drinks.beer_servings[drinks.continent=='EU'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Split Apply Combine**\n",
+    "\n",
+    "<img src=\"https://www.safaribooksonline.com/library/view/learning-pandas/9781783985128/graphics/5128OS_09_01.jpg\">\n",
+    "\n",
+    "Pandas uses the \"groupby\" command to split apply and combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "AF     61.471698\n",
+       "AS     37.045455\n",
+       "EU    193.777778\n",
+       "NA    145.434783\n",
+       "OC     89.687500\n",
+       "SA    175.083333\n",
+       "Name: beer_servings, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for each continent, calculate mean beer servings\n",
+    "drinks.groupby('continent').beer_servings.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AF    53\n",
+       "EU    45\n",
+       "AS    44\n",
+       "NA    23\n",
+       "OC    16\n",
+       "SA    12\n",
+       "Name: continent, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for each continent, count number of occurrences\n",
+    "drinks.groupby('continent').continent.count()\n",
+    "drinks.continent.value_counts() #same thing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "AF     94.075472\n",
+       "AS    106.954545\n",
+       "EU    468.555556\n",
+       "NA    335.695652\n",
+       "OC    183.750000\n",
+       "SA    352.250000\n",
+       "Name: total_servings, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can apply any function using .apply\n",
+    "drinks.groupby('continent').total_servings.apply(lambda x: x.mean())    # mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# on lambda functions:\n",
+    "\n",
+    "'''\n",
+    "Lamda Functions are ananonymous functions\n",
+    "\n",
+    "f = lambda x: x + 1   IS THE SAME AS \n",
+    "def f(x): return x + 1\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "AF    114.040622\n",
+       "AS    132.630446\n",
+       "EU    176.921857\n",
+       "NA    134.437696\n",
+       "OC    175.450848\n",
+       "SA     81.923606\n",
+       "Name: total_servings, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# note x here is an entire series\n",
+    "drinks.groupby('continent').total_servings.apply(lambda x: x.std())     # standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "continent\n",
+      "AF      0\n",
+      "AS      0\n",
+      "EU      0\n",
+      "NA    123\n",
+      "OC      0\n",
+      "SA    216\n",
+      "Name: total_servings, dtype: int64\n",
+      "continent\n",
+      "AF    504\n",
+      "AS    646\n",
+      "EU    695\n",
+      "NA    665\n",
+      "OC    545\n",
+      "SA    439\n",
+      "Name: total_servings, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for each continent, calculate the min, max, and range for total servings\n",
+    "print drinks.groupby('continent').total_servings.min()\n",
+    "print drinks.groupby('continent').total_servings.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "AF    504\n",
+       "AS    646\n",
+       "EU    695\n",
+       "NA    542\n",
+       "OC    545\n",
+       "SA    223\n",
+       "Name: total_servings, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What does this do?\n",
+    "drinks.groupby('continent').total_servings.apply(lambda x: x.max() - x.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11296bc50>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112053610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Plotting\n",
+    "'''\n",
+    "\n",
+    "# bar plot of number of countries in each continent\n",
+    "drinks.continent.value_counts().plot(kind='bar', title='Countries per Continent')\n",
+    "plt.xlabel('Continent')\n",
+    "plt.ylabel('Count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x112053a10>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112118c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# bar plot of average number of beer servings by continent\n",
+    "drinks.groupby('continent').beer_servings.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1120acf50>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9vKLpAJXk0i/MIWcOGcE5U8slZ10u7mZmHeSe+4T57rmbWRPcczczsxEu7ucVTQeoJJd+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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1120be110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# histogram of beer servings\n",
+    "drinks.beer_servings.hist(bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117fe4650>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1183a9050>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118427750>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118496a10>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118647d90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11844c450>]], dtype=object)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0SqASbOF7hMglSfgMIRJEL7hBVlGnu2yW6rLpccadCcAg8GpJv5R0TtOxG4CP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1180c1510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# grouped histogram of beer servings\n",
+    "drinks.beer_servings.hist(by=drinks.continent)\n",
+    "\n",
+    "# stop and think, does this make sense\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x118992310>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118b0fc50>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118b4e8d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118bb4b90>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118c378d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118be26d0>]], dtype=object)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cSdcCNWAfSU8CFwPLJS0FdpDNuHlG29mamVnPFBneeQGYCzwyeWUsSTcB1wFL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\ngMiiD48AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1189a3b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# same charts with the same scale for x and y axis\n",
+    "drinks.beer_servings.hist(by=drinks.continent, sharex=True, sharey=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x118f50fd0>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ZSTX+VcuFdyqNPHGj6AD6Io19mUo9G0UHsJOkGv8q5cKdc/WyZEnREewsqZx/\nKlIYz1C1OnrOvz5lVon380+MvyHKxxv/+pRZJf6Db5RGXhHKllvMQyr70utZH2WrY1KNv3POuaCd\na/ieBfwZsNnM/jCumwVcAOxHuIbvYjO7P963DDgR2EbJruFbtTxxp/yrcPl42qc+ZVZJt2mfs4E/\nnbBuKXC5mR0IXAEsiwUdBCwGFgJHAWeqm0tO9ZjPc++cK0rZTjynbfzN7IfAxEHbxwBr4u01wGvj\n7aOB881sm5mNARuARb0JtRcaRQfQFymMZxgebhQdQl+ULU+clxTqWbZrFnSa89/bzDYDmNkmYO+4\nfh5wZ+ZxG+M610cpjGdYs2b6xzjnJterH3wrknUbKjqAvhjq4ILf1TNUdAB9kca+TKWeQ0UHsJNO\nZ/XcLGmOmW2WNBe4O67fCOybedz8uK6l4eFhFixYAMDAwACDg4PjB0Hza2Cvl5s7IK/n9+X+LEOD\nRqM88ZQt3qJen36/v6r2fs473ubtsbExptPWIC9JC4BLzOz5cfl0YIuZnS7p/cAsM1saf/D9KnAo\nId2zHjigVbeeInr7DA83WL16qK9lFqHRaGQagXqSGpgNFR1G2zrtldLpvqxaz5sq1bNTRRyzU/X2\nmfbMX9J5hI+rvST9J7AcOA24UNKJwB2EHj6Y2S2S1gK3AI8Af1OmYbwp5MKdc+Xkc/s0C/bpHXKT\nwniGqtXR+/nXp8wq8bl9EuNviPLxxr8+ZU6lm2FNebSHPrdPlP1RpN4aRQeQu1T2pdezWsxs0r+R\nkZEp7++3pBp/55xzQVJpn6rliTtVtq/CztM+dSqzSjznP15mGgdKKvWsEm/861NmlXjOf1yj6AD6\nwuf2qY+65MKnk0I9y1bHxBr/NKQwnsHn9nGuO572cZVUtX3Z74nNZ82CLVv6W6anfcqnqxG+zrnu\nddpAeePm8lLJtM/s2eFNMdM/aHS0nRTKrIqy5Rbz0Sg6gD5pFB1AX6RwzJatjpVs/LduDWdDM/0b\nGelsO7NQpnPO1UUlc/6eW5xaCuMZUqgjVOu48/dl+dSun78fZFOrUqxualXal/6+LB/v5x+VLeeW\nn0bRAeQulX2ZwpgNSGN/lq2OSTX+zlVNCmM2XDE87VPiMjtVpVhdffj7snw87eOcc24nSTX+Zcu5\nTaXTsQzdjGeo0lgGn9unXlKoZ9nq2FXjL2lM0k8k3STp2rhulqR1kn4u6TJJe/Ym1LR0Opahm/EM\nVRrL4HPfH+JYAAAKQklEQVT7ONedrnL+kn4FvMDMtmbWnQ7ca2Yfk/R+YJaZLW2xref8S1ReUWV2\nqkqxdqNK4xn8mC2f3Pr5S7odeKGZ3ZtZdxtwuJltljQXaJjZc1ts641/icorqsxOVSnWblSpnn7M\nlk+eP/gasF7SdZL+Iq6bY2abAcxsE7B3l2X0TNlybnlJo56NogPok0bRAbTN6PBHKolGh9sZfZ4u\ntQtle192O6vnYWb2a0lPA9ZJ+jnhAyHLP5edS4Cwzs/CGw0YGpp5mfIGplNdNf5m9uv4/z2SLgYW\nAZslzcmkfe6ebPvh4WEWLFgAwMDAAIODgwzFA6D5Kdnr5aaZbg+NeHzmG18vyhsaGuri9elP/bpd\nXrIkrCtLPPkdr+WKJ694m+vq//owo8d38vyNRoOxsTGm03HOX9L/AnYxs99JeiKwDlgJvALYYman\n+w++1SmvqDLd1Kq0T/yYLZ+8cv5zgB9Kugm4GrjEzNYBpwOviimgVwCndVFGT0389K2rFOqZQh3B\n5/apk7LVseO0j5ndDgy2WL8FeGU3QTnnAp/bx+XF5/YpaZkp1NHVix+z5eNz+zjnnNtJUo1/2XJu\neUmhnj63T72kUM+y1TGpxt/Vh8/t41x3POdf0jJTqGM3qhRrN3xun/KVWSV+Dd8e8Ma/XKoUazeq\nVE8/Zsundj/4djqHSKfzh/gcImXUKDqAPmkUHUBfpHDMlq2OlWz8RZ8nujcLZTrnXE142qekZaZQ\nx25UKRfejSrtEz9my8dz/j3gjb8rQpX2iR+z5VO7nH+nypZzy0sK9UyhjuBz+9RJ2erY7Xz+zrkc\nVW1uH/W5X8SsWf0tr0487VPSMlOoo3NNfuzlY6q0j5/5l1ToztrvMnf865yrN8/5l1TH3VnNaHTY\npbVK3Vl9bp+6aRQdQO7Kti8r2/h3MlbrZS/reIyX5xZLxuf2ca47lcz5d15mdfKKnvOfWpVi7YaP\nZ3Dd8K6ezlXUypVFR9Afy5cXHUF6cmv8JR0p6TZJ/xEv5F4CjaID6Iuy5Rbz0Sg6gD5pFB1AXwwN\nNYoOIXdle1/m0vhL2gX4LPCnwPOA4yU9N4+yZma06AD6YnQ0hXqmUEdIpZ4pHLNlq2NeZ/6LgA1m\ndoeZPQKcDxyTU1kzcF/RAfTFfffVv56HH17/OgZp1DOFY7Zsdcyrn/884M7M8l2ED4RCHX540RHM\nTDejJTvJFVepR9PQUNEROFdtSQ3yWrBgrOgQ2tZNzwdprPY9J8bGxooOoWc0zae8NPkneVG99Xqt\nTvtzMmWrYy5dPSW9GFhhZkfG5aWAmdnpmcfU46h1zrkS6+uUzpJ2BX4OvAL4NXAtcLyZ3drzwpxz\nzs1YLmkfM3tU0juBdYQflc/yht8558qjsBG+zjnnilPrEb6SXitpu6TnxOX9JD0o6UZJN8X/K/2j\nd4s6StKnJd0s6aeSrpG0X9FxdkPSoxP22d/H9bdLmp153OGSLiku0s7FffjxzPLfSfqHCY8ZlXRe\n/6PrHUnzJF0cB39ukPSp5ntQ0iJJ35d0q6QbJP2LpN2LjrkTkj4o6d8l/SQesy+K6/eS9LCkvyo6\nxlo3/sCbgB8Ax2fW/cLMDjGzg+P/2wqKrVcm1vE4YB8ze76Z/SHwOqrfWfz3E/bZx+L6Vl9bq/pV\n9iHg9dkPs6w4SHIX4KWS9uhrZL11EXCRmT0HeA7wZOCfJO0NrAXeZ2YLzewFwKXx/kqJHV5eDQya\n2R8Br2RH1/djgavYuU0qRG0bf0lPBA4DTmLnF7rPs+TnZ5I67kP4kR0AM/svM7u/gPB6abJ9Vpt9\nCWwD/gV4zyT3Hw+cQ/gdrQQDJmdO0suB/zazcyB0/wPeDZwI/B2w2syubT7ezC4ys3sKCbY7+wC/\naZ5YmtkWM9sU7zueUNd5kp5eVIBQ48af8Aa51Mx+AfxG0sFx/bPi17AbJX2mwPh6oVUd1wJHx/p9\nQtJgsSH2xB4T0j7HFh1QDgz4HPBmSa3Odo8jjJQ/Hzihn4H10POAG7IrzOwB4D+BZ028r8LWAc+I\nc5t9TtKfAEiaD8w1s+sJ79Pjigyyzo3/8YQ3CsAF7HjDNNM+h5jZu4oJrWceU0cz20j4Or0M2A5c\nLullBcXXKw9OSPtcGNfXKe2Dmf0OWAOcnF0v6QWEM8m7gCuAgyUNFBCia4OZ/R44BPgr4B7gfElL\nCI392viwtRT8IV7pHzsnI2kW8HLgD+Jgsl3ZcWZVC1PU8X1xPqXLgMskbQZeC4wUFmx+fgPMArbE\n5dlxXZV9GrgRODuz7gTgQEm/IqS6ngy8ATir/+F15RbgjdkVkp4CPANYD7wQqOQP9hPFlNaVwJWS\nbgaWAE8H5kh6M2E/7iPpWWb2yyJirOuZ/7HAOWa2v5k908z2A24H9qU+eeKWdZT0Ukn7wPjsqn8I\n3FFkoD0w2T5rAG+F8YGFb6G6H3ICMLOthLPCkyD03iLs6z+I+3l/wod55VI/ZvY9QgrvLTC+zz4B\n/D/gDOCtzV4x8f7XSXpaIcF2QdJzJD07s2qQcHL2RDPbN7MfT6XA/VjXxv844BsT1n2dHamQOmhV\nx4uA1cC34tnGKPAIYXrtKtt9Qs7/n+L6jwDPljRKyBdvMLOvFBdmV7LpqjOAveLtlwJ3mdnmzP1X\nAgslzelXcD30OmCxpP8AbgP+G/igmd1N6Ll2Ruzq+TPgCOCB4kLt2JOANbGr5yiwELia1u/XN/U7\nuCYf5OWccwmq65m/c865KXjj75xzCfLG3znnEuSNv3POJcgbf+ecS5A3/s45lyBv/J2bIE79fXxm\n+QWSVuVQzjFxtk7n+s4bf+cea38yIy/N7AYzOyWHcl5LmOzMub7zxt/VjqS3xoto3CRpTTyT/168\nGMr6OLsiks6OF775kaRfSHp9fIpTgZfE0cQnZy8SI2m5pLMkjcRt3pUp980KF8+5UdLn49QMSHpA\n0kdi+T+W9DRJfwwcDXwsPn7//r5KLnXe+LtakXQQ8AFgyMwOBk4BPgOcbWaDwHlxuWmumR0GvAY4\nPa5bCvwgziD66bguOxT+QOBVwKHAckm7xvTNccD/NrNDCNOIvDk+/onAj2P5PwD+0syuAr5FmIjv\nEDO7vYcvg3PTquWsni5pLwcujBOkYWZb41n26+L957KjkQe4OD7u1ng1qXZ8J16o4944a+oc4BWE\naXyvi2f8uwPNC3g8bGbfjbdvIFzZyblCeePvUjDVBFYPZW63O+NrdptHCe8jAWvM7IMtHv9wi8c7\nVyhP+7i6uQI4tnkt3Pj/j9lxmcu3EFIvrTQb/wdo/9qxzW2+B7yxOQWxpFmS9p3wmIkeAJ7SZjnO\n9ZQ3/q5WzOwW4KPA9yXdRJgv/l3A2+L0um9mx5WyJn4jaC7/FNgefzA+malZLPdW4EPAOkk/IVzK\nb59Jymk6H3ifpBv8B1/Xbz6ls3POJcjP/J1zLkHe+DvnXIK88XfOuQR54++ccwnyxt855xLkjb9z\nziXIG3/nnEuQN/7OOZeg/w9o59RUDHomSgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118d7c210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# boxplot of beer servings by continent\n",
+    "drinks.boxplot(column='beer_servings', by='continent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11998c310>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jTJ8+3XcJJNuFUjAYZMqUKSxZckXRz9cY62cyHy9I8o0ljyxz8+EfesU60hfg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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119ef4850>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# scatterplot of beer servings versus wine servings\n",
+    "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['b', 'r', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b',\n",
+       "       'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n",
+       "       'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n",
+       "       'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b', 'r', 'b', 'b', 'b',\n",
+       "       'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'r', 'r', 'b', 'b', 'r',\n",
+       "       'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b',\n",
+       "       'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n",
+       "       'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b',\n",
+       "       'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b',\n",
+       "       'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b',\n",
+       "       'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'r', 'r', 'b', 'b',\n",
+       "       'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n",
+       "       'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b',\n",
+       "       'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'b',\n",
+       "       'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], \n",
+       "      dtype='|S1')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# same scatterplot, except all European countries are colored red\n",
+    "colors = np.where(drinks.continent=='EU', 'r', 'b')\n",
+    "colors      # is a series of 'r' and 'b' that \n",
+    "            # correspond to countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['a', 'b', 'b'], \n",
+       "      dtype='|S1')"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "np.where is like a condensed if statement\n",
+    "it's like a list comprehension for pandas!\n",
+    "\n",
+    "it will loop through drinks.continent which is a series\n",
+    "for each element:\n",
+    "    if it is \"EU\":\n",
+    "        make it 'r'\n",
+    "    else:\n",
+    "        make it 'b'\n",
+    "\n",
+    "More in depth:\n",
+    "    drinks.continent=='EU' is a logical statement\n",
+    "        It will return a bunch of Trues and Falses\n",
+    "        and np.where makes the True ones 'r' and\n",
+    "        the False ones 'b'\n",
+    "        \n",
+    "        Recall logical filtering!\n",
+    "\n",
+    "'''\n",
+    "\n",
+    "# Side quest\n",
+    "np.where([True, False, False], 'a', 'b')\n",
+    "\n",
+    "# 10 gold coins earned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11a96e210>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DeObSykWH6hbblEmTePbRR3FmZVGldiPWb/wNEeja9Qpmzpxaalc7U0qVjEAm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\nJYEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a97b1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', c=colors)\n",
+    "# passing colors into the chart makes the european dots, red!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n----UFO data----\\nScraped from: http://www.nuforc.org/webreports.html\\n'"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "----UFO data----\n",
+    "Scraped from: http://www.nuforc.org/webreports.html\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ufo = pd.read_csv('../data/ufo.csv')   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City</th>\n",
+       "      <th>Colors Reported</th>\n",
+       "      <th>Shape Reported</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ithaca</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Willingboro</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Holyoke</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abilene</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New York Worlds Fair</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   City Colors Reported Shape Reported State             Time\n",
+       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
+       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
+       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
+       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
+       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.head()              # Look at the top 5 observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City</th>\n",
+       "      <th>Colors Reported</th>\n",
+       "      <th>Shape Reported</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>80538</th>\n",
+       "      <td>Neligh</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>NE</td>\n",
+       "      <td>9/4/2014 23:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80539</th>\n",
+       "      <td>Uhrichsville</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>9/5/2014 1:14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80540</th>\n",
+       "      <td>Tucson</td>\n",
+       "      <td>RED BLUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>9/5/2014 2:40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80541</th>\n",
+       "      <td>Orland park</td>\n",
+       "      <td>RED</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>9/5/2014 3:43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80542</th>\n",
+       "      <td>Loughman</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>9/5/2014 5:30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               City Colors Reported Shape Reported State            Time\n",
+       "80538        Neligh             NaN         CIRCLE    NE  9/4/2014 23:20\n",
+       "80539  Uhrichsville             NaN          LIGHT    OH   9/5/2014 1:14\n",
+       "80540        Tucson        RED BLUE            NaN    AZ   9/5/2014 2:40\n",
+       "80541   Orland park             RED          LIGHT    IL   9/5/2014 3:43\n",
+       "80542      Loughman             NaN          LIGHT    FL   9/5/2014 5:30"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.tail()              # Look at the bottom 5 observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ufo['Location'] = ufo['City'] + ', ' + ufo['State']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City</th>\n",
+       "      <th>Colors Reported</th>\n",
+       "      <th>Shape Reported</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ithaca</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "      <td>Ithaca, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Willingboro</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "      <td>Willingboro, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Holyoke</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "      <td>Holyoke, CO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abilene</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "      <td>Abilene, KS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New York Worlds Fair</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "      <td>New York Worlds Fair, NY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   City Colors Reported Shape Reported State             Time  \\\n",
+       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00   \n",
+       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00   \n",
+       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00   \n",
+       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00   \n",
+       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00   \n",
+       "\n",
+       "                   Location  \n",
+       "0                Ithaca, NY  \n",
+       "1           Willingboro, NJ  \n",
+       "2               Holyoke, CO  \n",
+       "3               Abilene, KS  \n",
+       "4  New York Worlds Fair, NY  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# rename some columns\n",
+    "ufo.rename(columns={'Colors Reported':'Colors', 'Shape Reported':'Shape'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City</th>\n",
+       "      <th>Colors</th>\n",
+       "      <th>Shape</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ithaca</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "      <td>Ithaca, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Willingboro</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "      <td>Willingboro, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Holyoke</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "      <td>Holyoke, CO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abilene</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "      <td>Abilene, KS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New York Worlds Fair</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "      <td>New York Worlds Fair, NY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   City Colors     Shape State             Time  \\\n",
+       "0                Ithaca    NaN  TRIANGLE    NY   6/1/1930 22:00   \n",
+       "1           Willingboro    NaN     OTHER    NJ  6/30/1930 20:00   \n",
+       "2               Holyoke    NaN      OVAL    CO  2/15/1931 14:00   \n",
+       "3               Abilene    NaN      DISK    KS   6/1/1931 13:00   \n",
+       "4  New York Worlds Fair    NaN     LIGHT    NY  4/18/1933 19:00   \n",
+       "\n",
+       "                   Location  \n",
+       "0                Ithaca, NY  \n",
+       "1           Willingboro, NJ  \n",
+       "2               Holyoke, CO  \n",
+       "3               Abilene, KS  \n",
+       "4  New York Worlds Fair, NY  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "del ufo['City']                  # delete a column (permanently)\n",
+    "del ufo['State']                 # delete a column (permanently)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LIGHT        16332\n",
+       "TRIANGLE      7816\n",
+       "CIRCLE        7725\n",
+       "FIREBALL      6249\n",
+       "OTHER         5506\n",
+       "SPHERE        5231\n",
+       "DISK          5226\n",
+       "OVAL          3721\n",
+       "FORMATION     2405\n",
+       "CIGAR         1983\n",
+       "VARIOUS       1957\n",
+       "FLASH         1329\n",
+       "RECTANGLE     1295\n",
+       "CYLINDER      1252\n",
+       "DIAMOND       1152\n",
+       "CHEVRON        940\n",
+       "EGG            733\n",
+       "TEARDROP       723\n",
+       "CONE           310\n",
+       "CROSS          241\n",
+       "DELTA            7\n",
+       "CRESCENT         2\n",
+       "ROUND            2\n",
+       "HEXAGON          1\n",
+       "FLARE            1\n",
+       "PYRAMID          1\n",
+       "DOME             1\n",
+       "Name: Shape, dtype: int64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.Shape.value_counts()                # excludes missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LIGHT        16332\n",
+       "NaN           8402\n",
+       "TRIANGLE      7816\n",
+       "CIRCLE        7725\n",
+       "FIREBALL      6249\n",
+       "OTHER         5506\n",
+       "SPHERE        5231\n",
+       "DISK          5226\n",
+       "OVAL          3721\n",
+       "FORMATION     2405\n",
+       "CIGAR         1983\n",
+       "VARIOUS       1957\n",
+       "FLASH         1329\n",
+       "RECTANGLE     1295\n",
+       "CYLINDER      1252\n",
+       "DIAMOND       1152\n",
+       "CHEVRON        940\n",
+       "EGG            733\n",
+       "TEARDROP       723\n",
+       "CONE           310\n",
+       "CROSS          241\n",
+       "DELTA            7\n",
+       "CRESCENT         2\n",
+       "ROUND            2\n",
+       "HEXAGON          1\n",
+       "PYRAMID          1\n",
+       "DOME             1\n",
+       "FLARE            1\n",
+       "Name: Shape, dtype: int64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.Shape.value_counts(dropna=False)    # includes missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8402"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.Shape.isnull().sum() # count the missing values in the shape column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Colors      63509\n",
+       "Shape        8402\n",
+       "Time            0\n",
+       "Location       47\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.isnull().sum()       # returns a count of missing values in all columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Colors</th>\n",
+       "      <th>Shape</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>6/30/1939 20:00</td>\n",
+       "      <td>Belton, SC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>4/30/1943 23:00</td>\n",
+       "      <td>Bering Sea, AK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>FORMATION</td>\n",
+       "      <td>7/10/1945 1:30</td>\n",
+       "      <td>Portsmouth, VA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>6/30/1946 19:00</td>\n",
+       "      <td>Blairsden, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>CHEVRON</td>\n",
+       "      <td>7/15/1947 21:00</td>\n",
+       "      <td>San Jose, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>8/8/1947 22:00</td>\n",
+       "      <td>Modesto, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>5/10/1948 19:00</td>\n",
+       "      <td>Scipio, IN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>8/15/1949 22:00</td>\n",
+       "      <td>Tarrant City, AL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>6/10/1950 0:00</td>\n",
+       "      <td>Napa, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIGAR</td>\n",
+       "      <td>7/2/1950 13:00</td>\n",
+       "      <td>Coeur d'Alene, ID</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>4/15/1951 0:30</td>\n",
+       "      <td>Irving, KS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>6/15/1951 20:30</td>\n",
+       "      <td>Greenville, MS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>7/3/1951 12:00</td>\n",
+       "      <td>Green River, WY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>7/10/1951 23:30</td>\n",
+       "      <td>Provo, UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>4/15/1952 16:00</td>\n",
+       "      <td>Greenville, TX</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>FIREBALL</td>\n",
+       "      <td>6/1/1952 22:00</td>\n",
+       "      <td>Norfolk, VA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>7/13/1952 21:00</td>\n",
+       "      <td>Arlington, VA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>4/1/1953 18:00</td>\n",
+       "      <td>Cambridge, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>YELLOW</td>\n",
+       "      <td>FIREBALL</td>\n",
+       "      <td>4/15/1953 16:00</td>\n",
+       "      <td>Midwest City, OK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>FIREBALL</td>\n",
+       "      <td>6/30/1953 0:00</td>\n",
+       "      <td>Cleveland, OH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>7/4/1953 14:00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>8/15/1953 19:00</td>\n",
+       "      <td>Artesia, NM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>11/21/1953 22:30</td>\n",
+       "      <td>Pendleton, IN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>7/1/1954 21:00</td>\n",
+       "      <td>St. Louis Airport, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>7/1/1954 22:00</td>\n",
+       "      <td>Los Angeles, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>9/9/1954 12:30</td>\n",
+       "      <td>Beaumont, TX</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>12/15/1954 23:10</td>\n",
+       "      <td>Red Bank, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>YELLOW</td>\n",
+       "      <td>EGG</td>\n",
+       "      <td>5/1/1955 15:00</td>\n",
+       "      <td>Holbrook, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>6/15/1955 0:00</td>\n",
+       "      <td>Terre Haute, IN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CYLINDER</td>\n",
+       "      <td>6/1/1956 20:00</td>\n",
+       "      <td>Memphis, TN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80429</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>8/31/2014 20:50</td>\n",
+       "      <td>Bettendorf, IA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80430</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>8/31/2014 21:00</td>\n",
+       "      <td>Pompton Lakes, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80436</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>8/31/2014 21:45</td>\n",
+       "      <td>Montrose, MI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80438</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CYLINDER</td>\n",
+       "      <td>8/31/2014 22:00</td>\n",
+       "      <td>Brunswick, OH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80441</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>TEARDROP</td>\n",
+       "      <td>8/31/2014 22:30</td>\n",
+       "      <td>Scribner, NE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80453</th>\n",
+       "      <td>RED ORANGE GREEN BLUE</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>9/1/2014 11:56</td>\n",
+       "      <td>Minersville, PA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80457</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/1/2014 16:05</td>\n",
+       "      <td>Syracuse, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80458</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>FIREBALL</td>\n",
+       "      <td>9/1/2014 20:00</td>\n",
+       "      <td>Parma, OH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80459</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/1/2014 20:00</td>\n",
+       "      <td>Roswell, NM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80465</th>\n",
+       "      <td>GREEN</td>\n",
+       "      <td>VARIOUS</td>\n",
+       "      <td>9/1/2014 21:30</td>\n",
+       "      <td>South Londonderry, VT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80467</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/1/2014 22:00</td>\n",
+       "      <td>Gold Hill, NC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80469</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/1/2014 22:19</td>\n",
+       "      <td>Milford, DE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80473</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>9/2/2014 5:50</td>\n",
+       "      <td>Trevose, PA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80482</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>FIREBALL</td>\n",
+       "      <td>9/2/2014 22:30</td>\n",
+       "      <td>Spearfish, SD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80489</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>SPHERE</td>\n",
+       "      <td>9/3/2014 5:00</td>\n",
+       "      <td>Pleasure Island, NC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80494</th>\n",
+       "      <td>RED GREEN</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/3/2014 19:00</td>\n",
+       "      <td>Boca Raton, FL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80495</th>\n",
+       "      <td>YELLOW</td>\n",
+       "      <td>FLASH</td>\n",
+       "      <td>9/3/2014 19:00</td>\n",
+       "      <td>Kingston, NH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80496</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>FLASH</td>\n",
+       "      <td>9/3/2014 19:00</td>\n",
+       "      <td>New York City, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80500</th>\n",
+       "      <td>RED BLUE</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/3/2014 21:00</td>\n",
+       "      <td>Johnsburg, IL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80503</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>VARIOUS</td>\n",
+       "      <td>9/3/2014 21:15</td>\n",
+       "      <td>Bethel Park, PA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80505</th>\n",
+       "      <td>YELLOW</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>9/3/2014 21:30</td>\n",
+       "      <td>Clarence, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80509</th>\n",
+       "      <td>RED BLUE</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/3/2014 23:30</td>\n",
+       "      <td>Chesapeake Beach, MD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80510</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>9/4/2014 0:00</td>\n",
+       "      <td>Rogersville, TN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80519</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>9/4/2014 20:20</td>\n",
+       "      <td>Glen Ellyn, IL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80522</th>\n",
+       "      <td>ORANGE</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/4/2014 20:30</td>\n",
+       "      <td>Lawrence, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80524</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/4/2014 21:10</td>\n",
+       "      <td>Olympia, WA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80525</th>\n",
+       "      <td>BLUE</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/4/2014 21:11</td>\n",
+       "      <td>Iowa City, IA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80528</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>9/4/2014 21:30</td>\n",
+       "      <td>North Royalton, OH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80536</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>9/4/2014 23:00</td>\n",
+       "      <td>Wyoming, PA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80541</th>\n",
+       "      <td>RED</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>9/5/2014 3:43</td>\n",
+       "      <td>Orland park, IL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15516 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Colors      Shape              Time  \\\n",
+       "12                       RED     SPHERE   6/30/1939 20:00   \n",
+       "19                       RED      OTHER   4/30/1943 23:00   \n",
+       "36                       RED  FORMATION    7/10/1945 1:30   \n",
+       "44                     GREEN     SPHERE   6/30/1946 19:00   \n",
+       "82                      BLUE    CHEVRON   7/15/1947 21:00   \n",
+       "84                      BLUE       DISK    8/8/1947 22:00   \n",
+       "91                       RED     SPHERE   5/10/1948 19:00   \n",
+       "111                   ORANGE     CIRCLE   8/15/1949 22:00   \n",
+       "129                    GREEN       DISK    6/10/1950 0:00   \n",
+       "138                   ORANGE      CIGAR    7/2/1950 13:00   \n",
+       "152                     BLUE       DISK    4/15/1951 0:30   \n",
+       "157                    GREEN       DISK   6/15/1951 20:30   \n",
+       "163                    GREEN     SPHERE    7/3/1951 12:00   \n",
+       "164                     BLUE       DISK   7/10/1951 23:30   \n",
+       "174                   ORANGE   TRIANGLE   4/15/1952 16:00   \n",
+       "178                      RED   FIREBALL    6/1/1952 22:00   \n",
+       "202                    GREEN       OVAL   7/13/1952 21:00   \n",
+       "226                      RED     SPHERE    4/1/1953 18:00   \n",
+       "229                   YELLOW   FIREBALL   4/15/1953 16:00   \n",
+       "238                      RED   FIREBALL    6/30/1953 0:00   \n",
+       "241                     BLUE       DISK    7/4/1953 14:00   \n",
+       "249                   ORANGE      OTHER   8/15/1953 19:00   \n",
+       "256                    GREEN       DISK  11/21/1953 22:30   \n",
+       "288                      RED       OVAL    7/1/1954 21:00   \n",
+       "289                      RED     CIRCLE    7/1/1954 22:00   \n",
+       "304                      RED       DISK    9/9/1954 12:30   \n",
+       "311                   ORANGE     CIRCLE  12/15/1954 23:10   \n",
+       "314                   YELLOW        EGG    5/1/1955 15:00   \n",
+       "323                   ORANGE     CIRCLE    6/15/1955 0:00   \n",
+       "354                   ORANGE   CYLINDER    6/1/1956 20:00   \n",
+       "...                      ...        ...               ...   \n",
+       "80429                    RED     CIRCLE   8/31/2014 20:50   \n",
+       "80430                    RED     SPHERE   8/31/2014 21:00   \n",
+       "80436                 ORANGE     SPHERE   8/31/2014 21:45   \n",
+       "80438                 ORANGE   CYLINDER   8/31/2014 22:00   \n",
+       "80441                    RED   TEARDROP   8/31/2014 22:30   \n",
+       "80453  RED ORANGE GREEN BLUE   TRIANGLE    9/1/2014 11:56   \n",
+       "80457                 ORANGE     CIRCLE    9/1/2014 16:05   \n",
+       "80458                 ORANGE   FIREBALL    9/1/2014 20:00   \n",
+       "80459                 ORANGE     CIRCLE    9/1/2014 20:00   \n",
+       "80465                  GREEN    VARIOUS    9/1/2014 21:30   \n",
+       "80467                 ORANGE      LIGHT    9/1/2014 22:00   \n",
+       "80469                 ORANGE     CIRCLE    9/1/2014 22:19   \n",
+       "80473                   BLUE   TRIANGLE     9/2/2014 5:50   \n",
+       "80482                 ORANGE   FIREBALL    9/2/2014 22:30   \n",
+       "80489                 ORANGE     SPHERE     9/3/2014 5:00   \n",
+       "80494              RED GREEN     CIRCLE    9/3/2014 19:00   \n",
+       "80495                 YELLOW      FLASH    9/3/2014 19:00   \n",
+       "80496                    RED      FLASH    9/3/2014 19:00   \n",
+       "80500               RED BLUE      LIGHT    9/3/2014 21:00   \n",
+       "80503                 ORANGE    VARIOUS    9/3/2014 21:15   \n",
+       "80505                 YELLOW   TRIANGLE    9/3/2014 21:30   \n",
+       "80509               RED BLUE     CIRCLE    9/3/2014 23:30   \n",
+       "80510                    RED       OVAL     9/4/2014 0:00   \n",
+       "80519                    RED     CIRCLE    9/4/2014 20:20   \n",
+       "80522                 ORANGE      LIGHT    9/4/2014 20:30   \n",
+       "80524                    RED      LIGHT    9/4/2014 21:10   \n",
+       "80525                   BLUE      LIGHT    9/4/2014 21:11   \n",
+       "80528                    RED   TRIANGLE    9/4/2014 21:30   \n",
+       "80536                    RED       DISK    9/4/2014 23:00   \n",
+       "80541                    RED      LIGHT     9/5/2014 3:43   \n",
+       "\n",
+       "                    Location  \n",
+       "12                Belton, SC  \n",
+       "19            Bering Sea, AK  \n",
+       "36            Portsmouth, VA  \n",
+       "44             Blairsden, CA  \n",
+       "82              San Jose, CA  \n",
+       "84               Modesto, CA  \n",
+       "91                Scipio, IN  \n",
+       "111         Tarrant City, AL  \n",
+       "129                 Napa, CA  \n",
+       "138        Coeur d'Alene, ID  \n",
+       "152               Irving, KS  \n",
+       "157           Greenville, MS  \n",
+       "163          Green River, WY  \n",
+       "164                Provo, UT  \n",
+       "174           Greenville, TX  \n",
+       "178              Norfolk, VA  \n",
+       "202            Arlington, VA  \n",
+       "226            Cambridge, MA  \n",
+       "229         Midwest City, OK  \n",
+       "238            Cleveland, OH  \n",
+       "241                      NaN  \n",
+       "249              Artesia, NM  \n",
+       "256            Pendleton, IN  \n",
+       "288    St. Louis Airport, MO  \n",
+       "289          Los Angeles, CA  \n",
+       "304             Beaumont, TX  \n",
+       "311             Red Bank, NJ  \n",
+       "314             Holbrook, MA  \n",
+       "323          Terre Haute, IN  \n",
+       "354              Memphis, TN  \n",
+       "...                      ...  \n",
+       "80429         Bettendorf, IA  \n",
+       "80430      Pompton Lakes, NJ  \n",
+       "80436           Montrose, MI  \n",
+       "80438          Brunswick, OH  \n",
+       "80441           Scribner, NE  \n",
+       "80453        Minersville, PA  \n",
+       "80457           Syracuse, NY  \n",
+       "80458              Parma, OH  \n",
+       "80459            Roswell, NM  \n",
+       "80465  South Londonderry, VT  \n",
+       "80467          Gold Hill, NC  \n",
+       "80469            Milford, DE  \n",
+       "80473            Trevose, PA  \n",
+       "80482          Spearfish, SD  \n",
+       "80489    Pleasure Island, NC  \n",
+       "80494         Boca Raton, FL  \n",
+       "80495           Kingston, NH  \n",
+       "80496      New York City, NY  \n",
+       "80500          Johnsburg, IL  \n",
+       "80503        Bethel Park, PA  \n",
+       "80505           Clarence, NY  \n",
+       "80509   Chesapeake Beach, MD  \n",
+       "80510        Rogersville, TN  \n",
+       "80519         Glen Ellyn, IL  \n",
+       "80522           Lawrence, MA  \n",
+       "80524            Olympia, WA  \n",
+       "80525          Iowa City, IA  \n",
+       "80528     North Royalton, OH  \n",
+       "80536            Wyoming, PA  \n",
+       "80541        Orland park, IL  \n",
+       "\n",
+       "[15516 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Shows how many rows has a not null shape AND a not null color\n",
+    "ufo[(ufo.Shape.notnull()) & (ufo.Colors.notnull())]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(15510, 4)\n",
+      "(80543, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufo.dropna()             # drop a row if ANY values are missing\n",
+    "print ufo.dropna().shape\n",
+    "ufo.dropna(how='all')    # drop a row only if ALL values are missing\n",
+    "print ufo.dropna(how='all').shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Colors</th>\n",
+       "      <th>Shape</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "      <td>Ithaca, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "      <td>Willingboro, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "      <td>Holyoke, CO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "      <td>Abilene, KS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "      <td>New York Worlds Fair, NY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Colors     Shape             Time                  Location\n",
+       "0    NaN  TRIANGLE   6/1/1930 22:00                Ithaca, NY\n",
+       "1    NaN     OTHER  6/30/1930 20:00           Willingboro, NJ\n",
+       "2    NaN      OVAL  2/15/1931 14:00               Holyoke, CO\n",
+       "3    NaN      DISK   6/1/1931 13:00               Abilene, KS\n",
+       "4    NaN     LIGHT  4/18/1933 19:00  New York Worlds Fair, NY"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.head()                      # Without an inplace=True, the dataframe is unaffected!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ufo.Colors.fillna(value='Unknown', inplace=True) # Permanent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Colors</th>\n",
+       "      <th>Shape</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "      <td>Ithaca, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "      <td>Willingboro, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "      <td>Holyoke, CO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "      <td>Abilene, KS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "      <td>New York Worlds Fair, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>9/15/1934 15:30</td>\n",
+       "      <td>Valley City, ND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>CIRCLE</td>\n",
+       "      <td>6/15/1935 0:00</td>\n",
+       "      <td>Crater Lake, CA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>7/15/1936 0:00</td>\n",
+       "      <td>Alma, MI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>CIGAR</td>\n",
+       "      <td>10/15/1936 17:00</td>\n",
+       "      <td>Eklutna, AK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>CYLINDER</td>\n",
+       "      <td>6/15/1937 0:00</td>\n",
+       "      <td>Hubbard, OR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Colors     Shape              Time                  Location\n",
+       "0  Unknown  TRIANGLE    6/1/1930 22:00                Ithaca, NY\n",
+       "1  Unknown     OTHER   6/30/1930 20:00           Willingboro, NJ\n",
+       "2  Unknown      OVAL   2/15/1931 14:00               Holyoke, CO\n",
+       "3  Unknown      DISK    6/1/1931 13:00               Abilene, KS\n",
+       "4  Unknown     LIGHT   4/18/1933 19:00  New York Worlds Fair, NY\n",
+       "5  Unknown      DISK   9/15/1934 15:30           Valley City, ND\n",
+       "6  Unknown    CIRCLE    6/15/1935 0:00           Crater Lake, CA\n",
+       "7  Unknown      DISK    7/15/1936 0:00                  Alma, MI\n",
+       "8  Unknown     CIGAR  10/15/1936 17:00               Eklutna, AK\n",
+       "9  Unknown  CYLINDER    6/15/1937 0:00               Hubbard, OR"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufo.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ufo.fillna(value = 'Unknown', inplace = True)   # Permanent for the entire dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City</th>\n",
+       "      <th>Colors Reported</th>\n",
+       "      <th>Shape Reported</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Day</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ithaca</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRIANGLE</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>6/1/1930 22:00</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Willingboro</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>6/30/1930 20:00</td>\n",
+       "      <td>6</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Holyoke</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>OVAL</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>2/15/1931 14:00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abilene</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DISK</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>6/1/1931 13:00</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New York Worlds Fair</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIGHT</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>4/18/1933 19:00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1933</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   City Colors Reported Shape Reported State             Time  \\\n",
+       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00   \n",
+       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00   \n",
+       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00   \n",
+       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00   \n",
+       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00   \n",
+       "\n",
+       "   Month  Day  Year  \n",
+       "0      6    1  1930  \n",
+       "1      6   30  1930  \n",
+       "2      2   15  1931  \n",
+       "3      6    1  1931  \n",
+       "4      4   18  1933  "
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "''' Fun Stuff '''\n",
+    "\n",
+    "ufo = pd.read_csv('../data/ufo.csv')\n",
+    "\n",
+    "# Make a new month column\n",
+    "ufo['Month'] = ufo['Time'].apply(lambda x:int(x.split('/')[0]))\n",
+    "\n",
+    "'''\n",
+    "the apply function applys the lambda funciton to every element in the Series\n",
+    "lambda x:x.split('/')[0] will take in x and split it by '/' and return the first element\n",
+    "so if we pass in say 9/3/2014 01:22 into the function we would get:\n",
+    "9                i.e. the month\n",
+    "'''\n",
+    "\n",
+    "# similar for day\n",
+    "ufo['Day'] = ufo['Time'].apply(lambda x:int(x.split('/')[1]))\n",
+    "\n",
+    "# for year, I need the [:4] at the end to remove the time\n",
+    "ufo['Year'] = ufo['Time'].apply(lambda x:int(x.split('/')[2][:4]))\n",
+    "ufo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11e73b910>"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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S6iQtLCrrK2mupGWS5kg6pGjdZEkrJC2VdHZR+ShJCyUtl3Rd678UM6sahUD/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\nUSnRC3mjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11e7508d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sightings_per_year = ufo.groupby('Year').City.count()\n",
+    "\n",
+    "sightings_per_year.plot(kind='line', \n",
+    "                        color='r', \n",
+    "                        linewidth=1, \n",
+    "                        title='UFO Sightings by year')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11ac11890>"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CuPsc4J+Ef4knpkbgHeDFqHvoE8L4S5/m1JilOmsN4YutgiR9lqcAT7t7TdQF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a8cfcd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# -----Analysis-----\n",
+    "# Clearly, Aliens love the X-Files (which came out in 1993).\n",
+    "# Aliens are a natural extension of the target demographic so it makes sense.\n",
+    "\n",
+    "# Well hold on Sinan, the US population is always increasing\n",
+    "# So maybe there's a jump in population which would make sense!\n",
+    "# US Population data from 1930 as taken from the Census\n",
+    "\n",
+    "us_population = pd.read_csv('../data/us_population.csv')\n",
+    "us_population.plot(x = 'Date', y = 'Population', legend = False)\n",
+    "# Seems like a steady increase to me..\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11f0e2d50>"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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G5piZ5Wl4eJjh4eFasYqI7kHSbsBlEbFvp2WSFgAREaemZZcDCyPixhbrRZ26N4ZimL9V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\nA29USNyQpbAIAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11fdddc90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the sightings in in July \n",
+    "ufo[(ufo.Year==2014) & (ufo.Month == 7)].groupby('Day').City.count().plot(  kind='bar',\n",
+    "                                                        color='b', \n",
+    "                                                        title='UFO Sightings in July 2014')\n",
+    "                                                        \n",
+    "\n",
+    "# -----Analysis-----\n",
+    "# Aliens are love the 4th of July. The White House is still standing. Therefore\n",
+    "# it follows that Aliens are just here for the party.\n",
+    "\n",
+    "# Well maybe it's just 2014?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x11fe25a50>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1208b9390>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12093a950>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12099f8d0>], dtype=object)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HlbgOWD8QrBIrgMXJ8/OA28psw2qglENW9Xoep1Gu2avXv+9IaJT3sBTNvO9DlTNL8K3A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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1204cb690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot multiple plots on the same plot (plots neeed to be in column format)\n",
+    "ufo_fourth = ufo[(ufo.Year.isin([2011, 2012, 2013, 2014])) & (ufo.Month == 7)]\n",
+    "\n",
+    "\n",
+    "# unstack will take a groupby of multiple indices and split it by column (mainly great for sub plotting)\n",
+    "\n",
+    "# Hmm let's make that prettier by making it 4 seperate charts\n",
+    "ufo_fourth.groupby(['Year', 'Day']).City.count().unstack(0).plot(\n",
+    "                                        kind = 'bar',\n",
+    "                                        subplots=True, \n",
+    "                                        figsize = (7,9))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "'''\n",
+    "Further Exploration\n",
+    "'''\n",
+    "\n",
+    "'''\n",
+    "Joining Data\n",
+    "\n",
+    "MovieLens 100k data:\n",
+    "    main page: http://grouplens.org/datasets/movielens/\n",
+    "    data dictionary: http://files.grouplens.org/datasets/movielens/ml-100k-README.txt\n",
+    "    files: u.user, u.data, u.item\n",
+    "'''\n",
+    "\n",
+    "# read 'u.data' into 'ratings'\n",
+    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
+    "ratings = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.data', header=None, names=r_cols, sep='\\t')\n",
+    "\n",
+    "# read 'u.item' into 'movies'\n",
+    "m_cols = ['movie_id', 'title']\n",
+    "movies = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.item', header=None, names=m_cols, sep='|', usecols=[0,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>movie_id</th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>GoldenEye (1995)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Four Rooms (1995)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Get Shorty (1995)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Copycat (1995)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   movie_id              title\n",
+       "0         1   Toy Story (1995)\n",
+       "1         2   GoldenEye (1995)\n",
+       "2         3  Four Rooms (1995)\n",
+       "3         4  Get Shorty (1995)\n",
+       "4         5     Copycat (1995)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>movie_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>unix_timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>196</td>\n",
+       "      <td>242</td>\n",
+       "      <td>3</td>\n",
+       "      <td>881250949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>186</td>\n",
+       "      <td>302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>891717742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>377</td>\n",
+       "      <td>1</td>\n",
+       "      <td>878887116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>244</td>\n",
+       "      <td>51</td>\n",
+       "      <td>2</td>\n",
+       "      <td>880606923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>166</td>\n",
+       "      <td>346</td>\n",
+       "      <td>1</td>\n",
+       "      <td>886397596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  movie_id  rating  unix_timestamp\n",
+       "0      196       242       3       881250949\n",
+       "1      186       302       3       891717742\n",
+       "2       22       377       1       878887116\n",
+       "3      244        51       2       880606923\n",
+       "4      166       346       1       886397596"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>movie_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>unix_timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>308</td>\n",
+       "      <td>4</td>\n",
+       "      <td>887736532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>287</td>\n",
+       "      <td>5</td>\n",
+       "      <td>875334088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>148</td>\n",
+       "      <td>4</td>\n",
+       "      <td>877019411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>280</td>\n",
+       "      <td>4</td>\n",
+       "      <td>891700426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>66</td>\n",
+       "      <td>3</td>\n",
+       "      <td>883601324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   movie_id             title  user_id  rating  unix_timestamp\n",
+       "0         1  Toy Story (1995)      308       4       887736532\n",
+       "1         1  Toy Story (1995)      287       5       875334088\n",
+       "2         1  Toy Story (1995)      148       4       877019411\n",
+       "3         1  Toy Story (1995)      280       4       891700426\n",
+       "4         1  Toy Story (1995)       66       3       883601324"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# merge 'movies' and 'ratings' (inner join on 'movie_id')\n",
+    "movie_ratings = pd.merge(movies, ratings)\n",
+    "movie_ratings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Star Wars (1977)                                      583\n",
+       "Contact (1997)                                        509\n",
+       "Fargo (1996)                                          508\n",
+       "Return of the Jedi (1983)                             507\n",
+       "Liar Liar (1997)                                      485\n",
+       "English Patient, The (1996)                           481\n",
+       "Scream (1996)                                         478\n",
+       "Toy Story (1995)                                      452\n",
+       "Air Force One (1997)                                  431\n",
+       "Independence Day (ID4) (1996)                         429\n",
+       "Raiders of the Lost Ark (1981)                        420\n",
+       "Godfather, The (1972)                                 413\n",
+       "Pulp Fiction (1994)                                   394\n",
+       "Twelve Monkeys (1995)                                 392\n",
+       "Silence of the Lambs, The (1991)                      390\n",
+       "Jerry Maguire (1996)                                  384\n",
+       "Chasing Amy (1997)                                    379\n",
+       "Rock, The (1996)                                      378\n",
+       "Empire Strikes Back, The (1980)                       367\n",
+       "Star Trek: First Contact (1996)                       365\n",
+       "Back to the Future (1985)                             350\n",
+       "Titanic (1997)                                        350\n",
+       "Mission: Impossible (1996)                            344\n",
+       "Fugitive, The (1993)                                  336\n",
+       "Indiana Jones and the Last Crusade (1989)             331\n",
+       "Willy Wonka and the Chocolate Factory (1971)          326\n",
+       "Princess Bride, The (1987)                            324\n",
+       "Forrest Gump (1994)                                   321\n",
+       "Monty Python and the Holy Grail (1974)                316\n",
+       "Saint, The (1997)                                     316\n",
+       "                                                     ... \n",
+       "Brother's Kiss, A (1997)                                1\n",
+       "Coldblooded (1995)                                      1\n",
+       "Silence of the Palace, The (Saimt el Qusur) (1994)      1\n",
+       "Century (1993)                                          1\n",
+       "Homage (1995)                                           1\n",
+       "Hana-bi (1997)                                          1\n",
+       "Symphonie pastorale, La (1946)                          1\n",
+       "They Made Me a Criminal (1939)                          1\n",
+       "Brothers in Trouble (1995)                              1\n",
+       "Girls Town (1996)                                       1\n",
+       "Touki Bouki (Journey of the Hyena) (1973)               1\n",
+       "Marlene Dietrich: Shadow and Light (1996)               1\n",
+       "Sleepover (1995)                                        1\n",
+       "Sunchaser, The (1996)                                   1\n",
+       "Nothing Personal (1995)                                 1\n",
+       "Niagara, Niagara (1997)                                 1\n",
+       "Further Gesture, A (1996)                               1\n",
+       "Great Day in Harlem, A (1994)                           1\n",
+       "Eye of Vichy, The (Oeil de Vichy, L') (1993)            1\n",
+       "Daens (1992)                                            1\n",
+       "Woman in Question, The (1950)                           1\n",
+       "Someone Else's America (1995)                           1\n",
+       "Man from Down Under, The (1943)                         1\n",
+       "Jupiter's Wife (1994)                                   1\n",
+       "Spanish Prisoner, The (1997)                            1\n",
+       "Next Step, The (1995)                                   1\n",
+       "I, Worst of All (Yo, la peor de todas) (1990)           1\n",
+       "Two Friends (1986)                                      1\n",
+       "Gate of Heavenly Peace, The (1995)                      1\n",
+       "Crude Oasis, The (1995)                                 1\n",
+       "Name: title, dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for each movie, count number of ratings\n",
+    "movie_ratings.title.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: order is deprecated, use sort_values(...)\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "title\n",
+       "Marlene Dietrich: Shadow and Light (1996)                 5.000000\n",
+       "Prefontaine (1997)                                        5.000000\n",
+       "Santa with Muscles (1996)                                 5.000000\n",
+       "Star Kid (1997)                                           5.000000\n",
+       "Someone Else's America (1995)                             5.000000\n",
+       "Entertaining Angels: The Dorothy Day Story (1996)         5.000000\n",
+       "Saint of Fort Washington, The (1993)                      5.000000\n",
+       "Great Day in Harlem, A (1994)                             5.000000\n",
+       "They Made Me a Criminal (1939)                            5.000000\n",
+       "Aiqing wansui (1994)                                      5.000000\n",
+       "Pather Panchali (1955)                                    4.625000\n",
+       "Anna (1996)                                               4.500000\n",
+       "Everest (1998)                                            4.500000\n",
+       "Maya Lin: A Strong Clear Vision (1994)                    4.500000\n",
+       "Some Mother's Son (1996)                                  4.500000\n",
+       "Close Shave, A (1995)                                     4.491071\n",
+       "Schindler's List (1993)                                   4.466443\n",
+       "Wrong Trousers, The (1993)                                4.466102\n",
+       "Casablanca (1942)                                         4.456790\n",
+       "Wallace & Gromit: The Best of Aardman Animation (1996)    4.447761\n",
+       "Shawshank Redemption, The (1994)                          4.445230\n",
+       "Rear Window (1954)                                        4.387560\n",
+       "Usual Suspects, The (1995)                                4.385768\n",
+       "Star Wars (1977)                                          4.358491\n",
+       "12 Angry Men (1957)                                       4.344000\n",
+       "Third Man, The (1949)                                     4.333333\n",
+       "Letter From Death Row, A (1998)                           4.333333\n",
+       "Bitter Sugar (Azucar Amargo) (1996)                       4.333333\n",
+       "Citizen Kane (1941)                                       4.292929\n",
+       "Some Folks Call It a Sling Blade (1993)                   4.292683\n",
+       "                                                            ...   \n",
+       "Symphonie pastorale, La (1946)                            1.000000\n",
+       "Baton Rouge (1988)                                        1.000000\n",
+       "Boys in Venice (1996)                                     1.000000\n",
+       "Tigrero: A Film That Was Never Made (1994)                1.000000\n",
+       "Office Killer (1997)                                      1.000000\n",
+       "T-Men (1947)                                              1.000000\n",
+       "Terror in a Texas Town (1958)                             1.000000\n",
+       "Nobody Loves Me (Keiner liebt mich) (1994)                1.000000\n",
+       "The Courtyard (1995)                                      1.000000\n",
+       "King of New York (1990)                                   1.000000\n",
+       "Liebelei (1933)                                           1.000000\n",
+       "Stefano Quantestorie (1993)                               1.000000\n",
+       "Hostile Intentions (1994)                                 1.000000\n",
+       "Pharaoh's Army (1995)                                     1.000000\n",
+       "New Age, The (1994)                                       1.000000\n",
+       "Hungarian Fairy Tale, A (1987)                            1.000000\n",
+       "Death in the Garden (Mort en ce jardin, La) (1956)        1.000000\n",
+       "Promise, The (Versprechen, Das) (1994)                    1.000000\n",
+       "I, Worst of All (Yo, la peor de todas) (1990)             1.000000\n",
+       "Power 98 (1995)                                           1.000000\n",
+       "Police Story 4: Project S (Chao ji ji hua) (1993)         1.000000\n",
+       "Low Life, The (1994)                                      1.000000\n",
+       "Somebody to Love (1994)                                   1.000000\n",
+       "Invitation, The (Zaproszenie) (1986)                      1.000000\n",
+       "Lotto Land (1995)                                         1.000000\n",
+       "Touki Bouki (Journey of the Hyena) (1973)                 1.000000\n",
+       "JLG/JLG - autoportrait de d�cembre (1994)                 1.000000\n",
+       "Daens (1992)                                              1.000000\n",
+       "Butterfly Kiss (1995)                                     1.000000\n",
+       "Eye of Vichy, The (Oeil de Vichy, L') (1993)              1.000000\n",
+       "Name: rating, dtype: float64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movie_ratings.groupby('title').rating.mean().order(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/04_sklearn_knn-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/04_sklearn_knn-checkpoint.ipynb
new file mode 100644
index 0000000..0189d13
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/04_sklearn_knn-checkpoint.ipynb
@@ -0,0 +1,1041 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''\n",
+    "MACHINE LEARNING WITH KNN\n",
+    "'''\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# read in the iris data\n",
+    "data = pd.read_csv('https://raw.githubusercontent.com/sinanuozdemir/sfdat28/master/data/iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
+       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
+       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
+       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
+       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
+       "4           5.0          3.6           1.4          0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 4)\n",
+      "(150,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create X (features) and y (response)\n",
+    "# the .drop method removes a specific column\n",
+    "X, y = data.drop('species', axis = 1), data['species'] #recall axis 1 means columns\n",
+    "# So I am dropping the \"species\" column\n",
+    "print X.shape\n",
+    "print y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# predict y with KNN\n",
+    "from sklearn.neighbors import KNeighborsClassifier  # import class\n",
+    "# http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)           # instantiate the estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
+       "           weights='uniform')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X, y)                                       # fit with data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.95999999999999996"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use the models .score method to get accuracy (later classes we will see alternate metrics)\n",
+    "knn.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.3,  3.1,  5.1,  2.4]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict a new data point\n",
+    "np.array([6.3,3.1,5.1,2.4]).reshape(1, 4) # turn a single new data point into a matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.predict(np.array([6.3,3.1,5.1,2.4]).reshape(1, 4))                           # predict for a new observation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-virginica', 'Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict for multiple observations at once\n",
+    "X_new = [[6.3,3.1,5.1,2.4], [3, 5, 2, 2]]\n",
+    "knn.predict(X_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Iris-virginica' 'Iris-setosa']\n",
+      "[[ 0.  0.  1.]\n",
+      " [ 1.  0.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# try a different value of K\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(X, y)\n",
+    "print knn.predict(X_new)              # predictions\n",
+    "print knn.predict_proba(X_new)        # predicted probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SwsMpKdx2xRUMPeecqMZlCdxEQv/+gqr6HUBXmMQPqizSExilqueXKe8NDFPV80QkC3hS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\nhOsqV+kOct/nZlP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xCa1ateGhhxaTNGAu33z6DdPvn05eTh7d+nbj77f/nYTE0MdPYWkn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\nGGZJ3BhjYpglcWOMiWH/D/0RXnXb1BiwAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1175c6ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vUP/fbDaGQlH7o8A55Xg6Tqj5REq457p3fL4Dx02Ooh3kesTFji6R364NG5eC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\nEAR9sY93KmURMNI7Iq/2qmsBB1i/dD3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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11258dfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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tvIRR9/8nqPjB5qnCo8LXE1cV9+WOHdzidNLS8/gJl4uOO/yf6eev/z67ncFQ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\nXCmlopgWcaWUimJaxJVSKor9PyIyaOW3tuldAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11787d510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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VZe3ahaxduzCkvqIabHDt6yAyDrgK8AApwKHAu6o6wK/PS8ACVZ3ue/wD0L30dIqIqPoV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\nZ3Lcce3Iu2gv+liRt38HF+9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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1181e75d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MhieZ2McfP8e7700CswpoAjGDqVFzBcPuncZzz92J07kUaEJs7H20bXuEMWM+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\nwH7UWDkkBgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1176b1810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the decision boundaries of the model\n",
+    "# Notice that as we increase K, the number \n",
+    "# of small isolated \"prediction islands\" goes down\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "for n_neighbors in range(1,10, 2):\n",
+    "    h = .02  # step size in the mesh\n",
+    "\n",
+    "    # Create color maps\n",
+    "    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
+    "    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
+    "\n",
+    "    # we create an instance of Neighbours Classifier and fit the data.\n",
+    "    iris = load_iris()\n",
+    "    X_only_2 = iris.data[:, :2]  # we only take the first two features for ease\n",
+    "    y_only_2 = iris.target\n",
+    "    clf = KNeighborsClassifier(n_neighbors)\n",
+    "    clf.fit(X_only_2, y_only_2)\n",
+    "\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
+    "    x_min, x_max = X_only_2[:, 0].min() - 1, X_only_2[:, 0].max() + 1\n",
+    "    y_min, y_max = X_only_2[:, 1].min() - 1, X_only_2[:, 1].max() + 1\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.figure()\n",
+    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    plt.scatter(X_only_2[:, 0], X_only_2[:, 1], c=y_only_2, cmap=cmap_bold)\n",
+    "    plt.xlim(xx.min(), xx.max())\n",
+    "    plt.ylim(yy.min(), yy.max())\n",
+    "    plt.title(\"3-Class classification (k = %i)\"\n",
+    "              % (n_neighbors))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96666666666666667"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute the accuracy for K=5\n",
+    "# K = 5\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(X, y)\n",
+    "# the score function will return the accuracy of your prediction\n",
+    "# the number of correct prepdictions / the number of rows\n",
+    "knn.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# remember supervised learning is not ONLY about prediction but about finding relationships between variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sepal_length 0.76\n",
+      "sepal_width 0.533333333333\n",
+      "petal_length 0.953333333333\n",
+      "petal_width 0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for each column, use only that column to try and predict class\n",
+    "# This is called iterative feature selection\n",
+    "\n",
+    "for column in X.columns:\n",
+    "    X_subset = X[[column]]\n",
+    "    knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "    knn.fit(X_subset, y)\n",
+    "    print column, knn.score(X_subset, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85333333333333339"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X_subset = X[['sepal_length', 'sepal_width']] # using only sepal measurements\n",
+    "knn.fit(X_subset, y)\n",
+    "knn.score(X_subset, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.97999999999999998"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X_subset = X[['petal_length', 'petal_width']] # using only petal measurements\n",
+    "knn.fit(X_subset, y)\n",
+    "knn.score(X_subset, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# It seems as though the petal measurements \n",
+    "# are MORE important than sepal measurements\n",
+    "# That does not mean that the sepal\n",
+    "# measurements are UNimportant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.95999999999999996"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn.fit(X, y) # using ALL features\n",
+    "# IN this case, it does seem to take away from the overall predicting power...\n",
+    "knn.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# When to scale data\n",
+    "# You should normalize (scale) data when the scale of a column feature is \n",
+    "# either irrelevant and/or misleading. \n",
+    "# You should NOT normalize when the scale is meaningful.\n",
+    "\n",
+    "# KNN uses the Euclidean distance and considers it meaningful. \n",
+    "# scaling our data is likely important.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "s = StandardScaler()\n",
+    "\n",
+    "# standard scalr can only take in numpy arrays, no dataframes\n",
+    "X_scaled = s.fit_transform(X[['petal_length', 'petal_width']].as_matrix())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.97999999999999998"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# not scaled\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X_subset = X[['petal_length', 'petal_width']]\n",
+    "knn.fit(X_subset, y)\n",
+    "knn.score(X_subset, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98666666666666669"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# scaled\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn.fit(X_scaled, y)\n",
+    "knn.score(X_scaled, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x117f9ccd0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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lsoc14R5SqfQGSlrvoRTUITlOQHFLX3kVDfL9m6hUqsntrxFwzFAYJwm4iVSF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11747d250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# KNN is a discriminative, non-parametric model\n",
+    "\n",
+    "# discriminative: It attempts to model the conditional \n",
+    "# probability between the response and the features\n",
+    "\n",
+    "\n",
+    "# non-parametric: KNN model makes no assumptions \n",
+    "# about the shape of the underlying data, it is able to model\n",
+    "# data in funky shapes\n",
+    "\n",
+    "# Wednesday's model will not be so lucky on this dataset...\n",
+    "\n",
+    "from sklearn.datasets import make_circles\n",
+    "circles_X, circles_y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)\n",
+    "plt.scatter(circles_X[:,0], circles_X[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TUBAjfD4eRrmTegADgTrABq+Xc/nz88mJE2xC+c+x7/eXDgfVihen38GDfOP3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\nhbeIiA8pvEVEfOh/AasUFTQmoRdcAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1187a96d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9KdkYH0/T0aNZ+9FH/DZgACN+/plfjx+netGizGnShFkbNzJvyxYWrltHd8si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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a64f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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8dvw41YoWZXbjxszauJG5W7awYN06ulkWiajQwHwpls0LIcgUFMQon4+HUe6k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\nQwpvEREfUniLiPjQ/wIV8iWcxLOXngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11adf70d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yu3FjZm7cyNwtW1iwbh3dLItEVGhgvhTb5oUQZAoKYpTPx8Mod1JPYBBQF1jv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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1196af290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2d+tGzbNn+VRKtsTH02TECDZ88QW/9+3L0F9+4beTJ6lapAjzGjdmzpYtLNi+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\nOjqV184AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11968eed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for n_neighbors in range(1,10, 2):\n",
+    "    h = .02  # step size in the mesh\n",
+    "\n",
+    "    # Create color maps\n",
+    "    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
+    "    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
+    "\n",
+    "    # we create an instance of Neighbours Classifier and fit the data.\n",
+    "    clf = KNeighborsClassifier(n_neighbors)\n",
+    "    clf.fit(circles_X, circles_y)\n",
+    "\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
+    "    x_min, x_max = circles_X[:, 0].min() - 1, circles_X[:, 0].max() + 1\n",
+    "    y_min, y_max = circles_X[:, 1].min() - 1, circles_X[:, 1].max() + 1\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.figure()\n",
+    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    plt.scatter(circles_X[:, 0], circles_X[:, 1], c=circles_y, cmap=cmap_bold)\n",
+    "    plt.xlim(xx.min(), xx.max())\n",
+    "    plt.ylim(yy.min(), yy.max())\n",
+    "    plt.title(\"Circle classification (k = %i)\"\n",
+    "              % (n_neighbors))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.0,\n",
+       " 0.97999999999999998,\n",
+       " 0.95999999999999996,\n",
+       " 0.95999999999999996,\n",
+       " 0.96666666666666667,\n",
+       " 0.97333333333333338,\n",
+       " 0.97333333333333338,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97333333333333338,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.98666666666666669,\n",
+       " 0.98666666666666669,\n",
+       " 0.97999999999999998,\n",
+       " 0.97333333333333338,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97999999999999998,\n",
+       " 0.97333333333333338,\n",
+       " 0.97999999999999998,\n",
+       " 0.97333333333333338,\n",
+       " 0.97333333333333338,\n",
+       " 0.96666666666666667,\n",
+       " 0.97333333333333338]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's graph the accuracy of a few values of K\n",
+    "scores = []\n",
+    "for i in range(1, 30):\n",
+    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
+    "    knn.fit(X, y)\n",
+    "    scores.append(knn.score(X, y))\n",
+    "    \n",
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1187a9550>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9v/RSWq61WW6vPeaYtOZEM7SkGsno0en2V//e4JBD0sDcvH8XpVbPl79c+S3C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\nsjPwbNnPq7Nt5RYCEwEkjQV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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1182e6b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(1,30), scores)\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.xlabel(\"K\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# K = 1, odd... Why is this odd?\n",
+    "knn = KNeighborsClassifier(n_neighbors=1)\n",
+    "knn.fit(X, y)\n",
+    "knn.score(X, y)\n",
+    "# In our next class we will be looking into this phenomenom, called \"overfitting\"\n",
+    "# by exploring what is known as the bias varaince tradeoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb
new file mode 100644
index 0000000..ac51a79
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb
@@ -0,0 +1,3685 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Agenda\n",
+    "\n",
+    "1. Refresh your memory on how to do linear regression in scikit-learn\n",
+    "2. Attempt to use linear regression for classification\n",
