Regularized-Linear-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularized Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# surpress warnings for clean notebook\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Boston Housing Dataset first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the the Boston housing dataset\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "y = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features (X)\n",
    "CRIM: Per capita crime rate by town  \n",
    "ZN: Proportion of residential land zoned for lots over 25,000 sq.ft.  \n",
    "INDUS: Proportion of non-retail business acres per town  \n",
    "CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)  \n",
    "NOX: Nitric oxides concentration (parts per 10 million)  \n",
    "RM: Average number of rooms per dwelling  \n",
    "AGE: Proportion of owner-occupied units built prior to 1940  \n",
    "DIS: Weighted distances to five Boston employment centers  \n",
    "RAD: Index of accessibility to radial highways  \n",
    "TAX: Full-value property-tax rate per \\$10,000  \n",
    "PTRATIO: Pupil-teacher ratio by town  \n",
    "B: $1000(Bk-0.63)^2$ where Bk is the proportion of blacks by town  \n",
    "LSTAT: Percentage of the lower status of the population  \n",
    "\n",
    "#### Target (y)\n",
    "MEDV: Median value of owner-occupied homes in $1000's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
       "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
       "\n",
       "       11    12  \n",
       "0  396.90  4.98  \n",
       "1  396.90  9.14  \n",
       "2  392.83  4.03  \n",
       "3  394.63  2.94  \n",
       "4  396.90  5.33  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame(X).head(5))\n",
    "display(y[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display correlation matrix\n",
    "features = [\"CRIM\", \"ZN\", \"INDUS\", \"CHAS\", \"NOX\", \"RM\", \"AGE\", \"DIS\", \"RAD\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\"]\n",
    "df = pd.DataFrame(X, columns=features)\n",
    "df[\"MEDV\"] = y\n",
    "\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "plt.figure(figsize=(11, 11))\n",
    "plt.imshow(correlation_matrix, cmap=\"coolwarm\")\n",
    "for i in range(len(correlation_matrix)):\n",
    "    for j in range(len(correlation_matrix)):\n",
    "        plt.text(j, i, \"{:.2f}\".format(correlation_matrix.iloc[i, j]), ha='center', va='center', color='black')\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(correlation_matrix)), correlation_matrix.columns, rotation=90) \n",
    "plt.yticks(range(len(correlation_matrix)), correlation_matrix.columns) \n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train-test data split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression (L1)\n",
    "$\n",
    "J = MSE + \\text{Regularization Term} \\\\\n",
    "J = \\frac{1}{2N}\\sum_{i=1}^{N}\\left( y_i - y_{i}^{pred}\\right)^2 + \\alpha\\sum_{j=1}^{p}\\left| \\beta_j \\right|\n",
    "$   \n",
    "where,\n",
    "- $J$: cost function\n",
    "- $N$: number of samples\n",
    "- $y_i$: actual target values\n",
    "- $y_i^{pred}$: Predicted target value\n",
    "- $\\beta_j$: coeficients in the linear equation\n",
    "- $\\alpha$: regularization prameter\n",
    "- $p$: number of features ($X$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the lasss regression model\n",
    "lasso_reg = Lasso(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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: 1ex;\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>Lasso(alpha=0.1)</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 fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model for the training dataset\n",
    "lasso_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.10415691  0.03489335 -0.01678527  0.91995182 -0.          4.31168655\n",
      " -0.01512583 -1.15148729  0.23923695 -0.01296223 -0.73224678  0.01309057\n",
      " -0.56467442]\n"
     ]
    }
   ],
   "source": [
    "# get the coefficients trained linear reggression model\n",
    "print(lasso_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the prediction for the testing dataset from the trained model.\n",
    "y_pred = lasso_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the prediction against the target values of testing data set\n",
    "plt.figure()\n",
    "plt.scatter(y_test, y_pred, color=\"blue\", s=12)\n",
    "plt.plot(y_test, y_test, color=\"red\", linewidth=2)\n",
    "plt.title(\"$y_{pred}$ vs $y_{test}$\")\n",
    "plt.xlabel(\"$y_{test}$\")\n",
    "plt.ylabel(\"$y_{pred}$\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE = 25.15559375393417\n",
      "R-squared = 0.6569712802223937\n"
     ]
    }
   ],
   "source": [
