Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Car Price Prediction (Multi Linear Regression)\n",
    "\n",
    "Building a multiple linear regression model for the prediction of car `prices` with the help of [Car Data](https://archive.ics.uci.edu/ml/datasets/Automobile) with their specification offered by American companies in the USA market.\n",
    "\n",
    "Steps invlved in the predictive analysis:\n",
    "- Business Problem Understanding\n",
    "- Data Exploration\n",
    "- Data Cleaning and Standardization\n",
    "- Model Building\n",
    "- Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 1. Data Exploration and Understanding the Problem\n",
    "\n",
    "Importing the libraries and data, and understanding its parameters like shape, dtypes, and etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFECV,RFE\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "0  convertible        rwd          front       88.6      168.8      64.1   \n",
       "1  convertible        rwd          front       88.6      168.8      64.1   \n",
       "2    hatchback        rwd          front       94.5      171.2      65.5   \n",
       "3        sedan        fwd          front       99.8      176.6      66.2   \n",
       "4        sedan        4wd          front       99.4      176.6      66.4   \n",
       "\n",
       "   carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "0       48.8        2548       dohc           four         130       mpfi   \n",
       "1       48.8        2548       dohc           four         130       mpfi   \n",
       "2       52.4        2823       ohcv            six         152       mpfi   \n",
       "3       54.3        2337        ohc           four         109       mpfi   \n",
       "4       54.3        2824        ohc           five         136       mpfi   \n",
       "\n",
       "   boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "0       3.47    2.68               9.0         111     5000       21   \n",
       "1       3.47    2.68               9.0         111     5000       21   \n",
       "2       2.68    3.47               9.0         154     5000       19   \n",
       "3       3.19    3.40              10.0         102     5500       24   \n",
       "4       3.19    3.40               8.0         115     5500       18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  "
      ]
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   ],
   "source": [
    "# reading the data with all the columns visible\n",
    "pd.options.display.max_columns=None\n",
    "car = pd.read_csv('CarPrice_Assignment.csv')\n",
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# information about columns\n",
    "car.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of dataframe\n",
    "car.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID              0\n",
       "symboling           0\n",
       "CarName             0\n",
       "fueltype            0\n",
       "aspiration          0\n",
       "doornumber          0\n",
       "carbody             0\n",
       "drivewheel          0\n",
       "enginelocation      0\n",
       "wheelbase           0\n",
       "carlength           0\n",
       "carwidth            0\n",
       "carheight           0\n",
       "curbweight          0\n",
       "enginetype          0\n",
       "cylindernumber      0\n",
       "enginesize          0\n",
       "fuelsystem          0\n",
       "boreratio           0\n",
       "stroke              0\n",
       "compressionratio    0\n",
       "horsepower          0\n",
       "peakrpm             0\n",
       "citympg             0\n",
       "highwaympg          0\n",
       "price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of null values in every column\n",
    "car.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
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       "      <td>147</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>peugeot 504</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>6</td>\n",
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       "      <td>168</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.329756</td>\n",
       "      <td>3.255415</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>104.117073</td>\n",
       "      <td>5125.121951</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13276.710571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59.322565</td>\n",
       "      <td>1.245307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.270844</td>\n",
       "      <td>0.313597</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>39.544167</td>\n",
       "      <td>476.985643</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7988.852332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            car_ID   symboling      CarName fueltype aspiration doornumber  \\\n",
       "count   205.000000  205.000000          205      205        205        205   \n",
       "unique         NaN         NaN          147        2          2          2   \n",
       "top            NaN         NaN  peugeot 504      gas        std       four   \n",
       "freq           NaN         NaN            6      185        168        115   \n",
       "mean    103.000000    0.834146          NaN      NaN        NaN        NaN   \n",
       "std      59.322565    1.245307          NaN      NaN        NaN        NaN   \n",
       "min       1.000000   -2.000000          NaN      NaN        NaN        NaN   \n",
       "25%      52.000000    0.000000          NaN      NaN        NaN        NaN   \n",
       "50%     103.000000    1.000000          NaN      NaN        NaN        NaN   \n",
       "75%     154.000000    2.000000          NaN      NaN        NaN        NaN   \n",
       "max     205.000000    3.000000          NaN      NaN        NaN        NaN   \n",
       "\n",
       "       carbody drivewheel enginelocation   wheelbase   carlength    carwidth  \\\n",
       "count      205        205            205  205.000000  205.000000  205.000000   \n",
       "unique       5          3              2         NaN         NaN         NaN   \n",
       "top      sedan        fwd          front         NaN         NaN         NaN   \n",
       "freq        96        120            202         NaN         NaN         NaN   \n",
       "mean       NaN        NaN            NaN   98.756585  174.049268   65.907805   \n",
       "std        NaN        NaN            NaN    6.021776   12.337289    2.145204   \n",
       "min        NaN        NaN            NaN   86.600000  141.100000   60.300000   \n",
       "25%        NaN        NaN            NaN   94.500000  166.300000   64.100000   \n",
       "50%        NaN        NaN            NaN   97.000000  173.200000   65.500000   \n",
       "75%        NaN        NaN            NaN  102.400000  183.100000   66.900000   \n",
       "max        NaN        NaN            NaN  120.900000  208.100000   72.300000   \n",
       "\n",
       "         carheight   curbweight enginetype cylindernumber  enginesize  \\\n",
       "count   205.000000   205.000000        205            205  205.000000   \n",
       "unique         NaN          NaN          7              7         NaN   \n",
       "top            NaN          NaN        ohc           four         NaN   \n",
       "freq           NaN          NaN        148            159         NaN   \n",
       "mean     53.724878  2555.565854        NaN            NaN  126.907317   \n",
       "std       2.443522   520.680204        NaN            NaN   41.642693   \n",
       "min      47.800000  1488.000000        NaN            NaN   61.000000   \n",
       "25%      52.000000  2145.000000        NaN            NaN   97.000000   \n",
       "50%      54.100000  2414.000000        NaN            NaN  120.000000   \n",
       "75%      55.500000  2935.000000        NaN            NaN  141.000000   \n",