+    "3. Show you why logistic regression is a better alternative for classification\n",
+    "4. Brief overview of probability, odds, e, log, and log-odds\n",
+    "5. Explain the form of logistic regression\n",
+    "6. Explain how to interpret logistic regression coefficients\n",
+    "7. Compare logistic regression with other models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 1: Predicting a Continuous Response"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 46,
+=======
+   "execution_count": 1,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# glass identification dataset\n",
+    "import pandas as pd\n",
+    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data'\n",
+    "col_names = ['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type']\n",
+    "glass = pd.read_csv(url, names=col_names, index_col='id')\n",
+    "glass['assorted'] = glass.glass_type.map({1:0, 2:0, 3:0, 4:0, 5:1, 6:1, 7:1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 47,
+=======
+   "execution_count": 2,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ri</th>\n",
+       "      <th>na</th>\n",
+       "      <th>mg</th>\n",
+       "      <th>al</th>\n",
+       "      <th>si</th>\n",
+       "      <th>k</th>\n",
+       "      <th>ca</th>\n",
+       "      <th>ba</th>\n",
+       "      <th>fe</th>\n",
+       "      <th>glass_type</th>\n",
+       "      <th>assorted</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.52101</td>\n",
+       "      <td>13.64</td>\n",
+       "      <td>4.49</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>71.78</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.51761</td>\n",
+       "      <td>13.89</td>\n",
+       "      <td>3.60</td>\n",
+       "      <td>1.36</td>\n",
+       "      <td>72.73</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>7.83</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.51618</td>\n",
+       "      <td>13.53</td>\n",
+       "      <td>3.55</td>\n",
+       "      <td>1.54</td>\n",
+       "      <td>72.99</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>7.78</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.51766</td>\n",
+       "      <td>13.21</td>\n",
+       "      <td>3.69</td>\n",
+       "      <td>1.29</td>\n",
+       "      <td>72.61</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>8.22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.51742</td>\n",
+       "      <td>13.27</td>\n",
+       "      <td>3.62</td>\n",
+       "      <td>1.24</td>\n",
+       "      <td>73.08</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>8.07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         ri     na    mg    al     si     k    ca   ba   fe  glass_type  \\\n",
+       "id                                                                        \n",
+       "1   1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0           1   \n",
+       "2   1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0           1   \n",
+       "3   1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0           1   \n",
+       "4   1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0           1   \n",
+       "5   1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0           1   \n",
+       "\n",
+       "    assorted  \n",
+       "id            \n",
+       "1          0  \n",
+       "2          0  \n",
+       "3          0  \n",
+       "4          0  \n",
+       "5          0  "
+      ]
+     },
+<<<<<<< HEAD
+     "execution_count": 47,
+=======
+     "execution_count": 2,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glass.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pretend that we want to predict **ri**, and our only feature is **al**. How would we do it using machine learning? We would frame it as a regression problem, and use a linear regression model with **al** as the only feature and **ri** as the response.\n",
+    "\n",
+    "How would we **visualize** this model? Create a scatter plot with **al** on the x-axis and **ri** on the y-axis, and draw the line of best fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+=======
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    }
+   ],
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 49,
+=======
+   "execution_count": 4,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+<<<<<<< HEAD
+       "<seaborn.axisgrid.FacetGrid at 0x12ec7a6d0>"
+      ]
+     },
+     "execution_count": 49,
+=======
+       "<seaborn.axisgrid.FacetGrid at 0x1044cb850>"
+      ]
+     },
+     "execution_count": 4,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+<<<<<<< HEAD
+      "image/png": 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bvfeTN84yMBK6qkdyKj+mlWkqm8yFknCeTfdBnUt9Nxsf/NREfqlrkI6+Ua9+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\nCYuI+EhJWETER/8/CKsg8nZYJSAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12ec7acd0>"
+=======
+      "image/png": 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P2FtW+1JFRNY9hbQUJF8vmS64f39T9lvdsxYRKS4VjklBCqnsLuSetYiIFE4hLQUppJd8\nuCXEttD0QM69Zy0iIouj4W4pSCG95GDAz9NPHlB1t4hIkSikpSALVXZnBAN+FYmJiBSJQloKol6y\niMjqU0hLwfL1kudavESLmoiILJ9CWpZsrsVLPvH+/Xz75Uta1EREZJlU3S1LNte0rOePX9VGHCIi\nRaCQliWba1rW9YHxRZ0vIiL5KaRlyeaalrWrqWZR54uISH4KaVmyuRYv+fC79mhRExGRIlDhmCzZ\nfNOyNsp0LVWxi8hKUkjLssy1eMlGWNREW3OKyErTcLfIEhWy6YiIyHIopEWWSFtzishKU0iLLJG2\n5hSRlaaQFlkibc0pIitNhWMiS7SRqthFZG0opEWWYSNUsYvI2tFwt4iIiEcppEVERDzKU8Pdtm2X\nAyeAzxljXpvjnO8BTwEOYKX/fMoY80L68S8A/xwIAW8Cv2OMaU8/dhQ4lfNcgBPGmHesWKNERESW\nyDM96XRAPwscWuDUg8DHgG1Ac/rPl9Ov8Vng94DPAQ8AV4EXbduuSD/3EHA6/bzMf08Usx0iIiLF\n4ometG3bB4HvFHBeGbAXt/fbn+eUTwJfNca8mD7/XwAjwKPAK7gB326MGSjWtYuIiKwUr/SkH8MN\n0UeYGobOxwZSwOU5Hv8808M+M6xdl/7+EHBpWVcqIiKySjzRkzbGfCPztW3b8516EBgDvm3b9uPA\ndeBLxpiX0q9zfMb5vwn4gZ/mPN9n23YbbnC/CPwrY8ydIjRDRESkqLzSky7UAaASN1yfAF4AfmDb\n9v0zT7Rt+2Hgj4F/b4wZsG07ALTgfjD5DeBTuMPgz6zOpYuIiCyOJ3rShTLGfMW27a8ZY0bTh87Z\ntv0A8FvAZzPn2bb9CG6A/9AY86X0cxO2bYeASWNMMn3eJ4ETtm03G2NuFXodfv96+2wzJXPtasPa\nK4V2qA3eUQrtKKU2FMu6CmmAnIDOaCenIjw9DP4D4CXcKvDc547neS7ADqDgkK6trSz0VM9SG7zD\nK+2IxZOcaO+jZ2CcHU01PHhwK2XBwpY49UoblqMU2gCl0Y5SaEOxrKuQtm37W0DKGPPpnMNHgbb0\n4/cC3wN+CHzMGJPKee5B4A3gPmPMtfThY0Ac6FzMdYyNTZJMphY+0YP8fh+1tZVqgwd4qR2xRJJv\nPt/OzaGJ7LF/OHmdT334IGXzrEXupTYsVSm0AUqjHaXUhmLxfEjbtr0VGDXGRIDvA8/atv0qcBz4\nOO595c+kT/9PQDdulXdTThHaKHAR6AD+zLbt3wXqgW8A/zlP73xeyWSKRGJ9/gXKUBu8wwvtOG0G\n6BmcmHasZ3CC02agoLXJvdCG5SqFNkBptKMU2lAsXhz4d2Z83wt8FMAY8xzw28AXgXO4K489YYy5\nng7zd+IOfXcDN3P++6gxxgF+Bbc6/DXgOdxFUH5vpRsk4nW3hsOLOi4iq8NzPWljjH/G974Z338T\n+Gae5/XhTrea77V7gH9ShMsUKSnNDVWLOi4iq8OLPWkRWWWHW0JsC00P5G0hd39sEVk7nutJi8jq\nCwb8PP3kAdq6hrg1HKa5wQ3o4DxFYyKy8hTSIgK4QV1IkZiIrB6FtGw48URSPUYRWRcU0rKhxBNJ\nvvXiRXqHpqqWT14a4OknDyioRcRzVDgmG0pb19C0gAboHQrT1jW0RlckIjI3hbRsKJoPLCLriUJa\nNhTNBxaR9UT3pGVDOdwS4uSlgWlD3oXOB16tgjMVtolIhkJaNpSlzgderYIzFbaJSC6FtGw4S5kP\nPF/BWTHnFq/W+4jI+qB70iIFWK2CMxW2iUguhbRIAVar4EyFbSKSSyEtUoDV2oBCG12ISC7dk95g\nYokkx9tu0tk9zJbNlaoczjFfVfVqbUChjS5EJJdCegOJJ5L8xUuG/tuTJJIpHEeVwxmFVFWv1gYU\n2uhCRDI03L2BtHUNcXNoYtqx1VoSM55IctL088PXr3LS9BNPJFf8PRdDy4WKiBepJ72BrFXl8HqY\n+6uqahHxIvWkN5C1qhxeD71UVVWLiBcppDeQwy0htoeqpx1bjcrh9dBLVVW1iHiRhrs3kGDAz6c+\nfJCu3nE6u0fYsrliVSqH10MvVVXVIuJFCukNpizg512Ht3NwVx2JRGpV3nM5m1qsJlVVi4jXKKRl\nxamXurFoFy+R4lFIy6pQL3VjWA+V/CLrScEhbdv27wN/bIwJp7+ekzHmK8u+MhFZd7SLl0hxLaYn\n/TTwJ0A4/fVcHEAhvcHFE0lOXRrgbKc7zepIa4j79zctuzcVTyQ53TnIaDhOXVWQe/fUq4fmIeuh\nkl9kPSk4pI0xe3O+fb8xpnMFrkeKLByJ8/zxq1wfGGdXUw0fee9e6qle+InLEE8k+fMftnP+yjDx\ndHHauctDnO4Y5NMfOrjkUM0Mpd4aDhPw+0gkU7zV3qehVA9ZD5X8IuvJUudJ/4Nt2+8o6pVI0YUj\ncb78rbf48akeOq6P8uNTPfxff/Ym4+FYUd9n5pKfpy4NcKV3LBvQ7jkprvSOzbuAyUJLh66HRVE2\nOs03FymupRaOxdP/iYc9f/wqYxPTA3ksHON/vHKJf/TuvXM8a3HyFQqlUs60gJ46N5V32DMzNP7C\nL7qJxBJUlgewLGtWwZGGUr1PlfwixbXUkP6vwEu2bT8DdAKTuQ8aY55Z5nV5Vr7w8arrA+N5j1/p\nHSvae+Tr3UZiCVIpZ9a5wYBv1rBnJuS7ekYZHXc/UIQjCUJ1FbMKjjSUuj6okl+keJYa0pnq7s/n\necwBSjakP/77L3Lf3Q0caWnkvpYQNZXBtb6kOe1qqqHj+uis43u31RbtPfL1YivLA5QH/SRT0eyH\nmmDAx95ttbOGPTMhP3NofDKaoKoiOO31M4ui5B7TUKqIlLIlhbQxZsOu+T0ZTfBmez9vtvdjWbBv\nRx1H9zVxpDXEttDKFmQt1offtYcTZmDakHdtVRm//sv7iUeLc7ciXy/Wsiw+9MhdAAtWd/cMjhOO\nxInFkyRSDhZgWVMjFrmvnxlKffvqyJKqu1dikY1ivqYWARGRmbSYyTI4Dly6McqlG6P89U862Vpf\nyZHWRo7ta6R1Zx1+39p+lqmqCPLlpx/i+eNXuTEwwc6maj7y3r3UVJUxUqSQnmvJz0wgP3yoec7n\nxhNJLlwd4fadKImkQ2aA3AImo0n276qc1UsOBvw8dGAL9fXVjIxMFLy06UosslHM19QiICKSj0J6\nkXY0VdMzMJH3sb6RSf7+rev8/VvXqa4IcN/dIY60NnLf3Q1UVazNsHhVRZCP/tK+7PeBQHE/OCyn\nUKita4hILInPZ0Fy6h62z2exqSrIsX2NRQuolVhko5ivqUVARCQfhfQifeP/+J+40NnPyYsDnOkc\npOPGbZzZNVJMRBL84kIfv7jQh99nsX/XZo60NnJ0XyNbNleu/oWvoKUWCnX3j7m96NRUL9pnQXmZ\nn6qKAIOjkaJd40pUhhfzNW8Nh3Ech8logngiRTDgo7I8oMp1kQ1OIb0E20LVfPDhSj748F2MT8Y5\nd3mIMx2DvH1liMloctb5yZRD+7UR2q+N8N9f6WB7YzVHWkMcbW2kZXud25PcYMKROK+e7mU8HCf3\nM07KgVg8ydBohMa6iqK930pUhhfzNRvrKhgajUwroAtHEkX9GYjI+qOQXqaayiCP3NPMI/c0k0im\nuHT9Nmc6BznTMThnT/Dm4AQ3Byd48Rfd1FQGOdLiDovfs7eByvKN8St5/vhVorEklsWskQifVfwP\nLSuxXeZ62YJTRNavjZEIqyTg93FoTwOH9jTwz355HzcHJzjTOcjZziG6ekbJMyrO+GScn799i5+/\nfYuA3+LAXfXusHhrI6FV7kWtdHVx7uufvzqM4zjZ5T0z06oDfou6mjIqy4s73L0Si2wU8zUHRyOE\n6ipmDXcX82cgIuuPQnqFWJbFjqYadjTV8KFH9jA2EaOta4iznYO8fWWYaHz2sHgi6fD2lWHevjLM\nf3v5Ejubaji6zw3sPds2rUgPM2Olq4tnvv7oeIxkysHvc3vOTro7XVUeyBbZFXuRkpVYZGM5rxlL\nJDnedpPO7mEmJt1q+5kFhlqoRWRjU0ivktrqMt59eBvvPryNeCLJxe7b6V72IMNj0bzPuTEwzo2B\ncZ4/fpW66jIOt4Q4uq+RQ3saKA8Wd1rOSlcXz3z9uppywtEEjuPOi7Yst6q7rqYcKP1h43giyV+8\nZOi/PemOJKQcwpEEVRXukqhQ+j8DEVmYQnoNBAN+7rs7xH13h/jE+/dzvX88G9hXeu/kfc7oRIyf\ntvXy07ZeggEfB3fXc7S1kSOtjdRvKl/2Na30utgzX8fns9gWqqayzE9Z0M+2UBW7mzcxcidalKHo\nzNB6z+A4kWiSivIAOxqrPbNASFvXEDeHJgj43SlxlmVRVRHg0O56qiuDWsxERACF9JqzLIu7tm7i\nrq2b+JVH93J7PMrZ9H3sC1eHic2xUUVb15C7+9PfGXY3b+Jo+j72XVtrsj2xxVjpdbHzvY7PZ/H+\nh3YVfQg6M7R+c3AiWzEdDPgI1VV4ZoGQfB9+LMuiujLIhx7Zs/oXJCKepJD2mM015Tx2dAePHd1B\nNJ6k/doIZzoGOds1mN2AYqZrt+5w7dYdvvezK9RvKk8XnoU4uLvwJTNXulJ5NSuhM0PrmSIsmFoP\nfDUXCJmvEE+bhYhIIRTSHlYe9Gd7yCnH4dqtO5xNT+/q7s+/w9XInSivnu7h1dM9lAV93LOngaOt\njRxubaSuumzO91rpLQZXcwvDTC915o5lme9XY4GQhQrxDreEON0xSP/tqQ3kdA9aRGZSSK8TPsti\n77Za9m6r5R+9526GxyJuYHcO0X5tmERy9gSvWDzF6Y5BTncMYgF3b6/l2P4mHnvwLmrLZy8PutJb\nDK7WFoaZ3mhwxhKome9Xo7e6UCFeMODnUx8+SFfvOJ3dI2zZXLGoDy1e2owjFk/y1sV+bg6Mr/m1\niJQahfQ61VBbwfvu38n77t9JJJbg/JUR91521yB3wrM3z3CArptjdN0c429e7aKxriI7H9u+a3O2\ngKkUZIbWbw5OEI5Mn3e8Wr3VQgrxygJ+3nV4Owd31RW8UQh4azOOWCLJN//6DFd7R7OL0izmWrz0\nYUPEixTSJaCiLMADdhMP2E2kUg6Xe8fSvezBOTcDGRyN8MrJG7xy8gaV5X7u2RviaGuIwy2Nnt4j\nuxC5Q+uZ6u7K8gDbV7G6eyXvOXtpM46znUPc6J8+I6HQa/HShw0Rr1JIlxifz6J1Rx2tO+r4tcda\nGLg9mZ3eZbpvk0zNHhafjCY5cbGfExen9sg+kl5EpbmhaknV4mstM7S+2qE1NfVrgvKgj0gsWfR5\nzys9XW4xbg3l/xBYyLV46cOGiFd5KqRt2y4HTgCfM8a8Nsc53wOewh3BtdJ/PmWMeSH9+BeAfw6E\ngDeB3zHGtOc8/98BnwJ8wJ8bY76wci1ae02bK3n/g7t4/4O7CEcStHePcP7aCG9d6MuucpUrd4/s\n//GTLrbUV2aL1/btWvs9sr1sZs/QcRwqyvwc2lPPjsaaovXivVQZ3hyqho7BJV2Llz5siHiVZ0I6\nHdDPAocWOPUg8DHgxznHRtKv8Vng94DfADqALwAv2rZ9wBgTsW3788A/BT4ClAH/zbbtPmPMfyhm\nW7yqqiLAw4e28sFH72Zw6A7m2tRmIHP9w9ifs0d2VXmA+1rc3bvWco9sr5rZM7Qsi2g8xY7GmqL2\nDL20sceR1hDnLg9ztXd00dfipQ8bIl7liZC2bfsg8J0CzisD9gInjDH9eU75JPBVY8yL6fP/BW6A\nPwq8AvwO8EVjzOvpx78A/AGwIUI6l9/nY/+uzezftZmPvq+VvuFwNrA7boySyrNJdjia4I0LfbyR\n3iN73846t5e9r5Et9fqHdSV7hjMLrD7x/v20XxtZ84KrsoCff/nRo7x6onvR1d1e+rAh4lWeCGng\nMdwQ/SIw379oNpACLs/x+OeBqznfZ4bE62zb3gbsAn6a8/jPgN22bW81xvQt7dJLw9aGKp54x108\n8Y6pPbLPdg5y7vLce2Rf7L7Nxe7b/Pcfd7ItVJUN7I26R/ZK9QznK7Dywr3bsqCfhw5sIdHauKjn\nrebceZEuCT6+AAAgAElEQVT1yhMhbYz5RuZr27bnO/UgMAZ827btx4HrwJeMMS+lX+f4jPN/E/Dj\nhvFO3NC+mfN4H26I70x/Lcy9R/bZzkEGbuffOrF3KEzvUDcvvuHukX04PSy+kfbIXqmeYSkXWK3W\n3HmR9Wq9/et5AKgEXgT+EPhV4Ae2bT9sjDmVe6Jt2w8Dfwz8e2NMv23b+wGMMblra2a2n1r+DhUl\natYe2UNhznQMLLhH9vG3b3H87Vv4fRYHspuBhGisq1z1NqyWleoZLnUYXXOQRda/dRXSxpiv2Lb9\nNWNMpkrlnG3bDwC/BXw2c55t248ALwA/NMZ8KX04kn6sLCeoM+G8qJuG/nW88Efm2pfaht3Nm9jd\nvImPvOduxiZinO0c5HTHAG9fHiYSyz8sfv7KMOevDPPfXoZdW2o4tq+RY/ub2Lu9dkl7ZC+3DSsp\nEPDx8D3NBZ1baDu2N9VgXRrIezwQyP/cWHorzJs5U6ROdwzyqQ8fpKyIQe3l30WhSqENUBrtKKU2\nFMu6CmmAnIDOaCenIjw9DP4D4CXcKvCMnvSfzUB3ztcO0LuYa6itXf+9wWK0ob6+mt076/mVx/cR\nTyQ51znEmxdu8cb5WwzmrEmd63r/ONf7x/n+z6+yeVM5Dx3cyjvuaeboviYqFjksvha/h1g8yYn2\nPnoGxtnRVMODB7dStsy9vRdqx+MP3sW5y8PTFg3ZuWUTjz9415zvfbztJv23J6etJNd/e5Ku3nHe\ndXj7sq43H/1/wjtKoR2l0IZiWVchbdv2t4CUMebTOYePAm3px+8Fvgf8EPiYMSa71qIxpte27evA\nu5mqJH8P0L3YorGxsUmSycKXcVyKWCLJ2c4hbg1N0Byq5khrqCg9IL/fR21t5bQ2FOu99m6tZu/W\nFj76+N1c7x/n9KUBTncMcvnmWN7zb9+J8vKb3bz8ZjfBgDusfmyfW3zWUFuxqDashlgiyTefb5/W\nO/2Hk9eX3DtdTDs+8YF9nO0coqd/nMl4ksoyP6+e6J7zd9XZPUwiz2t2do9wcFfdoq+1GG3wqlJo\nA5RGO0qpDcXi+ZC2bXsrMGqMiQDfB561bftV4DjwcdzpVZ9Jn/6fcHvJnweacorQMs//U+CPbNvu\nwS0Y+0Pgq4u9pmQytai1lhcrXzXvW+19RV0uMdOGlXqv7aFqtj9SzYce2cPt8ShtXUOc6Ricd4/s\ns+niNF6koD2yV/r3MNNpM0DP4PQVtnoGJzhtBpZV/FRIO3xY3Lunnrfa+7K/q5NmYM7f1ZbNleSZ\nRceWzRUr8jNb7d/FSiiFNkBptKMU2lAsXgzpmf+09OIuTvKMMeY527Z/G3eq1i7gPPCEMeZ6Oszf\nmX5O94zXeBp4BjeQm4DvAgngvxhjvrYirViG1azmXY332lxTznuPbOe9R7YTiye5cG0kG8i3l7BH\n9lz3YVfaWq+QtZjfleYgi5QGz4W0McY/43vfjO+/CXwzz/P6cKdbzffaKeB/T//nWasZBqsdPGUz\n9sju7rvDmQ53M5DuvsL2yL53b4h3H93Bvu2bqF7FVc/WeoWsxfyuNAdZpDR4LqRldcOg2O+1mGk/\nPstiT3Mte5pz9shOD4u3XxvJe081Fk9x6tIApy4NYAF7t9dypLWRY62N7GiqXtRmIIudorTWvdPF\n/q40B1lk/VNIe9BqhkEx32u5Ww821FbwvmM7eN+xHURiCS5cHeFM5yBtnYOMzbFH9uWbY1y+OcZz\nr11298hucQvPFtoju9Br9dJynGv9IUFEVp/l5Ksukfk4IyMTK17UsFILUQQCPurrq8ltQ7He66Tp\n5/nXr806/uFHdi+rR5dyHK7cHHPXFp9nj+xcFWV+7t3bwJHWRg63hNhUVbboa80X5NtCVQsGeaE/\nv3y/i4WsxN+L5bzmQm1YDwuqLOX34EWl0I4SakPR1kVWT9qjVnOosljvtVL3t32WRcuOOlrSe2SP\njEcxN8Y4fraH9msjeffIjsSSnDADnDDuIiDbQlU8cs9WHrC30NxQRc/gBOFInHgiRTDgo7I8gGVZ\n0641X6HWzcEJnnvtMtWVQZobqji4u55vv3xpyaMHuQoJtGL/vVju6MdavbbIRqGQlqJZrXvpTZsr\n2b+3kXffu5U7EzHevjLMmY5B2roGmYgk8j6ndyjMd1+7wndfu8KWzZXEEknGw3Esy91SMhxJ0FBb\nwcRknB++fjUb5Lkcx2FoNMIvLvRRV1OO4zh897XLhKMJynKCfinV8WsVaCtZ3V/Ka46LrBaFtBTN\nWtwzrSwP8NCBLTx0YAvJVIquHndY/Bfnb805vat/xmpoPsshmXIYuRPhwrURLMvCcRziidS0AJ6M\nJognUlRVBLKBHYklsSyYTAd9qK5iVo+8EMUMtMUMMa9kdf9aT1kTKQUKaSmatZ72k7tHdnVFgNfP\n9zEZTRCOJojmWVc8I+UAjsP4ZIJ4cpLKMj/RWJJkysGyLCYjCcKRBAG/lR0azwS2ZeEuGmK5C7JM\nRhNUVQQXPXpQrEBbbI98JUc/1nrKmkgpUEhLUXll2k9zQxXBgI9goIza6jKSKYdINEH9pnKu9d0h\nFs9flBKNJacFennQl+05791Wy/CdKJZlEU8XtfgsC8vn9rzBDeqljB40N1ThOE42/DMfBhYbaIvt\nka/k6Ieq0UWWTyEtJWlmQPh9Fq0763j6yQMkkym+/rfnuNZ3h2gsSZ66s6xoPEU0HsNnWQyNRako\n8xOJJQmmVz0LBnw01FYQibnh+s5DW/nH77170aMHB3fX81c/7mRsYmqI3nHc44ux2B75So5+rPXI\nikgpUEhLSVooIGqry6ipDFIW8OHzWVSVBxmdiDE+OXs+NrjTwK73uyui+SyLUF05dTVlBDPPrwiy\nLVQ1LaAXc2+4/doIVRUBrPSweaYn3X5tZFEjE0sZYl7J0Q+vjKyIrFcKaSkZ+UIxX0C0dQ3RNzJJ\nZXprzHgiRSyR5H/5pRYcB850DnLp+m0mIom8m1SkHIeB25Hs97XVZezbUcfjR7dn95Jd7L3hW8Nh\nLMsN+5nHC2ln5jU1xCxSWhTSUhLCkThf/9s2+kcms73Qty72c2xfI4OjkWlhdms4nK3OjucsmPD3\nb93gCx87RmV5gB2NNbx1sY+xiRgpxw3yfPOxAcYmYpy8NMDJSwPUVpe5i6dUBukZmMDnm1rTYL57\nw4X2gBcKfw0xi5QWhbSse/FEkq//bRtXe+9kj01EEgyORrjSO5btnWbCrLmhKluglWsy6gZ9NJ4i\nHIkzOh4jEPBRU+HPVnL7LB+3x6Nz3scem4jxs7be7PeV5X4qywNUlgcI+H15e8axRJJEMkUq5RCJ\nJbLzrfP1gAspDFtoiNnrq4B5/fpEVpNCWta9tq4h+kemz32Oxd0K7bKcbS0zYXa4JcT3f36VUaaK\ntNxCMIv+kUnqasqzAR6JJojFkmBBKuWA5W7q7nccUo479D3fyrqT0SST0SQQpSzgo3tzBVdvjbF7\n66bsdX7z+XZ6BifI7A3iOPDkw7u4f38TwYB/Wmj1DEzgOM6sjUQKnarl9VXAvH59IqtNIS2r1nNZ\nqfe5NRzOVltnZIJz5vFbw2EesLfwP7/zLr772uVpRVpjE7FpVduZAE6mX8xJ/4/jpAj6ffgt8GNR\nWe7nnr0NBAP+effIjiVSvHVxgLcuDrC5poxj+5torK/ixuA4Fta0e9IBvy8b0LmhFY7Epy2aklHo\nVC2vrwLm9esTWW0K6Q1utXouK/k+zQ1VVJYHCEfcIWw3XB38PouKMv+scwHuuzvEKydv0DsUJpZI\n4TgOjXUV2a8dxw36TCfZSv+XXveElOPgS4dkWdDPsX1NPGBv4cTFPr770yvuAih5htQzbo/H+Mmp\nHve1LagoC1BZ7qeqPIA/Z1h8Zmhl2plZNAUWVxjm9VXAvH59IqtNIb3BrVbPZSXfJ1PR7DgO/SOT\nOCl3Jxq/z2J4LJrtdWbCLJ5I8szfGW4OhYnFk25vOZniri2b8PstLlwdyd6DzvBZbpgm06mdWWXM\nsqA86CeRTBFPJOkbmaQ86Kcs4HP3w04Pi1sWJFP5h8YdByajbvAOp4fFb9RXcr1/nN6h6euHW5ZF\nqK6C7aFqdjRVL3pEwuurgHn9+kRWm0J6g1utnstKvk+movm51y5zJ9yXHb4GCEcSVAT9NNRWcCTd\n22zrGuJK7xiJRMrtDaeX9rw+MM7R1kaqKgLEEykCfh9jE7Gc3rBFwAflZX621FcyOh7Hshx8PouX\n3rzO2a4hjrSEcByH23ei2XW9/T6LupoyKsoC3Lu3gTuT8Tn3yAZ3WPyN9n7eaO+npjKY7mn7cRyH\nRNIhGPBxpDXEw4eaF/2z8voULa9fn8hqU0hvcKvVc1np9wkG/FRXBqmrKc8eyyyz2Z9MEU2k6H0z\nzOmOQQDuhOOkMvea0z3dWCLFzaGJaXOVK8r83BoKk3QcLMBvWe75WFiWG5qTUbciu3cozD176glH\nEkxEEm6hGeD43NdxFz0J8OvvayXlOHT3jfOjkzd4q71vzmHxzOIqd9KBblnu/fI32/uzhWWL/Tl5\neYqW169PZLUppDe4Qnsuyy36Wo0e0szAz921CtzQPn9lGL/PIpFMkUiPXVs55/t9PkbHowQDPirK\nAozciWbvSzuOA45FXU05V2/dwUnfl7YsssVc56+4K4fF4kkmIgn3Xrbl7m9dVeHLXqPPcpcp7eod\no+P6CJFYMttTjsyzGYjjQCye4nTHIF/+5ls8engbR1ob2R6qmlXxPRevrwLm9esTWU0K6Q2ukJ5L\nMYq+VqOHNPODQCyezPaQicSz208Gyv3poJ6q2rYsN/wu99wmhbvt5Jjl9rZzp1nFkylu5uwz7U6H\nct9rMuruZW1ZFps3lZNIOcTi7trg4UiCvdtqp30oiSWSnOkYYHQilq1QCwZ87Gyq5p69DYxNxDl3\neWjOpUp7h8P8zatd/M2rXWzZXMmR1kaOtobYt2szAb8v73NEZH1RSMuCPZdiFX2tVA8pt5d/pCXE\nkZYQfSNh3rjQ785TjiSYJDOFyqEs6H4wSCRTpDJV3OmQnIgm8fssKssDxOJTld75ZPqtKQdS6R7w\ngd2b6c3cZ88pD/f7Zvdyz3YOEYkmCfp92eHuTM//V9NrgCdTKV54/RqvnLpBOOIuepJP/+1JXj5x\nnZdPXKeyPMB9dzdwtLWR+1pCVM9YalRE1g+FtCzIy9Ni8vXyt4WqONISSm9VORWAyZR7XzmVmh3O\nGY5D9j5zKuXMu0PWzIfGw3H++sedbGusZnQ8RiLpFpUFAz42byqnb2Qyu5hKW9cQ/3D2JhOROKG6\nimlbVN6zpyE7wuD3+fjgw3fROxymdyic3bM6mUwxPhnPe32T0QRvtvfzZns/Psti/666dC+7ka2q\nkhZZVxTSsiAvT4uZq5ePMzVdKRxJuEPKuKuGDY3lX2wkVzLpzArhhTi4K4z1p6dhVZb7KQv6s8t8\nAtwcnMgOyYcjccYm4gT87nVWVbjnbG+snva6c90qiCVSnLs8xJmOQc5dHs4Ot+dKOQ4Xu29zsfs2\nf/XjTpobqji6zw3slh21+H0aFhfxMoW0LMjL02IW6s1nwjGZTE1bnGQhiw3o3OfFEykqytyAnrmr\n1WQ0kf05VlYEmIwls/ezM9tdFvpzra4I8sD+JoJ+Hzsaq0k5DuOTcc51DdN/ezLvc24Nh3npjW5e\neqOb6ooAh1saObqvkXv3NmSnrYmId+j/lbIgL0+Lmas3f6Q1BF1ur3oymn/LyeXwWcw5FF6Wnqc9\n8z23haqoKJ/6mfksiy31ldyZiLG1vorHjm7P+3Odq3DvE+/fz7dfvjTrw9NXPv0Qg6NRznYOcqZz\nkM6e0bztn4gkeP38LV4/fwu/z+LAXZuzw+KNmysX/0NZA9qMQ0qdQloK4tVpMXP18u/f38T9+5to\n6xri+z+/SjQ2Me9mGD6flZ3X7O61MVX9Petcyw3iZHqlstyw9vncKVqWZfHkw7uyO19lAqSta4jT\nHUM5r2VRXRnksaPb5/z5zjWk//zxq3mPn7s8zAP2FrY3VvPkO3dzJxzj+z+/wutv9zEZy/+BJZly\nOH91hPNXR/jOjzrY2VSdDey922uzS6DmiieSnO4cZDQcp64qyL176lc1ILUZh2wECmlZ1xbq5T9g\nbyGRTPGXf3+Jycjse7YW7q5WNZUBKsoCxBMJkil3g4uJSJxoes6yz2dRFnDvM4+F4/j8PjJ3c33p\nQjS/z8fWhkp8Piv7QWFmWOT7ULE9VJ0d4s7XM5xrSP/GwPSpYJnis5NmYNrPYFNVGZuqyqiuDBAM\nWMQTKRIph0QiNedowI2BCW4MTPDD169RWxXkcDqw79nTQHnZ1MYft4bDBPzuEqhvtfdNC8iV7uVq\nMw7ZCBTSsu4t1Mu/f38Tr5y6wZWeMbeqm/SGGT6Luqog7zmyjV1bNnFwd/204ePM8qCT0UR2qdHJ\nqLtASXVFIPt9PJHioQNbuHt7LYOjkXkDKfdDRf/tCK131dOyrQYf1pw9wyNz3KPe2VRNe/dtHMdh\naDSSrWLvujnKt168mA3MeCLJhavD6YrzVHaFNb/PYldjNUf3NdLWNcyV3rG87zMWjvOztl5+1tZL\nwO/j4O56GmrLud4/Pm2XsdyAXI1erpdnHYgUi0JaSl4w4OfevSFGx2PZDTUsn0VZwMej9zXzK4/e\nnT13Zq88N7gdx2EiksjuhAVkC8Nqq8sKXks786EiEPBRX1/NyMgEiUQqb8/w5uAEmyqDpFIOkVgi\nWym+LVTFh9+1h9sTl+jqGc3u/mWlly29OTiRDcy2rqHsOuKZ63Ycd3TAAXY21fCRd9/N6HiUs11D\nnO0c5PzVYWLx2XOyE0m3ojyjLOCjujJIRbmf4Dy7d0Hxe7lennUgUiwKadkQdjRWU1URnFVtvaOx\nZtr3+XrlTz95gFOXBnjhF90EfBYJYGwixmR0al/nQoMhdwh4e1MNjz94V/axmT3ATA/5rYl+aqvL\n0sfgyYd3ZYfSn37yAH/+w/bsmuRWzrXdHJzgAdt9Xctyt+3MTC2zcNcTtywr+751NeW898h23ntk\nO7F4kvZrI9nis/n2yI7dicIdt2deUznCzqYaegbG855fzF6ul2cdiBSLQlo2hOX8gx4M+An4ffh8\n7nKfmaHlzFB4y466gl5n5hCwdWmAs51D2HfVca5ziKGxCBOTcaoq3N5y7trjlmVlh9fPdg4R8Puy\nQ+oNm8rTa4RbOe+Vys6bznyAKAv6mYxOrQueWXkt3weMsqCfI62NHGlt5H91HK713eFMhxvY3X35\nAziZcrhwdYQLV0cI+C2C6Xv4mT2y53qvpfLyrAORYlFIy4aw2H/QZxY99aTX684skJIJ0JbtdTz9\n5AEATpr+eV975hCw48BJ08cb53uzw9ApxyEcTdBYV5FdgcydzjV13/li9wi9w+HsPd6Kcv+0ldXc\n9voIBi1Omn56BicoD/pIpfyE0+cF/O6weCrlZPfCnutnYVkWe5pr2dNcy64tNTz7Sgej47F5q+UT\nSYdEMmeP7KCPLfWV1G8qT693XthmIAvx6qwDkWJRSMuGsdA/6Jlg7hmc4MLV4fR9XDdMyoO+bLhY\nlpUdNn/AbgKYs0gKyIZ9z8DEtICajCaIxtz1wTNre/ssi4DPYnuomkO767lwbQRg2v7Umc0zMvd4\ndzTWzFpatKLMj+kepa3Lfb7jOFSWB/jlBxqZjLpD2dH0BiSZvbALKeq6NRwmlXKv10qHPOlrtiyL\naDz/Dl6xeIob/RP838+cJFRbnp3eZd9VP634TESmU0jLhpXbW26sq+B0xyB9I5OEI3FGx2MEAz4a\nasuJxJLcHk9RVR4gGPBlQzYzXD5XkdSpSwOc7hjkSu8Y8USKZHpXrMxqZPFEKruLVi6fz2JHUzUf\neGgXwz9s5/wV9wNDKuWkt8WcGhK/NRzmAw/tmjWUXx70TfuQ4QZoil1bNgFw6cYoVf78ldnz/Zwm\nJuPZrT6dzM1tx8Hy+fjkE/tpqq9y72N3DE6bIpZraCzKj0/18ONTPZSX+bl3TwNH97mbgdRWlS32\n1yhS0hTSsiHNvD8cjsSze0K7gZoiHnHv6/p8lrtvNLCpqpJDe+rZ0VjDwd31tHUN8eqZHsKR+LQ1\nugFOdwxy/spwdhg6s+tWMukwGU1i+SwCAR/JZCq7u5XPZxHwu/tOBwN+ju1rzE6NiqZ70pkNQKoq\ngtnzZg7l9wxOZIvJcs1VuJVKpXj5reu8eqaHXU01fPhde6iqCM76OTmOQzSRmjbMbeX0pFu219Gy\nvY5ffW8Lg7cnOds1xJnOQS5eGyGZZ1J2NJbk5KUBTl4awAJadtRxdF/jovfI9iqtiCbLpZCWDWlm\n7zdTCBaOJAhH3AVNwL1vnEo6+CyHgN9HNJ5iR2MNh1tC2fDK9LwzIZ8Jltt3otmAzty/dRdPcYvA\nAn4fkViCaGwqvNwtLxN097vB3DcySVVFkMrywLS50PFEalrh2+yh/P68IZ2vcCuVSnFzMIxlucPt\nHddHOWEG+PLTD9F+bWTazykTyDVVAZIpd751TWUZ5WU+Bkcj0163cXMlv/zATn75gZ1MRhOcvzLM\nmc5B2rry75HtAJ09o3T2jPI3r3bRtLmCI62NHGttXJd7ZGtFNCkGhbRsSDN7lJn7opktKmfKPZJ5\nbnajjPIA4UgiW1Gd2SgjlXSAO+7zc17A7/ORSjmMRWLpuc1Tb+AA4UiCX5zvp61rZNq98Nz7zu88\ntJV/nN5zOp+FqtlzHxsdj6Xvi0+F4NhEjOePX6W6cvZe1MGADxLQUFuevUeeSKbmrdyuLA/w4IEt\nPHhgC6mUQ2fPaHZ618xbBRkDtyP86MQNfnTixrrcI3u+2yAzl4vNt167euACCmnZoGYGSiZoHaZW\nJMsNZp9lZYekmxuqpoV8boDmbpRx6tIA56+6w93ZUVsLwnm2lMzlOBCLJ6mqCBKJufewo/FUtmBt\nW6hq3oCGhavZcx97s72fSGx2wdeNgQkeO7p91vHK8gD1m9xryshd2nQhPp/F/l2b2b9rM7/+vlb6\nR8Kc6XQXUbl0/XbeYfHcPbItC/btqOPoviaO7fPuHtn5bi04jsMLv+jG55saxp/Zu1YPXHIppGVD\nyt/TrMbB4Xr/uNt7JR3U6WU0gwFftjeaSKYIR+LTpklVVUzfKOP+/U3ZwrFYupedbxWvmRymdtiy\nLCt7D3yxvarcIfB8PbPMYxOTcfryBMrOpur8a403VvOJ9++n/drIrKVNl2JLfRUfeKiKDzy0i3Ak\nzrnLw5xND4vn+0DjOG7h26Ubo/z1T5a/R3YskeR42006u4fZsrmyaL3WfCMLmbnruYvqzCza05rk\nkkshLRtSbk/z5uAE59NTrpycna0yIe3zuf/gfuiR3dy/351ydbpjMDvEDe4Q9T17G6b1JoMBP5/+\n0EFOpRctGRyd5HLvGKmFc5pkcuqkHY01y/rHeaGe2YfftYcTZoCxialVxWqry/jwu/bM2yPPt7Tp\nclVVBHn40FYePrSVRDJF541R/u6tbs5fGZ5zV7KZe2TvbKph86YyDt8d4sEDW+YN3HgiyV+8ZOi/\nPZmtWC9WrzXfB5yKssCsav5MG/J9Pdc5snEopGXDmupp9nOqYzC9ylc83RNL4ff7CPrdXvKHHtmd\nXZv7pOmnb2Ry1tzkY/sas49npnUlkin+/q0bRGKJ7LrhGTOH1HNljm8LueuHL7RQynwW6plVVQT5\n8tMP8fzxq9wYmGBbqIq7ttbwk9M9s3rdy7HY+6wBv48Du+vpujnKyJ1oevQiwWQ0Oed87IlIAnP9\nNgBvXOjnr3/SxQcfvosH9jfl3SO7rWuIm0MT04rSitVrzfcBJ5FM8dKb12edm9vr1prkkkshLRte\nbg8l0zP2+3xUlQeoqykHmFa5nDk/d1ETgP6RyWyPNbNCWKZ35jgOydT0ArK5AhrAb1lsra/k3r0N\nPPN3hr6Ryexji+3pFdIzq6oI8tFf2pftdf/oZM+S3y+f5dxnbW5wp2IFA37qavzU1UAylcLetZnR\n8Rhvp+eR5zM6EeOvftzJX/24kx1N1RydsUf2SvdaZ1bdxxNJzs740DRzeVqtSS65FNKy4eX2UHJX\nv8r9upCezmQ0kf2HNdPDTqamqrczC5fMtZRmrkg8yZXeMa70js2a2rVQT29mj7WxrmLBdmes1P3Q\n5bxuvtDa2VTDR9/Xmt6KM4W5PsL3fnqFq7fu5C08A+gZmKAnd4/slkY2Vbk7jDHjc8JK9VoLWZ5W\na5JLLoW0bHgHd9fz8onr9I9MEvD7CATcEqjKcvf/HoX2dCrKp/4RzfTI0wtyZbkbYZDeUtKdO50v\nUlIpZ9qc6MzUroy5enr5eqxb6yvZWl85rTc+V89spXqWy3ndhUIrGPBx794Q0ViSHxy/mv15hecp\n1BsLx/nZuV7A/X1UlgeoKPNTWR5g15aaFe21FrLeuNYklwyFtGxo8USSb798iUgsmd2kYnvInUZ1\nezy2qJ5OW9cQpzuG0ue4vXDLcrdwzGwR6Tjgs9zj6WWv8/asUw5MRpNsqgqmr3N62MzV08vXY+0b\nmeSD79i14Nzc+V53uT3L5b5uIaGV++GpLOinrqacUF059+5p4Nzl4Tn3yHYcsovYQJRoLMl//B9n\nuXdviF96YAflQf0zKWtHf/tkQ8uEWu795XjSoaIswIcemT1HOCNfaOSGRGbeNbiLfty+E2U8ksCf\nXmIUwLEg4LeIxVOzetM+yx0ed9/LN23ofb77k3P1TAdHI3zokT1ztidfGwp5v0Ktxn3W+Xrcjx/b\nSSye5GL3SHZO9sidaN7XGRyNMDgaof3abf6/n13hnYe2cmx/E4d212e39xRZLQpp2dCKObw7MyQy\n94IHRyPu/dDBcSKx5LS51Xu21RGNJei4fpvx9OYVuVtAJpIp7tnbwLF9jQyORha8P1mMHutK3A9d\nrXc+8c0AABxgSURBVPus8/W4y4J+Drc0crilEecD++nuG+dM5yBnOwe5eutO3ufEEyl+2tbLT9t6\nKQv4OJTeDORISyhbVOiepxXCZGV4KqRt2y4HTgCfM8a8Nsc53wOeYvrCUE8ZY16Ycd7/CbQaY57O\nOXYUOJXzXIATxph3FLstsj4Ue3h3rpA4afrpHQ5TVTHVI7YsePTIdg7uquON87d4/vVrgNuDLnT5\nz5mK0WNdqfuhXrrPalkWu5s3sbt5E7/2eAspn4+v//dTtF8bIRJN5q0TiCVSnEkvZQqwd9sm7rs7\nRFnAxy8u9BGNJ7ObrGiFMCkWz4R0OqCfBQ4tcOpB4GPAj3OOjcx4rX8GfBn4yxnPPQScBj7IVEjP\nXulfNozVmu6Sd+WuUDUPHtzKxHhk2uOLWf5zJlUGL02orpJHD29naCxKKuVucjIZTRKeYy13gCu9\nd7jSO9UD96VXpdtSX6kVwqRoPBHStm0fBL5TwHllwF7c3m9/nsf9wP8L/G9AZ56XOAi0G2MGlnfF\nUipWcxh25vscs5soC/qZKPJ1eKnHutYWMwx9pDXEW+199A6FqaoIUlUR5J6GSt53bAdvXxnmTMcQ\nNwbG53yvlAPReIrr/RNUlQf4xYU+9u3arD2yCxRPJDndOchoOE5dVZB799TrwyUeCWngMeAV4IvA\nfDcDbSAFXJ7j8RrgXuBh4PN5Hj8EnF36ZUopWq1Qm/k+gYBv3sdlefd6F7uAStk8H5T276p398ge\nneRsp7tH9oWrw3POeQ9HE5w0A5wyA7TsqONIa4ijrY1sb6xe93tkr4TM7+rWcDi7q9pb7X26ZYBH\nQtoY843M17Ztz3fqQWAM+LZt248D14EvGWNeSr/OKPCeeV7nIOCzbbsNqANeBP6VMSZ/1YiIrJnl\n7ga1lAVUFvqg1Fg3tUf262/38tzPrjAZSTARyb+zWe4e2X/7D5dprKvIbgayfx3ukb1SpmZZTB3T\nLQOXJ0J6EQ4Albjh+ofArwI/sG37YWPMqfmeaNt2AGgBuoDfAOqB/wg8A/zjFbxmEVmC5a5+ttJL\nfj54YAvnrgzTOxSmIZXiTjhOMuUQ8Pvmnd41tUe2n3v3hji6r5H77g5Rk2fv7o1Cm4rMbV2FtDHm\nK7Ztfy3dYwY4Z9v2A8BvAZ9d4LkJ27ZDwKQxJglg2/YngRO2bTcbY24Veh3+dfzpN3PtasPaK4V2\nrGQb+m9P5t0xqv92ZNatgny2N9VgXZpdfrK9qWba85fahkDAx2eeOsTZziFuDYVpDlVxpDVEWcBP\n33CY0x2DnOkYwHTPtUd2krcu9vPWxX58lsW+XXXcv7+Jo/sa2RaqXtS1LKcdXpD5XVnpel4LCyxn\n1u9qPSj2z39dhTRkh7RztbNwRXjmuTOrPtrTf+4ACg7p2trZu+msN2qDd5RCO1aiDa13NXC6YzDP\n8Xrq6xcOsccfvItzl4e50T91N2vnlk08/uBdeRclWWobPtBUO+tYfX01B1qa+GcfPMj4ZJxTF/t4\n83wfJy/2MT45e0JJynEw3bcx3bd59kcd7Giq4R33NPOOQ1s5uKdhUf/wr8e/TzN/V36/xc4ttXP+\nrjaSdRXStm1/C0gZYz6dc/go0FbAcw8CbwD3GWOupQ8fw52Cla8SfE5jY5PT9vtdT/x+H7W1lWqD\nB5RCO1ayDS3batiyuZKbQxPZY9tD1bRsq2FkZGKeZ075xAf2zerpToxHyH32avwe7ttTz3176vmN\nJ/fTcX2UU5cGON0xSN8cw7k9A+M892onz73aSXVlkCMtIY7tb+K+u0NUVeT/Z3u9/336xAf28fbl\nYYbHYzTUlHHv3Q2zflfrQeb3UCyeD2nbtrcCo8aYCPB94Fnbtl8FjgMfBx4FfrOAl7oIdAB/Ztv2\n7+Lek/4G8J/z9M7nlUymirLB/VpSG7yjFNqxEm3wYfHJD9qzqq19WAW/lw+LY62N0Dp1bK7nrtbv\noXVHHa076vjo+1rpHZpwq8U7BujoGc1bLT4xGef427c4/vYt/D6L/bs2u1tu7mukKc8e2ev175MP\ni/v3N1H//7d351F2l/d9x9+zSWhhhDQSktCGNCM/WmyNgAAmmEBoHUzipaGNDSZtYnzsprZP2jin\n9ekJLY7dUxrjOHbjxBw3iVvXCc1SO17wTkkUDIqRgAGBeAaNttGuGQ2S0Dpb//j9JI2GmdG9mju6\nz728X+dwRrr3ucPvyyP0mee3PN+Z0+jpOUZfX2XWUWophvTwP6Z7yW70+mqM8RshhI+QPaq1CHgR\nuCPGuPNC3zTGOBhCeDfwBWAd2aNcXwP+QwmPXVIJVftjafObpjG/aRrvuHExr53o5YWO7PGuF7Z2\nj9gju39gkM07eti8o4dHHnuFBbOn0ZoH9psWXVGGCjTRagYLaW6roQbP/JRXierra8/7SbUSVUMN\nUB11WMPE6OsfIHa+Stsr2TakXYdPXvAzl09t4IbV81i1ZCYrF1/BZZNSXIONLcW5KFZeQ8kehq+8\nWZSkKldfV8vqq2ex+upZ3PNPl7O76xhtW7p47pUutu45MuLe4keP9/LY05089nQn9XU1rFgyMzst\n3jKbWY2XXfIaVBqGtCQlrKamhoVzprNwznR+6aarOXzsNM93dNG2pZtN27pH7JHd1z/Ipq2H2LT1\nEF/7YTuLr5x+9rT4knmXn22XqvQZ0pJUQWZMm8Qta67iljVX0dvXz+Ydr9KWt9w8NMomKjsPvMbO\nA6/x7Se3M2P6JFqbsxX2yqtnMvkN/ohT6gxpSRPCHssTr6G+jjXNTaxpbuLXf3EFPcf7WLexk43t\nB9kxSo/sw6+dZl3bHta17TnbI7u1pYnWltlcMaRHttJgSEsqufHuu63i1dTU0LzwCmZNa+CdP3s1\nPUdP0daRXcfevKOH3hFuxDq/R3bk6nmXn91bfNGV020GkgBDWlLJjXffbY3fzMsnc9vaBdy2dgGn\nTvfz0o5D2c1nW7o5cuz0iJ/Zvu8o2/cd5W//YRuzGidn17FbZrNi8UwaKmx7zmphSEsqORsmpGXy\npDquWT6Ha5bPYWBwkO17j/Jcfh2788DIPbIPHTnF48/s5vFndjO5oY43L51Fa8ts1rQ02SP7EjKk\nJZXcvFlTi3pdl05tTQ3Lrmpk2VWN3PVzy+g+fDI7Lb6li5d39NDX//oHvE719rOx/SAb2w9SAyxb\n0Hj28S57ZE8sQ1pSya1pbmJj+8HzTnnPb8puHlNammZcxu3XLuT2axdy4lQfL20/lK+yu0dsBjII\ndOw+QsfuI+d6ZLfMpnX5bII9skvOkJZUcg31dXzgzhXe3V1hpkyu57pwJdeFKxkYGGTrniM8u+Ug\nbVu62dM1cquLrsMn+fHGXfx445Ae2S2zeUvzG7tHdqkY0pImRLXvu13tamtraFk4g5aFM/iV21o4\n0HM8awaypYv2zsJ6ZLcsnJGtsluaLqpHtgxpSVIBrpw5lbdfP5W3X7+I4yf72LQtbwbS0c2xk32v\nGz8wOEh756u0d77KXz2+hbmzprK2JVtltyycQV2tp8ULYUhLkooy9bJ6blg5lxtWzqV/YIAtuw5n\nz1u/0sX+nhMjfmb/oeP84KfH+cFPO5l2WT1vac4C+81LR++RLUNakjQOdbW1hMUzCYtn8r7blxfW\nI/tkH+tf3M/6F/ef1yP7uhVZP2mdY0hLUpU43dvPUy/u49l4EIDWliaufdOcS3rD3nh7ZC+aezmt\nzU2sWdbEsqsaqa19Yz/eZT/p4tlPusyqoQaojjqsIR0DDPLV77ez8eX9Z7cAbaivZfXSWXzwl1aW\n/c76i+2RvWZZE2uXz2b10lkV0SPbftJSlTnd18+z8aCPKmlc2rZ0097ZQ2//uR80evsG2Lb3SBLb\nsY7aI3tLF1t3j94j+yeb9vGTTfuyHtmLZ57dqrRpxhujR7YhLZXR6d5+/uw7m9k95BlUG1HoYuzr\nPkbvCL2le/sGktuOdXiP7CPHTtPW0cXzHd28uO3QiKfF+/oH2bTtEJu2HeLPf9TOorxH9jVV3iPb\nkJbKaMPm/ezpPn+TCBtR6GLMa5pGQ0MtDLu5uqG+NvntWBvzHtk/f+1Cpk2/jKfadrHx5YM8t6WL\nnlF6ZHceeI3OA6/xnSe3M2PaJFpbmljbMqfqemQb0lIZ7T44cnOD1FY+Sl9rSxNtW7pfd0166fzG\nitqOdVJDHWuaZ7NqySx+9RfeROeB184+3rV9tB7Zx06zrm0v69r20lBfy6olM1m7fHZV9Mg2pKUy\nWjBn+oivp77yUXom1dfxb+++hh//4/ay3t1dSjU1NSyeezmL517Ou29eSs/RUzzfke0r/uL2QyP2\nyO7tG6Cto5u2jm7O9shumc3a5ZXZI9uQlsroZ1bO5e83dp53TdpGFLpYkxrquGn1PK6v0kslMy+f\nzK1rF3Dr2gWc6u1n8/aerBlIRxeHX7tAj+wn8h7ZzVlgr1h8RUX88GJIS2U0qaGO+9650ru7pSJN\nbqhj7fIscAcGB9mx7yjP5Y93jdkj+9ndPP5s1iN79dJZtLY00do8m8ZpafbINqSlMptkIwppXGpr\nalg6v5Gl8xv55aE9sl/p4uWdo/fIfqb9IM8M65Hd2jKbBQn1yDakJUlVpRQ9slvz69jl7pFtSEuS\nqtZIPbKzwO46716QoboOn+Sxjbt4LO+RvXppE2tbmljTPPuS98g2pFXVevv6eb6j2+u9ks7rkf0v\nbmvmwKsnzm5TOlaP7A0vH2DDyweoqYHlC2bQujzb9exS9Mg2pFW1evv6+cr3XmZv97lnjt3NS9IZ\nV14xhbdfv6jgHtmDg9C+6zDtuw7z1493MHfmlLPblC5fNDE9sg1pVa3nO7rPC2hwNy9JIxupR3bb\nlm6e3dLF/lE2F9rfc4IfPt3JD5/Oe2Qva+LaMIc739ZcsuMypFW1Rtu1y928JI1laI/s997ewr5D\nx3nulew69iu7DjMwQvfIYyf7WP/Sfta/tN+Qlgox2q5d7uYlqRjzZk3lHTcuPtcje2s3bXmP7BOn\nXt8MpJQMaVWtNc1NbGw/eN4pb3fzkjQe06c0cNPqedy0eh59/QO0d756dm/xQnpkF8uQVtVqqK/j\nA3eu8O5uSROivq6WVVfPYtXVs7jnnyxnT9cxXtzeU9p/R0m/m5SYBnfzknQJ1NTUsGDOdJbMbyzp\n9y3fNiqSJGlMhrQkSYnydLf0BuIObFJlMaSlNwh3YJMqj6e7K0hvXz8b4wEefWo7G+MBevsm9vk8\nVZexdmCTlCZX0hXCVZDGyx3YpMrjSrpCuArSeLkDm1R5DOkK4SpI47WmuYn5TecHsjuwSWnzdHeF\ncBWk8XIHNqnyGNIVwn2oVQruwCZVFkO6QrgKkqQ3HkO6grgKkqQ3Fm8ckyQpUYa0JEmJMqQlSUpU\nUtekQwiTgQ3AR2OM60YZ803gXcAgUJN/fVeM8bvDxv0O0BJj/MCw1/8bcB/ZDyh/GmP8RMkLkSSp\nBJJZSecB/Qiw6gJDVwLvB+YD8/KvPxr2ve4BPkkW4ENf/23gbuA9wD8H7g0hfLwEhy9JUsklsZIO\nIawE/qKAcZOApcCGGOOBEd6vA74I/Ctgywjf4jeB+2OMT+XjPwF8GvjcxR+9JEkTI5WV9K3AY8BN\nZKewRxOAAWDrKO9PB94M3AisP++DIcwHFgH/MOTlJ4AlIYS5F3fYkiRNnCRW0jHGh8/8OoQw1tCV\nwBHgayGE24BO4IEY4/fz73MYuGWU7zOf7PT3niGv7Sf7oWBh/mtJkpKRREgXYQUwBfge8CBwF/Dt\nEMKNMcZnLvDZqQAxxtNDXjuVf51czEHU1aVyAqJ4Z47dGsqvGuqwhnRUQx3VVEOpVFRIxxg/FUL4\nQr5iBnghhHAd8GHgNy7w8ZOQXdceEtRnwrmoVlKNjVOKGZ4ka0hHNdRhDemohjqqoYZSqaiQhrOn\ntIfazIXvCAfYnX+dB+wc8utBYG8xx3DkyAn6+weK+Ugy6upqaWycYg0JqIY6rCEd1VBHNdVQKhUV\n0iGErwADMcYPDnl5LfD8hT4bY9wbQugE3sa5O8lvAXbGGIu6Ht3fP0BfX2X+ATrDGtJRDXVYQzqq\noY5qqKFUkg/p/M7rwzHGk8C3gEdCCH8HPAncC9wMfKjAb/cl4PdCCLvJbhh7EHio5ActSVIJpHh1\nfnDY7/cC7wWIMX4D+AhwP/AC2c5jd8QYd1KYh4C/BL6ef/1fMcYvlOKgJUkqtZrBweGZqAsY7Ok5\nVrGnYurra5k5cxrWUH7VUIc1pKMa6qiiGsba76MoKa6kJUkShrQkSckypCVJSpQhLUlSogxpSZIS\nZUhLkpQoQ1qSpEQZ0pIkJcqQliQpUYa0JEmJMqQlSUqUIS1JUqIMaUmSEmVIS5KUKENakqREGdKS\nJCXKkJYkKVGGtCRJiTKkJUlKlCEtSVKiDGlJkhJlSEuSlChDWpKkRBnSkiQlypCWJClRhrQkSYky\npCVJSpQhLUlSogxpSZISZUhLkpQoQ1qSpEQZ0pIkJcqQliQpUYa0JEmJMqQlSUqUIS1JUqIMaUmS\nEmVIS5KUKENakqREGdKSJCXKkJYkKVGGtCRJiTKkJUlKlCEtSVKiDGlJkhJlSEuSlChDWpKkRBnS\nkiQlqr7cBzBUCGEysAH4aIxx3Shjvgm8CxgEavKv74oxfjd//x7g08B84AfAh2KM3fl7a4FnhnwW\nYEOM8YYJK0qSpIuUzEo6D+hHgFUXGLoSeD9ZCM/Lv/4o/x43AH8CPADcCMwE/ueQz64Cns0/d+af\nO0pVgyRJpZTESjqEsBL4iwLGTQKWkq1+D4ww5KPAX8YY/zwf/y+BHSGEJTHGHWQBvznGeLB0Ry9J\n0sRIZSV9K/AYcBPnTkOPJAADwNZR3n8rcPY0eYxxF7Azfx2ylXT7eA9WkqRLIYmVdIzx4TO/DiGM\nNXQlcAT4WgjhNqATeCDG+P38/fnAnmGf2Q8sHPL52hDC88AM4HvAv48xHh1vDZIklVoSIV2EFcAU\nsnB9ELgL+HYI4cYY4zPAVODUsM+cAiaHEOqBZqAD+HWy69WfB74K/HIxB1FXl8oJiOKdOXZrKL9q\nqMMa0lENdVRTDaVSUSEdY/xUCOELMcbD+UsvhBCuAz4M/AZwEpg87GOTgeMxxr4QQhNwIsbYDxBC\n+DVgQwhhXoxxX4GHUdPYOGX8xZSZNaSjGuqwhnRUQx3VUEOpVFRIAwwJ6DM2c+6O8N1kd2wPNQ/Y\nm3/2tRE+C7AAKDSkJUm6JCrqnEII4SshhD8d9vJazoXteuBtQ8YvIrsevT6EsDKEcCSEsGTIZ68B\neoEtE3jYkiRdlORX0iGEucDhGONJ4FvAIyGEvwOeBO4FbgY+lA//EvB4CGE92aYonwe+HWPcEUKo\nAV4B/kcI4bfIrkk/DHx5hNW5JElll+JKenDY7/cC7wWIMX4D+AhwP/AC2c5jd8QYd+bvrwf+Ndlm\nJk8A3cB9+XuDwLvJ7g5fB3yDbBOUj09sOZIkXZyawcHhmShJklKQ4kpakiRhSEuSlCxDWpKkRBnS\nkiQlKvlHsC6lvF3mH5NtN3oc+P0Y4+dGGXsN2SNfbwE2Af8m35q07IqsY8z+3OVWYI/xZOcCStMn\nvZxCCFcB/x34ebI/T38F/McY4+kRxiY5F0XWkORchBCagT8ie+y0G/hijPGzo4xNch6g6DqSnIuh\nQgiPAvtjjPeN8v645sKV9Pk+C1wL3Eb2qNcDIYS7hg8KIUwFHgX+Ph//FPBoCCGVvewKqiM3an/u\nciukx3jqc1GKPukJ+L/AZWR/qd5N9pfmp4cPSnwuCqohl9xc5Ps8PErWMGgt2TbI94cQ7h5hbLLz\nUEwdueTmYqj8uO8c4/1xz4Ur6Vz+H/ODZM9dtwFtIYTPAB8Dvj5s+N1k+4F/Iv/9vwsh/CLwK2QN\nO8qmmDoK6M9dNoX2GCftuShVn/SyCVlbuhuAuTHGrvy1/ww8BHxi2PAk56KYGhKei7nAs8BHYozH\ngI4QwmNkOyz+n2Fjk5yHXMF1JDwXAIQQZgKfAX46xrBxz4Ur6XNayX5oeWrIa08AN44w9sb8vaF+\nQtYPu9yKqeNC/bnLqdAe4ynPRan6pJfTPuAdZ8ItV0PW6nW4VOeimBqSnIsY474Y4z15sBFCuBn4\nOeDxEYanOg/F1pHkXAzxWbKg3TzGmHHPhSvpc+YDXTHGviGv7QcuCyE0xRi7h43dNOzz+4HVE3yM\nhSimjgv15y6bInqMJzsXJeyTXjb5lrlnTy/mpys/Bvx4hOFJzkWRNSQ7F2eEELYDi4Dv8PqzfJDo\nPAxXQB3JzkUI4XbgFrLrzA+PMXTcc+FK+pzRelHD69tfjtq3egKOq1jF1DG0P/cdwHfJ+nNfO6FH\nWFopz0WhKmkeHiK7lvg7I7xXKXMxVg2VMBd3kV1Tv4asP8FwlTIPF6ojybnI7zN5mOyU/fD/zsON\ney4M6XNG60UN2d2ghYwdPq4cCq4jxvgpYEGM8X/HGF+IMf4u2f8QH574wyyZlOeiIJUyDyGE3wN+\nE7g3xjjSKb7k5+JCNVTCXMQYn8nvbv4t4MMhhOFnRJOfB7hwHQnPxSeBp2OMI52JGW7cc2FIn7Mb\nmB1CGPrfZB5wIsb46ghjR+1bXWbF1DFaf+4FE3h8pZbyXBQs9XkIIfwh2V+m98YY/3aUYUnPRYE1\nJDkXIYQrQwjvGfbyS8AkoHHY68nOQ5F1JDkXwPuAfxZCOBpCOErWjfFXQwhHRhg77rkwpM95jqy3\n9FuHvHYL8PQIY9cDPzvstZvz18ut4DrG6M/98sQdXsmlPBcFSX0eQggPkK1e3hdj/OsxhiY7F4XW\nkPBcLAW+HkKYP+S1nwEOxhgPDRub7DxQRB0Jz8WtZNeiW/N/vgV8M//1cOOeC28cy8UYT4QQvgo8\nHEK4D1gI/Dbwa/C6vtZ/AzwYQvgD4Mtkz/pNJdsgoayKrONC/bmTVClzMZZKmYf8MbL7gf8KPJkf\nNwAxxv2VMBdF1pDqXDxNtiHOn4UQPk4Wdp8B/gtU1P8TxdSR5FzEGDuH/j5fTQ/GGLflvy/pXLiS\nPt/HgY3A/wP+EPhPMcZv5u8N7Wt9FHgn2aMDG8iewbwzxnjikh/xyAqtY8z+3AkZq8d46nNxxkX3\nSS+zd5P9PXE/sCf/Z2/+FSpjLoqpIcm5iDEOAO8BjpEF1peBz8cYv5gPqYR5KLaOJOeiACWdC/tJ\nS5KUKFfSkiQlypCWJClRhrQkSYkypCVJSpQhLUlSogxpSZISZUhLkpQoQ1qSpEQZ0pIkJcqQllQS\nIYRPhhC2lfs4pGpiSEsqlUFev0e5pHEwpCVJSpStKiUVLITwZuBBspaB04BdwB/FGD9X1gOTqpQr\naUkFCSFMAX4IHATeCqwi64v7UAhhTTmPTapWhrSkQk0D/gD4WIyxPcbYAfwuUAO8paxHJlUpT3dL\nKkiMsSuE8CXg3hDCNUAL0Ep2s1hdWQ9OqlKGtKSChBDmAv8I7AO+BfwAeJrsurSkCWBISyrU+4Er\ngGUxxgGAEMKZ09w1ZTsqqYoZ0pIK1Ul2Xfp9IYQngJXA58hOd08u54FJ1cqQllSQGOPfhBCuBX4f\naAS2A38CvAe4Hk97SyVXMzjoBkGSJKXIR7AkSUqUIS1JUqIMaUmSEmVIS5KUKENakqREGdKSJCXK\nkJYkKVGGtCRJiTKkJUlKlCEtSVKiDGlJkhJlSEuSlKj/D0bgt0aphglbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116715b50>"
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='al', y='ri', data=glass, ci=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we had an **al** value of 2, what would we predict for **ri**? Roughly 1.517.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 51,
+=======
+   "execution_count": 5,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+<<<<<<< HEAD
+       "<matplotlib.axes._subplots.AxesSubplot at 0x130c7e250>"
+      ]
+     },
+     "execution_count": 51,
+=======
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11675ee50>"
+      ]
+     },
+     "execution_count": 5,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+<<<<<<< HEAD
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x130c8f150>"
+=======
+      "image/png": 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TKAXFZ+Ia4R1pCX0/k3nPHd3vR0RSlWpQMsz9m7Z4kxOH7EXhpBqJvninJq7lnlt+wK3r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\nIiIiIo7z/wGdl3N/Y9pF6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116224e50>"
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exercise: Draw the scatter plot using Pandas.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# scatter plot using Pandas\n",
+    "glass.plot(kind='scatter', x='al', y='ri')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 53,
+=======
+   "execution_count": 6,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+<<<<<<< HEAD
+     "execution_count": 53,
+=======
+     "execution_count": 6,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fit a linear regression model to predict ri from al\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "linreg = LinearRegression()\n",
+    "feature_cols = ['al']\n",
+    "X = glass[feature_cols]\n",
+    "y = glass.ri\n",
+    "linreg.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 54,
+=======
+   "execution_count": 7,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.52194533024\n",
+      "[-0.00247761]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# look at the coefficients to get the equation for the line, but then how do you plot the line?\n",
+    "print linreg.intercept_\n",
+    "print linreg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 55,
+=======
+   "execution_count": 8,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.51946772]\n",
+      "[ 1.51699012]\n",
+      "[ 1.51451251]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# you could make predictions for arbitrary points, and then plot a line connecting them\n",
+    "print linreg.predict(1)\n",
+    "print linreg.predict(2)\n",
+    "print linreg.predict(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 56,
+=======
+   "execution_count": 9,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+<<<<<<< HEAD
+       "[<matplotlib.lines.Line2D at 0x130d3f510>]"
+      ]
+     },
+     "execution_count": 56,
+=======
+       "[<matplotlib.lines.Line2D at 0x11782b650>]"
+      ]
+     },
+     "execution_count": 9,
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+<<<<<<< HEAD
+      "image/png": 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+HwePLqfl6HJarv1B+LquLnJr/xw4Kawl76+1vXdvb2foi6sY+uKqXuu6Ro+m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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x130d3fc90>"
+=======
+      "image/png": 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YUuYAc5ruWAT0chKPXf+7gCFnnt3sjTqTn3uRydcuko2ye/HxLBX8IA3V9IPU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\nUTIhIiIinvz/JjLFU0rUQ8oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11782ba10>"
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# or you could make predictions for all values of X, and then plot those predictions connected by a line\n",
+    "ri_pred = linreg.predict(X)\n",
+    "\n",
+    "# draw regression line with matplotlib and pandas\n",
+    "plt.scatter(glass.al, glass.ri)\n",
+    "plt.plot(glass.al, ri_pred, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Refresher: interpreting linear regression coefficients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear regression equation: $y = \\beta_0 + \\beta_1x$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.51699012])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute prediction for al=2 using the predict method\n",
+    "linreg.predict(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>al</td>\n",
+       "      <td>-0.002478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  feature      coef\n",
+       "0      al -0.002478"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine coefficient for al\n",
+    "pd.DataFrame(zip(feature_cols, linreg.coef_), columns=['feature', 'coef'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Note that we can't use a cross_val_score if we want to investigate variable relationships"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Interpretation:** A 1 unit increase in 'al' is associated with a 0.0025 unit decrease in 'ri'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.51451251])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute prediction for al=3 using the predict method\n",
+    "linreg.predict(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2: Predicting a Categorical Response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's change our task, so that we're predicting **assorted** using **al**. Let's visualize the relationship to figure out how to do this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1323058d0>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x130db5fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(glass.al, glass.assorted)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's draw a **regression line**, like we did before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# fit a linear regression model and store the predictions\n",
+    "feature_cols = ['al']\n",
+    "X = glass[feature_cols]\n",
+    "y = glass.assorted\n",
+    "linreg.fit(X, y)\n",
+    "assorted_pred = linreg.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x132156310>]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BTgkF1ZlfoamzpyfzL29dl1jhq8lJaNZyEKozeLk2/43AJ4BfGWMWAFsSB4wx5cBWY8yJ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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x132156390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# scatter plot that includes the regression line\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, assorted_pred, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If **al=3**, what class do we predict for assorted? **1**\n",
+    "\n",
+    "If **al=1.5**, what class do we predict for assorted? **0**\n",
+    "\n",
+    "So, we predict the 0 class for **lower** values of al, and the 1 class for **higher** values of al. What's our cutoff value? Around **al=2**, because that's where the linear regression line crosses the midpoint between predicting class 0 and class 1.\n",
+    "\n",
+    "So, we'll say that if **assorted_pred >= 0.5**, we predict a class of **1**, else we predict a class of **0**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# understanding np.where\n",
+    "import numpy as np\n",
+    "nums = np.array([5, 15, 8])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['small', 'big', 'small'], \n",
+       "      dtype='|S5')"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.where returns the first value if the condition is True, and the second value if the condition is False\n",
+    "np.where(nums > 10, 'big', 'small')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.06545853,  0.19576455,  0.28597641,  0.16068216,  0.13562331,\n",
+       "        0.32607057,  0.08550561,  0.04039968,  0.20077632,  0.19576455])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the predictions\n",
+    "assorted_pred[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
+       "       0, 1, 1, 1, 1, 0, 1])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform predictions to 1 or 0\n",
+    "assorted_pred_class = np.where(assorted_pred >= 0.5, 1, 0)\n",
+    "assorted_pred_class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x132413a10>]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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yt9lsMtA1W/G+LPVTgClFmJdAUGC0v/6M+YN3CZYrj33m/Ex/+iwYFs3P4b/v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\nk7eEXkSWhfNjwjcUJHfcW/lK1Ocj6blnIykgAY7+8gfG334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4axfurj0IXtAwtjAYxDZu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\ngiqIUP4CgUBQBRHKXyAQCKog/wcH/UmF4cXmAAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x132413f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the class predictions\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, assorted_pred_class, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What went wrong? This is a line plot, and it connects points in the order they are found. Let's sort the DataFrame by \"al\" to fix this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# add predicted class to DataFrame\n",
+    "glass['assorted_pred_class'] = assorted_pred_class\n",
+    "\n",
+    "# sort DataFrame by al\n",
+    "glass.sort('al', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x13270ea50>]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AeWa2wTn3DeAfCQJig5m9Xef6plrnOuApgjfLP5nZk+O0U08+QIP2Z1y1Ohul\nP9cBncCtzrn/ENa6nsbrz8nqbJT+/B/AA865/02QNTcAn3bONVJ/TlZjo/RlXE3+r+vePiIiOaSL\nvEREckjhLyKSQwp/EZEcUviLiOSQwl9EJIcU/iIiOaTwFxHJIYW/iEgO/X9iJWGiELd8wQAAAABJ\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x13270e6d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the class predictions again\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, glass.assorted_pred_class, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 3: Using Logistic Regression Instead\n",
+    "\n",
+    "Logistic regression can do what we just did, but better.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# fit a linear regression model and store the class predictions\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "feature_cols = ['al']\n",
+    "X = glass[feature_cols]\n",
+    "y = glass.assorted\n",
+    "logreg.fit(X, y)\n",
+    "assorted_pred_class = logreg.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the class predictions\n",
+    "assorted_pred_class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x130ad3f90>]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HueiTvKQI0k5eDgM9sccdZjYafv+7wALg+8CJwGzn3D+b2TcmarRU6plok0xQna2VhzpL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\naUMKfxGRNvT/AUbpkYIOEufSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1324b8d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the class predictions\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, assorted_pred_class, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we wanted the **predicted probabilities** instead of just the **class predictions**, to understand how confident we are in a given prediction?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# store the predicted probabilites of class 1\n",
+    "assorted_pred_prob = logreg.predict_proba(X)[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1331ca9d0>]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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H4j8k/G/wtzVfLyJJKb2Lf11dqLBv+ATvJ+tDBT5c6L0bP201it8mK4vA3vvi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\nSMVfRCQNqfiLiKSh/wfOtFi4ctYpUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1331caa50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the predicted probabilities\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, assorted_pred_prob, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.89253652  0.10746348]]\n",
+      "[[ 0.52645662  0.47354338]]\n",
+      "[[ 0.12953623  0.87046377]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# examine some example predictions\n",
+    "print logreg.predict_proba(1)\n",
+    "print logreg.predict_proba(2)\n",
+    "print logreg.predict_proba(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is this? The first column indicates the predicted probability of **class 0**, and the second column indicates the predicted probability of **class 1**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 4: Probability, odds, e, log, log-odds\n",
+    "\n",
+    "$$probability = \\frac {one\\ outcome} {all\\ outcomes}$$\n",
+    "\n",
+    "$$odds = \\frac {one\\ outcome} {all\\ other\\ outcomes}$$\n",
+    "\n",
+    "Examples:\n",
+    "\n",
+    "- Dice roll of 1: probability = 1/6, odds = 1/5\n",
+    "- Even dice roll: probability = 3/6, odds = 3/3 = 1\n",
+    "- Dice roll less than 5: probability = 4/6, odds = 4/2 = 2\n",
+    "\n",
+    "$$odds = \\frac {probability} {1 - probability}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>probability</th>\n",
+       "      <th>odds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.90</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   probability      odds\n",
+       "0         0.10  0.111111\n",
+       "1         0.20  0.250000\n",
+       "2         0.25  0.333333\n",
+       "3         0.50  1.000000\n",
+       "4         0.60  1.500000\n",
+       "5         0.80  4.000000\n",
+       "6         0.90  9.000000"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a table of probability versus odds\n",
+    "table = pd.DataFrame({'probability':[0.1, 0.2, 0.25, 0.5, 0.6, 0.8, 0.9]})\n",
+    "table['odds'] = table.probability/(1 - table.probability)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is **e**? It is the base rate of growth shared by all continually growing processes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7182818284590451"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# exponential function: e^1\n",
+    "e = np.exp(1)\n",
+    "e"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is a **(natural) log**? It gives you the time needed to reach a certain level of growth:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# time needed to grow 1 unit to 2.718 units\n",
+    "np.log(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also the **inverse** of the exponential function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.log(np.exp(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>probability</th>\n",
+       "      <th>odds</th>\n",
+       "      <th>logodds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>-2.197225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>-1.386294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-1.098612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>0.405465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.386294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.90</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2.197225</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   probability      odds   logodds\n",
+       "0         0.10  0.111111 -2.197225\n",
+       "1         0.20  0.250000 -1.386294\n",
+       "2         0.25  0.333333 -1.098612\n",
+       "3         0.50  1.000000  0.000000\n",
+       "4         0.60  1.500000  0.405465\n",
+       "5         0.80  4.000000  1.386294\n",
+       "6         0.90  9.000000  2.197225"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add log-odds to the table\n",
+    "table['logodds'] = np.log(table.odds)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 5: What is Logistic Regression?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Linear regression:** continuous response is modeled as a linear combination of the features:\n",
+    "\n",
+    "$$y = \\beta_0 + \\beta_1x$$\n",
+    "\n",
+    "**Logistic regression:** log-odds of a categorical response being \"true\" (1) is modeled as a linear combination of the features:\n",
+    "\n",
+    "$$\\log \\left({p\\over 1-p}\\right) = \\beta_0 + \\beta_1x$$\n",
+    "\n",
+    "This is called the **logit function**.\n",
+    "\n",
+    "Probability is sometimes written as pi:\n",
+    "\n",
+    "$$\\log \\left({\\pi\\over 1-\\pi}\\right) = \\beta_0 + \\beta_1x$$\n",
+    "\n",
+    "The equation can be rearranged into the **logistic function**:\n",
+    "\n",
+    "$$\\pi = \\frac{e^{\\beta_0 + \\beta_1x}} {1 + e^{\\beta_0 + \\beta_1x}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In other words:\n",
+    "\n",
+    "- Logistic regression outputs the **probabilities of a specific class**\n",
+    "- Those probabilities can be converted into **class predictions**\n",
+    "\n",
+    "The **logistic function** has some nice properties:\n",
+    "\n",
+    "- Takes on an \"s\" shape\n",
+    "- Output is bounded by 0 and 1\n",
+    "\n",
+    "Notes:\n",
+    "\n",
+    "- **Multinomial logistic regression** is used when there are more than 2 classes.\n",
+    "- Coefficients are estimated using **maximum likelihood estimation**, meaning that we choose parameters that maximize the likelihood of the observed data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 6: Interpreting Logistic Regression Coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1334d3d10>]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CxV8kSWSseJuS00/E+8nH1F49mZp7HgCfL9GxpJfStI9IEshcMJ/Ciy+AhgZqbr+bhvMn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\nSMVfRCQNqfiLiKSh/wfOtFi4ctYpUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1334d3750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the predicted probabilities again\n",
+    "plt.scatter(glass.al, glass.assorted)\n",
+    "plt.plot(glass.al, assorted_pred_prob, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.10592543]])"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute predicted log-odds for al=2 using the equation\n",
+    "logodds = logreg.intercept_ + logreg.coef_ * 2\n",
+    "logodds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.89949172]])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert log-odds to odds\n",
+    "odds = np.exp(logodds)\n",
+    "odds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.47354338]])"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert odds to probability\n",
+    "prob = odds/(1 + odds)\n",
+    "prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.47354338])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute predicted probability for al=2 using the predict_proba method\n",
+    "logreg.predict_proba(2)[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>al</td>\n",
+       "      <td>[2.01099096417]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  feature             coef\n",
+       "0      al  [2.01099096417]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the coefficient for al\n",
+    "pd.DataFrame(zip(feature_cols, logreg.coef_), columns=['feature', 'coef'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Interpretation:** A 1 unit increase in 'al' is associated with a 2.0109 unit increase in the log-odds of 'assorted'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.87045351351387434"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# increasing al by 1 (so that al=3) increases the log-odds by 2.0109\n",
+    "\n",
+    "# the -0.10592543 is the logodds we calculated a few cells ago for al=2\n",
+    "# I am stepping through the equation by one \"unit\" of al\n",
+    "\n",
+    "logodds = -0.10592543 + 2.0109\n",
+    "odds = np.exp(logodds)\n",
+    "prob = odds/(1 + odds)\n",
+    "prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.87046377])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute predicted probability for al=3 using the predict_proba method\n",
+    "logreg.predict_proba(3)[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Bottom line:** Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-4.12790736])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the intercept\n",
+    "logreg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Interpretation:** For an 'al' value of 0, the log-odds of 'assorted' is -4.127"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.01586095])"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert log-odds to probability\n",
+    "# Probability of assorted is low if al = 0\n",
+    "logodds = logreg.intercept_\n",
+    "odds = np.exp(logodds)\n",
+    "prob = odds/(1 + odds)\n",
+    "prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That makes sense from the plot above, because the probability of assorted=1 should be very low for such a low 'al' value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/logistic_betas.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Changing the $\\beta_0$ value shifts the curve **horizontally**, whereas changing the $\\beta_1$ value changes the **slope** of the curve."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 7: Comparing Logistic Regression with Other Models\n",
+    "\n",
+    "Advantages of logistic regression:\n",
+    "\n",
+    "- Highly interpretable (if you remember how)\n",
+    "- Model training and prediction are fast\n",
+    "- No tuning is required (excluding regularization)\n",
+    "- Features don't need scaling\n",
+    "- Can perform well with a small number of observations\n",
+    "- Outputs well-calibrated predicted probabilities\n",
+    "\n",
+    "Disadvantages of logistic regression:\n",
+    "\n",
+    "- Presumes a linear relationship between the features and the log-odds of the response\n",
+    "- Performance is (generally) not competitive with the best supervised learning methods\n",
+    "- Sensitive to irrelevant features\n",
+    "- Can't automatically learn feature interactions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Confusion Matrix\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[160   3]\n",
+      " [ 31  20]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "preds = logreg.predict(X)\n",
+    "print metrics.confusion_matrix(y, preds)\n",
+    "# Note that we can't make this martix using cross_val_score so a train_test_split has to do!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Top Left: True Negatives <Br>\n",
+    "##Top Right False Negatives <Br>\n",
+    "##Bottom Left: False Negatives <br>\n",
+    "##Bottom Right: True Positives <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise** Calculate:\n",
+    "Accuracy\n",
+    "Sensitivity\n",
+    "Specificity\n",
+    "Precision by hand\n",
+    "\n",
+    "\n",
+    "\n",
+    "<br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### Accuracy    = (157 + 28) / 214       == .8644\n",
+    "#### Sensitivity (Recall) =  28        / (23 + 28) == .5490\n",
+    "#### Specificity =  157       / (157 + 6) == .9631\n",
+    "#### Precision =  28       / (28 + 6) == .823"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             precision    recall  f1-score   support\n",
+      "\n",
+      "          0       0.84      0.98      0.90       163\n",
+      "          1       0.87      0.39      0.54        51\n",
+      "\n",
+      "avg / total       0.85      0.84      0.82       214\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print metrics.classification_report(y, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x133a5fd50>"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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E28AqrEjFKt2GcfenJg2Lz723vl7aeMzvw+ebSGPjbTQ2/gJRs1rbVZWUBELn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\neTRMAf4DUYw0C6hzOp1HXC5X00h0+lJjuNKftnO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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12f6ce690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# MORE DATA\n",
+    "\n",
+    "# Logistic Regression is a high bias low variance model that is also non-parametric\n",
+    "\n",
+    "from sklearn.datasets import make_circles\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "circles_X, circles_y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)\n",
+    "plt.scatter(circles_X[:,0], circles_X[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qAUGRBLu4n5cmb8BUDv34Kp2MZezx9iZw+HA+TE7G19eXCePG4e/tfd41tbQK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1326fc050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# It has a linear decision boundary, IE the shape is draws between classes are lines!\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "import numpy as np\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "\n",
+    "# Create color maps\n",
+    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
+    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
+    "\n",
+    "# we create an instance of Neighbours Classifier and fit the data.\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(circles_X, circles_y)\n",
+    "\n",
+    "# Plot the decision boundary. For that, we will assign a color to each\n",
+    "# point in the mesh [x_min, m_max]x[y_min, y_max].\n",
+    "x_min, x_max = circles_X[:, 0].min() - 1, circles_X[:, 0].max() + 1\n",
+    "y_min, y_max = circles_X[:, 1].min() - 1, circles_X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "# Put the result into a color plot\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "plt.figure()\n",
+    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
+    "\n",
+    "# Plot also the training points\n",
+    "plt.scatter(circles_X[:, 0], circles_X[:, 1], c=circles_y, cmap=cmap_bold)\n",
+    "plt.xlim(xx.min(), xx.max())\n",
+    "plt.ylim(yy.min(), yy.max())\n",
+    "plt.title(\"Circle classification Logistic Regression\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.48899999999999999"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, circles_X, circles_y, cv=5, scoring='accuracy').mean()\n",
+    "# lame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier  # compare to knn\n",
+    "knn = KNeighborsClassifier(n_neighbors=7)\n",
+    "cross_val_score(knn, circles_X, circles_y, cv=5, scoring='accuracy').mean()\n",
+    "# not as lame, remember?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0.,   0.,   5., ...,   0.,   0.,   0.],\n",
+       "       [  0.,   0.,   0., ...,  10.,   0.,   0.],\n",
+       "       [  0.,   0.,   0., ...,  16.,   9.,   0.],\n",
+       "       ..., \n",
+       "       [  0.,   0.,   1., ...,   6.,   0.,   0.],\n",
+       "       [  0.,   0.,   2., ...,  12.,   0.,   0.],\n",
+       "       [  0.,   0.,  10., ...,  12.,   1.,   0.]])"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "# new dataset, handwritten digits!\n",
+    "digits = datasets.load_digits()\n",
+    "digits.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD9CAYAAACV6WqFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADJ1JREFUeJzt3XGI5PV5x/H3XY2SiPEESdqgcSmSpw0EV6okEatzYrCW\nRDfloP9c29WENCa0Wf+wKGq8gkkDMckVxAQSc3sFW5oc3l3SQCRpd48oRY24kBB4bEiuhSYgSu5C\nezVobvvHzOHR7t3M/G6+s7v3vF8gOHLPPt8b97Pf38z+vvNsWV1dRdLZbet6L0BSewZdKsCgSwUY\ndKkAgy4VYNClAs5p8DXP6t/XLS8vd6q7+uqrefbZZ8euW1xcHLvmwQcf5L777hu7DmBlZWXsmn37\n9rFjx45O/RYWFsauufXWWzl48GCnfvPz853qNpEta/1Hd/QpOf/886fW65JLLplaL4DLL798qv0u\nuuiiqfY7Gxh0qQCDLhVg0KUCDLpUgEGXCjDoUgEGXSrAoEsFDL0zLiK2AI8AVwCvAB/OzJ+0Xpik\nyRllR58DzsvMa4B7gM+3XZKkSRsl6NcC3wbIzKeBq5quSNLEjRL0NwNHT3r8WkT42l7aRLYM+3DI\niPgc8K+ZuW/w+D8y8+2nKTmrT69JG9yap9dGOab6FPB+YF9EvAf4wSRXtdl0Paba6/U61XY5prq4\nuNj5OGaXY6orKyvMzs526tflmOr8/Hyn5+VEbUWjBH0/8L6IeGrw+LaG65HUwNCgZ+YqcMcU1iKp\nEd9Ukwow6FIBBl0qwKBLBRh0qQCDLhVg0KUCDLpUgEGXChh6qKWDqR5qOXLkSKe6bdu2dartOiVk\ndXWVLVvWPG9wWpdddtnYNYcPH2ZmZmbsOqBT3fLyMr1er1O/Q4cOjV3T9bkEeP7558eumZ2d7XQG\n4ETtlDmSSarKoEsFGHSpAIMuFWDQpQIMulSAQZcKMOhSAQZdKsCgSwWMFPSIeHdELLVejKQ2Rhmy\neBfwJ8B/tV+OpBZG2dF/DHyw9UIktTPS6bWIuAz4h8FE1WEcySStn84jmTY0j6n+fx5TPbUCx1TX\nNM677t2eWUnrbpyge0kubVIjXbpn5r8Do7w+l7QBecOMVIBBlwow6FIBBl0qwKBLBRh0qQCDLhVg\n0KUCDLpUwKY/1NL1sEGv1+tcO027du2aal3XQxi7d+/uVHfllVd2quuq6yGornUbhTu6VIBBlwow\n6FIBBl0qwKBLBRh0qQCDLhVg0KUCDLpUwGnvjIuIc4CvAjPAucCnMvObU1iXpAkatqPvBF7KzOuA\nm4GH2y9J0qQNu9f9a8DXB/++FXi17XIktXDaoGfmMYCIuIB+4O+dxqIkTdbQ2WsRcSnwOPBwZu4d\n4Ws66EFaP2tOVDpt0CPircAS8PHMHHU++lSDvry83Kmu1+t1qt2+fXunfl3nhe3Zs2fsmvn5eRYX\nF8eug27HVM9kNlmXY6pnMnttaWnUb+PXdf1eOVE7ZZ2GLN4DbAPuj4hP0g/xzZn5qwkvTlJDw16j\nLwALU1qLpEa8YUYqwKBLBRh0qQCDLhVg0KUCDLpUgEGXCjDoUgEGXSrAoEsFDD291sFZfXqt6yGF\n5eXlTrWHDh0au+ZMDn10sZn67d+/f+yaubk5Dhw40Knf3Nxcp7ozsOYT444uFWDQpQIMulSAQZcK\nMOhSAQZdKsCgSwUYdKkAgy4VMOxTYImIrcCXgQCOAx/NzB+1XpikyRllR/8AsJqZ1wL3A59uuyRJ\nkzY06Jl5EPjI4OEM8IuWC5I0eUMv3QEy83hELAJzwI6mK5I0cWOdXouItwDPAL+bmf9zij92Vp9e\nkza4TiOZiIidwCWZ+RngFeDX9N+UK8ljqpu7X4Fjqmsa5dL9cWBPRBwa/PlPOHtN2lyGBn0wI/2P\np7AWSY14w4xUgEGXCjDoUgEGXSrAoEsFGHSpAIMuFWDQpQIMulSAQZcKGOmYql63vLw81dqu/ZaW\nljrVddW13/bt2ye8ktNbWVkZu2Zubq5T3YnajcAdXSrAoEsFGHSpAIMuFWDQpQIMulSAQZcKMOhS\nAQZdKmCkO+MGn+f+feDGzHyh7ZIkTdrQHT0izgG+BBxrvxxJLYxy6f4Q8EXgZ43XIqmR0wY9IuaB\nFzPzO5xi1Iukje+0s9cG01lOjF+aBRK4JTNfPM3XdPaatH7W3JBHHrIYEUvAn4/wZpxBn6Aux1R7\nvd4ZHaedZr8ux1TPZPbaAw88MHbNrl272LVrV6d+XevOwJpPzDi/XjPA0iY18gdPZOYNLRciqR1v\nmJEKMOhSAQZdKsCgSwUYdKkAgy4VYNClAgy6VIBBlwow6FIBzl7b4Hq93lTruura78ILL5xq3ezs\n7FTrNgp3dKkAgy4VYNClAgy6VIBBlwow6FIBBl0qwKBLBRh0qYBRZ689BxwdPPxpZn6o3ZIkTdrQ\noEfEeeCnwEqb2Sg7+hXA+RHxBPAbwL2Z+XTbZUmapFFeox8DPpuZNwF3AI9FhK/tpU1k6EimiDgX\n2JqZrwwePw38UWb+5ylKnOgirZ81RzKNcul+O/Au4OMR8TbgAuDnE1yYCtu2bdvYNUeOHOlUB7C4\nuDh2zdzcHAcOHOjUb25urlPdpI0S9EeBPRHxPfqTVW/PzONDaiRtIEODnpmvAjunsBZJjfimmlSA\nQZcKMOhSAQZdKsCgSwUYdKkAgy4VYNClAgy6VIBBlwpw9tqYzuRwQ5faw4cPj12zsLDA7t27x67r\n6kz6HT16dPgfmmBd18MwXes2Cnd0qQCDLhVg0KUCDLpUgEGXCjDoUgEGXSrAoEsFGHSpgFFnr90N\n3AK8AXgkM/c0XZWkiRq6o0fE9cB7M/MaoAdc2npRkiZrlB39JuCHEXGA/vCGu9ouSdKkjRL0i4G3\nA+8Hfhv4BvA7LRclabJGmb32N8CLmfmFweMV4MbMfOkUJc5ek9ZP59lrTwJ/CXxhMHvtTcDLE1zY\npuIx1cn2u/POO8euWV1dZcuWNb+fh1paWhq7ptfrsby83Klfr9frVDdpQ9+My8xvAc9HxDPAQeBj\nmemuLW0iI/16LTPvbr0QSe14w4xUgEGXCjDoUgEGXSrAoEsFGHSpAIMuFWDQpQIMulSAQZcKGHp6\nrYOz+j74LodMAGZmZjrVdjkUcfjwYWZmZsauAzhy5Einmq6zyebn58eu2b17NwsLC536TfOwzzpZ\n87SPO7pUgEGXCjDoUgEGXSrAoEsFGHSpAIMuFWDQpQIMulTA0A+HjIg/A+bp3/H2RuAK4Dcz85dt\nlyZpUoYGPTP3AnsBIuJh4CuGXNpcRr50j4irgHdm5qMN1yOpgXFeo98D/HWrhUhqZ6TTaxFxIfBk\nZr5rhK95Vp9ekza4zrPXAK4D/nlya9m8PKa6do3HVDe2US/dA/hJy4VIamfU2WsPtV6IpHa8YUYq\nwKBLBRh0qQCDLhVg0KUCDLpUgEGXCjDoUgEGXSrAoEsFtJi9JmmDcUeXCjDoUgEGXSrAoEsFGHSp\nAIMuFTDqZ8Y1ERFbgEfoD4V4BfhwZjb/yKqIeDfwmczc3rjPOcBXgRngXOBTmfnNhv22Al+m/9Ff\nx4GPZuaPWvU7qe9bgO8DN2bmC417PQccHTz8aWZ+qHG/u4FbgDcAj2Tmnoa9mg1LWe8dfQ44LzOv\nof9x0p9v3TAi7qIfhvNa9wJ2Ai9l5nXAzcDDjft9AFjNzGuB+4FPN+534ofZl4BjU+h1HkBm3jD4\np3XIrwfeO/j+7AGXtuyXmXszc3tm3gA8B/zFpIalrHfQrwW+DZCZTwNXTaHnj4EPTqEPwNfoBw76\nz/WrLZtl5kHgI4OHM8AvWvYbeAj4IvCzKfS6Ajg/Ip6IiO8Orsxaugn4YUQcAL4B/FPjfkCbYSnr\nHfQ38/plGMBrg8vPZjJzP/Bayx4n9TqWmf8dERcAXwfunULP4xGxCPwt8FjLXhExD7yYmd/hFJ8n\nPmHHgM9m5k3AHcBjjb9fLgZ+D9gx6Pf3DXudbOLDUtY76L8ELjjp8dbMPL5ei2khIi4F/gXYm5n/\nOI2emTkPvAP4SkS8sWGr24D3RcQSMAv83eD1eisvMPjhlZn/BrwM/FbDfi8DT2Tma4P3Hl6JiIsb\n9jsxLOUdmXlokl93vYP+FPCHABHxHuAHU+zdfAeKiLcCTwB/NRhW2brfzsGbR9B/c/PX9N+UayIz\nrx+8ptwOrAB/mpkvtuoH3A58DiAi3kZ/k/h5w35PAn9wUr830Q9/S02Gpazru+7Afvo7wlODx7dN\nsfc0TvPcA2wD7o+ITw563pyZv2rU73FgT0Qcov//9hMNe/1f03g+H6X/9/se/R9gt7e8AszMb0XE\n70fEM/Q3ho9lZuu/Z5NhKZ5ekwpY70t3SVNg0KUCDLpUgEGXCjDoUgEGXSrAoEsFGHSpgP8FHhqJ\nkRtyXKgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1339809d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(digits.images[-5], cmap=plt.cm.gray_r, interpolation='nearest')\n",
+    "# the number 9\n",
+    "\n",
+    "\n",
+    "digits.target[-5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digits.data.shape\n",
+    "# 1,797 observations, 64 features (8 x 8 image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "digits_X, digits_y = digits.data, digits.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.92101881133607011"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, digits_X, digits_y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9627899114966898"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to KNN\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "cross_val_score(knn, digits_X, digits_y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Thought Exercise, why would KNN potentially be a better model than logsitci regression\n",
+    "# for handwriting?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# OK so wait, when should we use Logistic Regression?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Using dataset of a 1978 survey conducted to measure likliehood of women to perform extramarital affairs\n",
+    "# http://statsmodels.sourceforge.net/stable/datasets/generated/fair.html\n",
+    "\n",
+    "import statsmodels.api as sm\n",
+    "affairs_df = sm.datasets.fair.load_pandas().data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rate_marriage</th>\n",
+       "      <th>age</th>\n",
+       "      <th>yrs_married</th>\n",
+       "      <th>children</th>\n",
+       "      <th>religious</th>\n",
+       "      <th>educ</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>occupation_husb</th>\n",
+       "      <th>affairs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.230769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.666666</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
+       "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
+       "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
+       "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
+       "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
+       "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
+       "\n",
+       "   occupation_husb   affairs  \n",
+       "0              5.0  0.111111  \n",
+       "1              4.0  3.230769  \n",
+       "2              5.0  1.400000  \n",
+       "3              5.0  0.727273  \n",
+       "4              4.0  4.666666  "
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "affairs_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "affairs_df['affair_binary'] = (affairs_df['affairs'] > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x134050a90>"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rdh3q38CdObTdzGyxeNh3CUm6Cdion8cn1fy6zQCHe6sXJOls4OxBNc7MLCee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\nSHXzqob1vfug9D7tXjuIp9V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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x133fa0ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(affairs_df.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rate_marriage</th>\n",
+       "      <th>age</th>\n",
+       "      <th>yrs_married</th>\n",
+       "      <th>children</th>\n",
+       "      <th>religious</th>\n",
+       "      <th>educ</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>occupation_husb</th>\n",
+       "      <th>affairs</th>\n",
+       "      <th>affair_binary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>rate_marriage</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.111127</td>\n",
+       "      <td>-0.128978</td>\n",
+       "      <td>-0.129161</td>\n",
+       "      <td>0.078794</td>\n",
+       "      <td>0.079869</td>\n",
+       "      <td>0.039528</td>\n",
+       "      <td>0.027745</td>\n",
+       "      <td>-0.178068</td>\n",
+       "      <td>-0.331776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>-0.111127</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.894082</td>\n",
+       "      <td>0.673902</td>\n",
+       "      <td>0.136598</td>\n",
+       "      <td>0.027960</td>\n",
+       "      <td>0.106127</td>\n",
+       "      <td>0.162567</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>0.146519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yrs_married</th>\n",
+       "      <td>-0.128978</td>\n",
+       "      <td>0.894082</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.772806</td>\n",
+       "      <td>0.132683</td>\n",
+       "      <td>-0.109058</td>\n",
+       "      <td>0.041782</td>\n",
+       "      <td>0.128135</td>\n",
+       "      <td>-0.087737</td>\n",
+       "      <td>0.203109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>children</th>\n",
+       "      <td>-0.129161</td>\n",
+       "      <td>0.673902</td>\n",
+       "      <td>0.772806</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.141845</td>\n",
+       "      <td>-0.141918</td>\n",
+       "      <td>-0.015068</td>\n",
+       "      <td>0.086660</td>\n",
+       "      <td>-0.070278</td>\n",
+       "      <td>0.159833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>religious</th>\n",
+       "      <td>0.078794</td>\n",
+       "      <td>0.136598</td>\n",
+       "      <td>0.132683</td>\n",
+       "      <td>0.141845</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.032245</td>\n",
+       "      <td>0.035746</td>\n",
+       "      <td>0.004061</td>\n",
+       "      <td>-0.125933</td>\n",
+       "      <td>-0.129299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>educ</th>\n",
+       "      <td>0.079869</td>\n",
+       "      <td>0.027960</td>\n",
+       "      <td>-0.109058</td>\n",
+       "      <td>-0.141918</td>\n",
+       "      <td>0.032245</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.382286</td>\n",
+       "      <td>0.183932</td>\n",
+       "      <td>-0.017740</td>\n",
+       "      <td>-0.075280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>occupation</th>\n",
+       "      <td>0.039528</td>\n",
+       "      <td>0.106127</td>\n",
+       "      <td>0.041782</td>\n",
+       "      <td>-0.015068</td>\n",
+       "      <td>0.035746</td>\n",
+       "      <td>0.382286</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.201156</td>\n",
+       "      <td>0.004469</td>\n",
+       "      <td>0.028981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>occupation_husb</th>\n",
+       "      <td>0.027745</td>\n",
+       "      <td>0.162567</td>\n",
+       "      <td>0.128135</td>\n",
+       "      <td>0.086660</td>\n",
+       "      <td>0.004061</td>\n",
+       "      <td>0.183932</td>\n",
+       "      <td>0.201156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.015614</td>\n",
+       "      <td>0.017637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>affairs</th>\n",
+       "      <td>-0.178068</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>-0.087737</td>\n",
+       "      <td>-0.070278</td>\n",
+       "      <td>-0.125933</td>\n",
+       "      <td>-0.017740</td>\n",
+       "      <td>0.004469</td>\n",
+       "      <td>-0.015614</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.464046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>affair_binary</th>\n",
+       "      <td>-0.331776</td>\n",
+       "      <td>0.146519</td>\n",
+       "      <td>0.203109</td>\n",
+       "      <td>0.159833</td>\n",
+       "      <td>-0.129299</td>\n",
+       "      <td>-0.075280</td>\n",
+       "      <td>0.028981</td>\n",
+       "      <td>0.017637</td>\n",
+       "      <td>0.464046</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 rate_marriage       age  yrs_married  children  religious  \\\n",
+       "rate_marriage         1.000000 -0.111127    -0.128978 -0.129161   0.078794   \n",
+       "age                  -0.111127  1.000000     0.894082  0.673902   0.136598   \n",
+       "yrs_married          -0.128978  0.894082     1.000000  0.772806   0.132683   \n",
+       "children             -0.129161  0.673902     0.772806  1.000000   0.141845   \n",
+       "religious             0.078794  0.136598     0.132683  0.141845   1.000000   \n",
+       "educ                  0.079869  0.027960    -0.109058 -0.141918   0.032245   \n",
+       "occupation            0.039528  0.106127     0.041782 -0.015068   0.035746   \n",
+       "occupation_husb       0.027745  0.162567     0.128135  0.086660   0.004061   \n",
+       "affairs              -0.178068 -0.089964    -0.087737 -0.070278  -0.125933   \n",
+       "affair_binary        -0.331776  0.146519     0.203109  0.159833  -0.129299   \n",
+       "\n",
+       "                     educ  occupation  occupation_husb   affairs  \\\n",
+       "rate_marriage    0.079869    0.039528         0.027745 -0.178068   \n",
+       "age              0.027960    0.106127         0.162567 -0.089964   \n",
+       "yrs_married     -0.109058    0.041782         0.128135 -0.087737   \n",
+       "children        -0.141918   -0.015068         0.086660 -0.070278   \n",
+       "religious        0.032245    0.035746         0.004061 -0.125933   \n",
+       "educ             1.000000    0.382286         0.183932 -0.017740   \n",
+       "occupation       0.382286    1.000000         0.201156  0.004469   \n",
+       "occupation_husb  0.183932    0.201156         1.000000 -0.015614   \n",
+       "affairs         -0.017740    0.004469        -0.015614  1.000000   \n",
+       "affair_binary   -0.075280    0.028981         0.017637  0.464046   \n",
+       "\n",
+       "                 affair_binary  \n",
+       "rate_marriage        -0.331776  \n",
+       "age                   0.146519  \n",
+       "yrs_married           0.203109  \n",
+       "children              0.159833  \n",
+       "religious            -0.129299  \n",
+       "educ                 -0.075280  \n",
+       "occupation            0.028981  \n",
+       "occupation_husb       0.017637  \n",
+       "affairs               0.464046  \n",
+       "affair_binary         1.000000  "
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "affairs_df.corr()\n",
+    "# Obviously affairs will correlate to affair_binary but what else?\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# It seems children, yrs_married, rate_married, and age all correlate to affair_binary\n",
+    "# Remember correlations are NOT the single way to identify which features to use\n",
+    "# Correlations only give us a number determining how linearlly correlated the variables are\n",
+    "# We may find another variable that affects affairs by evaluating the coefficients of our LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "affairs_X = affairs_df.drop(['affairs', 'affair_binary'], axis=1)\n",
+    "affairs_y = affairs_df['affair_binary']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.71630094  0.69749216  0.74137931  0.71226415  0.70125786  0.73113208\n",
+      "  0.71855346  0.70125786  0.74685535  0.75314465]\n",
+      "0.72196378226\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LogisticRegression()\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "# check the accuracy on the training set\n",
+    "scores = cross_val_score(model, affairs_X, affairs_y, cv=10)\n",
+    "print scores\n",
+    "print scores.mean()\n",
+    "\n",
+    "# Looks pretty good"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rate_marriage</td>\n",
+       "      <td>[-0.702300201706]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>age</td>\n",
+       "      <td>[-0.0546769400998]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>yrs_married</td>\n",
+       "      <td>[0.105079088955]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>children</td>\n",
+       "      <td>[-0.00117231032]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>religious</td>\n",
+       "      <td>[-0.367121091053]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>educ</td>\n",
+       "      <td>[-0.0328106363897]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>occupation</td>\n",
+       "      <td>[0.161411859069]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>occupation_husb</td>\n",
+       "      <td>[0.0145734984752]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 0                   1\n",
+       "0    rate_marriage   [-0.702300201706]\n",
+       "1              age  [-0.0546769400998]\n",
+       "2      yrs_married    [0.105079088955]\n",
+       "3         children    [-0.00117231032]\n",
+       "4        religious   [-0.367121091053]\n",
+       "5             educ  [-0.0328106363897]\n",
+       "6       occupation    [0.161411859069]\n",
+       "7  occupation_husb   [0.0145734984752]"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Explore individual features that make the biggest impact\n",
+    "# religious, yrs_married, and occupation. But one of these variables doesn't quite make sense right?\n",
+    "pd.DataFrame(zip(affairs_X.columns, np.transpose(model.coef_)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Dummy Variables:\n",
+    "\n",
+    "# Encoding qualitiative (nominal) data using separate columns (see slides for linear regression for more)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"images/dummy.png\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rate_marriage</th>\n",
+       "      <th>age</th>\n",
+       "      <th>yrs_married</th>\n",
+       "      <th>children</th>\n",
+       "      <th>religious</th>\n",
+       "      <th>educ</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>occupation_husb</th>\n",
+       "      <th>affairs</th>\n",
+       "      <th>affair_binary</th>\n",
+       "      <th>occ__2.0</th>\n",
+       "      <th>occ__3.0</th>\n",
+       "      <th>occ__4.0</th>\n",
+       "      <th>occ__5.0</th>\n",
+       "      <th>occ__6.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.230769</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.400000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.666666</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
+       "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
+       "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
+       "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
+       "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
+       "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
+       "\n",
+       "   occupation_husb   affairs affair_binary  occ__2.0  occ__3.0  occ__4.0  \\\n",
+       "0              5.0  0.111111          True       1.0       0.0       0.0   \n",
+       "1              4.0  3.230769          True       0.0       1.0       0.0   \n",
+       "2              5.0  1.400000          True       0.0       1.0       0.0   \n",
+       "3              5.0  0.727273          True       0.0       0.0       0.0   \n",
+       "4              4.0  4.666666          True       0.0       1.0       0.0   \n",
+       "\n",
+       "   occ__5.0  occ__6.0  \n",
+       "0       0.0       0.0  \n",
+       "1       0.0       0.0  \n",
+       "2       0.0       0.0  \n",
+       "3       1.0       0.0  \n",
+       "4       0.0       0.0  "
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occuptation_dummies = pd.get_dummies(affairs_df['occupation'], prefix='occ_').iloc[:, 1:]\n",
+    "\n",
+    "# concatenate the dummy variable columns onto the original DataFrame (axis=0 means rows, axis=1 means columns)\n",
+    "affairs_df = pd.concat([affairs_df, occuptation_dummies], axis=1)\n",
+    "affairs_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rate_marriage</th>\n",
+       "      <th>age</th>\n",
+       "      <th>yrs_married</th>\n",
+       "      <th>children</th>\n",
+       "      <th>religious</th>\n",
+       "      <th>educ</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>occupation_husb</th>\n",
+       "      <th>affairs</th>\n",
+       "      <th>affair_binary</th>\n",
+       "      <th>occ__2.0</th>\n",
+       "      <th>occ__3.0</th>\n",
+       "      <th>occ__4.0</th>\n",
+       "      <th>occ__5.0</th>\n",
+       "      <th>occ__6.0</th>\n",
+       "      <th>occ_husb__2.0</th>\n",
+       "      <th>occ_husb__3.0</th>\n",
+       "      <th>occ_husb__4.0</th>\n",
+       "      <th>occ_husb__5.0</th>\n",
+       "      <th>occ_husb__6.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.230769</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.400000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.666666</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
