boston housing back elimination.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df['target'] = pd.Series(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>target</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",
       "      <td>24.0</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",
       "      <td>21.6</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",
       "      <td>34.7</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",
       "      <td>33.4</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",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "   PTRATIO       B  LSTAT  target  \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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CRIM',\n",
       " 'ZN',\n",
       " 'INDUS',\n",
       " 'CHAS',\n",
       " 'NOX',\n",
       " 'RM',\n",
       " 'AGE',\n",
       " 'DIS',\n",
       " 'RAD',\n",
       " 'TAX',\n",
       " 'PTRATIO',\n",
       " 'B',\n",
       " 'LSTAT',\n",
       " 'target']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x112dfa940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ctsBpG+FFnNQyAARYxnDcAyzZTa/jpjnltI5zYeS/HrulGU+83auPyeFTQzl9YK7rbd9/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\nNBGJp0YojyW5lJ8SN8pJkXyF+nK6/eNEfTuNpVb9euo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d18be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, x_vars = df.columns.tolist(), y_vars = ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRIM 506\n",
      "ZN 506\n",
      "INDUS 506\n",
      "CHAS 506\n",
      "NOX 506\n",
      "RM 506\n",
      "AGE 506\n",
      "DIS 506\n",
      "RAD 506\n",
      "TAX 506\n",
      "PTRATIO 506\n",
      "B 506\n",
      "LSTAT 506\n",
      "target 506\n"
     ]
    }
   ],
   "source": [
    "#check for missing values\n",
    "for col in df:\n",
    "    print (col, df[col].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.593761</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.596783</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.647423</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.593761   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.596783   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.647423   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT      target  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77742895278458135"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57711480570459739"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a182810b8>,\n",
       " <matplotlib.lines.Line2D at 0x1a18281080>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZhpVvYFSdj/R0wgufMjhJa5RLqSTnVoM+Fj2dAI0eCaR8P9dyGEFUScrgJK0cerEptyqZ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\nKGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17fdfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_test.index, y_test,'.',\n",
    "        X_test.index, y_pred,'*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a179c4748>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6zX1Fe2OUnZVnk0SSf+6551i6dCnPPfccJ598Ms8//3ymm5Yxr03albRzcf9U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\niEieU6IXEclzSvQiInlOiV5EJM/9f6rQBEPR2m5sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18000550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rss(true, pred):\n",
    "    return sum (( true - pred )**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def max_error(true, pred):\n",
    "    m = 0;\n",
    "    for i in true:\n",
    "        if (abs (true[i] - pred[i]) > m):\n",
    "            m = abs (true[i] - pred[i])\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def max2(true, pred):\n",
    "    return max(true - pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.595175867482766"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max2(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3732.3487796097065"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rss(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BACK ELIMINATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#back elimination preparation\n",
    "X_elim = np.append(arr = np.ones((X.shape[0],1)).astype(int), values = X, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.734</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   108.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>6.95e-135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:46</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3026.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   492</td>      <th>  BIC:               </th> <td>   3085.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   36.4911</td> <td>    5.104</td> <td>    7.149</td> <td> 0.000</td> <td>   26.462</td> <td>   46.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -0.1072</td> <td>    0.033</td> <td>   -3.276</td> <td> 0.001</td> <td>   -0.171</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0464</td> <td>    0.014</td> <td>    3.380</td> <td> 0.001</td> <td>    0.019</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0209</td> <td>    0.061</td> <td>    0.339</td> <td> 0.735</td> <td>   -0.100</td> <td>    0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    2.6886</td> <td>    0.862</td> <td>    3.120</td> <td> 0.002</td> <td>    0.996</td> <td>    4.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>  -17.7958</td> <td>    3.821</td> <td>   -4.658</td> <td> 0.000</td> <td>  -25.302</td> <td>  -10.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    3.8048</td> <td>    0.418</td> <td>    9.102</td> <td> 0.000</td> <td>    2.983</td> <td>    4.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0008</td> <td>    0.013</td> <td>    0.057</td> <td> 0.955</td> <td>   -0.025</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>   -1.4758</td> <td>    0.199</td> <td>   -7.398</td> <td> 0.000</td> <td>   -1.868</td> <td>   -1.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>    0.3057</td> <td>    0.066</td> <td>    4.608</td> <td> 0.000</td> <td>    0.175</td> <td>    0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>   -0.0123</td> <td>    0.004</td> <td>   -3.278</td> <td> 0.001</td> <td>   -0.020</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>   -0.9535</td> <td>    0.131</td> <td>   -7.287</td> <td> 0.000</td> <td>   -1.211</td> <td>   -0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>   <td>    0.0094</td> <td>    0.003</td> <td>    3.500</td> <td> 0.001</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th>   <td>   -0.5255</td> <td>    0.051</td> <td>  -10.366</td> <td> 0.000</td> <td>   -0.625</td> <td>   -0.426</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>178.029</td> <th>  Durbin-Watson:     </th> <td>   1.078</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 782.015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.521</td>  <th>  Prob(JB):          </th> <td>1.54e-170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.276</td>  <th>  Cond. No.          </th> <td>1.51e+04</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.741\n",
       "Model:                            OLS   Adj. R-squared:                  0.734\n",
       "Method:                 Least Squares   F-statistic:                     108.1\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):          6.95e-135\n",
       "Time:                        23:12:46   Log-Likelihood:                -1498.8\n",
       "No. Observations:                 506   AIC:                             3026.\n",
       "Df Residuals:                     492   BIC:                             3085.\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         36.4911      5.104      7.149      0.000      26.462      46.520\n",
       "x1            -0.1072      0.033     -3.276      0.001      -0.171      -0.043\n",
       "x2             0.0464      0.014      3.380      0.001       0.019       0.073\n",
       "x3             0.0209      0.061      0.339      0.735      -0.100       0.142\n",
       "x4             2.6886      0.862      3.120      0.002       0.996       4.381\n",
       "x5           -17.7958      3.821     -4.658      0.000     -25.302     -10.289\n",
       "x6             3.8048      0.418      9.102      0.000       2.983       4.626\n",
       "x7             0.0008      0.013      0.057      0.955      -0.025       0.027\n",
       "x8            -1.4758      0.199     -7.398      0.000      -1.868      -1.084\n",
       "x9             0.3057      0.066      4.608      0.000       0.175       0.436\n",
       "x10           -0.0123      0.004     -3.278      0.001      -0.020      -0.005\n",
       "x11           -0.9535      0.131     -7.287      0.000      -1.211      -0.696\n",
       "x12            0.0094      0.003      3.500      0.001       0.004       0.015\n",
       "x13           -0.5255      0.051    -10.366      0.000      -0.625      -0.426\n",
       "==============================================================================\n",
       "Omnibus:                      178.029   Durbin-Watson:                   1.078\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              782.015\n",
       "Skew:                           1.521   Prob(JB):                    1.54e-170\n",
       "Kurtosis:                       8.276   Cond. No.                     1.51e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 1\n",
    "X_opt = X_elim[:,:]\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.734</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   117.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>6.28e-136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:49</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3024.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   493</td>      <th>  BIC:               </th> <td>   3079.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   36.4666</td> <td>    5.081</td> <td>    7.177</td> <td> 0.000</td> <td>   26.483</td> <td>   46.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -0.1072</td> <td>    0.033</td> <td>   -3.279</td> <td> 0.001</td> <td>   -0.171</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0463</td> <td>    0.014</td> <td>    3.401</td> <td> 0.001</td> <td>    0.020</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0209</td> <td>    0.061</td> <td>    0.340</td> <td> 0.734</td> <td>   -0.100</td> <td>    0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    2.6911</td> <td>    0.860</td> <td>    3.130</td> <td> 0.002</td> <td>    1.002</td> <td>    4.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>  -17.7382</td> <td>    3.680</td> <td>   -4.820</td> <td> 0.000</td> <td>  -24.969</td> <td>  -10.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    3.8097</td> <td>    0.409</td> <td>    9.325</td> <td> 0.000</td> <td>    3.007</td> <td>    4.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>   -1.4791</td> <td>    0.191</td> <td>   -7.758</td> <td> 0.000</td> <td>   -1.854</td> <td>   -1.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>    0.3054</td> <td>    0.066</td> <td>    4.622</td> <td> 0.000</td> <td>    0.176</td> <td>    0.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>   -0.0123</td> <td>    0.004</td> <td>   -3.281</td> <td> 0.001</td> <td>   -0.020</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>   -0.9529</td> <td>    0.130</td> <td>   -7.313</td> <td> 0.000</td> <td>   -1.209</td> <td>   -0.697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>    0.0094</td> <td>    0.003</td> <td>    3.515</td> <td> 0.000</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>   <td>   -0.5245</td> <td>    0.048</td> <td>  -11.018</td> <td> 0.000</td> <td>   -0.618</td> <td>   -0.431</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>178.356</td> <th>  Durbin-Watson:     </th> <td>   1.077</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 785.541</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.523</td>  <th>  Prob(JB):          </th> <td>2.64e-171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.289</td>  <th>  Cond. No.          </th> <td>1.48e+04</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.741\n",
       "Model:                            OLS   Adj. R-squared:                  0.734\n",
       "Method:                 Least Squares   F-statistic:                     117.3\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):          6.28e-136\n",
       "Time:                        23:12:49   Log-Likelihood:                -1498.8\n",
       "No. Observations:                 506   AIC:                             3024.\n",
       "Df Residuals:                     493   BIC:                             3079.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         36.4666      5.081      7.177      0.000      26.483      46.450\n",
       "x1            -0.1072      0.033     -3.279      0.001      -0.171      -0.043\n",
       "x2             0.0463      0.014      3.401      0.001       0.020       0.073\n",
       "x3             0.0209      0.061      0.340      0.734      -0.100       0.142\n",
       "x4             2.6911      0.860      3.130      0.002       1.002       4.380\n",
       "x5           -17.7382      3.680     -4.820      0.000     -24.969     -10.508\n",
       "x6             3.8097      0.409      9.325      0.000       3.007       4.612\n",
       "x7            -1.4791      0.191     -7.758      0.000      -1.854      -1.104\n",
       "x8             0.3054      0.066      4.622      0.000       0.176       0.435\n",
       "x9            -0.0123      0.004     -3.281      0.001      -0.020      -0.005\n",
       "x10           -0.9529      0.130     -7.313      0.000      -1.209      -0.697\n",
       "x11            0.0094      0.003      3.515      0.000       0.004       0.015\n",
       "x12           -0.5245      0.048    -11.018      0.000      -0.618      -0.431\n",
       "==============================================================================\n",
       "Omnibus:                      178.356   Durbin-Watson:                   1.077\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              785.541\n",
       "Skew:                           1.523   Prob(JB):                    2.64e-171\n",
       "Kurtosis:                       8.289   Cond. No.                     1.48e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.48e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 2\n",
    "X_opt = X_elim[:,[0,1,2,3,4,5,6,8,9,10,11,12,13]]\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.735</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   128.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>5.74e-137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:49</td>     <th>  Log-Likelihood:    </th> <td> -1498.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3022.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   494</td>      <th>  BIC:               </th> <td>   3073.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   36.3694</td> <td>    5.069</td> <td>    7.176</td> <td> 0.000</td> <td>   26.411</td> <td>   46.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -0.1076</td> <td>    0.033</td> <td>   -3.296</td> <td> 0.001</td> <td>   -0.172</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0458</td> <td>    0.014</td> <td>    3.387</td> <td> 0.001</td> <td>    0.019</td> <td>    0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    2.7212</td> <td>    0.854</td> <td>    3.185</td> <td> 0.002</td> <td>    1.043</td> <td>    4.400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>  -17.3956</td> <td>    3.536</td> <td>   -4.920</td> <td> 0.000</td> <td>  -24.343</td> <td>  -10.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    3.7966</td> <td>    0.406</td> <td>    9.343</td> <td> 0.000</td> <td>    2.998</td> <td>    4.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>   -1.4934</td> <td>    0.186</td> <td>   -8.039</td> <td> 0.000</td> <td>   -1.858</td> <td>   -1.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.2991</td> <td>    0.063</td> <td>    4.719</td> <td> 0.000</td> <td>    0.175</td> <td>    0.424</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>   -0.0118</td> <td>    0.003</td> <td>   -3.488</td> <td> 0.001</td> <td>   -0.018</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>   -0.9471</td> <td>    0.129</td> <td>   -7.337</td> <td> 0.000</td> <td>   -1.201</td> <td>   -0.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>    0.0094</td> <td>    0.003</td> <td>    3.508</td> <td> 0.000</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>   -0.5232</td> <td>    0.047</td> <td>  -11.037</td> <td> 0.000</td> <td>   -0.616</td> <td>   -0.430</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>178.444</td> <th>  Durbin-Watson:     </th> <td>   1.078</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 786.944</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.524</td>  <th>  Prob(JB):          </th> <td>1.31e-171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.295</td>  <th>  Cond. No.          </th> <td>1.47e+04</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.741\n",
       "Model:                            OLS   Adj. R-squared:                  0.735\n",
       "Method:                 Least Squares   F-statistic:                     128.2\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):          5.74e-137\n",
       "Time:                        23:12:49   Log-Likelihood:                -1498.9\n",
       "No. Observations:                 506   AIC:                             3022.\n",
       "Df Residuals:                     494   BIC:                             3073.\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         36.3694      5.069      7.176      0.000      26.411      46.328\n",
       "x1            -0.1076      0.033     -3.296      0.001      -0.172      -0.043\n",
       "x2             0.0458      0.014      3.387      0.001       0.019       0.072\n",
       "x3             2.7212      0.854      3.185      0.002       1.043       4.400\n",
       "x4           -17.3956      3.536     -4.920      0.000     -24.343     -10.448\n",
       "x5             3.7966      0.406      9.343      0.000       2.998       4.595\n",
       "x6            -1.4934      0.186     -8.039      0.000      -1.858      -1.128\n",
       "x7             0.2991      0.063      4.719      0.000       0.175       0.424\n",
       "x8            -0.0118      0.003     -3.488      0.001      -0.018      -0.005\n",
       "x9            -0.9471      0.129     -7.337      0.000      -1.201      -0.693\n",
       "x10            0.0094      0.003      3.508      0.000       0.004       0.015\n",
       "x11           -0.5232      0.047    -11.037      0.000      -0.616      -0.430\n",
       "==============================================================================\n",
       "Omnibus:                      178.444   Durbin-Watson:                   1.078\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              786.944\n",
       "Skew:                           1.524   Prob(JB):                    1.31e-171\n",
       "Kurtosis:                       8.295   Cond. No.                     1.47e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.47e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 3\n",
    "X_opt = X_elim[:,[0,1,2,4,5,6,8,9,10,11,12,13]]\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7368245105969804"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_opt, y, test_size = 0.2)\n",
    "regressor2 = LinearRegression()\n",
    "regressor2.fit(X_train2, y_train2)\n",
    "y_pred2 = regressor2.predict(X_test2)\n",
    "regressor2.score(X_test2, y_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BACK ELIMINATION WITHOUT APPENDING CONSTANT TERM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   891.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:51</td>     <th>  Log-Likelihood:    </th> <td> -1523.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3074.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   493</td>      <th>  BIC:               </th> <td>   3129.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td>   -0.0916</td> <td>    0.034</td> <td>   -2.675</td> <td> 0.008</td> <td>   -0.159</td> <td>   -0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td>    0.0487</td> <td>    0.014</td> <td>    3.379</td> <td> 0.001</td> <td>    0.020</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>   -0.0038</td> <td>    0.064</td> <td>   -0.059</td> <td> 0.953</td> <td>   -0.130</td> <td>    0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>    2.8564</td> <td>    0.904</td> <td>    3.160</td> <td> 0.002</td> <td>    1.080</td> <td>    4.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>   -2.8808</td> <td>    3.359</td> <td>   -0.858</td> <td> 0.392</td> <td>   -9.481</td> <td>    3.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>    5.9252</td> <td>    0.309</td> <td>   19.168</td> <td> 0.000</td> <td>    5.318</td> <td>    6.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td>   -0.0072</td> <td>    0.014</td> <td>   -0.523</td> <td> 0.601</td> <td>   -0.034</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>   -0.9680</td> <td>    0.196</td> <td>   -4.947</td> <td> 0.000</td> <td>   -1.352</td> <td>   -0.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>    0.1704</td> <td>    0.067</td> <td>    2.554</td> <td> 0.011</td> <td>    0.039</td> <td>    0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>   -0.0094</td> <td>    0.004</td> <td>   -2.393</td> <td> 0.017</td> <td>   -0.017</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th> <td>   -0.3924</td> <td>    0.110</td> <td>   -3.571</td> <td> 0.000</td> <td>   -0.608</td> <td>   -0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th> <td>    0.0150</td> <td>    0.003</td> <td>    5.561</td> <td> 0.000</td> <td>    0.010</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th> <td>   -0.4170</td> <td>    0.051</td> <td>   -8.214</td> <td> 0.000</td> <td>   -0.517</td> <td>   -0.317</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>204.050</td> <th>  Durbin-Watson:     </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1372.527</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.609</td>  <th>  Prob(JB):          </th> <td>9.11e-299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.399</td>  <th>  Cond. No.          </th> <td>8.50e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.959\n",
       "Model:                            OLS   Adj. R-squared:                  0.958\n",
       "Method:                 Least Squares   F-statistic:                     891.1\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        23:12:51   Log-Likelihood:                -1523.8\n",
       "No. Observations:                 506   AIC:                             3074.\n",
       "Df Residuals:                     493   BIC:                             3129.\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0916      0.034     -2.675      0.008      -0.159      -0.024\n",
       "x2             0.0487      0.014      3.379      0.001       0.020       0.077\n",
       "x3            -0.0038      0.064     -0.059      0.953      -0.130       0.123\n",
       "x4             2.8564      0.904      3.160      0.002       1.080       4.633\n",
       "x5            -2.8808      3.359     -0.858      0.392      -9.481       3.720\n",
       "x6             5.9252      0.309     19.168      0.000       5.318       6.533\n",
       "x7            -0.0072      0.014     -0.523      0.601      -0.034       0.020\n",
       "x8            -0.9680      0.196     -4.947      0.000      -1.352      -0.584\n",
       "x9             0.1704      0.067      2.554      0.011       0.039       0.302\n",
       "x10           -0.0094      0.004     -2.393      0.017      -0.017      -0.002\n",
       "x11           -0.3924      0.110     -3.571      0.000      -0.608      -0.177\n",
       "x12            0.0150      0.003      5.561      0.000       0.010       0.020\n",
       "x13           -0.4170      0.051     -8.214      0.000      -0.517      -0.317\n",
       "==============================================================================\n",
       "Omnibus:                      204.050   Durbin-Watson:                   0.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1372.527\n",
       "Skew:                           1.609   Prob(JB):                    9.11e-299\n",
       "Kurtosis:                      10.399   Cond. No.                     8.50e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.5e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 1\n",
    "X_opt2 = X.iloc[:,:].values\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt2).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   967.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:52</td>     <th>  Log-Likelihood:    </th> <td> -1523.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3072.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   494</td>      <th>  BIC:               </th> <td>   3122.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td>   -0.0916</td> <td>    0.034</td> <td>   -2.677</td> <td> 0.008</td> <td>   -0.159</td> <td>   -0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td>    0.0488</td> <td>    0.014</td> <td>    3.409</td> <td> 0.001</td> <td>    0.021</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>    2.8510</td> <td>    0.898</td> <td>    3.173</td> <td> 0.002</td> <td>    1.086</td> <td>    4.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>   -2.9357</td> <td>    3.223</td> <td>   -0.911</td> <td> 0.363</td> <td>   -9.268</td> <td>    3.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>    5.9286</td> <td>    0.303</td> <td>   19.544</td> <td> 0.000</td> <td>    5.333</td> <td>    6.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>   -0.0072</td> <td>    0.014</td> <td>   -0.524</td> <td> 0.601</td> <td>   -0.034</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td>   -0.9652</td> <td>    0.189</td> <td>   -5.097</td> <td> 0.000</td> <td>   -1.337</td> <td>   -0.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>    0.1715</td> <td>    0.064</td> <td>    2.676</td> <td> 0.008</td> <td>    0.046</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>   -0.0095</td> <td>    0.004</td> <td>   -2.689</td> <td> 0.007</td> <td>   -0.016</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>   -0.3932</td> <td>    0.109</td> <td>   -3.608</td> <td> 0.000</td> <td>   -0.607</td> <td>   -0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th> <td>    0.0150</td> <td>    0.003</td> <td>    5.577</td> <td> 0.000</td> <td>    0.010</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th> <td>   -0.4172</td> <td>    0.051</td> <td>   -8.242</td> <td> 0.000</td> <td>   -0.517</td> <td>   -0.318</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>204.088</td> <th>  Durbin-Watson:     </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1373.221</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.610</td>  <th>  Prob(JB):          </th> <td>6.44e-299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.401</td>  <th>  Cond. No.          </th> <td>8.16e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.959\n",
       "Model:                            OLS   Adj. R-squared:                  0.958\n",
       "Method:                 Least Squares   F-statistic:                     967.3\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        23:12:52   Log-Likelihood:                -1523.8\n",
       "No. Observations:                 506   AIC:                             3072.\n",
       "Df Residuals:                     494   BIC:                             3122.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0916      0.034     -2.677      0.008      -0.159      -0.024\n",
       "x2             0.0488      0.014      3.409      0.001       0.021       0.077\n",
       "x3             2.8510      0.898      3.173      0.002       1.086       4.616\n",
       "x4            -2.9357      3.223     -0.911      0.363      -9.268       3.396\n",
       "x5             5.9286      0.303     19.544      0.000       5.333       6.525\n",
       "x6            -0.0072      0.014     -0.524      0.601      -0.034       0.020\n",
       "x7            -0.9652      0.189     -5.097      0.000      -1.337      -0.593\n",
       "x8             0.1715      0.064      2.676      0.008       0.046       0.297\n",
       "x9            -0.0095      0.004     -2.689      0.007      -0.016      -0.003\n",
       "x10           -0.3932      0.109     -3.608      0.000      -0.607      -0.179\n",
       "x11            0.0150      0.003      5.577      0.000       0.010       0.020\n",
       "x12           -0.4172      0.051     -8.242      0.000      -0.517      -0.318\n",
       "==============================================================================\n",
       "Omnibus:                      204.088   Durbin-Watson:                   0.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1373.221\n",
       "Skew:                           1.610   Prob(JB):                    6.44e-299\n",
       "Kurtosis:                      10.401   Cond. No.                     8.16e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.16e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 2\n",
    "X_opt2 = X.iloc[:,[0,1,3,4,5,6,7,8,9,10,11,12]].values\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt2).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1057.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:52</td>     <th>  Log-Likelihood:    </th> <td> -1524.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3070.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   495</td>      <th>  BIC:               </th> <td>   3116.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td>   -0.0915</td> <td>    0.034</td> <td>   -2.679</td> <td> 0.008</td> <td>   -0.159</td> <td>   -0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td>    0.0497</td> <td>    0.014</td> <td>    3.504</td> <td> 0.001</td> <td>    0.022</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>    2.8276</td> <td>    0.897</td> <td>    3.154</td> <td> 0.002</td> <td>    1.066</td> <td>    4.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>   -3.4000</td> <td>    3.096</td> <td>   -1.098</td> <td> 0.273</td> <td>   -9.483</td> <td>    2.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>    5.8950</td> <td>    0.296</td> <td>   19.899</td> <td> 0.000</td> <td>    5.313</td> <td>    6.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>   -0.9297</td> <td>    0.177</td> <td>   -5.261</td> <td> 0.000</td> <td>   -1.277</td> <td>   -0.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td>    0.1735</td> <td>    0.064</td> <td>    2.713</td> <td> 0.007</td> <td>    0.048</td> <td>    0.299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>   -0.0095</td> <td>    0.004</td> <td>   -2.706</td> <td> 0.007</td> <td>   -0.016</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>   -0.3952</td> <td>    0.109</td> <td>   -3.632</td> <td> 0.000</td> <td>   -0.609</td> <td>   -0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>    0.0149</td> <td>    0.003</td> <td>    5.564</td> <td> 0.000</td> <td>    0.010</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th> <td>   -0.4260</td> <td>    0.048</td> <td>   -8.933</td> <td> 0.000</td> <td>   -0.520</td> <td>   -0.332</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>200.438</td> <th>  Durbin-Watson:     </th> <td>   1.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1312.661</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.585</td>  <th>  Prob(JB):          </th> <td>9.10e-286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.226</td>  <th>  Cond. No.          </th> <td>7.79e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.959\n",
       "Model:                            OLS   Adj. R-squared:                  0.958\n",
       "Method:                 Least Squares   F-statistic:                     1057.\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        23:12:52   Log-Likelihood:                -1524.0\n",
       "No. Observations:                 506   AIC:                             3070.\n",
       "Df Residuals:                     495   BIC:                             3116.\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0915      0.034     -2.679      0.008      -0.159      -0.024\n",
       "x2             0.0497      0.014      3.504      0.001       0.022       0.078\n",
       "x3             2.8276      0.897      3.154      0.002       1.066       4.589\n",
       "x4            -3.4000      3.096     -1.098      0.273      -9.483       2.683\n",
       "x5             5.8950      0.296     19.899      0.000       5.313       6.477\n",
       "x6            -0.9297      0.177     -5.261      0.000      -1.277      -0.582\n",
       "x7             0.1735      0.064      2.713      0.007       0.048       0.299\n",
       "x8            -0.0095      0.004     -2.706      0.007      -0.016      -0.003\n",
       "x9            -0.3952      0.109     -3.632      0.000      -0.609      -0.181\n",
       "x10            0.0149      0.003      5.564      0.000       0.010       0.020\n",
       "x11           -0.4260      0.048     -8.933      0.000      -0.520      -0.332\n",
       "==============================================================================\n",
       "Omnibus:                      200.438   Durbin-Watson:                   1.004\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1312.661\n",
       "Skew:                           1.585   Prob(JB):                    9.10e-286\n",
       "Kurtosis:                      10.226   Cond. No.                     7.79e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.79e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 3\n",
    "X_opt2 = X.iloc[:,[0,1,3,4,5,7,8,9,10,11,12]].values\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt2).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1162.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:12:53</td>     <th>  Log-Likelihood:    </th> <td> -1524.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3069.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   496</td>      <th>  BIC:               </th> <td>   3111.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td>   -0.0898</td> <td>    0.034</td> <td>   -2.630</td> <td> 0.009</td> <td>   -0.157</td> <td>   -0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td>    0.0512</td> <td>    0.014</td> <td>    3.630</td> <td> 0.000</td> <td>    0.024</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>    2.7212</td> <td>    0.892</td> <td>    3.052</td> <td> 0.002</td> <td>    0.970</td> <td>    4.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>    5.7113</td> <td>    0.245</td> <td>   23.353</td> <td> 0.000</td> <td>    5.231</td> <td>    6.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>   -0.8664</td> <td>    0.167</td> <td>   -5.185</td> <td> 0.000</td> <td>   -1.195</td> <td>   -0.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>    0.1820</td> <td>    0.063</td> <td>    2.867</td> <td> 0.004</td> <td>    0.057</td> <td>    0.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td>   -0.0109</td> <td>    0.003</td> <td>   -3.292</td> <td> 0.001</td> <td>   -0.017</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>   -0.4004</td> <td>    0.109</td> <td>   -3.682</td> <td> 0.000</td> <td>   -0.614</td> <td>   -0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>    0.0146</td> <td>    0.003</td> <td>    5.475</td> <td> 0.000</td> <td>    0.009</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>   -0.4499</td> <td>    0.042</td> <td>  -10.596</td> <td> 0.000</td> <td>   -0.533</td> <td>   -0.366</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>198.034</td> <th>  Durbin-Watson:     </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1249.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.575</td>  <th>  Prob(JB):          </th> <td>6.07e-272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.022</td>  <th>  Cond. No.          </th> <td>2.24e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.959\n",
       "Model:                            OLS   Adj. R-squared:                  0.958\n",
       "Method:                 Least Squares   F-statistic:                     1162.\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        23:12:53   Log-Likelihood:                -1524.6\n",
       "No. Observations:                 506   AIC:                             3069.\n",
       "Df Residuals:                     496   BIC:                             3111.\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0898      0.034     -2.630      0.009      -0.157      -0.023\n",
       "x2             0.0512      0.014      3.630      0.000       0.024       0.079\n",
       "x3             2.7212      0.892      3.052      0.002       0.970       4.473\n",
       "x4             5.7113      0.245     23.353      0.000       5.231       6.192\n",
       "x5            -0.8664      0.167     -5.185      0.000      -1.195      -0.538\n",
       "x6             0.1820      0.063      2.867      0.004       0.057       0.307\n",
       "x7            -0.0109      0.003     -3.292      0.001      -0.017      -0.004\n",
       "x8            -0.4004      0.109     -3.682      0.000      -0.614      -0.187\n",
       "x9             0.0146      0.003      5.475      0.000       0.009       0.020\n",
       "x10           -0.4499      0.042    -10.596      0.000      -0.533      -0.366\n",
       "==============================================================================\n",
       "Omnibus:                      198.034   Durbin-Watson:                   0.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1249.000\n",
       "Skew:                           1.575   Prob(JB):                    6.07e-272\n",
       "Kurtosis:                      10.022   Cond. No.                     2.24e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.24e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iteration 4\n",
    "X_opt2 = X.iloc[:,[0,1,3,5,7,8,9,10,11,12]].values\n",
    "regressor_OLS = sm.OLS( endog = y, exog = X_opt2).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76289980389104572"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(X_opt2, y, test_size = 0.2)\n",
    "regressor3 = LinearRegression()\n",
    "regressor3.fit(X_train3, y_train3)\n",
    "y_pred3 = regressor3.predict(X_test3)\n",
    "regressor3.score(X_test3, y_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BACK ELIMINATION LOOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Backward Elimination with p-values only:\n",
    "\n",
    "import statsmodels.formula.api as sm\n",
    "def backwardElimination(x, sl):\n",
    "    numVars = len(x[0])\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.OLS(y, x).fit()\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        if maxVar > sl:\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    x = np.delete(x, j, 1)\n",
    "    print (regressor_OLS.summary())\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 target   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.958\n",
      "Method:                 Least Squares   F-statistic:                     1162.\n",