       "max      59.800000  4066.000000        NaN            NaN  326.000000   \n",
       "\n",
       "       fuelsystem   boreratio      stroke  compressionratio  horsepower  \\\n",
       "count         205  205.000000  205.000000        205.000000  205.000000   \n",
       "unique          8         NaN         NaN               NaN         NaN   \n",
       "top          mpfi         NaN         NaN               NaN         NaN   \n",
       "freq           94         NaN         NaN               NaN         NaN   \n",
       "mean          NaN    3.329756    3.255415         10.142537  104.117073   \n",
       "std           NaN    0.270844    0.313597          3.972040   39.544167   \n",
       "min           NaN    2.540000    2.070000          7.000000   48.000000   \n",
       "25%           NaN    3.150000    3.110000          8.600000   70.000000   \n",
       "50%           NaN    3.310000    3.290000          9.000000   95.000000   \n",
       "75%           NaN    3.580000    3.410000          9.400000  116.000000   \n",
       "max           NaN    3.940000    4.170000         23.000000  288.000000   \n",
       "\n",
       "            peakrpm     citympg  highwaympg         price  \n",
       "count    205.000000  205.000000  205.000000    205.000000  \n",
       "unique          NaN         NaN         NaN           NaN  \n",
       "top             NaN         NaN         NaN           NaN  \n",
       "freq            NaN         NaN         NaN           NaN  \n",
       "mean    5125.121951   25.219512   30.751220  13276.710571  \n",
       "std      476.985643    6.542142    6.886443   7988.852332  \n",
       "min     4150.000000   13.000000   16.000000   5118.000000  \n",
       "25%     4800.000000   19.000000   25.000000   7788.000000  \n",
       "50%     5200.000000   24.000000   30.000000  10295.000000  \n",
       "75%     5500.000000   30.000000   34.000000  16503.000000  \n",
       "max     6600.000000   49.000000   54.000000  45400.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing every column\n",
    "car.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##  Step 2. Data Cleaning and Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>201</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 145e (sw)</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 144ea</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>203</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 244dl</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 246</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 264gl</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  symboling                   CarName fueltype aspiration  \\\n",
       "0         1          3        alfa-romero giulia      gas        std   \n",
       "1         2          3       alfa-romero stelvio      gas        std   \n",
       "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
       "3         4          2               audi 100 ls      gas        std   \n",
       "4         5          2                audi 100ls      gas        std   \n",
       "..      ...        ...                       ...      ...        ...   \n",
       "200     201         -1           volvo 145e (sw)      gas        std   \n",
       "201     202         -1               volvo 144ea      gas      turbo   \n",
       "202     203         -1               volvo 244dl      gas        std   \n",
       "203     204         -1                 volvo 246   diesel      turbo   \n",
       "204     205         -1               volvo 264gl      gas      turbo   \n",
       "\n",
       "    doornumber      carbody drivewheel enginelocation  wheelbase  carlength  \\\n",
       "0          two  convertible        rwd          front       88.6      168.8   \n",
       "1          two  convertible        rwd          front       88.6      168.8   \n",
       "2          two    hatchback        rwd          front       94.5      171.2   \n",
       "3         four        sedan        fwd          front       99.8      176.6   \n",
       "4         four        sedan        4wd          front       99.4      176.6   \n",
       "..         ...          ...        ...            ...        ...        ...   \n",
       "200       four        sedan        rwd          front      109.1      188.8   \n",
       "201       four        sedan        rwd          front      109.1      188.8   \n",
       "202       four        sedan        rwd          front      109.1      188.8   \n",
       "203       four        sedan        rwd          front      109.1      188.8   \n",
       "204       four        sedan        rwd          front      109.1      188.8   \n",
       "\n",
       "     carwidth  carheight  curbweight enginetype cylindernumber  enginesize  \\\n",
       "0        64.1       48.8        2548       dohc           four         130   \n",
       "1        64.1       48.8        2548       dohc           four         130   \n",
       "2        65.5       52.4        2823       ohcv            six         152   \n",
       "3        66.2       54.3        2337        ohc           four         109   \n",
       "4        66.4       54.3        2824        ohc           five         136   \n",
       "..        ...        ...         ...        ...            ...         ...   \n",
       "200      68.9       55.5        2952        ohc           four         141   \n",
       "201      68.8       55.5        3049        ohc           four         141   \n",
       "202      68.9       55.5        3012       ohcv            six         173   \n",
       "203      68.9       55.5        3217        ohc            six         145   \n",
       "204      68.9       55.5        3062        ohc           four         141   \n",
       "\n",
       "    fuelsystem  boreratio  stroke  compressionratio  horsepower  peakrpm  \\\n",
       "0         mpfi       3.47    2.68               9.0         111     5000   \n",
       "1         mpfi       3.47    2.68               9.0         111     5000   \n",
       "2         mpfi       2.68    3.47               9.0         154     5000   \n",
       "3         mpfi       3.19    3.40              10.0         102     5500   \n",
       "4         mpfi       3.19    3.40               8.0         115     5500   \n",
       "..         ...        ...     ...               ...         ...      ...   \n",
       "200       mpfi       3.78    3.15               9.5         114     5400   \n",
       "201       mpfi       3.78    3.15               8.7         160     5300   \n",
       "202       mpfi       3.58    2.87               8.8         134     5500   \n",
       "203        idi       3.01    3.40              23.0         106     4800   \n",
       "204       mpfi       3.78    3.15               9.5         114     5400   \n",
       "\n",
       "     citympg  highwaympg    price  \n",
       "0         21          27  13495.0  \n",
       "1         21          27  16500.0  \n",
       "2         19          26  16500.0  \n",
       "3         24          30  13950.0  \n",
       "4         18          22  17450.0  \n",
       "..       ...         ...      ...  \n",
       "200       23          28  16845.0  \n",
       "201       19          25  19045.0  \n",
       "202       18          23  21485.0  \n",
       "203       26          27  22470.0  \n",
       "204       19          25  22625.0  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping duplicate rows if any\n",
    "car.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota corona        6\n",
       "toyota corolla       6\n",
       "peugeot 504          6\n",
       "subaru dl            4\n",
       "honda civic          3\n",
       "                    ..\n",
       "plymouth valiant     1\n",
       "porcshce panamera    1\n",
       "porsche boxter       1\n",
       "porsche macan        1\n",