    "# evaluate the accuracy of the predictions\n",
    "mae = mean_squared_error(y_test, y_pred)\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do hyperparameter tunning for the lasso regression, with differet $\\alpha$ values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the hyperparameter which need to be tunned\n",
    "alpha_arr = np.arange(0, 5.1, 0.1)\n",
    "params = {\n",
    "    'alpha': alpha_arr\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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: 1ex;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ])})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Lasso</label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={'alpha': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ])})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X.shape)\n",
    "lasso_grid_search = GridSearchCV(Lasso(), params, cv=5)\n",
    "lasso_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(alpha_arr, lasso_grid_search.cv_results_[\"mean_test_score\"], marker=\"o\", markersize=4)\n",
    "plt.xlabel(r\"$\\alpha$\")\n",
    "plt.ylabel(\"Test score\")\n",
    "plt.title(r\"Test score vs $\\alpha$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parmeters: {'alpha': 0.0}\n",
      "MSE = 24.291119474973527\n",
      "R-squared = 0.6687594935356318\n"
     ]
    }
   ],
   "source": [
    "best_params = lasso_grid_search.best_params_\n",
    "best_estimator = lasso_grid_search.best_estimator_\n",
    "\n",
    "# print the best hyperparameters\n",
    "print(\"Best parmeters:\", best_params)\n",
    "\n",
    "# evaluate the accuracy of the predictions with best parameters\n",
    "mae = mean_squared_error(y_test, best_estimator.predict(X_test))\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, best_estimator.predict(X_test))\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression (L2)\n",
    "$\n",
    "J = MSE + \\text{Regularization Term} \\\\\n",
    "J = \\frac{1}{2N}\\sum_{i=1}^{N}\\left( y_i - y_{i}^{pred}\\right)^2 + \\alpha\\sum_{j=1}^{p}\\beta_j^2\n",
    "$   \n",
    "where,\n",
    "- $J$: cost function\n",
    "- $N$: number of samples\n",
    "- $y_i$: actual target values\n",
    "- $y_i^{pred}$: Predicted target value\n",
    "- $\\beta_j$: coeficients in the linear equation\n",
    "- $\\alpha$: regularization prameter\n",
    "- $p$: number of features ($X$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the ridge regression model\n",
    "ridge_reg = Ridge(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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: 1ex;\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>Ridge(alpha=0.1)</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 fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model for the training dataset\n",
    "ridge_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.12399694e-01  3.04593914e-02  3.48958400e-02  2.75033318e+00\n",
      " -1.59244585e+01  4.44577949e+00 -7.30474388e-03 -1.42960751e+00\n",
      "  2.60042840e-01 -1.07802286e-02 -9.00771040e-01  1.24004789e-02\n",
      " -5.10902332e-01]\n"
     ]
    }
   ],
   "source": [
    "# get the coefficients trained linear reggression model\n",
    "print(ridge_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the prediction for the testing dataset from the trained model.\n",
    "y_pred = ridge_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the prediction against the target values of testing data set\n",
    "plt.figure()\n",
    "plt.scatter(y_test, y_pred, color=\"blue\", s=12)\n",
    "plt.plot(y_test, y_test, color=\"red\", linewidth=2)\n",
    "plt.title(\"$y_{pred}$ vs $y_{test}$\")\n",
    "plt.xlabel(\"$y_{test}$\")\n",
    "plt.ylabel(\"$y_{pred}$\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE = 24.30102550019274\n",
      "R-squared = 0.6686244122021412\n"
     ]
    }
   ],
   "source": [
    "# evaluate the accuracy of the predictions\n",
    "mae = mean_squared_error(y_test, y_pred)\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do hyperparameter tunning for the ridge regression, with differet $\\alpha$ values and solvers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the hyperparameter which need to be tunned\n",
    "alpha_arr = np.arange(0, 5.1, 0.1)\n",
    "params = {\n",
    "    'alpha': alpha_arr,\n",
    "    'solver': ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'saga']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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",
       "  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-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",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\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: 1ex;\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",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n",
