Multiple_Linear_Regression_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make plots interactive and enable inline backend\n",
    "%matplotlib inline\n",
    "\n",
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>16884.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>1725.55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>4449.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>21984.47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>3866.86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     bmi  children   charges  sex_female  sex_male  smoker_no  \\\n",
       "0   19  27.900         0  16884.92           1         0          0   \n",
       "1   18  33.770         1   1725.55           0         1          1   \n",
       "2   28  33.000         3   4449.46           0         1          1   \n",
       "3   33  22.705         0  21984.47           0         1          1   \n",
       "4   32  28.880         0   3866.86           0         1          1   \n",
       "\n",
       "   smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
       "0           1                 0                 0                 0   \n",
       "1           0                 0                 0                 1   \n",
       "2           0                 0                 0                 1   \n",
       "3           0                 0                 1                 0   \n",
       "4           0                 0                 1                 0   \n",
       "\n",
       "   region_southwest  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv file into a Pandas DataFrame\n",
    "df = pd.read_csv('Data/insurance_cleaned.csv')\n",
    "\n",
    "# Preview dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422280</td>\n",
       "      <td>0.494768</td>\n",
       "      <td>0.505232</td>\n",
       "      <td>0.795217</td>\n",
       "      <td>0.204783</td>\n",
       "      <td>0.242152</td>\n",
       "      <td>0.242900</td>\n",
       "      <td>0.272048</td>\n",
       "      <td>0.242900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011272</td>\n",
       "      <td>0.500160</td>\n",
       "      <td>0.500160</td>\n",
       "      <td>0.403694</td>\n",
       "      <td>0.403694</td>\n",
       "      <td>0.428546</td>\n",
       "      <td>0.428995</td>\n",
       "      <td>0.445181</td>\n",
       "      <td>0.428995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.870000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.915000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.430000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          bmi     children       charges   sex_female  \\\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000  1338.000000   \n",
       "mean     39.207025    30.663397     1.094918  13270.422280     0.494768   \n",
       "std      14.049960     6.098187     1.205493  12110.011272     0.500160   \n",
       "min      18.000000    15.960000     0.000000   1121.870000     0.000000   \n",
       "25%      27.000000    26.296250     0.000000   4740.287500     0.000000   \n",
       "50%      39.000000    30.400000     1.000000   9382.030000     0.000000   \n",
       "75%      51.000000    34.693750     2.000000  16639.915000     1.000000   \n",
       "max      64.000000    53.130000     5.000000  63770.430000     1.000000   \n",
       "\n",
       "          sex_male    smoker_no   smoker_yes  region_northeast  \\\n",
       "count  1338.000000  1338.000000  1338.000000       1338.000000   \n",
       "mean      0.505232     0.795217     0.204783          0.242152   \n",
       "std       0.500160     0.403694     0.403694          0.428546   \n",
       "min       0.000000     0.000000     0.000000          0.000000   \n",
       "25%       0.000000     1.000000     0.000000          0.000000   \n",
       "50%       1.000000     1.000000     0.000000          0.000000   \n",
       "75%       1.000000     1.000000     0.000000          0.000000   \n",
       "max       1.000000     1.000000     1.000000          1.000000   \n",
       "\n",
       "       region_northwest  region_southeast  region_southwest  \n",
       "count       1338.000000       1338.000000       1338.000000  \n",
       "mean           0.242900          0.272048          0.242900  \n",
       "std            0.428995          0.445181          0.428995  \n",
       "min            0.000000          0.000000          0.000000  \n",
       "25%            0.000000          0.000000          0.000000  \n",
       "50%            0.000000          0.000000          0.000000  \n",
       "75%            0.000000          1.000000          0.000000  \n",
       "max            1.000000          1.000000          1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 12 columns):\n",
      "age                 1338 non-null int64\n",
      "bmi                 1338 non-null float64\n",
      "children            1338 non-null int64\n",
      "charges             1338 non-null float64\n",
      "sex_female          1338 non-null int64\n",
      "sex_male            1338 non-null int64\n",
      "smoker_no           1338 non-null int64\n",
      "smoker_yes          1338 non-null int64\n",
      "region_northeast    1338 non-null int64\n",
      "region_northwest    1338 non-null int64\n",
      "region_southeast    1338 non-null int64\n",
      "region_southwest    1338 non-null int64\n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 125.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "display(df.describe())\n",
    "\n",
    "# Summary\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 float64\n",
       "bmi                 float64\n",
       "children            float64\n",
       "charges             float64\n",
       "sex                   int64\n",
       "sex_male            float64\n",
       "smoker                int64\n",