       "Nissan versa         1\n",
       "Name: CarName, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# carnames with their count in the data\n",
    "car['CarName'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As we can see that `CarName` column contains the car company name and the model name together.\n",
    "\n",
    "##### So we can extract the name of Car Company from `CarName` column and put it in some other column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "0  convertible        rwd          front       88.6      168.8      64.1   \n",
       "1  convertible        rwd          front       88.6      168.8      64.1   \n",
       "2    hatchback        rwd          front       94.5      171.2      65.5   \n",
       "3        sedan        fwd          front       99.8      176.6      66.2   \n",
       "4        sedan        4wd          front       99.4      176.6      66.4   \n",
       "\n",
       "   carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "0       48.8        2548       dohc           four         130       mpfi   \n",
       "1       48.8        2548       dohc           four         130       mpfi   \n",
       "2       52.4        2823       ohcv            six         152       mpfi   \n",
       "3       54.3        2337        ohc           four         109       mpfi   \n",
       "4       54.3        2824        ohc           five         136       mpfi   \n",
       "\n",
       "   boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "0       3.47    2.68               9.0         111     5000       21   \n",
       "1       3.47    2.68               9.0         111     5000       21   \n",
       "2       2.68    3.47               9.0         154     5000       19   \n",
       "3       3.19    3.40              10.0         102     5500       24   \n",
       "4       3.19    3.40               8.0         115     5500       18   \n",
       "\n",
       "   highwaympg    price        Brand  \n",
       "0          27  13495.0  alfa-romero  \n",
       "1          27  16500.0  alfa-romero  \n",
       "2          26  16500.0  alfa-romero  \n",
       "3          30  13950.0         audi  \n",
       "4          22  17450.0         audi  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting Car Brand name from 'CarName' column and putting it into a new column called 'Brand'\n",
    "def split(x):\n",
    "    return x.split(' ')[0]\n",
    "\n",
    "car['Brand'] = car['CarName'].apply(split)\n",
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota         31\n",
       "nissan         17\n",
       "mazda          15\n",
       "mitsubishi     13\n",
       "honda          13\n",
       "subaru         12\n",
       "volvo          11\n",
       "peugeot        11\n",
       "volkswagen      9\n",
       "dodge           9\n",
       "buick           8\n",
       "bmw             8\n",
       "plymouth        7\n",
       "audi            7\n",
       "saab            6\n",
       "porsche         4\n",
       "isuzu           4\n",
       "chevrolet       3\n",
       "alfa-romero     3\n",
       "jaguar          3\n",
       "vw              2\n",
       "maxda           2\n",
       "renault         2\n",
       "mercury         1\n",
       "porcshce        1\n",
       "toyouta         1\n",
       "vokswagen       1\n",
       "Nissan          1\n",
       "Name: Brand, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of car brands in the data\n",
    "car['Brand'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### We can see that there are some mistakes in the data.There are some repetitions in company names because of the writing mistake as mentioned below:\n",
    "\n",
    "- `toyota` : `toyouta`\n",
    "- `Nissan` : `Nissan`\n",
    "- `porsche` : `porcsche`\n",
    "- `mazda` : `maxda`\n",
    "- `volkswagen` : `vw`, `vokswagen`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all the Brand names to lowercase alphabets\n",
    "car['Brand'] = car['Brand'].apply(lambda y: y.lower())\n",
    "\n",
    "# corecting the wrongly marked data in the dataset\n",
    "car.loc[(car['Brand'] == 'vw') | (car['Brand'] == 'vokswagen'),'Brand'] = 'volkswagen'\n",
    "car.loc[(car['Brand'] == 'toyouta'),'Brand'] = 'toyota'\n",
    "car.loc[(car['Brand'] == 'maxda'),'Brand'] = 'mazda'\n",
    "car.loc[(car['Brand'] == 'porcshce'),'Brand'] = 'porsche'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota         32\n",
       "nissan         18\n",
       "mazda          17\n",
       "honda          13\n",
       "mitsubishi     13\n",
       "subaru         12\n",
       "volkswagen     12\n",
       "volvo          11\n",
       "peugeot        11\n",
       "dodge           9\n",
       "buick           8\n",
       "bmw             8\n",
       "plymouth        7\n",
       "audi            7\n",
       "saab            6\n",
       "porsche         5\n",
       "isuzu           4\n",
       "alfa-romero     3\n",
       "chevrolet       3\n",
       "jaguar          3\n",
       "renault         2\n",
       "mercury         1\n",
       "Name: Brand, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data in brand column after cleaning\n",
    "car['Brand'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four      159\n",
       "six        24\n",
       "five       11\n",
       "eight       5\n",
       "two         4\n",
       "twelve      1\n",
       "three       1\n",
       "Name: cylindernumber, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column cylinder number\n",
    "car['cylindernumber'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    115\n",
       "two      90\n",
       "Name: doornumber, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column doornumber\n",
    "car['doornumber'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### In the above two columns, we can see that the column values are in `object` datatype. We can convert it into `integer` or `int64` datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a separate dataframe for numeric columns\n",
    "numeric = car.select_dtypes(include=['int64','float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting doornumber and cylindernumber from object to integer form\n",
    "car['doornumber'] = car['doornumber'].map({'two':2,'four':4})\n",
    "car['cylindernumber'] = car['cylindernumber'].map({'four':4,'six':6,'five':5,'eight':8,'two':2,'twelve':12,'three':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     159\n",
       "6      24\n",
       "5      11\n",
       "8       5\n",
       "2       4\n",
       "12      1\n",
       "3       1\n",
       "Name: cylindernumber, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after conversion\n",
    "car['cylindernumber'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    115\n",
       "2     90\n",
       "Name: doornumber, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after conversion\n",
    "car['doornumber'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohc      148\n",
       "ohcf      15\n",
       "ohcv      13\n",
       "l         12\n",
       "dohc      12\n",
       "rotor      4\n",
       "dohcv      1\n",
       "Name: enginetype, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car['enginetype'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpfi    94\n",
       "2bbl    66\n",
       "idi     20\n",
       "1bbl    11\n",
       "spdi     9\n",
       "4bbl     3\n",
       "spfi     1\n",
       "mfi      1\n",
       "Name: fuelsystem, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column fuelsystem\n",
    "car['fuelsystem'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gas       185\n",
       "diesel     20\n",
       "Name: fueltype, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column fueltype\n",
    "car['fueltype'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "std      168\n",