       "       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. ]),\n",
       "                         &#x27;solver&#x27;: [&#x27;svd&#x27;, &#x27;cholesky&#x27;, &#x27;lsqr&#x27;, &#x27;sparse_cg&#x27;,\n",
       "                                    &#x27;saga&#x27;]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n",
       "       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. ]),\n",
       "                         &#x27;solver&#x27;: [&#x27;svd&#x27;, &#x27;cholesky&#x27;, &#x27;lsqr&#x27;, &#x27;sparse_cg&#x27;,\n",
       "                                    &#x27;saga&#x27;]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Ridge</label><div class=\"sk-toggleable__content fitted\"><pre>Ridge()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n",
       "       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. ]),\n",
       "                         'solver': ['svd', 'cholesky', 'lsqr', 'sparse_cg',\n",
       "                                    'saga']})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X.shape)\n",
    "ridge_grid_search = GridSearchCV(Ridge(), params, cv=5)\n",
    "ridge_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parmeters: {'alpha': 0.0, 'solver': 'svd'}\n",
      "MSE = 24.291119474973424\n",
      "R-squared = 0.6687594935356334\n"
     ]
    }
   ],
   "source": [
    "best_params = ridge_grid_search.best_params_\n",
    "best_estimator = ridge_grid_search.best_estimator_\n",
    "\n",
    "# print the best hyperparameters\n",
    "print(\"Best parmeters:\", best_params)\n",
    "\n",
    "# evaluate the accuracy of the predictions with best parameters\n",
    "mae = mean_squared_error(y_test, best_estimator.predict(X_test))\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, best_estimator.predict(X_test))\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Regression\n",
    "$\n",
    "J = MSE + \\text{Regularization Term} \\\\\n",
    "J = \\frac{1}{2N}\\sum_{i=1}^{N}\\left( y_i - y_{i}^{pred}\\right)^2 + \\alpha \\left[ \\gamma\\sum_{j=1}^{p}\\left| \\beta_j \\right| + \\frac{1}{2}(1-\\gamma)\\sum_{j=1}^{p}\\beta_j^2 \\right]\n",
    "$   \n",
    "where,\n",
    "- $J$: cost function\n",
    "- $N$: number of samples\n",
    "- $y_i$: actual target values\n",
    "- $y_i^{pred}$: Predicted target value\n",
    "- $\\beta_j$: coeficients in the linear equation\n",
    "- $\\alpha$: regularization prameter\n",
    "- $\\gamma$: Elastic net mixing parameter\n",
    "- $p$: number of features ($X$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the elastic regression model\n",
    "elastic_reg = ElasticNet(alpha=0.2, l1_ratio=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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: 1ex;\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>ElasticNet(alpha=0.2, l1_ratio=0.2)</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 fitted\">&nbsp;&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.2, l1_ratio=0.2)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ElasticNet(alpha=0.2, l1_ratio=0.2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model for the training dataset\n",
    "elastic_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.10916373  0.03915104 -0.04184112  0.57181201 -0.06179189  2.84069322\n",
      " -0.00321222 -1.14474427  0.28604475 -0.01430584 -0.80088631  0.01221336\n",
      " -0.66358643]\n"
     ]
    }
   ],
   "source": [
    "# get the coefficients trained linear reggression model\n",
    "print(elastic_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the prediction for the testing dataset from the trained model.\n",
    "y_pred = elastic_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the prediction against the target values of testing data set\n",
    "plt.figure()\n",
    "plt.scatter(y_test, y_pred, color=\"blue\", s=12)\n",
    "plt.plot(y_test, y_test, color=\"red\", linewidth=2)\n",
    "plt.title(\"$y_{pred}$ vs $y_{test}$\")\n",
    "plt.xlabel(\"$y_{test}$\")\n",
    "plt.ylabel(\"$y_{pred}$\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE = 23.70067730855687\n",
      "R-squared = 0.6768109282355959\n"
     ]
    }
   ],
   "source": [
    "# evaluate the accuracy of the predictions\n",
    "mae = mean_squared_error(y_test, y_pred)\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do hyperparameter tunning for the elastic regression, with differet $\\alpha$ values and $\\gamma$ values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the hyperparameter which need to be tunned\n",
    "alpha_arr = np.arange(0, 5.1, 0.1)\n",
    "gamma_arr = np.arange(0, 1.1, 0.1)\n",
    "params = {\n",
    "    'alpha': alpha_arr,\n",