       "smoker_yes          float64\n",
       "region_northeast    float64\n",
       "region_northwest    float64\n",
       "region_southeast    float64\n",
       "region_southwest    float64\n",
       "sex_female          float64\n",
       "smoker_no           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert all column values to the float type to make the values compatible with StandardScalar()\n",
    "df['age'] = df.age.astype(float)\n",
    "df['children'] = df.children.astype(float)\n",
    "df['sex_female'] = df.bmi.astype(float)\n",
    "df['sex_male'] = df.age.astype(float)\n",
    "df['smoker_no'] = df.age.astype(float)\n",
    "df['smoker_yes'] = df.age.astype(float)\n",
    "df['region_northeast'] = df.age.astype(float)\n",
    "df['region_northwest'] = df.age.astype(float)\n",
    "df['region_southeast'] = df.age.astype(float)\n",
    "df['region_southwest'] = df.age.astype(float)\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1338, 9) (1338, 1)\n"
     ]
    }
   ],
   "source": [
    "# Assign X to the independent variables we will use and assign y to the dependent variable that we will be trying to predict\n",
    "X = df[['age', 'bmi', 'children', 'sex_female', 'smoker_no', 'region_northeast', 'region_northwest', 'region_southeast', 'region_southwest']]\n",
    "y = df['charges'].values.reshape(-1, 1)\n",
    "\n",
    "# View dataframes' dimensionalities\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training data and testing data\n",
    "# Set the random_state to 42 to validate processing over multiple runs of the code\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler model and fit it to the training data\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the training and testing data using the X_scaler and y_scaler models\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "y_train_scaled = y_scaler.transform(y_train)\n",
    "y_test_scaled = y_scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a LinearRegression model and fit it to the scaled training data\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_scaled, y_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot X_test_scaled vs y_test_scaled\n",
    "# Plot both training data and testing data residuals on the same graph\n",
    "\n",
    "# Make predictions using the X_test_scaled data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# Construct scatter plots to visualize the relationships of y_test_scaled vs predictions\n",
    "plt.scatter(model.predict(X_train_scaled), model.predict(X_train_scaled) - y_train_scaled, c=\"blue\", label=\"Training Data\")\n",
    "plt.scatter(model.predict(X_test_scaled), model.predict(X_test_scaled) - y_test_scaled, c=\"orange\", label=\"Testing Data\")\n",
    "\n",
    "plt.legend()\n",
    "plt.hlines(y=0, xmin=y_test_scaled.min(), xmax=y_test_scaled.max())\n",
    "plt.title(\"Residuals\")\n",
    "\n",
    "# Display the scatter plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot y_test_scaled vs y_test_scaled\n",
    "\n",
    "# Make predictions using the X_test_scaled data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# Construct a scatter plot to visualize the relationships of y_test_scaled vs predictions\n",
    "plt.scatter(model.predict(X_train_scaled), model.predict(X_train_scaled) - y_train_scaled, c=\"blue\")\n",
    "\n",
    "plt.hlines(y=0, xmin=y_test_scaled.min(), xmax=y_test_scaled.max())\n",
    "plt.title(\"Residual Plot (training data)\")\n",
    "plt.xlabel(\"Predicted Value of Insurance Charge ($)\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "\n",
    "# Display the scatter plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions using the X_test_scaled data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# Construct a scatter plot to visualize the relationships of y_test_scaled vs predictions\n",
    "plt.scatter(model.predict(X_test_scaled), model.predict(X_test_scaled) - y_test_scaled, c=\"orange\")\n",
    "\n",
    "plt.hlines(y=0, xmin=y_test_scaled.min(), xmax=y_test_scaled.max())\n",
    "plt.title(\"Residual Plot (testing data)\")\n",
    "plt.xlabel(\"Predicted Value of Insurance Charge ($)\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "\n",
    "# Display the scatter plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.9075562333874532, R2: 0.12733317433736002\n"
     ]
    }
   ],
   "source": [
    "# Use X_test_scaled, y_test_scaled, and model.predict(X_test_scaled) to calculate MSE and R2\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = model.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "# View calculated MSE and R2 values\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.9075760903418219, R2: 0.12731408074877704\n"
     ]
    }
   ],
   "source": [
    "# LASSO model\n",
    "lasso = Lasso(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = lasso.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = lasso.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "# View calculated MSE and R2 values\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.9075562019475326, R2: 0.12733320456862796\n"
     ]
    }
   ],
   "source": [
    "# Ridge model\n",
    "ridge = Ridge(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = ridge.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = ridge.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "# View calculated MSE and R2 values\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.9074921847239236, R2: 0.12739476076234846\n"
     ]
    }
   ],
   "source": [
    "# ElasticNet model\n",
    "elasticnet = ElasticNet(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = elasticnet.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = elasticnet.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "# View calculated MSE and R2 values\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  }
 ],
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