+       "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
+       "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
+       "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
+       "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
+       "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
+       "\n",
+       "   occupation_husb   affairs affair_binary  occ__2.0  occ__3.0  occ__4.0  \\\n",
+       "0              5.0  0.111111          True       1.0       0.0       0.0   \n",
+       "1              4.0  3.230769          True       0.0       1.0       0.0   \n",
+       "2              5.0  1.400000          True       0.0       1.0       0.0   \n",
+       "3              5.0  0.727273          True       0.0       0.0       0.0   \n",
+       "4              4.0  4.666666          True       0.0       1.0       0.0   \n",
+       "\n",
+       "   occ__5.0  occ__6.0  occ_husb__2.0  occ_husb__3.0  occ_husb__4.0  \\\n",
+       "0       0.0       0.0            0.0            0.0            0.0   \n",
+       "1       0.0       0.0            0.0            0.0            1.0   \n",
+       "2       0.0       0.0            0.0            0.0            0.0   \n",
+       "3       1.0       0.0            0.0            0.0            0.0   \n",
+       "4       0.0       0.0            0.0            0.0            1.0   \n",
+       "\n",
+       "   occ_husb__5.0  occ_husb__6.0  \n",
+       "0            1.0            0.0  \n",
+       "1            0.0            0.0  \n",
+       "2            1.0            0.0  \n",
+       "3            1.0            0.0  \n",
+       "4            0.0            0.0  "
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occuptation_dummies = pd.get_dummies(affairs_df['occupation_husb'], prefix='occ_husb_').iloc[:, 1:]\n",
+    "\n",
+    "# concatenate the dummy variable columns onto the original DataFrame (axis=0 means rows, axis=1 means columns)\n",
+    "affairs_df = pd.concat([affairs_df, occuptation_dummies], axis=1)\n",
+    "affairs_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# remove appropiate columns for feature set\n",
+    "affairs_X = affairs_df.drop(['affairs', 'affair_binary', 'occupation', 'occupation_husb'], axis=1)\n",
+    "affairs_y = affairs_df['affair_binary']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.72588752748978946"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression()\n",
+    "model = model.fit(affairs_X, affairs_y)\n",
+    "\n",
+    "# check the accuracy on the training set\n",
+    "model.score(affairs_X, affairs_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>features</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rate_marriage</td>\n",
+       "      <td>[-0.697845453825]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>age</td>\n",
+       "      <td>[-0.0563368031972]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>yrs_married</td>\n",
+       "      <td>[0.103893444136]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>children</td>\n",
+       "      <td>[0.0181853982481]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>religious</td>\n",
+       "      <td>[-0.368506616998]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>educ</td>\n",
+       "      <td>[0.00864804494766]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>occ__2.0</td>\n",
+       "      <td>[0.298118794658]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>occ__3.0</td>\n",
+       "      <td>[0.608150180777]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>occ__4.0</td>\n",
+       "      <td>[0.346511273036]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>occ__5.0</td>\n",
+       "      <td>[0.942259498161]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>occ__6.0</td>\n",
+       "      <td>[0.918150144304]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>occ_husb__2.0</td>\n",
+       "      <td>[0.219957140288]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>occ_husb__3.0</td>\n",
+       "      <td>[0.32476602929]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>occ_husb__4.0</td>\n",
+       "      <td>[0.189354154353]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>occ_husb__5.0</td>\n",
+       "      <td>[0.21309298898]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>occ_husb__6.0</td>\n",
+       "      <td>[0.214179979671]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         features                coef\n",
+       "0   rate_marriage   [-0.697845453825]\n",
+       "1             age  [-0.0563368031972]\n",
+       "2     yrs_married    [0.103893444136]\n",
+       "3        children   [0.0181853982481]\n",
+       "4       religious   [-0.368506616998]\n",
+       "5            educ  [0.00864804494766]\n",
+       "6        occ__2.0    [0.298118794658]\n",
+       "7        occ__3.0    [0.608150180777]\n",
+       "8        occ__4.0    [0.346511273036]\n",
+       "9        occ__5.0    [0.942259498161]\n",
+       "10       occ__6.0    [0.918150144304]\n",
+       "11  occ_husb__2.0    [0.219957140288]\n",
+       "12  occ_husb__3.0     [0.32476602929]\n",
+       "13  occ_husb__4.0    [0.189354154353]\n",
+       "14  occ_husb__5.0     [0.21309298898]\n",
+       "15  occ_husb__6.0    [0.214179979671]"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(zip(affairs_X.columns, np.transpose(model.coef_)), columns = ['features', 'coef'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# compare KNN to LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.68630248906529234"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=7)\n",
+    "cross_val_score(knn, affairs_X, affairs_y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.72558005785768587"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, affairs_X, affairs_y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# When we are investigating individual correlations between features and categorical responses\n",
+    "# Logistic regression has a good shot :)\n",
+    "\n",
+    "# KNN relies on the entire n-space to make predictions while LR uses the model parameters to focus\n",
+    "# on one or more particular features\n",
+    "\n",
+    "# LR has concept of \"importance\" of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Final Thought Experiment\n",
+    "\n",
+    "# Why might KNN (a kind of look alike model) not perform well here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+<<<<<<< HEAD
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+=======
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [sfdat28]",
+   "language": "python",
+   "name": "Python [sfdat28]"
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb
new file mode 100644
index 0000000..29af553
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb
@@ -0,0 +1,2842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Natural Language Processing (NLP)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "*Adapted from [NLP Crash Course](http://files.meetup.com/7616132/DC-NLP-2013-09%20Charlie%20Greenbacker.pdf) by Charlie Greenbacker and [Introduction to NLP](http://spark-public.s3.amazonaws.com/nlp/slides/intro.pdf) by Dan Jurafsky*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is NLP?\n",
+    "\n",
+    "- Using computers to process (analyze, understand, generate) natural human languages\n",
+    "- Most knowledge created by humans is unstructured text, and we need a way to make sense of it\n",
+    "- Build probabilistic model using data about a language"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are some of the higher level task areas?\n",
+    "\n",
+    "- **Information retrieval**: Find relevant results and similar results\n",
+    "    - [Google](https://www.google.com/)\n",
+    "- **Information extraction**: Structured information from unstructured documents\n",
+    "    - [Events from Gmail](https://support.google.com/calendar/answer/6084018?hl=en)\n",
+    "- **Machine translation**: One language to another\n",
+    "    - [Google Translate](https://translate.google.com/)\n",
+    "- **Text simplification**: Preserve the meaning of text, but simplify the grammar and vocabulary\n",
+    "    - [Rewordify](https://rewordify.com/)\n",
+    "    - [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page)\n",
+    "- **Predictive text input**: Faster or easier typing\n",
+    "    - [A friend's application](https://justmarkham.shinyapps.io/textprediction/)\n",
+    "    - [A much better application](https://farsite.shinyapps.io/swiftkey-cap/)\n",
+    "- **Sentiment analysis**: Attitude of speaker\n",
+    "    - [Hater News](http://haternews.herokuapp.com/)\n",
+    "- **Automatic summarization**: Extractive or abstractive summarization\n",
+    "    - [autotldr](https://www.reddit.com/r/technology/comments/35brc8/21_million_people_still_use_aol_dialup/cr2zzj0)\n",
+    "- **Natural Language Generation**: Generate text from data\n",
+    "    - [How a computer describes a sports match](http://www.bbc.com/news/technology-34204052)\n",
+    "    - [Publishers withdraw more than 120 gibberish papers](http://www.nature.com/news/publishers-withdraw-more-than-120-gibberish-papers-1.14763)\n",
+    "- **Speech recognition and generation**: Speech-to-text, text-to-speech\n",
+    "    - [Google's Web Speech API demo](https://www.google.com/intl/en/chrome/demos/speech.html)\n",
+    "    - [Vocalware Text-to-Speech demo](https://www.vocalware.com/index/demo)\n",
+    "- **Question answering**: Determine the intent of the question, match query with knowledge base, evaluate hypotheses\n",
+    "    - [How did supercomputer Watson beat Jeopardy champion Ken Jennings?](http://blog.ted.com/how-did-supercomputer-watson-beat-jeopardy-champion-ken-jennings-experts-discuss/)\n",
+    "    - [IBM's Watson Trivia Challenge](http://www.nytimes.com/interactive/2010/06/16/magazine/watson-trivia-game.html)\n",
+    "    - [The AI Behind Watson](http://www.aaai.org/Magazine/Watson/watson.php)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are some of the lower level components?\n",
+    "\n",
+    "- **Tokenization**: breaking text into tokens (words, sentences, n-grams)\n",
+    "- **Stopword removal**: a/an/the\n",
+    "- **Stemming and lemmatization**: root word\n",
+    "- **TF-IDF**: word importance\n",
+    "- **Part-of-speech tagging**: noun/verb/adjective\n",
+    "- **Named entity recognition**: person/organization/location\n",
+    "- **Spelling correction**: \"New Yrok City\"\n",
+    "- **Word sense disambiguation**: \"buy a mouse\"\n",
+    "- **Segmentation**: \"New York City subway\"\n",
+    "- **Language detection**: \"translate this page\"\n",
+    "- **Machine learning**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Why is NLP hard?\n",
+    "\n",
+    "- **Ambiguity**:\n",
+    "    - Hospitals are Sued by 7 Foot Doctors\n",
+    "    - Juvenile Court to Try Shooting Defendant\n",
+    "    - Local High School Dropouts Cut in Half\n",
+    "- **Non-standard English**: text messages\n",
+    "- **Idioms**: \"throw in the towel\"\n",
+    "- **Newly coined words**: \"retweet\"\n",
+    "- **Tricky entity names**: \"Where is A Bug's Life playing?\"\n",
+    "- **World knowledge**: \"Mary and Sue are sisters\", \"Mary and Sue are mothers\"\n",
+    "\n",
+    "NLP requires an understanding of the **language** and the **world**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 1: Reading in the Yelp Reviews"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- \"corpus\" = collection of documents\n",
+    "- \"corpora\" = plural form of corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy as sp\n",
+    "from sklearn.cross_validation import train_test_split, cross_val_score\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn import metrics\n",
+    "from textblob import TextBlob, Word\n",
+    "from nltk.stem.snowball import SnowballStemmer\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# read yelp.csv into a DataFrame\n",
+    "url = '../data/yelp.csv'\n",
+    "yelp = pd.read_csv(url, encoding='unicode-escape')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE create a new DataFrame called yelp_best_worst \n",
+    "# that only contains the 5-star and 1-star reviews\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ANSWER\n",
+    "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>My wife took me here on my birthday for breakf...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>I have no idea why some people give bad review...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
+       "      <td>5</td>\n",
+       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>zp713qNhx8d9KCJJnrw1xA</td>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>riFQ3vxNpP4rWLk_CSri2A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Drop what you're doing and drive here. After I...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>wFweIWhv2fREZV_dYkz_1g</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              business_id        date               review_id  stars  \\\n",
+       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
+       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
+       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
+       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
+       "6  zp713qNhx8d9KCJJnrw1xA  2010-02-12  riFQ3vxNpP4rWLk_CSri2A      5   \n",
+       "\n",
+       "                                                text    type  \\\n",
+       "0  My wife took me here on my birthday for breakf...  review   \n",
+       "1  I have no idea why some people give bad review...  review   \n",
+       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
+       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
+       "6  Drop what you're doing and drive here. After I...  review   \n",
+       "\n",
+       "                  user_id  cool  useful  funny  \n",
+       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
+       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
+       "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
+       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  \n",
+       "6  wFweIWhv2fREZV_dYkz_1g     7       7      4  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp_best_worst.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5    3337\n",
+      "1     749\n",
+      "Name: stars, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define X and y\n",
+    "X = yelp_best_worst.text\n",
+    "y = yelp_best_worst.stars\n",
+    "print y.value_counts()\n",
+    "\n",
+    "# split the new DataFrame into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6841    FILLY-B's!!!!!  only 8 reviews?? NINE now!!!\\r...\n",
+       "1728    My husband and I absolutely LOVE this restaura...\n",
+       "3853    We went today after lunch. I got my usual of l...\n",
+       "671     Totally dissapointed.  I had purchased a coupo...\n",
+       "4920    Costco Travel - My husband and I recently retu...\n",
+       "Name: text, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2: Tokenization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **What:** Separate text into units such as sentences or words\n",
+    "- **Why:** Gives structure to previously unstructured text\n",
+    "- **Notes:** Relatively easy with English language text, not easy with some languages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# example documents\n",
+    "simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cab  call  me  please  tonight  you\n",
+       "0    0     1   0       0        1    1\n",
+       "1    1     1   1       0        0    0\n",
+       "2    0     1   1       2        0    0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Term Frequency\n",
+    "vect = CountVectorizer()\n",
+    "tf = pd.DataFrame(vect.fit_transform(simple_train).toarray(), columns=vect.get_feature_names())\n",
+    "tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cab  call  me  please  tonight  you\n",
+       "0    1     1   0       1        0    0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transforming a new sentence, what do you notice?\n",
+    "pd.DataFrame(vect.transform(['please call yourself a cab']).toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# use CountVectorizer to create document-term matrices from X_train and X_test\n",
+    "vect = CountVectorizer()\n",
+    "X_train_dtm = vect.fit_transform(X_train)\n",
+    "X_test_dtm = vect.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3064, 16825)\n",
+      "(1022, 16825)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rows are documents, columns are terms (phrases) (aka \"tokens\" or \"features\")\n",
+    "print X_train_dtm.shape\n",
+    "print X_test_dtm.shape\n",
+    "# Why do they have the same number of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'00', u'000', u'00a', u'00am', u'00pm', u'01', u'02', u'03', u'03342', u'04', u'05', u'06', u'07', u'09', u'0buxoc0crqjpvkezo3bqog', u'0l', u'10', u'100', u'1000', u'1000x', u'1001', u'100th', u'101', u'102', u'105', u'1070', u'108', u'10am', u'10ish', u'10min', u'10mins', u'10minutes', u'10pm', u'10th', u'10x', u'11', u'110', u'1100', u'111', u'111th', u'112', u'115th', u'118', u'11a', u'11am', u'11p', u'11pm', u'12', u'120', u'128i']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# first 50 features\n",
+    "print vect.get_feature_names()[:50]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'yyyyy', u'z11', u'za', u'zabba', u'zach', u'zam', u'zanella', u'zankou', u'zappos', u'zatsiki', u'zen', u'zero', u'zest', u'zexperience', u'zha', u'zhou', u'zia', u'zihuatenejo', u'zilch', u'zin', u'zinburger', u'zinburgergeist', u'zinc', u'zinfandel', u'zing', u'zip', u'zipcar', u'zipper', u'zippers', u'zipps', u'ziti', u'zoe', u'zombi', u'zombies', u'zone', u'zones', u'zoning', u'zoo', u'zoyo', u'zucca', u'zucchini', u'zuchinni', u'zumba', u'zupa', u'zuzu', u'zwiebel', u'zzed', u'\\xe9clairs', u'\\xe9cole', u'\\xe9m']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# last 50 features\n",
+    "print vect.get_feature_names()[-50:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
+       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
+       "        strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show vectorizer options\n",
+    "vect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[CountVectorizer documentation](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **lowercase:** boolean, True by default\n",
+    "- Convert all characters to lowercase before tokenizing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE: create a coun vectorizer that doesn't lowercase the words\n",
+    "# fit transform X_train and see how many features there are\n",
+    "# Hint there should be over 20k\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3064, 20838)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "vect = CountVectorizer(lowercase=False)\n",
+    "X_train_dtm = vect.fit_transform(X_train)\n",
+    "X_train_dtm.shape # has more features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **ngram_range:** tuple (min_n, max_n)\n",
+    "- The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3064, 169847)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# include 1-grams and 2-grams\n",
+    "vect = CountVectorizer(ngram_range=(1, 2))\n",
+    "X_train_dtm = vect.fit_transform(X_train)\n",
+    "X_train_dtm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'zone out', u'zone when', u'zones', u'zones dolls', u'zoning', u'zoning issues', u'zoo', u'zoo and', u'zoo is', u'zoo not', u'zoo the', u'zoo ve', u'zoyo', u'zoyo for', u'zucca', u'zucca appetizer', u'zucchini', u'zucchini and', u'zucchini bread', u'zucchini broccoli', u'zucchini carrots', u'zucchini fries', u'zucchini pieces', u'zucchini strips', u'zucchini veal', u'zucchini very', u'zucchini with', u'zuchinni', u'zuchinni again', u'zuchinni the', u'zumba', u'zumba class', u'zumba or', u'zumba yogalates', u'zupa', u'zupa flavors', u'zuzu', u'zuzu in', u'zuzu is', u'zuzu the', u'zwiebel', u'zwiebel kr\\xe4uter', u'zzed', u'zzed in', u'\\xe9clairs', u'\\xe9clairs napoleons', u'\\xe9cole', u'\\xe9cole len\\xf4tre', u'\\xe9m', u'\\xe9m all']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# last 50 features\n",
+    "print vect.get_feature_names()[-50:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Predicting the star rating:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.925636007828\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use default options for CountVectorizer\n",
+    "vect = CountVectorizer()\n",
+    "\n",
+    "# create document-term matrices :: only single words\n",
+    "X_train_dtm = vect.fit_transform(X_train)\n",
+    "X_test_dtm = vect.transform(X_test)\n",
+    "\n",
+    "# use logistic regression with document feature matrix, NOT the text column\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train_dtm, y_train)\n",
+    "y_pred_class = logreg.predict(X_test_dtm)\n",
+    "\n",
+    "# calculate accuracy\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.81996086105675148"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate null accuracy, which is the accuracy of our null model (just guessing the most common thing)\n",
+    "y_test_binary = np.where(y_test==5, 1, 0)\n",
+    "max(y_test_binary.mean(), 1 - y_test_binary.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE define a function, tokenize_test,  that does five things:\n",
+    "\n",
+    "# Has a single input, vect, that is a countvectorizer\n",
+    "# instantiates a logistic regression\n",
+    "# fit_transforms X using the vectorizer\n",
+    "# print the number of features (phrases)\n",
+    "# prints the output of a 5 fold cross validation using accuracy as our metric\n",
+    "\n",
+    "\n",
+    "# eg.\n",
+    "\n",
+    "# vect = CountVectorizer(ngram_range=(1, 2))\n",
+    "# tokenize_test(vect)\n",
+    "\n",
+    "# Should output:\n",
+    "# Features:  209471\n",
+    "# Accuracy:  0.933431652596\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# ANSWER\n",
+    "def tokenize_test(vect):\n",
+    "    logreg = LogisticRegression()\n",
+    "    X_dtm = vect.fit_transform(X)\n",
+    "    print 'Features: ', X_dtm.shape[1]\n",
+    "    print 'Accuracy: ', cross_val_score(logreg, X_dtm, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  209471\n",
+      "Accuracy:  0.933431652596\n"
+     ]
+    }
+   ],
+   "source": [
+    "# include 1-grams and 2-grams\n",
+    "vect = CountVectorizer(ngram_range=(1, 2))\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 3: Stopword Removal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **What:** Remove common words that will likely appear in any text\n",
+    "- **Why:** They don't tell you much about your text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
+       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 2), preprocessor=None, stop_words=None,\n",
+       "        strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show vectorizer options\n",
+    "vect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **stop_words:** string {'english'}, list, or None (default)\n",
+    "- If 'english', a built-in stop word list for English is used.\n",
+    "- If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens.\n",
+    "- If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  191339\n",
+      "Accuracy:  0.926581494928\n"
+     ]
+    }
+   ],
+   "source": [
+    "# remove English stop words\n",
+    "vect = CountVectorizer(stop_words='english', ngram_range=(1, 2))\n",
+    "tokenize_test(vect)\n",
+    "# made predictions worse! Why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "frozenset(['all', 'six', 'less', 'being', 'indeed', 'over', 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', 'fify', 'where', 'mill', 'only', 'find', 'before', 'one', 'whose', 'system', 'how', 'somewhere', 'with', 'thick', 'show', 'had', 'enough', 'should', 'to', 'must', 'whom', 'seeming', 'under', 'ours', 'has', 'might', 'thereafter', 'latterly', 'do', 'them', 'his', 'around', 'than', 'get', 'very', 'de', 'none', 'cannot', 'every', 'whether', 'they', 'front', 'during', 'thus', 'now', 'him', 'nor', 'name', 'several', 'hereafter', 'always', 'who', 'cry', 'whither', 'this', 'someone', 'either', 'each', 'become', 'thereupon', 'sometime', 'side', 'two', 'therein', 'twelve', 'because', 'often', 'ten', 'our', 'eg', 'some', 'back', 'up', 'go', 'namely', 'towards', 'are', 'further', 'beyond', 'ourselves', 'yet', 'out', 'even', 'will', 'what', 'still', 'for', 'bottom', 'mine', 'since', 'please', 'forty', 'per', 'its', 'everything', 'behind', 'un', 'above', 'between', 'it', 'neither', 'seemed', 'ever', 'across', 'she', 'somehow', 'be', 'we', 'full', 'never', 'sixty', 'however', 'here', 'otherwise', 'were', 'whereupon', 'nowhere', 'although', 'found', 'alone', 're', 'along', 'fifteen', 'by', 'both', 'about', 'last', 'would', 'anything', 'via', 'many', 'could', 'thence', 'put', 'against', 'keep', 'etc', 'amount', 'became', 'ltd', 'hence', 'onto', 'or', 'con', 'among', 'already', 'co', 'afterwards', 'formerly', 'within', 'seems', 'into', 'others', 'while', 'whatever', 'except', 'down', 'hers', 'everyone', 'done', 'least', 'another', 'whoever', 'moreover', 'couldnt', 'throughout', 'anyhow', 'yourself', 'three', 'from', 'her', 'few', 'together', 'top', 'there', 'due', 'been', 'next', 'anyone', 'eleven', 'much', 'call', 'therefore', 'interest', 'then', 'thru', 'themselves', 'hundred', 'was', 'sincere', 'empty', 'more', 'himself', 'elsewhere', 'mostly', 'on', 'fire', 'am', 'becoming', 'hereby', 'amongst', 'else', 'part', 'everywhere', 'too', 'herself', 'former', 'those', 'he', 'me', 'myself', 'made', 'twenty', 'these', 'bill', 'cant', 'us', 'until', 'besides', 'nevertheless', 'below', 'anywhere', 'nine', 'can', 'of', 'your', 'toward', 'my', 'something', 'and', 'whereafter', 'whenever', 'give', 'almost', 'wherever', 'is', 'describe', 'beforehand', 'herein', 'an', 'as', 'itself', 'at', 'have', 'in', 'seem', 'whence', 'ie', 'any', 'fill', 'again', 'hasnt', 'inc', 'thereby', 'thin', 'no', 'perhaps', 'latter', 'meanwhile', 'when', 'detail', 'same', 'wherein', 'beside', 'also', 'that', 'other', 'take', 'which', 'becomes', 'you', 'if', 'nobody', 'see', 'though', 'may', 'after', 'upon', 'most', 'hereupon', 'eight', 'but', 'serious', 'nothing', 'such', 'why', 'a', 'off', 'whereby', 'third', 'i', 'whole', 'noone', 'sometimes', 'well', 'amoungst', 'yours', 'their', 'rather', 'without', 'so', 'five', 'the', 'first', 'whereas', 'once'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set of stop words\n",
+    "print vect.get_stop_words()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 4: Other CountVectorizer Options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **max_features:** int or None, default=None\n",
+    "- If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  100\n",
+      "Accuracy:  0.888893346555\n"
+     ]
+    }
+   ],
+   "source": [
+    "# remove English stop words and only keep 100 features, MUCH FASTER\n",
+    "vect = CountVectorizer(stop_words='english', max_features=100)\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'amazing', u'area', u'asked', u'atmosphere', u'awesome', u'bad', u'bar', u'best', u'better', u'big', u'came', u'cheese', u'chicken', u'coffee', u'come', u'day', u'definitely', u'delicious', u'did', u'didn', u'dinner', u'don', u'eat', u'excellent', u'experience', u'favorite', u'feel', u'food', u'free', u'fresh', u'friendly', u'friends', u'going', u'good', u'got', u'great', u'happy', u'home', u'hot', u'hour', u'just', u'know', u'like', u'little', u'll', u'location', u'long', u'looking', u'lot', u'love', u'lunch', u'make', u'meal', u'menu', u'minutes', u'need', u'new', u'nice', u'night', u'order', u'ordered', u'people', u'perfect', u'phoenix', u'pizza', u'place', u'pretty', u'price', u'prices', u'really', u'recommend', u'restaurant', u'right', u'said', u'salad', u'sauce', u'say', u'service', u'staff', u'store', u'sure', u'table', u'thing', u'things', u'think', u'time', u'times', u'told', u'took', u'tried', u'try', u've', u'wait', u'want', u'way', u'went', u'wine', u'work', u'worth', u'years']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# all 100 features\n",
+    "print vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  100000\n",
+      "Accuracy:  0.93612563031\n"
+     ]
+    }
+   ],
+   "source": [
+    "# include 1-grams and 2-grams, and limit the number of features\n",
+    "vect = CountVectorizer(ngram_range=(1, 2), max_features=100000)\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **min_df:** float in range [0.0, 1.0] or int, default=1\n",
+    "- When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  32700\n",
+      "Accuracy:  0.93783741955\n"
+     ]
+    }
+   ],
+   "source": [
+    "# include 1-grams and 2-grams, and only include terms that appear at least 3 times\n",
+    "vect = CountVectorizer(ngram_range=(1, 2), min_df=3)\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 5: Introduction to TextBlob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TextBlob: \"Simplified Text Processing\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My wife took me here on my birthday for breakfast and it was excellent.  The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure.  Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning.  It looked like the place fills up pretty quickly so the earlier you get here the better.\r\n",
+      "\r\n",
+      "Do yourself a favor and get their Bloody Mary.  It was phenomenal and simply the best I've ever had.  I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it.  It was amazing.\r\n",
+      "\r\n",
+      "While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious.  It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete.  It was the best \"toast\" I've ever had.\r\n",
+      "\r\n",
+      "Anyway, I can't wait to go back!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print the first review\n",
+    "print yelp_best_worst.text[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# save it as a TextBlob object\n",
+    "review = TextBlob(yelp_best_worst.text[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WordList([u'My', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'was', u'excellent', u'The', u'weather', u'was', u'perfect', u'which', u'made', u'sitting', u'outside', u'overlooking', u'their', u'grounds', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'was', u'excellent', u'and', u'our', u'food', u'arrived', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'looked', u'like', u'the', u'place', u'fills', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'was', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'had', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredients', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'was', u'amazing', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'looks', u'excellent', u'I', u'had', u'the', u'white', u'truffle', u'scrambled', u'eggs', u'vegetable', u'skillet', u'and', u'it', u'was', u'tasty', u'and', u'delicious', u'It', u'came', u'with', u'2', u'pieces', u'of', u'their', u'griddled', u'bread', u'with', u'was', u'amazing', u'and', u'it', u'absolutely', u'made', u'the', u'meal', u'complete', u'It', u'was', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'had', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back'])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list the words\n",
+    "review.words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Sentence(\"My wife took me here on my birthday for breakfast and it was excellent.\"),\n",
+       " Sentence(\"The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure.\"),\n",
+       " Sentence(\"Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning.\"),\n",
+       " Sentence(\"It looked like the place fills up pretty quickly so the earlier you get here the better.\"),\n",
+       " Sentence(\"Do yourself a favor and get their Bloody Mary.\"),\n",
+       " Sentence(\"It was phenomenal and simply the best I've ever had.\"),\n",
+       " Sentence(\"I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it.\"),\n",
+       " Sentence(\"It was amazing.\"),\n",
+       " Sentence(\"While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious.\"),\n",
+       " Sentence(\"It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete.\"),\n",
+       " Sentence(\"It was the best \"toast\" I've ever had.\"),\n",
+       " Sentence(\"Anyway, I can't wait to go back!\")]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list the sentences\n",
+    "review.sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TextBlob(\"my wife took me here on my birthday for breakfast and it was excellent.  the weather was perfect which made sitting outside overlooking their grounds an absolute pleasure.  our waitress was excellent and our food arrived quickly on the semi-busy saturday morning.  it looked like the place fills up pretty quickly so the earlier you get here the better.\n",
+       "\n",
+       "do yourself a favor and get their bloody mary.  it was phenomenal and simply the best i've ever had.  i'm pretty sure they only use ingredients from their garden and blend them fresh when you order it.  it was amazing.\n",
+       "\n",
+       "while everything on the menu looks excellent, i had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious.  it came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete.  it was the best \"toast\" i've ever had.\n",
+       "\n",
+       "anyway, i can't wait to go back!\")"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# some string methods are available\n",
+    "review.lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 6: Stemming and Lemmatization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Stemming:**\n",
+    "\n",
+    "- **What:** Reduce a word to its base/stem/root form\n",
+    "- **Why:** Often makes sense to treat related words the same way\n",
+    "- **Notes:**\n",
+    "    - Uses a \"simple\" and fast rule-based approach\n",
+    "    - Stemmed words are usually not shown to users (used for analysis/indexing)\n",
+    "    - Some search engines treat words with the same stem as synonyms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'my', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'was', u'excel', u'the', u'weather', u'was', u'perfect', u'which', u'made', u'sit', u'outsid', u'overlook', u'their', u'ground', u'an', u'absolut', u'pleasur', u'our', u'waitress', u'was', u'excel', u'and', u'our', u'food', u'arriv', u'quick', u'on', u'the', u'semi-busi', u'saturday', u'morn', u'it', u'look', u'like', u'the', u'place', u'fill', u'up', u'pretti', u'quick', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'bloodi', u'mari', u'it', u'was', u'phenomen', u'and', u'simpli', u'the', u'best', u'i', u've', u'ever', u'had', u'i', u\"'m\", u'pretti', u'sure', u'they', u'onli', u'use', u'ingredi', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'it', u'was', u'amaz', u'while', u'everyth', u'on', u'the', u'menu', u'look', u'excel', u'i', u'had', u'the', u'white', u'truffl', u'scrambl', u'egg', u'veget', u'skillet', u'and', u'it', u'was', u'tasti', u'and', u'delici', u'it', u'came', u'with', u'2', u'piec', u'of', u'their', u'griddl', u'bread', u'with', u'was', u'amaz', u'and', u'it', u'absolut', u'made', u'the', u'meal', u'complet', u'it', u'was', u'the', u'best', u'toast', u'i', u've', u'ever', u'had', u'anyway', u'i', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize stemmer\n",
+    "stemmer = SnowballStemmer('english')\n",
+    "\n",
+    "# stem each word\n",
+    "print [stemmer.stem(word) for word in review.words]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Lemmatization**\n",
+    "\n",
+    "- **What:** Derive the canonical form ('lemma') of a word\n",
+    "- **Why:** Can be better than stemming\n",
+    "- **Notes:** Uses a dictionary-based approach (slower than stemming)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "word = Word('wolves')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'wolv'"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stemmer.stem(word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'My', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'wa', u'excellent', u'The', u'weather', u'wa', u'perfect', u'which', u'made', u'sitting', u'outside', u'overlooking', u'their', u'ground', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'wa', u'excellent', u'and', u'our', u'food', u'arrived', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'looked', u'like', u'the', u'place', u'fill', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'wa', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'had', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredient', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'wa', u'amazing', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'look', u'excellent', u'I', u'had', u'the', u'white', u'truffle', u'scrambled', u'egg', u'vegetable', u'skillet', u'and', u'it', u'wa', u'tasty', u'and', u'delicious', u'It', u'came', u'with', u'2', u'piece', u'of', u'their', u'griddled', u'bread', u'with', u'wa', u'amazing', u'and', u'it', u'absolutely', u'made', u'the', u'meal', u'complete', u'It', u'wa', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'had', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# assume every word is a noun\n",
+    "print [word.lemmatize() for word in review.words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[u'My', u'wife', u'take', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'be', u'excellent', u'The', u'weather', u'be', u'perfect', u'which', u'make', u'sit', u'outside', u'overlook', u'their', u'ground', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'be', u'excellent', u'and', u'our', u'food', u'arrive', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'look', u'like', u'the', u'place', u'fill', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'be', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'have', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredients', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'be', u'amaze', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'look', u'excellent', u'I', u'have', u'the', u'white', u'truffle', u'scramble', u'egg', u'vegetable', u'skillet', u'and', u'it', u'be', u'tasty', u'and', u'delicious', u'It', u'come', u'with', u'2', u'piece', u'of', u'their', u'griddle', u'bread', u'with', u'be', u'amaze', u'and', u'it', u'absolutely', u'make', u'the', u'meal', u'complete', u'It', u'be', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'have', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# assume every word is a verb\n",
+    "print [word.lemmatize(pos='v') for word in review.words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define a function that accepts text and returns a list of lemmas\n",
+    "def word_tokenize(text, how='lemma'):\n",
+    "    words = TextBlob(text).words\n",
+    "    if how == 'lemma':\n",
+    "        return [word.lemmatize() for word in words]\n",
+    "    elif how == 'stem':\n",
+    "        return [stemmer.stem(word) for word in words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  23856\n",
+      "Accuracy:  0.932208859444\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use word_tokenize LEMMA as the feature extraction function (WARNING: SLOW!)\n",
+    "# this will lemmatize each word\n",
+    "vect = CountVectorizer(analyzer=word_tokenize(x, how='lemma'))\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  15237\n",
+      "Accuracy:  0.938818413169\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use word_tokenize STEM as the feature extraction function (WARNING: SLOW!)\n",
+    "# this will lemmatize each word\n",
+    "vect = CountVectorizer(analyzer=lambda x:word_tokenize(x, how='stem'))\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 7: Term Frequency-Inverse Document Frequency (TF-IDF)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **What:** Computes \"relative frequency\" that a word appears in a document compared to its frequency across all documents\n",
+    "- **Why:** More useful than \"term frequency\" for identifying \"important\" words in each document (high frequency in that document, low frequency in other documents)\n",
+    "- **Notes:** Used for search engine scoring, text summarization, document clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# example documents\n",
+    "simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cab  call  me  please  tonight  you\n",
+       "0    0     1   0       0        1    1\n",
+       "1    1     1   1       0        0    0\n",
+       "2    0     1   1       2        0    0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Term Frequency\n",
+    "vect = CountVectorizer()\n",
+    "tf = pd.DataFrame(vect.fit_transform(simple_train).toarray(), columns=vect.get_feature_names())\n",
+    "tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cab  call  me  please  tonight  you\n",
+       "0    1     3   2       1        1    1"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Document Frequency\n",
+    "vect = CountVectorizer(binary=True)\n",
+    "df = vect.fit_transform(simple_train).toarray().sum(axis=0)\n",
+    "pd.DataFrame(df.reshape(1, 6), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cab      call   me  please  tonight  you\n",
+       "0  0.0  0.333333  0.0     0.0      1.0  1.0\n",
+       "1  1.0  0.333333  0.5     0.0      0.0  0.0\n",
+       "2  0.0  0.333333  0.5     2.0      0.0  0.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Term Frequency-Inverse Document Frequency (simple version)\n",
+    "tf/df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.385372</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.652491</td>\n",
+       "      <td>0.652491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.720333</td>\n",
+       "      <td>0.425441</td>\n",
+       "      <td>0.547832</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.266075</td>\n",
+       "      <td>0.342620</td>\n",
+       "      <td>0.901008</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        cab      call        me    please   tonight       you\n",
+       "0  0.000000  0.385372  0.000000  0.000000  0.652491  0.652491\n",
+       "1  0.720333  0.425441  0.547832  0.000000  0.000000  0.000000\n",
+       "2  0.000000  0.266075  0.342620  0.901008  0.000000  0.000000"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TfidfVectorizer\n",
+    "vect = TfidfVectorizer()\n",
+    "pd.DataFrame(vect.fit_transform(simple_train).toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**More details:** [TF-IDF is about what matters](http://planspace.org/20150524-tfidf_is_about_what_matters/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 28881)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a document-term matrix using TF-IDF\n",
+    "vect = TfidfVectorizer(stop_words='english')\n",
+    "dtm = vect.fit_transform(yelp.text)\n",
+    "features = vect.get_feature_names()\n",
+    "dtm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  18884\n",
+      "Accuracy:  0.873473450843\n"
+     ]
+    }
+   ],
+   "source": [
+    "vect = TfidfVectorizer(stop_words='english')\n",
+    "tokenize_test(vect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 8: Using TF-IDF to Summarize a Yelp Review\n",
+    "\n",
+    "Reddit's autotldr uses the [SMMRY](http://smmry.com/about) algorithm, which is based on TF-IDF!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def summarize():\n",
+    "    \n",
+    "    # choose a random review that is at least 300 characters\n",
+    "    review_length = 0\n",
+    "    while review_length < 300:\n",
+    "        review_id = np.random.randint(0, len(yelp))\n",
+    "        review_text = yelp.text[review_id]\n",
+    "        review_length = len(review_text)\n",
+    "    \n",
+    "    # create a dictionary of words and their TF-IDF scores\n",
+    "    word_scores = {}\n",
+    "    for word in TextBlob(review_text).words:\n",
+    "        word = word.lower()\n",
+    "        if word in features:\n",
+    "            word_scores[word] = dtm[review_id, features.index(word)]\n",
+    "    \n",
+    "    # print words with the top 5 TF-IDF scores\n",
+    "    print 'TOP SCORING WORDS:'\n",
+    "    top_scores = sorted(word_scores.items(), key=lambda x: x[1], reverse=True)[:5]\n",
+    "    for word, score in top_scores:\n",
+    "        print word\n",
+    "    \n",
+    "    # print 5 random words\n",
+    "    print '\\n' + 'RANDOM WORDS:'\n",
+    "    random_words = np.random.choice(word_scores.keys(), size=5, replace=False)\n",
+    "    for word in random_words:\n",
+    "        print word\n",
+    "    \n",
+    "    # print the review\n",
+    "    print '\\n' + review_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOP SCORING WORDS:\n",
+      "mussels\n",
+      "rushed\n",
+      "seared\n",
+      "dish\n",
+      "table\n",
+      "\n",
+      "RANDOM WORDS:\n",
+      "men\n",
+      "seared\n",
+      "sushi\n",
+      "pm\n",
+      "thrilled\n",
+      "\n",
+      "I stopped in with a friend on a Saturday night after a friend had recommended it to me and was totally thrilled we did. We sat at a table for sushi and waited no longer than 10 minutes for a table - amazing for 8 pm on a Saturday in old towne.\r\n",
+      "\r\n",
+      "Unfortunately, they had sold out of the oysters, but the other dishes we'd ordered were fabulous - starting with a seared tuna dish, then softshell crab roll, rainbow roll, and the green mussels. The seared tuna was cooked and cut perfectly - just the right temperature with a peppery \"rub,\" the rolls were a decent size with thick cuts of fish and plenty of flavorful crab, and the mussels were a tad overcooked for my preference but the creamy texture and buttery flavor still made it a winner. \r\n",
+      "\r\n",
+      "The thing I appreciated most was actually the service - we weren't rushed or hassled, and the food came out a dish at a time, giving us a chance to eat and enjoy before getting our next dish. At other sushi joints, I am constantly feeling rushed while trying to fit all of the dishes on the table (and I'm not what you'd call a slow eater!). This was a happy medium for me. The server confirmed that we were ready for the bill instead of just dropping it off without asking - it's the little details that make a big difference to me.\r\n",
+      "\r\n",
+      "My only other con was creepy old men leering at us from the bar...but that's what you get in Scottsdale.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summarize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 9: Sentiment Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My wife took me here on my birthday for breakfast and it was excellent.  The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure.  Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning.  It looked like the place fills up pretty quickly so the earlier you get here the better.\r\n",
+      "\r\n",
+      "Do yourself a favor and get their Bloody Mary.  It was phenomenal and simply the best I've ever had.  I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it.  It was amazing.\r\n",
+      "\r\n",
+      "While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious.  It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete.  It was the best \"toast\" I've ever had.\r\n",
+      "\r\n",
+      "Anyway, I can't wait to go back!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print review"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.40246913580246907"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# polarity ranges from -1 (most negative) to 1 (most positive)\n",
+    "review.sentiment.polarity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "      <th>length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>My wife took me here on my birthday for breakf...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>895</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              business_id        date               review_id  stars  \\\n",
+       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
+       "\n",
+       "                                                text    type  \\\n",
+       "0  My wife took me here on my birthday for breakf...  review   \n",
+       "\n",
+       "                  user_id  cool  useful  funny  length  \n",
+       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0     895  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# understanding the apply method\n",
+    "yelp['length'] = yelp.text.apply(len)\n",
+    "yelp.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1186a3a50>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7OXRoBVNTUzYfkqRWNL3y8b+Aq4D/CQTwa8DnI+K1KaW9EXEVsAZ4D/A94D8AuyJiUUrp\nifpzXA+8GXgn8Ciwnaq5eMPQ1/kMcDZwKXAqcAPwMWBFw3oLs4jy1qMox/r167nmmmu6LqN3zK05\nM8tjbmVr1HyklL40cuiDEfEB4GJgL3Al8OGU0hcBIuI9wAHgHcBnI+J0YBVweUrp6/WYlcDeiLgw\npXRPRCwCLqNa9HJvPWYt8KWI+M2U0oO5k5WOF68S5TG35swsj7mVLftW24g4KSIuB+YDd0bEucAC\n4KuHx6SUHgXuBpbWhy6ganiGx+wD9g+NuRg4eLjxqN0GJOCi3Hql42nt2rVdl9BL5tacmeUxt7I1\nXnAaEa8C7gJOAx4D/lVKaV9ELKVqEA6MfMgBqqYEqpdSnqibkqONWQA8NHwypfRURPxoaIwkSeqp\nnCsfk8BrgAuB/wR8KiJeeVyrek7eAgxGHkuBnSPjdtfnRq0Gdowcm6jHTo0c3whsHjm2vx47Ocvn\nvn7k+XQ9dnQnvjFgtreDXs7czANuuOGGGc/379/PYDBgcnLmPLZu3cr69etnHJuenmYwGByxo+DY\n2Nisb2u9fPlydu6cOY/du3czGBw5j9WrV7Njx8x5TExMMBgMmJqaOY+NGzeyefPM74fzcB7Ow3k4\nj5nzGBsbYzAYsHTpUhYsWMBgMGDdunVHfMxces6bjEXEV4D7gC3AXwGvTSl9Z+j814B7U0rrIuKN\nVC+hnDl89SMivgdcl1L6/XoNyO+llF48dP5k4BDwKymlzx+ljoI3Gft14JOUWdsEsITx8XEWLy6t\ntnJNTk7yylcW1HP3hLk1Z2Z5zK2ZPr6r7UnAvJTSd4EHqe5QAaBeYHoRcGd9aBx4cmTM+cBCqpdy\nqP97RkS8buhrXEp1d83dx6Fe6TnbsGFD1yX0krk1Z2Z5zK1sTff5+F3gv1K9tvD3gXcDvwgsq4dc\nT3UHzH1Ut9p+GPg+8HmoFqBGxA7g2og4SLVm5CPAHSmle+oxkxGxC/h4fSfNqcBWYMw7XVSKbdu2\ndV1CL5lbc2aWx9zK1nTB6UuBPwBeBjwCfAdYllL6U4CU0paImE+1J8cZwO3Am4f2+ABYR7Xj1q1U\nm4x9mWqBwrB3UW0ydhvVJmO3Ut3GKxXB2/jymFtzZpbH3MrWdJ+P9z6LMZuATcc4/ziwtn4cbczD\n9H5DMUmSNJvjseZDkiTpWbP5kDKM3iKnZ8fcmjOzPOZWNpsPKcP09HTXJfSSuTVnZnnMrWzPeZ+P\nUrjPRy73+ZCk57s+7vMhSZL0rNl8SJKkVtl8SBlG36dBz465NWdmecytbDYfUoZVq1Z1XUIvmVtz\nZpbH3Mpm8yFl2LRpU9cl9JK5NWdmecytbDYfUgbvDMpjbs2ZWR5zK5vNhyRJapXNhyRJapXNh5Rh\nx44dXZfQS+bWnJnlMbey2XxIGSYm5nwDwBOSuTVnZnnMrWw2H1KG7du3d11CL5lbc2aWx9zKZvMh\nSZJaZfMhSZJaZfMhSZJaZfMhZRgMBl2X0Evm1pyZ5TG3stl8SBnWrFnTdQm9ZG7NmVkecyubzYeU\nYdmyZV2X0Evm1pyZ5TG3stl8SJKkVtl8SJKkVtl8SBl27tzZdQm9ZG7NmVkecyubzYeUYWxsrOsS\nesncmjOzPOZWNpsPKcMtt9zSdQm9ZG7NmVkecyubzYckSWqVzYckSWqVzYckSWqVzYeUYeXKlV2X\n0Evm1pyZ5TG3stl8SBncPTGPuTVnZnnMrWw2H1KGK664ousSesncmjOzPOZWNpsPSZLUKpsPSZLU\nKpsPKcOePXu6LqGXzK05M8tjbmWz+ZAybNmypesSesncmjOzPOZWNpsPKcPNN9/cdQm9ZG7NmVke\ncyubzYeUYf78+V2X0Evm1pyZ5TG3stl8SJKkVtl8SJKkVtl8SBnWr1/fdQm9ZG7NmVkecyubzYeU\nYeHChV2X0Evm1pyZ5TG3stl8SBnWrl3bdQm9ZG7NmVkecyubzYckSWqVzYckSWqVzYeUYXJysusS\nesncmjOzPOZWNpsPKcOGDRu6LqGXzK05M8tjbmWz+ZAybNu2resSesncmjOzPOZWNpsPKYO38eUx\nt+bMLI+5lc3mQ5IktcrmQ5IktapR8xERvx0R90TEoxFxICL+KCJ+fpZxV0fEAxExHRFfiYjzRs7P\ni4jtETEVEY9FxK0R8dKRMWdGxE0R8UhEHIyIT0TEC/KmKR1fmzdv7rqEXjK35swsj7mVremVjzcA\nW4GLgDcBPwPsjoi/d3hARFwFrAHeB1wI/BjYFRGnDn2e64G3Au8ELgHOAT438rU+AywCLq3HXgJ8\nrGG90pyYnp7uuoReMrfmzCyPuZUtUkr5HxxxFvAQcElKaU997AHgmpTSdfXz04EDwL9NKX22fv63\nwOUppT+qx5wP7AUuTindExGLgL8AlqSU7q3HXAZ8CXh5SunBWWpZDIzDOLA4e05z49eBT1JmbRPA\nEsbHx1m8uLTaJEltmJiYYMmSJVD9uzsx11/vua75OANIwI8AIuJcYAHw1cMDUkqPAncDS+tDFwCn\njIzZB+wfGnMxcPBw41G7rf5aFz3HmiVJUoeym4+ICKqXT/aklP6yPryAqkE4MDL8QH0O4Gzgibop\nOdqYBVRXVP6vlNJTVE3OAiRJUm89lysfHwX+CXD5carlOHkLMBh5LAV2jozbXZ8btRrYMXJsoh47\nNXJ8IzC6qGl/PXa2rX2vH3k+XY/dM3J8DFg5y8cvZ27mATfccMOM5/v372cwGByxRfHWrVtZv379\njGPT09MMBgP27Jk5j7GxMVauPHIey5cvZ+fOmfPYvXs3g8GR81i9ejU7dsycx8TEBIPBgKmpmfPY\nuHHjEYvM5moeU1NTJ8Q8oN3vx7e+9a0TYh5tfj+mpqZOiHlAu9+PqampE2IecPy/H2NjYwwGA5Yu\nXcqCBQsYDAasW7fuiI+ZUymlxg9gG/A3wMKR4+cCTwP/dOT414Dr6j+/EXgKOH1kzPeAK+s/rwR+\nOHL+ZOCnwNuPUtNiIMF4glTYY1Uqt7bxBKTx8fGkZ+9tb3tb1yX0krk1Z2Z5zK2Z8fHx+t8pFqeM\nvqDpo/GVj4jYBrwdeGNKaf9II/Nd4EGqO1QOjz+dap3GnfWhceDJkTHnAwuBu+pDdwFnRMTrhj79\npUBQrR+ROrVp06auS+glc2vOzPKYW9lOaTI4Ij4KXEF17f7HEXF2feqRlNKh+s/XAx+MiPuormZ8\nGPg+8HmoFqBGxA7g2og4CDwGfAS4I6V0Tz1mMiJ2AR+PiA8Ap1Ld4juWZrnTRWqbdwblMbfmzCyP\nuZWtUfMBvJ/qsszXRo6vBD4FkFLaEhHzqfbkOAO4HXhzSumJofHrqF56uRWYB3yZapHCsHdRvbxz\nG9VLObcCVzasV5IkFaZR85FSelYv06SUNgGbjnH+cWBt/TjamIeBFU3qkyRJ5fO9XaQMoyva9eyY\nW3NmlsfcymbzIWWYmJjzDQBPSObWnJnlMbey2XxIGbZv3951Cb1kbs2ZWR5zK5vNhyRJapXNhyRJ\napXNhyRJapXNh5Rhtvd10DMzt+bMLI+5lc3mQ8qwZs2arkvoJXNrzszymFvZbD6kDMuWLeu6hF4y\nt+bMLI+5lc3mQ5IktcrmQ5IktcrmQ8qwc+fOrkvoJXNrzszymFvZbD6kDGNjY12X0Evm1pyZ5TG3\nstl8SBluueWWrkvoJXNrzszymFvZbD4kSVKrbD4kSVKrbD4kSVKrbD6kDCtXruy6hF4yt+bMLI+5\nlc3mQ8rg7ol5zK05M8tjbmWz+ZAyXHHFFV2X0Evm1pyZ5TG3stl8SJKkVtl8SJKkVtl8SBn27NnT\ndQm9ZG7NmVkecyubzYeUYcuWLV2X0Evm1pyZ5TG3stl8SBluvvnmrkvoJXNrzszymFvZbD6kDPPn\nz++6hF4yt+bMLI+5lc3mQ5IktcrmQ5IktcrmQ8qwfv36rkvoJXNrzszymFvZbD6kDAsXLuy6hF4y\nt+bMLI+5lc3mQ8qwdu3arkvoJXNrzszymFvZbD4kSVKrbD4kSVKrbD6kDJOTk12X0Evm1pyZ5TG3\nstl8SBk2bNjQdQm9ZG7NmVkecyubzYeUYdu2bV2X0Evm1pyZ5TG3stl8SBm8jS+PuTVnZnnMrWw2\nH5IkqVWndF2AyrB3796uS5jVWWed5W8wknSCsfl43vsBEKxYsaLrQmZ12mnz2bdvb3ENyObNm7nq\nqqu6LqN3zK05M8tjbmWz+XjeexhIwI3Aoo5rGbWXQ4dWMDU1VVzzMT093XUJvWRuzZlZHnMrW6SU\nuq7huIiIxcA4jAOLuy5nxK8Dn6TM2m4CVlBmbRPAEsbHx1m8uLTaJOnEMTExwZIlSwCWpJQm5vrr\nueBUkiS1yuZDkiS1yuZDyjA1NdV1Cb1kbs2ZWR5zK5vNh5Rh1apVXZfQS+bWnJnlMbey2XxIGTZt\n2tR1Cb1kbs2ZWR5zK5vNh5TBu2/ymFtzZpbH3Mpm8yFJklpl8yFJklpl8yFl2LFjR9cl9JK5NWdm\necytbI2bj4h4Q0R8ISLuj4inI2Iwy5irI+KBiJiOiK9ExHkj5+dFxPaImIqIxyLi1oh46ciYMyPi\npoh4JCIORsQnIuIFzacoHX8TE3O+AeAJydyaM7M85la2nCsfLwC+BfwG1ZuCzBARVwFrgPcBFwI/\nBnZFxKlDw64H3gq8E7gEOAf43Min+gzVm41cWo+9BPhYRr3Scbd9+/auS+glc2vOzPKYW9kav7Fc\nSunLwJcBIiJmGXIl8OGU0hfrMe8BDgDvAD4bEacDq4DLU0pfr8esBPZGxIUppXsiYhFwGdUe8/fW\nY9YCX4qI30wpPdi0bkmSVIbjuuYjIs4FFgBfPXwspfQocDewtD50AVXTMzxmH7B/aMzFwMHDjUft\nNqorLRcdz5olSVK7jveC0wVUDcKBkeMH6nMAZwNP1E3J0cYsAB4aPplSegr40dAYSZLUQyfg3S5v\nAQYjj6XAzpFxu+tzo1YDo6ukJ+qxo+8VsBHYPHJsfz12cpbPff3I8+l67J6R42PAylk+fjlzMw+A\nG0aeH20eW4H1I8fmch6wc+fMsRMTEwwGgyPeu2Hjxo1s3jzz+7F//34GgwGTkzPnsXXrVtavnzmP\n6elpBoMBe/bMnMfY2BgrV86cx2AwYPny5UfUtnv3bgaDI+exevXqI1bflzAPoNV5vOlNbzoh5tHm\n92MwGJwQ84B2vx+DweCEmAcc/+/H2NgYg8GApUuXsmDBAgaDAevWrTviY+ZUSin7ATwNDIaen1sf\n+6cj474GXFf/+Y3AU8DpI2O+B1xZ/3kl8MOR8ycDPwXefpRaFgMJxhOkwh6rUrm13VhwbeMJSOPj\n46k0u3bt6rqEXjK35swsj7k1Mz4+Xv9bwOI0y7+xx/txXK98pJS+CzxIdYcKAPUC04uAO+tD48CT\nI2POBxYCd9WH7gLOiIjXDX36S4GgWj8idWrZsmVdl9BL5tacmeUxt7I1vtul3mvjPKpGAOAfRsRr\ngB+llP4X1WsLH4yI+6iuZnwY+D7weagWoEbEDuDaiDgIPAZ8BLgjpXRPPWYyInYBH4+IDwCnUl3v\nH0ve6SJJUq81bj6o7lb5b1SXZxLwH+vjfwCsSiltiYj5VHtynAHcDrw5pfTE0OdYR/XSy63APKpb\nd1ePfJ13Aduo7nJ5uh57ZUa9kiSpII1fdkkpfT2ldFJK6eSRx6qhMZtSSueklOanlC5LKd038jke\nTymtTSmdlVL6+ymlf51SGr275eGU0oqU0otSSmemlP5dSmk6f6rS8TO6AE3Pjrk1Z2Z5zK1sJ+Dd\nLtLcGxsb67qEXjK35swsj7mVzeZDynDLLbd0XUIvmVtzZpbH3Mpm8yFJklpl8yFJklpl8yFJklpl\n8yFlmG37Yj0zc2vOzPKYW9lsPqQM7p6Yx9yaM7M85lY2mw8pwxVXXNF1Cb1kbs2ZWR5zK5vNhyRJ\napXNhyRJapXNh5Rhz549XZfQS+bWnJnlMbey2XxIGbZs2dJ1Cb1kbs2ZWR5zK5vNh5Th5ptv7rqE\nXjK35swsj7mVzeZDyjB//vyuS+glc2vOzPKYW9lsPiRJUqtsPiRJUqtsPqQM69ev77qEXjK35sws\nj7mVzeYD47e7AAAKH0lEQVRDyrBw4cKuS+glc2vOzPKYW9lsPqQMa9eu7bqEXjK35swsj7mVzeZD\nkiS1yuZDkiS1yuZDyjA5Odl1Cb1kbs2ZWR5zK9spXRcgPZO9e/d2XcIR1q1bx6c//WkXtTW0YcMG\nvvCFL3RdRq+YWR5zK5vNhwr2A+AkVqxY0XUhszr//EXs27fXBqSBbdu2dV1C75hZHnMrm82HCvYw\n8DRwI7Co41pG7eXQoRVMTU3ZfDRgVs2ZWR5zK5vNh3pgEbC46yIkSceJC04lSVKrbD6kLDd0XUAv\nbd68uesSesfM8phb2Ww+pCyHui6gl6anp7suoXfMLI+5lc3mQ8ry/q4L6KXf+Z3f6bqE3jGzPOZW\nNpsPSZLUKpsPSZLUKpsPKcvBrgvopampqa5L6B0zy2NuZbP5kLJc3XUBvbRq1aquS+gdM8tjbmWz\n+ZCyvK/rAnpp06ZNXZfQO2aWx9zKZvMhZSltu/d+WLzYnWqbMrM85lY2mw9JktQqmw9JktQqmw8p\ny86uC+ilHTt2dF1C75hZHnMrm82HlGWy6wJ6aWJiousSesfM8phb2Ww+pCy/1XUBvbR9+/auS+gd\nM8tjbmWz+ZAkSa06pesCpD7bu3dv1yXM6qyzzmLhwoVdlyFJs7L5kLL8ADiJFStWdF3IrE47bT77\n9u21AZFUJJsPKcuHgKeBGylvw7G9HDq0gqmpqeKaj8FgwBe+8IWuy+gVM8tjbmWz+ZCyLAPupWo8\n3Enx2VqzZk3XJfSOmeUxt7K54FTK8uquC+ilZcuWdV1C75hZHnMrm82HJElqlc2HJElqlc2HlOWb\nXRfQSzt3ui19U2aWx9zK5oJTKcsfd13AMypxD5IPfehDLF68uLi7cEq2efNm3vGOd3RdRu+YW9mK\nbz4iYjXwm8AC4NvA2pTSf++2Kun0rgs4hrL3IDn//EXuQdLAS17ykq5L6CVzK1vRzUdELAf+I/A+\n4B5gHbArIn4+pTTVaXFSsR6m3D1I3suhQ/dy++23s2hRabW5M6zUlqKbD6pm42MppU8BRMT7gbcC\nq4AtXRYmla/EPUh+hpKvyrgzrNSOYpuPiPgZYAnwu4ePpZRSRNwGLO2sMEnPwU8p96pMtTNsaVdl\nHnnkESYmJnj88ceZN29e1+UcVWn1Hc4NvKJVomKbD+As4GTgwMjxA8D5s4w/rfrPH1LenQiT9X//\nBChtEeAd9X+trZn/Uf+3xNr6kNt3O61idvcCUeRVmSVLllDdnPh016UcQ3n1VbnBqaeexh/+4a28\n7GUv67iicg0tUD+tja8XKaU2vk5jEfEy4H5gaUrp7qHjm4FLUkpLR8a/C7ip3SolSTqhvDul9Jm5\n/iIlX/mYAp4Czh45fjbw4CzjdwHvBr4HHJrTyiRJOrGcBryC6t/SOVfslQ+AiPgGcHdK6cr6eQD7\ngY+klK7ptDhJkpSl5CsfANcCN0TEOH93q+184IYui5IkSfmKbj5SSp+NiLOAq6lebvkWcFlK6W+7\nrUySJOUq+mUXSZJ04vGN5SRJUqtsPiRJUqtOiOYjIlZHxHcj4icR8Y2IeH3XNbUlIt4QEV+IiPsj\n4umIGMwy5uqIeCAipiPiKxFx3sj5eRGxPSKmIuKxiLg1Il46MubMiLgpIh6JiIMR8YmIeMFcz28u\nRMRvR8Q9EfFoRByIiD+KiJ+fZZy5DYmI90fEt+u5PBIRd0bEvxgZY2bHEBG/Vf9/eu3IcXMbEhEb\n65yGH385MsbMRkTEORHx6XrO0/X/r4tHxpSRW0qp1w9gOdW+Hu8BXgl8DPgRcFbXtbU0/39BtSD3\n7VT7ogxGzl9V5/EvgVcBO4G/Ak4dGvOfqPZH+UXgdcCdwO0jn+e/AhPABcA/o9qq8sau55+Z2Z8A\n/4Zqf+9XA1+s5//3zO2Yub21/nn7R8B5wH8AHgcWmdmzyu/1wF9TbaV6rT9rx8xqI/Ad4CXAS+vH\nz5rZMTM7g2rr4E9QvTXJPwDeBJxbYm6dB3YcAv8G8PtDzwP4PrCh69o6yOJpjmw+HgDWDT0/HfgJ\n8KtDzx8H/tXQmPPrz3Vh/XxR/fx1Q2MuA54EFnQ97+OQ21n1/P65uTXO7ofASjN7xpxeCOwDfhn4\nb8xsPsztyLw2AhPHOG9mR2by/wNff4YxxeTW65dd4u/efO6rh4+lKgnffA6IiHOBBczM51Hgbv4u\nnwuobrkeHrOPajO3w2MuBg6mlO4d+vS3AQm4aK7qb9EZVHP5EZjbsxERJ0XE5VT77txpZs9oO/DH\nKaU/HT5obsf0j6N6OfmvIuLGiPg5MLNjeBvwzYj4bP1y8kREvPfwydJy63XzwbHffG5B++UUZwHV\nD8Sx8jkbeKL+ITzamAXAQ8MnU0pPUf1j3eucIyKA64E9KaXDrymb21FExKsi4jGq344+SvUb0j7M\n7KjqJu21wG/PctrcZvcN4NeofqN+P3Au8Gf1ugIzm90/BD5AdYVtGdXLJx+JiH9Tny8qt6I3GZNa\n8FHgnwC/0HUhPTEJvAZ4EfArwKci4pJuSypXRLycqrl9U0rpp13X0xcppeH3F/nziLgH+BvgV/m7\ntwnXTCcB96SUPlQ//3ZEvIqqeft0d2XNru9XPpq++dzzzYNUa2COlc+DwKkRcfozjBld7Xwy8LP0\nOOeI2Aa8BfillNIPhk6Z21GklJ5MKf11SunelNK/B74NXImZHc0SqkWTExHx04j4KdVCvisj4gmq\n3yjN7RmklB6hWtR4Hv6sHc0PgL0jx/YCC+s/F5Vbr5uP+jeJceDSw8fqy+iXUq3QfV5LKX2X6odh\nOJ/TqV6XO5zPONVCoeEx51P9wN5VH7oLOCMiXjf06S+l+kG+e67qn0t14/F24I0ppf3D58ytkZOA\neWZ2VLdR3VH1WqorRq8BvgncCLwmpfTXmNsziogXUjUeD/izdlR3UC0OHXY+1RWj8v5e63qF7nFY\n4furwDQzb7X9IfCSrmtraf4voPoL7bVUK5D/3/r5z9XnN9R5vI3qL8GdwP9k5q1VH6W6ReuXqH5T\nu4Mjb636E6q/NF9P9RLFPuDTXc8/M7OPAgeBN1B19Icfpw2NMbcjc/vdOrN/QHWb3v9H9RfVL5tZ\noxxH73YxtyMzuga4pP5Z+2fAV6iuEr3YzI6a2QVUa7F+m+p2+HcBjwGXl/iz1nlgxyn036C6L/kn\nVF3ZBV3X1OLcf5Gq6Xhq5PHJoTGbqG6xmgZ2AeeNfI55wFaql7EeA/4L8NKRMWdQ/bb2CNU/3B8H\n5nc9/8zMZsvrKeA9I+PMbeZcPkG1T8VPqH6D2k3deJhZoxz/lKHmw9xmzWiMasuEn1DdafEZhvar\nMLOj5vYWqv1RpoG/AFbNMqaI3HxjOUmS1Kper/mQJEn9Y/MhSZJaZfMhSZJaZfMhSZJaZfMhSZJa\nZfMhSZJaZfMhSZJaZfMhSZJaZfMhSZJaZfMhSZJaZfMhSZJa9X8AfX/gPI7Z3T0AAAAASUVORK5C\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11563a610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Show a histogram of yelp review lengths\n",
+    "\n",
+    "yelp['length'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define a function that accepts text and returns the polarity\n",
+    "def detect_sentiment(text):\n",
+    "    return TextBlob(text).sentiment.polarity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create a new DataFrame column for sentiment (WARNING: SLOW!)\n",
+    "yelp['sentiment'] = yelp.text.apply(detect_sentiment)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11b4a6c90>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CQ4OKDcmeP/0JJkxI/nXOPjvZ51+zxn/BoWLDg8LLKBs2bNBlFBGRjOlq0Vi8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\n2PCgubmZhoaG7tudd96Zt63LKuk699x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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114965810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# EXERCISE: make a box plot of sentiment grouped by stars and a histogram of yelp sentiment\n",
+    "# You should have five boxplots in the same graph for sentiment for the first graph\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ANSWER\n",
+    "\n",
+    "\n",
+    "yelp.boxplot(column='sentiment', by='stars')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x118f84ad0>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7BTNrwGPUmvG7S6yyfHucWbV5jFozLjLMzMysEC4yzMzMrBAuMqyyZn7GgplV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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11836f350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yelp['sentiment'].hist() # Mostly positive!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "254    Our server Gary was awesome. Food was amazing....\n",
+       "347    3 syllables for this place. \\r\\nA-MAZ-ING!\\r\\n...\n",
+       "420                                    LOVE the food!!!!\n",
+       "459    Love it!!! Wish we still lived in Arizona as C...\n",
+       "679                                     Excellent burger\n",
+       "Name: text, dtype: object"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# EXERCISE Show the reviews with most positive sentiment (a score of 1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Answer \n",
+    "\n",
+    "yelp[yelp.sentiment == 1].text.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "773     This was absolutely horrible. I got the suprem...\n",
+       "1517                  Nasty workers and over priced trash\n",
+       "3266    Absolutely awful... these guys have NO idea wh...\n",
+       "4766                                       Very bad food!\n",
+       "5812        I wouldn't send my worst enemy to this place.\n",
+       "Name: text, dtype: object"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# reviews with most negative sentiment\n",
+    "yelp[yelp.sentiment == -1].text.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# widen the column display\n",
+    "pd.set_option('max_colwidth', 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "      <th>length</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>106JT5p8e8Chtd0CZpcARw</td>\n",
+       "      <td>2009-08-06</td>\n",
+       "      <td>KowGVoP_gygzdSu6Mt3zKQ</td>\n",
+       "      <td>5</td>\n",
+       "      <td>RIP AZ Coffee Connection.  :(  I stopped by two days ago unaware that they had closed.  I am severely bummed.  This place is irreplaceable!  Damn you, Starbucks and McDonalds!</td>\n",
+       "      <td>review</td>\n",
+       "      <td>jKeaOrPyJ-dI9SNeVqrbww</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>175</td>\n",
+       "      <td>-0.302083</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                business_id        date               review_id  stars  \\\n",
+       "390  106JT5p8e8Chtd0CZpcARw  2009-08-06  KowGVoP_gygzdSu6Mt3zKQ      5   \n",
+       "\n",
+       "                                                                                                                                                                                text  \\\n",
+       "390  RIP AZ Coffee Connection.  :(  I stopped by two days ago unaware that they had closed.  I am severely bummed.  This place is irreplaceable!  Damn you, Starbucks and McDonalds!   \n",
+       "\n",
+       "       type                 user_id  cool  useful  funny  length  sentiment  \n",
+       "390  review  jKeaOrPyJ-dI9SNeVqrbww     1       0      0     175  -0.302083  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Where sentiment can go wrong\n",
+    "\n",
+    "# negative sentiment in a 5-star review\n",
+    "yelp[(yelp.stars == 5) & (yelp.sentiment < -0.3)].head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "      <th>length</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1781</th>\n",
+       "      <td>53YGfwmbW73JhFiemNeyzQ</td>\n",
+       "      <td>2012-06-22</td>\n",
+       "      <td>Gi-4O3EhE175vujbFGDIew</td>\n",
+       "      <td>1</td>\n",
+       "      <td>If you like the stuck up Scottsdale vibe this is a good place for you. The food isn't impressive. Nice outdoor seating.</td>\n",
+       "      <td>review</td>\n",
+       "      <td>Hqgx3IdJAAaoQjvrUnbNvw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>119</td>\n",
+       "      <td>0.766667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 business_id        date               review_id  stars  \\\n",
+       "1781  53YGfwmbW73JhFiemNeyzQ  2012-06-22  Gi-4O3EhE175vujbFGDIew      1   \n",
+       "\n",
+       "                                                                                                                         text  \\\n",
+       "1781  If you like the stuck up Scottsdale vibe this is a good place for you. The food isn't impressive. Nice outdoor seating.   \n",
+       "\n",
+       "        type                 user_id  cool  useful  funny  length  sentiment  \n",
+       "1781  review  Hqgx3IdJAAaoQjvrUnbNvw     0       1      2     119   0.766667  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# positive sentiment in a 1-star review\n",
+    "yelp[(yelp.stars == 1) & (yelp.sentiment > 0.5)].head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# reset the column display width\n",
+    "pd.reset_option('max_colwidth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Adding Features to a Document-Term Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create a DataFrame that only contains the 5-star and 1-star reviews\n",
+    "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]\n",
+    "\n",
+    "# define X and y\n",
+    "feature_cols = ['text', 'sentiment', 'cool', 'useful', 'funny']\n",
+    "X = yelp_best_worst[feature_cols]\n",
+    "y = yelp_best_worst.stars\n",
+    "\n",
+    "# split into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3064, 16825)\n",
+      "(1022, 16825)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use CountVectorizer with text column only\n",
+    "vect = CountVectorizer()\n",
+    "X_train_dtm = vect.fit_transform(X_train.text)\n",
+    "X_test_dtm = vect.transform(X_test.text)\n",
+    "print X_train_dtm.shape\n",
+    "print X_test_dtm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6841</th>\n",
+       "      <td>0.197778</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1728</th>\n",
+       "      <td>0.412500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3853</th>\n",
+       "      <td>0.398148</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>0.129219</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4920</th>\n",
+       "      <td>0.473611</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      sentiment  cool  useful  funny\n",
+       "6841   0.197778     0       0      1\n",
+       "1728   0.412500     0       0      0\n",
+       "3853   0.398148     0       0      0\n",
+       "671    0.129219     0       0      0\n",
+       "4920   0.473611     0       0      0"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the other four feature columns that I want to use to predict stars, not just text\n",
+    "X_train.drop('text', axis=1).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3064, 4)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cast other feature columns to float and convert to a sparse matrix\n",
+    "# Why a sparse matrix, because the other matrix is sparse and the data types must match up\n",
+    "extra = sp.sparse.csr_matrix(X_train.drop('text', axis=1).astype(float))\n",
+    "extra.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3064, 16829)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# combine sparse matrices\n",
+    "X_train_dtm_extra = sp.sparse.hstack((X_train_dtm, extra))\n",
+    "X_train_dtm_extra.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1022, 16829)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# repeat for testing set\n",
+    "extra = sp.sparse.csr_matrix(X_test.drop('text', axis=1).astype(float))\n",
+    "X_test_dtm_extra = sp.sparse.hstack((X_test_dtm, extra))\n",
+    "X_test_dtm_extra.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.925636007828\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use logistic regression with text column only\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train_dtm, y_train)\n",
+    "y_pred_class = logreg.predict(X_test_dtm)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.937377690802\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use logistic regression with all features\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train_dtm_extra, y_train)\n",
+    "y_pred_class = logreg.predict(X_test_dtm_extra)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Fun TextBlob Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TextBlob(\"15 minutes late\")"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# spelling correction\n",
+    "TextBlob('15 minuets late').correct()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('part', 0.9929478138222849), (u'parrot', 0.007052186177715092)]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# spellcheck\n",
+    "Word('parot').spellcheck()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'tip laterally',\n",
+       " u'enclose with a bank',\n",
+       " u'do business with a bank or keep an account at a bank',\n",
+       " u'act as the banker in a game or in gambling',\n",
+       " u'be in the banking business',\n",
+       " u'put into a bank account',\n",
+       " u'cover with ashes so to control the rate of burning',\n",
+       " u'have confidence or faith in']"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# definitions\n",
+    "Word('bank').define('v')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "- NLP is a gigantic field\n",
+    "- Understanding the basics broadens the types of data you can work with\n",
+    "- Simple techniques go a long way\n",
+    "- Use scikit-learn for NLP whenever possible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+<<<<<<< HEAD
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+=======
+   "display_name": "Python [conda env:sfdat28]",
+   "language": "python",
+   "name": "conda-env-sfdat28-py"
+>>>>>>> cb83766255735d60d81c4112b7d26b26ef7d2572
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-Eugene-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-Eugene-checkpoint.ipynb
new file mode 100644
index 0000000..8b155eb
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-Eugene-checkpoint.ipynb
@@ -0,0 +1,2259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**CLASS**: Naive Bayes SMS spam classifier using sklearn\n",
+    "\n",
+    "Data source: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## READING IN THE DATA\n",
+    "\n",
+    "# read tab-separated file using pandas\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the data\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x95dfa90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9zIt169axbNmyncampqaYmpoaU0eSJE2O6elppqendxrbsWNH8+PnFBySXAG8B3hbVf3f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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x95df748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.label.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ham     0.865937\n",
+       "spam    0.134063\n",
+       "Name: label, dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the null accuracy rate\n",
+    "df.label.value_counts() / df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count                       5572\n",
+       "unique                      5169\n",
+       "top       Sorry, I'll call later\n",
+       "freq                          30\n",
+       "Name: msg, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.msg.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label                                                msg\n",
+       "0      0  Go until jurong point, crazy.. Available only ...\n",
+       "1      0                      Ok lar... Joking wif u oni...\n",
+       "2      1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3      0  U dun say so early hor... U c already then say...\n",
+       "4      0  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert label to a quantitative binary variable\n",
+    "df['label'] = df.label.map({'ham':0, 'spam':1})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into training and testing sets\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df.msg, df.label, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'44', u'cab', u'call', u'me', u'please', u'tonight', u'you']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "# start with a simple example\n",
+    "train_simple = ['call you tonight',\n",
+    "                'Call me a cab',\n",
+    "                'please call me... PLEASE 44!']\n",
+    "\n",
+    "# learn the 'vocabulary' of the training data\n",
+    "vect = CountVectorizer()\n",
+    "train_simple_dtm = vect.fit_transform(train_simple)\n",
+    "vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<3x7 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 10 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform training data into a 'document-term matrix'\n",
+    "train_simple_dtm = vect.transform(train_simple)\n",
+    "train_simple_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   0       0        1    1\n",
+       "1   0    1     1   1       0        0    0\n",
+       "2   1    0     1   1       2        0    0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the vocabulary and document-term matrix together\n",
+    "pd.DataFrame(train_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   1       1        0    0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix (using existing vocabulary, notice don't is missing)\n",
+    "test_simple = [\"please don't call me\"]\n",
+    "test_simple_dtm = vect.transform(test_simple)\n",
+    "test_simple_dtm.toarray()\n",
+    "pd.DataFrame(test_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4179x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 55209 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## REPEAT PATTERN WITH SMS DATA\n",
+    "\n",
+    "# instantiate the vectorizer\n",
+    "vect = CountVectorizer()\n",
+    "\n",
+    "# learn vocabulary and create document-term matrix in a single step\n",
+    "train_dtm = vect.fit_transform(X_train)\n",
+    "train_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1393x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 17604 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix\n",
+    "test_dtm = vect.transform(X_test)\n",
+    "test_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([u'00',\n",
+       "  u'000',\n",
+       "  u'008704050406',\n",
+       "  u'0121',\n",
+       "  u'01223585236',\n",
+       "  u'01223585334',\n",
+       "  u'0125698789',\n",
+       "  u'02',\n",
+       "  u'0207',\n",
+       "  u'02072069400'],\n",
+       " [u'zed',\n",
+       "  u'zeros',\n",
+       "  u'zhong',\n",
+       "  u'zindgi',\n",
+       "  u'zoe',\n",
+       "  u'zoom',\n",
+       "  u'zouk',\n",
+       "  u'zyada',\n",
+       "  u'\\xe8n',\n",
+       "  u'\\u3028ud'])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# store feature names and examine them\n",
+    "train_features = vect.get_feature_names()\n",
+    "len(train_features)\n",
+    "train_features[:10], train_features[-10:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert train_dtm to a regular array\n",
+    "train_arr = train_dtm.toarray()\n",
+    "# remember that each ROW is an sms\n",
+    "# and each COLUMN is a token\n",
+    "train_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## SIMPLE SUMMARIES OF THE TRAINING DATA\n",
+    "# refresher on numpy\n",
+    "import numpy as np\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[0, :]) #sum of the first row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[:,0]) # sum of the first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times the 0th token appears across ALL messages in train_arr\n",
+    "train_arr[:,0].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "#calculate the number of tokens in the 0th message in train_arr\n",
+    "train_arr[0,:].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr) # adds up all numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6,  8, 10, 12])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=0) # adds up by column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 26])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=1)  # adds up by row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5, 23,  2, ...,  1,  1,  1], dtype=int64)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times EACH token appears across ALL messages in train_arr\n",
+    "np.sum(train_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>1670</td>\n",
+       "      <td>to</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7420</th>\n",
+       "      <td>1660</td>\n",
+       "      <td>you</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6542</th>\n",
+       "      <td>1004</td>\n",
+       "      <td>the</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>929</th>\n",
+       "      <td>717</td>\n",
+       "      <td>and</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3502</th>\n",
+       "      <td>683</td>\n",
+       "      <td>in</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      count token\n",
+       "6656   1670    to\n",
+       "7420   1660   you\n",
+       "6542   1004   the\n",
+       "929     717   and\n",
+       "3502    683    in"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a dataframe with two columns, \"count\" and \"token\" that holds the token count for each token\n",
+    "# use the numpy sum methods\n",
+    "train_token_counts = pd.DataFrame({'token':train_features, 'count':np.sum(train_arr, axis=0)})\n",
+    "train_token_counts.sort_index(by='count', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## MODEL BUILDING WITH NAIVE BAYES\n",
+    "## http://scikit-learn.org/stable/modules/naive_bayes.html\n",
+    "\n",
+    "# train a Naive Bayes model using train_dtm\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(train_dtm, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 1, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make predictions on test data using test_dtm\n",
+    "preds = nb.predict(test_dtm)\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.988513998564\n",
+      "[[1203    5]\n",
+      " [  11  174]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compare predictions to true labels\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.986643100054\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict (poorly calibrated) probabilities and calculate AUC\n",
+    "probs = nb.predict_proba(test_dtm)[:, 1] #looks at the second column of the confusion matrix \n",
+    "probs\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1078</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yep, by the pretty sculpture</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4028</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes, princess. Are you going to make me moan?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>958</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Welp apparently he retired</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4642</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Havent.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4674</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>I forgot 2 ask ü all smth.. There's a card on ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                                               text\n",
+       "1078       0           0                       Yep, by the pretty sculpture\n",
+       "4028       0           0      Yes, princess. Are you going to make me moan?\n",
+       "958        0           0                         Welp apparently he retired\n",
+       "4642       0           0                                            Havent.\n",
+       "4674       0           0  I forgot 2 ask ü all smth.. There's a card on ..."
