diff --git a/01_materials/notebooks/Classification-1.ipynb b/01_materials/notebooks/Classification-1.ipynb
index 7b6959a7a..1e23cba9a 100644
--- a/01_materials/notebooks/Classification-1.ipynb
+++ b/01_materials/notebooks/Classification-1.ipynb
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/01_materials/notebooks/Classification-2.ipynb b/01_materials/notebooks/Classification-2.ipynb
index 96db650b8..8d637c26a 100644
--- a/01_materials/notebooks/Classification-2.ipynb
+++ b/01_materials/notebooks/Classification-2.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +424,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +453,7 @@
        "array(['Malignant', 'Benign'], dtype=object)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -864,7 +864,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -956,9 +956,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# set the seed\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mnp\u001b[49m.random.seed(\u001b[32m1\u001b[39m)\n\u001b[32m      4\u001b[39m \u001b[38;5;66;03m#split the data\u001b[39;00m\n\u001b[32m      5\u001b[39m cancer_train, cancer_test = train_test_split(\n\u001b[32m      6\u001b[39m     standardized_cancer, train_size=\u001b[32m0.75\u001b[39m, stratify=standardized_cancer[\u001b[33m\"\u001b[39m\u001b[33mdiagnosis\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m      7\u001b[39m )\n",
+      "\u001b[31mNameError\u001b[39m: name 'np' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# set the seed\n",
     "np.random.seed(1)\n",
@@ -978,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -986,7 +998,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 426 entries, 164 to 284\n",
+      "Index: 426 entries, 164 to 284\n",
       "Data columns (total 32 columns):\n",
       " #   Column                   Non-Null Count  Dtype  \n",
       "---  ------                   --------------  -----  \n",
@@ -1033,18 +1045,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "diagnosis\n",
        "Benign       0.626761\n",
        "Malignant    0.373239\n",
-       "Name: diagnosis, dtype: float64"
+       "Name: proportion, dtype: float64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1070,19 +1083,628 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1094,19 +1716,628 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +2355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1242,7 +2473,7 @@
        "[143 rows x 3 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,7 +2508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +2517,7 @@
        "0.9230769230769231"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2789,7 +4020,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -2803,7 +4034,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/01_materials/notebooks/Regression-1.ipynb b/01_materials/notebooks/Regression-1.ipynb
index c57a7d219..f2865d6a8 100644
--- a/01_materials/notebooks/Regression-1.ipynb
+++ b/01_materials/notebooks/Regression-1.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -379,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -938,7 +938,7 @@
        "661  Residential  Tue May 20 00:00:00 EDT 2008  385000  38.405527 -121.431746  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1007,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1154,7 @@
        "786   310  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1172,22 +1172,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1234b4b20>"
+       "<matplotlib.legend.Legend at 0x1b12f3242d0>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1264,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1275,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1284,7 +1284,7 @@
        "(609, 12)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1295,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1304,7 +1304,7 @@
        "(204, 12)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1392,18 +1392,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   n_neighbors  mean_test_score\n",
-      "0            1   -115096.457636\n",
-      "1            4    -93675.812662\n",
-      "2            7    -90334.266322\n",
-      "3           10    -88585.351601\n",
-      "4           13    -87276.736534\n",
-      "..         ...              ...\n",
-      "62         187    -96196.284147\n",
-      "63         190    -96501.008677\n",
-      "64         193    -96773.410839\n",
-      "65         196    -97024.993747\n",
-      "66         199    -97253.073041\n",
+      "    n_neighbors  mean_test_score\n",
+      "0             1   -115928.072257\n",
+      "1             4    -93824.786984\n",
+      "2             7    -90552.298398\n",
+      "3            10    -88405.903549\n",
+      "4            13    -87199.791789\n",
+      "..          ...              ...\n",
+      "62          187    -96198.137807\n",
+      "63          190    -96494.258024\n",
+      "64          193    -96770.368691\n",
+      "65          196    -97029.827067\n",
+      "66          199    -97251.873323\n",
       "\n",
       "[67 rows x 2 columns]\n"
      ]
@@ -1510,27 +1510,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>-115096.457636</td>\n",
+       "      <td>-115928.072257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>4</td>\n",
-       "      <td>-93675.812662</td>\n",
+       "      <td>-93824.786984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>7</td>\n",
-       "      <td>-90334.266322</td>\n",
+       "      <td>-90552.298398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10</td>\n",