       "turbo     37\n",
       "Name: aspiration, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column aspiration\n",
    "car['aspiration'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sedan          96\n",
       "hatchback      70\n",
       "wagon          25\n",
       "hardtop         8\n",
       "convertible     6\n",
       "Name: carbody, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column carbody\n",
    "car['carbody'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    120\n",
       "rwd     76\n",
       "4wd      9\n",
       "Name: drivewheel, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column drivewheel\n",
    "car['drivewheel'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "front    202\n",
       "rear       3\n",
       "Name: enginelocation, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column enginelocation\n",
    "car['enginelocation'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    67\n",
       " 1    54\n",
       " 2    32\n",
       " 3    27\n",
       "-1    22\n",
       "-2     3\n",
       "Name: symboling, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of column symboling\n",
    "car['symboling'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we need to drop the columns which are not suitable for predictions and training data\n",
    "\n",
    "- `car_ID` is an identity number given to every car and it does not have any affect on price.\n",
    "- `CarName` needed to be dropped as we have already extracted the company name (Brand column) from the column.\n",
    "- Columns like `enginetype`, `fuelsystem`, `enginelocation`, `aspiration`, and `fueltype` are highly imbalanced classes or attributes. So we need to drop them as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping all the required columns\n",
    "car.drop(columns=['CarName','car_ID','enginetype','fuelsystem','enginelocation','aspiration','fueltype'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap to show the correlation beyween all numeric columns\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(car.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Dropping columns again according to their correlation\n",
    "\n",
    "Columns `stroke`, `compressionratio`, and `peakrpm` needs to be dropped because they don't have much impact or a good correlation with any other variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns with low correlation with target column\n",
    "car.drop(columns=['stroke','compressionratio','peakrpm'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>6</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  doornumber      carbody drivewheel  wheelbase  carlength  \\\n",
       "0          3           2  convertible        rwd       88.6      168.8   \n",
       "1          3           2  convertible        rwd       88.6      168.8   \n",
       "2          1           2    hatchback        rwd       94.5      171.2   \n",
       "3          2           4        sedan        fwd       99.8      176.6   \n",
       "4          2           4        sedan        4wd       99.4      176.6   \n",
       "\n",
       "   carwidth  carheight  curbweight  cylindernumber  enginesize  boreratio  \\\n",
       "0      64.1       48.8        2548               4         130       3.47   \n",
       "1      64.1       48.8        2548               4         130       3.47   \n",
       "2      65.5       52.4        2823               6         152       2.68   \n",
       "3      66.2       54.3        2337               4         109       3.19   \n",
       "4      66.4       54.3        2824               5         136       3.19   \n",
       "\n",
       "   horsepower  citympg  highwaympg    price        Brand  \n",
       "0         111       21          27  13495.0  alfa-romero  \n",
       "1         111       21          27  16500.0  alfa-romero  \n",
       "2         154       19          26  16500.0  alfa-romero  \n",
       "3         102       24          30  13950.0         audi  \n",
       "4         115       18          22  17450.0         audi  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### Removing outliers from the data because it can affect the model during prediction and also affects the LinearRegression line while making the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting boxplot to identify outliers\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(2,5,1)\n",
    "sns.boxplot(y='wheelbase',data=car)\n",
    "plt.subplot(2,5,2)\n",
    "sns.boxplot(y='carlength',data=car)\n",
    "plt.subplot(2,5,3)\n",
    "sns.boxplot(y='carwidth',data=car)\n",
    "plt.subplot(2,5,4)\n",
    "sns.boxplot(y='carheight',data=car)\n",
    "plt.subplot(2,5,5)\n",
    "sns.boxplot(y='curbweight',data=car)\n",
    "plt.subplot(2,5,6)\n",
    "sns.boxplot(y='enginesize',data=car)\n",
    "plt.subplot(2,5,7)\n",
    "sns.boxplot(y='boreratio',data=car)\n",
    "plt.subplot(2,5,8)\n",
    "sns.boxplot(y='horsepower',data=car)\n",
    "plt.subplot(2,5,9)\n",
    "sns.boxplot(y='citympg',data=car)\n",
    "plt.subplot(2,5,10)\n",
    "sns.boxplot(y='highwaympg',data=car)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### From the above graphs, we can see that the column enginesize have a good amount of outliers which needs to be cleaned or removed for the further predictive analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 25th and 75th percentile of the values in enginesize column\n",
    "Q1_es = car.enginesize.quantile(0.25)\n",
    "Q3_es = car.enginesize.quantile(0.75)\n",
    "\n",
    "# onter quartile range of the values\n",
    "IQR_es = Q3_es - Q1_es\n",
    "\n",
    "# upper limit : The values greater than this are outliers\n",
    "upper_es = Q3_es + 1.5*IQR_es\n",
    "\n",
    "# removing rows with outliers from dataframe\n",
    "car = car[(car.enginesize < upper_es)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 17)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 rows got deleted after the outliers removal\n",
    "car.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### As `enginesize`, `horsepower`, and `curbweight` are highly correlated with `price`, we can categorize them into three parts.\n",
    "\n",
    "- curbweight : light, moderate and, heavy\n",
    "- enginesize : small, medium, and large\n",
    "- horsepower : low, medium, and high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# quantiles for all the columns\n",
    "Q1_hp = car.horsepower.quantile(0.25)\n",
    "Q3_hp = car.horsepower.quantile(0.75)\n",
    "Q1_cw = car.curbweight.quantile(0.25)\n",
    "Q3_cw = car.curbweight.quantile(0.75)\n",
    "\n",
    "# function definitions\n",
    "def curb_convert(x):\n",
    "    if x <= Q1_cw:\n",
    "        return 'light'\n",
    "    elif x < Q3_cw and x > Q1_cw:\n",
    "        return 'moderate'\n",
    "    else:\n",
    "        return 'heavy'\n",
    "        \n",
    "def es_convert(x):\n",
    "    if x <= Q1_es:\n",
    "        return 'small'\n",
    "    elif x < Q3_es and x > Q1_es:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'large'\n",
    "        \n",
    "def hp_convert(x):\n",
    "    if x <= Q1_hp:\n",
    "        return 'low'\n",
    "    elif x < Q3_hp and x > Q1_hp:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'\n",
    "\n",
    "# calling the functions\n",
    "car['curbtype'] = car['curbweight'].apply(curb_convert)\n",
    "car['engsize'] = car['enginesize'].apply(es_convert)\n",
    "car['hptype'] = car['horsepower'].apply(hp_convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>Brand</th>\n",
       "      <th>curbtype</th>\n",
       "      <th>engsize</th>\n",
       "      <th>hptype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>moderate</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>moderate</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>moderate</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>audi</td>\n",