    "    'l1_ratio': gamma_arr\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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: 1ex;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]),\n",
       "                         &#x27;l1_ratio&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={&#x27;alpha&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]),\n",
       "                         &#x27;l1_ratio&#x27;: array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: ElasticNet</label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={'alpha': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]),\n",
       "                         'l1_ratio': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X.shape)\n",
    "elastic_grid_search = GridSearchCV(ElasticNet(), params, cv=5)\n",
    "elastic_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parmeters: {'alpha': 0.0, 'l1_ratio': 0.0}\n",
      "MSE = 24.291119474973527\n",
      "R-squared = 0.6687594935356318\n"
     ]
    }
   ],
   "source": [
    "best_params = elastic_grid_search.best_params_\n",
    "best_estimator = elastic_grid_search.best_estimator_\n",
    "\n",
    "# print the best hyperparameters\n",
    "print(\"Best parmeters:\", best_params)\n",
    "\n",
    "# evaluate the accuracy of the predictions with best parameters\n",
    "mae = mean_squared_error(y_test, best_estimator.predict(X_test))\n",
    "print(\"MSE =\", mae)\n",
    "r2 = r2_score(y_test, best_estimator.predict(X_test))\n",
    "print(\"R-squared =\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use Melbourne Housing Market dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>3/09/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h        NaN     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u        NaN     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/09/2016       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3  4/02/2016       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"datasets/Melbourne_housing_FULL.csv\")\n",
    "\n",
    "display(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34857, 21)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34857 entries, 0 to 34856\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         34857 non-null  object \n",
      " 1   Address        34857 non-null  object \n",
      " 2   Rooms          34857 non-null  int64  \n",
      " 3   Type           34857 non-null  object \n",
      " 4   Price          27247 non-null  float64\n",
      " 5   Method         34857 non-null  object \n",
      " 6   SellerG        34857 non-null  object \n",
      " 7   Date           34857 non-null  object \n",
      " 8   Distance       34856 non-null  float64\n",
      " 9   Postcode       34856 non-null  float64\n",
      " 10  Bedroom2       26640 non-null  float64\n",
      " 11  Bathroom       26631 non-null  float64\n",
      " 12  Car            26129 non-null  float64\n",
      " 13  Landsize       23047 non-null  float64\n",
      " 14  BuildingArea   13742 non-null  float64\n",
      " 15  YearBuilt      15551 non-null  float64\n",
      " 16  CouncilArea    34854 non-null  object \n",
      " 17  Lattitude      26881 non-null  float64\n",
      " 18  Longtitude     26881 non-null  float64\n",
      " 19  Regionname     34854 non-null  object \n",
      " 20  Propertycount  34854 non-null  float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 5.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "print(dataset.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suburb             351\n",
      "Address          34009\n",
      "Rooms               12\n",
      "Type                 3\n",
      "Price             2871\n",
      "Method               9\n",
      "SellerG            388\n",
      "Date                78\n",
      "Distance           215\n",
      "Postcode           211\n",
      "Bedroom2            15\n",
      "Bathroom            11\n",
      "Car                 15\n",
      "Landsize          1684\n",
      "BuildingArea       740\n",
      "YearBuilt          160\n",
      "CouncilArea         33\n",
      "Lattitude        13402\n",
      "Longtitude       14524\n",
      "Regionname           8\n",
      "Propertycount      342\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove unnecessary columns\n",
    "dataset = dataset.drop([\"Address\", \"Date\", \"Postcode\", \"Lattitude\", \"Longtitude\", \"YearBuilt\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Suburb</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb  Rooms Type      Price Method SellerG  Distance  Bedroom2  \\\n",
       "0  Abbotsford      2    h        NaN     SS  Jellis       2.5       2.0   \n",
       "1  Abbotsford      2    h  1480000.0      S  Biggin       2.5       2.0   \n",