-       "      <td>-88585.351601</td>\n",
+       "      <td>-88405.903549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>13</td>\n",
-       "      <td>-87276.736534</td>\n",
+       "      <td>-87199.791789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1540,27 +1540,27 @@
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td>187</td>\n",
-       "      <td>-96196.284147</td>\n",
+       "      <td>-96198.137807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
        "      <td>190</td>\n",
-       "      <td>-96501.008677</td>\n",
+       "      <td>-96494.258024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64</th>\n",
        "      <td>193</td>\n",
-       "      <td>-96773.410839</td>\n",
+       "      <td>-96770.368691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65</th>\n",
        "      <td>196</td>\n",
-       "      <td>-97024.993747</td>\n",
+       "      <td>-97029.827067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66</th>\n",
        "      <td>199</td>\n",
-       "      <td>-97253.073041</td>\n",
+       "      <td>-97251.873323</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1568,18 +1568,18 @@
        "</div>"
       ],
       "text/plain": [
-       "   n_neighbors  mean_test_score\n",
-       "0            1   -115096.457636\n",
-       "1            4    -93675.812662\n",
-       "2            7    -90334.266322\n",
-       "3           10    -88585.351601\n",
-       "4           13    -87276.736534\n",
-       "..         ...              ...\n",
-       "62         187    -96196.284147\n",
-       "63         190    -96501.008677\n",
-       "64         193    -96773.410839\n",
-       "65         196    -97024.993747\n",
-       "66         199    -97253.073041\n",
+       "    n_neighbors  mean_test_score\n",
+       "0             1   -115928.072257\n",
+       "1             4    -93824.786984\n",
+       "2             7    -90552.298398\n",
+       "3            10    -88405.903549\n",
+       "4            13    -87199.791789\n",
+       "..          ...              ...\n",
+       "62          187    -96198.137807\n",
+       "63          190    -96494.258024\n",
+       "64          193    -96770.368691\n",
+       "65          196    -97029.827067\n",
+       "66          199    -97251.873323\n",
        "\n",
        "[67 rows x 2 columns]"
       ]
@@ -1636,27 +1636,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>115096.457636</td>\n",
+       "      <td>115928.072257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>4</td>\n",
-       "      <td>93675.812662</td>\n",
+       "      <td>93824.786984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>7</td>\n",
-       "      <td>90334.266322</td>\n",
+       "      <td>90552.298398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10</td>\n",
-       "      <td>88585.351601</td>\n",
+       "      <td>88405.903549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>13</td>\n",
-       "      <td>87276.736534</td>\n",
+       "      <td>87199.791789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1666,27 +1666,27 @@
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td>187</td>\n",
-       "      <td>96196.284147</td>\n",
+       "      <td>96198.137807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
        "      <td>190</td>\n",
-       "      <td>96501.008677</td>\n",
+       "      <td>96494.258024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64</th>\n",
        "      <td>193</td>\n",
-       "      <td>96773.410839</td>\n",
+       "      <td>96770.368691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65</th>\n",
        "      <td>196</td>\n",
-       "      <td>97024.993747</td>\n",
+       "      <td>97029.827067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66</th>\n",
        "      <td>199</td>\n",
-       "      <td>97253.073041</td>\n",
+       "      <td>97251.873323</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1694,18 +1694,18 @@
        "</div>"
       ],
       "text/plain": [
-       "   n_neighbors  mean_test_score\n",
-       "0            1    115096.457636\n",
-       "1            4     93675.812662\n",
-       "2            7     90334.266322\n",
-       "3           10     88585.351601\n",
-       "4           13     87276.736534\n",
-       "..         ...              ...\n",
-       "62         187     96196.284147\n",
-       "63         190     96501.008677\n",
-       "64         193     96773.410839\n",
-       "65         196     97024.993747\n",
-       "66         199     97253.073041\n",
+       "    n_neighbors  mean_test_score\n",
+       "0             1    115928.072257\n",
+       "1             4     93824.786984\n",
+       "2             7     90552.298398\n",
+       "3            10     88405.903549\n",
+       "4            13     87199.791789\n",
+       "..          ...              ...\n",
+       "62          187     96198.137807\n",
+       "63          190     96494.258024\n",
+       "64          193     96770.368691\n",
+       "65          196     97029.827067\n",
+       "66          199     97251.873323\n",
        "\n",
        "[67 rows x 2 columns]"
       ]
@@ -1769,15 +1769,15 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>19</td>\n",
-       "      <td>86621.515595</td>\n",
+       "      <td>86699.369388</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  n_neighbors  mean_test_score\n",
-       "6          19     86621.515595"
+       "   n_neighbors  mean_test_score\n",
+       "6           19     86699.369388"