       "      <td>moderate</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>audi</td>\n",
       "      <td>moderate</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       carbody drivewheel        Brand  curbtype engsize  hptype\n",
       "0  convertible        rwd  alfa-romero  moderate  medium  medium\n",
       "1  convertible        rwd  alfa-romero  moderate  medium  medium\n",
       "2    hatchback        rwd  alfa-romero  moderate   large    high\n",
       "3        sedan        fwd         audi  moderate  medium  medium\n",
       "4        sedan        4wd         audi  moderate  medium  medium"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all categorical columns or the columns with object dtype\n",
    "categorical = car.select_dtypes(include=['object'])\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### Creating `categorical columns` vs `price` box plots to understand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17455b22a88>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.subplot(2,4,1)\n",
    "sns.boxplot(x='symboling',y='price',data=car)\n",
    "plt.subplot(2,4,2)\n",
    "sns.boxplot(x='cylindernumber',y='price',data=car)\n",
    "plt.subplot(2,4,3)\n",
    "sns.boxplot(x='doornumber',y='price',data=car)\n",
    "plt.subplot(2,4,4)\n",
    "sns.boxplot(x='carbody',y='price',data=car)\n",
    "plt.subplot(2,4,5)\n",
    "sns.boxplot(x='drivewheel',y='price',data=car)\n",
    "plt.subplot(2,4,6)\n",
    "sns.boxplot(x='curbtype',y='price',data=car)\n",
    "plt.subplot(2,4,7)\n",
    "sns.boxplot(x='engsize',y='price',data=car)\n",
    "plt.subplot(2,4,8)\n",
    "sns.boxplot(x='hptype',y='price',data=car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17455808548>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(x='Brand',y='price',data=car)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation sfrom the above boplots:\n",
    "\n",
    "- The `Brand` boxplot clearly shows us that the car companies like `bmw`, `buick`, `porsche` and `volvo` has higher prices and the lowest belongs to `chevrolet`\n",
    "- cars having \"rwd\" `drivewheel` are costly withrespect to other categories.\n",
    "- Vehicles having more curbweight are supposed to be costly than the light ones.\n",
    "- `Doornumber` does not have much effect on the price of car.\n",
    "- Hatchback vehicles have the lowest median price of vehicles in the data set whereas hardtop vehicles have the highest median price.\n",
    "- Large `enginesize` vehicles are costly as compared to others.\n",
    "- The cars with extreme (-3 to 3) `symboling` rating have higher price that the average (0 or 1) ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Creating dummies of all the categorical columns left in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   carbody_hardtop  carbody_hatchback  carbody_sedan  carbody_wagon  \\\n",
       "0                0                  0              0              0   \n",
       "1                0                  0              0              0   \n",
       "2                0                  1              0              0   \n",
       "3                0                  0              1              0   \n",
       "4                0                  0              1              0   \n",
       "\n",
       "   drivewheel_fwd  drivewheel_rwd  Brand_audi  Brand_bmw  Brand_buick  \\\n",
       "0               0               1           0          0            0   \n",
       "1               0               1           0          0            0   \n",
       "2               0               1           0          0            0   \n",
       "3               1               0           1          0            0   \n",
       "4               0               0           1          0            0   \n",
       "\n",
       "   Brand_chevrolet  Brand_dodge  Brand_honda  Brand_isuzu  Brand_mazda  \\\n",
       "0                0            0            0            0            0   \n",
       "1                0            0            0            0            0   \n",
       "2                0            0            0            0            0   \n",
       "3                0            0            0            0            0   \n",
       "4                0            0            0            0            0   \n",
       "\n",
       "   Brand_mercury  Brand_mitsubishi  Brand_nissan  Brand_peugeot  \\\n",
       "0              0                 0             0              0   \n",
       "1              0                 0             0              0   \n",
       "2              0                 0             0              0   \n",
       "3              0                 0             0              0   \n",
       "4              0                 0             0              0   \n",
       "\n",
       "   Brand_plymouth  Brand_porsche  Brand_renault  Brand_saab  Brand_subaru  \\\n",
       "0               0              0              0           0             0   \n",
       "1               0              0              0           0             0   \n",
       "2               0              0              0           0             0   \n",
       "3               0              0              0           0             0   \n",
       "4               0              0              0           0             0   \n",
       "\n",
       "   Brand_toyota  Brand_volkswagen  Brand_volvo  curbtype_light  \\\n",
       "0             0                 0            0               0   \n",
       "1             0                 0            0               0   \n",
       "2             0                 0            0               0   \n",
       "3             0                 0            0               0   \n",
       "4             0                 0            0               0   \n",
       "\n",
       "   curbtype_moderate  engsize_medium  engsize_small  hptype_low  hptype_medium  \n",
       "0                  1               1              0           0              1  \n",
       "1                  1               1              0           0              1  \n",
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       "3                  1               1              0           0              1  \n",
       "4                  1               1              0           0              1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting to dummies\n",
    "dummies = pd.get_dummies(categorical,drop_first=True)\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concat the dummies dataframe to the main dataframe\n",
    "car = pd.concat([car,dummies],axis=1)\n",
    "\n",
    "# dropping all the categorical columns as the dummies are already created\n",
    "car.drop(columns=categorical.columns,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  doornumber  wheelbase  carlength  carwidth  carheight  \\\n",
       "0          3           2       88.6      168.8      64.1       48.8   \n",
       "1          3           2       88.6      168.8      64.1       48.8   \n",
       "2          1           2       94.5      171.2      65.5       52.4   \n",
       "3          2           4       99.8      176.6      66.2       54.3   \n",
       "4          2           4       99.4      176.6      66.4       54.3   \n",
       "\n",
       "   curbweight  cylindernumber  enginesize  boreratio  horsepower  citympg  \\\n",
       "0        2548               4         130       3.47         111       21   \n",
       "1        2548               4         130       3.47         111       21   \n",
       "2        2823               6         152       2.68         154       19   \n",
       "3        2337               4         109       3.19         102       24   \n",
       "4        2824               5         136       3.19         115       18   \n",
