diff --git a/.DS_Store b/.DS_Store
index 8ab2fa9..4c6813e 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/code/CustomerChurnPrediction.ipynb b/code/CustomerChurnPrediction.ipynb
index 567f03e..ec47975 100644
--- a/code/CustomerChurnPrediction.ipynb
+++ b/code/CustomerChurnPrediction.ipynb
@@ -13,40 +13,32 @@
"#### Rohit Chandra"
]
},
- {
- "cell_type": "markdown",
- "id": "0db512c9",
- "metadata": {},
- "source": [
- " ** Python statements that import the standard libraries **"
- ]
- },
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 1,
"id": "5225d155",
"metadata": {},
"outputs": [],
"source": [
+ "# Python statements that import the standard libraries \n",
+ "\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sn\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c5b547f2",
- "metadata": {},
- "source": [
- "** Reading file using pandas read_table function **"
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Initial settings\n",
+ "\n",
+ "pd.set_option(\"display.max_rows\", None)"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 2,
"id": "6ede23bd",
- "metadata": {},
+ "metadata": {
+ "scrolled": false
+ },
"outputs": [
{
"data": {
@@ -177,173 +169,31 @@
" | 64.5805 | \n",
" 0 | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 3145 | \n",
- " 21 | \n",
- " 0 | \n",
- " 19.0 | \n",
- " 2 | \n",
- " 6697 | \n",
- " 147.0 | \n",
- " 92.0 | \n",
- " 44.0 | \n",
- " 2 | \n",
- " 1 | \n",
- " 68 | \n",
- " 721.980 | \n",
- " 649.7820 | \n",
- " 122.1980 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 3146 | \n",
- " 17 | \n",
- " 0 | \n",
- " 17.0 | \n",
- " 1 | \n",
- " 9237 | \n",
- " 177.0 | \n",
- " 80.0 | \n",
- " 42.0 | \n",
- " 1 | \n",
- " 1 | \n",
- " 65 | \n",
- " 261.210 | \n",
- " 235.0890 | \n",
- " 76.1210 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 3147 | \n",
- " 13 | \n",
- " 0 | \n",
- " 18.0 | \n",
- " 4 | \n",
- " 3157 | \n",
- " -51.0 | \n",
- " 38.0 | \n",
- " 21.0 | \n",
- " 1 | \n",
- " 1 | \n",
- " 64 | \n",
- " 280.320 | \n",
- " 252.2880 | \n",
- " 78.0320 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 3148 | \n",
- " 7 | \n",
- " 0 | \n",
- " 11.0 | \n",
- " 2 | \n",
- " 4695 | \n",
- " 46.0 | \n",
- " 222.0 | \n",
- " 12.0 | \n",
- " 1 | \n",
- " 1 | \n",
- " 62 | \n",
- " 1077.640 | \n",
- " 969.8760 | \n",
- " 157.7640 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 3149 | \n",
- " 8 | \n",
- " 1 | \n",
- " 11.0 | \n",
- " 2 | \n",
- " 1792 | \n",
- " 25.0 | \n",
- " 7.0 | \n",
- " 9.0 | \n",
- " 1 | \n",
- " 1 | \n",
- " 68 | \n",
- " 100.680 | \n",
- " 90.6120 | \n",
- " 60.0680 | \n",
- " 1 | \n",
- "
\n",
" \n",
"\n",
- "3150 rows × 15 columns
\n",
""
],
"text/plain": [
- " Call Failure Complains Subscription Length Charge Amount \\\n",
- "0 8 0 38.0 0 \n",
- "1 O 0 39.0 0 \n",
- "2 10 0 37.0 0 \n",
- "3 10 0 38.0 0 \n",
- "4 3 0 NaN 0 \n",
- "... ... ... ... ... \n",
- "3145 21 0 19.0 2 \n",
- "3146 17 0 17.0 1 \n",
- "3147 13 0 18.0 4 \n",
- "3148 7 0 11.0 2 \n",
- "3149 8 1 11.0 2 \n",
- "\n",
- " Seconds of Use Frequency of use Frequency of SMS \\\n",
- "0 4370 71.0 5.0 \n",
- "1 NaN 5.0 7.0 \n",
- "2 2453 60.0 359.0 \n",
- "3 4198 66.0 1.0 \n",
- "4 2393 58.0 2.0 \n",
- "... ... ... ... \n",
- "3145 6697 147.0 92.0 \n",
- "3146 9237 177.0 80.0 \n",
- "3147 3157 -51.0 38.0 \n",
- "3148 4695 46.0 222.0 \n",
- "3149 1792 25.0 7.0 \n",
+ " Call Failure Complains Subscription Length Charge Amount Seconds of Use \\\n",
+ "0 8 0 38.0 0 4370 \n",
+ "1 O 0 39.0 0 NaN \n",
+ "2 10 0 37.0 0 2453 \n",
+ "3 10 0 38.0 0 4198 \n",
+ "4 3 0 NaN 0 2393 \n",
"\n",
- " Distinct Called Numbers Tariff Plan Status Age Customer Value \\\n",
- "0 17.0 1 1 5 197.640 \n",
- "1 4.0 1 1 6 46.035 \n",
- "2 24.0 1 1 2 1536.520 \n",
- "3 35.0 1 1 10 240.020 \n",
- "4 33.0 1 1 9 145.805 \n",
- "... ... ... ... ... ... \n",
- "3145 44.0 2 1 68 721.980 \n",
- "3146 42.0 1 1 65 261.210 \n",
- "3147 21.0 1 1 64 280.320 \n",
- "3148 12.0 1 1 62 1077.640 \n",
- "3149 9.0 1 1 68 100.680 \n",
+ " Frequency of use Frequency of SMS Distinct Called Numbers Tariff Plan \\\n",
+ "0 71.0 5.0 17.0 1 \n",
+ "1 5.0 7.0 4.0 1 \n",
+ "2 60.0 359.0 24.0 1 \n",
+ "3 66.0 1.0 35.0 1 \n",
+ "4 58.0 2.0 33.0 1 \n",
"\n",
- " FN FP Churn \n",
- "0 177.8760 69.7640 0 \n",
- "1 41.4315 60.0000 0 \n",
- "2 1382.8680 203.6520 0 \n",
- "3 216.0180 74.0020 0 \n",
- "4 131.2245 64.5805 0 \n",
- "... ... ... ... \n",
- "3145 649.7820 122.1980 0 \n",
- "3146 235.0890 76.1210 0 \n",
- "3147 252.2880 78.0320 0 \n",
- "3148 969.8760 157.7640 0 \n",
- "3149 90.6120 60.0680 1 \n",
- "\n",
- "[3150 rows x 15 columns]"
+ " Status Age Customer Value FN FP Churn \n",
+ "0 1 5 197.640 177.8760 69.7640 0 \n",
+ "1 1 6 46.035 41.4315 60.0000 0 \n",
+ "2 1 2 1536.520 1382.8680 203.6520 0 \n",
+ "3 1 10 240.020 216.0180 74.0020 0 \n",
+ "4 1 9 145.805 131.2245 64.5805 0 "
]
},
"metadata": {},
@@ -351,54 +201,2804 @@
}
],
"source": [
+ "# Reading file using pandas read_table function \n",
+ "\n",
"df = pd.read_csv('../data/Customer_churn_raw.csv')\n",
- "display(df)\n",
" \n",
- " "
+ "\n",
+ "display(df.head(5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a98463a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#column renaming\n",
+ "\n",
+ "df=df.rename(columns={\"Call Failure\": \"call_failure\", \"Complains\": \"complains\", \"Subscription Length\": \"subs_len\", \"Charge Amount\": \"charge_amount\",\n",
+ " \"Seconds of Use\": \"seconds_of_use\", \"Frequency of use\": \"freq_of_use\", \"Frequency of SMS\": \"freq_of_sms\", \"Distinct Called Numbers\": \"distinct_call_nums\",\n",
+ " \"Age Group\": \"age_group\", \"Tariff Plan\": \"tariff_plan\", \"Status\": \"status\", \"Age\": \"age\", \"Customer Value\": \"customer_value\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "00c63c0a",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "#Filter the dataframe for cleaning and analysis\n",
+ "\n",
+ "col_4_5_12 = df.filter(['charge_amount','customer_value','seconds_of_use','Churn'], axis=1)"
]
},
{
"cell_type": "markdown",
- "id": "7cbb83fa",
+ "id": "d401f2a3",
"metadata": {},
"source": [
- "** Check number of null values in each columns **"
+ "### Data Analysis"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 5,
+ "id": "0db9995c",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 3150 entries, 0 to 3149\n",
+ "Data columns (total 4 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 charge_amount 3148 non-null object \n",
+ " 1 customer_value 3058 non-null float64\n",
+ " 2 seconds_of_use 3111 non-null object \n",
+ " 3 Churn 3150 non-null int64 \n",
+ "dtypes: float64(1), int64(1), object(2)\n",
+ "memory usage: 98.6+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Summary of the dataframe\n",
+ "\n",
+ "col_4_5_12.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
"id": "21c334a0",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "charge_amount 2\n",
+ "customer_value 92\n",
+ "seconds_of_use 39\n",
+ "Churn 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#count the total number of null values in all the columns\n",
+ "\n",
+ "col_4_5_12.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "54b7344c",
"metadata": {},
"outputs": [
{
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+ "146.250 1\n",
+ "162.950 1\n",
+ "833.220 1\n",
+ "124.240 1\n",
+ "934.760 1\n",
+ "205.960 1\n",
+ "1904.130 1\n",
+ "114.345 1\n",
+ "48.680 1\n",
+ "97.200 1\n",
+ "95.655 1\n",
+ "48.690 1\n",
+ "65.040 1\n",
+ "220.280 1\n",
+ "186.900 1\n",
+ "155.080 1\n",
+ "212.480 1\n",
+ "724.545 1\n",
+ "113.190 1\n",
+ "860.130 1\n",
+ "272.520 1\n",
+ "742.995 1\n",
+ "99.640 1\n",
+ "270.160 1\n",
+ "174.000 1\n",
+ "199.960 1\n",
+ "200.025 1\n",
+ "431.910 1\n",
+ "711.630 1\n",
+ "889.650 1\n",
+ "1103.120 1\n",