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false positives\n",
+    "df2 = pd.DataFrame({'text':X_test,'actual':y_test,'prediction':preds})\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Waiting for your call.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3375</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Also andros ice etc etc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No calls..messages..missed calls</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3415</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No pic. Please re-send.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No calls..messages..missed calls</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                              text\n",
+       "574        0           1            Waiting for your call.\n",
+       "3375       0           1           Also andros ice etc etc\n",
+       "45         0           1  No calls..messages..missed calls\n",
+       "3415       0           1           No pic. Please re-send.\n",
+       "1988       0           1  No calls..messages..missed calls"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[(df2.prediction==1) & (df2.actual==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3132</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LookAtMe!: Thanks for your purchase of a video...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3530</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Xmas &amp; New Years Eve tickets are now on sale f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi I'm sue. I am 20 years old and work as a la...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1875</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Would you like to see my XXX pics they are so ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                                               text\n",
+       "3132       1           0  LookAtMe!: Thanks for your purchase of a video...\n",
+       "5          1           0  FreeMsg Hey there darling it's been 3 week's n...\n",
+       "3530       1           0  Xmas & New Years Eve tickets are now on sale f...\n",
+       "684        1           0  Hi I'm sue. I am 20 years old and work as a la...\n",
+       "1875       1           0  Would you like to see my XXX pics they are so ..."
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false negatives\n",
+    "df2[(df2.prediction==0) & (df2.actual==1)].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## COMPARE NAIVE BAYES AND LOGISTIC REGRESSION\n",
+    "## USING ALL DATA AND CROSS-VALIDATION\n",
+    "vect = CountVectorizer(ngram_range=(1,3), stop_words='english')\n",
+    "# create a document-term matrix using all data\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "\n",
+    "# instantiate logistic regression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "\n",
+    "# compare AUC using cross-validation\n",
+    "# note: this is slightly improper cross-validation... can you figure out why?\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.956237 Seconds for logistic regression 0.976848539122\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(logreg, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for logistic regression\", mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.160997 Seconds for naive bayes 0.967516659054\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for naive bayes\", mean\n",
+    "# about 4 times faster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>default</th>\n",
+       "      <th>student</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>729.526495</td>\n",
+       "      <td>44361.62507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>817.180407</td>\n",
+       "      <td>12106.13470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1073.549164</td>\n",
+       "      <td>31767.13895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>529.250605</td>\n",
+       "      <td>35704.49394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>785.655883</td>\n",
+       "      <td>38463.49588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   default student      balance       income\n",
+       "0        0      No   729.526495  44361.62507\n",
+       "1        0     Yes   817.180407  12106.13470\n",
+       "2        0      No  1073.549164  31767.13895\n",
+       "3        0      No   529.250605  35704.49394\n",
+       "4        0      No   785.655883  38463.49588"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Model evaluation metrics (confusion matrix, ROC/AUC)\n",
+    "'''\n",
+    "\n",
+    "## READ DATA AND SPLIT INTO TRAIN/TEST\n",
+    "\n",
+    "# read in the data\n",
+    "import pandas as pd\n",
+    "data = pd.read_csv('../data/Default.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create X and y\n",
+    "X = data[['balance']]\n",
+    "y = data.default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into train and test\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create logistic regression model\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9748"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict and calculate accuracy in one step\n",
+    "logreg.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9748\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict in one step, calculate accuracy in a separate step\n",
+    "preds = logreg.predict(X_test)\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9684"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to null accuracy rate\n",
+    "y_test.mean()\n",
+    "1 - y_test.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2416    5]\n",
+      " [  58   21]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "## CONFUSION MATRIX\n",
+    "\n",
+    "# print confusion matrix\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# nicer confusion matrix\n",
+    "from nltk import ConfusionMatrix\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.26582278481\n",
+      "0.997934737712\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sensitivity: percent of correct predictions when reference value is 'default'\n",
+    "21 / float(58 + 21)\n",
+    "print metrics.recall_score(y_test, preds)\n",
+    "\n",
+    "# specificity: percent of correct predictions when reference value is 'not default'\n",
+    "print 2416 / float(2416 + 5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  2.23000000e+03,   1.23000000e+02,   6.30000000e+01,\n",
+       "          2.90000000e+01,   2.30000000e+01,   1.10000000e+01,\n",
+       "          1.00000000e+01,   6.00000000e+00,   1.00000000e+00,\n",
+       "          4.00000000e+00]),\n",
+       " array([  1.87839293e-04,   8.91943925e-02,   1.78200946e-01,\n",
+       "          2.67207499e-01,   3.56214052e-01,   4.45220605e-01,\n",
+       "          5.34227159e-01,   6.23233712e-01,   7.12240265e-01,\n",
+       "          8.01246818e-01,   8.90253371e-01]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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oyeN67umxSXZ071F3J3nFSvYzZk+PS/IP3f+/W5OcPu9Kq2rVJ4YB9AXg54CfAO4ATp01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\nIEmN+z/CtF/SApB9NAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117b78210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# predict probabilities\n",
+    "import matplotlib.pyplot as plt\n",
+    "probs = logreg.predict_proba(X_test)[:, 1]\n",
+    "plt.hist(probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use 0.5 cutoff for predicting 'default'\n",
+    "import numpy as np\n",
+    "preds = np.where(probs > 0.5, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2340>  81 |\n",
+      "1 |   34  <45>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change cutoff for predicting default to 0.2\n",
+    "preds = np.where(probs > 0.2, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.954\n",
+      "0.569620253165\n",
+      "0.966542750929\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check accuracy, sensitivity, specificity\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print 45 / float(34 + 45)\n",
+    "print 2340 / float(2340 + 81)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x129d397d0>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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clu5T6w2gv54z15Etr3g42RKUNBnn94BPSdop3esPwBvKpow+qOr416timQf8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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1136ff310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## ROC CURVES and AUC\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(y_test, probs)\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
+    "plt.ylabel('True Positive Rate (Sensitivity)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.941011926236\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate AUC\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94819099843061938"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use AUC as evaluation metric for cross-validation\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "X = data[['balance']]\n",
+    "y = data.default\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.59503752064417714"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to a model with an additional feature\n",
+    "X = data[['balance', 'income']]\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# ROC/AUC are very meaningful when:\n",
+    "# 1. class sizes are inbalances\n",
+    "# 2. comparing across different binary classification models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## BONUS: CALCULATE THE 'SPAMMINESS' OF EACH TOKEN\n",
+    "\n",
+    "\n",
+    "# create separate DataFrames for ham and spam\n",
+    "df_ham = df[df.label==0]\n",
+    "df_spam = df[df.label==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# learn the vocabulary of ALL messages and save it\n",
+    "vect = CountVectorizer(ngram_range=(1,1), stop_words='english')\n",
+    "vect.fit(df.msg)\n",
+    "all_features = vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4825x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 32267 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of ham, then convert to a regular array\n",
+    "ham_dtm = vect.transform(df_ham.msg)\n",
+    "ham_arr = ham_dtm.toarray()\n",
+    "ham_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<747x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 11312 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of spam, then convert to a regular array\n",
+    "spam_dtm = vect.transform(df_spam.msg)\n",
+    "spam_arr = spam_dtm.toarray()\n",
+    "spam_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in ham_arr\n",
+    "ham_counts = np.sum(ham_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in spam_arr\n",
+    "spam_counts = np.sum(spam_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>000pes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>008704050406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0089</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ham  spam         token\n",
+       "0    0    10            00\n",
+       "1    0    29           000\n",
+       "2    1     0        000pes\n",
+       "3    0     2  008704050406\n",
+       "4    0     1          0089"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a DataFrame of tokens with their separate ham and spam counts\n",
+    "all_token_counts = pd.DataFrame({'token':all_features, 'ham':ham_counts, 'spam':spam_counts})\n",
+    "all_token_counts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# add one to ham counts and spam counts so that ratio calculations (below) make more sense\n",
+    "all_token_counts['ham'] = all_token_counts.ham + 1\n",
+    "all_token_counts['spam'] = all_token_counts.spam + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "      <th>spam_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>1</td>\n",
+       "      <td>114</td>\n",
+       "      <td>claim</td>\n",
+       "      <td>114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5935</th>\n",
+       "      <td>1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>prize</td>\n",
+       "      <td>94.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>1</td>\n",
+       "      <td>72</td>\n",
+       "      <td>150p</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7612</th>\n",
+       "      <td>1</td>\n",
+       "      <td>61</td>\n",
+       "      <td>tone</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52</td>\n",
+       "      <td>18</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3589</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>guaranteed</td>\n",
+       "      <td>51.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2314</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>cs</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>500</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>1</td>\n",
+       "      <td>39</td>\n",
+       "      <td>awarded</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ham  spam       token  spam_ratio\n",
+       "2013    1   114       claim       114.0\n",
+       "5935    1    94       prize        94.0\n",
+       "352     1    72        150p        72.0\n",
+       "7612    1    61        tone        61.0\n",
+       "369     1    52          18        52.0\n",
+       "3589    1    51  guaranteed        51.0\n",
+       "2314    1    45          cs        45.0\n",
+       "617     1    45         500        45.0\n",
+       "299     1    42        1000        42.0\n",
+       "1301    1    39     awarded        39.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate ratio of spam-to-ham for each token\n",
+    "all_token_counts['spam_ratio'] = all_token_counts.spam / all_token_counts.ham\n",
+    "all_token_counts.sort_index(by='spam_ratio', ascending = False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-checkpoint.ipynb
new file mode 100644
index 0000000..e00e0ad
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/09_naive_bayes_roc_auc-checkpoint.ipynb
@@ -0,0 +1,2016 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**CLASS**: Naive Bayes SMS spam classifier using sklearn\n",
+    "\n",
+    "Data source: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## READING IN THE DATA\n",
+    "\n",
+    "# read tab-separated file using pandas\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the data\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x95f3c88>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9zIt169axbNmyncampqaYmpoaU0eSJE2O6elppqendxrbsWNH8+PnFBySXAG8B3hbVf3f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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x95f3908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.label.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ham     0.865937\n",
+       "spam    0.134063\n",
+       "Name: label, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the null accuracy rate\n",
+    "df.label.value_counts() / df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count                       5572\n",
+       "unique                      5169\n",
+       "top       Sorry, I'll call later\n",
+       "freq                          30\n",
+       "Name: msg, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.msg.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label                                                msg\n",
+       "0      0  Go until jurong point, crazy.. Available only ...\n",
+       "1      0                      Ok lar... Joking wif u oni...\n",
+       "2      1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3      0  U dun say so early hor... U c already then say...\n",
+       "4      0  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert label to a quantitative binary variable\n",
+    "df['label'] = df.label.map({'ham':0, 'spam':1})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into training and testing sets\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df.msg, df.label, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'44', u'cab', u'call', u'me', u'please', u'tonight', u'you']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "# start with a simple example\n",
+    "train_simple = ['call you tonight',\n",
+    "                'Call me a cab',\n",
+    "                'please call me... PLEASE 44!']\n",
+    "\n",
+    "# learn the 'vocabulary' of the training data\n",
+    "vect = CountVectorizer()\n",
+    "train_simple_dtm = vect.fit_transform(train_simple)\n",
+    "vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<3x7 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 10 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform training data into a 'document-term matrix'\n",
+    "train_simple_dtm = vect.transform(train_simple)\n",
+    "train_simple_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   0       0        1    1\n",
+       "1   0    1     1   1       0        0    0\n",
+       "2   1    0     1   1       2        0    0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the vocabulary and document-term matrix together\n",
+    "pd.DataFrame(train_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   1       1        0    0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix (using existing vocabulary, notice don't is missing)\n",
+    "test_simple = [\"please don't call me\"]\n",
+    "test_simple_dtm = vect.transform(test_simple)\n",
+    "test_simple_dtm.toarray()\n",
+    "pd.DataFrame(test_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4179x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 55209 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## REPEAT PATTERN WITH SMS DATA\n",
+    "\n",
+    "# instantiate the vectorizer\n",
+    "vect = CountVectorizer()\n",
+    "\n",
+    "# learn vocabulary and create document-term matrix in a single step\n",
+    "train_dtm = vect.fit_transform(X_train)\n",
+    "train_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1393x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 17604 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix\n",
+    "test_dtm = vect.transform(X_test)\n",
+    "test_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([u'00',\n",
+       "  u'000',\n",
+       "  u'008704050406',\n",
+       "  u'0121',\n",
+       "  u'01223585236',\n",
+       "  u'01223585334',\n",
+       "  u'0125698789',\n",
+       "  u'02',\n",
+       "  u'0207',\n",
+       "  u'02072069400'],\n",
+       " [u'zed',\n",
+       "  u'zeros',\n",
+       "  u'zhong',\n",
+       "  u'zindgi',\n",
+       "  u'zoe',\n",
+       "  u'zoom',\n",
+       "  u'zouk',\n",
+       "  u'zyada',\n",
+       "  u'\\xe8n',\n",
+       "  u'\\u3028ud'])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# store feature names and examine them\n",
+    "train_features = vect.get_feature_names()\n",
+    "len(train_features)\n",
+    "train_features[:10], train_features[-10:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert train_dtm to a regular array\n",
+    "train_arr = train_dtm.toarray()\n",
+    "# remember that each ROW is an sms\n",
+    "# and each COLUMN is a token\n",
+    "train_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## SIMPLE SUMMARIES OF THE TRAINING DATA\n",
+    "# refresher on numpy\n",
+    "import numpy as np\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[0, :]) #sum of the first row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[:,0]) # sum of the first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times the 0th token appears across ALL messages in train_arr\n",
+    "train_arr[:,0].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "#calculate the number of tokens in the 0th message in train_arr\n",
+    "train_arr[0,:].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr) # adds up all numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6,  8, 10, 12])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=0) # adds up by column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 26])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=1)  # adds up by row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times EACH token appears across ALL messages in train_arr\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>1670</td>\n",
+       "      <td>to</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7420</th>\n",
+       "      <td>1660</td>\n",
+       "      <td>you</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6542</th>\n",
+       "      <td>1004</td>\n",
+       "      <td>the</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>929</th>\n",
+       "      <td>717</td>\n",
+       "      <td>and</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3502</th>\n",
+       "      <td>683</td>\n",
+       "      <td>in</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      count token\n",
+       "6656   1670    to\n",
+       "7420   1660   you\n",
+       "6542   1004   the\n",
+       "929     717   and\n",
+       "3502    683    in"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a dataframe with two columns, \"count\" and \"token\" that holds the token count for each token\n",
+    "# use the numpy sum methods\n",
+    "train_token_counts = pd.DataFrame({'token':train_features, 'count':np.sum(train_arr, axis=0)})\n",
+    "train_token_counts.sort_index(by='count', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## MODEL BUILDING WITH NAIVE BAYES\n",
+    "## http://scikit-learn.org/stable/modules/naive_bayes.html\n",
+    "\n",
+    "# train a Naive Bayes model using train_dtm\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(train_dtm, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 1, 0])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make predictions on test data using test_dtm\n",
+    "preds = nb.predict(test_dtm)\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.988513998564\n",
+      "[[1203    5]\n",
+      " [  11  174]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compare predictions to true labels\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.986643100054\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict (poorly calibrated) probabilities and calculate AUC\n",
+    "probs = nb.predict_proba(test_dtm)[:, 1]\n",
+    "probs\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false positives\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false negatives\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## COMPARE NAIVE BAYES AND LOGISTIC REGRESSION\n",
+    "## USING ALL DATA AND CROSS-VALIDATION\n",
+    "vect = CountVectorizer(ngram_range=(1,3), stop_words='english')\n",
+    "# create a document-term matrix using all data\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "\n",
+    "# instantiate logistic regression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "\n",
+    "# compare AUC using cross-validation\n",
+    "# note: this is slightly improper cross-validation... can you figure out why?\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.956237 Seconds for logistic regression 0.976848539122\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(logreg, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for logistic regression\", mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.160997 Seconds for naive bayes 0.967516659054\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for naive bayes\", mean\n",
+    "# about 4 times faster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>default</th>\n",
+       "      <th>student</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>729.526495</td>\n",
+       "      <td>44361.62507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>817.180407</td>\n",
+       "      <td>12106.13470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1073.549164</td>\n",
+       "      <td>31767.13895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>529.250605</td>\n",
+       "      <td>35704.49394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>785.655883</td>\n",
+       "      <td>38463.49588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   default student      balance       income\n",
+       "0        0      No   729.526495  44361.62507\n",
+       "1        0     Yes   817.180407  12106.13470\n",
+       "2        0      No  1073.549164  31767.13895\n",
+       "3        0      No   529.250605  35704.49394\n",
+       "4        0      No   785.655883  38463.49588"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Model evaluation metrics (confusion matrix, ROC/AUC)\n",
+    "'''\n",
+    "\n",
+    "## READ DATA AND SPLIT INTO TRAIN/TEST\n",
+    "\n",
+    "# read in the data\n",
+    "import pandas as pd\n",
+    "data = pd.read_csv('../data/Default.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create X and y\n",
+    "X = data[['balance']]\n",
+    "y = data.default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into train and test\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create logistic regression model\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9748"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict and calculate accuracy in one step\n",
+    "logreg.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9748\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict in one step, calculate accuracy in a separate step\n",
+    "preds = logreg.predict(X_test)\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9684"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to null accuracy rate\n",
+    "y_test.mean()\n",
+    "1 - y_test.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2416    5]\n",
+      " [  58   21]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "## CONFUSION MATRIX\n",
+    "\n",
+    "# print confusion matrix\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# nicer confusion matrix\n",
+    "from nltk import ConfusionMatrix\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.26582278481\n",
+      "0.997934737712\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sensitivity: percent of correct predictions when reference value is 'default'\n",
+    "21 / float(58 + 21)\n",
+    "print metrics.recall_score(y_test, preds)\n",
+    "\n",
+    "# specificity: percent of correct predictions when reference value is 'not default'\n",
+    "print 2416 / float(2416 + 5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  2.23000000e+03,   1.23000000e+02,   6.30000000e+01,\n",
+       "          2.90000000e+01,   2.30000000e+01,   1.10000000e+01,\n",
+       "          1.00000000e+01,   6.00000000e+00,   1.00000000e+00,\n",
+       "          4.00000000e+00]),\n",
+       " array([  1.87839293e-04,   8.91943925e-02,   1.78200946e-01,\n",
+       "          2.67207499e-01,   3.56214052e-01,   4.45220605e-01,\n",
+       "          5.34227159e-01,   6.23233712e-01,   7.12240265e-01,\n",
+       "          8.01246818e-01,   8.90253371e-01]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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oyeN67umxSXZ071F3J3nFSvYzZk+PS/IP3f+/W5OcPu9Kq2rVJ4YB9AXg54CfAO4ATp01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\nIEmN+z/CtF/SApB9NAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117b78210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# predict probabilities\n",
+    "import matplotlib.pyplot as plt\n",
+    "probs = logreg.predict_proba(X_test)[:, 1]\n",
+    "plt.hist(probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use 0.5 cutoff for predicting 'default'\n",
+    "import numpy as np\n",
+    "preds = np.where(probs > 0.5, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2340>  81 |\n",
+      "1 |   34  <45>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change cutoff for predicting default to 0.2\n",
+    "preds = np.where(probs > 0.2, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.954\n",
+      "0.569620253165\n",
+      "0.966542750929\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check accuracy, sensitivity, specificity\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print 45 / float(34 + 45)\n",
+    "print 2340 / float(2340 + 81)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x129d397d0>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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clu5T6w2gv54z15Etr3g42RKUNBnn94BPSdop3esPwBvKpow+qOr416timQf8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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1136ff310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## ROC CURVES and AUC\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(y_test, probs)\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
+    "plt.ylabel('True Positive Rate (Sensitivity)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.941011926236\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate AUC\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94819099843061938"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use AUC as evaluation metric for cross-validation\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "X = data[['balance']]\n",
+    "y = data.default\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.59503752064417714"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to a model with an additional feature\n",
+    "X = data[['balance', 'income']]\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# ROC/AUC are very meaningful when:\n",
+    "# 1. class sizes are inbalances\n",
+    "# 2. comparing across different binary classification models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## BONUS: CALCULATE THE 'SPAMMINESS' OF EACH TOKEN\n",
+    "\n",
+    "\n",
+    "# create separate DataFrames for ham and spam\n",
+    "df_ham = df[df.label==0]\n",
+    "df_spam = df[df.label==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# learn the vocabulary of ALL messages and save it\n",
+    "vect = CountVectorizer(ngram_range=(1,1), stop_words='english')\n",
+    "vect.fit(df.msg)\n",
+    "all_features = vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4825x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 32267 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of ham, then convert to a regular array\n",
+    "ham_dtm = vect.transform(df_ham.msg)\n",
+    "ham_arr = ham_dtm.toarray()\n",
+    "ham_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<747x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 11312 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of spam, then convert to a regular array\n",
+    "spam_dtm = vect.transform(df_spam.msg)\n",
+    "spam_arr = spam_dtm.toarray()\n",
+    "spam_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in ham_arr\n",
+    "ham_counts = np.sum(ham_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in spam_arr\n",
+    "spam_counts = np.sum(spam_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>000pes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>008704050406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0089</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ham  spam         token\n",
+       "0    0    10            00\n",
+       "1    0    29           000\n",
+       "2    1     0        000pes\n",
+       "3    0     2  008704050406\n",
+       "4    0     1          0089"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a DataFrame of tokens with their separate ham and spam counts\n",
+    "all_token_counts = pd.DataFrame({'token':all_features, 'ham':ham_counts, 'spam':spam_counts})\n",
+    "all_token_counts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# add one to ham counts and spam counts so that ratio calculations (below) make more sense\n",
+    "all_token_counts['ham'] = all_token_counts.ham + 1\n",
+    "all_token_counts['spam'] = all_token_counts.spam + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "      <th>spam_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>1</td>\n",
+       "      <td>114</td>\n",
+       "      <td>claim</td>\n",
+       "      <td>114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5935</th>\n",
+       "      <td>1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>prize</td>\n",
+       "      <td>94.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>1</td>\n",
+       "      <td>72</td>\n",
+       "      <td>150p</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7612</th>\n",
+       "      <td>1</td>\n",
+       "      <td>61</td>\n",
+       "      <td>tone</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52</td>\n",
+       "      <td>18</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3589</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>guaranteed</td>\n",
+       "      <td>51.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2314</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>cs</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>500</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>1</td>\n",
+       "      <td>39</td>\n",
+       "      <td>awarded</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ham  spam       token  spam_ratio\n",
+       "2013    1   114       claim       114.0\n",
+       "5935    1    94       prize        94.0\n",
+       "352     1    72        150p        72.0\n",
+       "7612    1    61        tone        61.0\n",
+       "369     1    52          18        52.0\n",
+       "3589    1    51  guaranteed        51.0\n",
+       "2314    1    45          cs        45.0\n",
+       "617     1    45         500        45.0\n",
+       "299     1    42        1000        42.0\n",
+       "1301    1    39     awarded        39.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate ratio of spam-to-ham for each token\n",
+    "all_token_counts['spam_ratio'] = all_token_counts.spam / all_token_counts.ham\n",
+    "all_token_counts.sort_index(by='spam_ratio', ascending = False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/10_advanced_sklearn-Eugene-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/10_advanced_sklearn-Eugene-checkpoint.ipynb
new file mode 100644
index 0000000..b5572a7
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/10_advanced_sklearn-Eugene-checkpoint.ipynb
@@ -0,0 +1,2159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.base import TransformerMixin\n",
+    "import pandas as pd\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Some text\n",
+    "\n",
+    "X = (\n",
+    "    \"the pizza pizza beer copyright\",\n",
+    "    \"the pizza burger beer copyright\",\n",
+    "    \"the the pizza beer beer copyright\",\n",
+    "    \"the burger beer beer copyright\",\n",
+    "    \"the coke burger coke copyright\",\n",
+    "    \"the coke burger burger\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
+       "      <th>pizza</th>\n",
+       "      <th>the</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the\n",
+       "0     1       0     0          1      2    1\n",
+       "1     1       1     0          1      1    1\n",
+       "2     2       0     0          1      1    2\n",
+       "3     2       1     0          1      0    1\n",
+       "4     0       1     2          1      0    1\n",
+       "5     0       2     1          0      0    1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# EXERCISE (WAKE UP ITS CODING TIME)\n",
+    "\n",
+    "# Use a count vectorizer to count vectorize X and show a \n",
+    "# user-friendly dataframe to see the count of words in each sentence\n",
+    "# Recall this is otherwise known as a document term matrix, make it\n",
+    "\n",
+    "vect = CountVectorizer(analyzer='word')\n",
+    "pd.DataFrame(vect.fit_transform(X).toarray(), columns=vect.get_feature_names())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>g</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
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+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <th>0</th>\n",
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+       "    </tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>3</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a  b  c  e  g  h  i  k  o  p  r  t  u  y  z\n",
+       "0  4  2  1  1  3  1  2  3  0  1  3  2  2  0  1  4\n",
+       "1  4  1  2  1  4  2  2  2  0  1  2  4  2  1  1  2\n",
+       "2  5  1  2  1  6  1  3  2  0  1  2  3  3  0  1  2\n",
+       "3  4  0  3  1  6  2  2  1  0  1  1  5  2  1  1  0\n",
+       "4  4  0  1  3  4  2  2  1  2  3  1  3  2  1  1  0\n",
+       "5  3  0  2  1  4  2  1  0  1  1  0  4  1  2  0  0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "word_vect = CountVectorizer(analyzer='char')\n",
+    "words = pd.DataFrame(word_vect.fit_transform(X).toarray(), columns=word_vect.get_feature_names())\n",
+    "words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>g</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
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+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a  b  c  e  g  h  i  k  o  p  r  t  u  y  z\n",
+       "0  4  2  1  1  3  1  2  3  0  1  3  2  2  0  1  4\n",
+       "1  4  1  2  1  4  2  2  2  0  1  2  4  2  1  1  2\n",
+       "2  5  1  2  1  6  1  3  2  0  1  2  3  3  0  1  2\n",
+       "3  4  0  3  1  6  2  2  1  0  1  1  5  2  1  1  0\n",
+       "4  4  0  1  3  4  2  2  1  2  3  1  3  2  1  1  0\n",
+       "5  3  0  2  1  4  2  1  0  1  1  0  4  1  2  0  0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "char_vect = CountVectorizer(analyzer='char')\n",
+    "chars = pd.DataFrame(char_vect.fit_transform(X).toarray(), columns=char_vect.get_feature_names())\n",
+    "chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This is a feature union. If automatically horizontally stacks our count and tfidf vectorizers\n",
+    "# http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html\n",
+    "fs = FeatureUnion([\n",
+    "    (\"words\", CountVectorizer(analyzer='word')),\n",
+    "    (\"chars\", CountVectorizer(analyzer='char')),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FeatureUnion(n_jobs=1,\n",
+       "       transformer_list=[('words', CountVectorizer(analyzer='word', binary=False, decode_error=u'strict',\n",
+       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,...  strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None))],\n",
+       "       transformer_weights=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fs #see our params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the     a  b  c ...  h  i  k  o  p  \\\n",
+       "0     1       0     0          1      2    1  4  2  1  1 ...  2  3  0  1  3   \n",
+       "1     1       1     0          1      1    1  4  1  2  1 ...  2  2  0  1  2   \n",
+       "2     2       0     0          1      1    2  5  1  2  1 ...  3  2  0  1  2   \n",
+       "3     2       1     0          1      0    1  4  0  3  1 ...  2  1  0  1  1   \n",
+       "4     0       1     2          1      0    1  4  0  1  3 ...  2  1  2  3  1   \n",
+       "5     0       2     1          0      0    1  3  0  2  1 ...  1  0  1  1  0   \n",
+       "\n",
+       "   r  t  u  y  z  \n",
+       "0  2  2  0  1  4  \n",
+       "1  4  2  1  1  2  \n",
+       "2  3  3  0  1  2  \n",
+       "3  5  2  1  1  0  \n",
+       "4  3  2  1  1  0  \n",
+       "5  4  1  2  0  0  \n",
+       "\n",
+       "[6 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To see them put together\n",
+    "\n",
+    "words_plus_chars = pd.DataFrame(fs.fit_transform(X).toarray(), columns=word_vect.get_feature_names()+char_vect.get_feature_names())\n",
+    "words_plus_chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6, 6)\n",
+      "+\n",
+      "(6, 16)\n",
+      "========\n",
+      "(6, 22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print words.shape\n",
+    "print \"+\"\n",
+    "print chars.shape\n",
+    "print \"========\"\n",
+    "print words_plus_chars.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Pipeline http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\n",
+    "# is a module that chains different sklearn modules together in order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Test the various methods of the pipeline (anova).\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test with selectkbest + LogisticRegression\n",
+    "# SelectKBest http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection\n",
+    "# will automatically attempt to reduce the number of features by performing \n",
+    "# statistical tests (including ANOVA or Chi Squared)\n",
+    "\n",
+    "logreg = LogisticRegression()\n",
+    "filter1 = SelectKBest(k=2) # select the best 2 features\n",
+    "pipe = Pipeline([('anova', filter1), ('logistic', logreg)])\n",
+    "\n",
+    "\n",
+    "mean = cross_val_score(pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You can use make_pipeline to make simpler pipelines\n",
+    "easy_pipe = make_pipeline(SelectKBest(k=2), LogisticRegression())\n",
+    "mean = cross_val_score(easy_pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'words_plus_chars' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-d5a5d989ffc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mnb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMultinomialNB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mnb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords_plus_chars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'words_plus_chars' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# cross validate two naive bayes models.\n",
+    "# One model should attempt to fit to the count vectorized text\n",
+    "# The other should fit to the tfidfed text\n",
+    "# Each model whould be fit over a 10 fold validation with accuracy\n",
+    "\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(words_plus_chars, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98026032061360824"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Answers\n",
+    "\n",
+    "vect = CountVectorizer()\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96320881558493299"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vect = TfidfVectorizer() #penalizes frequent words\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9840269835443396"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A more complicated pipeline\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "mean = cross_val_score(pipe, df.msg, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not only is this PROPER cross validation of count vectorizers, its better than either model combined!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'features__counts__ngram_range': [(1, 1), (1, 2)], 'features__tf_idf__ngram_range': [(1, 1), (1, 2)]}\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-5eb5c60c93a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Pipeline can also be used with GridSearchCV, WARNING SLOWWW\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "gram_range = [(1, n) for n in range(1, 3)]\n",
+    "param_grid = {\n",
+    "    'features__counts__ngram_range': gram_range,\n",
+    "    'features__tf_idf__ngram_range': gram_range,\n",
+    "}\n",
+    "print param_grid\n",
+    "print\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(df.msg, df.label)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# We can also make our own custom transformations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DatetimeTransformer(TransformerMixin):\n",
+    "    def transform(self, X, **transform_params): \n",
+    "        if 'date' in X:\n",
+    "            years = X['date'].apply(lambda x: x.year)\n",
+    "            months = X['date'].apply(lambda x: x.month)\n",
+    "            days = X['date'].apply(lambda x: x.day)\n",
+    "            return pd.DataFrame({'year':years, 'month':months, 'day':days})\n",
+    "        return X\n",
+    "\n",
+    "    def fit(self, X, y=None, **fit_params):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = '../data/yelp.csv'\n",
+    "yelp = pd.read_csv(url, encoding='unicode-escape', usecols=[1, 3])\n",
+    "yelp['date'] = pd.to_datetime(yelp['date'])\n",
+    "yelp = yelp[(yelp.stars == 1) | (yelp.stars == 5)] # only look at best and worst ratings\n",
+    "yelp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   day  month  year\n",
+       "0   26      1  2011\n",
+       "1   27      7  2011\n",
+       "3   27      5  2010\n",
+       "4    5      1  2012\n",
+       "6   12      2  2010"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = DatetimeTransformer() # our custom transformer\n",
+    "h.fit_transform(yelp).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp.head() # original data unscathed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'logreg' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-d4f02f7f7ab7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# pipeline with our custom transformer and logistic regression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpipe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'transform_datetime'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeTransformer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'logistic'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmean\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myelp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'stars'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myelp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'logreg' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# pipeline with our custom transformer and logistic regression\n",
+    "\n",
+    "pipe = Pipeline([('transform_datetime', DatetimeTransformer()), ('logistic', logreg)])\n",
+    "mean = cross_val_score(pipe, yelp.drop('stars', axis=1), yelp.stars, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Hmmmmmm not bad... but what was that null accuracy?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5    0.816691\n",
+       "1    0.183309\n",
+       "Name: stars, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp['stars'].value_counts() / yelp.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# It appears dates aren't helping at all.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>1480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>735</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label  color  proline\n",
+       "0      1   5.64     1065\n",
+       "1      1   4.38     1050\n",
+       "2      1   5.68     1185\n",
+       "3      1   7.80     1480\n",
+       "4      1   4.32      735"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read three columns from the dataset into a DataFrame\n",
+    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'\n",
+    "col_names = ['label', 'color', 'proline']\n",
+    "wine = pd.read_csv(url, header=None, names=col_names, usecols=[0, 10, 13])\n",
+    "wine.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.938202</td>\n",
+       "      <td>5.058090</td>\n",
+       "      <td>746.893258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.775035</td>\n",
+       "      <td>2.318286</td>\n",
+       "      <td>314.907474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.280000</td>\n",
+       "      <td>278.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>500.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.690000</td>\n",
+       "      <td>673.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>985.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1680.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            label       color      proline\n",
+       "count  178.000000  178.000000   178.000000\n",
+       "mean     1.938202    5.058090   746.893258\n",
+       "std      0.775035    2.318286   314.907474\n",
+       "min      1.000000    1.280000   278.000000\n",
+       "25%      1.000000    3.220000   500.500000\n",
+       "50%      2.000000    4.690000   673.500000\n",
+       "75%      3.000000    6.200000   985.000000\n",
+       "max      3.000000   13.000000  1680.000000"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define X and y\n",
+    "feature_cols = ['color', 'proline']\n",
+    "X = wine[feature_cols]\n",
+    "y = wine.label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.90664944003e-16\n",
+      "1.0\n",
+      "1.6027279754e-16\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# split into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
+    "\n",
+    "\n",
+    "# standardize X_train\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "\n",
+    "\n",
+    "# Recall that standard scalar does a basic z-score normalization\n",
+    "# So for each element, x, it computes z = (x - mean) / std\n",
+    "# This gives the entire series a mean of 0 and a standard deviation (and variance) of 1\n",
+    "\n",
+    "# check that it standardized properly\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_train_scaled[:, 0].mean() # 0\n",
+    "print X_train_scaled[:, 0].std()  # 1\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_train_scaled[:, 1].mean() # 0\n",
+    "print X_train_scaled[:, 1].std()  # 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# standardize X_test\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0305898576303\n",
+      "0.866822198488\n",
+      "0.0546533341088\n",
+      "1.14955947533\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Is anything wrong here?\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_test_scaled[:, 0].mean()\n",
+    "print X_test_scaled[:, 0].std()\n",
+    "\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_test_scaled[:, 1].mean()\n",
+    "print X_test_scaled[:, 1].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.64444444444444449"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Run a KNN (3 neighbors) on the training set and find the accuracy on the testing set\n",
+    "# Do this TWICE once for the unscaled data, and once for the scaled data\n",
+    "# What do you notice about the accuracies\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)           # instantiate the estimator\n",
+    "knn.fit(X_train, y_train)\n",
+    "knn.score(X_test, y_test)\n",
+    "\n",
+    "# knn.fit(X_train_scaled, y_train_scaled)\n",
+    "# knn.score(X_test, y_test)\n",
+    "\n",
+    "\n",
+    "# Thought experiment: Why am I not asking for a cross validation?\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.644444444444\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Answers\n",
+    "\n",
+    "# KNN accuracy on original data\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred_class = knn.predict(X_test)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.866666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# KNN accuracy on scaled data\n",
+    "knn.fit(X_train_scaled, y_train)\n",
+    "y_pred_class = knn.predict(X_test_scaled)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# So we agree, scaling data helps us out in KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# Cross validate a KNN with 3 neighbors on the X and y variables with  (UNSCALED)\n",
+    "# 5 folds and output average accuracy for the models\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.71983168041991563"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER proper cross-validation on the original (unscaled) data\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "cross_val_score(knn, X, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90104247104247115"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Why is this improper cross-validation on the scaled data?\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "cross_val_score(knn, X_scaled, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Make a pipeline that scales data and then runs a KNN with 3 neighbors\n",
+    "# cross validate your pipeline using cv=5 with accuacy metric\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.89516011810129448"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "\n",
+    "# fix the cross-validation process using Pipeline\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3))\n",
+    "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()\n",
+    "\n",
+    "# It is NOT as good... But it is more accurate to our true estimate (and also better than the unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.910112359551 {'kneighborsclassifier__n_neighbors': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
+    "neighbors_range = range(1, 21)\n",
+    "param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range)\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/10_advanced_sklearn-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/10_advanced_sklearn-checkpoint.ipynb
new file mode 100644
index 0000000..3b6ae84
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/10_advanced_sklearn-checkpoint.ipynb
@@ -0,0 +1,1965 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.base import TransformerMixin\n",
+    "import pandas as pd\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Some text\n",
+    "\n",
+    "X = (\n",
+    "    \"the pizza pizza beer copyright\",\n",
+    "    \"the pizza burger beer copyright\",\n",
+    "    \"the the pizza beer beer copyright\",\n",
+    "    \"the burger beer beer copyright\",\n",
+    "    \"the coke burger coke copyright\",\n",
+    "    \"the coke burger burger\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE (WAKE UP ITS CODING TIME)\n",
+    "\n",
+    "# Use a count vectorizer to count vectorize X and show a \n",
+    "# user-friendly dataframe to see the count of words in each sentence\n",
+    "# Recall this is otherwise known as a document term matrix, make it\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
+       "      <th>pizza</th>\n",
+       "      <th>the</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the\n",
+       "0     1       0     0          1      2    1\n",
+       "1     1       1     0          1      1    1\n",
+       "2     2       0     0          1      1    2\n",
+       "3     2       1     0          1      0    1\n",
+       "4     0       1     2          1      0    1\n",
+       "5     0       2     1          0      0    1"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "word_vect = CountVectorizer(analyzer='word')\n",
+    "words = pd.DataFrame(word_vect.fit_transform(X).toarray(), columns=word_vect.get_feature_names())\n",
+    "words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>g</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a  b  c  e  g  h  i  k  o  p  r  t  u  y  z\n",
+       "0  4  2  1  1  3  1  2  3  0  1  3  2  2  0  1  4\n",
+       "1  4  1  2  1  4  2  2  2  0  1  2  4  2  1  1  2\n",
+       "2  5  1  2  1  6  1  3  2  0  1  2  3  3  0  1  2\n",
+       "3  4  0  3  1  6  2  2  1  0  1  1  5  2  1  1  0\n",
+       "4  4  0  1  3  4  2  2  1  2  3  1  3  2  1  1  0\n",
+       "5  3  0  2  1  4  2  1  0  1  1  0  4  1  2  0  0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "char_vect = CountVectorizer(analyzer='char')\n",
+    "chars = pd.DataFrame(char_vect.fit_transform(X).toarray(), columns=char_vect.get_feature_names())\n",
+    "chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This is a feature union. If automatically horizontally stacks our count and tfidf vectorizers\n",
+    "# http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html\n",
+    "fs = FeatureUnion([\n",
+    "    (\"words\", CountVectorizer(analyzer='word')),\n",
+    "    (\"chars\", CountVectorizer(analyzer='char')),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FeatureUnion(n_jobs=1,\n",
+       "       transformer_list=[('words', CountVectorizer(analyzer='word', binary=False, decode_error=u'strict',\n",
+       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,...  strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None))],\n",
+       "       transformer_weights=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fs #see our params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
+       "      <th>pizza</th>\n",
+       "      <th>the</th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>...</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the     a  b  c ...  h  i  k  o  p  \\\n",
+       "0     1       0     0          1      2    1  4  2  1  1 ...  2  3  0  1  3   \n",
+       "1     1       1     0          1      1    1  4  1  2  1 ...  2  2  0  1  2   \n",
+       "2     2       0     0          1      1    2  5  1  2  1 ...  3  2  0  1  2   \n",
+       "3     2       1     0          1      0    1  4  0  3  1 ...  2  1  0  1  1   \n",
+       "4     0       1     2          1      0    1  4  0  1  3 ...  2  1  2  3  1   \n",
+       "5     0       2     1          0      0    1  3  0  2  1 ...  1  0  1  1  0   \n",
+       "\n",
+       "   r  t  u  y  z  \n",
+       "0  2  2  0  1  4  \n",
+       "1  4  2  1  1  2  \n",
+       "2  3  3  0  1  2  \n",
+       "3  5  2  1  1  0  \n",
+       "4  3  2  1  1  0  \n",
+       "5  4  1  2  0  0  \n",
+       "\n",
+       "[6 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To see them put together\n",
+    "\n",
+    "words_plus_chars = pd.DataFrame(fs.fit_transform(X).toarray(), columns=word_vect.get_feature_names()+char_vect.get_feature_names())\n",
+    "words_plus_chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6, 6)\n",
+      "+\n",
+      "(6, 16)\n",
+      "========\n",
+      "(6, 22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print words.shape\n",
+    "print \"+\"\n",
+    "print chars.shape\n",
+    "print \"========\"\n",
+    "print words_plus_chars.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Pipeline http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\n",
+    "# is a module that chains different sklearn modules together in order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Test the various methods of the pipeline (anova).\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test with selectkbest + LogisticRegression\n",
+    "# SelectKBest http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection\n",
+    "# will automatically attempt to reduce the number of features by performing \n",
+    "# statistical tests (including ANOVA or Chi Squared)\n",
+    "\n",
+    "logreg = LogisticRegression()\n",
+    "filter1 = SelectKBest(k=2) # select the best 2 features\n",
+    "pipe = Pipeline([('anova', filter1), ('logistic', logreg)])\n",
+    "\n",
+    "\n",
+    "mean = cross_val_score(pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You can use make_pipeline to make simpler pipelines\n",
+    "easy_pipe = make_pipeline(SelectKBest(k=2), LogisticRegression())\n",
+    "mean = cross_val_score(easy_pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# cross validate two naive bayes models.\n",
+    "# One model should attempt to fit to the count vectorized text\n",
+    "# The other should fit to the tfidfed text\n",
+    "# Each model whould be fit over a 10 fold validation with accuracy\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98026032061360824"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Answers\n",
+    "\n",
+    "vect = CountVectorizer()\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96320881558493299"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vect = TfidfVectorizer()\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9840269835443396"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A more complicated pipeline\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "mean = cross_val_score(pipe, df.msg, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not only is this PROPER cross validation of count vectorizers, its better than either model combined!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'features__counts__ngram_range': [(1, 1), (1, 2)], 'features__tf_idf__ngram_range': [(1, 1), (1, 2)]}\n",
+      "\n",
+      "0.985642498205 {'features__counts__ngram_range': (1, 2), 'features__tf_idf__ngram_range': (1, 1)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pipeline can also be used with GridSearchCV, WARNING SLOWWW\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "gram_range = [(1, n) for n in range(1, 3)]\n",
+    "param_grid = {\n",
+    "    'features__counts__ngram_range': gram_range,\n",
+    "    'features__tf_idf__ngram_range': gram_range,\n",
+    "}\n",
+    "print param_grid\n",
+    "print\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(df.msg, df.label)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# We can also make our own custom transformations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DatetimeTransformer(TransformerMixin):\n",
+    "    def transform(self, X, **transform_params): \n",
+    "        if 'date' in X:\n",
+    "            years = X['date'].apply(lambda x: x.year)\n",
+    "            months = X['date'].apply(lambda x: x.month)\n",
+    "            days = X['date'].apply(lambda x: x.day)\n",
+    "            return pd.DataFrame({'year':years, 'month':months, 'day':days})\n",
+    "        return X\n",
+    "\n",
+    "    def fit(self, X, y=None, **fit_params):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = '../data/yelp.csv'\n",
+    "yelp = pd.read_csv(url, encoding='unicode-escape', usecols=[1, 3])\n",
+    "yelp['date'] = pd.to_datetime(yelp['date'])\n",
+    "yelp = yelp[(yelp.stars == 1) | (yelp.stars == 5)] # only look at best and worst ratings\n",
+    "yelp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   day  month  year\n",
+       "0   26      1  2011\n",
+       "1   27      7  2011\n",
+       "3   27      5  2010\n",
+       "4    5      1  2012\n",
+       "6   12      2  2010"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = DatetimeTransformer() # our custom transformer\n",
+    "h.fit_transform(yelp).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp.head() # original data unscathed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.81669161774511989"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pipeline with our custom transformer and logistic regression\n",
+    "\n",
+    "pipe = Pipeline([('transform_datetime', DatetimeTransformer()), ('logistic', logreg)])\n",
+    "mean = cross_val_score(pipe, yelp.drop('stars', axis=1), yelp.stars, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Hmmmmmm not bad... but what was that null accuracy?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5    0.816691\n",
+       "1    0.183309\n",
+       "Name: stars, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp['stars'].value_counts() / yelp.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# It appears dates aren't helping at all.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>1480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>735</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label  color  proline\n",
+       "0      1   5.64     1065\n",
+       "1      1   4.38     1050\n",
+       "2      1   5.68     1185\n",
+       "3      1   7.80     1480\n",
+       "4      1   4.32      735"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read three columns from the dataset into a DataFrame\n",
+    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'\n",
+    "col_names = ['label', 'color', 'proline']\n",
+    "wine = pd.read_csv(url, header=None, names=col_names, usecols=[0, 10, 13])\n",
+    "wine.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.938202</td>\n",
+       "      <td>5.058090</td>\n",
+       "      <td>746.893258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.775035</td>\n",
+       "      <td>2.318286</td>\n",
+       "      <td>314.907474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.280000</td>\n",
+       "      <td>278.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>500.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.690000</td>\n",
+       "      <td>673.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>985.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1680.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            label       color      proline\n",
+       "count  178.000000  178.000000   178.000000\n",
+       "mean     1.938202    5.058090   746.893258\n",
+       "std      0.775035    2.318286   314.907474\n",
+       "min      1.000000    1.280000   278.000000\n",
+       "25%      1.000000    3.220000   500.500000\n",
+       "50%      2.000000    4.690000   673.500000\n",
+       "75%      3.000000    6.200000   985.000000\n",
+       "max      3.000000   13.000000  1680.000000"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define X and y\n",
+    "feature_cols = ['color', 'proline']\n",
+    "X = wine[feature_cols]\n",
+    "y = wine.label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.90664944003e-16\n",
+      "1.0\n",
+      "1.6027279754e-16\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# split into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
+    "\n",
+    "\n",
+    "# standardize X_train\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "\n",
+    "\n",
+    "# Recall that standard scalar does a basic z-score normalization\n",
+    "# So for each element, x, it computes z = (x - mean) / std\n",
+    "# This gives the entire series a mean of 0 and a standard deviation (and variance) of 1\n",
+    "\n",
+    "# check that it standardized properly\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_train_scaled[:, 0].mean() # 0\n",
+    "print X_train_scaled[:, 0].std()  # 1\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_train_scaled[:, 1].mean() # 0\n",
+    "print X_train_scaled[:, 1].std()  # 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# standardize X_test\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0305898576303\n",
+      "0.866822198488\n",
+      "0.0546533341088\n",
+      "1.14955947533\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Is anything wrong here?\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_test_scaled[:, 0].mean()\n",
+    "print X_test_scaled[:, 0].std()\n",
+    "\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_test_scaled[:, 1].mean()\n",
+    "print X_test_scaled[:, 1].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Run a KNN (3 neighbors) on the training set and find the accuracy on the testing set\n",
+    "# Do this TWICE once for the unscaled data, and once for the scaled data\n",
+    "# What do you notice about the accuracies\n",
+    "\n",
+    "\n",
+    "# Thought experiment: Why am I not asking for a cross validation?\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.644444444444\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Answers\n",
+    "\n",
+    "# KNN accuracy on original data\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred_class = knn.predict(X_test)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.866666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# KNN accuracy on scaled data\n",
+    "knn.fit(X_train_scaled, y_train)\n",
+    "y_pred_class = knn.predict(X_test_scaled)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# So we agree, scaling data helps us out in KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# Cross validate a KNN with 3 neighbors on the X and y variables with  (UNSCALED)\n",
+    "# 5 folds and output average accuracy for the models\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.71983168041991563"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER proper cross-validation on the original (unscaled) data\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "cross_val_score(knn, X, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90104247104247115"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Why is this improper cross-validation on the scaled data?\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "cross_val_score(knn, X_scaled, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Make a pipeline that scales data and then runs a KNN with 3 neighbors\n",
+    "# cross validate your pipeline using cv=5 with accuacy metric\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.89516011810129448"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "\n",
+    "# fix the cross-validation process using Pipeline\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3))\n",
+    "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()\n",
+    "\n",
+    "# It is NOT as good... But it is more accurate to our true estimate (and also better than the unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.910112359551 {'kneighborsclassifier__n_neighbors': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
+    "neighbors_range = range(1, 21)\n",
+    "param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range)\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/10_review-Eugene-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/10_review-Eugene-checkpoint.ipynb
new file mode 100644
index 0000000..07f8a96
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/10_review-Eugene-checkpoint.ipynb
@@ -0,0 +1,3224 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Review with bank marketing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "- Data from the UCI Machine Learning Repository: [data](https://github.com/sinanuozdemir/sfdat22/blob/master/data/bank.csv), [data dictionary](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)\n",
+    "- **Goal:** Predict whether a customer will purchase a bank product marketed over the phone\n",
+    "- `bank.csv` is already in our repo, so there is no need to download the data from the UCI website"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Read the data into Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
+       "1   39     services   single        high.school      no       no       no   \n",
+       "2   25     services  married        high.school      no      yes       no   \n",
+       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree      no      yes       no   \n",
+       "\n",
+       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
+       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
+       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
+       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
+       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
+       "\n",
+       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
+       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
+       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
+       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
+       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
+       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank = pd.read_csv('../data/bank.csv', sep=';')\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4119, 21)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "job               0\n",
+       "marital           0\n",
+       "education         0\n",
+       "default           0\n",
+       "housing           0\n",
+       "loan              0\n",
+       "contact           0\n",
+       "month             0\n",
+       "day_of_week       0\n",
+       "duration          0\n",
+       "campaign          0\n",
+       "pdays             0\n",
+       "previous          0\n",
+       "poutcome          0\n",
+       "emp.var.rate      0\n",
+       "cons.price.idx    0\n",
+       "cons.conf.idx     0\n",
+       "euribor3m         0\n",
+       "nr.employed       0\n",
+       "y                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Do we have any null values?\n",
+    "bank.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Prepare at least three features\n",
+    "\n",
+    "- Include both numeric and categorical features\n",
+    "- Choose features that you think might be related to the response (based on intuition or exploration)\n",
+    "- Think about how to handle missing values (encoded as \"unknown\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
+       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
+       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
+       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list all columns (for reference)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### y (response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    0\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert the response to numeric values and store as a new column called \"outcome\"\n",
+    "bank['outcome'] = bank.y.map({'no':0, 'yes':1})\n",
+    "bank['outcome'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's explore a few columns manually and visually"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc7a8470>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh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\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc7b5e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exercise!\n",
+    "# create a boxplot of age the data by outcome\n",
+    "# TODO\n",
+    "bank.boxplot(column='age',by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xcac4b38>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh\nQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUY\nGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlfe3eISL2\nAz4C/BGwBvhAZn6g3LcMuBg4GlgOnJmZV3WqWEmS1D1thwbgX4FbgccBhwGfi4jlmXkFcAXwI+Bw\n4FnAZRFxcGbe3qmCJUmdtXz5raxbd8929/f21hgcnM+6dZsYG6tv97jBwb1YtuzAnVGiZoie8fHx\nygdHxEJgLfCYzPxZue3LwErgMorQ8NDM3Fzuuwq4JjPPrtL+6tXrqxejWaWvr8bQ0ADDwyNs3br9\nPzqSpteaNWs47LBHUK9P/XXZ29vLTTfdzOLFiztQmabTkiULeqoc125PwyZgBPiriDgLeATwROAt\nwFHAjY3AULqWYqhCkjQDLV68mOuv/2HHehoMDHNbW6EhM0cj4tXAhcBrgV7gU5n5qYi4gKLHodkq\nYL+OVCpJ2il2NKRgT6EaJjOn4RDga8B7gN8DPhgR3wL2AEZbjh0F+qs2XKv1UKtV6iHRLNPbW3vA\nraTZw9evGtoKDRFxPPBSYL/MHAV+WJ5N8TbgW0Brv1Q/sLFq+4sWDdDTY2iYywYH53e7BEmT5OtX\n7fY0PA74VRkYGn5IMafhDoqzKZotBe6s2vjatSP2NMxRVcdEJc08d67dyIVf+V9e/ee/xz6L9uh2\nOdoJhoYGKh3XbmhYCTwyIvoyc2u57RCKUzCvB86KiP6mUHEMcE3Vxuv1cep1T6CYy8bG6o6JSrPM\n6OhWVqxaz+joVl+/u7h2Q8PXgfOBj0fEucDBwFnlv6uBFcAlEXEOcCJwBHBqx6qVJEld09aslsxc\nBxwP7AP8N/Be4OzM/Hhm1imCwlLgBuAU4GQXdpIkaW5o++yJzPwFcMJ29t0CHDvVoiRJ0szj+TOS\nJKkSQ4MkSarE0CBJkioxNEiSJrRwz36e/7Rg4Z6VF/jVHGVokCRNaOGCfk454WAWLjA07OoMDZIk\nqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZrQvVvGuO2367h3y1i3S1GXGRokSRNa\nedcIr373d1h510i3S1GXGRokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVdIz\nPj7e7Rrus3r1+plTjDqqr6/G0NAAw8MjbN1a73Y5ktpQHx9ncx12r0Gtp6fb5WgnWLJkQaX/WHsa\nJEkT2m1eLwcsHWS3eb3dLkVdZmiQJEmVGBokSVIlhgZJklRJXzsHR8RLgE8B40BP0209M/si4kDg\nY8DRwHLgzMy8qqMVS5Kkrmi3p+ELwFJgn/L2AOBm4J/L/ZcDK4HDgUuByyJiv86UKkmSuqmtnobM\nHAV+1/g+Is4qvzwrIo4DDgSOzMzNwHkRcTxwGnB2h+qVJEldMuk5DRExBLwJeHNmbgGOBG4sA0PD\ntRRDFZKkWeru9aN87t9/wd3rR7tdirpsKhMhTwfuyMzLyu/3oRiaaLYKcHhCkmaxuzeM8vkrk7s3\nGBp2dW0NT7R4KXBe0/d7AK2/UaNAf9UGa7UeajVXG5uLentrD7iVNHvUytdtrbdGX5+v4V3ZpEJD\nRBwBPAz4YtPmzcCilkP7gY1V2120aIAelyid0wYH53e7BEltWjOyBYCBgX6Ghga6XI26abI9DScA\nV2fmPU3b7gAObTluKXBn1UbXrh2xp2GO6u2tMTg4n3XrNjE25rUnpNlkZGT0vtvh4ZEuV6OdoWoY\nnGxoOBL4r5Zt1wNvjoj+8iwLgGOAa6o2Wq+PU697zaq5bGys7gWrpFmmXgb9uq/fXd5kQ8NjgM+0\nbPsusAK4JCLOAU4EjgBOnXR1mjWWL7+Vdevu2e7+qj0Ng4N7sWzZgTujREnSFE02NDwUGG7ekJn1\niDgJ+ARwA8WiTydn5u1TK1Ez3Zo1azjqqMdSr0/9E0hvby833XQzixcv7kBlkqROmlRoyMxtDn5k\n5i3AsVOqSLPO4sWLuf76H3asp8HAIM0s8/pq7L/3AuZ55sQur2d8fObMIVi9ev3MKUYd1ddXY2ho\ngOHhEcdEpVnG1+/ct2TJgkpnIRgbJUlSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZNCy+tK0mz\nn6FB08JL60rS7GdokCRN6I7VGzj9/G9zx+oN3S5FXWZokCRNaMvWOitWrWeLCzvt8gwNkiSpEkOD\nJEmqxNAgSZIqMTRIkqRKDA2aFl5aV5Jmv75uF6Bdw8OW7MmH33Scl9aVpFnMj32SpAkt3LOf5z8t\nWLhnf7dLUZcZGiRJE1q4oJ9TTjiYhQsMDbs6Q4MkSarE0CBJkioxNEiSpEoMDZIkqRJDgyRJqsTQ\noGnhpXUlafYzNGhaeGldafa6d8sYt/12HfduGet2KeoyQ4MkaUIr7xrh1e/+DivvGul2KeqytpeR\njojdgPcDzwdGgU9m5lvLfcuAi4GjgeXAmZl5VaeKlSRJ3TOZnoYLgOOBpwKnAC+LiJeV+64AVgKH\nA5cCl0XEfp0oVJIkdVdbPQ0RMQScBhyXmf9TbnsPcGRE3AwcCByZmZuB8yLi+PL4sztbtiRJmm7t\nDk8cA9ydmdc2NmTm+QARcRZwYxkYGq6lGKqQJEmzXLuh4SBgeUS8CHgLsBvwKeBcYB+KoYlmqwCH\nJyRJmgPaDQ17Ao8GXg6cShEUPgpsBPagmBjZbBSofFm0Wq2HWq2nzZI0Gyzaaz7Pf1qwaK/59PV5\n0o40m9R6a/fd+vrdtbUbGrYCC4DnZ+btABFxAHA6cCWwuOX4fopAUcmiRQP09Bga5qKhITjgYQu7\nXYakSVgzsgWAgYF+hoYGulyNuqnd0HAnsLkRGEpJMQRxB3BYy/FLy/tUsnbtiD0Nc1Rvb43Bwfms\nW7eJsTEXeJJmk4Xz+7jwjccyMK/G8LBrNcxFVcNgu6HhemD3iHhkZt5cbjuUYk2G64GzIqI/MxvD\nFMcA11RtvF4fp14fb7MkzSZjY3W2uiqkNKv09dU4YOkgw8Mjvn53cW2Fhsz8ZUR8A7gkIk6nmNPw\nZopTKq8GVpT7zgFOBI6gmPsgSZJmucnMaHkBcDNFD8IlwAWZ+aHMrFMEhaXADRQLP53cMpQhSZJm\nqZ7x8ZkzHLB69fqZU4w6qq+vxtDQgN2b0izk63fuW7JkQaUJhZ47I0mSKjE0aFp4aV1Jmv0MDZoW\nXlpXkmY/Q4MkaUJ3rx/lc//+C+5e37ror3Y1hgZJ0oTu3jDK569M7t5gaNjVGRokSVIlhgZJklSJ\noUGSJFViaJAkSZUYGiRJUiXtXuVSmpR9HzLAhW88lt2NqZI0a/knXNNit3m9HLB0kN3m9Xa7FElt\nmtdXY/+9FzCvz7eMXZ0XrNK08II30uzl63fu84JVkiSpowwNkiSpEkODJEmqxNAgSZIqMTRIkqRK\nDA2aFl5aV5JmP0ODpoWX1pWk2c/QIEma0B2rN3D6+d/mjtUbul2KuszQIEma0JatdVasWs8WF3ba\n5RkaJElSJYYGSZJUiaFBkiRV0valsSPiZOCrwDjQU95+JTP/IiKWARcDRwPLgTMz86qOVStJkrpm\nMj0NhwJfA5aW//YB/rrcdwWwEjgcuBS4LCL260CdmuW8tK4kzX5t9zQAhwA3Zebq5o0RcRxwIHBk\nZm4GzouI44HTgLOnXKlmtYct2ZMPv+k4L60rTbNVazey+d6xqbUxvBGAlXeNMDY2PqW2dt+tl70X\n7TGlNtQ9kwkNhwLbGnI4ErixDAwN11IMVUiSptmqtRs562PXd6y9i674aUfa+aeXH2VwmKUmExoC\neHpEvBXoBf4VeDvFMMXKlmNXAQ5PSFIXNHoYXvbMQ9l38cCk2+nt7WHB4HzWr9s0pZ6GlWtGuPjr\nP5tyz4e6p63QEBEPB+YDm4DnUgxHXFBu2wNoXSN4FOiv2n6t1kOt1tNOSZolentrD7iVtPP19hZ/\nT/d/6J4s22dwCu3UGBycz7p1/YyNTX54sVFPb28Pfc5vmpXaCg2Z+ZuIWJyZd5ebfhIRvRSTHj8F\nDLXcpR/YWLX9RYsG6OkxNMxlg4Pzu12CtMtYM7IFgAWD8xkamnxPQ8NUX7+drkfTr+3hiabA0PBz\nYHfgtxSTJJstBe6s2vbatSP2NMxR939S2TSlTyqSqlu/btN9t8PD8ybdTqdev52qR51XNcS1Ozzx\nNOBzwH5NEx4fC9wFXAO8ISL6M7MxTHFMub2Sen2cen1qM3M1s42N1T17QpomjfkHY2PjHXndTfX1\n2+l6NP3a7Wn4HsVww8cj4mzgEcD5wLuAq4EVwCURcQ5wInAEcGrHqpUkSV3T1kyUzNwAnAAsAX5A\nsfrjRZn53sysUwSFpcANwCnAyZl5e2dL1mzkpXUlafabzJyGn1MEh23tuwU4dqpFae7x0rqSNPt5\nzoskSarE0CBJkioxNEiSpEoMDZIkqRJDgyRJqmQyF6zSLsZL60qSwNCgHfDSupKkBkODJuSldSVJ\nDYYGVbLv4gEOWLpg0vfv66sxNDTA8PA815yXpFnKiZCSJKkSQ4MkSarE0CBJkioxNEiSpEoMDZIk\nqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZIqMTRIkqRKDA2SJKkSQ4MkSarE0CBJ\nkioxNEiSpEoMDZIkqZK+yd4xIr4BrMrM08rvlwEXA0cDy4EzM/OqDtQoSZJmgEn1NETEXwJ/0rL5\ncmAlcDhwKXBZROw3tfIkSdJM0XZoiIgh4Hzgv5u2HQccBLwiC+cB1wGndapQSZLUXZMZnngP8Gng\nYU3bjgRuzMzNTduupRiqkCRJc0BbPQ1lj8KTgHNadu1DMTTRbBXg8IQkSXNE5Z6GiOgHLgJOz8zR\niGjevQcw2nKXUaC/nWJqtR5qtZ527qKdrLe3577bvr7Jn2zT21t7wG2365F2Bb5+1WntDE/8PfCD\nzPyPbezbDCxq2dYPbGynmEWLBujpMTTMJGtGtgCwYHA+Q0MDU25vcHD+jKpHmst8/arT2gkNzwP2\njoj15ff9ABHxHOAfgUNbjl8K3NlOMWvXjtjTMMOsX7fpvtvh4XmTbqe3t8bg4HzWrdvE2Fi96/VI\nuwJfv6qqaohrJzQ8GWj+Xz4fGAfeBCwD/jYi+jOzMUxxDHBNG+1Tr49Tr4+3cxftZGNj4/fdbt06\n+T8W97dXn1I7na5Hmst8/arTKoeGzFzR/H3Z4zCembdGxG3ACuCSiDgHOBE4Aji1g7VKktq015b1\n1FeuYPPoHpNuo7e3xry189k0xZ6G+pqN7LVl/Y4P1Iw16RUhm2VmPSJOAj4B3ADcDJycmbd3on1J\nUvvGRzbwitsuZ8uHx/lNt4spvYIexkeeACzodimahEmHhsz8q5bvbwGOnXJFkqSO6BnYk48ecDJn\n/Nmj2Gfx1HoaOjGn4c41G7ngG7/itQN7TroNdVdHeho0t9m9Kc1e98xbQG3f/dl96eQ/2ff11dhz\naIAtwyNTmotQ61/PPfN+O+n7q/sMDZqQ3ZuSpAZDgyZk96YkqcHQoB2ye1OSBJO8NLYkSdr1GBok\nSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFB\nkiRVYmiQJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVElftwvQ7HDbqvVTun9v\nbw9rRrawft0mxsbGJ93OyjUjU6pDkjR5hgZNaKxevMFf8s1fdLmSB9p9t95ulyBJu5y2Q0NEPAL4\nEPBEYA1wYWa+p9y3DLgYOBpYDpyZmVd1qlhNv4P2HeRtL348vbWeKbWzangjF13xU1550mHsPbTH\nlNrafbde9l40tTYkSe1rKzRERA/wDeD7wP8BHgV8ISJuz8wvAFcAPwIOB54FXBYRB2fm7Z0tW9Pp\noH0Hp9xGb28ROvZ9yAD7Ldlzyu1JkqZfuz0NewM/BE7PzBHg1xHxLeCYiFgFHAgcmZmbgfMi4njg\nNODsThY+9fhvAAAMQklEQVQtSarOOUnqlLZCQ2b+Fnh+4/uIeCLwJOB04CjgxjIwNFxLMVQhSZpm\nzklSp016ImRELAf2B/4v8FXgn4GVLYetAvab7GNIkibPOUnqtKmcPfFsYCnwEeD9wB7AaMsxo0B/\n1QZrtR5qU/zl1sxU663dd9vX5/Ig0nR59MMXTrmNeWXPwH57L+DhD3VO0q5s0qEhM28EiIjXAZ8F\nPgEMtRzWD2ys2uaiRQP09Bga5qI1I1sAGBjoZ2hooMvVSGqHr181tHv2xEOBozPziqbNPwN2A+4E\nDmm5y9JyeyVr147Y0zBH3Tu6hf33XsC9o1sYHnYylDSbjIyM3nfr63duqhoG2+1pOBD4akTsl5mN\nMPB44HcUkx7fGBH9mdkYpjgGuKZq4/X6OPX65GfmaubaZ9EefPhNxzE8PMLWrfVulyOpDfWx+n23\nvn53be2Ghh8ANwCfLIclDgTOB/4BuBpYAVwSEecAJwJHAKd2rFpJktQ1bc1Iy8w6cBIwAnwP+Bjw\nz5l5YbnvRIohiRuAU4CTXdhJkqS5oe2JkOVaDc/Zzr5bgGOnWpQkaeaY11dj/70XMM8zn3Z5PePj\nM2cOwerV62dOMeqovr4aQ0MDzmmQZiFfv3PfkiULKp2FYGyUJEmVGBokSVIlhgZJklSJoUHT4o7V\nGzj9/G9zx+oN3S5FkjRJhgZNiy1b66xYtZ4tTqKSpFnL0CBJkioxNEiSpEoMDZKkCTknSQ2GBknS\nhJyTpAZDgyRJqsTQIEmSKjE0aFos3LOf5z8tWLhnf7dLkSRNkqFB02Lhgn5OOeFgFi4wNEjSbGVo\nkCRJlRgaJElSJYYGSdKEnJOkBkODJGlCzklSg6FBkiRVYmiQJEmVGBo0Le7dMsZtv13HvVvGul2K\nJGmSDA2aFivvGuHV7/4OK+8a6XYpkqRJMjRIkqRKDA2SJKkSQ4MkaULOSVKDoUGSNCHnJKmhr52D\nI2Jf4ALgWGAj8CXgrMy8NyKWARcDRwPLgTMz86qOVitJkrqm3Z6GrwC7A08E/hJ4JnBOue8KYCVw\nOHApcFlE7NehOiVJUpdV7mmIiACeAOydmXeV294OvDsi/g04EDgyMzcD50XE8cBpwNmdL1uSJE23\ndoYnfgs8vREYmuwFHAXcWAaGhmsphiok9n3IABe+8Vh2dxaNJM1alUNDZt4D3DdHISJ6gFcD3wL2\noRiaaLYKcHhCAOw2r5e9hwYYHh5h69Z6t8uRJE1CWxMhW7wbeCxwBPA6YLRl/yjQ1iXRarUearWe\nKZSkmaq3t/aAW0mzR6183dZ6a/T1+RrelU0qNETEu4AzgL/IzJ9FxGZgUcth/RRnWFS2aNEAPT2G\nhrlscHB+t0uQ1KY1I1sAGBjoZ2hooMvVqJvaDg0R8UHgFcALMvPycvMdwKEthy4F7myn7bVrR+xp\nmKN6e2sMDs5n3bpNjI05PCHNJgvn93HhG49lYF6N4WHXapiLqobBdtdpeAfwcuB5mXlZ067rgTdH\nRH9mNoYpjgGuaaf9en2cen28nbtolhkbqzunQZpl+vpqHLB00DlJauuUy0OAtwH/CHwvIvZu2v1d\nYAVwSUScA5xIMdfh1M6VKkmSuqmdGS0nlse/jeJMiZUUww8rM7MOnEwxJHEDcApwcmbe3tlyJUlS\nt/SMj8+c4YDVq9fPnGLUURs2beG6n/+Oow95KHvOn9ftciS1oa+vxpCnTM9pS5YsqDShcCqnXEr3\nWb78Vtatu2e7+1cNb+Siy38KJx/G3kN7bPe4wcG9WLbswJ1RoqTt2NHrt+pEZl+/c589DZqyNWvW\ncNhhj6Ben/onkN7eXm666WYWL17cgcok7YivX0H1ngZDgzrCTyrS7OXrV4YGzSiOiUqzl6/fua9q\naHA9UEmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZJklSJoUGS\nJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBok\nSVIlhgZJklRJ32TvGBH9wA3AqzLz6nLbMuBi4GhgOXBmZl419TIlSVK3TaqnoQwMnwcObdl1ObAS\nOBy4FLgsIvabUoWSJGlGaDs0RMQhwPXAgS3bjwMOAl6RhfOA64DTOlGoJEnqrsn0NDwZ+BbFEERP\n0/YjgRszc3PTtmvL4yRJ0izX9pyGzLyo8XVENO/ah2JootkqwOEJSZLmgElPhNyGPYDRlm2jQH/V\nBmq1Hmq1nh0fqFmnt7f2gFtJs4evXzV0MjRsBha1bOsHNlZtYPHiPU0Mc9zg4PxulyBpknz9qpOx\n8Q5gacu2pcCdHXwMSZLUJZ0MDdcDjytPx2w4ptwuSZJmuU4OT3wXWAFcEhHnACcCRwCndvAxJElS\nl0y1p2G88UVm1oGTKIYkbgBOAU7OzNun+BiSJGkG6BkfH9/xUZIkaZfn+TOSJKkSQ4MkSarE0CBJ\nkioxNEiSpEoMDZIkqZJOrtMgbVO54NeHgWdTLCv+3sx8X3erktSO8nV8A/CqzLy62/WoO+xp0HR4\nD/A44CnA6cA7IuLZXa1IUmVlYPg8cGi3a1F3GRq0U0XEHsBLgTMy88eZeQVwPvDq7lYmqYqIOITi\ncgAHdrsWdZ+hQTvbH1AMg13XtO1a4MjulCOpTU8GvgUcDXgl4l2ccxq0s+0D3JWZW5u2rQJ2j4jF\nmbmmS3VJqiAzL2p8HRHdLEUzgD0N2tn2AEZbtjW+70eSNGsYGrSzbebB4aDx/cZprkWSNAWGBu1s\ndwAPiYjm37WlwKbMvLtLNUmSJsHQoJ3tR8AW4KimbU8CftCdciRJk+VESO1UmbkpIj4NXBQRpwH7\nAa8HXtLdyiRJ7TI0aDq8jmJFyG8D9wB/V67XIGl2Ge92AequnvFxfwckSdKOOadBkiRVYmiQJEmV\nGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokLRdEbF/RDyv23VImhkM\nDZIm8i/ACd0uQtLMYGiQNJGebhcgaebwglXSHBYRQ8A/AM8EHgLcCLw1M78bEX8PvCQzD2w6/r5t\nEfEd4MnlruWZeVBE9AFvB14MLAF+CrwlM/+jvP/BwLuAJ1JcRfcq4PWZ+Zty/3eAHwD7ACcD64F3\nAv8LfAh4NPAj4MWZ+evyPvsC76Po8RgD/qts8+bOPluSdsSeBmmOiogaxZv2E4FTgMdRvDn/e0Qc\nTnGZ49ZPDc3bng1cB3wReHy57QLg5cCZwGOAK4GvRcSjIuLh5fGbKMLGU4GlwNURsWfTY5xBEV5+\nD7gc+CDFpdPPAJ5EESjOK3+GPYD/pAgLTwL+CFgNfD8i9pn0kyNpUvq6XYCkneYE4LHAYzLz5+W2\nv4mIJwBvBH6+3XsCmTkcEfcCmzJzbfnGfxrwqsy8rDzsbREBMAg8l6Ln4EWZuQUgIp4D3Aq8ELio\nvM8PM/P95f4LgVcCF2TmNeW2LwEnlcc+H9irbLNe7n8ZcBzwMuDsST0zkibFngZp7noMcE9TYGi4\nmuJTfrsCmAd8v3ljZr4tM/+nbPOGRmAo960CsuXxmocVRsrbW5q2bQL6y68fCywG7omI9RGxHlgH\n7AccMomfQdIU2NMgzV3bm8RYA7ZsZ99EfxO2TNBmO4+3rceuT3DfX1DMyWhtf8MEtUjaCexpkOau\nnwB7RcShLduPAX4G3AssaNn36Jbvm+c8/IriDf+I5gMi4rqI+P/KxzsiIuY17dsbeBTFhMnJuAk4\ngKLH5JbMvAX4DcVkyz+aZJuSJsmeBmnuuhL4MfC5iDgD+B3wGophi1cCA8CiiHg98GXg6eW/NU1t\nbACWRcTDMvOOiPgg8A8RcRdFEPjrsr1vUASKVwKfiYhzgfnAu8vH/eIkf4ZLgTcDX4mIN1MMTby9\nrPNtk2xT0iTZ0yDNUeXEwacCPwS+SnGq46HAcZn5g8z8T+AdwOspAsAfU7whN7uIYj7CjyOiBzgL\n+DTwEYqehScDf5KZN2fmbeX3QxRnUXwTuAM4JjPXTVDqds/7Lu/3R8BdwL9RzKfYB/jjzMxqz4Sk\nTnGdBkmSVIk9DZIkqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZIqMTRIkqRKDA2S\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcad0ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "bank.boxplot(column='age', by='outcome')\n",
+    "# probably not a great feature, why?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x112e19410>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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baP1+bQe8AHhLrYJLSdcAzye9rt3O8iMwo37zM5aThPtIc5631Y6lU65oPZ60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\n8WCDpirPIu3Z06g+JRGxdeex3Ob9TOA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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112760e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('job').outcome.mean().plot(kind='bar')\n",