       "2  Abbotsford      2    h  1035000.0      S  Biggin       2.5       2.0   \n",
       "3  Abbotsford      3    u        NaN     VB  Rounds       2.5       3.0   \n",
       "4  Abbotsford      3    h  1465000.0     SP  Biggin       2.5       3.0   \n",
       "\n",
       "   Bathroom  Car  Landsize  BuildingArea         CouncilArea  \\\n",
       "0       1.0  1.0     126.0           NaN  Yarra City Council   \n",
       "1       1.0  1.0     202.0           NaN  Yarra City Council   \n",
       "2       1.0  0.0     156.0          79.0  Yarra City Council   \n",
       "3       2.0  1.0       0.0           NaN  Yarra City Council   \n",
       "4       2.0  0.0     134.0         150.0  Yarra City Council   \n",
       "\n",
       "              Regionname  Propertycount  \n",
       "0  Northern Metropolitan         4019.0  \n",
       "1  Northern Metropolitan         4019.0  \n",
       "2  Northern Metropolitan         4019.0  \n",
       "3  Northern Metropolitan         4019.0  \n",
       "4  Northern Metropolitan         4019.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34857, 15)\n"
     ]
    }
   ],
   "source": [
    "display(dataset.head())\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suburb               0\n",
      "Rooms                0\n",
      "Type                 0\n",
      "Price             7610\n",
      "Method               0\n",
      "SellerG              0\n",
      "Distance             1\n",
      "Bedroom2          8217\n",
      "Bathroom          8226\n",
      "Car               8728\n",
      "Landsize         11810\n",
      "BuildingArea     21115\n",
      "CouncilArea          3\n",
      "Regionname           3\n",
      "Propertycount        3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suburb               0\n",
      "Rooms                0\n",
      "Type                 0\n",
      "Price             7610\n",
      "Method               0\n",
      "SellerG              0\n",
      "Distance             0\n",
      "Bedroom2             0\n",
      "Bathroom             0\n",
      "Car                  0\n",
      "Landsize         11810\n",
      "BuildingArea     21115\n",
      "CouncilArea          3\n",
      "Regionname           3\n",
      "Propertycount        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# fill zero for NaN values for suitable columns.\n",
    "columns_fill_0 = [\"Distance\", \"Bedroom2\", \"Bathroom\", \"Car\", \"Propertycount\"]\n",
    "dataset[columns_fill_0] = dataset[columns_fill_0].fillna(0)\n",
    "print(dataset.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suburb              0\n",
      "Rooms               0\n",
      "Type                0\n",
      "Price            7610\n",
      "Method              0\n",
      "SellerG             0\n",
      "Distance            0\n",
      "Bedroom2            0\n",
      "Bathroom            0\n",
      "Car                 0\n",
      "Landsize            0\n",
      "BuildingArea        0\n",
      "CouncilArea         3\n",
      "Regionname          3\n",
      "Propertycount       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# fill mean for NaN values for suitable columns.\n",
    "columns_fill_mean = [\"Landsize\", \"BuildingArea\"]\n",
    "dataset[columns_fill_mean] = dataset[columns_fill_mean].fillna(dataset[columns_fill_mean].mean())\n",
    "print(dataset.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all remaining rows which contain NaN values.\n",
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27244, 15)\n",
      "Suburb           0\n",
      "Rooms            0\n",
      "Type             0\n",
      "Price            0\n",
      "Method           0\n",
      "SellerG          0\n",
      "Distance         0\n",
      "Bedroom2         0\n",
      "Bathroom         0\n",
      "Car              0\n",
      "Landsize         0\n",
      "BuildingArea     0\n",
      "CouncilArea      0\n",
      "Regionname       0\n",
      "Propertycount    0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 27244 entries, 1 to 34856\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         27244 non-null  object \n",
      " 1   Rooms          27244 non-null  int64  \n",
      " 2   Type           27244 non-null  object \n",
      " 3   Price          27244 non-null  float64\n",
      " 4   Method         27244 non-null  object \n",
      " 5   SellerG        27244 non-null  object \n",
      " 6   Distance       27244 non-null  float64\n",
      " 7   Bedroom2       27244 non-null  float64\n",
      " 8   Bathroom       27244 non-null  float64\n",
      " 9   Car            27244 non-null  float64\n",
      " 10  Landsize       27244 non-null  float64\n",