+ "213.885 1\n",
+ "1620.600 1\n",
+ "190.125 1\n",
+ "155.055 1\n",
+ "200.880 1\n",
+ "166.400 1\n",
+ "101.280 1\n",
+ "194.865 1\n",
+ "1370.790 1\n",
+ "Name: customer_value, dtype: int64"
]
},
- "execution_count": 14,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df['Customer Value'].isnull().sum()\n",
- " "
+ "# Count the unique values in 'customer_value' column\n",
+ "\n",
+ "col_4_5_12['customer_value'].value_counts()"
]
},
{
- "cell_type": "markdown",
- "id": "1243d7d1",
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e3c3222d",
"metadata": {},
+ "outputs": [],
"source": [
- "** Replace null values **"
+ "# Replace the null values with zero\n",
+ "\n",
+ "col_4_5_12['customer_value'].replace(np.nan, 0, inplace=True )\n"
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "59ac1f1b",
+ "execution_count": 9,
+ "id": "cbe4abb8",
"metadata": {
"scrolled": true
},
@@ -406,36 +3006,3797 @@
{
"data": {
"text/plain": [
- "0"
+ "0 1755\n",
+ "1 616\n",
+ "2 393\n",
+ "3 198\n",
+ "4 76\n",
+ "5 30\n",
+ "8 19\n",
+ "7 14\n",
+ "9 13\n",
+ "6 11\n",
+ "10 7\n",
+ "OOO 1\n",
+ "2.00001 1\n",
+ "o4 1\n",
+ "-9 1\n",
+ "o0 1\n",
+ "o3 1\n",
+ "o6 1\n",
+ "o 1\n",
+ "OO 1\n",
+ "o7 1\n",
+ "o1 1\n",
+ "o8 1\n",
+ "-1 1\n",
+ "o2 1\n",
+ "-3 1\n",
+ "o5 1\n",
+ "Name: charge_amount, dtype: int64"
]
},
- "execution_count": 15,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df[\"Customer Value\"].replace(np.nan, 0, inplace=True )\n",
- "df['Customer Value'].isnull().sum()"
+ "# Count the unique values in 'charge_amount' column\n",
+ "\n",
+ "col_4_5_12['charge_amount'].value_counts()"
]
},
{
- "cell_type": "markdown",
- "id": "b4565a4b",
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "d3e75abf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Replace the null values with zero\n",
+ "\n",
+ "col_4_5_12['charge_amount'].replace(np.nan, 0, inplace=True )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fdca6352",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a5d447d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Replace incorrect values with correct values\n",
+ "\n",
+ "col_4_5_12['charge_amount'].replace(['OOO','OO','0','o','o0'], 0, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o1'], 1, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o7'], 7, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o2'], 2, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o4'], 4, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o8'], 8, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o5'], 5, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o3'], 3, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['o6'], 6, inplace=True )\n",
+ "col_4_5_12['charge_amount'].replace(['2.00001'], 2, inplace=True )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "0a9a5541",
"metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert datatype to intgere and make negatives positive\n",
+ "\n",
+ "col_4_5_12['charge_amount'] = col_4_5_12['charge_amount'].astype(int)\n",
+ "col_4_5_12['charge_amount'] = col_4_5_12['charge_amount'].abs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "44aa19bf",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1761\n",
+ "1 618\n",
+ "2 395\n",
+ "3 200\n",
+ "4 77\n",
+ "5 31\n",
+ "8 20\n",
+ "7 15\n",
+ "9 14\n",
+ "6 12\n",
+ "10 7\n",
+ "Name: charge_amount, dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "** Correlation with 'Customer value' and 'Churn'**"
+ "# Count the unique values in 'charge_amount' column\n",
+ "\n",
+ "col_4_5_12['charge_amount'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "fd363f9b",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ "5897 1\n",
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+ "7530 1\n",
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+ "6490 1\n",
+ "770 1\n",
+ "1958 1\n",
+ "15190 1\n",
+ "960 1\n",