       "\n",
       "   highwaympg    price  carbody_hardtop  carbody_hatchback  carbody_sedan  \\\n",
       "0          27  13495.0                0                  0              0   \n",
       "1          27  16500.0                0                  0              0   \n",
       "2          26  16500.0                0                  1              0   \n",
       "3          30  13950.0                0                  0              1   \n",
       "4          22  17450.0                0                  0              1   \n",
       "\n",
       "   carbody_wagon  drivewheel_fwd  drivewheel_rwd  Brand_audi  Brand_bmw  \\\n",
       "0              0               0               1           0          0   \n",
       "1              0               0               1           0          0   \n",
       "2              0               0               1           0          0   \n",
       "3              0               1               0           1          0   \n",
       "4              0               0               0           1          0   \n",
       "\n",
       "   Brand_buick  Brand_chevrolet  Brand_dodge  Brand_honda  Brand_isuzu  \\\n",
       "0            0                0            0            0            0   \n",
       "1            0                0            0            0            0   \n",
       "2            0                0            0            0            0   \n",
       "3            0                0            0            0            0   \n",
       "4            0                0            0            0            0   \n",
       "\n",
       "   Brand_mazda  Brand_mercury  Brand_mitsubishi  Brand_nissan  Brand_peugeot  \\\n",
       "0            0              0                 0             0              0   \n",
       "1            0              0                 0             0              0   \n",
       "2            0              0                 0             0              0   \n",
       "3            0              0                 0             0              0   \n",
       "4            0              0                 0             0              0   \n",
       "\n",
       "   Brand_plymouth  Brand_porsche  Brand_renault  Brand_saab  Brand_subaru  \\\n",
       "0               0              0              0           0             0   \n",
       "1               0              0              0           0             0   \n",
       "2               0              0              0           0             0   \n",
       "3               0              0              0           0             0   \n",
       "4               0              0              0           0             0   \n",
       "\n",
       "   Brand_toyota  Brand_volkswagen  Brand_volvo  curbtype_light  \\\n",
       "0             0                 0            0               0   \n",
       "1             0                 0            0               0   \n",
       "2             0                 0            0               0   \n",
       "3             0                 0            0               0   \n",
       "4             0                 0            0               0   \n",
       "\n",
       "   curbtype_moderate  engsize_medium  engsize_small  hptype_low  hptype_medium  \n",
       "0                  1               1              0           0              1  \n",
       "1                  1               1              0           0              1  \n",
       "2                  1               0              0           0              0  \n",
       "3                  1               1              0           0              1  \n",
       "4                  1               1              0           0              1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 46)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "## Step 3. Model Building\n",
    "---\n",
    "### Train-Test Split\n",
    "\n",
    "You now need to split our variable into training and testing sets. You'll perform this by importing `train_test_split` from the `sklearn.model_selection` library. It is usually a good practice to keep 70% of the data in your train dataset and the rest 30% in your test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the main data into train and test data\n",
    "car_train, car_test = train_test_split(car, train_size=0.7, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = car_train.drop('price',1)\n",
    "y_train = car_train['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Scaling\n",
    "\n",
    "Now we need to scale the variables for better interpretability.\n",
    "<br>\n",
    "As all the final independent variables in our dataset are numeric, we'll scale all the variables\n",
    "<br>\n",
    "Let's scale all these columns using StandardScaler. \n",
    "You can use any other scaling method as well; it is totally up to you. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler object\n",
    "scaler = StandardScaler()\n",
    "x_train[x_train.columns] = scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>carbody_hardtop</th>\n",
       "      <th>carbody_hatchback</th>\n",
       "      <th>carbody_sedan</th>\n",
       "      <th>carbody_wagon</th>\n",
       "      <th>drivewheel_fwd</th>\n",
       "      <th>drivewheel_rwd</th>\n",
       "      <th>Brand_audi</th>\n",
       "      <th>Brand_bmw</th>\n",
       "      <th>Brand_buick</th>\n",
       "      <th>Brand_chevrolet</th>\n",
       "      <th>Brand_dodge</th>\n",
       "      <th>Brand_honda</th>\n",
       "      <th>Brand_isuzu</th>\n",
       "      <th>Brand_mazda</th>\n",
       "      <th>Brand_mercury</th>\n",
       "      <th>Brand_mitsubishi</th>\n",
       "      <th>Brand_nissan</th>\n",
       "      <th>Brand_peugeot</th>\n",
       "      <th>Brand_plymouth</th>\n",
       "      <th>Brand_porsche</th>\n",
       "      <th>Brand_renault</th>\n",
       "      <th>Brand_saab</th>\n",
       "      <th>Brand_subaru</th>\n",
       "      <th>Brand_toyota</th>\n",
       "      <th>Brand_volkswagen</th>\n",
       "      <th>Brand_volvo</th>\n",
       "      <th>curbtype_light</th>\n",
       "      <th>curbtype_moderate</th>\n",
       "      <th>engsize_medium</th>\n",
       "      <th>engsize_small</th>\n",
       "      <th>hptype_low</th>\n",
       "      <th>hptype_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.938539</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>0.300864</td>\n",
       "      <td>0.349295</td>\n",
       "      <td>0.291222</td>\n",
       "      <td>0.210773</td>\n",
       "      <td>-0.357333</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>-0.375031</td>\n",
       "      <td>-0.436398</td>\n",
       "      <td>0.128117</td>\n",
       "      <td>-0.263558</td>\n",
       "      <td>-0.182816</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>4.654747</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1.500467</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>1.156879</td>\n",
       "      <td>1.462104</td>\n",
       "      <td>0.846708</td>\n",
       "      <td>1.481250</td>\n",
       "      <td>1.220104</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.743637</td>\n",
       "      <td>1.901159</td>\n",
       "      <td>0.513884</td>\n",
       "      <td>-0.430274</td>\n",
       "      <td>-0.505713</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>2.481511</td>\n",
       "      <td>-1.270978</td>\n",
       "      <td>1.470643</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>4.292851</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>-1.014816</td>\n",
       "      <td>-0.956814</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.751542</td>\n",
       "      <td>-1.159667</td>\n",
       "      <td>-0.441016</td>\n",
       "      <td>0.039168</td>\n",