+    "# Ask yourself, WHY is this going to be useful?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>job_blue-collar</th>\n",
+       "      <th>job_entrepreneur</th>\n",
+       "      <th>job_housemaid</th>\n",
+       "      <th>job_management</th>\n",
+       "      <th>job_retired</th>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <th>job_services</th>\n",
+       "      <th>job_student</th>\n",
+       "      <th>job_technician</th>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <th>job_unknown</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   job_blue-collar  job_entrepreneur  job_housemaid  job_management  \\\n",
+       "0              1.0               0.0            0.0             0.0   \n",
+       "1              0.0               0.0            0.0             0.0   \n",
+       "2              0.0               0.0            0.0             0.0   \n",
+       "3              0.0               0.0            0.0             0.0   \n",
+       "4              0.0               0.0            0.0             0.0   \n",
+       "\n",
+       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
+       "0          0.0                0.0           0.0          0.0             0.0   \n",
+       "1          0.0                0.0           1.0          0.0             0.0   \n",
+       "2          0.0                0.0           1.0          0.0             0.0   \n",
+       "3          0.0                0.0           1.0          0.0             0.0   \n",
+       "4          0.0                0.0           0.0          0.0             0.0   \n",
+       "\n",
+       "   job_unemployed  job_unknown  \n",
+       "0             0.0          0.0  \n",
+       "1             0.0          0.0  \n",
+       "2             0.0          0.0  \n",
+       "3             0.0          0.0  \n",
+       "4             0.0          0.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create job_dummies (we will add it to the bank DataFrame later)\n",
+    "job_dummies = pd.get_dummies(bank.job, prefix='job')\n",
+    "# removing the first job from the list (making it k-1 columns) where k is the number of jobs\n",
+    "job_dummies.drop(job_dummies.columns[0], axis=1, inplace=True)\n",
+    "job_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x18ad20f0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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lWtta4B2FvkfW66a7CTgaWEU1EvEbUysy817gOQ3rkSRJPdI0JOwHPJSZO6a1bQD2jIhl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\npKL/D62ylSnSyXCKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x18a9b550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('default').outcome.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "default  outcome\n",
+       "no       0          2913\n",
+       "         1           402\n",
+       "unknown  0           754\n",
+       "         1            49\n",
+       "yes      0             1\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# How many people have a value of no, yes, and unknown?\n",
+    "# TODO\n",
+    "bank.groupby('default').outcome.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "no         3315\n",
+       "unknown     803\n",
+       "yes           1\n",
+       "Name: default, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "bank.default.value_counts()\n",
+    "# only one person in the dataset has a status of yes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "      <th>outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education  default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y        0      yes       no   \n",
+       "1   39     services   single        high.school        0       no       no   \n",
+       "2   25     services  married        high.school        0      yes       no   \n",
+       "3   38     services  married           basic.9y        0  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree        0      yes       no   \n",
+       "\n",
+       "     contact month day_of_week   ...     pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri   ...       999         0  nonexistent   \n",
+       "1  telephone   may         fri   ...       999         0  nonexistent   \n",
+       "2  telephone   jun         wed   ...       999         0  nonexistent   \n",
+       "3  telephone   jun         fri   ...       999         0  nonexistent   \n",
+       "4   cellular   nov         mon   ...       999         0  nonexistent   \n",
+       "\n",
+       "   emp.var.rate cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \\\n",
+       "0          -1.8         92.893          -46.2      1.313       5099.1  no   \n",
+       "1           1.1         93.994          -36.4      4.855       5191.0  no   \n",
+       "2           1.4         94.465          -41.8      4.962       5228.1  no   \n",
+       "3           1.4         94.465          -41.8      4.959       5228.1  no   \n",
+       "4          -0.1         93.200          -42.0      4.191       5195.8  no   \n",
+       "\n",
+       "   outcome  \n",
+       "0        0  \n",
+       "1        0  \n",
+       "2        0  \n",
+       "3        0  \n",
+       "4        0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# So, let's treat this as a 2-class feature rather than a 3-class feature\n",
+    "bank['default'] = bank.default.map({'no':0, 'unknown':1, 'yes':1})\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### contact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contact\n",
+       "cellular     0.141403\n",
+       "telephone    0.051806\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('contact').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Convert the feature to numeric values, 0 for cellular, 1 for telephone\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "bank['contact'] = bank.contact.map({'cellular':0, 'telephone':1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "month\n",
+       "apr    0.167442\n",
+       "aug    0.100629\n",
+       "dec    0.545455\n",
+       "jul    0.082982\n",
+       "jun    0.128302\n",
+       "mar    0.583333\n",
+       "may    0.065312\n",
+       "nov    0.096413\n",
+       "oct    0.362319\n",
+       "sep    0.406250\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature at first glance\n",
+    "bank.groupby('month').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dec</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0.545455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mar</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0.406250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.362319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>apr</th>\n",
+       "      <td>215</td>\n",
+       "      <td>0.167442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nov</th>\n",
+       "      <td>446</td>\n",
+       "      <td>0.096413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jun</th>\n",
+       "      <td>530</td>\n",
+       "      <td>0.128302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aug</th>\n",
+       "      <td>636</td>\n",
+       "      <td>0.100629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jul</th>\n",
+       "      <td>711</td>\n",
+       "      <td>0.082982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>may</th>\n",
+       "      <td>1378</td>\n",
+       "      <td>0.065312</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count      mean\n",
+       "month                 \n",
+       "dec       22  0.545455\n",
+       "mar       48  0.583333\n",
+       "sep       64  0.406250\n",
+       "oct       69  0.362319\n",
+       "apr      215  0.167442\n",
+       "nov      446  0.096413\n",
+       "jun      530  0.128302\n",
+       "aug      636  0.100629\n",
+       "jul      711  0.082982\n",
+       "may     1378  0.065312"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# but, it looks like their success rate is actually just correlated with number of calls\n",
+    "# thus, the month feature is unlikely to generalize\n",
+    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.766364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-0.766364</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          count      mean\n",
+       "count  1.000000 -0.766364\n",
+       "mean  -0.766364  1.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count').corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1133cd090>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ga7bRmJmZDRujSdbaWRkRz/dUsa8tHY0kN4dqkVRcyGcEcKKk80iWTBZJa0Zpa8d4oNhV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\nhZMOMzMzq4r/Hwja06m9XpGSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1133d5610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like an excellent feature, can you think of a reason why duration isn't helpful in practice?\n",
+    "bank.boxplot(column='duration', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Answer: You can't know the duration of a call beforehand, thus it can't be used in your model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### previous"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "previous\n",
+       "0    0.082884\n",
+       "1    0.208421\n",
+       "2    0.410256\n",
+       "3    0.600000\n",
+       "4    0.714286\n",
+       "5    1.000000\n",
+       "6    0.500000\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('previous').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### poutcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "poutcome\n",
+       "failure        0.147577\n",
+       "nonexistent    0.082884\n",
+       "success        0.647887\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('poutcome').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create poutcome_dummies\n",
+    "poutcome_dummies = pd.get_dummies(bank.poutcome, prefix='poutcome')\n",
+    "poutcome_dummies.drop(poutcome_dummies.columns[0], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# concatenate bank DataFrame with job_dummies and poutcome_dummies\n",
+    "bank = pd.concat([bank, job_dummies, poutcome_dummies], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### euribor3m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1133bb450>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/AoOAJEm1ZhCQJKnGDAKSJNWYQUClNBqNqkuQVJD9KzAIqKS1a9dWXYKkguxf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113723410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like an excellent feature\n",
+    "bank.boxplot(column='euribor3m', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Model building\n",
+    "\n",
+    "- Use cross-validation to evaluate the AUC of a logistic regression model with your chosen features\n",
+    "- Try to increase the AUC by selecting different sets of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
+       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
+       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
+       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y', u'outcome',\n",
+       "       u'job_blue-collar', u'job_entrepreneur', u'job_housemaid',\n",
+       "       u'job_management', u'job_retired', u'job_self-employed',\n",
+       "       u'job_services', u'job_student', u'job_technician', u'job_unemployed',\n",
+       "       u'job_unknown', u'poutcome_nonexistent', u'poutcome_success'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# new list of columns (including dummy columns)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create X (including 13 dummy columns)\n",
+    "feature_cols = ['default', 'contact', 'previous', 'euribor3m'] + list(bank.columns[-13:])\n",
+    "X = bank[feature_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create y\n",
+    "y = bank.outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# calculate cross-validated Accuracy for logisic regression, and knn with 5 neighbors\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90215602496498271"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90046229940191136"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(5)\n",
+    "cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# use grid search to try k in range from 1-29 (odd only) for knn, and graph the accuracy from a cross val score vs the k\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score='raise',\n",
+       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
+       "           weights='uniform'),\n",
+       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       param_grid={'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27]},\n",
+       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "knn = KNeighborsClassifier()\n",
+    "param_grid = {'n_neighbors': range(1, 29, 2)}\n",
+    "grid = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x11670de90>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0mDev7EokSSqXQaKA2oqN6HuCb0mSWoxBogBXbEiSlBkkCujqcqKlJElgkGjY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\n99oAImJSRLyNfN6n5c18n42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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116473410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
+    "plt.figure()\n",
+    "plt.plot(range(1, 29, 2), grid_mean_scores)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=27, p=2,\n",
+       "           weights='uniform')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# look's like adding to k after 5 doesn't even help that much...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Create a confusion matrix from the best estimator in the gridsearch and find the accuracy, sensitivity, and specificity\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3625   43]\n",
+      " [ 351  100]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution! \n",
+    "from sklearn import metrics\n",
+    "preds = grid.best_estimator_.predict(X)\n",
+    "print metrics.confusion_matrix(y, preds)\n",
+    "# Accuracy = (3625  + 100) / 4119  == .904\n",
+    "# Sensitivity = 100 / (100 + 351)  == .222\n",
+    "# Specificity = 3625 / (3625 + 43) == .988"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Compare the accuracy to the null accuracy rate\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.89050740470988099"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "\n",
+    "null_accuracy_rate = bank['outcome'].value_counts()[0] / float(bank.shape[0])\n",
+    "null_accuracy_rate\n",
+    "# accuracy is greater than the null accuracy rate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# What can we do to make the model better?\n",
+    "\n",
+    "# Thought Experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we want to regress to the duration of the call so the rep can better plan their day?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>default</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>...</th>\n",
+       "      <th>job_management</th>\n",
+       "      <th>job_retired</th>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <th>job_services</th>\n",
+       "      <th>job_student</th>\n",
+       "      <th>job_technician</th>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <th>job_unknown</th>\n",
+       "      <th>poutcome_nonexistent</th>\n",
+       "      <th>poutcome_success</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.161427</td>\n",
+       "      <td>0.012700</td>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>-0.014169</td>\n",
+       "      <td>-0.043425</td>\n",
+       "      <td>0.050931</td>\n",
+       "      <td>-0.019192</td>\n",
+       "      <td>-0.000482</td>\n",
+       "      <td>0.098135</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.065606</td>\n",
+       "      <td>0.412542</td>\n",
+       "      <td>0.010991</td>\n",
+       "      <td>-0.050379</td>\n",
+       "      <td>-0.185453</td>\n",
+       "      <td>-0.064930</td>\n",
+       "      <td>-0.009394</td>\n",
+       "      <td>0.063831</td>\n",
+       "      <td>-0.031205</td>\n",
+       "      <td>0.043341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>default</th>\n",
+       "      <td>0.161427</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.150494</td>\n",
+       "      <td>-0.020140</td>\n",
+       "      <td>0.021239</td>\n",
+       "      <td>0.089514</td>\n",
+       "      <td>-0.092754</td>\n",
+       "      <td>0.184389</td>\n",
+       "      <td>0.162098</td>\n",
+       "      <td>0.021570</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.043783</td>\n",
+       "      <td>0.023665</td>\n",
+       "      <td>-0.019191</td>\n",
+       "      <td>0.021453</td>\n",
+       "      <td>-0.017566</td>\n",
+       "      <td>-0.081766</td>\n",
+       "      <td>-0.010086</td>\n",
+       "      <td>0.065701</td>\n",
+       "      <td>0.091124</td>\n",
+       "      <td>-0.086343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contact</th>\n",
+       "      <td>0.012700</td>\n",
+       "      <td>0.150494</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.028185</td>\n",
+       "      <td>0.085654</td>\n",
+       "      <td>0.120670</td>\n",
+       "      <td>-0.215464</td>\n",
+       "      <td>0.383405</td>\n",
+       "      <td>0.574452</td>\n",
+       "      <td>0.252035</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.045940</td>\n",
+       "      <td>-0.038983</td>\n",
+       "      <td>0.003609</td>\n",
+       "      <td>0.024214</td>\n",
+       "      <td>-0.018891</td>\n",
+       "      <td>-0.021849</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>0.021516</td>\n",
+       "      <td>0.251147</td>\n",
+       "      <td>-0.109971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>-0.020140</td>\n",
+       "      <td>-0.028185</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.085348</td>\n",
+       "      <td>-0.046998</td>\n",
+       "      <td>0.025724</td>\n",
+       "      <td>-0.028848</td>\n",
+       "      <td>0.016672</td>\n",
+       "      <td>-0.034745</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011460</td>\n",
+       "      <td>0.044257</td>\n",
+       "      <td>-0.001466</td>\n",
+       "      <td>-0.030936</td>\n",
+       "      <td>0.016982</td>\n",
+       "      <td>-0.006173</td>\n",
+       "      <td>-0.004565</td>\n",
+       "      <td>-0.008591</td>\n",
+       "      <td>-0.021259</td>\n",
+       "      <td>0.046154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>campaign</th>\n",
+       "      <td>-0.014169</td>\n",
+       "      <td>0.021239</td>\n",
+       "      <td>0.085654</td>\n",
+       "      <td>-0.085348</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.058742</td>\n",
+       "      <td>-0.091490</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.145021</td>\n",
+       "      <td>0.007882</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011967</td>\n",
+       "      <td>-0.011147</td>\n",
+       "      <td>0.021876</td>\n",
+       "      <td>0.038893</td>\n",
+       "      <td>-0.032527</td>\n",
+       "      <td>-0.004871</td>\n",
+       "      <td>0.005467</td>\n",
+       "      <td>-0.007765</td>\n",
+       "      <td>0.098957</td>\n",
+       "      <td>-0.054564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pdays</th>\n",
+       "      <td>-0.043425</td>\n",
+       "      <td>0.089514</td>\n",
+       "      <td>0.120670</td>\n",
+       "      <td>-0.046998</td>\n",
+       "      <td>0.058742</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.587941</td>\n",
+       "      <td>0.270684</td>\n",
+       "      <td>0.058472</td>\n",
+       "      <td>-0.092090</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011278</td>\n",
+       "      <td>-0.067405</td>\n",
+       "      <td>0.007591</td>\n",
+       "      <td>0.031058</td>\n",
+       "      <td>-0.043072</td>\n",
+       "      <td>0.009654</td>\n",
+       "      <td>-0.028664</td>\n",
+       "      <td>-0.006368</td>\n",
+       "      <td>0.488761</td>\n",
+       "      <td>-0.940565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>previous</th>\n",
+       "      <td>0.050931</td>\n",
+       "      <td>-0.092754</td>\n",
+       "      <td>-0.215464</td>\n",
+       "      <td>0.025724</td>\n",
+       "      <td>-0.091490</td>\n",
+       "      <td>-0.587941</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.415238</td>\n",
+       "      <td>-0.164922</td>\n",
+       "      <td>-0.051420</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.030516</td>\n",
+       "      <td>0.051054</td>\n",
+       "      <td>0.006366</td>\n",
+       "      <td>-0.007326</td>\n",
+       "      <td>0.078260</td>\n",
+       "      <td>-0.017419</td>\n",
+       "      <td>0.013485</td>\n",
+       "      <td>-0.001958</td>\n",
+       "      <td>-0.854241</td>\n",
+       "      <td>0.510865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <td>-0.019192</td>\n",
+       "      <td>0.184389</td>\n",
+       "      <td>0.383405</td>\n",
+       "      <td>-0.028848</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.270684</td>\n",
+       "      <td>-0.415238</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.755155</td>\n",
+       "      <td>0.195022</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.025176</td>\n",
+       "      <td>-0.087204</td>\n",
+       "      <td>0.000153</td>\n",
+       "      <td>-0.003592</td>\n",
+       "      <td>-0.116507</td>\n",
+       "      <td>0.047053</td>\n",
+       "      <td>-0.028425</td>\n",
+       "      <td>0.013772</td>\n",
+       "      <td>0.482106</td>\n",
+       "      <td>-0.256587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <td>-0.000482</td>\n",
+       "      <td>0.162098</td>\n",
+       "      <td>0.574452</td>\n",
+       "      <td>0.016672</td>\n",
+       "      <td>0.145021</td>\n",
+       "      <td>0.058472</td>\n",
+       "      <td>-0.164922</td>\n",
+       "      <td>0.755155</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.045835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048750</td>\n",
+       "      <td>-0.032616</td>\n",
+       "      <td>-0.001529</td>\n",
+       "      <td>0.025988</td>\n",
+       "      <td>-0.032322</td>\n",
+       "      <td>-0.002939</td>\n",
+       "      <td>-0.003210</td>\n",
+       "      <td>0.016884</td>\n",
+       "      <td>0.289799</td>\n",
+       "      <td>-0.060257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <td>0.098135</td>\n",
+       "      <td>0.021570</td>\n",
+       "      <td>0.252035</td>\n",
+       "      <td>-0.034745</td>\n",
+       "      <td>0.007882</td>\n",
+       "      <td>-0.092090</td>\n",
+       "      <td>-0.051420</td>\n",
+       "      <td>0.195022</td>\n",
+       "      <td>0.045835</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.005887</td>\n",
+       "      <td>0.065338</td>\n",
+       "      <td>0.007642</td>\n",
+       "      <td>-0.073024</td>\n",
+       "      <td>0.025850</td>\n",
+       "      <td>0.058946</td>\n",
+       "      <td>-0.000228</td>\n",
+       "      <td>0.032504</td>\n",
+       "      <td>0.100217</td>\n",
+       "      <td>0.087382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euribor3m</th>\n",
+       "      <td>-0.015033</td>\n",
+       "      <td>0.176800</td>\n",
+       "      <td>0.390313</td>\n",
+       "      <td>-0.032329</td>\n",
+       "      <td>0.159435</td>\n",
+       "      <td>0.301478</td>\n",
+       "      <td>-0.458851</td>\n",
+       "      <td>0.970308</td>\n",
+       "      <td>0.657159</td>\n",
+       "      <td>0.276595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011447</td>\n",
+       "      <td>-0.088148</td>\n",
+       "      <td>0.005542</td>\n",
+       "      <td>-0.012849</td>\n",
+       "      <td>-0.119714</td>\n",
+       "      <td>0.046468</td>\n",
+       "      <td>-0.023823</td>\n",
+       "      <td>0.017662</td>\n",
+       "      <td>0.502340</td>\n",
+       "      <td>-0.282272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nr.employed</th>\n",
+       "      <td>-0.041936</td>\n",
+       "      <td>0.167754</td>\n",
+       "      <td>0.261496</td>\n",
+       "      <td>-0.044218</td>\n",
+       "      <td>0.161037</td>\n",
+       "      <td>0.381983</td>\n",
+       "      <td>-0.514853</td>\n",
+       "      <td>0.897173</td>\n",
+       "      <td>0.472560</td>\n",
+       "      <td>0.107054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.002022</td>\n",
+       "      <td>-0.112450</td>\n",
+       "      <td>0.001342</td>\n",
+       "      <td>-0.002975</td>\n",
+       "      <td>-0.144023</td>\n",
+       "      <td>0.042848</td>\n",
+       "      <td>-0.030453</td>\n",
+       "      <td>0.010605</td>\n",
+       "      <td>0.508717</td>\n",
+       "      <td>-0.354030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outcome</th>\n",
+       "      <td>0.060374</td>\n",
+       "      <td>-0.076567</td>\n",
+       "      <td>-0.137401</td>\n",
+       "      <td>0.418565</td>\n",
+       "      <td>-0.076091</td>\n",
+       "      <td>-0.332012</td>\n",
+       "      <td>0.255697</td>\n",
+       "      <td>-0.283216</td>\n",
+       "      <td>-0.098326</td>\n",
+       "      <td>0.054393</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.015814</td>\n",
+       "      <td>0.078373</td>\n",
+       "      <td>-0.017796</td>\n",
+       "      <td>-0.021253</td>\n",
+       "      <td>0.055781</td>\n",
+       "      <td>0.009032</td>\n",
+       "      <td>0.032872</td>\n",
+       "      <td>-0.002169</td>\n",
+       "      <td>-0.207179</td>\n",
+       "      <td>0.325804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_blue-collar</th>\n",
+       "      <td>-0.042976</td>\n",
+       "      <td>0.167771</td>\n",
+       "      <td>0.098988</td>\n",
+       "      <td>0.010396</td>\n",
+       "      <td>-0.021404</td>\n",
+       "      <td>0.062240</td>\n",
+       "      <td>-0.041762</td>\n",
+       "      <td>0.050353</td>\n",
+       "      <td>0.080969</td>\n",
+       "      <td>-0.122025</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.152741</td>\n",
+       "      <td>-0.107122</td>\n",
+       "      <td>-0.104747</td>\n",
+       "      <td>-0.169771</td>\n",
+       "      <td>-0.074502</td>\n",
+       "      <td>-0.234697</td>\n",
+       "      <td>-0.086993</td>\n",
+       "      <td>-0.051108</td>\n",
+       "      <td>0.026745</td>\n",
+       "      <td>-0.053400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_entrepreneur</th>\n",
+       "      <td>0.039110</td>\n",
+       "      <td>0.020113</td>\n",
+       "      <td>0.028028</td>\n",
+       "      <td>-0.005750</td>\n",
+       "      <td>-0.024137</td>\n",
+       "      <td>0.032033</td>\n",
+       "      <td>-0.017265</td>\n",
+       "      <td>0.022054</td>\n",
+       "      <td>0.011657</td>\n",
+       "      <td>-0.014320</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.056409</td>\n",
+       "      <td>-0.039561</td>\n",
+       "      <td>-0.038684</td>\n",
+       "      <td>-0.062698</td>\n",
+       "      <td>-0.027514</td>\n",
+       "      <td>-0.086676</td>\n",
+       "      <td>-0.032128</td>\n",
+       "      <td>-0.018875</td>\n",
+       "      <td>0.008954</td>\n",
+       "      <td>-0.029330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_housemaid</th>\n",
+       "      <td>0.089297</td>\n",
+       "      <td>0.036203</td>\n",
+       "      <td>0.018311</td>\n",
+       "      <td>-0.017642</td>\n",
+       "      <td>-0.004750</td>\n",
+       "      <td>0.017684</td>\n",
+       "      <td>-0.027623</td>\n",
+       "      <td>0.036182</td>\n",
+       "      <td>0.022542</td>\n",
+       "      <td>0.042351</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048400</td>\n",
+       "      <td>-0.033944</td>\n",
+       "      <td>-0.033192</td>\n",
+       "      <td>-0.053796</td>\n",
+       "      <td>-0.023608</td>\n",
+       "      <td>-0.074370</td>\n",
+       "      <td>-0.027566</td>\n",
+       "      <td>-0.016195</td>\n",
+       "      <td>0.042448</td>\n",
+       "      <td>-0.014792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_management</th>\n",
+       "      <td>0.065606</td>\n",
+       "      <td>-0.043783</td>\n",
+       "      <td>-0.045940</td>\n",
+       "      <td>-0.011460</td>\n",
+       "      <td>-0.011967</td>\n",
+       "      <td>-0.011278</td>\n",
+       "      <td>0.030516</td>\n",
+       "      <td>-0.025176</td>\n",
+       "      <td>-0.048750</td>\n",
+       "      <td>-0.005887</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.059877</td>\n",
+       "      <td>-0.058549</td>\n",
+       "      <td>-0.094895</td>\n",
+       "      <td>-0.041643</td>\n",
+       "      <td>-0.131185</td>\n",
+       "      <td>-0.048626</td>\n",
+       "      <td>-0.028567</td>\n",
+       "      <td>-0.007995</td>\n",
+       "      <td>0.004104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_retired</th>\n",
+       "      <td>0.412542</td>\n",
+       "      <td>0.023665</td>\n",
+       "      <td>-0.038983</td>\n",
+       "      <td>0.044257</td>\n",
+       "      <td>-0.011147</td>\n",
+       "      <td>-0.067405</td>\n",
+       "      <td>0.051054</td>\n",
+       "      <td>-0.087204</td>\n",
+       "      <td>-0.032616</td>\n",
+       "      <td>0.065338</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.059877</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.041062</td>\n",
+       "      <td>-0.066553</td>\n",
+       "      <td>-0.029206</td>\n",
+       "      <td>-0.092005</td>\n",
+       "      <td>-0.034103</td>\n",
+       "      <td>-0.020035</td>\n",
+       "      <td>-0.045550</td>\n",
+       "      <td>0.056007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <td>0.010991</td>\n",
+       "      <td>-0.019191</td>\n",
+       "      <td>0.003609</td>\n",
+       "      <td>-0.001466</td>\n",
+       "      <td>0.021876</td>\n",
+       "      <td>0.007591</td>\n",
+       "      <td>0.006366</td>\n",
+       "      <td>0.000153</td>\n",
+       "      <td>-0.001529</td>\n",
+       "      <td>0.007642</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.058549</td>\n",
+       "      <td>-0.041062</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>-0.028558</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>-0.033346</td>\n",
+       "      <td>-0.019591</td>\n",
+       "      <td>-0.014306</td>\n",
+       "      <td>-0.003325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_services</th>\n",
+       "      <td>-0.050379</td>\n",
+       "      <td>0.021453</td>\n",
+       "      <td>0.024214</td>\n",
+       "      <td>-0.030936</td>\n",
+       "      <td>0.038893</td>\n",
+       "      <td>0.031058</td>\n",
+       "      <td>-0.007326</td>\n",
+       "      <td>-0.003592</td>\n",
+       "      <td>0.025988</td>\n",
+       "      <td>-0.073024</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.094895</td>\n",
+       "      <td>-0.066553</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.046286</td>\n",
+       "      <td>-0.145812</td>\n",
+       "      <td>-0.054047</td>\n",
+       "      <td>-0.031752</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>-0.025132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_student</th>\n",
+       "      <td>-0.185453</td>\n",
+       "      <td>-0.017566</td>\n",
+       "      <td>-0.018891</td>\n",
+       "      <td>0.016982</td>\n",
+       "      <td>-0.032527</td>\n",
+       "      <td>-0.043072</td>\n",
+       "      <td>0.078260</td>\n",
+       "      <td>-0.116507</td>\n",
+       "      <td>-0.032322</td>\n",
+       "      <td>0.025850</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.041643</td>\n",
+       "      <td>-0.029206</td>\n",
+       "      <td>-0.028558</td>\n",
+       "      <td>-0.046286</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.063988</td>\n",
+       "      <td>-0.023718</td>\n",
+       "      <td>-0.013934</td>\n",
+       "      <td>-0.084656</td>\n",
+       "      <td>0.011176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_technician</th>\n",
+       "      <td>-0.064930</td>\n",
+       "      <td>-0.081766</td>\n",
+       "      <td>-0.021849</td>\n",
+       "      <td>-0.006173</td>\n",
+       "      <td>-0.004871</td>\n",
+       "      <td>0.009654</td>\n",
+       "      <td>-0.017419</td>\n",
+       "      <td>0.047053</td>\n",
+       "      <td>-0.002939</td>\n",
+       "      <td>0.058946</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.131185</td>\n",
+       "      <td>-0.092005</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>-0.145812</td>\n",
+       "      <td>-0.063988</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.074716</td>\n",
+       "      <td>-0.043896</td>\n",
+       "      <td>0.020288</td>\n",
+       "      <td>-0.013611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <td>-0.009394</td>\n",
+       "      <td>-0.010086</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>-0.004565</td>\n",
+       "      <td>0.005467</td>\n",
+       "      <td>-0.028664</td>\n",
+       "      <td>0.013485</td>\n",
+       "      <td>-0.028425</td>\n",
+       "      <td>-0.003210</td>\n",
+       "      <td>-0.000228</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048626</td>\n",
+       "      <td>-0.034103</td>\n",
+       "      <td>-0.033346</td>\n",
+       "      <td>-0.054047</td>\n",
+       "      <td>-0.023718</td>\n",
+       "      <td>-0.074716</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.016270</td>\n",
+       "      <td>-0.012524</td>\n",
+       "      <td>0.034295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_unknown</th>\n",
+       "      <td>0.063831</td>\n",
+       "      <td>0.065701</td>\n",
+       "      <td>0.021516</td>\n",
+       "      <td>-0.008591</td>\n",
+       "      <td>-0.007765</td>\n",
+       "      <td>-0.006368</td>\n",
+       "      <td>-0.001958</td>\n",
+       "      <td>0.013772</td>\n",
+       "      <td>0.016884</td>\n",
+       "      <td>0.032504</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.028567</td>\n",
+       "      <td>-0.020035</td>\n",
+       "      <td>-0.019591</td>\n",
+       "      <td>-0.031752</td>\n",
+       "      <td>-0.013934</td>\n",
+       "      <td>-0.043896</td>\n",
+       "      <td>-0.016270</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.004583</td>\n",
+       "      <td>0.009007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poutcome_nonexistent</th>\n",
+       "      <td>-0.031205</td>\n",
+       "      <td>0.091124</td>\n",
+       "      <td>0.251147</td>\n",
+       "      <td>-0.021259</td>\n",
+       "      <td>0.098957</td>\n",
+       "      <td>0.488761</td>\n",
+       "      <td>-0.854241</td>\n",
+       "      <td>0.482106</td>\n",
+       "      <td>0.289799</td>\n",
+       "      <td>0.100217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.007995</td>\n",
+       "      <td>-0.045550</td>\n",
+       "      <td>-0.014306</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>-0.084656</td>\n",
+       "      <td>0.020288</td>\n",
+       "      <td>-0.012524</td>\n",
+       "      <td>0.004583</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.459409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poutcome_success</th>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>-0.086343</td>\n",
+       "      <td>-0.109971</td>\n",
+       "      <td>0.046154</td>\n",
+       "      <td>-0.054564</td>\n",
+       "      <td>-0.940565</td>\n",
+       "      <td>0.510865</td>\n",
+       "      <td>-0.256587</td>\n",
+       "      <td>-0.060257</td>\n",
+       "      <td>0.087382</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004104</td>\n",
+       "      <td>0.056007</td>\n",
+       "      <td>-0.003325</td>\n",
+       "      <td>-0.025132</td>\n",
+       "      <td>0.011176</td>\n",
+       "      <td>-0.013611</td>\n",
+       "      <td>0.034295</td>\n",
+       "      <td>0.009007</td>\n",
+       "      <td>-0.459409</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           age   default   contact  duration  campaign  \\\n",
+       "age                   1.000000  0.161427  0.012700  0.041299 -0.014169   \n",
+       "default               0.161427  1.000000  0.150494 -0.020140  0.021239   \n",
+       "contact               0.012700  0.150494  1.000000 -0.028185  0.085654   \n",
+       "duration              0.041299 -0.020140 -0.028185  1.000000 -0.085348   \n",
+       "campaign             -0.014169  0.021239  0.085654 -0.085348  1.000000   \n",
+       "pdays                -0.043425  0.089514  0.120670 -0.046998  0.058742   \n",
+       "previous              0.050931 -0.092754 -0.215464  0.025724 -0.091490   \n",
+       "emp.var.rate         -0.019192  0.184389  0.383405 -0.028848  0.176079   \n",
+       "cons.price.idx       -0.000482  0.162098  0.574452  0.016672  0.145021   \n",
+       "cons.conf.idx         0.098135  0.021570  0.252035 -0.034745  0.007882   \n",
+       "euribor3m            -0.015033  0.176800  0.390313 -0.032329  0.159435   \n",
+       "nr.employed          -0.041936  0.167754  0.261496 -0.044218  0.161037   \n",
+       "outcome               0.060374 -0.076567 -0.137401  0.418565 -0.076091   \n",
+       "job_blue-collar      -0.042976  0.167771  0.098988  0.010396 -0.021404   \n",
+       "job_entrepreneur      0.039110  0.020113  0.028028 -0.005750 -0.024137   \n",
+       "job_housemaid         0.089297  0.036203  0.018311 -0.017642 -0.004750   \n",
+       "job_management        0.065606 -0.043783 -0.045940 -0.011460 -0.011967   \n",
+       "job_retired           0.412542  0.023665 -0.038983  0.044257 -0.011147   \n",
+       "job_self-employed     0.010991 -0.019191  0.003609 -0.001466  0.021876   \n",
+       "job_services         -0.050379  0.021453  0.024214 -0.030936  0.038893   \n",
+       "job_student          -0.185453 -0.017566 -0.018891  0.016982 -0.032527   \n",
+       "job_technician       -0.064930 -0.081766 -0.021849 -0.006173 -0.004871   \n",
+       "job_unemployed       -0.009394 -0.010086  0.001462 -0.004565  0.005467   \n",
+       "job_unknown           0.063831  0.065701  0.021516 -0.008591 -0.007765   \n",
+       "poutcome_nonexistent -0.031205  0.091124  0.251147 -0.021259  0.098957   \n",
+       "poutcome_success      0.043341 -0.086343 -0.109971  0.046154 -0.054564   \n",
+       "\n",
+       "                         pdays  previous  emp.var.rate  cons.price.idx  \\\n",
+       "age                  -0.043425  0.050931     -0.019192       -0.000482   \n",
+       "default               0.089514 -0.092754      0.184389        0.162098   \n",
+       "contact               0.120670 -0.215464      0.383405        0.574452   \n",
+       "duration             -0.046998  0.025724     -0.028848        0.016672   \n",
+       "campaign              0.058742 -0.091490      0.176079        0.145021   \n",
+       "pdays                 1.000000 -0.587941      0.270684        0.058472   \n",
+       "previous             -0.587941  1.000000     -0.415238       -0.164922   \n",
+       "emp.var.rate          0.270684 -0.415238      1.000000        0.755155   \n",
+       "cons.price.idx        0.058472 -0.164922      0.755155        1.000000   \n",
+       "cons.conf.idx        -0.092090 -0.051420      0.195022        0.045835   \n",
+       "euribor3m             0.301478 -0.458851      0.970308        0.657159   \n",
+       "nr.employed           0.381983 -0.514853      0.897173        0.472560   \n",
+       "outcome              -0.332012  0.255697     -0.283216       -0.098326   \n",
+       "job_blue-collar       0.062240 -0.041762      0.050353        0.080969   \n",
+       "job_entrepreneur      0.032033 -0.017265      0.022054        0.011657   \n",
+       "job_housemaid         0.017684 -0.027623      0.036182        0.022542   \n",
+       "job_management       -0.011278  0.030516     -0.025176       -0.048750   \n",
+       "job_retired          -0.067405  0.051054     -0.087204       -0.032616   \n",
+       "job_self-employed     0.007591  0.006366      0.000153       -0.001529   \n",
+       "job_services          0.031058 -0.007326     -0.003592        0.025988   \n",
+       "job_student          -0.043072  0.078260     -0.116507       -0.032322   \n",
+       "job_technician        0.009654 -0.017419      0.047053       -0.002939   \n",
+       "job_unemployed       -0.028664  0.013485     -0.028425       -0.003210   \n",
+       "job_unknown          -0.006368 -0.001958      0.013772        0.016884   \n",
+       "poutcome_nonexistent  0.488761 -0.854241      0.482106        0.289799   \n",
+       "poutcome_success     -0.940565  0.510865     -0.256587       -0.060257   \n",
+       "\n",
+       "                      cons.conf.idx        ...         job_management  \\\n",
+       "age                        0.098135        ...               0.065606   \n",
+       "default                    0.021570        ...              -0.043783   \n",
+       "contact                    0.252035        ...              -0.045940   \n",
+       "duration                  -0.034745        ...              -0.011460   \n",
+       "campaign                   0.007882        ...              -0.011967   \n",
+       "pdays                     -0.092090        ...              -0.011278   \n",
+       "previous                  -0.051420        ...               0.030516   \n",
+       "emp.var.rate               0.195022        ...              -0.025176   \n",
+       "cons.price.idx             0.045835        ...              -0.048750   \n",
+       "cons.conf.idx              1.000000        ...              -0.005887   \n",
+       "euribor3m                  0.276595        ...              -0.011447   \n",
+       "nr.employed                0.107054        ...              -0.002022   \n",
+       "outcome                    0.054393        ...              -0.015814   \n",
+       "job_blue-collar           -0.122025        ...              -0.152741   \n",
+       "job_entrepreneur          -0.014320        ...              -0.056409   \n",
+       "job_housemaid              0.042351        ...              -0.048400   \n",
+       "job_management            -0.005887        ...               1.000000   \n",
+       "job_retired                0.065338        ...              -0.059877   \n",
+       "job_self-employed          0.007642        ...              -0.058549   \n",
+       "job_services              -0.073024        ...              -0.094895   \n",
+       "job_student                0.025850        ...              -0.041643   \n",
+       "job_technician             0.058946        ...              -0.131185   \n",
+       "job_unemployed            -0.000228        ...              -0.048626   \n",
+       "job_unknown                0.032504        ...              -0.028567   \n",
+       "poutcome_nonexistent       0.100217        ...              -0.007995   \n",
+       "poutcome_success           0.087382        ...               0.004104   \n",
+       "\n",
+       "                      job_retired  job_self-employed  job_services  \\\n",
+       "age                      0.412542           0.010991     -0.050379   \n",
+       "default                  0.023665          -0.019191      0.021453   \n",
+       "contact                 -0.038983           0.003609      0.024214   \n",
+       "duration                 0.044257          -0.001466     -0.030936   \n",
+       "campaign                -0.011147           0.021876      0.038893   \n",
+       "pdays                   -0.067405           0.007591      0.031058   \n",
+       "previous                 0.051054           0.006366     -0.007326   \n",
+       "emp.var.rate            -0.087204           0.000153     -0.003592   \n",
+       "cons.price.idx          -0.032616          -0.001529      0.025988   \n",
+       "cons.conf.idx            0.065338           0.007642     -0.073024   \n",
+       "euribor3m               -0.088148           0.005542     -0.012849   \n",
+       "nr.employed             -0.112450           0.001342     -0.002975   \n",
+       "outcome                  0.078373          -0.017796     -0.021253   \n",
+       "job_blue-collar         -0.107122          -0.104747     -0.169771   \n",
+       "job_entrepreneur        -0.039561          -0.038684     -0.062698   \n",
+       "job_housemaid           -0.033944          -0.033192     -0.053796   \n",
+       "job_management          -0.059877          -0.058549     -0.094895   \n",
+       "job_retired              1.000000          -0.041062     -0.066553   \n",
+       "job_self-employed       -0.041062           1.000000     -0.065077   \n",
+       "job_services            -0.066553          -0.065077      1.000000   \n",
+       "job_student             -0.029206          -0.028558     -0.046286   \n",
+       "job_technician          -0.092005          -0.089964     -0.145812   \n",
+       "job_unemployed          -0.034103          -0.033346     -0.054047   \n",
+       "job_unknown             -0.020035          -0.019591     -0.031752   \n",
+       "poutcome_nonexistent    -0.045550          -0.014306      0.002033   \n",
+       "poutcome_success         0.056007          -0.003325     -0.025132   \n",
+       "\n",
+       "                      job_student  job_technician  job_unemployed  \\\n",
+       "age                     -0.185453       -0.064930       -0.009394   \n",
+       "default                 -0.017566       -0.081766       -0.010086   \n",
+       "contact                 -0.018891       -0.021849        0.001462   \n",
+       "duration                 0.016982       -0.006173       -0.004565   \n",
+       "campaign                -0.032527       -0.004871        0.005467   \n",
+       "pdays                   -0.043072        0.009654       -0.028664   \n",
+       "previous                 0.078260       -0.017419        0.013485   \n",
+       "emp.var.rate            -0.116507        0.047053       -0.028425   \n",
+       "cons.price.idx          -0.032322       -0.002939       -0.003210   \n",
+       "cons.conf.idx            0.025850        0.058946       -0.000228   \n",
+       "euribor3m               -0.119714        0.046468       -0.023823   \n",
+       "nr.employed             -0.144023        0.042848       -0.030453   \n",
+       "outcome                  0.055781        0.009032        0.032872   \n",
+       "job_blue-collar         -0.074502       -0.234697       -0.086993   \n",
+       "job_entrepreneur        -0.027514       -0.086676       -0.032128   \n",
+       "job_housemaid           -0.023608       -0.074370       -0.027566   \n",
+       "job_management          -0.041643       -0.131185       -0.048626   \n",
+       "job_retired             -0.029206       -0.092005       -0.034103   \n",
+       "job_self-employed       -0.028558       -0.089964       -0.033346   \n",
+       "job_services            -0.046286       -0.145812       -0.054047   \n",
+       "job_student              1.000000       -0.063988       -0.023718   \n",
+       "job_technician          -0.063988        1.000000       -0.074716   \n",
+       "job_unemployed          -0.023718       -0.074716        1.000000   \n",
+       "job_unknown             -0.013934       -0.043896       -0.016270   \n",
+       "poutcome_nonexistent    -0.084656        0.020288       -0.012524   \n",
+       "poutcome_success         0.011176       -0.013611        0.034295   \n",
+       "\n",
+       "                      job_unknown  poutcome_nonexistent  poutcome_success  \n",
+       "age                      0.063831             -0.031205          0.043341  \n",
+       "default                  0.065701              0.091124         -0.086343  \n",
+       "contact                  0.021516              0.251147         -0.109971  \n",
+       "duration                -0.008591             -0.021259          0.046154  \n",
+       "campaign                -0.007765              0.098957         -0.054564  \n",
+       "pdays                   -0.006368              0.488761         -0.940565  \n",
+       "previous                -0.001958             -0.854241          0.510865  \n",
+       "emp.var.rate             0.013772              0.482106         -0.256587  \n",
+       "cons.price.idx           0.016884              0.289799         -0.060257  \n",
+       "cons.conf.idx            0.032504              0.100217          0.087382  \n",
+       "euribor3m                0.017662              0.502340         -0.282272  \n",
+       "nr.employed              0.010605              0.508717         -0.354030  \n",
+       "outcome                 -0.002169             -0.207179          0.325804  \n",
+       "job_blue-collar         -0.051108              0.026745         -0.053400  \n",
+       "job_entrepreneur        -0.018875              0.008954         -0.029330  \n",
+       "job_housemaid           -0.016195              0.042448         -0.014792  \n",
+       "job_management          -0.028567             -0.007995          0.004104  \n",
+       "job_retired             -0.020035             -0.045550          0.056007  \n",
+       "job_self-employed       -0.019591             -0.014306         -0.003325  \n",
+       "job_services            -0.031752              0.002033         -0.025132  \n",
+       "job_student             -0.013934             -0.084656          0.011176  \n",
+       "job_technician          -0.043896              0.020288         -0.013611  \n",
+       "job_unemployed          -0.016270             -0.012524          0.034295  \n",
+       "job_unknown              1.000000              0.004583          0.009007  \n",
+       "poutcome_nonexistent     0.004583              1.000000         -0.459409  \n",
+       "poutcome_success         0.009007             -0.459409          1.000000  \n",
+       "\n",
+       "[26 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1179dcd50>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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izWjgCKy8yyDgTkwSbl9gd0z39+icfovJxy1CVd/GnLf9ci79isU1AGswDd+f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\nU6vafAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1179dc490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a heatmap out of the correlation matrix\n",
+    "sns.heatmap(bank.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.041299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               age  duration\n",
+       "age       1.000000  0.041299\n",
+       "duration  0.041299  1.000000"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank[['age', 'duration']].corr()\n",
+    "# age won't be useful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1179dc3d0>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9gRXV538H7D/xKCUzl0bE2ymJd647gDKf/ocAEXEZpTfog5n5zoi4nbL4zreb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\nAA3LdXEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117cbfd50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('education').duration.mean().plot(kind='bar')\n",
+    "# or education, except that illiterate column is interesting.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1180c4390>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gSZKKDAmSJKnIkCBJkooMCZIkqciQIEmSigwJkiSpyJAgSZKKDAmSJKnIkCBJkooMCZIk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\nSZKKDAmSJKnIkCBJkooMCZIkqej/A/cqp5Yh6xBLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118134e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('marital').duration.mean().plot(kind='bar')\n",
+    "# or marital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x117c86590>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EzJhZh5lNNrNXpY6lkZmtYmYPmdmGqWMRScnMjjGzPRo+vgx4xsyeMLNNE8b1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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11818d990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('job').duration.mean().plot(kind='bar')\n",
+    "# job maybe.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1184d3590>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JKjIkSJKkIkOCJEkqMiRIkqQiQ4IkSSoyJEiSpCJDgiRJKjIkSJKkIkOCJEkqMiRIkqQi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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1184e1b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('day_of_week').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x118538810>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BPB34LdWMwWsz85sAmXlxROwGXEZ1MqWbgcMzc6jlMc4BhoGrqU6mdANwxlRehCRJ6rx2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\nkiSpyJAgSZKKDAmSJKno/wMlveBi+JuAkgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118479cd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('previous').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "duration_response = bank['duration']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "255.17246331494047"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X, duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not that great.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1186fdfd0>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EzJhZh5lNNrNXpY6lkZmtYmYPmdmGqWMRScnMjjGzPRo+vgx4xsyeMLNNE8b1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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118794b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# job field\n",
+    "bank.groupby('job').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "254.46828669453504"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Only with the retired field\n",
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X[['job_retired']], duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# How can we make this a bit better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/10_review-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/10_review-checkpoint.ipynb
new file mode 100644
index 0000000..333d5cc
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/10_review-checkpoint.ipynb
@@ -0,0 +1,3242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Review with bank marketing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "- Data from the UCI Machine Learning Repository: [data](https://github.com/sinanuozdemir/sfdat22/blob/master/data/bank.csv), [data dictionary](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)\n",
+    "- **Goal:** Predict whether a customer will purchase a bank product marketed over the phone\n",
+    "- `bank.csv` is already in our repo, so there is no need to download the data from the UCI website"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Read the data into Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
+       "1   39     services   single        high.school      no       no       no   \n",
+       "2   25     services  married        high.school      no      yes       no   \n",
+       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree      no      yes       no   \n",
+       "\n",
+       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
+       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
+       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
+       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
+       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
+       "\n",
+       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
+       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
+       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
+       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
+       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
+       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank = pd.read_csv('../data/bank.csv', sep=';')\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4119, 21)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "job               0\n",
+       "marital           0\n",
+       "education         0\n",
+       "default           0\n",
+       "housing           0\n",
+       "loan              0\n",
+       "contact           0\n",
+       "month             0\n",
+       "day_of_week       0\n",
+       "duration          0\n",
+       "campaign          0\n",
+       "pdays             0\n",
+       "previous          0\n",
+       "poutcome          0\n",
+       "emp.var.rate      0\n",
+       "cons.price.idx    0\n",
+       "cons.conf.idx     0\n",
+       "euribor3m         0\n",
+       "nr.employed       0\n",
+       "y                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Do we have any null values?\n",
+    "bank.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Prepare at least three features\n",
+    "\n",
+    "- Include both numeric and categorical features\n",
+    "- Choose features that you think might be related to the response (based on intuition or exploration)\n",
+    "- Think about how to handle missing values (encoded as \"unknown\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
+       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
+       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
+       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list all columns (for reference)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### y (response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    0\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert the response to numeric values and store as a new column called \"outcome\"\n",
+    "bank['outcome'] = bank.y.map({'no':0, 'yes':1})\n",
+    "bank['outcome'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's explore a few columns manually and visually"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise!\n",
+    "# create a boxplot of age the data by outcome\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1127256d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SFc1kQZI0qDlzstu1S56GkCRJVZksqOE2b95cdAiS6mT7FZgsaAx0d3cXHYKk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\npKr+P05C7qpjaA6+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112725e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "bank.boxplot(column='age', by='outcome')\n",
+    "# probably not a great feature, why?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x112e19410>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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baP1+bQe8AHhLrYJLSdcAzye9rt3O8iMwo37zM5aThPtIc5631Y6lU65oPZ60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\n8WCDpirPIu3Z06g+JRGxdeex3Ob9TOA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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112760e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('job').outcome.mean().plot(kind='bar')\n",
+    "# Ask yourself, WHY is this going to be useful?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>job_blue-collar</th>\n",
+       "      <th>job_entrepreneur</th>\n",
+       "      <th>job_housemaid</th>\n",
+       "      <th>job_management</th>\n",
+       "      <th>job_retired</th>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <th>job_services</th>\n",
+       "      <th>job_student</th>\n",
+       "      <th>job_technician</th>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <th>job_unknown</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   job_blue-collar  job_entrepreneur  job_housemaid  job_management  \\\n",
+       "0              1.0               0.0            0.0             0.0   \n",
+       "1              0.0               0.0            0.0             0.0   \n",
+       "2              0.0               0.0            0.0             0.0   \n",
+       "3              0.0               0.0            0.0             0.0   \n",
+       "4              0.0               0.0            0.0             0.0   \n",
+       "\n",
+       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
+       "0          0.0                0.0           0.0          0.0             0.0   \n",
+       "1          0.0                0.0           1.0          0.0             0.0   \n",
+       "2          0.0                0.0           1.0          0.0             0.0   \n",
+       "3          0.0                0.0           1.0          0.0             0.0   \n",
+       "4          0.0                0.0           0.0          0.0             0.0   \n",
+       "\n",
+       "   job_unemployed  job_unknown  \n",
+       "0             0.0          0.0  \n",
+       "1             0.0          0.0  \n",
+       "2             0.0          0.0  \n",
+       "3             0.0          0.0  \n",
+       "4             0.0          0.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create job_dummies (we will add it to the bank DataFrame later)\n",
+    "job_dummies = pd.get_dummies(bank.job, prefix='job')\n",
+    "# removing the first job from the list (making it k-1 columns) where k is the number of jobs\n",
+    "job_dummies.drop(job_dummies.columns[0], axis=1, inplace=True)\n",
+    "job_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1130ac8d0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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U1NQD2jZv3rxDYwcKCVV1X5LrgZXAlQBJ0r2/oM+wa+mdQpjpmK592seB2b/VlwFf31ZA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\nSJKkJkOCJElqMiRIkqSm/w+vAKlfwEo5IAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112760fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('default').outcome.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# How many people have a value of no, yes, and unknown?\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "no         3315\n",
+       "unknown     803\n",
+       "yes           1\n",
+       "Name: default, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "bank.default.value_counts()\n",
+    "# only one person in the dataset has a status of yes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "      <th>outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education  default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y        0      yes       no   \n",
+       "1   39     services   single        high.school        0       no       no   \n",
+       "2   25     services  married        high.school        0      yes       no   \n",
+       "3   38     services  married           basic.9y        0  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree        0      yes       no   \n",
+       "\n",
+       "     contact month day_of_week   ...     pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri   ...       999         0  nonexistent   \n",
+       "1  telephone   may         fri   ...       999         0  nonexistent   \n",
+       "2  telephone   jun         wed   ...       999         0  nonexistent   \n",
+       "3  telephone   jun         fri   ...       999         0  nonexistent   \n",
+       "4   cellular   nov         mon   ...       999         0  nonexistent   \n",
+       "\n",
+       "   emp.var.rate cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \\\n",
+       "0          -1.8         92.893          -46.2      1.313       5099.1  no   \n",
+       "1           1.1         93.994          -36.4      4.855       5191.0  no   \n",
+       "2           1.4         94.465          -41.8      4.962       5228.1  no   \n",
+       "3           1.4         94.465          -41.8      4.959       5228.1  no   \n",
+       "4          -0.1         93.200          -42.0      4.191       5195.8  no   \n",
+       "\n",
+       "   outcome  \n",
+       "0        0  \n",
+       "1        0  \n",
+       "2        0  \n",
+       "3        0  \n",
+       "4        0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# So, let's treat this as a 2-class feature rather than a 3-class feature\n",
+    "bank['default'] = bank.default.map({'no':0, 'unknown':1, 'yes':1})\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### contact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contact\n",
+       "cellular     0.141403\n",
+       "telephone    0.051806\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('contact').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Convert the feature to numeric values, 0 for cellular, 1 for telephone\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "bank['contact'] = bank.contact.map({'cellular':0, 'telephone':1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "month\n",
+       "apr    0.167442\n",
+       "aug    0.100629\n",
+       "dec    0.545455\n",
+       "jul    0.082982\n",
+       "jun    0.128302\n",
+       "mar    0.583333\n",
+       "may    0.065312\n",
+       "nov    0.096413\n",
+       "oct    0.362319\n",
+       "sep    0.406250\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature at first glance\n",
+    "bank.groupby('month').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dec</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0.545455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mar</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0.406250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.362319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>apr</th>\n",
+       "      <td>215</td>\n",
+       "      <td>0.167442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nov</th>\n",
+       "      <td>446</td>\n",
+       "      <td>0.096413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jun</th>\n",
+       "      <td>530</td>\n",
+       "      <td>0.128302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aug</th>\n",
+       "      <td>636</td>\n",
+       "      <td>0.100629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jul</th>\n",
+       "      <td>711</td>\n",
+       "      <td>0.082982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>may</th>\n",
+       "      <td>1378</td>\n",
+       "      <td>0.065312</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count      mean\n",
+       "month                 \n",
+       "dec       22  0.545455\n",
+       "mar       48  0.583333\n",
+       "sep       64  0.406250\n",
+       "oct       69  0.362319\n",
+       "apr      215  0.167442\n",
+       "nov      446  0.096413\n",
+       "jun      530  0.128302\n",
+       "aug      636  0.100629\n",
+       "jul      711  0.082982\n",
+       "may     1378  0.065312"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# but, it looks like their success rate is actually just correlated with number of calls\n",
+    "# thus, the month feature is unlikely to generalize\n",
+    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.766364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-0.766364</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          count      mean\n",
+       "count  1.000000 -0.766364\n",
+       "mean  -0.766364  1.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count').corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1133cd090>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ga7bRmJmZDRujSdbaWRkRz/dUsa8tHY0kN4dqkVRcyGcEcKKk80iWTBZJa0Zpa8d4oNhV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\nhZMOMzMzq4r/Hwja06m9XpGSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1133d5610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like an excellent feature, can you think of a reason why duration isn't helpful in practice?\n",
+    "bank.boxplot(column='duration', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Answer: You can't know the duration of a call beforehand, thus it can't be used in your model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### previous"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "previous\n",
+       "0    0.082884\n",
+       "1    0.208421\n",
+       "2    0.410256\n",
+       "3    0.600000\n",
+       "4    0.714286\n",
+       "5    1.000000\n",
+       "6    0.500000\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('previous').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### poutcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "poutcome\n",
+       "failure        0.147577\n",
+       "nonexistent    0.082884\n",
+       "success        0.647887\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('poutcome').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create poutcome_dummies\n",
+    "poutcome_dummies = pd.get_dummies(bank.poutcome, prefix='poutcome')\n",
+    "poutcome_dummies.drop(poutcome_dummies.columns[0], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# concatenate bank DataFrame with job_dummies and poutcome_dummies\n",
+    "bank = pd.concat([bank, job_dummies, poutcome_dummies], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### euribor3m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1133bb450>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/AoOAJEm1ZhCQJKnGDAKSJNWYQUClNBqNqkuQVJD9KzAIqKS1a9dWXYKkguxf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113723410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like an excellent feature\n",
+    "bank.boxplot(column='euribor3m', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Model building\n",
+    "\n",
+    "- Use cross-validation to evaluate the AUC of a logistic regression model with your chosen features\n",
+    "- Try to increase the AUC by selecting different sets of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
+       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
+       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
+       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y', u'outcome',\n",
+       "       u'job_blue-collar', u'job_entrepreneur', u'job_housemaid',\n",
+       "       u'job_management', u'job_retired', u'job_self-employed',\n",
+       "       u'job_services', u'job_student', u'job_technician', u'job_unemployed',\n",
+       "       u'job_unknown', u'poutcome_nonexistent', u'poutcome_success'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# new list of columns (including dummy columns)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create X (including 13 dummy columns)\n",
+    "feature_cols = ['default', 'contact', 'previous', 'euribor3m'] + list(bank.columns[-13:])\n",
+    "X = bank[feature_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create y\n",
+    "y = bank.outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# calculate cross-validated Accuracy for logisic regression, and knn with 5 neighbors\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90215602496498271"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90046229940191136"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(5)\n",
+    "cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# use grid search to try k in range from 1-29 (odd only) for knn, and graph the accuracy from a cross val score vs the k\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score='raise',\n",
+       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
+       "           weights='uniform'),\n",
+       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       param_grid={'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27]},\n",
+       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "knn = KNeighborsClassifier()\n",
+    "param_grid = {'n_neighbors': range(1, 29, 2)}\n",
+    "grid = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x11670de90>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0mDev7EokSSqXQaKA2oqN6HuCb0mSWoxBogBXbEiSlBkkCujqcqKlJElgkGjY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\n99oAImJSRLyNfN6n5c18n42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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116473410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
+    "plt.figure()\n",
+    "plt.plot(range(1, 29, 2), grid_mean_scores)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=27, p=2,\n",
+       "           weights='uniform')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# look's like adding to k after 5 doesn't even help that much...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Create a confusion matrix from the best estimator in the gridsearch and find the accuracy, sensitivity, and specificity\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3625   43]\n",
+      " [ 351  100]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution! \n",
+    "from sklearn import metrics\n",
+    "preds = grid.best_estimator_.predict(X)\n",
+    "print metrics.confusion_matrix(y, preds)\n",
+    "# Accuracy = (3625  + 100) / 4119  == .904\n",
+    "# Sensitivity = 100 / (100 + 351)  == .222\n",
+    "# Specificity = 3625 / (3625 + 43) == .988"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# Compare the accuracy to the null accuracy rate\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.89050740470988099"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "\n",
+    "null_accuracy_rate = bank['outcome'].value_counts()[0] / float(bank.shape[0])\n",
+    "null_accuracy_rate\n",
+    "# accuracy is greater than the null accuracy rate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# What can we do to make the model better?\n",
+    "\n",
+    "# Thought Experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we want to regress to the duration of the call so the rep can better plan their day?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>default</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>...</th>\n",
+       "      <th>job_management</th>\n",
+       "      <th>job_retired</th>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <th>job_services</th>\n",
+       "      <th>job_student</th>\n",
+       "      <th>job_technician</th>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <th>job_unknown</th>\n",
+       "      <th>poutcome_nonexistent</th>\n",
+       "      <th>poutcome_success</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.161427</td>\n",
+       "      <td>0.012700</td>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>-0.014169</td>\n",
+       "      <td>-0.043425</td>\n",
+       "      <td>0.050931</td>\n",
+       "      <td>-0.019192</td>\n",
+       "      <td>-0.000482</td>\n",
+       "      <td>0.098135</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.065606</td>\n",
+       "      <td>0.412542</td>\n",
+       "      <td>0.010991</td>\n",
+       "      <td>-0.050379</td>\n",
+       "      <td>-0.185453</td>\n",
+       "      <td>-0.064930</td>\n",
+       "      <td>-0.009394</td>\n",
+       "      <td>0.063831</td>\n",
+       "      <td>-0.031205</td>\n",
+       "      <td>0.043341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>default</th>\n",
+       "      <td>0.161427</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.150494</td>\n",
+       "      <td>-0.020140</td>\n",
+       "      <td>0.021239</td>\n",
+       "      <td>0.089514</td>\n",
+       "      <td>-0.092754</td>\n",
+       "      <td>0.184389</td>\n",
+       "      <td>0.162098</td>\n",
+       "      <td>0.021570</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.043783</td>\n",
+       "      <td>0.023665</td>\n",
+       "      <td>-0.019191</td>\n",
+       "      <td>0.021453</td>\n",
+       "      <td>-0.017566</td>\n",
+       "      <td>-0.081766</td>\n",
+       "      <td>-0.010086</td>\n",
+       "      <td>0.065701</td>\n",
+       "      <td>0.091124</td>\n",
+       "      <td>-0.086343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contact</th>\n",
+       "      <td>0.012700</td>\n",
+       "      <td>0.150494</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.028185</td>\n",
+       "      <td>0.085654</td>\n",
+       "      <td>0.120670</td>\n",
+       "      <td>-0.215464</td>\n",
+       "      <td>0.383405</td>\n",
+       "      <td>0.574452</td>\n",
+       "      <td>0.252035</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.045940</td>\n",
+       "      <td>-0.038983</td>\n",
+       "      <td>0.003609</td>\n",
+       "      <td>0.024214</td>\n",
+       "      <td>-0.018891</td>\n",
+       "      <td>-0.021849</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>0.021516</td>\n",
+       "      <td>0.251147</td>\n",
+       "      <td>-0.109971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>-0.020140</td>\n",
+       "      <td>-0.028185</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.085348</td>\n",
+       "      <td>-0.046998</td>\n",
+       "      <td>0.025724</td>\n",
+       "      <td>-0.028848</td>\n",
+       "      <td>0.016672</td>\n",
+       "      <td>-0.034745</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011460</td>\n",
+       "      <td>0.044257</td>\n",
+       "      <td>-0.001466</td>\n",
+       "      <td>-0.030936</td>\n",
+       "      <td>0.016982</td>\n",
+       "      <td>-0.006173</td>\n",
+       "      <td>-0.004565</td>\n",
+       "      <td>-0.008591</td>\n",
+       "      <td>-0.021259</td>\n",
+       "      <td>0.046154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>campaign</th>\n",
+       "      <td>-0.014169</td>\n",
+       "      <td>0.021239</td>\n",
+       "      <td>0.085654</td>\n",
+       "      <td>-0.085348</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.058742</td>\n",
+       "      <td>-0.091490</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.145021</td>\n",
+       "      <td>0.007882</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011967</td>\n",
+       "      <td>-0.011147</td>\n",
+       "      <td>0.021876</td>\n",
+       "      <td>0.038893</td>\n",
+       "      <td>-0.032527</td>\n",
+       "      <td>-0.004871</td>\n",
+       "      <td>0.005467</td>\n",
+       "      <td>-0.007765</td>\n",
+       "      <td>0.098957</td>\n",
+       "      <td>-0.054564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pdays</th>\n",
+       "      <td>-0.043425</td>\n",
+       "      <td>0.089514</td>\n",
+       "      <td>0.120670</td>\n",
+       "      <td>-0.046998</td>\n",
+       "      <td>0.058742</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.587941</td>\n",
+       "      <td>0.270684</td>\n",
+       "      <td>0.058472</td>\n",
+       "      <td>-0.092090</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011278</td>\n",
+       "      <td>-0.067405</td>\n",
+       "      <td>0.007591</td>\n",
+       "      <td>0.031058</td>\n",
+       "      <td>-0.043072</td>\n",
+       "      <td>0.009654</td>\n",
+       "      <td>-0.028664</td>\n",
+       "      <td>-0.006368</td>\n",
+       "      <td>0.488761</td>\n",
+       "      <td>-0.940565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>previous</th>\n",
+       "      <td>0.050931</td>\n",
+       "      <td>-0.092754</td>\n",
+       "      <td>-0.215464</td>\n",
+       "      <td>0.025724</td>\n",
+       "      <td>-0.091490</td>\n",
+       "      <td>-0.587941</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.415238</td>\n",
+       "      <td>-0.164922</td>\n",
+       "      <td>-0.051420</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.030516</td>\n",
+       "      <td>0.051054</td>\n",
+       "      <td>0.006366</td>\n",
+       "      <td>-0.007326</td>\n",
+       "      <td>0.078260</td>\n",
+       "      <td>-0.017419</td>\n",
+       "      <td>0.013485</td>\n",
+       "      <td>-0.001958</td>\n",
+       "      <td>-0.854241</td>\n",
+       "      <td>0.510865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <td>-0.019192</td>\n",
+       "      <td>0.184389</td>\n",
+       "      <td>0.383405</td>\n",
+       "      <td>-0.028848</td>\n",
+       "      <td>0.176079</td>\n",
+       "      <td>0.270684</td>\n",
+       "      <td>-0.415238</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.755155</td>\n",
+       "      <td>0.195022</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.025176</td>\n",
+       "      <td>-0.087204</td>\n",
+       "      <td>0.000153</td>\n",
+       "      <td>-0.003592</td>\n",
+       "      <td>-0.116507</td>\n",
+       "      <td>0.047053</td>\n",
+       "      <td>-0.028425</td>\n",
+       "      <td>0.013772</td>\n",
+       "      <td>0.482106</td>\n",
+       "      <td>-0.256587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <td>-0.000482</td>\n",
+       "      <td>0.162098</td>\n",
+       "      <td>0.574452</td>\n",
+       "      <td>0.016672</td>\n",
+       "      <td>0.145021</td>\n",
+       "      <td>0.058472</td>\n",
+       "      <td>-0.164922</td>\n",
+       "      <td>0.755155</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.045835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048750</td>\n",
+       "      <td>-0.032616</td>\n",
+       "      <td>-0.001529</td>\n",
+       "      <td>0.025988</td>\n",
+       "      <td>-0.032322</td>\n",
+       "      <td>-0.002939</td>\n",
+       "      <td>-0.003210</td>\n",
+       "      <td>0.016884</td>\n",
+       "      <td>0.289799</td>\n",
+       "      <td>-0.060257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <td>0.098135</td>\n",
+       "      <td>0.021570</td>\n",
+       "      <td>0.252035</td>\n",
+       "      <td>-0.034745</td>\n",
+       "      <td>0.007882</td>\n",
+       "      <td>-0.092090</td>\n",
+       "      <td>-0.051420</td>\n",
+       "      <td>0.195022</td>\n",
+       "      <td>0.045835</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.005887</td>\n",
+       "      <td>0.065338</td>\n",
+       "      <td>0.007642</td>\n",
+       "      <td>-0.073024</td>\n",
+       "      <td>0.025850</td>\n",
+       "      <td>0.058946</td>\n",
+       "      <td>-0.000228</td>\n",
+       "      <td>0.032504</td>\n",
+       "      <td>0.100217</td>\n",
+       "      <td>0.087382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euribor3m</th>\n",
+       "      <td>-0.015033</td>\n",
+       "      <td>0.176800</td>\n",
+       "      <td>0.390313</td>\n",
+       "      <td>-0.032329</td>\n",
+       "      <td>0.159435</td>\n",
+       "      <td>0.301478</td>\n",
+       "      <td>-0.458851</td>\n",
+       "      <td>0.970308</td>\n",
+       "      <td>0.657159</td>\n",
+       "      <td>0.276595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011447</td>\n",
+       "      <td>-0.088148</td>\n",
+       "      <td>0.005542</td>\n",
+       "      <td>-0.012849</td>\n",
+       "      <td>-0.119714</td>\n",
+       "      <td>0.046468</td>\n",
+       "      <td>-0.023823</td>\n",
+       "      <td>0.017662</td>\n",
+       "      <td>0.502340</td>\n",
+       "      <td>-0.282272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nr.employed</th>\n",
+       "      <td>-0.041936</td>\n",
+       "      <td>0.167754</td>\n",
+       "      <td>0.261496</td>\n",
+       "      <td>-0.044218</td>\n",
+       "      <td>0.161037</td>\n",
+       "      <td>0.381983</td>\n",
+       "      <td>-0.514853</td>\n",
+       "      <td>0.897173</td>\n",
+       "      <td>0.472560</td>\n",
+       "      <td>0.107054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.002022</td>\n",
+       "      <td>-0.112450</td>\n",
+       "      <td>0.001342</td>\n",
+       "      <td>-0.002975</td>\n",
+       "      <td>-0.144023</td>\n",
+       "      <td>0.042848</td>\n",
+       "      <td>-0.030453</td>\n",
+       "      <td>0.010605</td>\n",
+       "      <td>0.508717</td>\n",
+       "      <td>-0.354030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outcome</th>\n",
+       "      <td>0.060374</td>\n",
+       "      <td>-0.076567</td>\n",
+       "      <td>-0.137401</td>\n",
+       "      <td>0.418565</td>\n",
+       "      <td>-0.076091</td>\n",
+       "      <td>-0.332012</td>\n",
+       "      <td>0.255697</td>\n",
+       "      <td>-0.283216</td>\n",
+       "      <td>-0.098326</td>\n",
+       "      <td>0.054393</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.015814</td>\n",
+       "      <td>0.078373</td>\n",
+       "      <td>-0.017796</td>\n",
+       "      <td>-0.021253</td>\n",
+       "      <td>0.055781</td>\n",
+       "      <td>0.009032</td>\n",
+       "      <td>0.032872</td>\n",