+ "2615 1\n",
+ "6383 1\n",
+ "4913 1\n",
+ "15690 1\n",
+ "325 1\n",
+ "9500 1\n",
+ "3220 1\n",
+ "403 1\n",
+ "2260 1\n",
+ "9410 1\n",
+ "15970 1\n",
+ "15730 1\n",
+ "10900 1\n",
+ "6088 1\n",
+ "2738 1\n",
+ "3813 1\n",
+ "2530 1\n",
+ "2683 1\n",
+ "6680 1\n",
+ "5420 1\n",
+ "7170 1\n",
+ "3458 1\n",
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+ "1700 1\n",
+ "5813 1\n",
+ "1235 1\n",
+ "Name: seconds_of_use, dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Count the unique values in 'seconds_of_use' column\n",
+ "\n",
+ "\n",
+ "col_4_5_12['seconds_of_use'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "b60129d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Replace the null values with zeros\n",
+ "\n",
+ "col_4_5_12['seconds_of_use'].replace(np.nan, 0, inplace=True )"
]
},
{
"cell_type": "code",
"execution_count": 16,
- "id": "d33c6064",
+ "id": "63ade2d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert datatype to intgere and make negatives positive\n",
+ "\n",
+ "col_4_5_12['seconds_of_use'].replace(['O3915'], 3915, inplace=True )\n",
+ "col_4_5_12['seconds_of_use'] = col_4_5_12['seconds_of_use'].astype(int)\n",
+ "col_4_5_12['seconds_of_use'] = col_4_5_12['seconds_of_use'].abs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "559c56e0",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ "3633 1\n",
+ "7753 1\n",
+ "1608 1\n",
+ "1602 1\n",
+ "1800 1\n",
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+ "14645 1\n",
+ "3745 1\n",
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+ "15905 1\n",
+ "9427 1\n",
+ "5658 1\n",
+ "1560 1\n",
+ "15895 1\n",
+ "1558 1\n",
+ "3595 1\n",
+ "3867 1\n",
+ "3673 1\n",
+ "1628 1\n",
+ "1630 1\n",
+ "5790 1\n",
+ "6383 1\n",
+ "1680 1\n",
+ "16015 1\n",
+ "9870 1\n",
+ "5770 1\n",
+ "3715 1\n",
+ "3713 1\n",
+ "5758 1\n",
+ "14845 1\n",
+ "15995 1\n",
+ "3703 1\n",
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+ "9838 1\n",
+ "11885 1\n",
+ "1638 1\n",
+ "3685 1\n",
+ "6143 1\n",
+ "Name: seconds_of_use, dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Count the unique values in 'seconds_of_use' column\n",
+ "\n",
+ "col_4_5_12['seconds_of_use'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "2f47d873",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "charge_amount 0\n",
+ "customer_value 0\n",
+ "seconds_of_use 0\n",
+ "Churn 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Count the total number fo null values \n",
+ "\n",
+ "col_4_5_12.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "b45504db",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 3150 entries, 0 to 3149\n",
+ "Data columns (total 4 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 charge_amount 3150 non-null int64 \n",
+ " 1 customer_value 3150 non-null float64\n",
+ " 2 seconds_of_use 3150 non-null int64 \n",
+ " 3 Churn 3150 non-null int64 \n",
+ "dtypes: float64(1), int64(3)\n",
+ "memory usage: 98.6 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get the summary of dataframe\n",
+ "\n",
+ "col_4_5_12.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "44c7b7af",
+ "metadata": {
+ "scrolled": false
+ },
"outputs": [
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
""
]
@@ -447,22 +6808,30 @@
}
],
"source": [
- "df = pd.DataFrame(df, columns=['Customer Value','Churn'])\n",
- "corrMatrix = df.corr()\n",
+ "##\n",
+ "\n",
+ "col_4_5_12= pd.DataFrame(col_4_5_12, columns=['customer_value','seconds_of_use','charge_amount','Churn'])\n",
+ "corrMatrix = col_4_5_12.corr()\n",
"sn.heatmap(corrMatrix, annot=True)\n",
"plt.show()\n",
- " "
+ " \n"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "47b0f4ef",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": []
+ "cell_type": "markdown",
+ "id": "dfdbb9b7",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "675934b6",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
}
],
"metadata": {