       "      <td>0.346770</td>\n",
       "      <td>-1.417026</td>\n",
       "      <td>0.715415</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>1.268012</td>\n",
       "      <td>1.188006</td>\n",
       "      <td>1.510452</td>\n",
       "      <td>-1.097138</td>\n",
       "      <td>-1.151506</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>1.354006</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>3.756476</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>-0.956814</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>-0.687465</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>-0.402970</td>\n",
       "      <td>0.796243</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.568095</td>\n",
       "      <td>0.190357</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.429011</td>\n",
       "      <td>0.633331</td>\n",
       "      <td>-0.257651</td>\n",
       "      <td>-0.430274</td>\n",
       "      <td>-0.021368</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>2.481511</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>8.185353</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.938539</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>0.167706</td>\n",
       "      <td>1.261434</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.925416</td>\n",
       "      <td>0.452886</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.044469</td>\n",
       "      <td>0.950288</td>\n",
       "      <td>0.385295</td>\n",
       "      <td>-0.763706</td>\n",
       "      <td>-0.505713</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>8.185353</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  doornumber  wheelbase  carlength  carwidth  carheight  \\\n",
       "3     0.938539    0.862316   0.300864   0.349295  0.291222   0.210773   \n",
       "195  -1.500467    0.862316   1.156879   1.462104  0.846708   1.481250   \n",
       "29    1.751542   -1.159667  -0.441016   0.039168  0.346770  -1.417026   \n",
       "130  -0.687465    0.862316  -0.402970   0.796243  0.457868   0.568095   \n",
       "133   0.938539    0.862316   0.167706   1.261434  0.457868   0.925416   \n",
       "\n",
       "     curbweight  cylindernumber  enginesize  boreratio  horsepower   citympg  \\\n",
       "3     -0.357333       -0.301113   -0.375031  -0.436398    0.128117 -0.263558   \n",
       "195    1.220104       -0.301113    0.743637   1.901159    0.513884 -0.430274   \n",
       "29     0.715415       -0.301113    1.268012   1.188006    1.510452 -1.097138   \n",
       "130    0.190357       -0.301113    0.429011   0.633331   -0.257651 -0.430274   \n",
       "133    0.452886       -0.301113    0.044469   0.950288    0.385295 -0.763706   \n",
       "\n",
       "     highwaympg  carbody_hardtop  carbody_hatchback  carbody_sedan  \\\n",
       "3     -0.182816        -0.150188          -0.738549       1.076443   \n",
       "195   -0.505713        -0.150188          -0.738549      -0.928985   \n",
       "29    -1.151506        -0.150188           1.354006      -0.928985   \n",
       "130   -0.021368        -0.150188          -0.738549      -0.928985   \n",
       "133   -0.505713        -0.150188          -0.738549       1.076443   \n",
       "\n",
       "     carbody_wagon  drivewheel_fwd  drivewheel_rwd  Brand_audi  Brand_bmw  \\\n",
       "3        -0.402980        0.786796       -0.679975    4.654747  -0.174078   \n",
       "195       2.481511       -1.270978        1.470643   -0.214834  -0.174078   \n",
       "29       -0.402980        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "130       2.481511        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "133      -0.402980        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "\n",
       "     Brand_buick  Brand_chevrolet  Brand_dodge  Brand_honda  Brand_isuzu  \\\n",
       "3      -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "195    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "29     -0.150188        -0.122169     3.756476    -0.266207    -0.122169   \n",
       "130    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "133    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "\n",
       "     Brand_mazda  Brand_mercury  Brand_mitsubishi  Brand_nissan  \\\n",
       "3      -0.352089            0.0         -0.266207     -0.281718   \n",
       "195    -0.352089            0.0         -0.266207     -0.281718   \n",
       "29     -0.352089            0.0         -0.266207     -0.281718   \n",
       "130    -0.352089            0.0         -0.266207     -0.281718   \n",
       "133    -0.352089            0.0         -0.266207     -0.281718   \n",
       "\n",
       "     Brand_peugeot  Brand_plymouth  Brand_porsche  Brand_renault  Brand_saab  \\\n",
       "3        -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "195      -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "29       -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "130      -0.266207       -0.214834      -0.122169       8.185353   -0.122169   \n",
       "133      -0.266207       -0.214834      -0.122169      -0.122169    8.185353   \n",
       "\n",
       "     Brand_subaru  Brand_toyota  Brand_volkswagen  Brand_volvo  \\\n",
       "3       -0.281718     -0.377964         -0.281718    -0.232945   \n",
       "195     -0.281718     -0.377964         -0.281718     4.292851   \n",
       "29      -0.281718     -0.377964         -0.281718    -0.232945   \n",
       "130     -0.281718     -0.377964         -0.281718    -0.232945   \n",
       "133     -0.281718     -0.377964         -0.281718    -0.232945   \n",
       "\n",
       "     curbtype_light  curbtype_moderate  engsize_medium  engsize_small  \\\n",
       "3          -0.57735           0.985401        1.045135        -0.6227   \n",
       "195        -0.57735          -1.014816       -0.956814        -0.6227   \n",
       "29         -0.57735           0.985401       -0.956814        -0.6227   \n",
       "130        -0.57735           0.985401        1.045135        -0.6227   \n",
       "133        -0.57735           0.985401        1.045135        -0.6227   \n",
       "\n",
       "     hptype_low  hptype_medium  \n",
       "3     -0.611341            1.0  \n",
       "195   -0.611341            1.0  \n",
       "29    -0.611341           -1.0  \n",
       "130   -0.611341            1.0  \n",
       "133   -0.611341            1.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Building the first Linear Regression model with all the features\n",
    "\n",
    "Equation of linear regression<br>\n",
    "$y = c + m_1x_1 + m_2x_2 + ... + m_nx_n$\n",
    "\n",
    "-  $y$ is the response\n",
    "-  $c$ is the intercept\n",
    "-  $m_1$ is the coefficient for the first feature\n",
    "-  $m_n$ is the coefficient for the nth feature<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649285454615241"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The model is currently 96.4% accurate which is a very good value during model building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Model Building Using RFE\n",
    "\n",
    "Now, you have close to 45 features. It is not adviced to manually eliminate these features. So let's now build a model using recursive feature elimination to select features. \n",
    "\n",
    "We'll first start off by finding out the optimal number of features using RFECV, and then use the `statsmodels` library to build models using the shortlisted features (this is also because `SKLearn` doesn't have `Adjusted R-squared` that `statsmodels` has)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 38\n"
     ]
    }
   ],
   "source": [
    "rfecv = RFECV(estimator=lm,step=1,cv=3)\n",
    "rfecv.fit(x_train,y_train)\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('symboling', True, 1),\n",
       " ('doornumber', True, 1),\n",
       " ('wheelbase', True, 1),\n",