       ]
      },
      "execution_count": 17,
@@ -1856,16 +1856,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "74240.70943389146"
+       "74244.70520282978"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,16 +1903,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.49526130381694955"
+       "0.49520697046011675"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1944,20 +1944,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/juliagallucci/miniconda3/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+      "c:\\Users\\CECi\\Desktop\\DSI\\LCR\\lcr-env\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2014,7 +2014,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -2028,7 +2028,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index a05da5cd3..79ca11a9c 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -50,10 +50,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "\n",
@@ -82,12 +200,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5d79f1cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Display the number of rows and columns contained in the dataset\n",
+    "mpg_data.shape"
    ]
   },
   {
@@ -100,12 +230,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ac306190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "mpg_data.dtypes[\"mpg\"]"
    ]
   },
   {
@@ -116,6 +257,14 @@
     "Your explanation... \n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "417a244b",
+   "metadata": {},
+   "source": [
+    "The response variable \"mpg\" is a float variable. A float type variable can store positive or negative non-whole numbers, containing one or more decimals. See: https://www.w3schools.com/python/python_numbers.asp"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "be0119ad",
@@ -126,12 +275,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9f034a5d",
+   "execution_count": 5,
+   "id": "555e9ff7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+      "116  16.0          8         400.0       230.0    4278           9.5   \n",
+      "8    14.0          8         455.0       225.0    4425          10.0   \n",
+      "13   14.0          8         455.0       225.0    3086          10.0   \n",
+      "95   12.0          8         455.0       225.0    4951          11.0   \n",
+      "6    14.0          8         454.0       220.0    4354           9.0   \n",
+      "\n",
+      "     model_year origin                      name  \n",
+      "116          73    usa        pontiac grand prix  \n",
+      "8            70    usa          pontiac catalina  \n",
+      "13           70    usa   buick estate wagon (sw)  \n",
+      "95           73    usa  buick electra 225 custom  \n",
+      "6            70    usa          chevrolet impala  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here... "
+    "print(mpg_data.nlargest(5, \"horsepower\"))  #https://www.w3schools.com/python/pandas/trypandas.asp?filename=demo_ref_df_nlargest"
    ]
   },
   {
@@ -144,12 +313,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "1b91233e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "print(mpg_data.shape[1] - 1)"
    ]
   },
   {
@@ -173,10 +350,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "732784d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
     "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
@@ -225,7 +463,7 @@
    "id": "f67e57ab",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> The fitted associations between the target variable (miles per gallon) and the features variables are negative for number of cylinders, displacement, horsepower, and vehicle weight. However, the association is positive for the accerleration and model year. "
    ]
   },
   {
@@ -241,7 +479,7 @@
    "id": "843f9eef",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> How much the target (response) variable (y) changes for a one-unit change in the feature (predictor) variable (X), based on the linear function of y and X, where the marginal effect of X on y is a constant slope. "
    ]
   },
   {
@@ -257,7 +495,7 @@
    "id": "2ea782fc",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> The data points deviate from the plotted line because while each data point is an actual paired observation of a predictor variable and its corresponding response variable measure, the plotted line (i.e., straight line of best fit) is calculated to minimize the sum of the squared distances between each observed value of y and its predicted value (for a given value of x). The deviations of the data points from their predicted values (i.e., the residuals) indicate the degree of predictive accuracy of the plotted linear regression line. "
    ]
   },
   {
@@ -275,11 +513,43 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "c5977b2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mpg                                  18.0\n",
+      "cylinders                               8\n",
+      "displacement                        307.0\n",
+      "horsepower                          130.0\n",
+      "weight                               3504\n",
+      "acceleration                         12.0\n",
+      "model_year                             70\n",
+      "origin                                usa\n",
+      "name            chevrolet chevelle malibu\n",
+      "Name: 0, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# List the target and feature variables for easy reference\n",
+    "print(mpg_data.loc[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "399225f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here..."
