diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index 6316b23b..f7cd90de 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "6ffd05a8",
    "metadata": {},
    "outputs": [
@@ -71,7 +71,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -92,7 +92,8 @@
     "plt.xlabel('Scaled BMI')\n",
     "plt.ylabel('Diabetes Progression')\n",
     "plt.title('A graph plot showng  Diabetes Progression  Against Scaled BMI')  \n",
-    "plt.show()"
+    "plt.show()\n",
+    "#end of regression example"
    ]
   },
   {
diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index aa1dc7b0..38e53549 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -17,6 +17,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+
+   "execution_count": 3,
+
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +217,11 @@
        "[5 rows x 26 columns]"
       ]
      },
+
      "execution_count": 2,
+
+     "execution_count": 3,
+
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -227,19 +234,28 @@
     "\n",
     "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
     "\n",
-    "pumpkins.head()\n"
+    "pumpkins.head()\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
+
    "execution_count": 3,
+
+   "execution_count": 4,
+
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+
       "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+
       "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
      ]
     },
@@ -350,7 +366,11 @@
        "74        15.0  13.636364  "
       ]
      },
+
      "execution_count": 3,
+
+     "execution_count": 4,
+
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -391,16 +411,27 @@
   },
   {
    "cell_type": "code",
+
    "execution_count": 4,
+
+   "execution_count": 6,
+
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+
        "<matplotlib.collections.PathCollection at 0x1ae64410440>"
       ]
      },
      "execution_count": 4,
+
+       "<matplotlib.collections.PathCollection at 0x25f051f1e50>"
+      ]
+     },
+     "execution_count": 6,
+
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -422,16 +453,27 @@
   },
   {
    "cell_type": "code",
+
    "execution_count": 5,
+
+   "execution_count": 8,
+
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+
        "<matplotlib.collections.PathCollection at 0x1ae644ea490>"
       ]
      },
      "execution_count": 5,
+
+       "<matplotlib.collections.PathCollection at 0x25f0528b610>"
+      ]
+     },
+     "execution_count": 8,
+
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -453,6 +495,7 @@
   },
   {
    "cell_type": "code",
+
    "execution_count": 6,
    "metadata": {},
    "outputs": [
@@ -473,6 +516,16 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -481,16 +534,33 @@
       "-0.14878293554077535\n",
       "-0.16673322492745407\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\2521659294.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
+
+     ]
     }
    ],
    "source": [
+
+
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+
     "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
     "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
    ]
   },
   {
    "cell_type": "code",
+
    "execution_count": 14,
+
+   "execution_count": 11,
+
    "metadata": {},
    "outputs": [
     {
@@ -509,12 +579,20 @@
     "colors = ['red', 'blue', 'green', 'yellow']\n",
     "for i, var in enumerate(new_pumpkins['Variety'].unique()):\n",
     "    df = new_pumpkins[new_pumpkins['Variety'] == var]\n",
+
     "    ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, c=colors[i], label=var)\n"
+
+    "    ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, color=colors[i], label=var)  "
+
    ]
   },
   {
    "cell_type": "code",
+
    "execution_count": 15,
+
+   "execution_count": 14,
+
    "metadata": {},
    "outputs": [
     {
@@ -523,7 +601,11 @@
        "<Axes: xlabel='Variety'>"
       ]
      },
+
      "execution_count": 15,
+
+     "execution_count": 14,
+
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -544,7 +626,11 @@
   },
   {
    "cell_type": "code",
+
    "execution_count": 18,
+
+   "execution_count": 21,
+
    "metadata": {},
    "outputs": [
     {
@@ -553,13 +639,19 @@
        "<Axes: xlabel='DayOfYear', ylabel='Price'>"
       ]
      },
-     "execution_count": 18,
+
+     "execution_count": 21,
+
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+
       "image/png": 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",
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+      "image/png": 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",
+
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -570,6 +662,7 @@
    ],
    "source": [
     "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
+
     "pie_pumpkins.plot.scatter('DayOfYear', 'Price', c='red', label='PIE TYPE')"
    ]
   },
@@ -591,11 +684,18 @@
     "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
     "correlation = pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price'])\n",
     "print('Correlation between Day of Year and Price for PIE TYPE:', correlation)"
+
+    "pie_pumpkins.plot.scatter('DayOfYear', 'Price', color='orange', label='PIE TYPE')\n"
+
    ]
   },
   {
    "cell_type": "code",
+
    "execution_count": 20,
+
+   "execution_count": 22,
+
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +723,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+
       "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\3144308612.py:1: SettingWithCopyWarning: \n",
+
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\3144308612.py:1: SettingWithCopyWarning: \n",
+
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -638,6 +742,7 @@
   },
   {
    "cell_type": "code",
+
    "execution_count": 23,
    "metadata": {},
    "outputs": [
@@ -736,10 +841,14 @@
   {
    "cell_type": "code",
    "execution_count": 32,
+
+   "execution_count": null,
+
    "metadata": {},
    "outputs": [
     {
      "data": {
+
       "text/plain": [
        "<matplotlib.collections.PathCollection at 0x1ae73a71a90>"
       ]
@@ -792,6 +901,445 @@
     "pipeline.fit(X_train,y_train)\n",
     "print(\"Score on test set:\", pipeline.score(X_test, y_test))\n",
     "pred = pipeline.predict(X_test)"
+
+      "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",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\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",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\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",
+       "</style><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/1.6/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\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1, 1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train, y_train)\n",
+    "#Remaining\n"
+
    ]
   }
  ],