      " 11  BuildingArea   27244 non-null  float64\n",
      " 12  CouncilArea    27244 non-null  object \n",
      " 13  Regionname     27244 non-null  object \n",
      " 14  Propertycount  27244 non-null  float64\n",
      "dtypes: float64(8), int64(1), object(6)\n",
      "memory usage: 3.3+ MB\n",
      "None\n",
      "Suburb            344\n",
      "Rooms              12\n",
      "Type                3\n",
      "Price            2871\n",
      "Method              5\n",
      "SellerG           349\n",
      "Distance          213\n",
      "Bedroom2           14\n",
      "Bathroom           10\n",
      "Car                13\n",
      "Landsize         1558\n",
      "BuildingArea      663\n",
      "CouncilArea        33\n",
      "Regionname          8\n",
      "Propertycount     336\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "print(dataset.isna().sum())\n",
    "print(dataset.info())\n",
    "print(dataset.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Suburb_Aberfeldie</th>\n",
       "      <th>...</th>\n",
       "      <th>CouncilArea_Wyndham City Council</th>\n",
       "      <th>CouncilArea_Yarra City Council</th>\n",
       "      <th>CouncilArea_Yarra Ranges Shire Council</th>\n",
       "      <th>Regionname_Eastern Victoria</th>\n",
       "      <th>Regionname_Northern Metropolitan</th>\n",
       "      <th>Regionname_Northern Victoria</th>\n",
       "      <th>Regionname_South-Eastern Metropolitan</th>\n",
       "      <th>Regionname_Southern Metropolitan</th>\n",
       "      <th>Regionname_Western Metropolitan</th>\n",
       "      <th>Regionname_Western Victoria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 745 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms      Price  Distance  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "1      2  1480000.0       2.5       2.0       1.0  1.0     202.0   \n",
       "2      2  1035000.0       2.5       2.0       1.0  0.0     156.0   \n",
       "4      3  1465000.0       2.5       3.0       2.0  0.0     134.0   \n",
       "5      3   850000.0       2.5       3.0       2.0  1.0      94.0   \n",
       "6      4  1600000.0       2.5       3.0       1.0  2.0     120.0   \n",
       "\n",
       "   BuildingArea  Propertycount  Suburb_Aberfeldie  ...  \\\n",
       "1      160.2564         4019.0              False  ...   \n",
       "2       79.0000         4019.0              False  ...   \n",
       "4      150.0000         4019.0              False  ...   \n",
       "5      160.2564         4019.0              False  ...   \n",
       "6      142.0000         4019.0              False  ...   \n",
       "\n",
       "   CouncilArea_Wyndham City Council  CouncilArea_Yarra City Council  \\\n",
       "1                             False                            True   \n",
       "2                             False                            True   \n",
       "4                             False                            True   \n",
       "5                             False                            True   \n",
       "6                             False                            True   \n",
       "\n",
       "   CouncilArea_Yarra Ranges Shire Council  Regionname_Eastern Victoria  \\\n",
       "1                                   False                        False   \n",
       "2                                   False                        False   \n",
       "4                                   False                        False   \n",
       "5                                   False                        False   \n",
       "6                                   False                        False   \n",
       "\n",
       "   Regionname_Northern Metropolitan  Regionname_Northern Victoria  \\\n",
       "1                              True                         False   \n",
       "2                              True                         False   \n",
       "4                              True                         False   \n",
       "5                              True                         False   \n",
       "6                              True                         False   \n",
       "\n",
       "   Regionname_South-Eastern Metropolitan  Regionname_Southern Metropolitan  \\\n",
       "1                                  False                             False   \n",
       "2                                  False                             False   \n",
       "4                                  False                             False   \n",
       "5                                  False                             False   \n",
       "6                                  False                             False   \n",
       "\n",
       "   Regionname_Western Metropolitan  Regionname_Western Victoria  \n",
       "1                            False                        False  \n",
       "2                            False                        False  \n",
       "4                            False                        False  \n",
       "5                            False                        False  \n",
       "6                            False                        False  \n",
       "\n",
       "[5 rows x 745 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27244, 745)\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.get_dummies(dataset, drop_first=True)\n",
    "display(dataset.head())\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's prepare for regresion\n",
    "X = dataset.drop(\"Price\", axis=1)\n",
    "y = dataset[\"Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's develop linear regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearReg:\n",
    "    def __init__(self, X, y, test_size=0.3, random_state=42) -> None:\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
    "\n",
    "        self.linear_reg = LinearRegression()\n",
    "        self.linear_reg.fit(self.X_train, self.y_train)\n",
    "\n",
    "    def get_predict(self):\n",
    "        return self.linear_reg.predict(self.X_test)\n",
    "    \n",
    "    def get_score(self, dataset=\"test\"):\n",
    "        dataset = dataset.lower()\n",
    "        if dataset == \"test\":\n",
    "            return self.linear_reg.score(self.X_test, self.y_test)\n",
    "        elif dataset == \"train\":\n",
    "            return self.linear_reg.score(self.X_train, self.y_train)\n",
    "        else:\n",
    "            return None\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.6827792395792723\n",
      "Test score: 0.13853683161571284\n"
     ]
    }
   ],
   "source": [
    "linear_reg = LinearReg(X, y, random_state=2)\n",
    "\n",
    "print(\"Train score:\", linear_reg.get_score(\"train\"))\n",
    "print(\"Test score:\", linear_reg.get_score(\"test\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clearly see that there is a overfitting with the random_state=2. Now let's develop lasso and ridge regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LassoReg:\n",
    "    def __init__(self, X, y, test_size=0.3, random_state=42, alpha=1, tol=0.001, max_iter=1000) -> None:\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
    "\n",
    "        self.lasso_reg = Lasso(alpha=alpha, tol=tol, max_iter=max_iter)\n",
    "        self.lasso_reg.fit(self.X_train, self.y_train)\n",
    "\n",
    "    def get_predict(self):\n",
    "        return self.lasso_reg.predict(self.X_test)\n",
    "    \n",
    "    def get_score(self, dataset=\"test\"):\n",
    "        dataset = dataset.lower()\n",
    "        if dataset == \"test\":\n",
    "            return self.lasso_reg.score(self.X_test, self.y_test)\n",
    "        elif dataset == \"train\":\n",
    "            return self.lasso_reg.score(self.X_train, self.y_train)\n",
    "        else:\n",
    "            return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.6767356948457683\n",
      "Test score: 0.6637669697137103\n"
     ]
    }
   ],
   "source": [
    "lasso_reg = LassoReg(X, y, random_state=2, alpha=50, max_iter=100, tol=0.01)\n",
    "\n",
    "print(\"Train score:\", lasso_reg.get_score(\"train\"))\n",
    "print(\"Test score:\", lasso_reg.get_score(\"test\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RidgeReg:\n",
    "    def __init__(self, X, y, test_size=0.3, random_state=42, alpha=1, tol=0.001, max_iter=1000) -> None:\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
    "\n",
    "        self.ridge_reg = Ridge(alpha=alpha, tol=tol, max_iter=max_iter)\n",
    "        self.ridge_reg.fit(self.X_train, self.y_train)\n",
    "\n",
    "    def get_predict(self):\n",
    "        return self.ridge_reg.predict(self.X_test)\n",
    "    \n",
    "    def get_score(self, dataset=\"test\"):\n",
    "        dataset = dataset.lower()\n",
    "        if dataset == \"test\":\n",
    "            return self.ridge_reg.score(self.X_test, self.y_test)\n",
    "        elif dataset == \"train\":\n",
    "            return self.ridge_reg.score(self.X_train, self.y_train)\n",
    "        else:\n",
    "            return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.6622376739684328\n",
      "Test score: 0.6670848945194956\n"
     ]
    }
   ],
   "source": [
    "ridge_reg = RidgeReg(X, y, random_state=2, alpha=50, max_iter=100, tol=0.01)\n",
    "\n",
    "print(\"Train score:\", ridge_reg.get_score(\"train\"))\n",
    "print(\"Test score:\", ridge_reg.get_score(\"test\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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