+    "#Split the mpg dataset into 75% training data and 25% test data, using a random state = 42 for reproducibility each time the cell block is run\n",
+    "mpg_train, mpg_test = train_test_split(\n",
+    "    mpg_data, train_size=0.75, random_state=42\n",
+    ")\n",
+    "\n"
    ]
   },
   {
@@ -290,23 +560,643 @@
     "**Step 2: Fit the linear regression model.**"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5bb83131",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  Initialize the Multivariable Linear Regression model - also see https://realpython.com/linear-regression-in-python/#multiple-linear-regression for some output explanation\n",
+    "mlm = LinearRegression()\n",
+    "\n",
+    "# Fit the multiple linear regression model training data \n",
+    "mlm.fit(\n",
+    "    mpg_train[[\"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\"]],  # Six predictors: exclude the non-numeric feature variables\n",
+    "    mpg_train[\"mpg\"]  # Target variable: miles per gallon\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "ac1e1117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      predictor     slope  intercept\n",
+      "0     cylinders -0.160143 -15.047371\n",
+      "1  displacement  0.000373 -15.047371\n",
+      "2    horsepower -0.001899 -15.047371\n",
+      "3        weight -0.006457 -15.047371\n",
+      "4  acceleration  0.057588 -15.047371\n",
+      "5    model_year  0.762270 -15.047371\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here ...\n",
-    "\n",
-    "numeric_predictors = 🤷‍♂️\n",
-    "\n",
-    "\n",
-    "# Create a DataFrame containing the slope (coefficients) and intercept\n",
+    "# Create a DataFrame containing the slope (coefficients) and intercept. List the numeric variables in the data library.\n",
+    "# Note: See the Regression-2 notebook - use mlm (i.e., multivariable) coefficients and intercept, not lm (single variable) and intercept parameters \n",
     "coefficients_df = pd.DataFrame({\n",
-    "    \"predictor\": numeric_predictors.columns,\n",
-    "    \"slope\": lm.coef_,\n",
-    "    \"intercept\": [lm.intercept_] * len(lm.coef_)\n",
+    "    \"predictor\": [\"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\"],\n",
+    "    \"slope\": mlm.coef_,\n",
+    "    \"intercept\": [mlm.intercept_] * len(mlm.coef_)\n",
     "})\n",
     "\n",
     "# Display the coefficients DataFrame\n",
@@ -330,12 +1220,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ffefa9f2",
+   "execution_count": 8,
+   "id": "910b0e25",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here ..."
+    "# Generate a numeric predictors variable for the feature variables used in the fitted multiple regression model\n",
+    "mpg_test[\"numeric_predictors\"] = mlm.predict(mpg_test[[\"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\"]]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1854749513391605"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate RMSPE for the multivariable model.\n",
+    "lm_mult_test_RMSPE = mean_squared_error(\n",
+    "    y_true= mpg_test[\"mpg\"],\n",
+    "    y_pred= mpg_test[\"numeric_predictors\"]\n",
+    ")**(1/2)\n",
+    "\n",
+    "lm_mult_test_RMSPE\n"
    ]
   },
   {
@@ -386,7 +1304,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -400,12 +1318,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/04_this_cohort/live_code/live-code-11-13-2025.ipynb b/04_this_cohort/live_code/live-code-11-13-2025.ipynb
index 2a137ada8..1351ab8c4 100644
--- a/04_this_cohort/live_code/live-code-11-13-2025.ipynb
+++ b/04_this_cohort/live_code/live-code-11-13-2025.ipynb
@@ -2,17 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib is building the font cache; this may take a moment.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#import our libraries\n",
     "import pandas as pd\n",
@@ -409,20 +401,21 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#load in data set\n",
-    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "# cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer = pd.read_csv('../../01_materials/notebooks/dataset/wdbc.csv')\n",
     "cancer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
diff --git a/04_this_cohort/live_code/live-code-11-18-2025.ipynb b/04_this_cohort/live_code/live-code-11-18-2025.ipynb
index 28ac5469a..0c5c6b7b2 100644
--- a/04_this_cohort/live_code/live-code-11-18-2025.ipynb
+++ b/04_this_cohort/live_code/live-code-11-18-2025.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -406,20 +406,21 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#load in our dataset\n",
-    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "# cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer = pd.read_csv('../../01_materials/notebooks/dataset/wdbc.csv')\n",
     "cancer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +806,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -820,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1253,7 +1254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1266,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1321,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1376,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1386,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1397,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2030,7 +2031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2041,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2438,7 +2439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2461,7 +2462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2532,7 +2533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2558,7 +2559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2584,7 +2585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2594,7 +2595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3255,7 +3256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3358,7 +3359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3422,7 +3423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3434,7 +3435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3448,7 +3449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4877,7 +4878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5265,7 +5266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5296,7 +5297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {