Ridge_Lasso_Elastic.ipynb

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   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn import datasets \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Loading pre-defined Boston Dataset\n",
    "boston_dataset = datasets.load_boston()\n",
    "#print(boston_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  House Price  \n",
      "0     15.3  396.90   4.98         24.0  \n",
      "1     17.8  396.90   9.14         21.6  \n",
      "2     17.8  392.83   4.03         34.7  \n",
      "3     18.7  394.63   2.94         33.4  \n",
      "4     18.7  396.90   5.33         36.2  \n"
     ]
    }
   ],
   "source": [
    "#Load the data and divide into X and Y varaiable\n",
    "boston_pd = pd.DataFrame(boston_dataset.data) \n",
    "boston_pd.columns = boston_dataset.feature_names \n",
    "boston_pd_target = np.asarray(boston_dataset.target) \n",
    "boston_pd['House Price'] = pd.Series(boston_pd_target) \n",
    "\n",
    "# input \n",
    "X = boston_pd.iloc[:, :-1] \n",
    "\n",
    "#output \n",
    "Y = boston_pd.iloc[:, -1] \n",
    "\n",
    "print(boston_pd.head()) \n"
   ]
  },
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   "execution_count": 4,
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</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",
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       "    <tr>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
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       "      <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",
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       "    <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",
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       "      <th>...</th>\n",
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       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
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       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
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       "      <td>0.06076</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
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      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
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    "X"
   ]
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    {
     "data": {
      "text/plain": [
       "((379, 13), (379,), (127, 13), (127,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(boston_pd.iloc[:, :-1],\n",
    "                                                    boston_pd.iloc[:, -1],\n",
    "                                                    test_size=0.25)\n",
    "\n",
    "(x_train.shape, y_train.shape,x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T07:20:24.605642Z",
     "start_time": "2021-09-02T07:20:22.960604Z"
    },
    "execution": {
     "iopub.execute_input": "2021-09-02T07:06:57.422578Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared Error on test set :  21.370636909749347\n",
      "    Columns  Coefficient Estimate\n",
      "0      CRIM             -0.138093\n",
      "1        ZN              0.049392\n",
      "2     INDUS              0.027685\n",
      "3      CHAS              2.543397\n",
      "4       NOX            -17.040075\n",
      "5        RM              3.806995\n",
      "6       AGE              0.008233\n",
      "7       DIS             -1.322260\n",
      "8       RAD              0.373468\n",
      "9       TAX             -0.013674\n",
      "10  PTRATIO             -0.940085\n",
      "11        B              0.011322\n",
      "12    LSTAT             -0.567177\n"
     ]
    }
   ],
   "source": [
    "# Apply multiple Linear Regression Model \n",
    "lreg = LinearRegression() \n",
    "lreg.fit(x_train, y_train) \n",
    "\n",
    "# Generate Prediction on test set \n",
    "lreg_y_pred = lreg.predict(x_test) \n",
    "\n",
    "# calculating Mean Squared Error (mse) \n",
    "mean_squared_error = np.mean((lreg_y_pred - y_test)**2) \n",
    "print(\"Mean squared Error on test set : \", mean_squared_error) \n",
    "\n",
    "# Putting together the coefficient and their corrsponding variable names \n",
    "lreg_coefficient = pd.DataFrame() \n",
    "lreg_coefficient[\"Columns\"] = x_train.columns \n",
    "lreg_coefficient['Coefficient Estimate'] = pd.Series(lreg.coef_) \n",
    "print(lreg_coefficient) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T07:20:31.883194Z",
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Coefficient Estimate', ylabel='Columns'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "\n",
    "sns.barplot(x='Coefficient Estimate' , y='Columns', data=lreg_coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T07:20:32.989262Z",
     "start_time": "2021-09-02T07:20:31.890175Z"
    },
    "execution": {
     "iopub.execute_input": "2021-09-02T07:06:57.901074Z",
     "iopub.status.busy": "2021-09-02T07:06:57.900697Z",
     "iopub.status.idle": "2021-09-02T07:06:57.932402Z",
     "shell.execute_reply": "2021-09-02T07:06:57.931531Z",
     "shell.execute_reply.started": "2021-09-02T07:06:57.901039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.75577516850526\n",
      "    Columns  Coefficient Estimate\n",
      "0      CRIM             -0.136610\n",
      "1        ZN              0.051582\n",
      "2     INDUS             -0.006944\n",
      "3      CHAS              2.412324\n",
      "4       NOX             -9.016429\n",
      "5        RM              3.841423\n",
      "6       AGE              0.001483\n",
      "7       DIS             -1.213516\n",
      "8       RAD              0.357216\n",
      "9       TAX             -0.014341\n",
      "10  PTRATIO             -0.847665\n",
      "11        B              0.011743\n",
      "12    LSTAT             -0.577194\n"
     ]
    }
   ],
   "source": [
    "# import ridge regression from sklearn library \n",
    "from sklearn.linear_model import Ridge \n",
    "\n",
    "# Train the model \n",
    "ridgeR = Ridge(alpha = 1) \n",
    "ridgeR.fit(x_train, y_train) \n",
    "y_pred = ridgeR.predict(x_test) \n",
    "\n",
    "# calculate mean square error \n",
    "mean_squared_error_ridge = np.mean((y_pred - y_test)**2) \n",
    "print(mean_squared_error_ridge) \n",
    "\n",
    "# get ridge coefficient and print them \n",
    "ridge_coefficient = pd.DataFrame() \n",
    "ridge_coefficient[\"Columns\"]= x_train.columns \n",
    "ridge_coefficient['Coefficient Estimate'] = pd.Series(ridgeR.coef_) \n",
    "print(ridge_coefficient) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### # import Lasso regression from sklearn library \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T07:20:35.880220Z",
     "start_time": "2021-09-02T07:20:35.793453Z"
    },
    "execution": {
     "iopub.execute_input": "2021-09-02T07:06:57.934244Z",
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     "shell.execute_reply.started": "2021-09-02T07:06:57.934206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error on test set 27.171023702377944\n",
      "    Columns  Coefficient Estimate\n",
      "0      CRIM             -0.102649\n",
      "1        ZN              0.060930\n",
      "2     INDUS             -0.000000\n",
      "3      CHAS              0.000000\n",
      "4       NOX             -0.000000\n",
      "5        RM              0.712211\n",
      "6       AGE              0.027991\n",
      "7       DIS             -0.649902\n",
      "8       RAD              0.357682\n",
      "9       TAX             -0.017069\n",
      "10  PTRATIO             -0.690664\n",
      "11        B              0.009903\n",
      "12    LSTAT             -0.817838\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso \n",
    "\n",
    "# Train the model \n",
    "lasso = Lasso(alpha = 1) \n",
    "lasso.fit(x_train, y_train) \n",
    "y_pred1 = lasso.predict(x_test) \n",
    "\n",
    "# Calculate Mean Squared Error \n",
    "mean_squared_error = np.mean((y_pred1 - y_test)**2) \n",
    "print(\"Mean squared error on test set\", mean_squared_error) \n",
    "lasso_coeff = pd.DataFrame() \n",
    "lasso_coeff[\"Columns\"] = x_train.columns \n",
    "lasso_coeff['Coefficient Estimate'] = pd.Series(lasso.coef_) \n",
    "\n",
    "print(lasso_coeff) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T07:20:37.028288Z",
     "start_time": "2021-09-02T07:20:36.979418Z"
    },
    "execution": {
     "iopub.execute_input": "2021-09-02T07:06:57.960510Z",
     "iopub.status.busy": "2021-09-02T07:06:57.959976Z",
     "iopub.status.idle": "2021-09-02T07:06:57.987724Z",
     "shell.execute_reply": "2021-09-02T07:06:57.986781Z",
     "shell.execute_reply.started": "2021-09-02T07:06:57.960471Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error on test set 24.770017673679895\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.130557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.062056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>-0.029702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>1.889641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>0.013962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-0.970202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>0.395017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.017769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.787063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.011070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.737008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Columns  Coefficient Estimate\n",
       "0      CRIM             -0.130557\n",
       "1        ZN              0.062056\n",
       "2     INDUS             -0.029702\n",
       "3      CHAS              0.000000\n",
       "4       NOX             -0.000000\n",
       "5        RM              1.889641\n",
       "6       AGE              0.013962\n",
       "7       DIS             -0.970202\n",
       "8       RAD              0.395017\n",
       "9       TAX             -0.017769\n",
       "10  PTRATIO             -0.787063\n",
       "11        B              0.011070\n",
       "12    LSTAT             -0.737008"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import model \n",
    "from sklearn.linear_model import ElasticNet \n",
    "\n",
    "# Train the model \n",
    "e_net = ElasticNet(alpha = .4,l1_ratio=.5) \n",
    "e_net.fit(x_train, y_train) \n",
    "\n",
    "# calculate the prediction and mean square error \n",
    "y_pred_elastic = e_net.predict(x_test) \n",
    "mean_squared_error = np.mean((y_pred_elastic - y_test)**2) \n",
    "print(\"Mean Squared Error on test set\", mean_squared_error) \n",
    "\n",
    "e_net_coeff = pd.DataFrame() \n",
    "e_net_coeff[\"Columns\"] = x_train.columns \n",
    "e_net_coeff['Coefficient Estimate'] = pd.Series(e_net.coef_) \n",
    "e_net_coeff \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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