+       "      <td>-0.002169</td>\n",
+       "      <td>-0.207179</td>\n",
+       "      <td>0.325804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_blue-collar</th>\n",
+       "      <td>-0.042976</td>\n",
+       "      <td>0.167771</td>\n",
+       "      <td>0.098988</td>\n",
+       "      <td>0.010396</td>\n",
+       "      <td>-0.021404</td>\n",
+       "      <td>0.062240</td>\n",
+       "      <td>-0.041762</td>\n",
+       "      <td>0.050353</td>\n",
+       "      <td>0.080969</td>\n",
+       "      <td>-0.122025</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.152741</td>\n",
+       "      <td>-0.107122</td>\n",
+       "      <td>-0.104747</td>\n",
+       "      <td>-0.169771</td>\n",
+       "      <td>-0.074502</td>\n",
+       "      <td>-0.234697</td>\n",
+       "      <td>-0.086993</td>\n",
+       "      <td>-0.051108</td>\n",
+       "      <td>0.026745</td>\n",
+       "      <td>-0.053400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_entrepreneur</th>\n",
+       "      <td>0.039110</td>\n",
+       "      <td>0.020113</td>\n",
+       "      <td>0.028028</td>\n",
+       "      <td>-0.005750</td>\n",
+       "      <td>-0.024137</td>\n",
+       "      <td>0.032033</td>\n",
+       "      <td>-0.017265</td>\n",
+       "      <td>0.022054</td>\n",
+       "      <td>0.011657</td>\n",
+       "      <td>-0.014320</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.056409</td>\n",
+       "      <td>-0.039561</td>\n",
+       "      <td>-0.038684</td>\n",
+       "      <td>-0.062698</td>\n",
+       "      <td>-0.027514</td>\n",
+       "      <td>-0.086676</td>\n",
+       "      <td>-0.032128</td>\n",
+       "      <td>-0.018875</td>\n",
+       "      <td>0.008954</td>\n",
+       "      <td>-0.029330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_housemaid</th>\n",
+       "      <td>0.089297</td>\n",
+       "      <td>0.036203</td>\n",
+       "      <td>0.018311</td>\n",
+       "      <td>-0.017642</td>\n",
+       "      <td>-0.004750</td>\n",
+       "      <td>0.017684</td>\n",
+       "      <td>-0.027623</td>\n",
+       "      <td>0.036182</td>\n",
+       "      <td>0.022542</td>\n",
+       "      <td>0.042351</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048400</td>\n",
+       "      <td>-0.033944</td>\n",
+       "      <td>-0.033192</td>\n",
+       "      <td>-0.053796</td>\n",
+       "      <td>-0.023608</td>\n",
+       "      <td>-0.074370</td>\n",
+       "      <td>-0.027566</td>\n",
+       "      <td>-0.016195</td>\n",
+       "      <td>0.042448</td>\n",
+       "      <td>-0.014792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_management</th>\n",
+       "      <td>0.065606</td>\n",
+       "      <td>-0.043783</td>\n",
+       "      <td>-0.045940</td>\n",
+       "      <td>-0.011460</td>\n",
+       "      <td>-0.011967</td>\n",
+       "      <td>-0.011278</td>\n",
+       "      <td>0.030516</td>\n",
+       "      <td>-0.025176</td>\n",
+       "      <td>-0.048750</td>\n",
+       "      <td>-0.005887</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.059877</td>\n",
+       "      <td>-0.058549</td>\n",
+       "      <td>-0.094895</td>\n",
+       "      <td>-0.041643</td>\n",
+       "      <td>-0.131185</td>\n",
+       "      <td>-0.048626</td>\n",
+       "      <td>-0.028567</td>\n",
+       "      <td>-0.007995</td>\n",
+       "      <td>0.004104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_retired</th>\n",
+       "      <td>0.412542</td>\n",
+       "      <td>0.023665</td>\n",
+       "      <td>-0.038983</td>\n",
+       "      <td>0.044257</td>\n",
+       "      <td>-0.011147</td>\n",
+       "      <td>-0.067405</td>\n",
+       "      <td>0.051054</td>\n",
+       "      <td>-0.087204</td>\n",
+       "      <td>-0.032616</td>\n",
+       "      <td>0.065338</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.059877</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.041062</td>\n",
+       "      <td>-0.066553</td>\n",
+       "      <td>-0.029206</td>\n",
+       "      <td>-0.092005</td>\n",
+       "      <td>-0.034103</td>\n",
+       "      <td>-0.020035</td>\n",
+       "      <td>-0.045550</td>\n",
+       "      <td>0.056007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <td>0.010991</td>\n",
+       "      <td>-0.019191</td>\n",
+       "      <td>0.003609</td>\n",
+       "      <td>-0.001466</td>\n",
+       "      <td>0.021876</td>\n",
+       "      <td>0.007591</td>\n",
+       "      <td>0.006366</td>\n",
+       "      <td>0.000153</td>\n",
+       "      <td>-0.001529</td>\n",
+       "      <td>0.007642</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.058549</td>\n",
+       "      <td>-0.041062</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>-0.028558</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>-0.033346</td>\n",
+       "      <td>-0.019591</td>\n",
+       "      <td>-0.014306</td>\n",
+       "      <td>-0.003325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_services</th>\n",
+       "      <td>-0.050379</td>\n",
+       "      <td>0.021453</td>\n",
+       "      <td>0.024214</td>\n",
+       "      <td>-0.030936</td>\n",
+       "      <td>0.038893</td>\n",
+       "      <td>0.031058</td>\n",
+       "      <td>-0.007326</td>\n",
+       "      <td>-0.003592</td>\n",
+       "      <td>0.025988</td>\n",
+       "      <td>-0.073024</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.094895</td>\n",
+       "      <td>-0.066553</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.046286</td>\n",
+       "      <td>-0.145812</td>\n",
+       "      <td>-0.054047</td>\n",
+       "      <td>-0.031752</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>-0.025132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_student</th>\n",
+       "      <td>-0.185453</td>\n",
+       "      <td>-0.017566</td>\n",
+       "      <td>-0.018891</td>\n",
+       "      <td>0.016982</td>\n",
+       "      <td>-0.032527</td>\n",
+       "      <td>-0.043072</td>\n",
+       "      <td>0.078260</td>\n",
+       "      <td>-0.116507</td>\n",
+       "      <td>-0.032322</td>\n",
+       "      <td>0.025850</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.041643</td>\n",
+       "      <td>-0.029206</td>\n",
+       "      <td>-0.028558</td>\n",
+       "      <td>-0.046286</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.063988</td>\n",
+       "      <td>-0.023718</td>\n",
+       "      <td>-0.013934</td>\n",
+       "      <td>-0.084656</td>\n",
+       "      <td>0.011176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_technician</th>\n",
+       "      <td>-0.064930</td>\n",
+       "      <td>-0.081766</td>\n",
+       "      <td>-0.021849</td>\n",
+       "      <td>-0.006173</td>\n",
+       "      <td>-0.004871</td>\n",
+       "      <td>0.009654</td>\n",
+       "      <td>-0.017419</td>\n",
+       "      <td>0.047053</td>\n",
+       "      <td>-0.002939</td>\n",
+       "      <td>0.058946</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.131185</td>\n",
+       "      <td>-0.092005</td>\n",
+       "      <td>-0.089964</td>\n",
+       "      <td>-0.145812</td>\n",
+       "      <td>-0.063988</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.074716</td>\n",
+       "      <td>-0.043896</td>\n",
+       "      <td>0.020288</td>\n",
+       "      <td>-0.013611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <td>-0.009394</td>\n",
+       "      <td>-0.010086</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>-0.004565</td>\n",
+       "      <td>0.005467</td>\n",
+       "      <td>-0.028664</td>\n",
+       "      <td>0.013485</td>\n",
+       "      <td>-0.028425</td>\n",
+       "      <td>-0.003210</td>\n",
+       "      <td>-0.000228</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048626</td>\n",
+       "      <td>-0.034103</td>\n",
+       "      <td>-0.033346</td>\n",
+       "      <td>-0.054047</td>\n",
+       "      <td>-0.023718</td>\n",
+       "      <td>-0.074716</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.016270</td>\n",
+       "      <td>-0.012524</td>\n",
+       "      <td>0.034295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>job_unknown</th>\n",
+       "      <td>0.063831</td>\n",
+       "      <td>0.065701</td>\n",
+       "      <td>0.021516</td>\n",
+       "      <td>-0.008591</td>\n",
+       "      <td>-0.007765</td>\n",
+       "      <td>-0.006368</td>\n",
+       "      <td>-0.001958</td>\n",
+       "      <td>0.013772</td>\n",
+       "      <td>0.016884</td>\n",
+       "      <td>0.032504</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.028567</td>\n",
+       "      <td>-0.020035</td>\n",
+       "      <td>-0.019591</td>\n",
+       "      <td>-0.031752</td>\n",
+       "      <td>-0.013934</td>\n",
+       "      <td>-0.043896</td>\n",
+       "      <td>-0.016270</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.004583</td>\n",
+       "      <td>0.009007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poutcome_nonexistent</th>\n",
+       "      <td>-0.031205</td>\n",
+       "      <td>0.091124</td>\n",
+       "      <td>0.251147</td>\n",
+       "      <td>-0.021259</td>\n",
+       "      <td>0.098957</td>\n",
+       "      <td>0.488761</td>\n",
+       "      <td>-0.854241</td>\n",
+       "      <td>0.482106</td>\n",
+       "      <td>0.289799</td>\n",
+       "      <td>0.100217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.007995</td>\n",
+       "      <td>-0.045550</td>\n",
+       "      <td>-0.014306</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>-0.084656</td>\n",
+       "      <td>0.020288</td>\n",
+       "      <td>-0.012524</td>\n",
+       "      <td>0.004583</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.459409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poutcome_success</th>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>-0.086343</td>\n",
+       "      <td>-0.109971</td>\n",
+       "      <td>0.046154</td>\n",
+       "      <td>-0.054564</td>\n",
+       "      <td>-0.940565</td>\n",
+       "      <td>0.510865</td>\n",
+       "      <td>-0.256587</td>\n",
+       "      <td>-0.060257</td>\n",
+       "      <td>0.087382</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004104</td>\n",
+       "      <td>0.056007</td>\n",
+       "      <td>-0.003325</td>\n",
+       "      <td>-0.025132</td>\n",
+       "      <td>0.011176</td>\n",
+       "      <td>-0.013611</td>\n",
+       "      <td>0.034295</td>\n",
+       "      <td>0.009007</td>\n",
+       "      <td>-0.459409</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           age   default   contact  duration  campaign  \\\n",
+       "age                   1.000000  0.161427  0.012700  0.041299 -0.014169   \n",
+       "default               0.161427  1.000000  0.150494 -0.020140  0.021239   \n",
+       "contact               0.012700  0.150494  1.000000 -0.028185  0.085654   \n",
+       "duration              0.041299 -0.020140 -0.028185  1.000000 -0.085348   \n",
+       "campaign             -0.014169  0.021239  0.085654 -0.085348  1.000000   \n",
+       "pdays                -0.043425  0.089514  0.120670 -0.046998  0.058742   \n",
+       "previous              0.050931 -0.092754 -0.215464  0.025724 -0.091490   \n",
+       "emp.var.rate         -0.019192  0.184389  0.383405 -0.028848  0.176079   \n",
+       "cons.price.idx       -0.000482  0.162098  0.574452  0.016672  0.145021   \n",
+       "cons.conf.idx         0.098135  0.021570  0.252035 -0.034745  0.007882   \n",
+       "euribor3m            -0.015033  0.176800  0.390313 -0.032329  0.159435   \n",
+       "nr.employed          -0.041936  0.167754  0.261496 -0.044218  0.161037   \n",
+       "outcome               0.060374 -0.076567 -0.137401  0.418565 -0.076091   \n",
+       "job_blue-collar      -0.042976  0.167771  0.098988  0.010396 -0.021404   \n",
+       "job_entrepreneur      0.039110  0.020113  0.028028 -0.005750 -0.024137   \n",
+       "job_housemaid         0.089297  0.036203  0.018311 -0.017642 -0.004750   \n",
+       "job_management        0.065606 -0.043783 -0.045940 -0.011460 -0.011967   \n",
+       "job_retired           0.412542  0.023665 -0.038983  0.044257 -0.011147   \n",
+       "job_self-employed     0.010991 -0.019191  0.003609 -0.001466  0.021876   \n",
+       "job_services         -0.050379  0.021453  0.024214 -0.030936  0.038893   \n",
+       "job_student          -0.185453 -0.017566 -0.018891  0.016982 -0.032527   \n",
+       "job_technician       -0.064930 -0.081766 -0.021849 -0.006173 -0.004871   \n",
+       "job_unemployed       -0.009394 -0.010086  0.001462 -0.004565  0.005467   \n",
+       "job_unknown           0.063831  0.065701  0.021516 -0.008591 -0.007765   \n",
+       "poutcome_nonexistent -0.031205  0.091124  0.251147 -0.021259  0.098957   \n",
+       "poutcome_success      0.043341 -0.086343 -0.109971  0.046154 -0.054564   \n",
+       "\n",
+       "                         pdays  previous  emp.var.rate  cons.price.idx  \\\n",
+       "age                  -0.043425  0.050931     -0.019192       -0.000482   \n",
+       "default               0.089514 -0.092754      0.184389        0.162098   \n",
+       "contact               0.120670 -0.215464      0.383405        0.574452   \n",
+       "duration             -0.046998  0.025724     -0.028848        0.016672   \n",
+       "campaign              0.058742 -0.091490      0.176079        0.145021   \n",
+       "pdays                 1.000000 -0.587941      0.270684        0.058472   \n",
+       "previous             -0.587941  1.000000     -0.415238       -0.164922   \n",
+       "emp.var.rate          0.270684 -0.415238      1.000000        0.755155   \n",
+       "cons.price.idx        0.058472 -0.164922      0.755155        1.000000   \n",
+       "cons.conf.idx        -0.092090 -0.051420      0.195022        0.045835   \n",
+       "euribor3m             0.301478 -0.458851      0.970308        0.657159   \n",
+       "nr.employed           0.381983 -0.514853      0.897173        0.472560   \n",
+       "outcome              -0.332012  0.255697     -0.283216       -0.098326   \n",
+       "job_blue-collar       0.062240 -0.041762      0.050353        0.080969   \n",
+       "job_entrepreneur      0.032033 -0.017265      0.022054        0.011657   \n",
+       "job_housemaid         0.017684 -0.027623      0.036182        0.022542   \n",
+       "job_management       -0.011278  0.030516     -0.025176       -0.048750   \n",
+       "job_retired          -0.067405  0.051054     -0.087204       -0.032616   \n",
+       "job_self-employed     0.007591  0.006366      0.000153       -0.001529   \n",
+       "job_services          0.031058 -0.007326     -0.003592        0.025988   \n",
+       "job_student          -0.043072  0.078260     -0.116507       -0.032322   \n",
+       "job_technician        0.009654 -0.017419      0.047053       -0.002939   \n",
+       "job_unemployed       -0.028664  0.013485     -0.028425       -0.003210   \n",
+       "job_unknown          -0.006368 -0.001958      0.013772        0.016884   \n",
+       "poutcome_nonexistent  0.488761 -0.854241      0.482106        0.289799   \n",
+       "poutcome_success     -0.940565  0.510865     -0.256587       -0.060257   \n",
+       "\n",
+       "                      cons.conf.idx        ...         job_management  \\\n",
+       "age                        0.098135        ...               0.065606   \n",
+       "default                    0.021570        ...              -0.043783   \n",
+       "contact                    0.252035        ...              -0.045940   \n",
+       "duration                  -0.034745        ...              -0.011460   \n",
+       "campaign                   0.007882        ...              -0.011967   \n",
+       "pdays                     -0.092090        ...              -0.011278   \n",
+       "previous                  -0.051420        ...               0.030516   \n",
+       "emp.var.rate               0.195022        ...              -0.025176   \n",
+       "cons.price.idx             0.045835        ...              -0.048750   \n",
+       "cons.conf.idx              1.000000        ...              -0.005887   \n",
+       "euribor3m                  0.276595        ...              -0.011447   \n",
+       "nr.employed                0.107054        ...              -0.002022   \n",
+       "outcome                    0.054393        ...              -0.015814   \n",
+       "job_blue-collar           -0.122025        ...              -0.152741   \n",
+       "job_entrepreneur          -0.014320        ...              -0.056409   \n",
+       "job_housemaid              0.042351        ...              -0.048400   \n",
+       "job_management            -0.005887        ...               1.000000   \n",
+       "job_retired                0.065338        ...              -0.059877   \n",
+       "job_self-employed          0.007642        ...              -0.058549   \n",
+       "job_services              -0.073024        ...              -0.094895   \n",
+       "job_student                0.025850        ...              -0.041643   \n",
+       "job_technician             0.058946        ...              -0.131185   \n",
+       "job_unemployed            -0.000228        ...              -0.048626   \n",
+       "job_unknown                0.032504        ...              -0.028567   \n",
+       "poutcome_nonexistent       0.100217        ...              -0.007995   \n",
+       "poutcome_success           0.087382        ...               0.004104   \n",
+       "\n",
+       "                      job_retired  job_self-employed  job_services  \\\n",
+       "age                      0.412542           0.010991     -0.050379   \n",
+       "default                  0.023665          -0.019191      0.021453   \n",
+       "contact                 -0.038983           0.003609      0.024214   \n",
+       "duration                 0.044257          -0.001466     -0.030936   \n",
+       "campaign                -0.011147           0.021876      0.038893   \n",
+       "pdays                   -0.067405           0.007591      0.031058   \n",
+       "previous                 0.051054           0.006366     -0.007326   \n",
+       "emp.var.rate            -0.087204           0.000153     -0.003592   \n",
+       "cons.price.idx          -0.032616          -0.001529      0.025988   \n",
+       "cons.conf.idx            0.065338           0.007642     -0.073024   \n",
+       "euribor3m               -0.088148           0.005542     -0.012849   \n",
+       "nr.employed             -0.112450           0.001342     -0.002975   \n",
+       "outcome                  0.078373          -0.017796     -0.021253   \n",
+       "job_blue-collar         -0.107122          -0.104747     -0.169771   \n",
+       "job_entrepreneur        -0.039561          -0.038684     -0.062698   \n",
+       "job_housemaid           -0.033944          -0.033192     -0.053796   \n",
+       "job_management          -0.059877          -0.058549     -0.094895   \n",
+       "job_retired              1.000000          -0.041062     -0.066553   \n",
+       "job_self-employed       -0.041062           1.000000     -0.065077   \n",
+       "job_services            -0.066553          -0.065077      1.000000   \n",
+       "job_student             -0.029206          -0.028558     -0.046286   \n",
+       "job_technician          -0.092005          -0.089964     -0.145812   \n",
+       "job_unemployed          -0.034103          -0.033346     -0.054047   \n",
+       "job_unknown             -0.020035          -0.019591     -0.031752   \n",
+       "poutcome_nonexistent    -0.045550          -0.014306      0.002033   \n",
+       "poutcome_success         0.056007          -0.003325     -0.025132   \n",
+       "\n",
+       "                      job_student  job_technician  job_unemployed  \\\n",
+       "age                     -0.185453       -0.064930       -0.009394   \n",
+       "default                 -0.017566       -0.081766       -0.010086   \n",
+       "contact                 -0.018891       -0.021849        0.001462   \n",
+       "duration                 0.016982       -0.006173       -0.004565   \n",
+       "campaign                -0.032527       -0.004871        0.005467   \n",
+       "pdays                   -0.043072        0.009654       -0.028664   \n",
+       "previous                 0.078260       -0.017419        0.013485   \n",
+       "emp.var.rate            -0.116507        0.047053       -0.028425   \n",
+       "cons.price.idx          -0.032322       -0.002939       -0.003210   \n",
+       "cons.conf.idx            0.025850        0.058946       -0.000228   \n",
+       "euribor3m               -0.119714        0.046468       -0.023823   \n",
+       "nr.employed             -0.144023        0.042848       -0.030453   \n",
+       "outcome                  0.055781        0.009032        0.032872   \n",
+       "job_blue-collar         -0.074502       -0.234697       -0.086993   \n",
+       "job_entrepreneur        -0.027514       -0.086676       -0.032128   \n",
+       "job_housemaid           -0.023608       -0.074370       -0.027566   \n",
+       "job_management          -0.041643       -0.131185       -0.048626   \n",
+       "job_retired             -0.029206       -0.092005       -0.034103   \n",
+       "job_self-employed       -0.028558       -0.089964       -0.033346   \n",
+       "job_services            -0.046286       -0.145812       -0.054047   \n",
+       "job_student              1.000000       -0.063988       -0.023718   \n",
+       "job_technician          -0.063988        1.000000       -0.074716   \n",
+       "job_unemployed          -0.023718       -0.074716        1.000000   \n",
+       "job_unknown             -0.013934       -0.043896       -0.016270   \n",
+       "poutcome_nonexistent    -0.084656        0.020288       -0.012524   \n",
+       "poutcome_success         0.011176       -0.013611        0.034295   \n",
+       "\n",
+       "                      job_unknown  poutcome_nonexistent  poutcome_success  \n",
+       "age                      0.063831             -0.031205          0.043341  \n",
+       "default                  0.065701              0.091124         -0.086343  \n",
+       "contact                  0.021516              0.251147         -0.109971  \n",
+       "duration                -0.008591             -0.021259          0.046154  \n",
+       "campaign                -0.007765              0.098957         -0.054564  \n",
+       "pdays                   -0.006368              0.488761         -0.940565  \n",
+       "previous                -0.001958             -0.854241          0.510865  \n",
+       "emp.var.rate             0.013772              0.482106         -0.256587  \n",
+       "cons.price.idx           0.016884              0.289799         -0.060257  \n",
+       "cons.conf.idx            0.032504              0.100217          0.087382  \n",
+       "euribor3m                0.017662              0.502340         -0.282272  \n",
+       "nr.employed              0.010605              0.508717         -0.354030  \n",
+       "outcome                 -0.002169             -0.207179          0.325804  \n",
+       "job_blue-collar         -0.051108              0.026745         -0.053400  \n",
+       "job_entrepreneur        -0.018875              0.008954         -0.029330  \n",
+       "job_housemaid           -0.016195              0.042448         -0.014792  \n",
+       "job_management          -0.028567             -0.007995          0.004104  \n",
+       "job_retired             -0.020035             -0.045550          0.056007  \n",
+       "job_self-employed       -0.019591             -0.014306         -0.003325  \n",
+       "job_services            -0.031752              0.002033         -0.025132  \n",
+       "job_student             -0.013934             -0.084656          0.011176  \n",
+       "job_technician          -0.043896              0.020288         -0.013611  \n",
+       "job_unemployed          -0.016270             -0.012524          0.034295  \n",
+       "job_unknown              1.000000              0.004583          0.009007  \n",
+       "poutcome_nonexistent     0.004583              1.000000         -0.459409  \n",
+       "poutcome_success         0.009007             -0.459409          1.000000  \n",
+       "\n",
+       "[26 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1179dcd50>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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izWjgCKy8yyDgTkwSbl9gd0z39+icfovJxy1CVd/GnLf9ci79isU1AGswDd+f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\nU6vafAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1179dc490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a heatmap out of the correlation matrix\n",
+    "sns.heatmap(bank.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.041299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <td>0.041299</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               age  duration\n",
+       "age       1.000000  0.041299\n",
+       "duration  0.041299  1.000000"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank[['age', 'duration']].corr()\n",
+    "# age won't be useful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1179dc3d0>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9gRXV538H7D/xKCUzl0bE2ymJd647gDKf/ocAEXEZpTfog5n5zoi4nbL4zreb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\nAA3LdXEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117cbfd50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('education').duration.mean().plot(kind='bar')\n",
+    "# or education, except that illiterate column is interesting.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1180c4390>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gSZKKDAmSJKnIkCBJkooMCZIkqciQIEmSigwJkiSpyJAgSZKKDAmSJKnIkCBJkooMCZIk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\nSZKKDAmSJKnIkCBJkooMCZIkqej/A/cqp5Yh6xBLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118134e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('marital').duration.mean().plot(kind='bar')\n",
+    "# or marital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x117c86590>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EzJhZh5lNNrNXpY6lkZmtYmYPmdmGqWMRScnMjjGzPRo+vgx4xsyeMLNNE8b1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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11818d990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('job').duration.mean().plot(kind='bar')\n",
+    "# job maybe.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1184d3590>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JKjIkSJKkIkOCJEkqMiRIkqQiQ4IkSSoyJEiSpCJDgiRJKjIkSJKkIkOCJEkqMiRIkqQi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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1184e1b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('day_of_week').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x118538810>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BPB34LdWMwWsz85sAmXlxROwGXEZ1MqWbgcMzc6jlMc4BhoGrqU6mdANwxlRehCRJ6rx2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\nkiSpyJAgSZKKDAmSJKno/wMlveBi+JuAkgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118479cd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bank.groupby('previous').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "duration_response = bank['duration']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "255.17246331494047"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X, duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not that great.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1186fdfd0>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EzJhZh5lNNrNXpY6lkZmtYmYPmdmGqWMRScnMjjGzPRo+vgx4xsyeMLNNE8b1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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118794b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# job field\n",
+    "bank.groupby('job').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "254.46828669453504"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Only with the retired field\n",
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X[['job_retired']], duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# How can we make this a bit better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/11_decision_trees-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/11_decision_trees-checkpoint.ipynb
new file mode 100644
index 0000000..095c32d
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/11_decision_trees-checkpoint.ipynb
@@ -0,0 +1,2075 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Decision Trees\n",
+    "\n",
+    "*Adapted from Chapter 8 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why are we learning about decision trees?\n",
+    "\n",
+    "- Can be applied to both regression and classification problems\n",
+    "- Many useful properties\n",
+    "- Very popular\n",
+    "- Basis for more sophisticated models\n",
+    "- Have a different way of \"thinking\" than the other models we have studied"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lesson objectives\n",
+    "\n",
+    "Students will be able to:\n",
+    "\n",
+    "- Explain how a decision tree is created\n",
+    "- Build a decision tree model in scikit-learn\n",
+    "- Tune a decision tree model and explain how tuning impacts the model\n",
+    "- Interpret a tree diagram\n",
+    "- Describe the key differences between regression and classification trees\n",
+    "- Decide whether a decision tree is an appropriate model for a given problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part 1: Regression trees\n",
+    "\n",
+    "Major League Baseball player data from 1986-87:\n",
+    "\n",
+    "- **Years** (x-axis): number of years playing in the major leagues\n",
+    "- **Hits** (y-axis): number of hits in the previous year\n",
+    "- **Salary** (color): low salary is blue/green, high salary is red/yellow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Salary data](images/salary_color.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Group exercise:\n",
+    "\n",
+    "- The data above is our **training data**.\n",
+    "- We want to build a model that predicts the Salary of **future players** based on Years and Hits.\n",
+    "- We are going to \"segment\" the feature space into regions, and then use the **mean Salary in each region** as the predicted Salary for future players.\n",
+    "- Intuitively, you want to **maximize** the similarity (or \"homogeneity\") within a given region, and **minimize** the similarity between different regions.\n",
+    "\n",
+    "Rules for segmenting:\n",
+    "\n",
+    "- You can only use **straight lines**, drawn one at a time.\n",
+    "- Your line must either be **vertical or horizontal**.\n",
+    "- Your line **stops** when it hits an existing line."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Salary regions](images/salary_regions.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above are the regions created by a computer:\n",
+    "\n",
+    "- $R_1$: players with **less than 5 years** of experience, mean Salary of **\\$166,000 **\n",
+    "- $R_2$: players with **5 or more years** of experience and **less than 118 hits**, mean Salary of **\\$403,000 **\n",
+    "- $R_3$: players with **5 or more years** of experience and **118 hits or more**, mean Salary of **\\$846,000 **\n",
+    "\n",
+    "**Note:** Years and Hits are both integers, but the convention is to use the **midpoint** between adjacent values to label a split.\n",
+    "\n",
+    "These regions are used to make predictions on **out-of-sample data**. Thus, there are only three possible predictions! (Is this different from how **linear regression** makes predictions?)\n",
+    "\n",
+    "Below is the equivalent regression tree:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Salary tree](images/salary_tree.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first split is **Years < 4.5**, thus that split goes at the top of the tree. When a splitting rule is **True**, you follow the left branch. When a splitting rule is **False**, you follow the right branch.\n",
+    "\n",
+    "For players in the **left branch**, the mean Salary is \\$166,000, thus you label it with that value. (Salary has been divided by 1000 and log-transformed to 5.11.)\n",
+    "\n",
+    "For players in the **right branch**, there is a further split on **Hits < 117.5**, dividing players into two more Salary regions: \\$403,000 (transformed to 6.00), and \\$846,000 (transformed to 6.74)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Salary tree annotated](images/salary_tree_annotated.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What does this tree tell you about your data?**\n",
+    "\n",
+    "- Years is the most important factor determining Salary, with a lower number of Years corresponding to a lower Salary.\n",
+    "- For a player with a lower number of Years, Hits is not an important factor determining Salary.\n",
+    "- For a player with a higher number of Years, Hits is an important factor determining Salary, with a greater number of Hits corresponding to a higher Salary.\n",
+    "\n",
+    "**Question:** What do you like and dislike about decision trees so far?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building a regression tree by hand\n",
+    "\n",
+    "Your **training data** is a tiny dataset of [used vehicle sale prices](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/vehicles_train.csv). Your goal is to **predict price** for testing data.\n",
+    "\n",
+    "1. Read the data into a Pandas DataFrame.\n",
+    "2. Explore the data by sorting, plotting, or split-apply-combine (aka `group_by`).\n",
+    "3. Decide which feature is the most important predictor, and use that to create your first splitting rule.\n",
+    "    - Only binary splits are allowed.\n",
+    "4. After making your first split, split your DataFrame into two parts, and then explore each part to figure out what other splits to make.\n",
+    "5. Stop making splits once you are convinced that it strikes a good balance between underfitting and overfitting.\n",
+    "    - Your goal is to build a model that generalizes well.\n",
+    "    - You are allowed to split on the same variable multiple times!\n",
+    "6. Draw your tree, labeling the leaves with the mean price for the observations in that region.\n",
+    "    - Make sure nothing is backwards: You follow the **left branch** if the rule is true, and the **right branch** if the rule is false."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How does a computer build a regression tree?\n",
+    "\n",
+    "**Ideal approach:** Consider every possible partition of the feature space (computationally infeasible)\n",
+    "\n",
+    "**\"Good enough\" approach:** recursive binary splitting\n",
+    "\n",
+    "1. Begin at the top of the tree.\n",
+    "2. For **every feature**, examine **every possible cutpoint**, and choose the feature and cutpoint such that the resulting tree has the lowest possible mean squared error (MSE). Make that split.\n",
+    "3. Examine the two resulting regions, and again make a **single split** (in one of the regions) to minimize the MSE.\n",
+    "4. Keep repeating step 3 until a **stopping criterion** is met:\n",
+    "    - maximum tree depth (maximum number of splits required to arrive at a leaf)\n",
+    "    - minimum number of observations in a leaf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Demo: Choosing the ideal cutpoint for a given feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# vehicle data\n",
+    "import pandas as pd\n",
+    "url = '../data/vehicles_train.csv'\n",
+    "train = pd.read_csv(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>year</th>\n",
+       "      <th>miles</th>\n",
+       "      <th>doors</th>\n",
+       "      <th>vtype</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22000</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>13000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>73500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9500</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>78000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9000</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>47000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4000</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>124000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>177000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>209000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>62000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1800</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>163000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1300</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>6571.428571</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    price  year   miles  doors  vtype   prediction\n",
+       "0   22000  2012   13000      2    car  6571.428571\n",
+       "1   14000  2010   30000      2    car  6571.428571\n",
+       "2   13000  2010   73500      4    car  6571.428571\n",
+       "3    9500  2009   78000      4    car  6571.428571\n",
+       "4    9000  2007   47000      4    car  6571.428571\n",
+       "5    4000  2006  124000      2    car  6571.428571\n",
+       "6    3000  2004  177000      4    car  6571.428571\n",
+       "7    2000  2004  209000      4  truck  6571.428571\n",
+       "8    3000  2003  138000      2    car  6571.428571\n",
+       "9    1900  2003  160000      4    car  6571.428571\n",
+       "10   2500  2003  190000      2  truck  6571.428571\n",
+       "11   5000  2001   62000      4    car  6571.428571\n",
+       "12   1800  1999  163000      2  truck  6571.428571\n",
+       "13   1300  1997  138000      4    car  6571.428571"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# before splitting anything, just predict the mean of the entire dataset\n",
+    "train['prediction'] = train.price.mean()\n",
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5936.9819859959835"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate RMSE for those predictions\n",
+    "from sklearn import metrics\n",
+    "import numpy as np\n",
+    "np.sqrt(metrics.mean_squared_error(train.price, train.prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define a function that calculates the RMSE for a given split of miles\n",
+    "def mileage_split(miles):\n",
+    "    lower_mileage_price = train[train.miles < miles].price.mean()\n",
+    "    higher_mileage_price = train[train.miles >= miles].price.mean()\n",
+    "    train['prediction'] = np.where(train.miles < miles, lower_mileage_price, higher_mileage_price)\n",
+    "    return np.sqrt(metrics.mean_squared_error(train.price, train.prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3984.09174254\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>year</th>\n",
+       "      <th>miles</th>\n",
+       "      <th>doors</th>\n",
+       "      <th>vtype</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22000</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>13000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>15000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>15000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>73500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9500</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>78000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9000</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>47000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>15000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4000</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>124000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>177000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>209000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>62000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1800</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>163000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1300</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4272.727273</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    price  year   miles  doors  vtype    prediction\n",
+       "0   22000  2012   13000      2    car  15000.000000\n",
+       "1   14000  2010   30000      2    car  15000.000000\n",
+       "2   13000  2010   73500      4    car   4272.727273\n",
+       "3    9500  2009   78000      4    car   4272.727273\n",
+       "4    9000  2007   47000      4    car  15000.000000\n",
+       "5    4000  2006  124000      2    car   4272.727273\n",
+       "6    3000  2004  177000      4    car   4272.727273\n",
+       "7    2000  2004  209000      4  truck   4272.727273\n",
+       "8    3000  2003  138000      2    car   4272.727273\n",
+       "9    1900  2003  160000      4    car   4272.727273\n",
+       "10   2500  2003  190000      2  truck   4272.727273\n",
+       "11   5000  2001   62000      4    car   4272.727273\n",
+       "12   1800  1999  163000      2  truck   4272.727273\n",
+       "13   1300  1997  138000      4    car   4272.727273"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate RMSE for tree which splits on miles < 50000\n",
+    "print 'RMSE:', mileage_split(50000)\n",
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3530.14653008\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>year</th>\n",
+       "      <th>miles</th>\n",
+       "      <th>doors</th>\n",
+       "      <th>vtype</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22000</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>13000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>73500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9500</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>78000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9000</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>47000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4000</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>124000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>177000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>209000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>62000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>12083.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1800</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>163000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>truck</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1300</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>138000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2437.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    price  year   miles  doors  vtype    prediction\n",
+       "0   22000  2012   13000      2    car  12083.333333\n",
+       "1   14000  2010   30000      2    car  12083.333333\n",
+       "2   13000  2010   73500      4    car  12083.333333\n",
+       "3    9500  2009   78000      4    car  12083.333333\n",
+       "4    9000  2007   47000      4    car  12083.333333\n",
+       "5    4000  2006  124000      2    car   2437.500000\n",
+       "6    3000  2004  177000      4    car   2437.500000\n",
+       "7    2000  2004  209000      4  truck   2437.500000\n",
+       "8    3000  2003  138000      2    car   2437.500000\n",
+       "9    1900  2003  160000      4    car   2437.500000\n",
+       "10   2500  2003  190000      2  truck   2437.500000\n",
+       "11   5000  2001   62000      4    car  12083.333333\n",
+       "12   1800  1999  163000      2  truck   2437.500000\n",
+       "13   1300  1997  138000      4    car   2437.500000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate RMSE for tree which splits on miles < 100000\n",
+    "print 'RMSE:', mileage_split(100000)\n",
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# check all possible mileage splits\n",
+    "mileage_range = range(train.miles.min(), train.miles.max(), 1000)\n",
+    "RMSE = [mileage_split(miles) for miles in mileage_range]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# allow plots to appear in the notebook\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['figure.figsize'] = (6, 4)\n",
+    "plt.rcParams['font.size'] = 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x114a83690>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wX0u1s6Y4bJl1N4cts/6UN2y9HfhxRFw+TJmlwF/l3L7l4LBl1t0ctsz6U96w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\nxFt1AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114a4f0d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot mileage cutpoint (x-axis) versus RMSE (y-axis)\n",
+    "plt.plot(mileage_range, RMSE)\n",
+    "plt.xlabel('Mileage cutpoint')\n",
+    "plt.ylabel('RMSE (lower is better)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Recap:** Before every split, this process is repeated for every feature, and the feature and cutpoint that produces the lowest MSE is chosen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building a regression tree in scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# GROUP EXERCISE\n",
+    "\n",
+    "# Build your own decision tree\n",
+    "# You may make only 3 splits and each leaf must predict a price \n",
+    "# Think hard about which variables are better to split on first and which variables (might be the same as the first)\n",
+    "# will be the second split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# encode car as 0 and truck as 1\n",
+    "train['vtype'] = train.vtype.map({'car':0, 'truck':1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define X and y\n",
+    "feature_cols = ['year', 'miles', 'doors', 'vtype']\n",
+    "X = train[feature_cols]\n",
+    "y = train.price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
+       "           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
+       "           min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
+       "           splitter='best')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# instantiate a DecisionTreeRegressor (with random_state=1)\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "treereg = DecisionTreeRegressor(random_state=1)\n",
+    "treereg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3107.1428571428573"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use leave-one-out cross-validation (LOOCV) to estimate the RMSE for this model\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "scores = cross_val_score(treereg, X, y, cv=14, scoring='mean_squared_error')\n",
+    "np.mean(np.sqrt(-scores))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What happens when we grow a tree too deep?\n",
+    "\n",
+    "- Left: Regression tree for Salary **grown deeper**\n",
+    "- Right: Comparison of the **training, testing, and cross-validation errors** for trees with different numbers of leaves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Salary tree grown deep](images/salary_tree_deep.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **training error** continues to go down as the tree size increases (due to overfitting), but the lowest **cross-validation error** occurs for a tree with 3 leaves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tuning a regression tree\n",
+    "\n",
+    "Let's try to reduce the RMSE by tuning the **max_depth** parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4050.1443001442999"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# try different values one-by-one\n",
+    "treereg = DecisionTreeRegressor(max_depth=1, random_state=1)\n",
+    "scores = cross_val_score(treereg, X, y, cv=14, scoring='mean_squared_error')\n",
+    "np.mean(np.sqrt(-scores))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or, we could write a loop to try a range of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# list of values to try\n",
+    "max_depth_range = range(1, 8)\n",
+    "\n",
+    "# list to store the average RMSE for each value of max_depth\n",
+    "RMSE_scores = []\n",
+    "\n",
+    "# use LOOCV with each value of max_depth\n",
+    "for depth in max_depth_range:\n",
+    "    treereg = DecisionTreeRegressor(max_depth=depth, random_state=1)\n",
+    "    MSE_scores = cross_val_score(treereg, X, y, cv=14, scoring='mean_squared_error')\n",
+    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x114e46710>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/2GmntETdCY+ZmbVWuR5pRcS/G/rKG1hpspMdW0EaxRHQvejUWFIyMrI02Sm6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\nASHgywSo+0FdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114d7e910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot max_depth (x-axis) versus RMSE (y-axis)\n",
+    "plt.plot(max_depth_range, RMSE_scores)\n",
+    "plt.xlabel('max_depth')\n",
+    "plt.ylabel('RMSE (lower is better)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\n",
+       "           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
+       "           min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
+       "           splitter='best')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# max_depth=3 was best, so fit a tree using that parameter\n",
+    "treereg = DecisionTreeRegressor(max_depth=3, random_state=1)\n",
+    "treereg.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>year</td>\n",
+       "      <td>0.798744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>miles</td>\n",
+       "      <td>0.201256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doors</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>vtype</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  feature  importance\n",
+       "0    year    0.798744\n",
+       "1   miles    0.201256\n",
+       "2   doors    0.000000\n",
+       "3   vtype    0.000000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# \"Gini importance\" of each feature: the (normalized) total reduction of error brought by that feature\n",
+    "pd.DataFrame({'feature':feature_cols, 'importance':treereg.feature_importances_})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a tree diagram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create a Graphviz file\n",
+    "from sklearn.tree import export_graphviz\n",
+    "export_graphviz(treereg, out_file='tree_vehicles.dot', feature_names=feature_cols)\n",
+    "\n",
+    "# At the command line, run this to convert to PNG:\n",
+    "#   dot -Tpng tree_vehicles.dot -o tree_vehicles.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Tree for vehicle data](images/tree_vehicles.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading the internal nodes:\n",
+    "\n",
+    "- **samples:** number of observations in that node before splitting\n",
+    "- **mse:** MSE calculated by comparing the actual response values in that node against the mean response value in that node\n",
+    "- **rule:** rule used to split that node (go left if true, go right if false)\n",
+    "\n",
+    "Reading the leaves:\n",
+    "\n",
+    "- **samples:** number of observations in that node\n",
+    "- **value:** mean response value in that node\n",
+    "- **mse:** MSE calculated by comparing the actual response values in that node against \"value\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making predictions for the testing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>year</th>\n",
+       "      <th>miles</th>\n",
+       "      <th>doors</th>\n",
+       "      <th>vtype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>130000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6000</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>82500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   price  year   miles  doors  vtype\n",
+       "0   3000  2003  130000      4      1\n",
+       "1   6000  2005   82500      4      0\n",
+       "2  12000  2010   60000      2      0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the testing data\n",
+    "url = '../data/vehicles_test.csv'\n",
+    "test = pd.read_csv(url)\n",
+    "test['vtype'] = test.vtype.map({'car':0, 'truck':1})\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question:** Using the tree diagram above, what predictions will the model make for each observation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  4000.,   5000.,  13500.])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use fitted model to make predictions on testing data\n",
+    "X_test = test[feature_cols]\n",
+    "y_test = test.price\n",
+    "y_pred = treereg.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1190.2380714238084"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate RMSE\n",
+    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part 2: Classification trees\n",
+    "\n",
+    "**Example:** Predict whether Barack Obama or Hillary Clinton will win the Democratic primary in a particular county in 2008:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Obama-Clinton decision tree](images/obama_clinton_tree.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Questions:**\n",
+    "\n",
+    "- What are the observations? How many observations are there?\n",
+    "- What is the response variable?\n",
+    "- What are the features?\n",
+    "- What is the most predictive feature?\n",
+    "- Why does the tree split on high school graduation rate twice in a row?\n",
+    "- What is the class prediction for the following county: 15% African-American, 90% high school graduation rate, located in the South, high poverty, high population density?\n",
+    "- What is the predicted probability for that same county?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing regression trees and classification trees\n",
+    "\n",
+    "|regression trees|classification trees|\n",
+    "|---|---|\n",
+    "|predict a continuous response|predict a categorical response|\n",
+    "|predict using mean response of each leaf|predict using most commonly occuring class of each leaf|\n",
+    "|splits are chosen to minimize MSE|splits are chosen to minimize Gini index (discussed below)|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing classification error rate and Gini index\n",
+    "\n",
+    "- Gini index is generally preferred because it will make splits that **increase node purity**, even if that split does not change the classification error rate.\n",
+    "- Node purity is important because we're interested in the **class proportions** in each region, since that's how we calculate the **predicted probability** of each class.\n",
+    "- scikit-learn's default splitting criteria for classification trees is Gini index.\n",
+    "\n",
+    "Note: There is another common splitting criteria called **cross-entropy**. It's numerically similar to Gini index, but slower to compute, thus it's not as popular as Gini index."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building a classification tree in scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll build a classification tree using the Titanic data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
+       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
+       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
+       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
+       "\n",
+       "             Ticket     Fare Cabin Embarked  Embarked_Q  Embarked_S  \n",
+       "0         A/5 21171   7.2500   NaN        S         0.0         1.0  \n",
+       "1          PC 17599  71.2833   C85        C         0.0         0.0  \n",
+       "2  STON/O2. 3101282   7.9250   NaN        S         0.0         1.0  \n",
+       "3            113803  53.1000  C123        S         0.0         1.0  \n",
+       "4            373450   8.0500   NaN        S         0.0         1.0  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in the data\n",
+    "url = '../data/titanic.csv'\n",
+    "titanic = pd.read_csv(url)\n",
+    "\n",
+    "# encode female as 0 and male as 1\n",
+    "titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1})\n",
+    "\n",
+    "# fill in the missing values for age with the median age\n",
+    "titanic.Age.fillna(titanic.Age.median(), inplace=True)\n",
+    "\n",
+    "# create a DataFrame of dummy variables for Embarked\n",
+    "embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked')\n",
+    "embarked_dummies.drop(embarked_dummies.columns[0], axis=1, inplace=True)\n",
+    "\n",
+    "# concatenate the original DataFrame and the dummy DataFrame\n",
+    "titanic = pd.concat([titanic, embarked_dummies], axis=1)\n",
+    "\n",
+    "# print the updated DataFrame\n",
+    "titanic.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- **Survived:** 0=died, 1=survived (response variable)\n",
+    "- **Pclass:** 1=first class, 2=second class, 3=third class\n",
+    "    - What will happen if the tree splits on this feature?\n",
+    "- **Sex:** 0=female, 1=male\n",
+    "- **Age:** numeric value\n",
+    "- **Embarked:** C or Q or S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define X and y\n",
+    "feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S']\n",
+    "X = titanic[feature_cols]\n",
+    "y = titanic.Survived"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
+       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
+       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "            presort=False, random_state=1, splitter='best')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fit a classification tree with max_depth=3 on all data\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "treeclf = DecisionTreeClassifier(max_depth=3, random_state=1)\n",
+    "treeclf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create a Graphviz file\n",
+    "export_graphviz(treeclf, out_file='tree_titanic.dot', feature_names=feature_cols)\n",
+    "\n",
+    "# At the command line, run this to convert to PNG:\n",
+    "#   dot -Tpng tree_titanic.dot -o tree_titanic.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Tree for Titanic data](images/tree_titanic.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the split in the bottom right: the **same class** is predicted in both of its leaves. That split didn't affect the **classification error rate**, though it did increase the **node purity**, which is important because it increases the accuracy of our predicted probabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pclass</td>\n",
+       "      <td>0.242664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sex</td>\n",
+       "      <td>0.655584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Age</td>\n",
+       "      <td>0.064494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Embarked_Q</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Embarked_S</td>\n",
+       "      <td>0.037258</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      feature  importance\n",
+       "0      Pclass    0.242664\n",
+       "1         Sex    0.655584\n",
+       "2         Age    0.064494\n",
+       "3  Embarked_Q    0.000000\n",
+       "4  Embarked_S    0.037258"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute the feature importances\n",
+    "pd.DataFrame({'feature':feature_cols, 'importance':treeclf.feature_importances_})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part 3: Comparing decision trees with other models\n",
+    "\n",
+    "**Advantages of decision trees:**\n",
+    "\n",
+    "- Can be used for regression or classification\n",
+    "- Can be displayed graphically\n",
+    "- Highly interpretable\n",
+    "- Can be specified as a series of rules, and more closely approximate human decision-making than other models\n",
+    "- Prediction is fast\n",
+    "- Features don't need scaling\n",
+    "- Automatically learns feature interactions\n",
+    "- Tends to ignore irrelevant features\n",
+    "- Non-parametric (will outperform linear models if relationship between features and response is highly non-linear)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Trees versus linear models](images/tree_vs_linear.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Disadvantages of decision trees:**\n",
+    "\n",
+    "- Performance is (generally) not competitive with the best supervised learning methods (low bias)\n",
+    "- Can easily overfit the training data (tuning is required)\n",
+    "- Small variations in the data can result in a completely different tree (high variance)\n",
+    "- Recursive binary splitting makes \"locally optimal\" decisions that may not result in a globally optimal tree\n",
+    "- Doesn't tend to work well if the classes are highly unbalanced\n",
+    "- Doesn't tend to work well with very small datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BONUS Dive into Gini\n",
+    "\n",
+    "## Splitting criteria for classification trees\n",
+    "\n",
+    "Common options for the splitting criteria:\n",
+    "\n",
+    "- **classification error rate:** fraction of training observations in a region that don't belong to the most common class\n",
+    "- **Gini index:** measure of total variance across classes in a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example of classification error rate\n",
+    "\n",
+    "Pretend we are predicting whether someone buys an iPhone or an Android:\n",
+    "\n",
+    "- At a particular node, there are **25 observations** (phone buyers), of whom **10 bought iPhones and 15 bought Androids**.\n",
+    "- Since the majority class is **Android**, that's our prediction for all 25 observations, and thus the classification error rate is **10/25 = 40%**.\n",
+    "\n",
+    "Our goal in making splits is to **reduce the classification error rate**. Let's try splitting on gender:\n",
+    "\n",
+    "- **Males:** 2 iPhones and 12 Androids, thus the predicted class is Android\n",
+    "- **Females:** 8 iPhones and 3 Androids, thus the predicted class is iPhone\n",
+    "- Classification error rate after this split would be **5/25 = 20%**\n",
+    "\n",
+    "Compare that with a split on age:\n",
+    "\n",
+    "- **30 or younger:** 4 iPhones and 8 Androids, thus the predicted class is Android\n",
+    "- **31 or older:** 6 iPhones and 7 Androids, thus the predicted class is Android\n",
+    "- Classification error rate after this split would be **10/25 = 40%**\n",
+    "\n",
+    "The decision tree algorithm will try **every possible split across all features**, and choose the split that **reduces the error rate the most.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example of Gini index\n",
+    "\n",
+    "Calculate the Gini index before making a split:\n",
+    "\n",
+    "$$1 - \\left(\\frac {iPhone} {Total}\\right)^2 - \\left(\\frac {Android} {Total}\\right)^2 = 1 - \\left(\\frac {10} {25}\\right)^2 - \\left(\\frac {15} {25}\\right)^2 = 0.48$$\n",
+    "\n",
+    "- The **maximum value** of the Gini index is 0.5, and occurs when the classes are perfectly balanced in a node.\n",
+    "- The **minimum value** of the Gini index is 0, and occurs when there is only one class represented in a node.\n",
+    "- A node with a lower Gini index is said to be more \"pure\".\n",
+    "\n",
+    "Evaluating the split on **gender** using Gini index:\n",
+    "\n",
+    "$$\\text{Males: } 1 - \\left(\\frac {2} {14}\\right)^2 - \\left(\\frac {12} {14}\\right)^2 = 0.24$$\n",
+    "$$\\text{Females: } 1 - \\left(\\frac {8} {11}\\right)^2 - \\left(\\frac {3} {11}\\right)^2 = 0.40$$\n",
+    "$$\\text{Weighted Average: } 0.24 \\left(\\frac {14} {25}\\right) + 0.40 \\left(\\frac {11} {25}\\right) = 0.31$$\n",
+    "\n",
+    "Evaluating the split on **age** using Gini index:\n",
+    "\n",
+    "$$\\text{30 or younger: } 1 - \\left(\\frac {4} {12}\\right)^2 - \\left(\\frac {8} {12}\\right)^2 = 0.44$$\n",
+    "$$\\text{31 or older: } 1 - \\left(\\frac {6} {13}\\right)^2 - \\left(\\frac {7} {13}\\right)^2 = 0.50$$\n",
+    "$$\\text{Weighted Average: } 0.44 \\left(\\frac {12} {25}\\right) + 0.50 \\left(\\frac {13} {25}\\right) = 0.47$$\n",
+    "\n",
+    "Again, the decision tree algorithm will try **every possible split**, and will choose the split that **reduces the Gini index (and thus increases the \"node purity\") the most.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('DecisionTree with features', [0, 1], 'has a score of', 0.92666666666666664)\n",
+      "('DecisionTree with features', [0, 2], 'has a score of', 0.99333333333333329)\n",
+      "('DecisionTree with features', [2, 3], 'has a score of', 0.99333333333333329)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GIWIubgT6BT2rRwdJehq09fuE2bN1rIHH05htICDMnaNtvV6zF3aOUFCYO0eR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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1183fd150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting decision tree boundaries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn import clone\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,\n",
+    "                              AdaBoostClassifier)\n",
+    "from sklearn.externals.six.moves import xrange\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "# Parameters\n",
+    "n_classes = 3\n",
+    "n_estimators = 30\n",
+    "plot_colors = \"ryb\"\n",
+    "cmap = plt.cm.RdYlBu\n",
+    "plot_step = 0.01  # fine step width for decision surface contours\n",
+    "plot_step_coarser = 0.5  # step widths for coarse classifier guesses\n",
+    "RANDOM_SEED = 13  # fix the seed on each iteration\n",
+    "\n",
+    "# Load data\n",
+    "iris = load_iris()\n",
+    "\n",
+    "plot_idx = 2\n",
+    "\n",
+    "models = [DecisionTreeClassifier(max_depth=None)]\n",
+    "          \n",
+    "for pair in ([0, 1], [0, 2], [2, 3]):\n",
+    "    for model in models:\n",
+    "        # We only take the two corresponding features\n",
+    "        X = iris.data[:, pair]\n",
+    "        y = iris.target\n",
+    "\n",
+    "        # Shuffle\n",
+    "        idx = np.arange(X.shape[0])\n",
+    "        np.random.seed(RANDOM_SEED)\n",
+    "        np.random.shuffle(idx)\n",
+    "        X = X[idx]\n",
+    "        y = y[idx]\n",
+    "\n",
+    "        # Standardize\n",
+    "        mean = X.mean(axis=0)\n",
+    "        std = X.std(axis=0)\n",
+    "        X = (X - mean) / std\n",
+    "\n",
+    "        # Train\n",
+    "        clf = clone(model)\n",
+    "        clf = model.fit(X, y)\n",
+    "\n",
+    "        scores = clf.score(X, y)\n",
+    "        # Create a title for each column and the console by using str() and\n",
+    "        # slicing away useless parts of the string\n",
+    "        model_title = str(type(model)).split(\".\")[-1][:-2][:-len(\"Classifier\")]\n",
+    "        model_details = model_title\n",
+    "        if hasattr(model, \"estimators_\"):\n",
+    "            model_details += \" with {} estimators\".format(len(model.estimators_))\n",
+    "        print( model_details + \" with features\", pair, \"has a score of\", scores )\n",
+    "\n",
+    "        plt.subplot(2, 2, plot_idx)\n",
+    "        if plot_idx <= len(models):\n",
+    "            # Add a title at the top of each column\n",
+    "            plt.title(model_title)\n",
+    "\n",
+    "        # Now plot the decision boundary using a fine mesh as input to a\n",
+    "        # filled contour plot\n",
+    "        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
+    "                             np.arange(y_min, y_max, plot_step))\n",
+    "\n",
+    "        # Plot either a single DecisionTreeClassifier or alpha blend the\n",
+    "        # decision surfaces of the ensemble of classifiers\n",
+    "        if isinstance(model, DecisionTreeClassifier):\n",
+    "            Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "            Z = Z.reshape(xx.shape)\n",
+    "            cs = plt.contourf(xx, yy, Z, cmap=cmap)\n",
+    "        else:\n",
+    "            # Choose alpha blend level with respect to the number of estimators\n",
+    "            # that are in use (noting that AdaBoost can use fewer estimators\n",
+    "            # than its maximum if it achieves a good enough fit early on)\n",
+    "            estimator_alpha = 1.0 / len(model.estimators_)\n",
+    "            for tree in model.estimators_:\n",
+    "                Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "                Z = Z.reshape(xx.shape)\n",
+    "                cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)\n",
+    "\n",
+    "        # Build a coarser grid to plot a set of ensemble classifications\n",
+    "        # to show how these are different to what we see in the decision\n",
+    "        # surfaces. These points are regularly space and do not have a black outline\n",
+    "        xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),\n",
+    "                                             np.arange(y_min, y_max, plot_step_coarser))\n",
+    "        Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)\n",
+    "        cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors=\"none\")\n",
+    "\n",
+    "        # Plot the training points, these are clustered together and have a\n",
+    "        # black outline\n",
+    "        for i, c in zip(xrange(n_classes), plot_colors):\n",
+    "            idx = np.where(y == i)\n",
+    "            plt.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],\n",
+    "                        cmap=cmap)\n",
+    "\n",
+    "        plot_idx += 1  # move on to the next plot in sequence\n",
+    "\n",
+    "plt.suptitle(\"Classifiers on feature subsets of the Iris dataset\")\n",
+    "plt.axis(\"tight\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/Project-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Project-checkpoint.ipynb
new file mode 100644
index 0000000..6f9a18c
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Project-checkpoint.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Featured</th>\n",
+       "      <th>Pct_of_Active_(7d)_Teams_with_App_Installed</th>\n",
+       "      <th>Active_(7d)_Installed_Teams</th>\n",
+       "      <th>Pct_of_Installed_Teams_Using_App</th>\n",
+       "      <th>App_Active_(7d)_Teams</th>\n",
+       "      <th>App_Active_(7d)_Teams_Last_Week</th>\n",
+       "      <th>Net_Growth</th>\n",
+       "      <th>Pct_Growth</th>\n",
+       "      <th>New</th>\n",
+       "      <th>Pct_New</th>\n",
+       "      <th>Resurrected</th>\n",
+       "      <th>Retained</th>\n",
+       "      <th>Pct_Retention</th>\n",
+       "      <th>Churned</th>\n",
+       "      <th>Pct_Churn</th>\n",
+       "      <th>Stale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>167239</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>49359</td>\n",
+       "      <td>45135</td>\n",
+       "      <td>4224</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>6839</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>14117</td>\n",
+       "      <td>28403</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>16732</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>112999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>151886</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>65718</td>\n",
+       "      <td>61338</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>4048</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>13135</td>\n",
+       "      <td>48535</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>12803</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>77256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>105937</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>59309</td>\n",
+       "      <td>58475</td>\n",
+       "      <td>834</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5098</td>\n",
+       "      <td>52213</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>6262</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>34088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>99137</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>14681</td>\n",
+       "      <td>14445</td>\n",
+       "      <td>236</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>447</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>2337</td>\n",
+       "      <td>11897</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>16424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>95338</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>13384</td>\n",
+       "      <td>12731</td>\n",
+       "      <td>653</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>1757</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>4091</td>\n",
+       "      <td>7536</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>5195</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>30987</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID Featured  Pct_of_Active_(7d)_Teams_with_App_Installed  \\\n",
+       "0   1       no                                         0.26   \n",
+       "1   2       no                                         0.23   \n",
+       "2   3       no                                         0.16   \n",
+       "3   4       no                                         0.15   \n",
+       "4   5       no                                         0.15   \n",
+       "\n",
+       "   Active_(7d)_Installed_Teams  Pct_of_Installed_Teams_Using_App  \\\n",
+       "0                       167239                              0.30   \n",
+       "1                       151886                              0.43   \n",
+       "2                       105937                              0.56   \n",
+       "3                        99137                              0.15   \n",
+       "4                        95338                              0.14   \n",
+       "\n",
+       "   App_Active_(7d)_Teams  App_Active_(7d)_Teams_Last_Week  Net_Growth  \\\n",
+       "0                  49359                            45135        4224   \n",
+       "1                  65718                            61338        4380   \n",
+       "2                  59309                            58475         834   \n",
+       "3                  14681                            14445         236   \n",
+       "4                  13384                            12731         653   \n",
+       "\n",
+       "   Pct_Growth   New  Pct_New  Resurrected  Retained  Pct_Retention  Churned  \\\n",
+       "0        0.09  6839     0.15        14117     28403           0.63    16732   \n",
+       "1        0.07  4048     0.07        13135     48535           0.79    12803   \n",
+       "2        0.01  1998     0.03         5098     52213           0.89     6262   \n",
+       "3        0.02   447     0.03         2337     11897           0.82     2548   \n",
+       "4        0.05  1757     0.14         4091      7536           0.59     5195   \n",
+       "\n",
+       "   Pct_Churn   Stale  \n",
+       "0       0.37  112999  \n",
+       "1       0.21   77256  \n",
+       "2       0.11   34088  \n",
+       "3       0.18   16424  \n",
+       "4       0.41   30987  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('https://raw.githubusercontent.com/bersonperson/sfdat28/master/data/App%20Weekly%20Review.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xe1e7780>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BkiQVMTRIkqQihgZJklTE0CBJkooYGiRJUhFDgyRJKmJokCRJRQwNkiSpiKFBkiQVMTRI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\nRQwNkiSpiKFBkiQV+X/OSp6oO/QSNgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe1e7c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Featured'] = df.Featured.map({'no':0, 'yes':1})\n",
+    "df.groupby('Featured').Pct_Growth.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    173\n",
+       "1     38\n",
+       "Name: Featured, dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Featured.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Featured\n",
+       "0    0.216994\n",
+       "1    0.020789\n",
+       "Name: Pct_Growth, dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Featured').Pct_Growth.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x117af320>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MYwhERERknEzHVV4LCMoH7CR4Uuxi4Bp3/xmAu3+VIIl5kGB0pwK41t2jlbtv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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117bad30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='Pct_Churn', y='App_Active_(7d)_Teams', kind='scatter', alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10fc9320>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Mt4/a9iHgoMz8wyqFlyRJ7ddUB8yImEcxCc7lI9vKcd+XAYdMcNiOwP1jtt0PHDRmyl9J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\n8v8BuqyjHEh7CxcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113d1278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot(column='Pct_Churn', by = 'Featured')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19464080>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zSqlngZdhPAZCKeVIfv0ny1xWCUSyjsUxVuCaSrCCwTCxWNxUzNuZy+XE53MX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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1873fb70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='Pct_Churn', y='Pct_of_Installed_Teams_Using_App', kind='scatter', alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x158317f0>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bbTaU9HpquwH4Q+q7ADA38FDBogJ8UMV5BUEQBAEQ2bGKCSUkmEpyZ1kX2Bm8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5IOwsvKnp9lsLSdZxS+7aINWBQYJ+l84G0z26/AXEuXgqUb8eZ/g/C8kI/x4G4A8NuM482Jr3h8\nL33Mjwcdd6ax7i3h5GtNqgIQADN7J1UgK7LN5VtUBSBp3EmSjqF+9/c+kbUAgaQzgJGNJi2n3J39\nJB2Ob0ubADybJ6/AzP5Q73bJzsK/Pz+mhDyLpLSAqc4K0CLAG5LG07VCWNbg5g08KH1Z0rTACvgW\nrYoZa98j9J0IQkIIoTXVVqupNPl6rvqkMYcJdPxt+B+wEJ6QPQnf2pHV8fjV/sWAR6uOX41fYe5X\nQQje12QssDKdk5GvJV9lof8BL+HB2w74SlDZSbGf4ydXtYbhCcl5fYjnBdV2dB8OfFRg3L72S7w8\ndNbKSXMBswJ3mdnnkgaU3N+jLIsCPzWz0hohlhwwNWUFKLkJOFrSfniO1Kd0Lie9DL5qGPqBCEJC\nCKE1teFX0TsFHJIGS/punn3fyYPAqviJ5y3AMZIWA36Gb2XIqpJ4+pz3pWtn5Ch5OxmsCaxhZhNq\n5jsOmDfHeKOBNfBVj6WBpSX9y8zGFJ5ph78B56R9+5WtXd/Bg6bRBcb9C97XZB+8ASL4NrVj8QTw\nVtXw1jxo39p2JR74t6/m4V+b98wsV9+JVExgT2Az6heBmDXPuMAD+OpC6d3Yy9DEFSDw7V3X4iuN\nHwNbm1l1IL4dUGbltFBABCEhhNCa/oXngNSWDv1aeixvJZt96KgONRKYGe+T8CywW47xZqL+VfNZ\nKHaVPqtGr1h393dxHvykJhMz+0nKMVkWP4ldFzg8NVi8Cz9ZuqNgb4/d8MZs99Gx1WQwHoDsXmDc\nffCv25/p+LpMBM7EeztMLU7E/93z4atkFX/Bq7HlbX53CDACXy08AjgSD8x/Qr6u5hWnAsdLmgvv\ndl+7xSlzlbCU/N9TEn2m/28kzYKvSF1sZh/WPPY1vBLdpWb2XpZx0xa776YxPq5THe5n5Pg9Ds0R\nQUgIIbSm7roiz0ZN0mgWZmZVtz8Etsk7VnIP3vTu8HS/Muc9KdDNO4dGr37fiq9a7JLut0kaip8w\n/j3PG6ctO4+ljxNTULI8sCl+8jkDBf4em9n7wEapSlallO7Yottx0hXk3VPDxYXT4edLSiBuJesC\n65nZKzWrY8/iOT55bYnn3Nwo6VDgcjN7XtIT+HbAU3KOe036fEHVsfYqYeS7QLFxzf1KX6Kt6Zxz\n0ahdgGXM7NTaB8zsA0lr4BdZspYEbx+jm+NlNgkNBUUQEkIILURSpUJVG3CRpOr8gkH4nud7u7yw\n8fHHAKvX/rFOVxbvy1Hacl/gdkkr4NtNDpe0FL61abW888xhtpptGd3ZG7g1lVIdAlyIl7v9hIIB\nWUrSX5OORPWl8dLHubt6p0Zy/wX+L5U5LWULTp0GdWV1YW9FM+C5BbVmpaPSVR6VlQrwq/NfS7f/\nRkfQnkfp1cK6aSx5taSn8W7ymQpW4AF4TytIZ+MrRLmCkNAaIggJIYTWUrnCNwDf5lRdMvYL4H7g\n3ALjf5P6fxumo+NqeMPM7PGUU7IHHiTNg682nGxmLxeYJwCS7qP+ilAbXkb1OXzLR0OBmZm9KKlS\nIWxZfGva1WmMzMnYkjbEg4618ACxsiryD+D3wL/NLPf2kNRIbrq8r+9l3GY1qGs1/8a3Bx2c7rel\nPhz74lsf83oFv9r/Ep4svS7wCN6LJXdwk7VaWEH3A+fkeN3C9FxK/Fm8KEaYgkUQEkIILaTSyC2V\nszzOzHJvvaomad2qu2tJer/q/iBgHfxkqdHxRqb5fWpm79BxAle2e/B99c/SuaHeYnivhKWAf0va\nwMxu6mG+FwC7m9lHacUk65Xd7vwVeBw/WR2JV1b6sOeXZHY6XkJ2RMHKaLWOBP4oqdQGdc0iaT7g\n5dqKVWn723Azq/z8XopX/mrUvsBtqXnjtHiTwiXxlZAiq3l/xZsePoDncVyaigvMh+eh5CZpK2BH\nfFVklRRc7wG80M2qRp73GIrnI72a4+Vf4hckuvs/ZR684l+YgkUQEkIILagJFWYq+Q5twBU1j7Xh\nV233yDDeIXh1pmbnD0wHnGpmnYIcSYcBs5vZdyUdhff56DYIwfe270/5pWdnz5Jcm04UL8gYqHwb\nP5ldV9KTdG0kt0mGsao1q0Fds7xA/WINs6bHBgGY2U5ZBjWzp9Jq3i74z8cwvALT6Wb2et7Jmtn+\nVbf/kr7Gq+I9SG7IO66knfDE9pOAA+lYzXof/x3OHIRIeo/OK44D8LLQn+IJ5lk9iifg39/N4xvT\nuaR3mAJFEBJCCC1K0k/pvrxn1hPEofiJxQv4Se1bVY9NqlNlpjeZyqAWsCU+31qX4LkWO+PVnXpr\nMtiU+Wat7oOfPI4m25X69+lIRi5TM/s5NEN3xRqG4VvzMksd4g/AA8MjC8yt3tjfparMtpndD9xf\nQpntXfGE9+tSw8qKh/D+KHnUXoCo9CV6IMfPOMBpwBWSXgHOrPz/ksoW74wXrvhFzrmGFhFBSAgh\ntCBJu+HbZS4CNsITqBfGT8hPzzpeVQO9ueu813T49omsJkcjt0nASnRNyP42HaVJ22isHPCMkno8\nWW3CVqpamYOhyha9sjW5n0NpJJ2QbrbhhQ+qV98G4T1THsszduoQvy8eyJatWWW2F6T+KsLneJJ9\nZmZ2cc65dDfeNZKOwSuAHSlpXHpoITxoPNbMri7zPUP/E0FICCG0pp2BHczscknbAMeY2bi0DSlv\nkzMk7Ynvq7863b8E+EXaKrKBmT2dYbhnJPUYiBRoyFZxJnC2pGXpqDL1bfzrUzk5/QGel9GbZ3p4\nrEh505YmaUY6B0dfFUmmb4Ll0+cBeMWx6oDzC/x7n3cFAOA2vKrZ+AJj1NOUMtv4auZyQG2C+g/p\n3Ockk1TdbSVgTmBg9WNmljlIM7MDJV2Pr2Yugn897gQuM7MHe3xxmCJEEBJCCK1pPjpK8U7A92eD\nb0O6n44+F1ntgudHIOn7wIb4/uyf4idyP8ow1iF0VPNqCjMbmQKkXej4Nz+DJ5lX+iRcjK8Y9ean\nQL9PwK6QtDBwoJltl+6/REejSfDVq9Wre780OO5ywB/NbP106DVg+qqntElaxcxylxYuk5l9D0DS\nhfj3vezVqpuBoyUtDTxM19yYTF3pm11mGw++T08rmAOAlSRtgVdjG5FnQEkbAKPwn68P6Rw8VRpa\nZpaCjV4DDklnACNTM8IwhYggJIQQWtP/8BWPF/EKMyvjV3wXpFh+wzx0XEHdALjSzEZLMrwjdxZX\nmFntVpPSmdn59FDNKsNJ6T2TY74l2hV4o+r+LHhOSeXf8HN8b/2OOca9u+bYVngVpAHAdnhVpK0y\njttse1DnvEbSrHheU97g5Iz0ea86j+VZHWtqmW0zO0/SBLwR5vR4lbjX8ACttuhEo47Hmx8e0EfN\nKn+JXwSJIGQKEkFICCG0ptvxVYpH8XyQE1Oi+rfwyj15vY8HIi/j2zcOTcfb8C7JjZoc+SDtUhnW\n2ei6TaSVgoqs1ga2rzl2jZmNg/YyzuflGHdVPHG42v1V404ArswxbrNdgVd+Oqvm+Gb478r6XV7R\nADMb2PuzMo3XlDLbNe8xChglaXpgWAm/B98ATumjAAQmX6GLMBlFEBJCCK1pB9IJt5mdLukd/ORx\nNN5tOK/R+MnLf/GOzjen48sC47p9VVeZThokzQu8ZmaZegNIWhBvlrYmna9IZ83heJEMyfeSVgMe\nqkroL8uDNF7JaQH8CnfFeXTe/jYe70yf1fx0ro42ks5XoF8Hvp5j3Gb7DvXLSN+BF3HILHWO/zuw\no5n11FwvMzP7Q6qEtQ5eVOIyM/tI0jzAh3nzbiTdDmxiZu+noOHTdHwm4Doz+36OYW/BL3Bk+T8g\nhB5FEBJCCC0onax/VXX/Crr298hjN+B3wHBgvaotLAuQIbjJcfV4DJ5Mm/Uk5yK8PPHP8ZPjXCsw\nZrZgxpfcTIb5popN85vZWzXHZwVeMbPp0zzWzjCHr/BVq1fSa/esefzrdFQIy+IzPBCpjFvbOG84\nze//kscQakpVJ9PgJagzS53jlyk0q25Imh8PcObD5/5PfHvWful+1m10FWtR/+swHbBGhvltWHX3\nRuBYSUsAT1Lzc5U1LyYEiCAkhBBalqQ1gN/gV1F/amavpk7JL5hZ7Z7+hqQr+0fUOX5socn2Lu92\nixWBb5tZ7qo/OWWdbyVJuNYQaraQZfA03sm+u8Te9YCncoxbaSR3TzePb0L/bCT3IL5CuGvN8R3x\nhPK8LsW3ve3f2xMzOhnv3bEs8E7V8b+SIyekJlhaQtJcVfcH4dsrs3Q3r9cnZmSdY1Nl1bhQXAQh\nIYTQgiRtilfCGoWXKB2SHvoa3lwt1/73NPb8+PameqU4j8k7bpM8A8zc15PojqQd0s02YCtJ1R3Z\nB+FXrXsqDdyTC4GTJD1uZjfWvO8G+Elzli73FWfgjeTG443kvkpjVhrJ7Ur/bCR3EHBrKtd8Wzq2\nNl6yed0C4w4GtkvbpupVx6qXsN6INYBVzewLSdXHx+M5GFk9hv+cteE5Y7Um0DVA61bZuTAh1Iog\nJIQQWtNB+D71P0vavOr4PemxXCRtjV+F/QSvvFRbirO/BSF74OVT96P+NpFGmhQ2U6Xh3wBgX6q2\n0OGVkMbjJ/aZmdm5qYzyDSmHp1KKV+njGjPLfEU9NZI7ATgV+GOdRnIn9MdGcmZ2j6SV8a/zZvhJ\n9xPA9gXzOZYCHkm3F6t5rEgBhoHUX0GYF9+WlVWlMt44vJ9H9da/L4A3K53JG5V+vk4DVq6tLibp\na3gp4b3M7JYc80XSfHhforaa4wOA4Wb2Ujp0KV4aOExBIggJIYTWJOCuOsc/oNjKwKHA4cARtScG\n/dQd6XN3W4f6dJuImc0NIOk+YH0ze6/k8bdIDd82x38mAJ4FDitQjhUz20/SX4EtgEXT4buAy83s\n/iJzLpukgcA+wEZ4LsTtwAgzm9DjCxtU6UPSBP/Ag+j21TJJw/DA9aasg5lZpbR2mSsYewDn1itv\nbGYfSDobX13JFYTgjRXrdY2fNT02KL3XTjnHD/1YBCEhhNCa/od3GR5fc3x1ilWwmR0Y1QcBSN73\ny9I8sUyZ5mtmq1TfT1d6hV8FLlSitdGiBJL2B84ys/cbHPd+vGdFb+P2dSO5A/HGmLfiSfW7AXPg\n/Uz6s72BWySNwXOGLsMDvnfw4C+3lBu2I746soqZvShpT2CcmV2fYahl8UT57vwDDwDz6q5r/DAa\nrxIXWlQEISGE0JrOBU6WtB3+R3weSavgDb0OLzDutXiewuQuxZkrMT3vNpASZC1BfAwwxswuSlfu\nb8Pzbj6StL6ZdbeSU6YD8P4eDQUhGfR1I7lfATub2TkAKXfjRkkjspZ8rkfSv+gh6MxZ8hYzeyXl\nr2yOd0kfhjfdHJVnvApJO+FNK0/CA7TKauB7+MpGliCktwprk/CAL+scT0g324DDU/W4ikF4ueXH\nso4bWksEISGE0JqOxrdd3IZ3Rb4L+BxvfnZqgXGfAI6StBL1cyzOKTB2T5agc8+LbklaDHjWzNrS\n7W6ZWd6k7x6Z2YwZX7I5sGm6/WNgcbzE75b497Lh0qkFNKvhW183kpuPjn42mNmtktqoKl9cUO3J\n8DT4924p4OIiA5vZJDzfAQBJQ4Df4nktc3X3ul7sCvzazK5Lq18VD+HBYhav4v/O57p5fBm8NHZW\ny6fPA4Cl8ZyVii+Ax8k+19BiIggJIYQWImkhvARvG3CkpGPxbVnD8CvtuRqcVdkHT57eKH1Ua8Mb\nA/Y2x4Y7tpvZJunzyxnmWGmk+Ga6Xe8qddZmhUhaHA8W1sD7ZEyPJ/c+iu95v6ZAc8I56ThZ+zFw\npZk9Ielj8veDCG4wXbfuTMSDhcLq9F8BQNKh+O9dJinQOBT4AX7CfUwKGLbFmyp+CdT2ZsliQeqX\nUP4cmCHjWDfhKxV/N7NOX2NJQ/H8lb9lnWAlz0bShcDu9XJOwpQvgpAQQmgtz9I5kfMSYDczG1PG\n4JVE6oKqu3YPADZOxx5Kx1bEk+cbDlZqLE5H5Z/Fc47RTtIKeNWv1fEE9wfwXlsI2WMAACAASURB\nVA0T8ATZpfCTw1PTtqqTcgQjb/pb6TW8X8Nu6fh0FKuwFPxn7CJJ1d+T6YCzJLXn21QC3hJdivcm\nyZoTcRje3+efwGrAVelkfGVgL+CqrFWsaryAr9S8WHP8h0DWfjpH4H1hnpF0Gh0V2L6Jr9gMImc3\n+mQP6pyLpiaekyI4mbJFEBJCCK2lduvL+sDv+2Ii3TGzbSu3Jf0Jz0PYsXJilfpNnEHOkptmZvVu\nF3ANcCze8LHbfImUc7M7nlD8x4zvcQnwF3x7y2A8oRe8h0UZ/4apWb0tUZfWOVa2VciXPP0z4Fdm\nNlrSUvgWyMHAsiUVhDgBOF1SpUHmSpK2wP+fGJFlIDN7Q9KqwJnAUXT8/9OGrw7+1szeKDDXK/Ac\nlbNqjm8GbEiBfkeh/4sgJIQQApIaOqk2swMyDr0dsHr1lV0z+zIlpt4L/C7jeJ2kk6t3Kwnqkg7D\nS56OwU/0GskJWMzMekq+BcDM7gPuk5R5m4+ZHShpLDAcuKJqa8tgPAAKOVUHvc1QZ3vhAHw18lvk\nKwIxL6mDu5k9lVZwTiyrIp2ZnSdpAr6KMT1edes1fNtT5rLNqfTv+pJmwbd+DsBzssooN/0d6jfU\nvINiKyyhBUQQEkIIraXSEbn2WFGN9ELI8z6D8a0btVf7v0k5/QxGkrpAS/o2HtTsh5fuPRG/6tyj\nRgKQIs+vel2Xq/Nmdn6esXL6N77FrCHRSK7dBzX3v8J/nkea2T/qPL83g+iciD0JKJrL1YmZjQJG\nSZoeGGZmtX048oz5HvCfwpPrbAje26XWNMDQkt8r9DMRhIQQQmup3f/eZe87ZN//XtvHokQXAudL\nWhjfPw9+9XP/9FhR8wOVClib4Mnjp6Syqrc1MoCk3Xp/ljOzU7JPsf19huA5APNRc+JVpOqYpC+B\nuWtPNCXNhnfJrjR8y7q1JRrJ0ZSVlqb8DtdjZp8CnwKk7Vm7mFl/qjr1IL5yuWvN8R1Jq0VhyhVB\nSAghtJba/e+TY+97F5LeBFYys/G9PHUfvLHi3vgJLXiVqGOB40uYyif4SfFLeLWh06qON1oJqLb6\n0Rz4NpZKfsjM+Incm0CuIETS0nilodnwAOQTYEY8p+B9Gqg61oPuSuQOofMV9zzjTvWN5CQNB9oq\nW/tS+epf4NXo8nzfmvY7LGkOPMj/ArgtbX2cBtgZzwkZTP8qfXsQcGvql1K5aLA2niu1bp/NKkwW\nEYSEEEILafb+9wyG0sB2qtQs7hjgGEkzpWNlbt25Db+K/DCwJHBjOr4EXasDdTfHBSu3Jf0CP2Hb\nvpL0Lkl4c8izC8zzxDTXXwPv4n0SBgIXAX/KM2DVCk4bMCKV+60YBHwXL2GcddxoJNfZZXiQeImk\nufDO7E8BW0qay8wOyzJYs36HJa2Ol8udCf/ePZTK/l6Hb/k6lIJ9TcpmZvdIWhnvi7IZvl3wCfz3\n79k+nVxoughCQgghNJWkwXgX9oXxEzokzQN8WEJfk9/iJ/GLAJubWaV07yrAVTnGOxyvktWpApek\nPYGryd/NekVgVzObKOkrYIiZjZW0L3AecEOOMSsrOAPw7SvVZV2/AMaTrwdJNJLrbCk6thJuBjxp\nZqtJWhev6pQpCGmiI/DVtiOBbfFyv38FDjCzq/tyYrUkDcRXSTfCVwZvB0aYWcM5S6H1RRASQgih\naSTND/wdz4MYgvdG+AhPHh9CwUZ9ZvYOdcqOmtmBOYecm/p/GwcBX885JniAUElofxOvkjUWeBtY\nIM+AlRWclP+ySUnViqKRXFfT4I3+ANYBRqfb/6Vji2F/sDSws5mNkXQwHqTua2bX9/G86jkQOARf\nVfoM75szB15NL0wlIggJIYTQTCfjTQqXBd6pOv5XfItTLukqdD0f4OVD38059G3A2ZJGmNkj6b1W\nxPsk3JpzTPDtSysCz+FVqg6RNCOwNfB0gXGrg4Zp8W7Zz5vZpCJjJtFIzj0N7CjpRjzv6OB0fB46\n/0z3tVnwoBYzm5C20T3Vt1Pq1q/wgOkcAEnrADem37uv+nZqYXKJICSEEEIejZbrXQNY1cy+8NSK\nduOBbxR4/7/38NhXki7GG6llTaDeDt83/5CkysrFYLwxW6ZGbzUOxhO6wa8CX45vTXsOD0RykzQU\nT8ivjLMYME7SqcCrZnZ0zqGjkZzbDw+afwdcbGaPp+Mb0rFNq79YIuWtgG+nk6ROBRrM7InJP60u\n5gNurtwxs1slteGBXSO9fcIUIIKQEEIIeXRXkanWQFIp1xrz4tuy8uquh8DM+IrDycABeB+RhqWc\nkvUlLQosng7/18ye6eFljYx7X9Xt1/EcmbIcja80rUXn4OxWPBk5bxASjeQAM7tD0uzATDVb3s4h\nlb/tR26j8+/m39LnNjqqndX7fZzcBtO1wtpEfOtbmEpEEBJCCCGPn+BdmHvzD/xEdod0v03SMOAP\neBJtLmb2eTcPvQHclFZdjidjEFI1/rNA06vzSFoKuNfMZiowzE+An5vZ/elqcsXTeDGAvKKRXIcB\nwIqp381lZvYRnqTfn4KQBXt/Sr9R2ysF6vRLKaNXSui/IggJIYTQiaQt8aTWb6ZD/wVOqu74bWYN\nNQLE+4PcImkMfpJxGbAovnd9i9Im3dUYPPm7IWnr0pVm9u/mTamuQTTez6Q7c9C1oSBp3Dxd7iui\nkRzNL65QFjNrqCR1haQz8K7vbzdpSj2pVyq4T3oehb4TQUgIIYR2kg7CtzGdjff3AC93e7akBczs\niCzjmdkrqRHZ5sAyeF7E+cCoJpfjnB9vitio3wI7S3oen9/FZva/psysfA8BPwZOTfcrgccI4L66\nr2hMNJJzTSmu0A/8Ei+1PNmDkH7U7yj0oQhCQgghVNsN+I2ZXVJ17EpJj+InLJmCEIBUqWmyXeWU\n9E08gOopeb2edYEN8P4Fh0u6GT/JvKmfV+w5ALhZ0hL43/Xd0+1VgTXzDhqN5No1q7hCX2s0ryuE\npoggJIQQQrUhwP11jt+Hb6fqlaQNG30zMxvd+7Pqvsfr1N9qNBOer3AXHaVUG/Wkmd0m6XfAxnil\nrOuANyRdBFxoZs9lnGe9nIpqvT3eKzO7W9JywP7Ak3gw9Qiwipk9mXW8aCTXRbOKK4QwVYsgJIQQ\nQrXLge3xE9pq2+IlWxtxXYPPK1Kp59Bujn+INzl/JOe4mNlE4Ep8BWg+PBjZBv+aZJ3vZ/SclzGg\nl8cbYmbPA78uOk4SjeQ6a0pxhRCmdhGEhBBCqPYJsJOkH9CRT7Aynkx+rqQ/Vp5oZgfUG8DMBjZ7\nkmZ2dpbnS9oDuCBrgz0zewk4VNIf8G7ZWf0ox2saIukreg9g2sws69/6aCTXWXfFFd6hucUVQpii\nRRASQgih2qp4ZSnwfhvg9fvHAKtVPa/w1fvJ7DBgNL5SUs+LwJfdvdjM2vCqSJmY2S1Znp8xWNq4\nh8dWwVcw8gSE0UiuSk/FFfp0YiG0uAhCQgghtDOzVYqOIWm3DO93StH3a1CPSbhm1l96LPQWLLUz\ns+trj8kzp4/GE+xHka9PSjSSq1FbXEHSELyi2r7AXN29ri+kLYQvp8C5+vgAYHha3QP/92RaGQyh\nTBGEhBBCqEvSHNDeRTyLPRt8XhswuYKQVpGrYpGkefAcha2BW4DlzOypAnOY6hvJpUDjUOAHeGPC\nY8zsOknb4h3jvwRO7LsZdusFYG669o6ZNT02CMDMdprM8wqhkwhCQgghtEuVkX6PJ+LOmo69i/dK\nOMrMut2yVNGPVhUaIuks4Agz63WrkaSfA4PNrF9sxZH0NbxE767AY8DaJTRcjEZy7jDgN/g2vNWA\nqyRdiOdI7QVc1cjvQx/ortjBMLqucIXQZyIICSGEUO0k4BfA4XQkpq+CV0yaE88zmNK8BTwt6R7g\nBrwx3Wv4CdsswBLA6nhOwGt0VEnqU5L2xbt2/w/Yot72rDyikVy7nwG/MrPRkpbCe6QMBpat3erU\nH0g6Id1sw/vcfFr18CDgO3igGkK/EEFICCGEar8CtjKzG6qOPShpHHAJOYIQSfMCG+IJz536YpjZ\nXgXmWgozO1jSaXiH8Z3xoKPaR3i52h3MLGsDxGY6Gm8g+BywtaSt6z1pSt821UTzAg8DmNlTaXva\nif0xAEmWT58HAEvjW8gqvgAexxuOhtAvRBASQgih2kSgXjfsZ4FJWQeTtDaeaD0O+CbwFLAAfqKU\nu5dHDg/Sw1YUM3sD3+d/pKRZ8IBpKPA28Hw/PfH8M61XpayVDKLzifwk4OM+mkuvzOx7AGnL2O5Z\ny1GHMLlFEBJCCKHaWcD+kn6dmvYhaTBeBShTb47kKOA4MztE0kfApnjC7Cig8KqCpKWBSWY2Nt3/\nEZ6cPQb4Y6pqhJmt3eiYZvYe8F4v73sGMNLM3s479270GCxVM7NtSn7v0Fltgn6X5HzolytNe1Dn\n/E7SrPjvSgQnoV+IICSEEEK1RYAfA+tKqqxUrADMANwk6bLKE83sFw2MtzgdDd0mAUPN7GNJI4Hr\ngTMLzvc8fIvJWEnzA9fgwc22wEzAPgXH784v0/s2FIQ0I1gKTVeboN8qyflX4L9bZ9Uc3wzfFrn+\nZJ9RCHVEEBJCCKHWjTX376y6nbWE7Cd05IG8DiwMPJ3uz559al18E3g03d4MuNfMNpG0Br7a0qwg\nJOvXoa+CpZBTCyfofwdfDal1B77lMIR+IYKQEEII7cxsi96flcn9eGWpscBNwPFpVWCT9FhR1R3B\n16EjgHoRmKOE8cvSV8FSmPoMoaYARDINnucUQr8wsPenhBBCmJpJml7StpLuzvHyvYAH0u1DgNuA\nnwPjge1LmN4jwL6SfgZ8Dw90AOana7O2vtQqwVJofQ9Sv4z0jqRqXyH0B7ESEkIIoS5JqwDb4Vfu\nJ+L7zDMxs3FVtz/BT4TKtCfwF2Ar4Hgzs3R8Uzr6nPQHlWDpn3iwVCl13N+CpdD6DgJulbQsHvQD\nrA18G1i3z2YVQo0IQkIIIbSTNAfeK2Q7vE/CMGAb4LI83aFTf5Fvm9k7NcdnBh4xs4WKzNfMHgEW\nrfPQSDxw6i9aJVgKLc7M7pG0Ml7RbjO8l8wTwPZmVq/8dgh9IoKQEEIISPoxvj1qPeB24DC8v8eH\nwMN5ApBkAbzfQq0hwDdyjllXKiVc2fbUUJnbAi7FvzYNaaFgKbQoSQPx3KKN8JyQ24ERZjahTycW\nQjciCAkhhAC+1epY/KTl3cpBSbkGk7Rh1d31JH1QdX8Qvj1kfK7BO7/PcOAkfIvT1+o8pV4A1NuY\nawC/wSt5/dTMXpW0FfCCmd0NYGY7FZjz5AyWwtTjQDzv6lb852o3PN9ou76cVAjdiSAkhBACeIWm\n3wIrSboEuNrMinSHvi59bqNrv4WJeACyd4HxK0bhFX/2BN6gYAdxSZsCl6Rxl8dXbMADnAPI2WOh\nGcFSCDV+BexsZucASFoHuFHSCDP7qm+nFkJXEYSEEELAzLaWtAvwC2An4HRJfysw3kAASS/gOSFl\ndxavWCGNP7ak8Q4CdjSzP0vavOr4PemxvEoNlkKoYz7g5sodM7tVUhswD/BKn80qhG5EEBJCCAEA\nM/sIOBs4W9JS+DaO94C7JI3GV0du6mmMOmMuWHtM0sxm9n4Zc8Z7b8yF9yEpg4C76hz/AJi5wLhl\nB0sh1BpM1+19E/H+ICH0OxGEhBBC6MLMngL2krQf8BM8af16Mp7QpNePN7O/pPtXAZtKeh1Y38we\nLzjVEfiqzZzAU9QkeZvZMxnH+x+wCF3zVVYHxnV5duPKDpZCqDUAuEjS51XHpgPOkvRJ5YCZbTLZ\nZxZCHRGEhBBC6JaZTQSuAq6SNG/luKRr8P3nb/QyxI7Aluk1P8Ab9f0QLx16LMX7FgzDSwlfTuct\nTgPS/ay5FucCJ0vaLr1+ntQv5Tjg8ALzLDtYCqFWbe4VeBW3EPqlCEJCCCE0xMyq95WvC8zQwMvm\nAl5Ot/8PuNLM/iFpPB2d1Iu4CHger2ZVRq7F0XjlqtuA6fGtWZ8Dx5nZqQXGLTtYCqETM9u2r+cQ\nQhYRhIQQQmim94DheCDyQzqSuwdQzon3QsDGZvZcCWNhZm3AkZKOxbdlDQPGFKwUBuUHSyGE0NIi\nCAkhhNBM1wKXSXoWmI2O6j3LA2UEDncBS5Y0FpJ+CVxrZp8CY8oYMyk1WAohhFYXQUgIIYRm2hNP\n8h4O7Fu1ojA3cEYJ418JnCRpceBJuuZa/CPjeCfiibyj8f30txToFl+t1GAphBBa3YC2tlgRDiGE\nkI2kj4BlzaxIxagy5tFTE7Y2M8u05St1M/8hsAWwEfApnpg/yszuLTDPbYGReAnkMoKlEEJoabES\nEkIIoakkLYp3Cp8TT/puZ2aHFRx+aMHXd2Jmk4C/AX+TND2wMd7A8V+SXjGzhXMOfX76/Mc6j0Vi\neghhqhNBSAghhDyOx5POeyTp18CZwNt4D47q5fc2oFAQYmaf9/6s3GN/KukWYBZgfmDxAsOVGiyF\nEEKri+1YIYQQOpE0P7Am9Vcujsk41ovAGWb2p/Jm2OU9hgCrAfMB01Y/Zmbn5BivsgKyJbA2Xtnr\ncnxL1n8LTziEEEKshIQQQuggaWu8Yd8ndC0l2wZkCkLwVYSrypldV5KWBm7CK29Ni897RuAz4H0g\nUxAi6Qq8n8mneNL74WZ2X0lzLTVYCiGEVhZBSAghhGqH4p3Bj0g9M4q6Cm9seFYJY9VzIt5Y8NfA\nu3jp34F4X448qy9f4t3cy6qKBZQfLIUQQquLICSEEEK12fFtR2Xt1X0OOFzSytSvCnVKwfFXBHY1\ns4mpUtYQMxsraV/gPOCGLIOZ2ZYF59OdsoOlEEJoaRGEhBBCqHYtsBZQVundHYCP8RyTNWseawOK\nBiFf0hHYvIn3IxmLJ8Iv0MgAknYDzjGzz9LtbhUImkoNlkIIodVFEBJCCFM5STtU3X0COErSStRf\nuci0bcjMFiw+wx49hp/gPwf8GzhE0ozA1sDTDY6xJzAK3xq1Zw/PKxI0FQ6WQghhShJBSAghhD/U\n3P8Kb9S3Uc3xNvpf7sLBwLB0+0C8itVleFDyq0YGqA6Umhg0lREshRDCFCNK9IYQQiidpBMaeZ6Z\n7dXsuWQhaSRwnJl9WnN8KPC7vM0VJa0CDDOzf0qaGw+WViEFS2b2cMGphxBCS4kgJIQQQruUo3Cq\nmU2oOT4dsFujfUIk/auBp7WZ2fdzTLP2vQbgpW8XBq4xs48lzQ58UvvvaGCsL4G5zezNmuOzAW+a\nWXQ2DyGEEsR2rBBCCNWOwis21Z68z5AeaygIMbPvlTut+iTNC9wIfBMYhG91+hgvNTwA+G3GIQfQ\nuTdKxbJ4VavcygyWQgih1UUQEkIIoVp3J+GLA+81600lfQgsZ2ZZq3KdjCd4r4wnfFdcS4beJJLe\nw//dbcAzkqq/BoPwvJPcvU6aECyFEEJLiyAkhBACkl6n4yT8yVRGtmIQMCu+QtIsA3K+bk1gDTOb\nIKn6+Dhg3gzj7JHmcAFwCPBB1WNfAOMLdk4vJVgKIYQpRQQhIYQQoOOK/BnA8cCHVY9VTsIbyfOY\n3Lr7OzYPvtLQEDO7GEDSC8A9ZjaphLlVKytYCiGEKUIEISGEEDCzs6H9JPx2M5vYy0v6i1vxrUy7\npPttqZLVIcDfc4w3A7A2cEv1QUnrAQPN7Oac8ywlWAohhClFBCEhhDCVkzStmX2R7v4LGCBp2nrP\nrXpef7E3cKukR4AhwIWAgE+AbXKMdzTwuzrHB6TH8gYhZQdLIYTQ0gb29QRCCCH0uQmS5ky3P8Mr\nY3X30Sy56sWb2YvAksDpeG7FOOBIYHkzez3HkIsCVuf4f4FF8swx2RtYryZYGodXytqvwLghhNCS\nYiUkhBDC+nSUn12fnAFBQZkS0yVdAOxuZh+l1ZnzS5rHB8BCwPia44vgqyu5mNmLkpYEtsLL/Q4D\nrgYuNrOP8o4bQgitKpoVhhBC6HOSVgf+Y2afN/j8uk0FS5jH2Xgn843N7Pl0bBHgmjS/ERnHaw+W\nypxnCCG0ulgJCSGE0E7SP4A7gDuBB4pUiZI0kFQVCpgfmB54C3gUuNXMXq4818zuzjh83pK+vdkX\nz9H4r6RX0rF58b4e++QYb2tgfyCCkBBCqBIrISGEENpJOgL4LrASMAm4n46g5P5GqmalhOu9gZ3w\n/iKPAa/hOSWzAkvhVaH+ARxmZvfnmOdXeP7GWz09z8w+7OnxbsYeAPwA3zY1AXjCzO7KOk7VPOcq\ne8UmhBBaXQQhIYQQupA0Hb4taU1gLWBV4AszG9bAa18G7sObG/6zXuAiaX7gF8BvgCPN7NyM8/uK\nnnNXBgBtZjYoy7g9vN/MwC/N7LSMr2tasBRCCK0stmOFEEKoZ05gOL6Nan68atY9Db52XTMb29MT\nUlWroyQdB8yXc44/pSOhvikkrQ1sD2wMfApkCkKSZ3p4bAAeTJUSLIUQQquIICSEEEK7lEi9FjAb\nHnTciZe+fcjMvmxkjN4CkJrnTgSezz5TwDubl77NSdJwYNv0MR/wFzwIuS3nkE0PlkIIodVEEBJC\nCKHaNsDbwMl4g737szYolLRMo881sycyza5JJE0D/AQYgSfS/x1vWng5cISZjSkwfFOCpRBCaGUR\nhIQQQqg2Nx15IGcCC0h6AE9Ov6PBBO3H8C1Gla1GPcm7DelFoKGVGQBJq+GrOd2VAH4Vb0h4KbC5\nmb2XXnd5zvmFEELoQQQhIYQQ2pnZG8CV6aPSI+Mg4GDgEBoLGhasur08cBxwLJ6sDp7wvjdeDjfv\nPBfs/Vmd3Awsh3cpr2cwHjC1kSG4aUDZwVIIIUwRIggJIYTQTtKM+HaktdLHcniZ2n/i+SG9Sknn\nlfGuAnYzs5uqnvJEqqB1OHBdKRPvXW99ReYBNsWT0E+WdDO+KlKohGQTgqUQQpgiRBASQgih2rvA\nx3hS+lXAb4FHGk1Kr2Np4IU6x18Alsg5ZunM7DNgFDBK0sJ4Uvop+N/JAyVdBNxe4OvQqGY1YQwh\nhH4lgpAQQgjVVgYeNbOvenqSpBXxJn69NS8cC/xe0ohKgrukaYHfp8f6HTN7HjhI0khgPXx15G94\n1/PZ+3JuIYQwpYggJIQQQjsze7jBp/6LxrYN7QjcALwiqVIJaxl8m9MGuSY5maRA7GbgZklzAFtV\nHpO0BTDazD7pq/mFEEIriyAkhBBCHg1tGzKzByUtBGwJfDMd/gtw2WQ+gS+a2/EWcELVobOBB4jc\njRBCyCWCkBBCCE2Vgo1z+ngaZedaNCt3o1CwFEIIrSKCkBBCCKWTtClws5l92tdzATCzGft6Dg2K\nxPQQwlQhgpAQQgjNcBXwkaS/AOeb2QNlDSzpURpcMTCzFcp638mhhYKlEEIoJIKQEEIIeTQSBBwH\nbAyMkDQGOA+4xMzeKfje1b1FpgN2BsbQ0QxxZWBJ4IyC71MKSYsDm+P9V+YHpgfeAh4FbgGuieaE\nIYSpzYC2tth+GkIIIRtJHwHLmlndxGxJXwFzmdmbqZzv9sAWwFBgNHCumf2zhHmcB7xuZgfXHP8D\nMNzMtiv6Ht28b4///vScFYBjgNXxvisPAq/hzR9nBZbCA5OZ0vNOimAkhDC1iJWQEEII7SRNA1Dp\n/yFpHmBDYKyZVXdMn63S96M3qezvw5L2An4GbAf8XdJLOTqK1/oZ8K06xy8FHkrv1QwvAr31SLkG\nOBb4qZm9392TJK0C7A7sDfyxtBmGEEI/FkFICCGEajekj9MlzYSfyA8CZpa0s5mdD9BAANJlmT11\nJb8EuETSInhX8qImAKsBz9YcXw34LO+gkr4FLJ7ujjWzh6ofN7OlGhhmsQaaOWJm9wH3VQLAEEKY\nGkQQEkIIodqKwD7p9k+Bd4Dl8RWHg4DzGxynxypPZvYccGDOOVY7CTgzbX16MB37Dr4CcnjWwSTN\nC1yOBzGV1YuZJd0LbG5mrzQ6ViMBSJHnhxBCK4sgJIQQQrVhwAfp9rrAtWY2SdI9wAIZxlkQT75u\nKjM7WtI4fDvTL9PhscC2ZnZljiHPA6YBFjczA5Ak4ML02A8bHUjSbo0+18xOyTjPEEJoaRGEhBBC\nqPY8sL6kvwLrAaem47MDHzc6iJm92IS5dfdeVwJ5Ao561gRWrQQgaXyTtCvw74xj7Vlzfw68Mlb7\nCgvwKfAmEEFICGGqMrCvJxBCCKFfORI4Da/i9LiZ3ZOOrwM81sgAkjZv9M0kDZe0WuZZdh5jZkkj\nJP1R0qzp2AqSvpFjuJfxlZBag/CvScPMbMHKB7717DF8hWVWM5sVzzl5BDi4p3FCCGFKFEFICCGE\ndmZ2ObAwXjp2naqH7sWrNzViJ0ljJe2bemR0IulrktaXdBl+Ej5b3vlKWgZ4BtgP+B2+ugCwCXBU\njiF/B5yaEtMr7/Et4GQ6cmXyOBzYtXaFBV8tOaLAuCGE0JJiO1YIIYROzOwl4KWaY3dneP2akjYE\ndgWOkvQJ8AZerWoWYC7gbeAiYCkze6PAdE8ALjKzfVPvjoqbgMtyjHcRvmXqAUmT0rHBwCTgAkkX\nVJ6YVjMaNTf1/+YOAr6eY54hhNDSIggJIYTQTtK0wG+A7wFzUrNibmarNjKOmY0GRkuaHW/WNz/e\nqPBtvFP4o2b2VQlT/naab61X8WAnqz2KTadbtwFnSxphZo8ApCaOZwK3Nuk9Qwih34ogJIQQQrVz\n8OaE1+PbnLr0+8jCzN4GruvteZK2AEab2ScZ3+JzvON4rcXIUZ3LzC7O+poGbQdcDDwkqVKKdzBw\nCzCiSe8ZQgj91oC2tkJ/X0IIIUxBJH0AbFjTHX1yvO+HwHJmNi7j687Dc0o2A94FlgG+xAOfu8ws\n88qGpEHAxnQ0KxwDXG9mk7p/VcNjL1o17n/N7JmiY4YQQiuKlZAQQgjVXscbFE5uPTY37MHewNV4\nmduhwJ34Nqz7yNEMUdKSwOg0RiWJfD/gLUkbmNlTOecJgJk9S9fu7iGEjzqxnAAAG9FJREFUMNWJ\n6lghhBCq7QscLaklkqXN7AMz+wGwAbAbXl54fTNbM8fWLvCGhE8D85rZCma2AjAceALfqpaJpFMl\nrZFjHiGEMEWL7VghhBDaSZoFuApPTH8XmFj9uJnN06T3/QhYNut2rCbMYwLwLTN7uub4UsB/zGxo\nxvG+wvNqngfOBy42s/+VNd8QQmhVsR0rhBBCtcuAhYDD8LK6/e5KlaTdGn2umWXtRP4MXjL36Zrj\ncwLPZRyrYl18pWYf4HBJNwPnAjeVVCEshBBaTgQhIYQQqq0JrF4pI9tP7dng89qArEHI74FTJB0K\n3J+OrQyMBPaT1F6Jy8w+bHDMJ83sNkm/wxPet8MT59+QdBFwoZnlDXBCCKElRRASQgih2rN4A73J\n7UVqtn51x8wWbOI8/pY+X0nHKlAlaf6GqvttZPw6mdnENO6VkubDg5FtgP2zjhVCCK0ugpAQQgjV\n9gKOk7Qf8CRdc0K+yDqgpJmBnwILA8ea2buSVgDeMLNX07hLFZ55Ob7Xw2PL4AnqhaWu9IdK+gOw\nThljhhBCK4kgJIQQQrV/ps/3dPN4piv2kpbBO4J/ACyA50K8C2wCzAf8KusEJZ3Q6HPNbK8sY9f2\nR5E0I7AF3lBwRTPLumLxIt63pLv3a6Pjax5CCFONCEJCCCFU+1HJ450AXGRm+6YKWBU34UnweSzf\n4PNyJ9VL+i6wPbAp8BpwLfDbrOM0eetYCCG0rAhCQgghtDOzW0oe8tvAb+ocfxVvCJiZmfW0ZSo3\nSXPhORrbAzPh+RtDgJ+Y2ZhmvGcIIUytIggJIYTQhaTBwLzAtNXHzeyZjEN9jp/Q11oMeCvf7Mon\n6Qbgu8CNwB7A383sS0k7FhjzLOAIM3ulgef+HBhsZqPyvl8IIbSSCEJCCCG0kzQbcDawETCwzlOy\n5kSMBkZK2izdb0uVof4EXJN7olUkfQvYDM8xqQ2aNmlwmB/h5XzPNLNny5gXHmQ9LekevLLWQ/jW\nrs+AWYAlgNWBzdPxHUp63xBC6Pfq/YEJIYQw9ToBGI5XiZqAByO/AcbhPS6y2hsYBrwJDAXuxJv+\nfQQcWHSykjYH7gUWT/ObBlgS+D6eDN+o1YEZgYclPSBpF0mzF5mbmR2Mr/jcA+yM9x15Cf9aGPBn\nvDHkDma2spmVUnkrhBBawYC2tn7XDDeEEEIfkfQasImZ3S/pQ7wi1LOSNgF2M7O1co67Ol7idhjw\niJndWtJ8nwDONrPTU+L7ssAL+GrO62Z2SMbxZgB+jvfwWAlf+dkLuMDMPurptQ2MPQu+WjMUeBt4\nPlXHCiGEqU5sxwohhFBtRuD1dPs9YA68geEj+El5JpKGm9nLZnY3cHdps+ywMJ7HAfAFMIOZtUk6\nEbgdyBSEmNknwAXABZKEJ6nvDxwt6Z9mtmHeiZrZe/jXtFuSzgBGmtnbed8nhBBaQWzHCiGEUO0Z\nYNF0+0lgu5Qnsh3wRo7xxku6U9Kv00pA2d7DAyfwiluVpoczA9MXGdjcvniC/hZFxsrgl9RP5A8h\nhClKrISEEEKodhreVBDgcODm/2/v3qMsK8s7j3+LVhElQBpNAKERxfzU0eYSlUTwFiRR16AoRkVE\nBYmiIsEbhsACE2WSGZUVL4OJqBDBgBpZCjFiBElQhDghCIsBHokIAdqIjgod7peaP95dndPF6aa6\nLmdXn/p+1qpVp959ep8ff1FPvft5XuBg4D7agX0b6hnAa4HjgI8nORc4HTinqu6ec1q4ENiHVjB9\nCfhokt/p1s6fh/tTVfcDX+m+FtrECD5DknpnT4gkaZ2SbElr9L6+qlbN4T4TwPNpBcn+tJ34s6rq\nkDnmWw48sqpWJdkEOAp4Nu0Rsg92j0BtNKb6Wqrqur6zSNJCsgiRJD1I9wv9DsBN3U7AfN57d+Az\nwMqq2tCRv2PNIkTSUuHjWJKkNZI8EvgI7dGrZbQRs9d1jd43VtWJs7zv9rRdkNfS+jYuBt4+D3nv\nB7atqlumrW8N3GKRI0mLk0WIJGnQB4E9gZcAXx1YvxA4lnaOyIwleQut8NgTuAb4PPCyqrphXtKu\nu4diU9q0LEnSImQRIkka9ErgwKq6KMng87pXAjvP4n7HAmfQzhi5fD4CAiQ5ons5CRya5D8HLi8D\nnksrejY2pwO39R1CkhaaRYgkadCvAcMa0DdjdpObVizQgXzv7L5PAIcBg30r9wDXd+uLRpLn0E6f\nfyLwyqq6OclBwI+6c1Soqrf2mVGSRsUiRJI06DLgRcAnp62/EfjnmdwgyUrgyqp6AHh6O/NvuKq6\nYjYhq2qn7rMuoJ3wvqinYCXZHziN9jjabrTHxQC2BP6Y9vibJC0ZFiGSpEHHAmcn+Q3aY01vSfJU\n4IW0Ebsz8X1gG+CW7vUka++iTP082X3GrFXVCwZ/TrIMeDpwwyIrTI4FDquqzyV5zcD6Rd01SVpS\nPDFdkrRGVV0APAt4DPBvwO8DdwN7VtWMdkKAnYCfDrx+Qvd9p2k/P2GueZP8RZI3da+X0Rro/xW4\nMcnz53r/eRRatulupZ3uLklLijshkiSSvB74QlXdXVVXAwfN9l7TJl/tCHy3qu6b9nkPox0qONcp\nWb9Pa+YG2Jd22vuTaflPoE3lWgz+g9bYf/209b0AzwSRtOS4EyJJAjiF1p8w3y4Alg9Z37K7Nldb\n037Bh9ZX8aWq+gHwWdpjWYvFycBHk+xBewxtuyQHAh/mwf03kjT23AmRJMHsJl/N9L7DpmNtDdw+\nD/f/CfDUJD+mNdRPTZd6FGtPzOrbn9P+8Hc+LduFtMfcPlxVH+8zmCT1wSJEkjRl3kbpJjlr4J6n\nJrl74PIyYCXw3Xn4qFOALwI/7j7rvG59DxbROSHdmOITknyI9ljW5sBVVfWf6/+XkjSeLEIkSVPO\nT3Lf+t5QVbvP8F63dt8ngNXAnQPX7gEuoT2iNCdV9f4kVwI70B7Fmip27qftPiwKSV4HnFVVdwBX\n9Z1Hkvo2MTm5EGdISZI2JkkeAD4CrPcv81X1Jxt43+OBD3W/fC9ZSX5KO/DxbFoj/TeqajE9LiZJ\nI+VOiCRpyoeq6pZ5vufngMcB1w4uJnkScG9VXT/XD0iyN7A37bT3tQauVNUhc73/PNmW1rNyAO3x\nsTuSfAn4fFXNx2NpkrRRcTqWJAnmsR9kmlNp/RnT7dFdm5Nup+UfaEXIY4Bfnfa1KFTVfVX1d1V1\nIK1YeidtnPAFSX7YazhJ6oE7IZIkWLjpWLsBFw9ZvwT4xDzc/zDgjVV12jzcaySq6o4k36AVSTsC\nT+k5kiSNnEWIJAnWPuX8ISW5Ddi1qh7qoL1JYIsh61vSpmTN1SOYnylbCy7Jo4CXAwfSdm5uBM4A\nXtlnLknqg0WIJGn6KeczMdOdkwuBo5McMNWInWQZcDTwnQ38zGE+DbwW+MA83GvBJDkT+O/AHbSe\nkA9U1bAdIklaEixCJEkL6X20QqSSfLtbew5tJ+QF83D/RwJvTvJC4Arg3sGLVfWuefiM+XA/8Cqc\niiVJgEWIJGkBVdVVSVYChwO70M4L+RzwcdrUrLlaCXy/e/20adcWzQz6riFdktSxCJEkLaiqWgX8\nMUCSLYDXAOcCz2COfSFVNR+7KQsiyRHAp6rqru71OlXVx0YUS5IWBYsQSdJsbNAuQ5LnAm8C9gdW\nAWfRdkfG2TuBzwN3da/XZRKwCJG0pFiESJJm4yEb05NsA7yRVnxsQWvI3hTYr6qumsuHJzlrJu+r\nqlfM5XPmoqp2GvZakuRhhZKk2XkxcPO6LiY5Byhaz8aRwHZV9Y55/PxbZ/i1KCQ5rhvRO319syTH\n9ZFJkvrkTogkCYAkmwDPo02v2hF4FO3skMuA86rqxqn3VtVDjdd9Me0Ro09W1bXznbWqDt6Q9yfZ\nHlhVVQ/Md5YZOh74S9qI3kGP6q796cgTSVKPLEIkaYlLshnwbuCtwHLatKlVtElWOwP7AScn+Qfg\nT6vqkhncdi/aY1iXJrkaOA04cwHiz9RVwK7AQx2uuFAmGN5Hswvw8xFnkaTeWYRIkn4AXAz8AfDN\nqrp3+huS7Eg7FPDMJCdU1cnru2FXqFyS5Ejg1cAhwIm0x4D3SXJjVa2e5/+O9Znp4YrzKskvaMXH\nJPCDJIOFyDJgc9oOiSQtKROTk4tmjLokqQdJnlJVV8/wvQ8HVlTVD2fxOaHtjhwEbEUreF66ofeZ\njSSrgV2qaqQ7IUneQCuAPkvrjRnsU7kHuN6T0yUtRRYhkqSRSrIM2Bc4ZNyLkIHPfx5wUVXd18fn\nS9Ji4+NYkrTEdSeaz0hVXTHXz6uq+4GvdF9LxaOBvYFvDC4m+T1gk6r6ei+pJKknFiGSpO/TehbW\n1Tw9aE4nnPeo723/PwfeO2R9ortmESJpSfGcEEnSTsATuu/7Az8C3gbs1n29Dfhhd613SVZ244Q3\nRC+N6QOeRDs3ZbpraBPIJGlJcSdEkpa4qrph6nWSLwFHVNXfD7zliiQ3Ah9gcTxCdRmwLXBLkuuA\nZ1bV/3uIf/NU2tjhvtxKK/Sun7a+M3D7yNNIUs/cCZEkDXo6bSdkuh/RfpFfDH5J27UBeDwz+H9Z\nVd3Y9aL05avAXyR54tRCkp2BjwBn95ZKknriTogkadDVwNFJDq2qewCSPAI4uru2GHwZ+KckP6b1\nevxLkqEFRlU9YaTJ1u0o4FzgmiQ3dWvbA98G3tNbKknqiSN6JUlrJHkWcA6th2JqEtZK2i/7+1bV\n9/rKNijJi2iPMn0MOA4YevBhVX10lLnWJ8kEsA/tlPQ7gSuq6sJ+U0lSPyxCJElrSfJo4EDgyd3S\n1cDfVNWi611Icgqth2WUp6/PmyRbAa+rqk/0nUWSRskiRJI0FpJsD1BVNz3Ue/uWZG/a6fEvB+6o\nqq17jiRJI2VPiCSJJPsDX6+qO/rOsiG6Ub3HAu8GNu/WVtMavk+oqgd6jLeWJDsAB3dfK4Av0IqQ\n8/vMJUl9sAiRJAF8CVid5AvAZ6rqn/sONEMn0HYU/gi4qFvbC3g/8EjgmH5iNUkeDuwHHAo8h9ac\n/l7gDOCDVXVVj/EkqTcWIZKkKR+m/WX+0CRXAZ8GTpvBGRx9egNwaFUNjrm9IsnNwEn0XIQAN9MO\nJDwdeE1V/QIgyRm9ppKknnlOiCRpyl9V1e7AM4ELgeOBm5N8Mck+/UZbp+W0X/Knu6a71reH0SaL\nTQJ9nlMiSYuKRYgkaS1VdWlVvY12KvkfAI8Fzk0y7BDDvl0OHD5k/fDuWt+2Az4FHAD8R5IvJ3k5\nrSiRpCXL6ViSJLrD/ratqlvWcX1n4OCq6vvxprUkeR7wNeDfgYu75d8GdgBeUlXf7ivbdN1p6QfT\nHiF7HK0v5FTgWz2f5i5JI2cRIkkiyQPANusqQhazJNsBb2ftc01OqqpV/aVat26i1+/RGur3BVZX\n1WP6TSVJo2URIkkiyY7Av1fVWP5PIclJwHFV9bO+swxK8ljgoKo6sfv5AODsxXgwpCTNJ4sQSdLY\nS3IbsGtVXdd3lvXZWHJK0lzZmC5JS1yS12zAe3dIsudC5lkgE30HmKGNJackzYnnhEiS3prkeOAU\n4JyqunrwYpItgT2B1wH70HoZJEmaNYsQSVriqup5SV4KvAP4syS3Az8B7gJ+FdgG+BltktPTquon\nfWWVJI0HixBJEt2J42cneQywF7AjsBmt+LgMuKyqHugxoiRpjFiESJLW6KZHfeWh3ucUJ0nSXNiY\nLkmajb8Cfr3vEElWJHlQM3eSiSQrBpZOB24bXbJZuwG4t+8QkrTQ3AmRJM3GYpni9CNgW2D6IYvL\nu2vLAKrqrSPONVSSZwBP6X68uqr+ZfB6VT1t9KkkafQsQiRJG7MJYNiBV5vTGusXhSTbA2fQpoz9\nslveKsl3gddU1U29hZOkHliESJI2OklO7F5OAh9IcsfA5WXAHsD3Rx5s3T4NPBx4SlUVQJLQxiJ/\nGnhRj9kkaeQsQiRJG6Pduu8TwNOBewau3QNcDnx41KHW43nAs6cKEICqqiTvAL7dXyxJ6odFiCRp\no1NVLwBIcgrwh1W12JvOb6TthEy3DFg14iyS1DunY0mSZmOxTHE6kiF/UEuyPMkWPeRZl/cCH+8a\n04E1TeofBd7TWypJ6snE5OSwfj5J0lKVZCvglcATgQ9V1c+T7A78pKpu7jfd2pJ8HfhqVf3ltPXD\ngJdW1Uv6Sba2JL8AHkUrmO7rlqder3XWSlUtH206SRo9H8eSJK2RZCVwHnAr8HjgZODnwCuAFcDr\news33B603ZDp/hE4YbRR1mtYRklasixCJEmDTgROraqjkqweWP974G96yrQ+mwKPGLL+cGCzEWdZ\np6r6674zSNJiYhEiSRr0TOAtQ9ZvBrYZcZaZ+B7wZuAd09YPAy4dfZx1S7IMeDn/dVjhVbRHye5b\n97+SpPFkESJJGnQ3MKyh+zeAn444y0wcC5yXZBfg/G5tb1ox9bu9pZomyX8DzqYVclNjet8H/DTJ\nvlV1ZW/hJKkHTseSJA06GzguydQ42ckkK4D/CXy5v1jDVdVFwG/RRuC+CtgX+DdgZVUtpvM3Pg38\nX2D7qtq9qnYHdgCuAD7VazJJ6oE7IZKkQe8G/ha4hdZT8U+0v95fDBzTY661JNmENtr2ZbSekG8B\nh1bVnb0GW7ddgWdU1S+mFqrqF0mOAf5Pf7EkqR8WIZKkNarqVmCfJHsBK4HNgX+tqvP6TfYgxwDH\n0yZ53QUcATwWOKTPUOvxA+DXabshg36NtnMjSUuK54RIktZIskNV3dh3joeS5FraGSaf6n5+IfA1\nYLOqeqDXcEMkeQnwv4D3A5d0y78FHAf8EfCdqfduBKe/S9KcuRMiSRp0fZLvAKcDfzv4+NAiswL4\n+tQPVXVekklgO+Cm3lKt2991378ITP31b6L7fs7Az5PAshHmkqReWIRIkgY9A3gt7S/0H09yLq0g\nOaeq7u412doeRnsMa9C9tPNBFqMXrOfaSlqDuiQtGT6OJUl6kCQTwPNpBcn+tGmKZ1XVoui5SPIA\nbSdksDDal9agfvvUQlW9YsTRZiTJrwAHAIcCv1lV7n5IWlLcCZEkPUhVTQIXABck+STwGeANLJ7G\n72EnkJ8+8hQbKMlzgTfRCrtVwFnA23sNJUk9sAiRJD1Iku1puyCvBZ5GG9G7aH5ZrqqD+84wU0m2\nAd5IKz62oPWFbArsV1VX9RhNknpjESJJWiPJW2iFx57ANcDngZdV1Q29BttIJTkHeC5tcteRwLlV\ndX+Sw/pNJkn9sgiRJA06FjgDOKKqLu87zBh4MfAx4JNVdW3fYSRpsbAIkSQNWtH1g2h+7EV7DOvS\nJFcDpwFn9htJkvrndCxJWuKSrASurKoHutfrVFWOkp2FJI8GXk1r7H8W7SyQdwGfrarVfWaTpD5Y\nhEjSEteNu92mqm7pXk/yXwfpMfDzpKNk5y5JaLsjBwFbAd+sqpf2m0qSRsvHsSRJOwE/HXitBVRV\nBRyV5Gja2SaLZeyxJI2MOyGSpDW6cyy+W1X3TVt/GPDsqrqwn2SSpHGySd8BJEmLygXA8iHrW3bX\nJEmaM4sQSdKgCVoPyHRbA7ePOIskaUzZEyJJIslZ3ctJ4NQkdw9cXgasBL478mCSpLFkESJJAri1\n+z4BrAbuHLh2D3AJcPKoQ0mSxpON6ZKkNZIcD3yoqu7oO4skaXzZEyJJGvQ54HHTF5M8KcnjRx9H\nkjSOLEIkSYNOBfYYsr5Hd02SpDmzCJEkDdoNuHjI+iXAriPOIkkaUxYhkqRBk8AWQ9a3pE3JkiRp\nzixCJEmDLgSOTrKm4OheHw18p7dUkqSx4oheSdKg99EKkUry7W7tObTdkd/pLZUkaaw4oleStJYk\n2wGHA7vQzgu5AvhEVf2812CSpLFhESJJmpEkT6uqK/vOIUna+Pk4liRpnZL8CnAAcCjwm9icLkma\nBxYhkqQHSfJc4E3A/sAq4Czg7b2GkiSNDYsQSRIASbYB3kgrPrYAvghsCuxXVVf1GE2SNGYc0StJ\nIsk5QAErgSOB7arqHf2mkiSNK3dCJEkALwY+Bnyyqq7tO4wkabxZhEiSAPaiPYZ1aZKrgdOAM/uN\nJEkaV47olSStkeTRwKuBQ4Bn0aZhvQv4bFWt7jObJGl8WIRIkoZKEtruyEHAVsA3q+ql/aaSJI0D\nG9MlSUNVcxSwPe2skDWSbJ/E/4dIkmbFnRBJ0gZLchuwa1Vd13cWSdLGx79iSZJmY6LvAJKkjZdF\niCRJkqSRsgiRJEmSNFIWIZIkSZJGyiJEkjQbTjWRJM2aRYgkLXFJVs5i3K6N6ZKkWbMIkSRdBjwG\nIMl1Sbaewb95KnDDgqaSJI2th/UdQJLUu18COwG3AI9nBn+gqqobFziTJGmMeVihJC1xST4FvB74\nMbACuAm4f9h7q+oJI4wmSRpT7oRI0hJXVW9OchawM/Ax4GRgdb+pJEnjzJ0QSdIaSU4BjqgqixBJ\n0oKxCJEkDZVke4CquqnvLJKk8WIRIklaoxvVeyzwbmDzbnk18BHghKp6oK9skqTxYU+IJGnQCcCb\ngD8CLurW9gLeDzwSOKafWJKkcWIRIkka9Abg0Ko6e2DtiiQ3AydhESJJmgceVihJGrQcuGbI+jXd\nNUmS5swiRJI06HLg8CHrh3fXJEmaMx/HkiQNOgr4WpIXAhd3a78N7AC8pLdUkqSx4nQsSdJakmwH\nvB14crd0NXBSVa3qL5UkaZxYhEiSNliSk4DjqupnfWeRJG187AmRJM3G64At+g4hSdo4WYRIkmZj\nou8AkqSNl0WIJEmSpJGyCJEkSZI0UhYhkiRJkkbKIkSSJEnSSFmESJLWSLIiyYOazpNMJFkxsHQ6\ncNvokkmSxoknpkuSBv0I2Ba4Zdr68u7aMoCqeuuIc0mSxog7IZKkQRPAsFNsNwfuGnEWSdKYcidE\nkkSSE7uXk8AHktwxcHkZsAfw/ZEHkySNJYsQSRLAbt33CeDpwD0D1+4BLgc+POpQkqTxNDE5OWzX\nXZK0FCU5BfjDqrLpXJK0YOwJkSQNOpIhu+RJlifZooc8kqQxZBEiSRp0JvCqIeuv6q5JkjRnFiGS\npEF7ABcMWf/H7pokSXNmESJJGrQp8Igh6w8HNhtxFknSmLIIkSQN+h7w5iHrhwGXjjiLJGlMOaJX\nkjToWOC8JLsA53drewPPBH63t1SSpLHiiF5J0lq6AuQoYFfgTuAK4M+q6tpeg0mSxoY7IZIkkmwC\nvAd4Ga0n5FvAoVV1Z6/BJEljyZ4QSRLAMcD/AFYDNwNHAP+710SSpLFlESJJAng98LaqelFV7Qfs\nCxzY7ZBIkjSv/J+LJAlgBfD1qR+q6jxgEtiut0SSpLFlESJJgtYjeNe0tXtp54NIkjSvnI4lSSLJ\nA7SdkLsHlvelNajfPrVQVa8YcTRJ0hhyOpYkCeCvh6ydPvIUkqQlwZ0QSZIkSSNlT4gkSZKkkbII\nkSRJkjRSFiGSJEmSRsoiRJIkSdJIWYRIkiRJGimLEEmSJEkjZREiSZIkaaT+PwaTOuhCUG4kAAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x156df588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "actives = df['Active_(7d)_Installed_Teams']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'ID', u'Featured', u'Pct_of_Active_(7d)_Teams_with_App_Installed',\n",
+       "       u'Active_(7d)_Installed_Teams', u'Pct_of_Installed_Teams_Using_App',\n",
+       "       u'App_Active_(7d)_Teams', u'App_Active_(7d)_Teams_Last_Week',\n",
+       "       u'Net_Growth', u'Pct_Growth', u'New', u'Pct_New', u'Resurrected',\n",
+       "       u'Retained', u'Pct_Retention', u'Churned', u'Pct_Churn', u'Stale'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "feature_cols = ['Net_Growth', 'Active_(7d)_Installed_Teams', 'Pct_Churn']\n",
+    "X = df[feature_cols]\n",
+    "y = df.Featured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.76567099567099561"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.76079365079365091"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(5)\n",
+    "cross_val_score(knn, X, y, cv=25, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score='raise',\n",
+       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
+       "           weights='uniform'),\n",
+       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]},\n",
+       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.grid_search import GridSearchCV\n",
+    "knn = KNeighborsClassifier()\n",
+    "param_grid = {'n_neighbors': range(1, 29)}\n",
+    "grid = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a5ec550>]"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Xv/RjE8smz/YO8W8f3sf9j7xA78DI6+5bOGsC3AVSksrJ0KCK+NvnT/HfH4sArGxv4dMf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\nSZJUlP8N7HAtnkFrw4MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a4b89e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
+    "plt.figure()\n",
+    "plt.plot(range(1, 29), grid_mean_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[173   0]\n",
+      " [ 38   0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "preds = grid.best_estimator_.predict(X)\n",
+    "print metrics.confusion_matrix(y, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.81990521327014221"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_accuracy_rate = df['Featured'].value_counts()[0] / float(df.shape[0])\n",
+    "null_accuracy_rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Net_Growth</th>\n",
+       "      <th>Active_(7d)_Installed_Teams</th>\n",
+       "      <th>Pct_Churn</th>\n",
+       "      <th>Featured</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Net_Growth</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.623705</td>\n",
+       "      <td>-0.007920</td>\n",
+       "      <td>-0.052708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Active_(7d)_Installed_Teams</th>\n",
+       "      <td>0.623705</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.143220</td>\n",
+       "      <td>-0.045415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pct_Churn</th>\n",
+       "      <td>-0.007920</td>\n",
+       "      <td>0.143220</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.065146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Featured</th>\n",
+       "      <td>-0.052708</td>\n",
+       "      <td>-0.045415</td>\n",
+       "      <td>0.065146</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Net_Growth  Active_(7d)_Installed_Teams  \\\n",
+       "Net_Growth                     1.000000                     0.623705   \n",
+       "Active_(7d)_Installed_Teams    0.623705                     1.000000   \n",
+       "Pct_Churn                     -0.007920                     0.143220   \n",
+       "Featured                      -0.052708                    -0.045415   \n",
+       "\n",
+       "                             Pct_Churn  Featured  \n",
+       "Net_Growth                   -0.007920 -0.052708  \n",
+       "Active_(7d)_Installed_Teams   0.143220 -0.045415  \n",
+       "Pct_Churn                     1.000000  0.065146  \n",
+       "Featured                      0.065146  1.000000  "
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Net_Growth', 'Active_(7d)_Installed_Teams', 'Pct_Churn','Featured']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9635852922459972"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X, y, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/Yelp_Eugene-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Yelp_Eugene-checkpoint.ipynb
new file mode 100644
index 0000000..7ab0ba3
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Yelp_Eugene-checkpoint.ipynb
@@ -0,0 +1,861 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>review_id</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>text</th>\n",
+       "      <th>type</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>My wife took me here on my birthday for breakf...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>I have no idea why some people give bad review...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
+       "      <td>2012-06-14</td>\n",
+       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
+       "      <td>4</td>\n",
+       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
+       "      <td>5</td>\n",
+       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
+       "      <td>review</td>\n",
+       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              business_id        date               review_id  stars  \\\n",
+       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
+       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
+       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
+       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
+       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
+       "\n",
+       "                                                text    type  \\\n",
+       "0  My wife took me here on my birthday for breakf...  review   \n",
+       "1  I have no idea why some people give bad review...  review   \n",
+       "2  love the gyro plate. Rice is so good and I als...  review   \n",
+       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
+       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
+       "\n",
+       "                  user_id  cool  useful  funny  \n",
+       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
+       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
+       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
+       "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
+       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn import metrics\n",
+    "import statsmodels.formula.api as smf\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "# visualization\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data = pd.read_csv('http://localhost:8888/files/data/yelp.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x12e573c8>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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DOoxpmppPZ+UPRmUYnbUL1+qWfvjWnH789rwalt00Pj4U09kzk7p9grLDdnEc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5+f5u1TE6Rzjcntz2+k+PxGip39QY4Nb2NXPLTlv0xxulw4l0wNTU3M4XYalCpB7JqBw9H+lVO3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\np7P8wfdG6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Zlkgf5O36ap8tnqC/K2Ap2jlekcrU6rnicVJVhZq1T5nE0V6QrfrvKZypYJ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12e57048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data, x_vars=['cool','useful','funny'], y_vars='stars', size=6, aspect=0.7, kind='reg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x125f4748>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7vKR0MqrwpXmFTFOGEVAmW1Sx7GhqLquhZEwHh2LqMa4cmxsz7WZ9s5qtR1Jb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\niagiYVO5fElL2WX1RntkGAGVy44uz+W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yf4x2j2+pCl1nJfufK7vHJ9k/RuIrHSv+Nsqw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S7g103TfCab2xQRERERERHJhmwm4EPA7xmG8TzQBBwwDGPY6o2maS54HnDDMD4K3El68/YngB8D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\nbJx7vOHSyOZ7drZw/PzQnGtkz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C4NTaZciAF8/SmTbZv8rK92Rkc4VyKRKJFI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6dGCncrhZHbqo/TxhrjetA5vZ4qLKSXRMaFh/zcppeztSHDvcfSXwTK7MU7NfTy1qOwZqD/i2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\niIhIAv8PHDnCfQbnykwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125f4550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stars</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>stars</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.052555</td>\n",
+       "      <td>-0.023479</td>\n",
+       "      <td>-0.061306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cool</th>\n",
+       "      <td>0.052555</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.887102</td>\n",
+       "      <td>0.764342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>useful</th>\n",
+       "      <td>-0.023479</td>\n",
+       "      <td>0.887102</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.723406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>funny</th>\n",
+       "      <td>-0.061306</td>\n",
+       "      <td>0.764342</td>\n",
+       "      <td>0.723406</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           stars      cool    useful     funny\n",
+       "stars   1.000000  0.052555 -0.023479 -0.061306\n",
+       "cool    0.052555  1.000000  0.887102  0.764342\n",
+       "useful -0.023479  0.887102  1.000000  0.723406\n",
+       "funny  -0.061306  0.764342  0.723406  1.000000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x148448d0>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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urfa2K7OyliSpj06y3b+z9K4b7Xl9PbD9dMVispYktcbYzPSCN47JWpLUGuMzdM26aUzW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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1629dbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(data.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.750433\n",
+       "cool         0.030870\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ cool', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stars</th>\n",
+       "      <th>cool</th>\n",
+       "      <th>useful</th>\n",
+       "      <th>funny</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.777500</td>\n",
+       "      <td>0.876800</td>\n",
+       "      <td>1.409300</td>\n",
+       "      <td>0.701300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.214636</td>\n",
+       "      <td>2.067861</td>\n",
+       "      <td>2.336647</td>\n",
+       "      <td>1.907942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              stars          cool        useful         funny\n",
+       "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
+       "mean       3.777500      0.876800      1.409300      0.701300\n",
+       "std        1.214636      2.067861      2.336647      1.907942\n",
+       "min        1.000000      0.000000      0.000000      0.000000\n",
+       "25%        3.000000      0.000000      0.000000      0.000000\n",
+       "50%        4.000000      0.000000      1.000000      0.000000\n",
+       "75%        5.000000      1.000000      2.000000      1.000000\n",
+       "max        5.000000     77.000000     76.000000     57.000000"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 10)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.794700\n",
+       "useful      -0.012205\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ useful', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.804871\n",
+       "funny       -0.039029\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### STATSMODELS ###\n",
+    "\n",
+    "# create a fitted model\n",
+    "lm = smf.ols(formula='stars ~ funny', data=data).fit()\n",
+    "\n",
+    "# print the coefficients\n",
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 5, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 1, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "features = np.array([data.cool, data.useful, data.funny]).T\n",
+    "features  # 2D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5],\n",
+       "       [5],\n",
+       "       [4],\n",
+       "       ..., \n",
+       "       [4],\n",
+       "       [2],\n",
+       "       [5]], dtype=int64)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = np.array([data.stars]).T \n",
+    "response  # 1D array\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# step 1: split data into training set and test set\n",
+    "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n",
+    "# the random_state allows us all to get the same random numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 6, 4],\n",
+       "       [1, 2, 1],\n",
+       "       [0, 1, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [2, 3, 2],\n",
+       "       [0, 2, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 3, 1],\n",
+       "       [0, 1, 0],\n",
+       "       [0, 0, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3],\n",
+       "       [5],\n",
+       "       [5],\n",
+       "       ..., \n",
+       "       [4],\n",
+       "       [2],\n",
+       "       [1]], dtype=int64)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5],\n",
+       "       [5],\n",
+       "       [5],\n",
+       "       ..., \n",
+       "       [3],\n",
+       "       [4],\n",
+       "       [3]], dtype=int64)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.35320000000000001"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(features_train, response_train)      # Note that I fit to the training\n",
+    "knn.score(features_test, response_test)      # and scored on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = data[['cool', 'useful', 'funny']]\n",
+    "y = data['stars']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import cross_val_score\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.28657014,  0.24787606,  0.22011006,  0.17558779,  0.24274274])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/nov_8_eugene-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/nov_8_eugene-checkpoint.ipynb
new file mode 100644
index 0000000..c07e693
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/nov_8_eugene-checkpoint.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "r = requests.get('https://www.strava.com/api/v3/athlete', data={'access_token': '1a870acccbdde888dacddbcb4c0f14bd55df8ddf'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{u'athlete_type': 0,\n",
+       " u'badge_type_id': 0,\n",
+       " u'bikes': [{u'distance': 271861.0,\n",
+       "   u'id': u'b1443262',\n",
+       "   u'name': u'Cannondale Slice 4 Rival',\n",
+       "   u'primary': False,\n",
+       "   u'resource_state': 2},\n",
+       "  {u'distance': 7136859.0,\n",
+       "   u'id': u'b1314028',\n",
+       "   u'name': u'Fuji Altamira 3.0 LE 2012',\n",
+       "   u'primary': True,\n",
+       "   u'resource_state': 2}],\n",
+       " u'city': u'San Francisco',\n",
+       " u'clubs': [{u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/4453767/5/large.jpg',\n",
+       "   u'cover_photo_small': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/4453767/5/small.jpg',\n",
+       "   u'featured': False,\n",
+       "   u'id': 15879,\n",
+       "   u'member_count': 1095,\n",
+       "   u'name': u'San Francisco Running Company',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/314178/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/314178/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'running',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'san-francisco-running-company',\n",
+       "   u'verified': True},\n",
+       "  {u'city': u'Palo Alto',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 2052,\n",
+       "   u'member_count': 42,\n",
+       "   u'name': u'SurveyMonkey',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/2052/21885/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/2052/21885/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'surveymonkey',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 220,\n",
+       "   u'member_count': 456,\n",
+       "   u'name': u'Golden Gate Triathlon Club',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/220/596/3/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/220/596/3/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'triathlon',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'golden-gate-triathlon-club',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 9,\n",
+       "   u'member_count': 824,\n",
+       "   u'name': u'SF2G',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/9/552/2/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/9/552/2/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'sf2g',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 148324,\n",
+       "   u'member_count': 36,\n",
+       "   u'name': u'Slack',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/148324/2971770/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/148324/2971770/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'slack',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551525/1/large.jpg',\n",
+       "   u'cover_photo_small': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551525/1/small.jpg',\n",
+       "   u'featured': False,\n",
+       "   u'id': 207515,\n",
+       "   u'member_count': 11,\n",
+       "   u'name': u'Blood, Slack, and Tears',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551517/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551517/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'running',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'slackstars',\n",
+       "   u'verified': False}],\n",
+       " u'country': u'United States',\n",
+       " u'created_at': u'2011-07-29T16:39:08Z',\n",
+       " u'date_preference': u'%m/%d/%Y',\n",
+       " u'email': u'eugene.berson@gmail.com',\n",
+       " u'firstname': u'Eugene',\n",
+       " u'follower': None,\n",
+       " u'follower_count': 110,\n",
+       " u'friend': None,\n",
+       " u'friend_count': 109,\n",
+       " u'ftp': None,\n",
+       " u'id': 117433,\n",
+       " u'lastname': u'Berson',\n",
+       " u'measurement_preference': u'feet',\n",
+       " u'mutual_friend_count': 0,\n",
+       " u'premium': False,\n",
+       " u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/athletes/117433/851602/2/large.jpg',\n",
+       " u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/athletes/117433/851602/2/medium.jpg',\n",
+       " u'resource_state': 3,\n",
+       " u'sex': u'M',\n",
+       " u'shoes': [{u'distance': 385463.0,\n",
+       "   u'id': u'g375794',\n",
+       "   u'name': u'Newton Gravitas',\n",
+       "   u'primary': True,\n",
+       "   u'resource_state': 2}],\n",
+       " u'state': u'CA',\n",
+       " u'updated_at': u'2016-01-01T21:18:39Z',\n",
+       " u'username': u'eberson',\n",
+       " u'weight': 77.1107}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def myfunction (x):\n",
+    "    weight = x['weight']\n",
+    "    city = x['city']\n",
+    "    user_name = x['username']\n",
+    "    shoe = x['shoes'][0]['name']\n",
+    "    sex = x['sex']\n",
+    "    return [sex, weight, city, user_name, shoe]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'M', 77.1107, u'San Francisco', u'eberson', u'Newton Gravitas']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myfunction(r.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/02_pandas.ipynb b/notebooks/02_pandas.ipynb
index 722e3b2..b826e3f 100644
--- a/notebooks/02_pandas.ipynb
+++ b/notebooks/02_pandas.ipynb
@@ -7191,9 +7191,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/04_sklearn_knn.ipynb b/notebooks/04_sklearn_knn.ipynb
index defcee3..0189d13 100644
--- a/notebooks/04_sklearn_knn.ipynb
+++ b/notebooks/04_sklearn_knn.ipynb
@@ -1019,9 +1019,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/05_bias_variance_tradeoff.ipynb b/notebooks/05_bias_variance_tradeoff.ipynb
index 82fc5ab..f41c77b 100644
--- a/notebooks/05_bias_variance_tradeoff.ipynb
+++ b/notebooks/05_bias_variance_tradeoff.ipynb
@@ -589,9 +589,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/05_linear_regression.ipynb b/notebooks/05_linear_regression.ipynb
index 2a46cce..6902367 100644
--- a/notebooks/05_linear_regression.ipynb
+++ b/notebooks/05_linear_regression.ipynb
@@ -1627,9 +1627,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/05_model_evaluation.ipynb b/notebooks/05_model_evaluation.ipynb
index e2cb051..2f2ac4d 100644
--- a/notebooks/05_model_evaluation.ipynb
+++ b/notebooks/05_model_evaluation.ipynb
@@ -96,7 +96,8 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -126,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -137,7 +138,7 @@
        "['even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,9 +699,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/06_logistic_regression.ipynb b/notebooks/06_logistic_regression.ipynb
index 3e08915..bc28e36 100644
--- a/notebooks/06_logistic_regression.ipynb
+++ b/notebooks/06_logistic_regression.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -180,7 +180,7 @@
        "5          0  "
       ]
      },
-     "execution_count": 47,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -221,18 +221,18 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x12ec7a6d0>"
+       "<seaborn.axisgrid.FacetGrid at 0xb1ed5f8>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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bvfeTN84yMBK6qkdyKj+mlWkqm8yFknCeTfdBnUt9Nxsf/NREfqlrkI6+Ua9+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\n/5rGmBrghDFmOzAK7Ae+O81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Ny32pIiKrnkJaCpKvl0wn3L+/Jfut7lmLiBSXCsekIIVUdhdyz1pERAqnkJaCFNJLPtwa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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12ec7acd0>"
+       "<matplotlib.figure.Figure at 0xb1ed550>"
       ]
      },
      "metadata": {},
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -261,18 +261,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x130c7e250>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0xb312ba8>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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GSE9JiW1KYhVwc2xh38f/991Mnb808b7DO17iPQunJ1aPR8priAxA49wlALz7+q+orm/m\nVF83p47vo6axhYFwLw1zljAnuG/UBJgc43AxxY1U1tlU3qN9n2zKciz3yqdSejJWnLnT0FDj2e6p\nkuTl86yLNctmuCmhfd2hlNbDscMHaF9wRcrqccdxEuMLlTVTaZp/Ke3bf82C9y9PuX7WOY1ZxXS4\nK4Kv9nRMh09EUq6PVNbZtNhyOR1WU2ulVClplKhCzbJJP4GwqX4y+/Z04A9UEu7v5T1zY5sDRnpo\n2/IMwep6eruPUlbhT2yUmDyeMBjuY//WHxLxT+XdN16g78RR3n39l8xY9Gf4K6spr6hMJEC3rYAD\n7e9QX3N+IvEcaH9nyHvcbkkyUuWdy0StqbVSqpQ0SlShFgbGTyCMV25vb/khcy/95OlzQiKvA1AW\nqGbBxR9NvL7zlR/S39tN1+G9OA6JridfRSXdJwZZdPGHE+9t395KxztbmWEuxx/p4MZP/g1r1m7k\n9Z0d9PY7NM1dQg+TM7YCQqEQvX2n2PXqT/AHowdfzZ7RPCRJhCNhOiouGNKiGKnyTr/Hjcuu5olN\nP89Jos4m6Wv8Q7xESaNI8lUR5Pq+6ScQllfWETl1En9lNT6fj+5wIHqt3M9+20qFv5JIfy+Ta6Zw\nfNcLLLj0E4kKeccrP2buhVdzaHAgdSvzQCW+wT6afZZ1D0fPBml3DFPnt+A4Drte/QmV1fWUTx+6\n5flDj22i+cJPpCSg6XXBId1OR99tZer8oS2KkSrv9Hs8sennOUvU2SR9bS0iXjLaGeGSJ5nO+/bi\nfeMnEEJ0K4/yQBWHdm9N/Hnv7rf58t0PcexoJzPM5TTNv5QZLUvp6T5GWWVdSnKorJmK40D3sf0c\nbNtC+1sv09/bTbi/j+5jnfT39wOxMYqkz1XXNzOjZSmdh9qHxJf+3sqK6OFRyWen+3w+jh/tyHg+\nd7zy3nDPF1LOVQeG3KNY4w5eiUME1NIompH60sfTWsj1AOvD93yRG257kMn1s4iE+6ifYWjf/jJ7\n33iRvpNHmLHocv6421J1VlNq66FqKkc6DlA/73TXz6kTR9nzx19gLluWeM3+5gfgK+OcxVfR6W/k\n+i/dy+DgANPOOz1GEQn34fP5aGialYgrvuX4druduX9y+r2+ge7odNu0bqeySbUctZuZec4C111L\nXhl38EocIqCkUTQjVQTj6Y7IZQUTCoV4YtPPCVZOonHeJfh8Pva99WsWXPpXifvve/NFero6wXFS\nZk+dPHaQyupptG9vjQ2a99HXc4yzGuelJJe6sxfSOO8S9ttWqs5q4mSkklMnDtH35osMRPqpCFTR\nNPdiHMeh89DeREL9wxs7aTjvamZdMJP27a0MDoYpL/czq6kZiHY7ferv7sUXbCQS7qN50WXUDu7L\nalqyV7b08EocIqCkUTQjVQTjaS3ksoKJJ69pC6MVczAAwUBqV4njOCz6078mcupkovIuK/MDZZRV\nVBB7Kz3HDlBTP4vy8oqU5BJvRVT4K6PdX/4AC96/LJpsJkX/eh5tf5NIuI9ZTc2JmJzKrmiLprKa\nmecu5dDOLTTOu4TpPgtEu50WLzqHTv8Fp1d5R/pdteLS35Npr6pCcjv+oQFzKQQljSIZqSIYT2sh\nl7Oq4skrXjEHe7ZTF3RSVlefOnl0SOXdNP9SDuz4HT5fGdMXXIrjOBzvfIfeEx3MMJ9gv22lvKKS\nY4d2MH/Jx6PXO3bT39fN9PmXRo+pDUB/TxfTF5+euTU9tkOtr8o35EyPgZMHOGo3M6l5Dmse2MCq\nFcsYHBxIaelMnVuTSDqRspPs2L6V629fzwXzGlIq2PTV7Z+59W6+98jdY1ql3t1fRrV/IOX++arc\nNWAuhaCBcA9atWIZzT5LsGc7zT5btO6IuqAzZPA4Htu+bb+kfXsrgWBtynsi4b5oJR7pJ3RwO+9u\newH72x/Q3HIFVVOms2vrc/QcP0TfySMMDkQ4uOtV9ttWHGDmeVfir6zGcRwumN/IDzbcmVION37y\nI+zb08bBti1Ewn3sffNFjuz8Nc0+y+JF51BvrqG/ZjHttHD/w9/lzbZ2/IHobK6muRfTOxhMDCof\n3LmF5pbLmTp/Ke20sPaR03tjbt97LKU11etUZzWhYPfu3Vx741d55Y0D/M9rf2TH8fqUz+drEoQG\nzKUQ1NLwIK8czpSpqyse2+49u7lh5QMEamew45UfUzm5jt4THUyqquXA6/+Jmd1Ixezz2dl+gv4y\nH53vbCHS103VlLOZdd6VQ1aI2//+N97+7feZPGU6kzjJXfetTN272HHY8NQz1CetLj/85n8yp2k6\nW7e1EfEF8ZX9msa5S/BXVvPGjn1MX5y0buTVn1BX7WfhnBn0OA4OTkoFu23nwcT+V6HO/SkD+L1d\nh/jDDn+iBTNaq2DlNx5h7iV/k/h82++fYdri8xPX87UaXAPmUghKGjKskZLX3NlzefbJb8WSSiPB\nsl7Ky5voDgeoCzr0nerjyKQLOWvWSfoiWwmf6sbxBWAwwv7tpyv3Ct8gR+1mGs6eTW8YmuYuoWLS\nZJ7Y9HPC/f0p3US7X3+GeZdcBEQr21MEeWvvKZrPH7pNyUDZpJSkUFPfTP28SyiPRA9i2nUydduT\n7uNHaXc+jK/KxznvnUnblmeonTaLrsN7mXvRxwgEq3mnt5trP/tVqutmUB45zsP3fJG5s+cO6W7q\ni1SkfO9JVVNSKvB8Ve5eGDDXuMqZT0lDxiw5qSQ24PNHK/hDbZuZvvgiOt7ZSnPL5ey3rcx570eG\nVO4VTnfKivP4meChHth34FjKgryK4JSUir63+zi106akVNAD4VMc3b4ZwuGMA+7d4QAb7vwCX/76\ng/wxabzjrPrpifsEgtXUT50G9FFZVUMgWA1A556tKS2IlV9/hOeeWjdkLKH7+P+kxnn8AKtu/nKi\n3PJVuXuhhZpeFmv/z9P4AwFXW+YryZQGJQ3JifQulwgBDrZtYSASzrgHlb8cGsOv00Eg5fX4LKq6\nKtjV05VS+fp8vsSq8+6j7fiDtYTTBsR7T3Qy6/3X0fH6M9HZXANhysr9iWm78af6r/39jUkVdy39\n/f10xu7T39sNkRPMPGcB+/bsoL+3m0Awui9WSoIqr834s581rfn06vhwH4uMSakIx1u5j2UG2EiV\ncSgU4r5//g4HQuHxb/meVhbb9x6jft7lww7Oa/C+9ChpSE6kd7lQXsn0BZey762Xo0/6aZX7hQuj\nO9mGnUkprw/2HOKo3Ux5UzNETiZmP3Ud3svs9/w5gWB1ouXQOOdidr+2mV2v/oTyQCX9vSeI9J/i\nYNsrRMIOleUhwmVB+ro6mdR7FtMrJyWe6tMr7uTt04+820a9uYZen4/6FpNYFNhzZBfO/EtOd2mF\n9nPLXRvZt2cH9S2ntzn3D56kKWnspTk2DTiTsTxpu6los6mMc1lxp/89CPd2jTg4r91+S4+ShuRE\nvMvlcFeE7dYy64I/B6JjFG1b/i8Vk6rY+bsfct7iC5hWU8Gqm69jzbpNNM1dkngqd3o7OH/hTI5M\nupBBn49TezsZ6D6Cv76ZgYF+Du7aAo6TaDlUTJoMwODgILMWXU7nnq0px7q2b29l5rlLE9N1M1WE\nmdZkrFm3id6kim7mOQvYcE908H/l16M7/naH9tO06Ep6q5qomTMzZbX5XffcmtjY8Ow6P7fcMHz3\n01gqbDcVbTaVcS4r7vSut6nzZ3BkhPEbDd6XHiUNyYnEKXlrNxKonpao0CsmTabqrEZmmMuHHDRU\nF3ToYTLNLUsTT+TJ00aDk6cQ8UdXowM0zb+UcF83He9s5eDO33Py2H7OXngZxw6+TSBYPbQLLFCZ\n+Hq4ijBTpT1cRTZ39lyee2odED8EqwmIjoHEE0tc/Occ7TCesVTYbirabCrj9Pfu3tXmaqbYcK2k\n0Q7ASuaFwXvJjtZpSE6Feki0Hg7s+B27tvyIWU01GdebZFqPkrw2JNwf3U59v23leOeeaNdPZTUz\nzOX0HD/A/CUfp7ZhDv29J1K6wCC6ZiTc35f4erhKM9PaBjfrZDKtYRmLsdzHTXzZrPVZtWIZ8yt3\ncWTnr2nf3srUBR8ccf1IKBRizdqNXP+V9Wx56xBdZTOHff9IG0K6uS7eo+NeC6iEjoAcc5xrHthA\nOy0p/fnZ9I8nP5lGp/GW0x0O8E7bG4T9DYnZToFIJ+VVDZw6GaIr1ElF1VTK/ZOInDpJXX0D586p\nZ2BggN7B4IjH6Y41XrfH9Y7a0sji2N+xzjRy87mGhhr+esW3XB0hnH5UbXzGWy6PHB6O1/8NjbQT\ngBfpuFcpuvF2Nww3syi1cq3lga+tYWCgIsO17M5bH2u8uZreWohzNdx+zm2XVnqXWvKMt4kuUdYB\nH8fP0NlgShqSU/laK5B+3/r600+c4/meXljb4NZYB6zdfs5tAk1PLk5vR1G3u/GSiTAbTElDpESM\ndaaR28+5TaDpyWXdv9zp6S6YQpoIs8E0plFAXu+PjSuFOEshRshtnGPthnPzuYlYnvkQL+vu/jKq\nA4NZdZUWw1jGNJQ0Csjrf+HjSiHOUogRMsfpxa0zSrk8vaiE4sw6aWjKrUiB5WtrdJFCUNIQKTCd\neyGlTElDpMBytTBQpBg0e0qkwLR1xtgdORpizdpve2o8aKJR0pCS5sVB5dGU0toQr7nnwSdTFipe\n/6V7eWq9pvwWkrqnpKRpUHliOXxiIGU8KOxv1O+8wJQ0pKRpUHlimVpTljIeNBDp0++8wNQ9JSVt\nIqzAldPu/vJn+einv0LY38hApI+G2RdTF9xX7LAmFLU0pKRlswW4lL76+jqeWn8nFy+oZcE5jcwJ\n7tPvvMDU0pCseWnwWYPKE49+58WlloZkTYPPIhOXkoZkTYPPIhOXkoZkTSuaRSYujWlkwUt9+cU0\nEVY063ctkpmSRhbGetzmmWYiDETqdy2SmbqnsqC+/IlDv2uRzJQ0sqC+/IlDv2uRzPKeNIwx7zPG\nvJjh9ZXGmG3GmP+K/bdwtM8UmxaSTRz6XYtkltcxDWPM7cByoDvD5SXAcmvta1l8pqgmQl++ROl3\nLZJZvlsabcC1w1xbAqw2xrQaY+5w+RkRESmivCYNa+2zQGSYy5uAm4ErgQ8YY6528RkRESmiYk65\nXW+t7QIwxmwGLgJ+NtabNTTU5CquvFKcuVMKMYLizDXFWVyFShq+5D8YY2qBbcaYFqAXuAp4YqTP\njKaz88S4AiyEhoYaxZkjpRAjKM5cU5y5NZbEVqik4QAYY64DJltrHzfGrAZeAvqAF6y1z2f6jIiI\neIcvPhe9xDmlktUVZ26UQoygOHNNceZWQ0NNVj06oMV9IiKSBSUNERFxTUlDRERcU9IQERHXlDRE\nRMQ1JQ0REXFNSUNERFxT0hAREdeUNERExDUlDRERcU1JQ0REXFPSEBER15Q0RETENSUNERFxTUlD\nRERcU9IQERHXlDRERMQ1JQ0REXFNSUNERFxT0hAREdeUNERExDUlDRERcU1JQ0REXFPSEBER15Q0\nRETENSUNERFxTUlDRERcU9IQERHXlDRERMQ1JQ0REXFNSUNERFxT0hAREdeUNERExDUlDRERcU1J\nQ0REXFPSEBER15Q0RETENSUNERFxTUlDRERcU9IQERHXlDRERMQ1JQ0REXGtIt/fwBjzPmCttfbK\ntNdXAjcBHbGXPg+0ARuB9wJ9wE3W2l35jlFERNzJa9IwxtwOLAe6M1xeAiy31r6W9P5rgUnW2sti\nyWYd8Jf5jFFERNzLd/dUG3DtMNeWAKuNMa3GmK/GXvsA8DyAtfYV4E/yHJ+IiGQhr0nDWvssEBnm\n8ibgZuBK4APGmGuAWuB40nsixhiNu4iIeETexzRGsN5a2wVgjPkZcBHRhFGT9J4ya+2gi3v5Ghpq\nRn+XByjO3CmFGEFx5priLK5CPcX7kv9gjKkFthljqowxPuAq4FXgt8A1sff8KfB6geITEREXCtXS\ncACMMdcBk621jxtjVgMvEZ0l9YK19vlYAvmwMeY3sc/dUKD4RETEBZ/jOMWOQURESoQGmUVExDUl\nDRERcU1JQ0REXCvmlNusxAbJh91ixBjzF8CdQBh40lr7uEfjHLJ9irV2R8EDPR3PcNu8eKI8k+Jx\nvR1NMcp+WNZsAAADt0lEQVTTGFMBfBeYAwSA+621P026XvTydBGjV8qyDPgOYIBB4GZr7ZtJ14te\nli7j9ER5JsXTSHSW6oestW8nvZ5VeZZM0iC6nUjGLUZi/xjWEV1l3gv8xhjznLW200txxgzZPqVY\nhtvmxWPlmfV2NEXyaeCwtfYzxpg64A/AT8FT5TlsjDFeKcu/ABxr7QeMMVcA38Sb/9aHjTPGK+UZ\nL7dHgZ4Mr2dVnqXUPTXSFiPnAjustV3W2jDw38DSwocIjL4VSvL2KXcUOrg0w23z4qXyBPfb0RSz\nPP+d6NMaRP9dhZOueaU8R4oRPFKW1trngM/F/jgHCCVd9kpZjhYneKQ8Yx4E/hXYn/Z61uVZSklj\npC1G0q+dAM4qVGBpRtsKJX37lKsLGVyyEbZ58VJ5ZrsdTVHK01rbY609aYypAX4MfC3psifKc5QY\nwSNlCWCtHTTGPAWsB76fdMkTZRk3QpzgkfI0xlwPdFhrf0XaQmvGUJ6llDS6GH6LkS6iP3xcDXCs\nUIGlGSlOiG6fctRaGwE2E90+xWu8VJ6j8Ux5GmNmAf8FPG2t/VHSJc+U5wgxgofKEsBaez2wCHjc\nGBOMveyZsowbJk7wTnneQHTR9IvAhcD3YuMbMIbyLKUxjd8AHwX+I8MWI28BC4wxU4j22S0F/qnw\nIQIjxJm0fUoL0f7Dq4AnihJlqvSnDy+VZ7LhtqMpenkaY5qAXwC3WGtfTLvsifIcKUaPleWngZnW\n2rVEJ5MMEB1oBo+U5Whxeqk8rbVXxL+OJY7PW2vjg/NZl2cpJY1nSdtiJG1bktuAXxKtWB631h7w\naJxDtk8pUpzJMm3z4pXyTOZqO5oixbYamALcaYy5Kxbrd/BWeY4Wo1fK8hngSWPMy0TrqJXAJ4wx\nXipLN3F6pTyTjfvfurYRERER10ppTENERIpMSUNERFxT0hAREdeUNERExDUlDRERcU1JQ0REXFPS\nEMkzY8xsY8zuYschkgtKGiKFoQVRckYopRXhIp5njCknupvoYqARsMCqogYlkkNqaYjk1mXAKWvt\nZcBCoAoo2m6xIrmmloZIDllrW40xR4wxXwBagAVAdZHDEskZtTREcsgY8zGi5yp0Ez1atRXYU9Sg\nRHJISUMkt/4X8CNr7feIng29FCgvbkgiuaNdbkVyyBizGPgB0aNUTwHtRLfG/jNr7bxixiaSC0oa\nIiLimrqnRETENSUNERFxTUlDRERcU9IQERHXlDRERMQ1JQ0REXFNSUNERFxT0hAREdf+P/r+DZ+v\nwnqfAAAAAElFTkSuQmCC\n",
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TKAXFZ+Ia4R1pCX0/k3nPHd3vR0RSlWpQMsz9m7Z4kxOH7EXhpBqJvninJq7lnlt+wK3r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\nIiIiIo7z/wGdl3N/Y9pF6wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x130c8f150>"
+       "<matplotlib.figure.Figure at 0x883edd8>"
       ]
      },
      "metadata": {},
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -323,7 +323,7 @@
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -386,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -394,18 +394,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x130d3f510>]"
+       "[<matplotlib.lines.Line2D at 0xc61a8d0>]"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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YM+dNdu7sTPfu+5g8+fspucGoC6FtZUo3mYjET8lEmnj22cVcc80m6uvnAT727fNz7733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\nUTIhIiIinvz/JjLFU0rUQ8oAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x130d3fc90>"
+       "<matplotlib.figure.Figure at 0xc4fc3c8>"
       ]
      },
      "metadata": {},
@@ -823,16 +823,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
+     "ename": "NameError",
+     "evalue": "name 'assorted_pred_class' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-441a9144174f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# add predicted class to DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mglass\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'assorted_pred_class'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0massorted_pred_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;31m# sort DataFrame by al\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mglass\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'al'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'assorted_pred_class' is not defined"
      ]
     }
    ],
@@ -3559,9 +3563,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/07_nlp.ipynb b/notebooks/07_nlp.ipynb
index e1a9c7b..c1bd7cd 100644
--- a/notebooks/07_nlp.ipynb
+++ b/notebooks/07_nlp.ipynb
@@ -116,17 +116,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
-      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     "ename": "ImportError",
+     "evalue": "No module named textblob",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-33-6f8f2fec0037>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[1;32mfrom\u001b[0m \u001b[0mtextblob\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWord\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msnowball\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSnowballStemmer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named textblob"
      ]
     }
    ],
@@ -145,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -159,16 +162,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import nltk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "# EXERCISE create a new DataFrame called yelp_best_worst \n",
-    "# that only contains the 5-star and 1-star reviews\n",
-    "\n",
-    "\n",
+    "# that only contains thimport nltke 5-star and 1-star reviews\n",
     "\n",
     "\n",
     "\n",
@@ -183,13 +195,37 @@
     "\n",
     "\n",
     "\n",
-    "# ANSWER\n",
-    "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]"
+    "    \n",
+    "# ANSWERyelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'business_id', u'date', u'review_id', u'stars', u'text', u'type',\n",
+       "       u'user_id', u'cool', u'useful', u'funny'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -307,7 +343,7 @@
        "6  wFweIWhv2fREZV_dYkz_1g     7       7      4  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -318,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -345,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -361,7 +397,7 @@
        "Name: text, dtype: object"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 46,
    "metadata": {
     "collapsed": true
    },
@@ -400,7 +436,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import nltk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['how', 'now', 'brown', 'cow']"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.word_tokenize(\"how now brown cow\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -460,7 +529,7 @@
        "2    0     1   1       2        0    0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -474,7 +543,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -514,7 +601,7 @@
        "0    1     1   0       1        0    0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -526,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -540,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -563,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -583,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -603,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -619,7 +706,7 @@
        "        tokenizer=None, vocabulary=None)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -646,30 +733,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 62,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3064, 20838)\n",
+      "(1022, 20838)\n"
+     ]
+    }
+   ],
    "source": [
-    "# EXERCISE: create a coun vectorizer that doesn't lowercase the words\n",
+    "# EXERCISE: create a count vectorizer that doesn't lowercase the words\n",
     "# fit transform X_train and see how many features there are\n",
     "# Hint there should be over 20k\n",
     "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
+    "# use CountVectorizer to create document-term matrices from X_train and X_test\n",
+    "vect = CountVectorizer(lowercase=False)\n",
+    "X_train_dtm = vect.fit_transform(X_train)\n",
+    "X_test_dtm = vect.transform(X_test)\n",
+    "print X_train_dtm.shape\n",
+    "print X_test_dtm.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -680,7 +773,7 @@
        "(3064, 20838)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -702,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -713,7 +806,7 @@
        "(3064, 169847)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -727,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -754,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -786,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -797,7 +890,7 @@
        "0.81996086105675148"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,7 +903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -887,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {
     "collapsed": true
    },
@@ -903,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -940,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -956,7 +1049,7 @@
        "        tokenizer=None, vocabulary=None)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2814,9 +2907,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:sfdat28]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "conda-env-sfdat28-py"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/09_naive_bayes_roc_auc-Eugene.ipynb b/notebooks/09_naive_bayes_roc_auc-Eugene.ipynb
new file mode 100644
index 0000000..8b155eb
--- /dev/null
+++ b/notebooks/09_naive_bayes_roc_auc-Eugene.ipynb
@@ -0,0 +1,2259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**CLASS**: Naive Bayes SMS spam classifier using sklearn\n",
+    "\n",
+    "Data source: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## READING IN THE DATA\n",
+    "\n",
+    "# read tab-separated file using pandas\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the data\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x95dfa90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9zIt169axbNmyncampqaYmpoaU0eSJE2O6elppqendxrbsWNH8+PnFBySXAG8B3hbVf3f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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x95df748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.label.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ham     0.865937\n",
+       "spam    0.134063\n",
+       "Name: label, dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the null accuracy rate\n",
+    "df.label.value_counts() / df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count                       5572\n",
+       "unique                      5169\n",
+       "top       Sorry, I'll call later\n",
+       "freq                          30\n",
+       "Name: msg, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.msg.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label                                                msg\n",
+       "0      0  Go until jurong point, crazy.. Available only ...\n",
+       "1      0                      Ok lar... Joking wif u oni...\n",
+       "2      1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3      0  U dun say so early hor... U c already then say...\n",
+       "4      0  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert label to a quantitative binary variable\n",
+    "df['label'] = df.label.map({'ham':0, 'spam':1})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into training and testing sets\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df.msg, df.label, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'44', u'cab', u'call', u'me', u'please', u'tonight', u'you']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "# start with a simple example\n",
+    "train_simple = ['call you tonight',\n",
+    "                'Call me a cab',\n",
+    "                'please call me... PLEASE 44!']\n",
+    "\n",
+    "# learn the 'vocabulary' of the training data\n",
+    "vect = CountVectorizer()\n",
+    "train_simple_dtm = vect.fit_transform(train_simple)\n",
+    "vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<3x7 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 10 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform training data into a 'document-term matrix'\n",
+    "train_simple_dtm = vect.transform(train_simple)\n",
+    "train_simple_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   0       0        1    1\n",
+       "1   0    1     1   1       0        0    0\n",
+       "2   1    0     1   1       2        0    0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# examine the vocabulary and document-term matrix together\n",
+    "pd.DataFrame(train_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>44</th>\n",
+       "      <th>cab</th>\n",
+       "      <th>call</th>\n",
+       "      <th>me</th>\n",
+       "      <th>please</th>\n",
+       "      <th>tonight</th>\n",
+       "      <th>you</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   44  cab  call  me  please  tonight  you\n",
+       "0   0    0     1   1       1        0    0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix (using existing vocabulary, notice don't is missing)\n",
+    "test_simple = [\"please don't call me\"]\n",
+    "test_simple_dtm = vect.transform(test_simple)\n",
+    "test_simple_dtm.toarray()\n",
+    "pd.DataFrame(test_simple_dtm.toarray(), columns=vect.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4179x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 55209 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## REPEAT PATTERN WITH SMS DATA\n",
+    "\n",
+    "# instantiate the vectorizer\n",
+    "vect = CountVectorizer()\n",
+    "\n",
+    "# learn vocabulary and create document-term matrix in a single step\n",
+    "train_dtm = vect.fit_transform(X_train)\n",
+    "train_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1393x7456 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 17604 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transform testing data into a document-term matrix\n",
+    "test_dtm = vect.transform(X_test)\n",
+    "test_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([u'00',\n",
+       "  u'000',\n",
+       "  u'008704050406',\n",
+       "  u'0121',\n",
+       "  u'01223585236',\n",
+       "  u'01223585334',\n",
+       "  u'0125698789',\n",
+       "  u'02',\n",
+       "  u'0207',\n",
+       "  u'02072069400'],\n",
+       " [u'zed',\n",
+       "  u'zeros',\n",
+       "  u'zhong',\n",
+       "  u'zindgi',\n",
+       "  u'zoe',\n",
+       "  u'zoom',\n",
+       "  u'zouk',\n",
+       "  u'zyada',\n",
+       "  u'\\xe8n',\n",
+       "  u'\\u3028ud'])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# store feature names and examine them\n",
+    "train_features = vect.get_feature_names()\n",
+    "len(train_features)\n",
+    "train_features[:10], train_features[-10:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ..., \n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert train_dtm to a regular array\n",
+    "train_arr = train_dtm.toarray()\n",
+    "# remember that each ROW is an sms\n",
+    "# and each COLUMN is a token\n",
+    "train_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## SIMPLE SUMMARIES OF THE TRAINING DATA\n",
+    "# refresher on numpy\n",
+    "import numpy as np\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[0, :]) #sum of the first row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(arr[:,0]) # sum of the first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times the 0th token appears across ALL messages in train_arr\n",
+    "train_arr[:,0].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "#calculate the number of tokens in the 0th message in train_arr\n",
+    "train_arr[0,:].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr) # adds up all numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6,  8, 10, 12])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=0) # adds up by column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 26])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(arr, axis=1)  # adds up by row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5, 23,  2, ...,  1,  1,  1], dtype=int64)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# count how many times EACH token appears across ALL messages in train_arr\n",
+    "np.sum(train_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>1670</td>\n",
+       "      <td>to</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7420</th>\n",
+       "      <td>1660</td>\n",
+       "      <td>you</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6542</th>\n",
+       "      <td>1004</td>\n",
+       "      <td>the</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>929</th>\n",
+       "      <td>717</td>\n",
+       "      <td>and</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3502</th>\n",
+       "      <td>683</td>\n",
+       "      <td>in</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      count token\n",
+       "6656   1670    to\n",
+       "7420   1660   you\n",
+       "6542   1004   the\n",
+       "929     717   and\n",
+       "3502    683    in"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a dataframe with two columns, \"count\" and \"token\" that holds the token count for each token\n",
+    "# use the numpy sum methods\n",
+    "train_token_counts = pd.DataFrame({'token':train_features, 'count':np.sum(train_arr, axis=0)})\n",
+    "train_token_counts.sort_index(by='count', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## MODEL BUILDING WITH NAIVE BAYES\n",
+    "## http://scikit-learn.org/stable/modules/naive_bayes.html\n",
+    "\n",
+    "# train a Naive Bayes model using train_dtm\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(train_dtm, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 1, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make predictions on test data using test_dtm\n",
+    "preds = nb.predict(test_dtm)\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.988513998564\n",
+      "[[1203    5]\n",
+      " [  11  174]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compare predictions to true labels\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.986643100054\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict (poorly calibrated) probabilities and calculate AUC\n",
+    "probs = nb.predict_proba(test_dtm)[:, 1] #looks at the second column of the confusion matrix \n",
+    "probs\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1078</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yep, by the pretty sculpture</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4028</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes, princess. Are you going to make me moan?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>958</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Welp apparently he retired</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4642</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Havent.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4674</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>I forgot 2 ask ü all smth.. There's a card on ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                                               text\n",
+       "1078       0           0                       Yep, by the pretty sculpture\n",
+       "4028       0           0      Yes, princess. Are you going to make me moan?\n",
+       "958        0           0                         Welp apparently he retired\n",
+       "4642       0           0                                            Havent.\n",
+       "4674       0           0  I forgot 2 ask ü all smth.. There's a card on ..."
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false positives\n",
+    "df2 = pd.DataFrame({'text':X_test,'actual':y_test,'prediction':preds})\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Waiting for your call.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3375</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Also andros ice etc etc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No calls..messages..missed calls</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3415</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No pic. Please re-send.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No calls..messages..missed calls</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                              text\n",
+       "574        0           1            Waiting for your call.\n",
+       "3375       0           1           Also andros ice etc etc\n",
+       "45         0           1  No calls..messages..missed calls\n",
+       "3415       0           1           No pic. Please re-send.\n",
+       "1988       0           1  No calls..messages..missed calls"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[(df2.prediction==1) & (df2.actual==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3132</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LookAtMe!: Thanks for your purchase of a video...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3530</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Xmas &amp; New Years Eve tickets are now on sale f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi I'm sue. I am 20 years old and work as a la...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1875</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Would you like to see my XXX pics they are so ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      actual  prediction                                               text\n",
+       "3132       1           0  LookAtMe!: Thanks for your purchase of a video...\n",
+       "5          1           0  FreeMsg Hey there darling it's been 3 week's n...\n",
+       "3530       1           0  Xmas & New Years Eve tickets are now on sale f...\n",
+       "684        1           0  Hi I'm sue. I am 20 years old and work as a la...\n",
+       "1875       1           0  Would you like to see my XXX pics they are so ..."
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "################\n",
+    "### EXERCISE ###\n",
+    "################\n",
+    "# show the message text for the false negatives\n",
+    "df2[(df2.prediction==0) & (df2.actual==1)].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## COMPARE NAIVE BAYES AND LOGISTIC REGRESSION\n",
+    "## USING ALL DATA AND CROSS-VALIDATION\n",
+    "vect = CountVectorizer(ngram_range=(1,3), stop_words='english')\n",
+    "# create a document-term matrix using all data\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "\n",
+    "# instantiate logistic regression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "\n",
+    "# compare AUC using cross-validation\n",
+    "# note: this is slightly improper cross-validation... can you figure out why?\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.956237 Seconds for logistic regression 0.976848539122\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(logreg, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for logistic regression\", mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.160997 Seconds for naive bayes 0.967516659054\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "print (datetime.now()-now).total_seconds(), \"Seconds for naive bayes\", mean\n",
+    "# about 4 times faster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>default</th>\n",
+       "      <th>student</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>729.526495</td>\n",
+       "      <td>44361.62507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>817.180407</td>\n",
+       "      <td>12106.13470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1073.549164</td>\n",
+       "      <td>31767.13895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>529.250605</td>\n",
+       "      <td>35704.49394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>785.655883</td>\n",
+       "      <td>38463.49588</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   default student      balance       income\n",
+       "0        0      No   729.526495  44361.62507\n",
+       "1        0     Yes   817.180407  12106.13470\n",
+       "2        0      No  1073.549164  31767.13895\n",
+       "3        0      No   529.250605  35704.49394\n",
+       "4        0      No   785.655883  38463.49588"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Model evaluation metrics (confusion matrix, ROC/AUC)\n",
+    "'''\n",
+    "\n",
+    "## READ DATA AND SPLIT INTO TRAIN/TEST\n",
+    "\n",
+    "# read in the data\n",
+    "import pandas as pd\n",
+    "data = pd.read_csv('../data/Default.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create X and y\n",
+    "X = data[['balance']]\n",
+    "y = data.default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# split into train and test\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create logistic regression model\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9748"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict and calculate accuracy in one step\n",
+    "logreg.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9748\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict in one step, calculate accuracy in a separate step\n",
+    "preds = logreg.predict(X_test)\n",
+    "from sklearn import metrics\n",
+    "print metrics.accuracy_score(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9684"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to null accuracy rate\n",
+    "y_test.mean()\n",
+    "1 - y_test.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2416    5]\n",
+      " [  58   21]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "## CONFUSION MATRIX\n",
+    "\n",
+    "# print confusion matrix\n",
+    "print metrics.confusion_matrix(y_test, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# nicer confusion matrix\n",
+    "from nltk import ConfusionMatrix\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.26582278481\n",
+      "0.997934737712\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sensitivity: percent of correct predictions when reference value is 'default'\n",
+    "21 / float(58 + 21)\n",
+    "print metrics.recall_score(y_test, preds)\n",
+    "\n",
+    "# specificity: percent of correct predictions when reference value is 'not default'\n",
+    "print 2416 / float(2416 + 5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  2.23000000e+03,   1.23000000e+02,   6.30000000e+01,\n",
+       "          2.90000000e+01,   2.30000000e+01,   1.10000000e+01,\n",
+       "          1.00000000e+01,   6.00000000e+00,   1.00000000e+00,\n",
+       "          4.00000000e+00]),\n",
+       " array([  1.87839293e-04,   8.91943925e-02,   1.78200946e-01,\n",
+       "          2.67207499e-01,   3.56214052e-01,   4.45220605e-01,\n",
+       "          5.34227159e-01,   6.23233712e-01,   7.12240265e-01,\n",
+       "          8.01246818e-01,   8.90253371e-01]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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oyeN67umxSXZ071F3J3nFSvYzZk+PS/IP3f+/W5OcPu9Kq2rVJ4YB9AXg54CfAO4ATp01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\nIEmN+z/CtF/SApB9NAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117b78210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# predict probabilities\n",
+    "import matplotlib.pyplot as plt\n",
+    "probs = logreg.predict_proba(X_test)[:, 1]\n",
+    "plt.hist(probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2416>   5 |\n",
+      "1 |   58  <21>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use 0.5 cutoff for predicting 'default'\n",
+    "import numpy as np\n",
+    "preds = np.where(probs > 0.5, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  |    0    1 |\n",
+      "--+-----------+\n",
+      "0 |<2340>  81 |\n",
+      "1 |   34  <45>|\n",
+      "--+-----------+\n",
+      "(row = reference; col = test)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change cutoff for predicting default to 0.2\n",
+    "preds = np.where(probs > 0.2, 1, 0)\n",
+    "print ConfusionMatrix(list(y_test), list(preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.954\n",
+      "0.569620253165\n",
+      "0.966542750929\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check accuracy, sensitivity, specificity\n",
+    "print metrics.accuracy_score(y_test, preds)\n",
+    "print 45 / float(34 + 45)\n",
+    "print 2340 / float(2340 + 81)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x129d397d0>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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clu5T6w2gv54z15Etr3g42RKUNBnn94BPSdop3esPwBvKpow+qOr416timQf8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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1136ff310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## ROC CURVES and AUC\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(y_test, probs)\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
+    "plt.ylabel('True Positive Rate (Sensitivity)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.941011926236\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate AUC\n",
+    "print metrics.roc_auc_score(y_test, probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94819099843061938"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use AUC as evaluation metric for cross-validation\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "X = data[['balance']]\n",
+    "y = data.default\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.59503752064417714"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare to a model with an additional feature\n",
+    "X = data[['balance', 'income']]\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# ROC/AUC are very meaningful when:\n",
+    "# 1. class sizes are inbalances\n",
+    "# 2. comparing across different binary classification models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## BONUS: CALCULATE THE 'SPAMMINESS' OF EACH TOKEN\n",
+    "\n",
+    "\n",
+    "# create separate DataFrames for ham and spam\n",
+    "df_ham = df[df.label==0]\n",
+    "df_spam = df[df.label==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# learn the vocabulary of ALL messages and save it\n",
+    "vect = CountVectorizer(ngram_range=(1,1), stop_words='english')\n",
+    "vect.fit(df.msg)\n",
+    "all_features = vect.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4825x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 32267 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of ham, then convert to a regular array\n",
+    "ham_dtm = vect.transform(df_ham.msg)\n",
+    "ham_arr = ham_dtm.toarray()\n",
+    "ham_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<747x8445 sparse matrix of type '<type 'numpy.int64'>'\n",
+       "\twith 11312 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create document-term matrix of spam, then convert to a regular array\n",
+    "spam_dtm = vect.transform(df_spam.msg)\n",
+    "spam_arr = spam_dtm.toarray()\n",
+    "spam_dtm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in ham_arr\n",
+    "ham_counts = np.sum(ham_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# count how many times EACH token appears across ALL messages in spam_arr\n",
+    "spam_counts = np.sum(spam_arr, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>000pes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>008704050406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0089</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ham  spam         token\n",
+       "0    0    10            00\n",
+       "1    0    29           000\n",
+       "2    1     0        000pes\n",
+       "3    0     2  008704050406\n",
+       "4    0     1          0089"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a DataFrame of tokens with their separate ham and spam counts\n",
+    "all_token_counts = pd.DataFrame({'token':all_features, 'ham':ham_counts, 'spam':spam_counts})\n",
+    "all_token_counts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# add one to ham counts and spam counts so that ratio calculations (below) make more sense\n",
+    "all_token_counts['ham'] = all_token_counts.ham + 1\n",
+    "all_token_counts['spam'] = all_token_counts.spam + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ham</th>\n",
+       "      <th>spam</th>\n",
+       "      <th>token</th>\n",
+       "      <th>spam_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>1</td>\n",
+       "      <td>114</td>\n",
+       "      <td>claim</td>\n",
+       "      <td>114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5935</th>\n",
+       "      <td>1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>prize</td>\n",
+       "      <td>94.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>1</td>\n",
+       "      <td>72</td>\n",
+       "      <td>150p</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7612</th>\n",
+       "      <td>1</td>\n",
+       "      <td>61</td>\n",
+       "      <td>tone</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52</td>\n",
+       "      <td>18</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3589</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>guaranteed</td>\n",
+       "      <td>51.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2314</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>cs</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>500</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>1</td>\n",
+       "      <td>39</td>\n",
+       "      <td>awarded</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ham  spam       token  spam_ratio\n",
+       "2013    1   114       claim       114.0\n",
+       "5935    1    94       prize        94.0\n",
+       "352     1    72        150p        72.0\n",
+       "7612    1    61        tone        61.0\n",
+       "369     1    52          18        52.0\n",
+       "3589    1    51  guaranteed        51.0\n",
+       "2314    1    45          cs        45.0\n",
+       "617     1    45         500        45.0\n",
+       "299     1    42        1000        42.0\n",
+       "1301    1    39     awarded        39.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate ratio of spam-to-ham for each token\n",
+    "all_token_counts['spam_ratio'] = all_token_counts.spam / all_token_counts.ham\n",
+    "all_token_counts.sort_index(by='spam_ratio', ascending = False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/09_naive_bayes_roc_auc.ipynb b/notebooks/09_naive_bayes_roc_auc.ipynb
index ceb3234..1273e7e 100644
--- a/notebooks/09_naive_bayes_roc_auc.ipynb
+++ b/notebooks/09_naive_bayes_roc_auc.ipynb
@@ -15,16 +15,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
-      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "## READING IN THE DATA\n",
     "\n",
@@ -35,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -47,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -103,7 +94,7 @@
        "4   ham  Nah I don't think he goes to usf, he lives aro..."
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -123,18 +114,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x113bb7e50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x9dc4a90>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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9zIt169axbNmyncampqaYmpoaU0eSJE2O6elppqendxrbsWNH8+PnFBySXAG8B3hbVf3f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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x113bb7590>"
+       "<matplotlib.figure.Figure at 0x9c1f8d0>"
       ]
      },
      "metadata": {},
@@ -147,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -155,12 +146,12 @@
     {
      "data": {
       "text/plain": [
-       "0    0.865937\n",
-       "1    0.134063\n",
+       "ham     0.865937\n",
+       "spam    0.134063\n",
        "Name: label, dtype: float64"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -198,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -254,7 +245,7 @@
        "4      0  Nah I don't think he goes to usf, he lives aro..."