      "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
      "Time:                        23:22:46   Log-Likelihood:                -1524.6\n",
      "No. Observations:                 506   AIC:                             3069.\n",
      "Df Residuals:                     496   BIC:                             3111.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            -0.0898      0.034     -2.630      0.009      -0.157      -0.023\n",
      "x2             0.0512      0.014      3.630      0.000       0.024       0.079\n",
      "x3             2.7212      0.892      3.052      0.002       0.970       4.473\n",
      "x4             5.7113      0.245     23.353      0.000       5.231       6.192\n",
      "x5            -0.8664      0.167     -5.185      0.000      -1.195      -0.538\n",
      "x6             0.1820      0.063      2.867      0.004       0.057       0.307\n",
      "x7            -0.0109      0.003     -3.292      0.001      -0.017      -0.004\n",
      "x8            -0.4004      0.109     -3.682      0.000      -0.614      -0.187\n",
      "x9             0.0146      0.003      5.475      0.000       0.009       0.020\n",
      "x10           -0.4499      0.042    -10.596      0.000      -0.533      -0.366\n",
      "==============================================================================\n",
      "Omnibus:                      198.034   Durbin-Watson:                   0.999\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1249.000\n",
      "Skew:                           1.575   Prob(JB):                    6.07e-272\n",
      "Kurtosis:                      10.022   Cond. No.                     2.24e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.24e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "SL = 0.05\n",
    "X_opt3 = X.iloc[:,:].values\n",
    "X_Modeled = backwardElimination(X_opt3, SL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 10)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Modeled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Backward Elimination with p-values and Adjusted R Squared:\n",
    "\n",
    "import statsmodels.formula.api as sm\n",
    "def backwardElimination2(x, SL):\n",
    "    numVars = len(x[0])\n",
    "    temp = np.zeros((X.shape[0],6)).astype(int)\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.OLS(y, x).fit()\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        adjR_before = regressor_OLS.rsquared_adj.astype(float)\n",
    "        if maxVar > SL:\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    temp[:,j] = x[:, j]\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    tmp_regressor = sm.OLS(y, x).fit()\n",
    "                    adjR_after = tmp_regressor.rsquared_adj.astype(float)\n",
    "                    if (adjR_before >= adjR_after):\n",
    "                        x_rollback = np.hstack((x, temp[:,[0,j]]))\n",
    "                        x_rollback = np.delete(x_rollback, j, 1)\n",
    "                        print (regressor_OLS.summary())\n",
    "                        return x_rollback\n",
    "                    else:\n",
    "                        continue\n",
    "    regressor_OLS.summary()\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 target   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.958\n",
      "Method:                 Least Squares   F-statistic:                     1057.\n",
      "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
      "Time:                        23:24:00   Log-Likelihood:                -1524.0\n",
      "No. Observations:                 506   AIC:                             3070.\n",
      "Df Residuals:                     495   BIC:                             3116.\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            -0.0915      0.034     -2.679      0.008      -0.159      -0.024\n",
      "x2             0.0497      0.014      3.504      0.001       0.022       0.078\n",
      "x3             2.8276      0.897      3.154      0.002       1.066       4.589\n",
      "x4            -3.4000      3.096     -1.098      0.273      -9.483       2.683\n",
      "x5             5.8950      0.296     19.899      0.000       5.313       6.477\n",
      "x6            -0.9297      0.177     -5.261      0.000      -1.277      -0.582\n",
      "x7             0.1735      0.064      2.713      0.007       0.048       0.299\n",
      "x8            -0.0095      0.004     -2.706      0.007      -0.016      -0.003\n",
      "x9            -0.3952      0.109     -3.632      0.000      -0.609      -0.181\n",
      "x10            0.0149      0.003      5.564      0.000       0.010       0.020\n",
      "x11           -0.4260      0.048     -8.933      0.000      -0.520      -0.332\n",
      "==============================================================================\n",
      "Omnibus:                      200.438   Durbin-Watson:                   1.004\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1312.661\n",
      "Skew:                           1.585   Prob(JB):                    9.10e-286\n",
      "Kurtosis:                      10.226   Cond. No.                     7.79e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.79e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "SL = 0.05\n",
    "X_opt4 = X.iloc[:,:].values\n",
    "X_Modeled2 = backwardElimination2(X_opt4, SL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 11)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Modeled2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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