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
@@ -280,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -291,7 +282,7 @@
        "[u'44', u'cab', u'call', u'me', u'please', u'tonight', u'you']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -311,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -323,7 +314,7 @@
        "\twith 10 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -400,7 +391,7 @@
        "2   1    0     1   1       2        0    0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -454,7 +445,7 @@
        "0   0    0     1   1       1        0    0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -481,7 +472,7 @@
        "\twith 55209 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -511,7 +502,7 @@
        "\twith 17604 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -554,7 +545,7 @@
        "  u'\\u3028ud'])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -568,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -582,10 +573,10 @@
        "       ..., \n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
-       "       [0, 0, 0, ..., 0, 0, 0]])"
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -600,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -612,7 +603,7 @@
        "       [5, 6, 7, 8]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -638,7 +629,7 @@
        "10"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -660,7 +651,7 @@
        "6"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -671,30 +662,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "################\n",
     "### EXERCISE ###\n",
     "################\n",
-    "# count how many times the 0th token appears across ALL messages in train_arr\n"
+    "# count how many times the 0th token appears across ALL messages in train_arr\n",
+    "train_arr[:,0].sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "################\n",
     "### EXERCISE ###\n",
     "################\n",
-    "#calculate the number of tokens in the 0th message in train_arr\n"
+    "#calculate the number of tokens in the 0th message in train_arr\n",
+    "train_arr[0,:].sum()"
    ]
   },
   {
@@ -1979,9 +1994,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat26-env]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat26-env]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/10_advanced_sklearn-Eugene.ipynb b/notebooks/10_advanced_sklearn-Eugene.ipynb
new file mode 100644
index 0000000..a331b41
--- /dev/null
+++ b/notebooks/10_advanced_sklearn-Eugene.ipynb
@@ -0,0 +1,2179 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.base import TransformerMixin\n",
+    "import pandas as pd\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Some text\n",
+    "\n",
+    "X = (\n",
+    "    \"the pizza pizza beer copyright\",\n",
+    "    \"the pizza burger beer copyright\",\n",
+    "    \"the the pizza beer beer copyright\",\n",
+    "    \"the burger beer beer copyright\",\n",
+    "    \"the coke burger coke copyright\",\n",
+    "    \"the coke burger burger\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
+       "      <th>pizza</th>\n",
+       "      <th>the</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the\n",
+       "0     1       0     0          1      2    1\n",
+       "1     1       1     0          1      1    1\n",
+       "2     2       0     0          1      1    2\n",
+       "3     2       1     0          1      0    1\n",
+       "4     0       1     2          1      0    1\n",
+       "5     0       2     1          0      0    1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# EXERCISE (WAKE UP ITS CODING TIME)\n",
+    "\n",
+    "# Use a count vectorizer to count vectorize X and show a \n",
+    "# user-friendly dataframe to see the count of words in each sentence\n",
+    "# Recall this is otherwise known as a document term matrix, make it\n",
+    "\n",
+    "vect = CountVectorizer(analyzer='word')\n",
+    "pd.DataFrame(vect.fit_transform(X).toarray(), columns=vect.get_feature_names())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>g</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a  b  c  e  g  h  i  k  o  p  r  t  u  y  z\n",
+       "0  4  2  1  1  3  1  2  3  0  1  3  2  2  0  1  4\n",
+       "1  4  1  2  1  4  2  2  2  0  1  2  4  2  1  1  2\n",
+       "2  5  1  2  1  6  1  3  2  0  1  2  3  3  0  1  2\n",
+       "3  4  0  3  1  6  2  2  1  0  1  1  5  2  1  1  0\n",
+       "4  4  0  1  3  4  2  2  1  2  3  1  3  2  1  1  0\n",
+       "5  3  0  2  1  4  2  1  0  1  1  0  4  1  2  0  0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "word_vect = CountVectorizer(analyzer='char')\n",
+    "words = pd.DataFrame(word_vect.fit_transform(X).toarray(), columns=word_vect.get_feature_names())\n",
+    "words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>g</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a  b  c  e  g  h  i  k  o  p  r  t  u  y  z\n",
+       "0  4  2  1  1  3  1  2  3  0  1  3  2  2  0  1  4\n",
+       "1  4  1  2  1  4  2  2  2  0  1  2  4  2  1  1  2\n",
+       "2  5  1  2  1  6  1  3  2  0  1  2  3  3  0  1  2\n",
+       "3  4  0  3  1  6  2  2  1  0  1  1  5  2  1  1  0\n",
+       "4  4  0  1  3  4  2  2  1  2  3  1  3  2  1  1  0\n",
+       "5  3  0  2  1  4  2  1  0  1  1  0  4  1  2  0  0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "char_vect = CountVectorizer(analyzer='char')\n",
+    "chars = pd.DataFrame(char_vect.fit_transform(X).toarray(), columns=char_vect.get_feature_names())\n",
+    "chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This is a feature union. If automatically horizontally stacks our count and tfidf vectorizers\n",
+    "# http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html\n",
+    "fs = FeatureUnion([\n",
+    "    (\"words\", CountVectorizer(analyzer='word')),\n",
+    "    (\"chars\", CountVectorizer(analyzer='char')),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FeatureUnion(n_jobs=1,\n",
+       "       transformer_list=[('words', CountVectorizer(analyzer='word', binary=False, decode_error=u'strict',\n",
+       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,...  strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None))],\n",
+       "       transformer_weights=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fs #see our params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>beer</th>\n",
+       "      <th>burger</th>\n",
+       "      <th>coke</th>\n",
+       "      <th>copyright</th>\n",
+       "      <th>pizza</th>\n",
+       "      <th>the</th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>...</th>\n",
+       "      <th>h</th>\n",
+       "      <th>i</th>\n",
+       "      <th>k</th>\n",
+       "      <th>o</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>t</th>\n",
+       "      <th>u</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>4</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   beer  burger  coke  copyright  pizza  the     a  b  c ...  h  i  k  o  p  \\\n",
+       "0     1       0     0          1      2    1  4  2  1  1 ...  2  3  0  1  3   \n",
+       "1     1       1     0          1      1    1  4  1  2  1 ...  2  2  0  1  2   \n",
+       "2     2       0     0          1      1    2  5  1  2  1 ...  3  2  0  1  2   \n",
+       "3     2       1     0          1      0    1  4  0  3  1 ...  2  1  0  1  1   \n",
+       "4     0       1     2          1      0    1  4  0  1  3 ...  2  1  2  3  1   \n",
+       "5     0       2     1          0      0    1  3  0  2  1 ...  1  0  1  1  0   \n",
+       "\n",
+       "   r  t  u  y  z  \n",
+       "0  2  2  0  1  4  \n",
+       "1  4  2  1  1  2  \n",
+       "2  3  3  0  1  2  \n",
+       "3  5  2  1  1  0  \n",
+       "4  3  2  1  1  0  \n",
+       "5  4  1  2  0  0  \n",
+       "\n",
+       "[6 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To see them put together\n",
+    "\n",
+    "words_plus_chars = pd.DataFrame(fs.fit_transform(X).toarray(), columns=word_vect.get_feature_names()+char_vect.get_feature_names())\n",
+    "words_plus_chars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6, 6)\n",
+      "+\n",
+      "(6, 16)\n",
+      "========\n",
+      "(6, 22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print words.shape\n",
+    "print \"+\"\n",
+    "print chars.shape\n",
+    "print \"========\"\n",
+    "print words_plus_chars.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Pipeline http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\n",
+    "# is a module that chains different sklearn modules together in order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Test the various methods of the pipeline (anova).\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test with selectkbest + LogisticRegression\n",
+    "# SelectKBest http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection\n",
+    "# will automatically attempt to reduce the number of features by performing \n",
+    "# statistical tests (including ANOVA or Chi Squared)\n",
+    "\n",
+    "logreg = LogisticRegression()\n",
+    "filter1 = SelectKBest(k=2) # select the best 2 features\n",
+    "pipe = Pipeline([('anova', filter1), ('logistic', logreg)])\n",
+    "\n",
+    "\n",
+    "mean = cross_val_score(pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85999999999999999"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You can use make_pipeline to make simpler pipelines\n",
+    "easy_pipe = make_pipeline(SelectKBest(k=2), LogisticRegression())\n",
+    "mean = cross_val_score(easy_pipe, X, y, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>msg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label                                                msg\n",
+       "0   ham  Go until jurong point, crazy.. Available only ...\n",
+       "1   ham                      Ok lar... Joking wif u oni...\n",
+       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3   ham  U dun say so early hor... U c already then say...\n",
+       "4   ham  Nah I don't think he goes to usf, he lives aro..."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_table('../data/sms.tsv',\n",
+    "                   sep='\\t', header=None, names=['label', 'msg'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'words_plus_chars' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-d5a5d989ffc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mnb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMultinomialNB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mnb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords_plus_chars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'words_plus_chars' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# cross validate two naive bayes models.\n",
+    "# One model should attempt to fit to the count vectorized text\n",
+    "# The other should fit to the tfidfed text\n",
+    "# Each model whould be fit over a 10 fold validation with accuracy\n",
+    "\n",
+    "nb = MultinomialNB()\n",
+    "nb.fit(words_plus_chars, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98026032061360824"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Answers\n",
+    "\n",
+    "vect = CountVectorizer()\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96320881558493299"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vect = TfidfVectorizer() #penalizes frequent words\n",
+    "all_dtm = vect.fit_transform(df.msg)\n",
+    "nb = MultinomialNB()\n",
+    "mean = cross_val_score(nb, all_dtm, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9840269835443396"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A more complicated pipeline\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "mean = cross_val_score(pipe, df.msg, df.label, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not only is this PROPER cross validation of count vectorizers, its better than either model combined!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'features__counts__ngram_range': [(1, 1), (1, 2)], 'features__tf_idf__ngram_range': [(1, 1), (1, 2)]}\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-5eb5c60c93a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Pipeline can also be used with GridSearchCV, WARNING SLOWWW\n",
+    "pipe = Pipeline([\n",
+    "  ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "gram_range = [(1, n) for n in range(1, 3)]\n",
+    "param_grid = {\n",
+    "    'features__counts__ngram_range': gram_range,\n",
+    "    'features__tf_idf__ngram_range': gram_range,\n",
+    "}\n",
+    "print param_grid\n",
+    "print\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(df.msg, df.label)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# We can also make our own custom transformations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DatetimeTransformer(TransformerMixin):\n",
+    "    def transform(self, X, **transform_params): \n",
+    "        if 'date' in X:\n",
+    "            years = X['date'].apply(lambda x: x.year)\n",
+    "            months = X['date'].apply(lambda x: x.month)\n",
+    "            days = X['date'].apply(lambda x: x.day)\n",
+    "            return pd.DataFrame({'year':years, 'month':months, 'day':days})\n",
+    "        return X\n",
+    "\n",
+    "    def fit(self, X, y=None, **fit_params):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = '../data/yelp.csv'\n",
+    "yelp = pd.read_csv(url, encoding='unicode-escape', usecols=[1, 3])\n",
+    "yelp['date'] = pd.to_datetime(yelp['date'])\n",
+    "yelp = yelp[(yelp.stars == 1) | (yelp.stars == 5)] # only look at best and worst ratings\n",
+    "yelp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   day  month  year\n",
+       "0   26      1  2011\n",
+       "1   27      7  2011\n",
+       "3   27      5  2010\n",
+       "4    5      1  2012\n",
+       "6   12      2  2010"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = DatetimeTransformer() # our custom transformer\n",
+    "h.fit_transform(yelp).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-01-26</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-05-27</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-05</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-02-12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  stars\n",
+       "0 2011-01-26      5\n",
+       "1 2011-07-27      5\n",
+       "3 2010-05-27      5\n",
+       "4 2012-01-05      5\n",
+       "6 2010-02-12      5"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp.head() # original data unscathed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'logreg' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-d4f02f7f7ab7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# pipeline with our custom transformer and logistic regression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpipe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'transform_datetime'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeTransformer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'logistic'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmean\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myelp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'stars'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myelp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'logreg' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# pipeline with our custom transformer and logistic regression\n",
+    "\n",
+    "pipe = Pipeline([('transform_datetime', DatetimeTransformer()), ('logistic', logreg)])\n",
+    "mean = cross_val_score(pipe, yelp.drop('stars', axis=1), yelp.stars, cv=10, scoring='accuracy').mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Hmmmmmm not bad... but what was that null accuracy?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5    0.816691\n",
+       "1    0.183309\n",
+       "Name: stars, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yelp['stars'].value_counts() / yelp.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# It appears dates aren't helping at all.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>1480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>735</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label  color  proline\n",
+       "0      1   5.64     1065\n",
+       "1      1   4.38     1050\n",
+       "2      1   5.68     1185\n",
+       "3      1   7.80     1480\n",
+       "4      1   4.32      735"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read three columns from the dataset into a DataFrame\n",
+    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'\n",
+    "col_names = ['label', 'color', 'proline']\n",
+    "wine = pd.read_csv(url, header=None, names=col_names, usecols=[0, 10, 13])\n",
+    "wine.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>color</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.938202</td>\n",
+       "      <td>5.058090</td>\n",
+       "      <td>746.893258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.775035</td>\n",
+       "      <td>2.318286</td>\n",
+       "      <td>314.907474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.280000</td>\n",
+       "      <td>278.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>500.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.690000</td>\n",
+       "      <td>673.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>985.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1680.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            label       color      proline\n",
+       "count  178.000000  178.000000   178.000000\n",
+       "mean     1.938202    5.058090   746.893258\n",
+       "std      0.775035    2.318286   314.907474\n",
+       "min      1.000000    1.280000   278.000000\n",
+       "25%      1.000000    3.220000   500.500000\n",
+       "50%      2.000000    4.690000   673.500000\n",
+       "75%      3.000000    6.200000   985.000000\n",
+       "max      3.000000   13.000000  1680.000000"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define X and y\n",
+    "feature_cols = ['color', 'proline']\n",
+    "X = wine[feature_cols]\n",
+    "y = wine.label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3.90664944003e-16\n",
+      "1.0\n",
+      "1.6027279754e-16\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# split into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
+    "\n",
+    "\n",
+    "# standardize X_train\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "\n",
+    "\n",
+    "# Recall that standard scalar does a basic z-score normalization\n",
+    "# So for each element, x, it computes z = (x - mean) / std\n",
+    "# This gives the entire series a mean of 0 and a standard deviation (and variance) of 1\n",
+    "\n",
+    "# check that it standardized properly\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_train_scaled[:, 0].mean() # 0\n",
+    "print X_train_scaled[:, 0].std()  # 1\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_train_scaled[:, 1].mean() # 0\n",
+    "print X_train_scaled[:, 1].std()  # 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# standardize X_test\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0305898576303\n",
+      "0.866822198488\n",
+      "0.0546533341088\n",
+      "1.14955947533\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Is anything wrong here?\n",
+    "\n",
+    "# First column of X_train_scaled\n",
+    "print X_test_scaled[:, 0].mean()\n",
+    "print X_test_scaled[:, 0].std()\n",
+    "\n",
+    "\n",
+    "# Second column of X_train_scaled\n",
+    "print X_test_scaled[:, 1].mean()\n",
+    "print X_test_scaled[:, 1].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.64444444444444449"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Run a KNN (3 neighbors) on the training set and find the accuracy on the testing set\n",
+    "# Do this TWICE once for the unscaled data, and once for the scaled data\n",
+    "# What do you notice about the accuracies\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)           # instantiate the estimator\n",
+    "knn.fit(X_train, y_train)\n",
+    "knn.score(X_test, y_test)\n",
+    "\n",
+    "# knn.fit(X_train_scaled, y_train_scaled)\n",
+    "# knn.score(X_test, y_test)\n",
+    "\n",
+    "\n",
+    "# Thought experiment: Why am I not asking for a cross validation?\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.644444444444\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Answers\n",
+    "\n",
+    "# KNN accuracy on original data\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred_class = knn.predict(X_test)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.866666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# KNN accuracy on scaled data\n",
+    "knn.fit(X_train_scaled, y_train)\n",
+    "y_pred_class = knn.predict(X_test_scaled)\n",
+    "print metrics.accuracy_score(y_test, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# So we agree, scaling data helps us out in KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "\n",
+    "# Cross validate a KNN with 3 neighbors on the X and y variables with  (UNSCALED)\n",
+    "# 5 folds and output average accuracy for the models\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.71983168041991563"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER proper cross-validation on the original (unscaled) data\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "cross_val_score(knn, X, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90104247104247115"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Why is this improper cross-validation on the scaled data?\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "cross_val_score(knn, X_scaled, y, cv=5, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "# Make a pipeline that scales data and then runs a KNN with 3 neighbors\n",
+    "# cross validate your pipeline using cv=5 with accuacy metric\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "# scale the data\n",
+    "        scaler = StandardScaler()\n",
+    "        X_scaled = scaler.fit_transform(X)\n",
+    "# KNN 3\n",
+    "\n",
+    "        \n",
+    "        # CV = 5\n",
+    "        \n",
+    "        ('features', FeatureUnion([\n",
+    "        ('counts', CountVectorizer()),\n",
+    "        ('tf_idf', TfidfVectorizer())\n",
+    "  ])),\n",
+    "  ('classifier', MultinomialNB())\n",
+    "])\n",
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "gram_range = [(1, n) for n in range(1, 3)]\n",
+    "param_grid = {\n",
+    "    'features__counts__ngram_range': gram_range,\n",
+    "    'features__tf_idf__ngram_range': gram_range,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.89516011810129448"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ANSWER\n",
+    "\n",
+    "\n",
+    "# fix the cross-validation process using Pipeline\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3))\n",
+    "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()\n",
+    "\n",
+    "# It is NOT as good... But it is more accurate to our true estimate (and also better than the unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.910112359551 {'kneighborsclassifier__n_neighbors': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# search for an optimal n_neighbors value using GridSearchCV\n",
+    "pipe = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
+    "neighbors_range = range(1, 21)\n",
+    "param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range)\n",
+    "grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)\n",
+    "print grid.best_score_, grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/10_advanced_sklearn.ipynb b/notebooks/10_advanced_sklearn.ipynb
index 824a36f..3b6ae84 100644
--- a/notebooks/10_advanced_sklearn.ipynb
+++ b/notebooks/10_advanced_sklearn.ipynb
@@ -1943,9 +1943,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat28]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat28]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/10_review-Eugene.ipynb b/notebooks/10_review-Eugene.ipynb
new file mode 100644
index 0000000..bc09140
--- /dev/null
+++ b/notebooks/10_review-Eugene.ipynb
@@ -0,0 +1,1663 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Review with bank marketing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "- Data from the UCI Machine Learning Repository: [data](https://github.com/sinanuozdemir/sfdat22/blob/master/data/bank.csv), [data dictionary](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)\n",
+    "- **Goal:** Predict whether a customer will purchase a bank product marketed over the phone\n",
+    "- `bank.csv` is already in our repo, so there is no need to download the data from the UCI website"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Read the data into Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
+       "1   39     services   single        high.school      no       no       no   \n",
+       "2   25     services  married        high.school      no      yes       no   \n",
+       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree      no      yes       no   \n",
+       "\n",
+       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
+       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
+       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
+       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
+       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
+       "\n",
+       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
+       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
+       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
+       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
+       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
+       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank = pd.read_csv('../data/bank.csv', sep=';')\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4119, 21)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "job               0\n",
+       "marital           0\n",
+       "education         0\n",
+       "default           0\n",
+       "housing           0\n",
+       "loan              0\n",
+       "contact           0\n",
+       "month             0\n",
+       "day_of_week       0\n",
+       "duration          0\n",
+       "campaign          0\n",
+       "pdays             0\n",
+       "previous          0\n",
+       "poutcome          0\n",
+       "emp.var.rate      0\n",
+       "cons.price.idx    0\n",
+       "cons.conf.idx     0\n",
+       "euribor3m         0\n",
+       "nr.employed       0\n",
+       "y                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Do we have any null values?\n",
+    "bank.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Prepare at least three features\n",
+    "\n",
+    "- Include both numeric and categorical features\n",
+    "- Choose features that you think might be related to the response (based on intuition or exploration)\n",
+    "- Think about how to handle missing values (encoded as \"unknown\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
+       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
+       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
+       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list all columns (for reference)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### y (response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    0\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert the response to numeric values and store as a new column called \"outcome\"\n",
+    "bank['outcome'] = bank.y.map({'no':0, 'yes':1})\n",
+    "bank['outcome'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's explore a few columns manually and visually"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xa454978>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh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\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa384a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exercise!\n",
+    "# create a boxplot of age the data by outcome\n",
+    "# TODO\n",
+    "bank.boxplot(column='age',by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xa468c88>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh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\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa5357b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "bank.boxplot(column='age', by='outcome')\n",
+    "# probably not a great feature, why?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc05b0b8>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SEIsWLeTYOfMYP3FKQ473zNIlzJ4F06dv15DjSf2UJIiISMOMnziF9SdvnjsMaRCNSRAR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\nJElq9QuzNbJXcib2fAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc06b940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('job').outcome.mean().plot(kind='bar')\n",
+    "# Ask yourself, WHY is this going to be useful?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>job_blue-collar</th>\n",
+       "      <th>job_entrepreneur</th>\n",
+       "      <th>job_housemaid</th>\n",
+       "      <th>job_management</th>\n",
+       "      <th>job_retired</th>\n",
+       "      <th>job_self-employed</th>\n",
+       "      <th>job_services</th>\n",
+       "      <th>job_student</th>\n",
+       "      <th>job_technician</th>\n",
+       "      <th>job_unemployed</th>\n",
+       "      <th>job_unknown</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   job_blue-collar  job_entrepreneur  job_housemaid  job_management  \\\n",
+       "0              1.0               0.0            0.0             0.0   \n",
+       "1              0.0               0.0            0.0             0.0   \n",
+       "2              0.0               0.0            0.0             0.0   \n",
+       "3              0.0               0.0            0.0             0.0   \n",
+       "4              0.0               0.0            0.0             0.0   \n",
+       "\n",
+       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
+       "0          0.0                0.0           0.0          0.0             0.0   \n",
+       "1          0.0                0.0           1.0          0.0             0.0   \n",
+       "2          0.0                0.0           1.0          0.0             0.0   \n",
+       "3          0.0                0.0           1.0          0.0             0.0   \n",
+       "4          0.0                0.0           0.0          0.0             0.0   \n",
+       "\n",
+       "   job_unemployed  job_unknown  \n",
+       "0             0.0          0.0  \n",
+       "1             0.0          0.0  \n",
+       "2             0.0          0.0  \n",
+       "3             0.0          0.0  \n",
+       "4             0.0          0.0  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create job_dummies (we will add it to the bank DataFrame later)\n",
+    "job_dummies = pd.get_dummies(bank.job, prefix='job')\n",
+    "# removing the first job from the list (making it k-1 columns) where k is the number of jobs\n",
+    "job_dummies.drop(job_dummies.columns[0], axis=1, inplace=True)\n",
+    "job_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc20f710>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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lWtta4B2FvkfW66a7CTgaWEU1EvEbUysy817gOQ3rkSRJPdI0JOwHPJSZO6a1bQD2jIhl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\npKL/D62ylSnSyXCKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc4553c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('default').outcome.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "default  outcome\n",
+       "no       0          2913\n",
+       "         1           402\n",
+       "unknown  0           754\n",
+       "         1            49\n",
+       "yes      0             1\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exercise! \n",
+    "# How many people have a value of no, yes, and unknown?\n",
+    "# TODO\n",
+    "bank.groupby('default').outcome.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['unknown', 'yes', 'no']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(set(bank.default))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "bank.default.value_counts()\n",
+    "# only one person in the dataset has a status of yes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# So, let's treat this as a 2-class feature rather than a 3-class feature\n",
+    "bank['default'] = bank.default.map({'no':0, 'unknown':1, 'yes':1})\n",
+    "bank.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### contact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contact\n",
+       "cellular     0.141403\n",
+       "telephone    0.051806\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0fVS0NJn+DbguItYA91A9PvxXMtPbdEvt+QTVLfPPAMae8vsm4LPApcB/a6ku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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe093780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('contact').outcome.mean().plot(kind='bar')\n",
+    "bank.groupby('contact').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Exercise! \n",
+    "# Convert the feature to numeric values, 0 for cellular, 1 for telephone\n",
+    "# TODO\n",
+    "bank['contact'] = bank.default.map({'no':0, 'telephone':1})\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contact\n",
+       "0    0.141403\n",
+       "1    0.051806\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution!\n",
+    "bank['contact'] = bank.contact.map({'cellular':0, 'telephone':1})\n",
+    "bank.groupby('contact').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "month\n",
+       "apr    0.167442\n",
+       "aug    0.100629\n",
+       "dec    0.545455\n",
+       "jul    0.082982\n",
+       "jun    0.128302\n",
+       "mar    0.583333\n",
+       "may    0.065312\n",
+       "nov    0.096413\n",
+       "oct    0.362319\n",
+       "sep    0.406250\n",
+       "Name: outcome, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# looks like a useful feature at first glance\n",
+    "bank.groupby('month').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "int_months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
+    "index_four=int_months[3]\n",
+    "last = len(int_months)\n",
+    "last_value=int_months[last-1]\n",
+    "print(index_four)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
+      "  app.launch_new_instance()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dec</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0.545455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mar</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0.406250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.362319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>apr</th>\n",
+       "      <td>215</td>\n",
+       "      <td>0.167442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nov</th>\n",
+       "      <td>446</td>\n",
+       "      <td>0.096413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jun</th>\n",
+       "      <td>530</td>\n",
+       "      <td>0.128302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aug</th>\n",
+       "      <td>636</td>\n",
+       "      <td>0.100629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jul</th>\n",
+       "      <td>711</td>\n",
+       "      <td>0.082982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>may</th>\n",
+       "      <td>1378</td>\n",
+       "      <td>0.065312</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count      mean\n",
+       "month                 \n",
+       "dec       22  0.545455\n",
+       "mar       48  0.583333\n",
+       "sep       64  0.406250\n",
+       "oct       69  0.362319\n",
+       "apr      215  0.167442\n",
+       "nov      446  0.096413\n",
+       "jun      530  0.128302\n",
+       "aug      636  0.100629\n",
+       "jul      711  0.082982\n",
+       "may     1378  0.065312"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# but, it looks like their success rate is actually just correlated with number of calls\n",
+    "# thus, the month feature is unlikely to generalize\n",
+    "df = bank.groupby('month').outcome.agg(['count', 'mean']).sort('count')\n",
+    "df\n",
+    "\n",
+    "# plt.plot(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count').corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# looks like an excellent feature, can you think of a reason why duration isn't helpful in practice?\n",
+    "bank.boxplot(column='duration', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Answer: You can't know the duration of a call beforehand, thus it can't be used in your model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### previous"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('previous').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### poutcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# looks like a useful feature\n",
+    "bank.groupby('poutcome').outcome.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create poutcome_dummies\n",
+    "poutcome_dummies = pd.get_dummies(bank.poutcome, prefix='poutcome')\n",
+    "poutcome_dummies.drop(poutcome_dummies.columns[0], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# concatenate bank DataFrame with job_dummies and poutcome_dummies\n",
+    "bank = pd.concat([bank, job_dummies, poutcome_dummies], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### euribor3m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# looks like an excellent feature\n",
+    "bank.boxplot(column='euribor3m', by='outcome')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Model building\n",
+    "\n",
+    "- Use cross-validation to evaluate the AUC of a logistic regression model with your chosen features\n",
+    "- Try to increase the AUC by selecting different sets of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# new list of columns (including dummy columns)\n",
+    "bank.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create X (including 13 dummy columns)\n",
+    "feature_cols = ['default', 'contact', 'previous', 'euribor3m'] + list(bank.columns[-13:])\n",
+    "X = bank[feature_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create y\n",
+    "y = bank.outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Exercise! \n",
+    "# calculate cross-validated Accuracy for logisic regression, and knn with 5 neighbors\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "logreg = LogisticRegression()\n",
+    "cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(5)\n",
+    "cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Exercise! \n",
+    "# use grid search to try k in range from 1-29 (odd only) for knn, and graph the accuracy from a cross val score vs the k\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "knn = KNeighborsClassifier()\n",
+    "param_grid = {'n_neighbors': range(1, 29, 2)}\n",
+    "grid = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
+    "grid.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
+    "plt.figure()\n",
+    "plt.plot(range(1, 29, 2), grid_mean_scores)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# look's like adding to k after 5 doesn't even help that much...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Exercise! \n",
+    "# Create a confusion matrix from the best estimator in the gridsearch and find the accuracy, sensitivity, and specificity\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution! \n",
+    "from sklearn import metrics\n",
+    "preds = grid.best_estimator_.predict(X)\n",
+    "print metrics.confusion_matrix(y, preds)\n",
+    "# Accuracy = (3625  + 100) / 4119  == .904\n",
+    "# Sensitivity = 100 / (100 + 351)  == .222\n",
+    "# Specificity = 3625 / (3625 + 43) == .988"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Exercise! \n",
+    "# Compare the accuracy to the null accuracy rate\n",
+    "# TODO\n",
+    "print \"\\n\"*20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution!\n",
+    "\n",
+    "null_accuracy_rate = bank['outcome'].value_counts()[0] / float(bank.shape[0])\n",
+    "null_accuracy_rate\n",
+    "# accuracy is greater than the null accuracy rate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# What can we do to make the model better?\n",
+    "\n",
+    "# Thought Experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we want to regress to the duration of the call so the rep can better plan their day?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# make a heatmap out of the correlation matrix\n",
+    "sns.heatmap(bank.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank[['age', 'duration']].corr()\n",
+    "# age won't be useful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('education').duration.mean().plot(kind='bar')\n",
+    "# or education, except that illiterate column is interesting.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('marital').duration.mean().plot(kind='bar')\n",
+    "# or marital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('job').duration.mean().plot(kind='bar')\n",
+    "# job maybe.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('day_of_week').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bank.groupby('previous').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "duration_response = bank['duration']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X, duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Not that great.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# job field\n",
+    "bank.groupby('job').duration.mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Only with the retired field\n",
+    "linreg = LinearRegression()\n",
+    "rmse = np.sqrt(abs(cross_val_score(linreg, X[['job_retired']], duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# How can we make this a bit better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/10_review.ipynb b/notebooks/10_review.ipynb
index bd6dace..07f8a96 100644
--- a/notebooks/10_review.ipynb
+++ b/notebooks/10_review.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -368,7 +368,7 @@
        "Name: outcome, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -404,49 +404,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc7a8470>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh\nQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUY\nGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlfe3eISL2\nAz4C/BGwBvhAZn6g3LcMuBg4GlgOnJmZV3WqWEmS1D1thwbgX4FbgccBhwGfi4jlmXkFcAXwI+Bw\n4FnAZRFxcGbe3qmCJUmdtXz5raxbd8929/f21hgcnM+6dZsYG6tv97jBwb1YtuzAnVGiZoie8fHx\nygdHxEJgLfCYzPxZue3LwErgMorQ8NDM3Fzuuwq4JjPPrtL+6tXrqxejWaWvr8bQ0ADDwyNs3br9\nPzqSpteaNWs47LBHUK9P/XXZ29vLTTfdzOLFiztQmabTkiULeqoc125PwyZgBPiriDgLeATwROAt\nwFHAjY3AULqWYqhCkjQDLV68mOuv/2HHehoMDHNbW6EhM0cj4tXAhcBrgV7gU5n5qYi4gKLHodkq\nYL+OVCpJ2il2NKRgT6EaJjOn4RDga8B7gN8DPhgR3wL2AEZbjh0F+qs2XKv1UKtV6iHRLNPbW3vA\nraTZw9evGtoKDRFxPPBSYL/MHAV+WJ5N8TbgW0Brv1Q/sLFq+4sWDdDTY2iYywYH53e7BEmT5OtX\n7fY0PA74VRkYGn5IMafhDoqzKZotBe6s2vjatSP2NMxRVcdEJc08d67dyIVf+V9e/ee/xz6L9uh2\nOdoJhoYGKh3XbmhYCTwyIvoyc2u57RCKUzCvB86KiP6mUHEMcE3Vxuv1cep1T6CYy8bG6o6JSrPM\n6OhWVqxaz+joVl+/u7h2Q8PXgfOBj0fEucDBwFnlv6uBFcAlEXEOcCJwBHBqx6qVJEld09aslsxc\nBxwP7AP8N/Be4OzM/Hhm1imCwlLgBuAU4GQXdpIkaW5o++yJzPwFcMJ29t0CHDvVoiRJ0szj+TOS\nJKkSQ4MkSarE0CBJkioxNEiSJrRwz36e/7Rg4Z6VF/jVHGVokCRNaOGCfk454WAWLjA07OoMDZIk\nqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZrQvVvGuO2367h3y1i3S1GXGRokSRNa\nedcIr373d1h510i3S1GXGRokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVdIz\nPj7e7Rrus3r1+plTjDqqr6/G0NAAw8MjbN1a73Y5ktpQHx9ncx12r0Gtp6fb5WgnWLJkQaX/WHsa\nJEkT2m1eLwcsHWS3eb3dLkVdZmiQJEmVGBokSVIlhgZJklRJXzsHR8RLgE8B40BP0209M/si4kDg\nY8DRwHLgzMy8qqMVS5Kkrmi3p+ELwFJgn/L2AOBm4J/L/ZcDK4HDgUuByyJiv86UKkmSuqmtnobM\nHAV+1/g+Is4qvzwrIo4DDgSOzMzNwHkRcTxwGnB2h+qVJEldMuk5DRExBLwJeHNmbgGOBG4sA0PD\ntRRDFZKkWeru9aN87t9/wd3rR7tdirpsKhMhTwfuyMzLyu/3oRiaaLYKcHhCkmaxuzeM8vkrk7s3\nGBp2dW0NT7R4KXBe0/d7AK2/UaNAf9UGa7UeajVXG5uLentrD7iVNHvUytdtrbdGX5+v4V3ZpEJD\nRBwBPAz4YtPmzcCilkP7gY1V2120aIAelyid0wYH53e7BEltWjOyBYCBgX6Ghga6XI26abI9DScA\nV2fmPU3b7gAObTluKXBn1UbXrh2xp2GO6u2tMTg4n3XrNjE25rUnpNlkZGT0vtvh4ZEuV6OdoWoY\nnGxoOBL4r5Zt1wNvjoj+8iwLgGOAa6o2Wq+PU697zaq5bGys7gWrpFmmXgb9uq/fXd5kQ8NjgM+0\nbPsusAK4JCLOAU4EjgBOnXR1mjWWL7+Vdevu2e7+qj0Ng4N7sWzZgTujREnSFE02NDwUGG7ekJn1\niDgJ+ARwA8WiTydn5u1TK1Ez3Zo1azjqqMdSr0/9E0hvby833XQzixcv7kBlkqROmlRoyMxtDn5k\n5i3AsVOqSLPO4sWLuf76H3asp8HAIM0s8/pq7L/3AuZ55sQur2d8fObMIVi9ev3MKUYd1ddXY2ho\ngOHhEcdEpVnG1+/ct2TJgkpnIRgbJUlSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZNCy+tK0mz\nn6FB08JL60rS7GdokCRN6I7VGzj9/G9zx+oN3S5FXWZokCRNaMvWOitWrWeLCzvt8gwNkiSpEkOD\nJEmqxNAgSZIqMTRIkqRKDA2aFl5aV5Jmv75uF6Bdw8OW7MmH33Scl9aVpFnMj32SpAkt3LOf5z8t\nWLhnf7dLUZcZGiRJE1q4oJ9TTjiYhQsMDbs6Q4MkSarE0CBJkioxNEiSpEoMDZIkqRJDgyRJqsTQ\noGnhpXUlafYzNGhaeGldafa6d8sYt/12HfduGet2KeoyQ4MkaUIr7xrh1e/+DivvGul2KeqytpeR\njojdgPcDzwdGgU9m5lvLfcuAi4GjgeXAmZl5VaeKlSRJ3TOZnoYLgOOBpwKnAC+LiJeV+64AVgKH\nA5cCl0XEfp0oVJIkdVdbPQ0RMQScBhyXmf9TbnsPcGRE3AwcCByZmZuB8yLi+PL4sztbtiRJmm7t\nDk8cA9ydmdc2NmTm+QARcRZwYxkYGq6lGKqQJEmzXLuh4SBgeUS8CHgLsBvwKeBcYB+KoYlmqwCH\nJyRJmgPaDQ17Ao8GXg6cShEUPgpsBPagmBjZbBSofFm0Wq2HWq2nzZI0Gyzaaz7Pf1qwaK/59PV5\n0o40m9R6a/fd+vrdtbUbGrYCC4DnZ+btABFxAHA6cCWwuOX4fopAUcmiRQP09Bga5qKhITjgYQu7\nXYakSVgzsgWAgYF+hoYGulyNuqnd0HAnsLkRGEpJMQRxB3BYy/FLy/tUsnbtiD0Nc1Rvb43Bwfms\nW7eJsTEXeJJmk4Xz+7jwjccyMK/G8LBrNcxFVcNgu6HhemD3iHhkZt5cbjuUYk2G64GzIqI/MxvD\nFMcA11RtvF4fp14fb7MkzSZjY3W2uiqkNKv09dU4YOkgw8Mjvn53cW2Fhsz8ZUR8A7gkIk6nmNPw\nZopTKq8GVpT7zgFOBI6gmPsgSZJmucnMaHkBcDNFD8IlwAWZ+aHMrFMEhaXADRQLP53cMpQhSZJm\nqZ7x8ZkzHLB69fqZU4w6qq+vxtDQgN2b0izk63fuW7JkQaUJhZ47I0mSKjE0aFp4aV1Jmv0MDZoW\nXlpXkmY/Q4MkaUJ3rx/lc//+C+5e37ror3Y1hgZJ0oTu3jDK569M7t5gaNjVGRokSVIlhgZJklSJ\noUGSJFViaJAkSZUYGiRJUiXtXuVSmpR9HzLAhW88lt2NqZI0a/knXNNit3m9HLB0kN3m9Xa7FElt\nmtdXY/+9FzCvz7eMXZ0XrNK08II30uzl63fu84JVkiSpowwNkiSpEkODJEmqxNAgSZIqMTRIkqRK\nDA2aFl5aV5JmP0ODpoWX1pWk2c/QIEma0B2rN3D6+d/mjtUbul2KuszQIEma0JatdVasWs8WF3ba\n5RkaJElSJYYGSZJUiaFBkiRV0valsSPiZOCrwDjQU95+JTP/IiKWARcDRwPLgTMz86qOVStJkrpm\nMj0NhwJfA5aW//YB/rrcdwWwEjgcuBS4LCL260CdmuW8tK4kzX5t9zQAhwA3Zebq5o0RcRxwIHBk\nZm4GzouI44HTgLOnXKlmtYct2ZMPv+k4L60rTbNVazey+d6xqbUxvBGAlXeNMDY2PqW2dt+tl70X\n7TGlNtQ9kwkNhwLbGnI4ErixDAwN11IMVUiSptmqtRs562PXd6y9i674aUfa+aeXH2VwmKUmExoC\neHpEvBXoBf4VeDvFMMXKlmNXAQ5PSFIXNHoYXvbMQ9l38cCk2+nt7WHB4HzWr9s0pZ6GlWtGuPjr\nP5tyz4e6p63QEBEPB+YDm4DnUgxHXFBu2wNoXSN4FOiv2n6t1kOt1tNOSZolentrD7iVtPP19hZ/\nT/d/6J4s22dwCu3UGBycz7p1/YyNTX54sVFPb28Pfc5vmpXaCg2Z+ZuIWJyZd5ebfhIRvRSTHj8F\nDLXcpR/YWLX9RYsG6OkxNMxlg4Pzu12CtMtYM7IFgAWD8xkamnxPQ8NUX7+drkfTr+3hiabA0PBz\nYHfgtxSTJJstBe6s2vbatSP2NMxR939S2TSlTyqSqlu/btN9t8PD8ybdTqdev52qR51XNcS1Ozzx\nNOBzwH5NEx4fC9wFXAO8ISL6M7MxTHFMub2Sen2cen1qM3M1s42N1T17QpomjfkHY2PjHXndTfX1\n2+l6NP3a7Wn4HsVww8cj4mzgEcD5wLuAq4EVwCURcQ5wInAEcGrHqpUkSV3T1kyUzNwAnAAsAX5A\nsfrjRZn53sysUwSFpcANwCnAyZl5e2dL1mzkpXUlafabzJyGn1MEh23tuwU4dqpFae7x0rqSNPt5\nzoskSarE0CBJkioxNEiSpEoMDZIkqRJDgyRJqmQyF6zSLsZL60qSwNCgHfDSupKkBkODJuSldSVJ\nDYYGVbLv4gEOWLpg0vfv66sxNDTA8PA815yXpFnKiZCSJKkSQ4MkSarE0CBJkioxNEiSpEoMDZIk\nqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZIqMTRIkqRKDA2SJKkSQ4MkSarE0CBJ\nkioxNEiSpEoMDZIkqZK+yd4xIr4BrMrM08rvlwEXA0cDy4EzM/OqDtQoSZJmgEn1NETEXwJ/0rL5\ncmAlcDhwKXBZROw3tfIkSdJM0XZoiIgh4Hzgv5u2HQccBLwiC+cB1wGndapQSZLUXZMZnngP8Gng\nYU3bjgRuzMzNTduupRiqkCRJc0BbPQ1lj8KTgHNadu1DMTTRbBXg8IQkSXNE5Z6GiOgHLgJOz8zR\niGjevQcw2nKXUaC/nWJqtR5qtZ527qKdrLe3577bvr7Jn2zT21t7wG2365F2Bb5+1WntDE/8PfCD\nzPyPbezbDCxq2dYPbGynmEWLBujpMTTMJGtGtgCwYHA+Q0MDU25vcHD+jKpHmst8/arT2gkNzwP2\njoj15ff9ABHxHOAfgUNbjl8K3NlOMWvXjtjTMMOsX7fpvtvh4XmTbqe3t8bg4HzWrdvE2Fi96/VI\nuwJfv6qqaohrJzQ8GWj+Xz4fGAfeBCwD/jYi+jOzMUxxDHBNG+1Tr49Tr4+3cxftZGNj4/fdbt06\n+T8W97dXn1I7na5Hmst8/arTKoeGzFzR/H3Z4zCembdGxG3ACuCSiDgHOBE4Aji1g7VKktq015b1\n1FeuYPPoHpNuo7e3xry189k0xZ6G+pqN7LVl/Y4P1Iw16RUhm2VmPSJOAj4B3ADcDJycmbd3on1J\nUvvGRzbwitsuZ8uHx/lNt4spvYIexkeeACzodimahEmHhsz8q5bvbwGOnXJFkqSO6BnYk48ecDJn\n/Nmj2Gfx1HoaOjGn4c41G7ngG7/itQN7TroNdVdHeho0t9m9Kc1e98xbQG3f/dl96eQ/2ff11dhz\naIAtwyNTmotQ61/PPfN+O+n7q/sMDZqQ3ZuSpAZDgyZk96YkqcHQoB2ye1OSBJO8NLYkSdr1GBok\nSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFB\nkiRVYmiQJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVElftwvQ7HDbqvVTun9v\nbw9rRrawft0mxsbGJ93OyjUjU6pDkjR5hgZNaKxevMFf8s1fdLmSB9p9t95ulyBJu5y2Q0NEPAL4\nEPBEYA1wYWa+p9y3DLgYOBpYDpyZmVd1qlhNv4P2HeRtL348vbWeKbWzangjF13xU1550mHsPbTH\nlNrafbde9l40tTYkSe1rKzRERA/wDeD7wP8BHgV8ISJuz8wvAFcAPwIOB54FXBYRB2fm7Z0tW9Pp\noH0Hp9xGb28ROvZ9yAD7Ldlzyu1JkqZfuz0NewM/BE7PzBHg1xHxLeCYiFgFHAgcmZmbgfMi4njg\nNODsThY+9fhvAAAMQklEQVQtSarOOUnqlLZCQ2b+Fnh+4/uIeCLwJOB04CjgxjIwNFxLMVQhSZpm\nzklSp016ImRELAf2B/4v8FXgn4GVLYetAvab7GNIkibPOUnqtKmcPfFsYCnwEeD9wB7AaMsxo0B/\n1QZrtR5qU/zl1sxU663dd9vX5/Ig0nR59MMXTrmNeWXPwH57L+DhD3VO0q5s0qEhM28EiIjXAZ8F\nPgEMtRzWD2ys2uaiRQP09Bga5qI1I1sAGBjoZ2hooMvVSGqHr181tHv2xEOBozPziqbNPwN2A+4E\nDmm5y9JyeyVr147Y0zBH3Tu6hf33XsC9o1sYHnYylDSbjIyM3nfr63duqhoG2+1pOBD4akTsl5mN\nMPB44HcUkx7fGBH9mdkYpjgGuKZq4/X6OPX65GfmaubaZ9EefPhNxzE8PMLWrfVulyOpDfWx+n23\nvn53be2Ghh8ANwCfLIclDgTOB/4BuBpYAVwSEecAJwJHAKd2rFpJktQ1bc1Iy8w6cBIwAnwP+Bjw\nz5l5YbnvRIohiRuAU4CTXdhJkqS5oe2JkOVaDc/Zzr5bgGOnWpQkaeaY11dj/70XMM8zn3Z5PePj\nM2cOwerV62dOMeqovr4aQ0MDzmmQZiFfv3PfkiULKp2FYGyUJEmVGBokSVIlhgZJklSJoUHT4o7V\nGzj9/G9zx+oN3S5FkjRJhgZNiy1b66xYtZ4tTqKSpFnL0CBJkioxNEiSpEoMDZKkCTknSQ2GBknS\nhJyTpAZDgyRJqsTQIEmSKjE0aFos3LOf5z8tWLhnf7dLkSRNkqFB02Lhgn5OOeFgFi4wNEjSbGVo\nkCRJlRgaJElSJYYGSdKEnJOkBkODJGlCzklSg6FBkiRVYmiQJEmVGBo0Le7dMsZtv13HvVvGul2K\nJGmSDA2aFivvGuHV7/4OK+8a6XYpkqRJMjRIkqRKDA2SJKkSQ4MkaULOSVKDoUGSNCHnJKmhr52D\nI2Jf4ALgWGAj8CXgrMy8NyKWARcDRwPLgTMz86qOVitJkrqm3Z6GrwC7A08E/hJ4JnBOue8KYCVw\nOHApcFlE7NehOiVJUpdV7mmIiACeAOydmXeV294OvDsi/g04EDgyMzcD50XE8cBpwNmdL1uSJE23\ndoYnfgs8vREYmuwFHAXcWAaGhmsphiok9n3IABe+8Vh2dxaNJM1alUNDZt4D3DdHISJ6gFcD3wL2\noRiaaLYKcHhCAOw2r5e9hwYYHh5h69Z6t8uRJE1CWxMhW7wbeCxwBPA6YLRl/yjQ1iXRarUearWe\nKZSkmaq3t/aAW0mzR6183dZ6a/T1+RrelU0qNETEu4AzgL/IzJ9FxGZgUcth/RRnWFS2aNEAPT2G\nhrlscHB+t0uQ1KY1I1sAGBjoZ2hooMvVqJvaDg0R8UHgFcALMvPycvMdwKEthy4F7myn7bVrR+xp\nmKN6e2sMDs5n3bpNjI05PCHNJgvn93HhG49lYF6N4WHXapiLqobBdtdpeAfwcuB5mXlZ067rgTdH\nRH9mNoYpjgGuaaf9en2cen28nbtolhkbqzunQZpl+vpqHLB00DlJauuUy0OAtwH/CHwvIvZu2v1d\nYAVwSUScA5xIMdfh1M6VKkmSuqmdGS0nlse/jeJMiZUUww8rM7MOnEwxJHEDcApwcmbe3tlyJUlS\nt/SMj8+c4YDVq9fPnGLUURs2beG6n/+Oow95KHvOn9ftciS1oa+vxpCnTM9pS5YsqDShcCqnXEr3\nWb78Vtatu2e7+1cNb+Siy38KJx/G3kN7bPe4wcG9WLbswJ1RoqTt2NHrt+pEZl+/c589DZqyNWvW\ncNhhj6Ben/onkN7eXm666WYWL17cgcok7YivX0H1ngZDgzrCTyrS7OXrV4YGzSiOiUqzl6/fua9q\naHA9UEmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBokSVIlhgZJklSJoUGS\nJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokCRJlRgaJElSJYYGSZJUiaFBkiRVYmiQJEmVGBok\nSVIlhgZJklRJ32TvGBH9wA3AqzLz6nLbMuBi4GhgOXBmZl419TIlSVK3TaqnoQwMnwcObdl1ObAS\nOBy4FLgsIvabUoWSJGlGaDs0RMQhwPXAgS3bjwMOAl6RhfOA64DTOlGoJEnqrsn0NDwZ+BbFEERP\n0/YjgRszc3PTtmvL4yRJ0izX9pyGzLyo8XVENO/ah2JootkqwOEJSZLmgElPhNyGPYDRlm2jQH/V\nBmq1Hmq1nh0fqFmnt7f2gFtJs4evXzV0MjRsBha1bOsHNlZtYPHiPU0Mc9zg4PxulyBpknz9qpOx\n8Q5gacu2pcCdHXwMSZLUJZ0MDdcDjytPx2w4ptwuSZJmuU4OT3wXWAFcEhHnACcCRwCndvAxJElS\nl0y1p2G88UVm1oGTKIYkbgBOAU7OzNun+BiSJGkG6BkfH9/xUZIkaZfn+TOSJKkSQ4MkSarE0CBJ\nkioxNEiSpEoMDZIkqZJOrtMgbVO54NeHgWdTLCv+3sx8X3erktSO8nV8A/CqzLy62/WoO+xp0HR4\nD/A44CnA6cA7IuLZXa1IUmVlYPg8cGi3a1F3GRq0U0XEHsBLgTMy88eZeQVwPvDq7lYmqYqIOITi\ncgAHdrsWdZ+hQTvbH1AMg13XtO1a4MjulCOpTU8GvgUcDXgl4l2ccxq0s+0D3JWZW5u2rQJ2j4jF\nmbmmS3VJqiAzL2p8HRHdLEUzgD0N2tn2AEZbtjW+70eSNGsYGrSzbebB4aDx/cZprkWSNAWGBu1s\ndwAPiYjm37WlwKbMvLtLNUmSJsHQoJ3tR8AW4KimbU8CftCdciRJk+VESO1UmbkpIj4NXBQRpwH7\nAa8HXtLdyiRJ7TI0aDq8jmJFyG8D9wB/V67XIGl2Ge92AequnvFxfwckSdKOOadBkiRVYmiQJEmV\nGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImhQZIkVWJokLRdEbF/RDyv23VImhkM\nDZIm8i/ACd0uQtLMYGiQNJGebhcgaebwglXSHBYRQ8A/AM8EHgLcCLw1M78bEX8PvCQzD2w6/r5t\nEfEd4MnlruWZeVBE9AFvB14MLAF+CrwlM/+jvP/BwLuAJ1JcRfcq4PWZ+Zty/3eAHwD7ACcD64F3\nAv8LfAh4NPAj4MWZ+evyPvsC76Po8RgD/qts8+bOPluSdsSeBmmOiogaxZv2E4FTgMdRvDn/e0Qc\nTnGZ49ZPDc3bng1cB3wReHy57QLg5cCZwGOAK4GvRcSjIuLh5fGbKMLGU4GlwNURsWfTY5xBEV5+\nD7gc+CDFpdPPAJ5EESjOK3+GPYD/pAgLTwL+CFgNfD8i9pn0kyNpUvq6XYCkneYE4LHAYzLz5+W2\nv4mIJwBvBH6+3XsCmTkcEfcCmzJzbfnGfxrwqsy8rDzsbREBMAg8l6Ln4EWZuQUgIp4D3Aq8ELio\nvM8PM/P95f4LgVcCF2TmNeW2LwEnlcc+H9irbLNe7n8ZcBzwMuDsST0zkibFngZp7noMcE9TYGi4\nmuJTfrsCmAd8v3ljZr4tM/+nbPOGRmAo960CsuXxmocVRsrbW5q2bQL6y68fCywG7omI9RGxHlgH\n7AccMomfQdIU2NMgzV3bm8RYA7ZsZ99EfxO2TNBmO4+3rceuT3DfX1DMyWhtf8MEtUjaCexpkOau\nnwB7RcShLduPAX4G3AssaNn36Jbvm+c8/IriDf+I5gMi4rqI+P/KxzsiIuY17dsbeBTFhMnJuAk4\ngKLH5JbMvAX4DcVkyz+aZJuSJsmeBmnuuhL4MfC5iDgD+B3wGophi1cCA8CiiHg98GXg6eW/NU1t\nbACWRcTDMvOOiPgg8A8RcRdFEPjrsr1vUASKVwKfiYhzgfnAu8vH/eIkf4ZLgTcDX4mIN1MMTby9\nrPNtk2xT0iTZ0yDNUeXEwacCPwS+SnGq46HAcZn5g8z8T+AdwOspAsAfU7whN7uIYj7CjyOiBzgL\n+DTwEYqehScDf5KZN2fmbeX3QxRnUXwTuAM4JjPXTVDqds/7Lu/3R8BdwL9RzKfYB/jjzMxqz4Sk\nTnGdBkmSVIk9DZIkqRJDgyRJqsTQIEmSKjE0SJKkSgwNkiSpEkODJEmqxNAgSZIqMTRIkqRKDA2S\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc7b5e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Exercise!\n",
     "# create a boxplot of age the data by outcome\n",
     "# TODO\n",
-    "print \"\\n\"*20"
+    "bank.boxplot(column='age',by='outcome')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -454,18 +447,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1127256d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0xcac4b38>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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oCThFtUZdZ2cnnZ2dLyzfc889lEqlF5YXL17M4sWLiwhNUo0uuOCCokNQEzBZ0KirTAZK\npRI33XRTgRFJkkbC0xCSJKkqkwU13M6dO4sOQVKdVq5cWXQIagImC2q4t73tbUWHIKlOd911V9Eh\nqAmYLKjhtmzZUnQIkur0rW99q+gQ1ARMFiRJUlUmC2q48ssoJUmtx2RBDbdp06aiQ5A0TCtWrGDa\ntGkvPPbt29dvecWKFUWHqAJ4nwU1XE9PT9EhSBqm9evXs379+heWjz76aB599NECI1IzsGdBDXfa\naacVHYKkOk2ZMqXoENQE7FnQqKu83fPtt9/u7Z6lFnXkkUcWHYKagMmCRp23e5bGj9/6rd8qOgQ1\nAU9DSJIOq3z8giYukwU13O7du4sOQVKd1q5dW3QIagImC2q42bNnFx2CpDr19vYWHYKagMmCGu4L\nX/hC0SFIqtOaNWuKDkFNwGRBkiRVZbIgSZKqMllQw+3fv7/oECTV6aKLLio6BDUBkwU13IUXXlh0\nCJLqdM011xQdgpqAyYIarqOjo+gQJNXpmGOOKToENQGTBTXc/fffX3QIkur0ohe9qOgQ1ARMFtRw\n5fNESGpuTlGtwdQ0N0REHAGsAd4DTAMeAbaklD5eUe8y4CLgeOCrwAdTSt7GT5KaXOUU1dOmTXOK\natU8kdQfA78LLAF2Am8EtkTET1JKGwAi4hJgeV7nIeDjwK0RMTuldGi0Alfr2LNnT9EhSKrQ29vL\nrl27hqz31FNP0d3dPWS9WbNm0dbWNhqhqQnVmizMA25MKd2SL++JiHcDby6rczFweUrpZoCIWALs\nA84Frh9hvGoBlVNU33PPPU5RLTWZXbt20d7ePqy6w6nX1dXF3LlzRxqWmlStycJ/AO+LiFNSSt+O\niNOBtwIfAoiIk8hOT9zet0FK6YmIuIss0TBZmAAqk4Fp06Y5RbXUZGbNmkVXV9eQ9dauXcsll1wy\nrP1p/Ko1WfgkMAXYFRHPkQ2Q/EhK6bp8/TQgkfUklNuXr5MkNYG2trZh9QRs3bp1DKJRs6s1WVgE\nvBv4bbIxC2cAn4qIR1JK3rlDkqRxqNZLJ9cBn0wp3ZBSui+l9HfAlcCH8/WPAgFMrdhuar7usBYs\nWECpVOr3mDdvHtu2betXb/v27f3Of/dZtmwZmzdv7lfW3d1NqVQacLvh1atXD5ijfc+ePZRKpQED\nftavX8/KlSv7lfX29lIqldixY0e/8s7OTpYuXTogtkWLFk2o4+js7OSkk07ijDPOoFQqsW/fPkql\nEu94xzuYNm0aV199dUscB4yP98Pj8Dg8Do9j06ZN/b5fZ86cycKFCwfs43AipTT8yhH7gT9JKW0q\nK/swcH5KaVa+/AhwRUrpynx5CtlpiCUppRsG2edcoMvBMeOXl15JrWnnTnjLW0rcdddNzJlTdDQa\nbd3d3X2DV9tTSlUvean1NMQ/AZdGxMPAfcBcssGNf1NW56q8zm6ySycvBx4GbqzxuTROnHjiiUWH\nIKkOBw/CU08t5+DBoiNR0WpNFpaTfflvBF5OdlOmv8rLAEgprYuINuBqspsy3QGc4z0WJq6Xv/zl\nRYcgqW7ziw5ATaCmZCGl9DTwB/mjWr0OoKPuqDSuPPPMM0WHIEkaAeeGUMPdc889RYcgSRoBkwU1\n3EFPeEotbNvQVTTumSyo4Xp7e4sOQVLdnDVWJgtqgMopbp999lmnuJValndwVO1XQ0hDqpzi9uij\nj/Y+C1ILmj4dVq/O/mpiM1mQJA1q+nTo6Cg6CjUDkwWNusopqg8dOuQU1ZLUwkwWNOoqk4GIcIpq\nqUUtXbqUz372s0WHoYI5wFENd+SRRxYdgqQ6zZ/vHRxlsqAxYLIgtS5PGQpMFtQAlZdOHjp0yEsn\nJamFOWZBo67y0kmnqJak1mbPghru0CEnHJVa0YEDcM01OzhwoOhIVDSTBTXc008/XXQIkurQ0wNL\nlqyjp6foSFQ0kwU13CmnnFJ0CJLqdl3RAagJmCyo4fbv3190CJLq1lZ0AGoCJguSJKkqkwU13BNP\nPFF0CJKkETBZ0KirvM/CgQMHvM+C1LJWFh2AmoD3WdCoq7zPQkR4nwWpZc0oOgA1AXsW1HARUXQI\nkupmT6BMFtQAlachUkqehpBa0OzZcO+92V9NbJFSKjaAiLlAV1dXF3Pnzi00FjVGRFD050yS1F93\ndzft7e0A7Sml7mp17VmQJB3Wrl27ig5BTcBkQZJ0WKtWrSo6BDUBkwWNusoxC4BjFqQWtWHDhqJD\nUBPw0kmNOi+dlMaPGTO8dFI19ixExIMR8fwgj/VldS6LiEciojcibouIk0c/bEmSNFZqPQ3xRmBa\n2eNXgARcDxARlwDLgfcDbwaeBm6NiBePVsCSJGls1ZQspJR+mFL6Qd8DeCfwnZTSHXmVi4HLU0o3\np5TuBZYArwDOHdWo1dTOPvtsjj766BceQL/ls88+u+AIJQ3H3r1w1llr2bu36EhUtLrHLETEi4D3\nAH+eL59E1ttwe1+dlNITEXEXMI+890Hj36233tpvOSJ45plnCopGUr327oXbb+9l716YPr3oaFSk\nkVwN8RvAccDn8uVpZKck9lXU25evkyS1nDVFB6AmMJJk4ULgiyklh7lLkjSO1ZUsRMQM4Czgr8uK\nHwUCmFpRfWq+rqoFCxZQKpX6PebNm8e2bdv61du+fTulUmnA9suWLWPz5s39yrq7uymVSuzfv79f\n+erVq1m7dm2/sj179lAqlQbcrWz9+vWsXNl/itbe3l5KpRI7duzoV97Z2cnSpUsHxLZo0aIJdRwr\nVqygra2N4447rt99Fl760pdy9NFHc9FFF7XEccD4eD88Do9jpMdx553j4zjGy/tRz3Fs2rSp3/fr\nzJkzWbhw4YB9HE5dc0NERAfwPuDnUkrPl5U/AlyRUroyX55CdhpiSUrphsPsy7khxrmXv/zl/OAH\nPyg6DEk16u6G9vb9dHWdgP89jz+1zA1R8wDHyOYbvgDYUp4o5K4CLo2I3cBDwOXAw8CNtT6Pml9v\nb++w7hv/k5/8hO7uqp9DAGbNmkVbW9tohCZp1FwI3FR0ECpYPVdDnAX8HPDZyhUppXUR0QZcDRwP\n3AGck1I6NKIo1ZR27drVl5UOaTj17F2SmlFH0QGoCdScLKSUbgOOrLK+Az9dE8KsWbPo6uoast7a\ntWu55JJLhrU/Sc1j0iSYM2cukyYVHYmK5twQqltbW9uwegK2bt06BtFIGm1z5sB99xUdhZqBs05K\nkqSqTBbUcJWX8khqHbZfgcmCxsBwroSQ1JxsvwKTBY2BjRs3Fh2CpDrZfgUmC5IkaQgmC2qovXuh\nowOnuJWkFmayoIbauxfWrDFZkKRWZrKgMTBwwhNJzW/nTjj22BI7dxYdiYpmsqAxsLzoACTV4eBB\neOqp5Rw8WHQkKprJgsbA/KIDkFQ3269MFiRJ0hBMFiRJUlUmCxoD24oOQFLdbL8yWVCDTZoEU6Z0\nOsWt1LI6iw5ATcApqtVQc+bA4487RbXUumy/smdBknQY06fD6tXZX01s9ixIkgY1fXp2u3bJngVJ\nklSVyYIabunSpUWHIKlOtl+ByYLGwPz53gFOalW2X4HJgsbA4sWLiw5BUp1svwKTBUmSNASTBTXU\nzp1w6qk4xa0ktTCTBTXUwYOwc+cOp7iVWtCBA3DNNTs4cKDoSFQ0kwWNgXVFByCpDj09sGTJOnp6\nio5ERas5WYiIV0TENRGxPyJ6I+KeiJhbUeeyiHgkX39bRJw8eiGr9VxXdACS6mb7VY3JQkQcD3wV\neAY4G5gN/CHw47I6lwDLgfcDbwaeBm6NiBePUsxqOW1FByCpbrZf1X675z8G9qSULior+25FnYuB\ny1NKNwNExBJgH3AucH29gUqSpGLUehrincDXI+L6iNgXEd0R8ULiEBEnAdOA2/vKUkpPAHcB80Yj\nYEmSNLZqTRZeDXwQuB+YD/wV8OmIeG++fhqQyHoSyu3L12lCWll0AJLqZvtV7achjgC+llL6aL58\nT0ScBnwAuGZUI9O4MH06/OqvznCKW6llzSg6ADWBWnsW9gKVF9H08N+fpkeBAKZW1JmarzusBQsW\nUCqV+j3mzZvHtm3b+tXbvn07pVJpwPbLli1j8+bN/cq6u7splUrs37+/X/nq1atZu3Ztv7I9e/ZQ\nKpXYtWtXv/L169ezcmX/zLq3t5dSqcSOHTv6lXd2dg466cqiRYsm7HFMnw5f/OIKpk9v7eMo53F4\nHBPrOFZw553j4TjGy/tR33Fs2rSp3/frzJkzWbhw4YB9HE6klIZfOeLvgFemlM4sK7sSeFNK6W35\n8iPAFSmlK/PlKWSnIZaklG4YZJ9zga6uri7mzp1buVqSVJADB+CBB+DVr4bJk4uORqOtu7ub9vZ2\ngPaUUne1urWehrgS+GpEfJjsyoa3ABcB7yurcxVwaUTsBh4CLgceBm6s8bkkSQWaPDm7XbtU02mI\nlNLXgd8AFgPfBD4CXJxSuq6szjpgPXA12VUQk4FzUkqHRitotZbKrjlJrcP2K6jjDo4ppX9JKb0+\npdSWUjo1pfS3g9TpSCm9Iq9zdkpp9+iEq1a0atWqokOQVCfbr8C5ITQGNmzYUHQIkupk+xWYLGgM\nzJjhpVdSq7L9CkwW1GAHDsB99+EUt5LUwkwW1FA9PXDaaTjFrSS1MJMFjYG1Q1eR1HT27oWzzlrL\n3r1FR6KimSxoDPQWHYCkOuzdC7ff3muyIJMFjYU1RQcgqW62X5ksSJKkIZgsSJKkqkwWNAb2D11F\nUpOy/cpkQWPiwqIDkFQ3269MFtRgs2fD9dd3MHt20ZFIqk9H0QGoCdQ6RbVUk8mT4bd+a27RYUiq\nw6RJMGfOXCZNKjoSFc1kQZI0qDlzstu1S56GkCRJVZksqOE2b95cdAiS6mT7FZgsaAx0d3cXHYKk\nOtl+BSYLGgMbN24sOgRJdbL9CkwWJEnSEEwW1FB790JHB85aJ0ktzGRBDbV3L6xZY7IgSa3MZEFj\noFR0AJLqsHMnHHtsiZ07i45ERTNZ0BhYXnQAkupw8CA89dRyDh4sOhIVzWRBY2B+0QFIqpvtVyYL\nkiRpCCYLkiSpKpMFjYFtRQcgqW62X9WYLETE6oh4vuKxs6LOZRHxSET0RsRtEXHy6IasVjJpEkyZ\n0ukUt1LL6iw6ADWBeqaovhf4ZSDy5Z/2rYiIS8iGvi8BHgI+DtwaEbNTSodGFqpa0Zw58PjjW4sO\nQ5pwvv1tePLJke2jpwdga/53ZI49Fk45ZeT7UTHqSRZ+mlJ67DDrLgYuTyndDBARS4B9wLnA9fWF\nKEmqxbe/Da997ejt77zzRmc/3/qWCUOrqidZOCUivg8cBO4EPpxS+l5EnARMA27vq5hSeiIi7gLm\nYbIgSWOir0fh2mth9uxiY4Gsh+K880be06Hi1Jos/CdwAXA/MB3oAP49Ik4jSxQSWU9CuX35OknS\nGJo9G+bOLToKjQc1DXBMKd2aUvpCSunelNJtwALgJcC7GhKdxoWlS5cWHYKkOtl+BSO8dDKl9Djw\nLeBk4FGyQY9TK6pNzddVtWDBAkqlUr/HvHnz2Lat/2U727dvp1QaONfAsmXL2Lx5c7+y7u5uSqUS\n+/fv71e+evVq1q5d269sz549lEoldu3a1a98/fr1rFy5sl9Zb28vpVKJHTt29Cvv7OwctGEtWrRo\nQh/H/Pnzx8Vx9PE4PI6JdBzz588fleOA1WzZ4vtR1HFs2rSp3/frzJkzWbhw4YB9HE6klIZdecDG\nEccAe4CPppQ2RsQjwBUppSvz9VPITkMsSSndcJh9zAW6urq6mGt/mSSNWHc3tLdDV1dznIZotniU\n6e7upr29HaA9pdRdrW5NYxYi4grgn4DvAv8DWAM8C1yXV7kKuDQidpNdOnk58DBwYy3PI0mSmket\npyFeCfw9sIssQXgM+PmU0g8BUkrrgPXA1cBdwGTgHO+xMHHt3AmnnopT3EpSC6t1gOPilNIrU0qT\nU0ozUkrvTik9WFGnI6X0ipRSW0rp7JTS7tENWa3k4EHYuXOHU9xKLary3LomJueG0BhYV3QAkuq0\nbp3tVyYLGhPXDV1FUlO67jrbr0wWNCbaig5AUp3a2my/MlmQJElDMFmQJElV1TORlCaI0ZvidiU9\nPVeMOB7RxpjSAAATyElEQVSnuJXG3sqVK7niipG3X7U2kwUNanSnuJ3hFLdSi5oxY0bRIagJmCxo\nUKM7xe2Kke7AKW6lgqxYMfL2q9ZnsqCqnOJWkuQAR0mSVJXJghquctpWSa3D9iswWdAYWLVqVdEh\nSKqT7VdgsqAxsGHDhqJDkFQn26/AZEFjwEuvpNZl+xWYLEiSpCGYLEiSpKpMFtRwa9euLToESXWy\n/QpMFjQGent7iw5BUp1svwKTBY2BNWvWFB2CpDrZfgUmC5IkaQgmC5IkqSqTBTXc/v37iw5BUp1s\nvwKTBY2BCy+8sOgQJNXJ9iswWdAY6OjoKDoESXWy/QpMFjQG5s6dW3QIkupk+xWYLEiSpCGMKFmI\niD+OiOcj4i8ryi+LiEciojcibouIk0cWpiRJKkrdyUJEvAl4P3BPRfklwPJ83ZuBp4FbI+LFI4hT\nLWzz5s1FhyCpTrZfQZ3JQkQcA1wLXAT8pGL1xcDlKaWbU0r3AkuAVwDnjiRQta7u7u6iQ5BUJ9uv\noP6ehY3AP6WUvlReGBEnAdOA2/vKUkpPAHcB8+oNUq1t48aNRYcgqU62XwEcVesGEfHbwBnAGwdZ\nPQ1IwL6K8n35OkmS1GJqShYi4pXAVcBZKaVnGxOSJElqJrWehmgHXgZ0R8SzEfEscCZwcUQcIutB\nCGBqxXZTgUer7XjBggWUSqV+j3nz5rFt27Z+9bZv306pVBqw/bJlywYMxOnu7qZUKg24Xenq1asH\nzNG+Z88eSqUSu3bt6le+fv16Vq5c2a+st7eXUqnEjh07+pV3dnaydOnSAbEtWrSo5Y6jo6M5j+O6\n6ybm++FxeBytfhywmi1bWv84WvX92LRpU7/v15kzZ7Jw4cIB+zicSCkNv3LEzwCvqijeAvQAn0wp\n9UTEI8AVKaUr822mkCURS1JKNwyyz7lAV1dXlzf/aCLd3dDeDl1dMNK3pVQqcdNNNzVNPNJ4Z/vV\ncHR3d9Pe3g7QnlKqOpK1ptMQKaWngZ3lZRHxNPDDlFJPXnQVcGlE7AYeAi4HHgZurOW5NH4sX768\n6BAk1cn2K6hjgOMg+nVNpJTWRUQbcDVwPHAHcE5K6dAoPJda0Pz584sOQZpQ4kAvb2AXk3uGrjuU\n+SeckHUNjMDkHngDEAdmAW0jD0pjbsTJQkrplwYp6wA6RrpvSVLtJj20i27a4byiI8nMBrqBnoe6\n4K2eh2hFo9GzIElqIgdPnMVcuvi7a2H27KKjgZ4eeM95sPnEWUWHojqZLKjhtm3bxrnnegNPaayk\nyW3czVwOzAZG+EN+NNrvAeBuIE0eWSwqjsmCBjWa5zw7P/MZzp0xY0T78JynVIzOzk6TfZksaHCj\nec5zK8Btt41oH57zlIqxdevWokNQEzBZ0KA85ylJ6mOyoEGN5jnP0eA5T0kqTr2zTkqSpAnCZEEN\nN9j9ziW1BtuvwGRBY8A7OEqty/YrMFnQGFi8eHHRIUiqk+1XYLIgSZKGYLIgSZKqMllQw+3YsaPo\nECTVyfYrMFnQGFi3bl3RIUiqk+1XYLKgMXDdddcVHYKkOtl+BSYLGgNtbU78JLUq26/AZEGSJA3B\nZEGSJFVlsqCGW7lyZdEhSKqT7VdgsqAxMGPGjKJDkFQn26/AZEFjYMWKFUWHIKlOtl+ByYIkSRqC\nyYIkSarKZEENt2vXrqJDkFQn268Ajio6ADWn3t7sb3f3yPf1oQ+t4sorbxrRPnp6Rh6HpNqtWrWK\nm24aWftV6zNZ0KD6fky8732jsbcNtLePxn7g2GNHZz+ShmfDhg1Fh6AmYLKgQZ17bvZ31iwYyd1e\ne3rgvPNmcO21MHv2yGI69lg45ZSR7UNSbbx0UlBjshARHwA+CJyYF90HXJZSuqWszmXARcDxwFeB\nD6aUdo9KtBozJ5wAF100evubPRvmzh29/UmSxk6tPQvfAy4Bvg0EcAFwY0SckVLqiYhLgOXAEuAh\n4OPArRExO6V0aNSiliQd1miOORoNjjlqfTUlCymlf64oujQiPgj8PNADXAxcnlK6GSAilgD7gHOB\n60cerlrTWrIcU9JYGN0xR6PXfh1z1LrqHrMQEUcA7wLagP+IiJOAacDtfXVSSk9ExF3APEwWJrDe\nogOQJpTRHXPU65gj1Z4sRMRpwJ3AJOBJ4DdSSvdHxDwgkfUklNtHlkRowlpTdADShDK6Y47WOOZI\ndfUs7AJOB44DFgKfj4h3jGpUkiSpadR8B8eU0k9TSg+klO5OKX0EuIdsrMKjZIMep1ZsMjVfV9WC\nBQsolUr9HvPmzWPbtm396m3fvp1SqTRg+2XLlrF58+Z+Zd3d3ZRKJfbv39+vfPXq1axdu7Zf2Z49\neyiVSgPuVrZ+/foBU7T29vZSKpXYsWNHv/LOzk6WLl06ILZFixZN2OOYNAnmzIFJk1r7OMp5HB7H\nRDuOO+8cH8cxXt6Peo5j06ZN/b5fZ86cycKFCwfs43AipTTsyoPuIOJ24LsppQsj4hHgipTSlfm6\nKWSnIZaklG44zPZzga6uri7m2s81Lu3fv58TTjih6DAk1ai7G9rb99PVdYKnIcah7u5u2rM75rWn\nlKpeO1NTz0JE/FlEvD0iXhURp0XEJ4AzgWvzKleRXSHxzoh4HfB54GHgxpqPQuPGhRdeWHQIkupm\n+1XtYxZeDnwOmA48DnwDmJ9S+hJASmldRLQBV5PdlOkO4BzvsTCxdXR0FB2CpLp1FB2AmkCt91kY\ncnxtSqkDP10q4+klqTVlY47mMmlS0ZGoaM4NIUka1Jw5cN99RUehZlDz1RCSJGliMVlQw1VeyiOp\nddh+BSYLGgPdzTKbjaSa2X4FJgtqsJ074Stf2cjOnUVHIqkeGzduLDoENQGTBTXUwYNZwnDwYNGR\nSJLqZbIgSZKqMlmQJElVmSxoDAyc8ERS89u5E449tuSYI5ksaCwsLzoASXU4eBCeemq5Y45ksqCx\nML/oACTVzfYrkwVJkjQEkwU11PTpsHp19leS1JpMFtRQ06fDGWdsM1mQWta2ogNQEzBZUMN1dnYW\nHYKkutl+ZbKgMbB169aiQ5BUN9uvTBYkSYfhmCP1OaroACRJzWn6dOjoKDoKNQN7FiRJUlUmC2q4\npUuXFh2CpDrZfgUmC2qwAwdgzpz5HDhQdCSS6jF/vndwlMmCGqynB1atWkxPT9GRSKrH4sWLiw5B\nTcBkQZIkVWWyIEmSqjJZ0BjYUXQAkupw4ABcc80OxxzJZEFjYV3RAUiqQ08PLFmyzjFHqi1ZiIgP\nR8TXIuKJiNgXEf8YEa8dpN5lEfFIRPRGxG0RcfLohazWc13RAUiqm+1XtfcsvB1YD7wFOAt4EbA9\nIib3VYiIS4DlwPuBNwNPA7dGxItHJWK1oLaiA5BUN9uvarzdc0ppQflyRFwA/ABo579PTF8MXJ5S\nujmvswTYB5wLXD/CeCVJ0hgb6ZiF44EE/AggIk4CpgG391VIKT0B3AXMG+FzqQXNng333pv9lSS1\nprqThYgI4CpgR0ppZ148jSx52FdRfV++ThPM5MmwZctKJk8euq6kZrSy6ADUBEYy6+RngDnAW0cp\nFo1TM2bMKDoESXWz/arOnoWI2AAsAH4hpbS3bNWjQABTKzaZmq87rAULFlAqlfo95s2bx7Zt2/rV\n2759O6VSacD2y5YtY/Pmzf3Kuru7KZVK7N+/v1/56tWrWbt2bb+yPXv2UCqV2LVrV7/y9evXs3Jl\n/8y6t7eXUqnEjh397x/Q2dk56KQrixYtmtDHsWLFinFxHH08Do9jYh3HCu68czwcx3h5P+o7jk2b\nNvX7fp05cyYLFy4csI/DiZTSsCvDC4nCrwNnppQeGGT9I8AVKaUr8+UpZKchlqSUbhik/lygq6ur\ni7lz59YUiySpcQ4cgAcegFe/Gk8ljkPd3d20t7cDtKeUuqvVrek0RER8BlgMlICnI6KvB+HxlNLB\n/N9XAZdGxG7gIeBy4GHgxlqeS5JUrMmT4dRTi45CzaDW0xAfAKYAXwEeKXu8q69CSmkd2b0Yria7\nCmIycE5K6dAoxKsWVNk1J6l12H4FNSYLKaUjUkpHDvL4fEW9jpTSK1JKbSmls1NKu0c3bLWSVatW\nFR2CpDrZfgXODaEG27sXXvOaDezdO3RdSc1nw4YNRYegJmCyoIbauxeuumqGyYLUorz0WTCy+yxo\nguvt7R3yfGbfbHXDmbVu1qxZtLV5H3ppLAyn/dbC9ju+mSyobrt27eq77GZI5503dB0vn5XGTi3t\ndzhsv+ObyYLqNmvWLLq6uoast2XLFi644IJh7U/S2LD9qhYmC6pbW1vbsH5J3Hjjjf7ikJqM7Ve1\ncICjGm7NmjVFhyCpTrZfgcmCJEkagsmCJEmqymRBDVc5a5uk1mH7FZgsaAxceOGFRYcgqU62X4HJ\ngsZAR0dH0SFIqpPtV2CyoDHgZVdS67L9CkwWJEnSEEwWJElSVSYLarjNmzcXHYKkOtl+BSYLGgPd\n3d1FhyCpTrZfgcmCxsDGjRuLDkFSnWy/ApMFSZI0BJMFSZJUlcmCJEmqymRBDVcqlYoOQVKdbL8C\nkwWNgeXLlxcdgqQ62X4FJgsaA/Pnzy86BEl1sv0KTBYkSdIQTBYkSVJVJgtquG3bthUdgqQ62X4F\ndSQLEfH2iLgpIr4fEc9HxIChshFxWUQ8EhG9EXFbRJw8OuGqFa1du7boECTVyfYrqK9n4WeA/wJ+\nD0iVKyPiEmA58H7gzcDTwK0R8eIRxKkW9rKXvazoECTVyfYrgKNq3SCldAtwC0BExCBVLgYuTynd\nnNdZAuwDzgWurz9USZJUhFEdsxARJwHTgNv7ylJKTwB3AfNG87kkSdLYGO0BjtPITk3sqyjfl6+T\nJEktpubTEA0wCaCnp6foONQgX/va1+ju7i46DEl1sP2OX2Xfu5OGqjvaycKjQABT6d+7MBW4+zDb\nnAhw3nnnjXIoaibt7e1FhyCpTrbfce9E4D+qVRjVZCGl9GBEPAr8MvANgIiYArwF2HiYzW4F3gM8\nBBwczXgkSdJhTSJLFG4dqmLNyUJE/AxwMlkPAsCrI+J04Ecppe8BVwGXRsRusgTgcuBh4MbB9pdS\n+iHw97XGIUmSRqxqj0KfSGnArRKqbxBxJvBlBt5j4XMppQvzOh1k91k4HrgDWJZS2l3TE0mSpKZQ\nc7IgSZImFueGkCRJVZksSJKkqkwW1FARsSwiHoyIAxHxnxHxpqJjklTdcCYM1MRisqCGiYhFwF8A\nq4E3APeQTSp2QqGBSRpK1QkDNfE4wFENExH/CdyVUro4Xw7ge8CnU0rrCg1O0rBExPPAuSmlm4qO\nRcWxZ0ENEREvAtrpP6lYAv4VJxWTpJZisqBGOQE4EicVk6SWZ7IgSZKqMllQo+wHniObRKzcVLIJ\nxyRJLcJkQQ2RUnoW6CKbVAx4YYDjLzPMe5FLkprDaE9RLZX7S2BLRHQBXwM+BLQBW4oMSlJ1w5gw\nUBOMl06qoSLi94BVZKcf/gtYkVL6erFRSapmOBMGamIxWZAkSVU5ZkGSJFVlsiBJkqoyWZAkSVWZ\nLEiSpKpMFiRJUlUmC5IkqSqTBUmSVJXJgiRJqspkQZIkVWWyIKmfiFgdEXcXHYek5mGyIGkw3gde\n0gtMFqRxJiJeHBGfjoh9EXEgIu6IiDfm6y6IiB9X1D83Ip7P/30+sBo4PSKej4jnImJJvu64iLg6\nIh7N9/uNiFhQtp/fjIh7I+JgRDwYEX9Q8TwPRsRHIuJzEfFkRDwUEe+MiBMiYltedk9EtFds97aI\n+PeI6I2I70bEpyKirTGvnqTBmCxI488VwG8A7wXeAOwGbomI48l6DCp7DcrLtgJ/AdxHNlPodGBr\nRARwCzAPeDcwG1gJPAeQf8FvBf4eOI0s4bi8L9Eo83+AO4AzgJuBa4DP5X/fAHwnXybf72uALwI3\n5PtdBLwVWF/PCyOpPs46KY0j+S/uHwNLUkpb87KjgIeAK4H9wJUppZ8t2+bXgX9IKR2ZL68Gfj2l\nNLesznzgn4FZKaXvDPK81wInpJR+taxsLbAgpfS6fPlB4N9SShfky1OBvcCalNKavOwtwH8A01NK\nP4iIvwZ+mlL6YNl+3wZ8BWhLKR0awcslaZjsWZDGl9cAR5F94QKQUvop8DWy3oB6nQ48PFiikJsN\nfLWi7KvAKXmvRJ9vlsW1L//nvWXr9wEBvLzseS/IT1E8GRFPkvVwAJxU+2FIqsdRRQcgaUw9T/Zl\nXO5Fw9juwCg9/7NDlPV1dfb9kDkGuBr4FAPj3jNKMUkagj0L0vjyHbIv37f2FeSnId4E7AQeA46N\niMll27yhYh+HgCMryr4BvDIiTj7M8/aUP2fubcC30sjOdXYDc1JKD6aUHqh4/HQE+5VUA3sWpHEk\npdQbEX8FXJFf9fA9YBUwGdhM9gOhF/hERHwa+Hng/IrdPAScFBGnAw8DT6aU/j0i7gC+EBF/SDZo\nclb2lOlWskGRX4uIS8kGOv5PYBnwgREe0lrgzohYD/wN8DRwKnBWSmnFCPctaZjsWZDGnz8GvgB8\nHvg68Gpgfkrp8ZTSj4HzgHPIegsWkV25UO4LZOMCvgz8APjtvPx/A/+P7IqH+8i+yI8ASCndDbwr\n3983gQ7g0pTSNWX7HayHoWpZSumbwJnAKcC/k/U0dADfr/oKSBpVXg0hSZKqsmdBkiRVZbIgSZKq\nMlmQJElVmSxIkqSqTBYkSVJVJguSJKkqkwVJklSVyYIkSarKZEGSJFVlsiBJkqoyWZAkSVWZLEiS\npKr+P05C7qpjaA6+AAAAAElFTkSuQmCC\n",
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6nfuWLFlQaUKhF6ySJEmVGBokSVIlhgZJklSJoUGSJFViaJAkSZUYGiRJUiWGBkmSVImh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\nJKkSQ4MkSarE0CBJkir5fyAl2OkyfEtdAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112725e90>"
+       "<matplotlib.figure.Figure at 0xcad0ef0>"
       ]
      },
      "metadata": {},
@@ -676,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -684,18 +677,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1130ac8d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x18ad20f0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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lWtta4B2FvkfW66a7CTgaWEU1EvEbUysy817gOQ3rkSRJPdI0JOwHPJSZO6a1bQD2jIhl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\npKL/D62ylSnSyXCKAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112760fd0>"
+       "<matplotlib.figure.Figure at 0x18a9b550>"
       ]
      },
      "metadata": {},
@@ -709,44 +702,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
+     "data": {
+      "text/plain": [
+       "default  outcome\n",
+       "no       0          2913\n",
+       "         1           402\n",
+       "unknown  0           754\n",
+       "         1            49\n",
+       "yes      0             1\n",
+       "Name: outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Exercise! \n",
     "# How many people have a value of no, yes, and unknown?\n",
     "# TODO\n",
-    "print \"\\n\"*20"
+    "bank.groupby('default').outcome.value_counts()"
    ]
   },
   {
@@ -3220,9 +3202,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [sfdat28]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat28]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/11_decision_trees.ipynb b/notebooks/11_decision_trees.ipynb
index 940ab2a..ef291fa 100644
--- a/notebooks/11_decision_trees.ipynb
+++ b/notebooks/11_decision_trees.ipynb
@@ -23,8 +23,12 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "## Lesson objectives\n",
     "\n",
@@ -2053,9 +2057,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [sfdat28]",
+   "display_name": "Python [bersonenv]",
    "language": "python",
-   "name": "Python [sfdat28]"
+   "name": "Python [bersonenv]"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/nov_8_eugene.ipynb b/notebooks/nov_8_eugene.ipynb
new file mode 100644
index 0000000..c07e693
--- /dev/null
+++ b/notebooks/nov_8_eugene.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "r = requests.get('https://www.strava.com/api/v3/athlete', data={'access_token': '1a870acccbdde888dacddbcb4c0f14bd55df8ddf'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{u'athlete_type': 0,\n",
+       " u'badge_type_id': 0,\n",
+       " u'bikes': [{u'distance': 271861.0,\n",
+       "   u'id': u'b1443262',\n",
+       "   u'name': u'Cannondale Slice 4 Rival',\n",
+       "   u'primary': False,\n",
+       "   u'resource_state': 2},\n",
+       "  {u'distance': 7136859.0,\n",
+       "   u'id': u'b1314028',\n",
+       "   u'name': u'Fuji Altamira 3.0 LE 2012',\n",
+       "   u'primary': True,\n",
+       "   u'resource_state': 2}],\n",
+       " u'city': u'San Francisco',\n",
+       " u'clubs': [{u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/4453767/5/large.jpg',\n",
+       "   u'cover_photo_small': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/4453767/5/small.jpg',\n",
+       "   u'featured': False,\n",
+       "   u'id': 15879,\n",
+       "   u'member_count': 1095,\n",
+       "   u'name': u'San Francisco Running Company',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/314178/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/15879/314178/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'running',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'san-francisco-running-company',\n",
+       "   u'verified': True},\n",
+       "  {u'city': u'Palo Alto',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 2052,\n",
+       "   u'member_count': 42,\n",
+       "   u'name': u'SurveyMonkey',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/2052/21885/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/2052/21885/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'surveymonkey',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 220,\n",
+       "   u'member_count': 456,\n",
+       "   u'name': u'Golden Gate Triathlon Club',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/220/596/3/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/220/596/3/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'triathlon',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'golden-gate-triathlon-club',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 9,\n",
+       "   u'member_count': 824,\n",
+       "   u'name': u'SF2G',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/9/552/2/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/9/552/2/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'sf2g',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': None,\n",
+       "   u'cover_photo_small': None,\n",
+       "   u'featured': False,\n",
+       "   u'id': 148324,\n",
+       "   u'member_count': 36,\n",
+       "   u'name': u'Slack',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/148324/2971770/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/148324/2971770/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'cycling',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'slack',\n",
+       "   u'verified': False},\n",
+       "  {u'city': u'San Francisco',\n",
+       "   u'country': u'United States',\n",
+       "   u'cover_photo': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551525/1/large.jpg',\n",
+       "   u'cover_photo_small': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551525/1/small.jpg',\n",
+       "   u'featured': False,\n",
+       "   u'id': 207515,\n",
+       "   u'member_count': 11,\n",
+       "   u'name': u'Blood, Slack, and Tears',\n",
+       "   u'private': False,\n",
+       "   u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551517/1/large.jpg',\n",
+       "   u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/clubs/207515/4551517/1/medium.jpg',\n",
+       "   u'resource_state': 2,\n",
+       "   u'sport_type': u'running',\n",
+       "   u'state': u'California',\n",
+       "   u'url': u'slackstars',\n",
+       "   u'verified': False}],\n",
+       " u'country': u'United States',\n",
+       " u'created_at': u'2011-07-29T16:39:08Z',\n",
+       " u'date_preference': u'%m/%d/%Y',\n",
+       " u'email': u'eugene.berson@gmail.com',\n",
+       " u'firstname': u'Eugene',\n",
+       " u'follower': None,\n",
+       " u'follower_count': 110,\n",
+       " u'friend': None,\n",
+       " u'friend_count': 109,\n",
+       " u'ftp': None,\n",
+       " u'id': 117433,\n",
+       " u'lastname': u'Berson',\n",
+       " u'measurement_preference': u'feet',\n",
+       " u'mutual_friend_count': 0,\n",
+       " u'premium': False,\n",
+       " u'profile': u'https://dgalywyr863hv.cloudfront.net/pictures/athletes/117433/851602/2/large.jpg',\n",
+       " u'profile_medium': u'https://dgalywyr863hv.cloudfront.net/pictures/athletes/117433/851602/2/medium.jpg',\n",
+       " u'resource_state': 3,\n",
+       " u'sex': u'M',\n",
+       " u'shoes': [{u'distance': 385463.0,\n",
+       "   u'id': u'g375794',\n",
+       "   u'name': u'Newton Gravitas',\n",
+       "   u'primary': True,\n",
+       "   u'resource_state': 2}],\n",
+       " u'state': u'CA',\n",
+       " u'updated_at': u'2016-01-01T21:18:39Z',\n",
+       " u'username': u'eberson',\n",
+       " u'weight': 77.1107}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def myfunction (x):\n",
+    "    weight = x['weight']\n",
+    "    city = x['city']\n",
+    "    user_name = x['username']\n",
+    "    shoe = x['shoes'][0]['name']\n",
+    "    sex = x['sex']\n",
+    "    return [sex, weight, city, user_name, shoe]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'M', 77.1107, u'San Francisco', u'eberson', u'Newton Gravitas']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myfunction(r.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [bersonenv]",
+   "language": "python",
+   "name": "Python [bersonenv]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}