       " ('carlength', True, 1),\n",
       " ('carwidth', True, 1),\n",
       " ('carheight', True, 1),\n",
       " ('curbweight', True, 1),\n",
       " ('cylindernumber', True, 1),\n",
       " ('enginesize', True, 1),\n",
       " ('boreratio', True, 1),\n",
       " ('horsepower', True, 1),\n",
       " ('citympg', False, 7),\n",
       " ('highwaympg', False, 3),\n",
       " ('carbody_hardtop', True, 1),\n",
       " ('carbody_hatchback', True, 1),\n",
       " ('carbody_sedan', True, 1),\n",
       " ('carbody_wagon', True, 1),\n",
       " ('drivewheel_fwd', True, 1),\n",
       " ('drivewheel_rwd', True, 1),\n",
       " ('Brand_audi', True, 1),\n",
       " ('Brand_bmw', True, 1),\n",
       " ('Brand_buick', True, 1),\n",
       " ('Brand_chevrolet', False, 2),\n",
       " ('Brand_dodge', True, 1),\n",
       " ('Brand_honda', True, 1),\n",
       " ('Brand_isuzu', False, 4),\n",
       " ('Brand_mazda', True, 1),\n",
       " ('Brand_mercury', False, 8),\n",
       " ('Brand_mitsubishi', True, 1),\n",
       " ('Brand_nissan', True, 1),\n",
       " ('Brand_peugeot', True, 1),\n",
       " ('Brand_plymouth', True, 1),\n",
       " ('Brand_porsche', True, 1),\n",
       " ('Brand_renault', False, 6),\n",
       " ('Brand_saab', True, 1),\n",
       " ('Brand_subaru', True, 1),\n",
       " ('Brand_toyota', True, 1),\n",
       " ('Brand_volkswagen', False, 5),\n",
       " ('Brand_volvo', True, 1),\n",
       " ('curbtype_light', True, 1),\n",
       " ('curbtype_moderate', True, 1),\n",
       " ('engsize_medium', True, 1),\n",
       " ('engsize_small', True, 1),\n",
       " ('hptype_low', True, 1),\n",
       " ('hptype_medium', True, 1)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  RFE object\n",
    "lm = LinearRegression()\n",
    "\n",
    "# RFE with 38 columns\n",
    "rfe = RFE(lm,38)\n",
    "\n",
    "# fit with 38 columns\n",
    "rfe.fit(x_train,y_train)\n",
    "\n",
    "list(zip(x_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Building a Linear Model using statsmodels\n",
    "\n",
    "By default, the `statsmodels` library fits a line on the dataset which passes through the origin. But in order to have an intercept, you need to manually use the `add_constant` attribute of `statsmodels`. And once you've added the constant to your `x_train` dataset, you can go ahead and fit a regression line using the `OLS` (Ordinary Least Squares) attribute of `statsmodels` as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>symboling</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>carbody_hardtop</th>\n",
       "      <th>carbody_hatchback</th>\n",
       "      <th>carbody_sedan</th>\n",
       "      <th>carbody_wagon</th>\n",
       "      <th>drivewheel_fwd</th>\n",
       "      <th>drivewheel_rwd</th>\n",
       "      <th>Brand_audi</th>\n",
       "      <th>Brand_bmw</th>\n",
       "      <th>Brand_buick</th>\n",
       "      <th>Brand_dodge</th>\n",
       "      <th>Brand_honda</th>\n",
       "      <th>Brand_mazda</th>\n",
       "      <th>Brand_mitsubishi</th>\n",
       "      <th>Brand_nissan</th>\n",
       "      <th>Brand_peugeot</th>\n",
       "      <th>Brand_plymouth</th>\n",
       "      <th>Brand_porsche</th>\n",
       "      <th>Brand_saab</th>\n",
       "      <th>Brand_subaru</th>\n",
       "      <th>Brand_toyota</th>\n",
       "      <th>Brand_volvo</th>\n",
       "      <th>curbtype_light</th>\n",
       "      <th>curbtype_moderate</th>\n",
       "      <th>engsize_medium</th>\n",
       "      <th>engsize_small</th>\n",
       "      <th>hptype_low</th>\n",
       "      <th>hptype_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938539</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>0.300864</td>\n",
       "      <td>0.349295</td>\n",
       "      <td>0.291222</td>\n",
       "      <td>0.210773</td>\n",
       "      <td>-0.357333</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>-0.375031</td>\n",
       "      <td>-0.436398</td>\n",
       "      <td>0.128117</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>4.654747</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.500467</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>1.156879</td>\n",
       "      <td>1.462104</td>\n",
       "      <td>0.846708</td>\n",
       "      <td>1.481250</td>\n",
       "      <td>1.220104</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.743637</td>\n",
       "      <td>1.901159</td>\n",
       "      <td>0.513884</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>2.481511</td>\n",
       "      <td>-1.270978</td>\n",
       "      <td>1.470643</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>4.292851</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>-1.014816</td>\n",
       "      <td>-0.956814</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.751542</td>\n",
       "      <td>-1.159667</td>\n",
       "      <td>-0.441016</td>\n",
       "      <td>0.039168</td>\n",
       "      <td>0.346770</td>\n",
       "      <td>-1.417026</td>\n",
       "      <td>0.715415</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>1.268012</td>\n",
       "      <td>1.188006</td>\n",
       "      <td>1.510452</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>1.354006</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>3.756476</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>-0.956814</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.687465</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>-0.402970</td>\n",
       "      <td>0.796243</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.568095</td>\n",
       "      <td>0.190357</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.429011</td>\n",
       "      <td>0.633331</td>\n",
       "      <td>-0.257651</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>2.481511</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938539</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>0.167706</td>\n",
       "      <td>1.261434</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.925416</td>\n",
       "      <td>0.452886</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.044469</td>\n",
       "      <td>0.950288</td>\n",
       "      <td>0.385295</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.402980</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>8.185353</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.57735</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     const  symboling  doornumber  wheelbase  carlength  carwidth  carheight  \\\n",
       "3      1.0   0.938539    0.862316   0.300864   0.349295  0.291222   0.210773   \n",
       "195    1.0  -1.500467    0.862316   1.156879   1.462104  0.846708   1.481250   \n",
       "29     1.0   1.751542   -1.159667  -0.441016   0.039168  0.346770  -1.417026   \n",
       "130    1.0  -0.687465    0.862316  -0.402970   0.796243  0.457868   0.568095   \n",
       "133    1.0   0.938539    0.862316   0.167706   1.261434  0.457868   0.925416   \n",
       "\n",
       "     curbweight  cylindernumber  enginesize  boreratio  horsepower  \\\n",
       "3     -0.357333       -0.301113   -0.375031  -0.436398    0.128117   \n",
       "195    1.220104       -0.301113    0.743637   1.901159    0.513884   \n",
       "29     0.715415       -0.301113    1.268012   1.188006    1.510452   \n",
       "130    0.190357       -0.301113    0.429011   0.633331   -0.257651   \n",
       "133    0.452886       -0.301113    0.044469   0.950288    0.385295   \n",
       "\n",
       "     carbody_hardtop  carbody_hatchback  carbody_sedan  carbody_wagon  \\\n",
       "3          -0.150188          -0.738549       1.076443      -0.402980   \n",
       "195        -0.150188          -0.738549      -0.928985       2.481511   \n",
       "29         -0.150188           1.354006      -0.928985      -0.402980   \n",
       "130        -0.150188          -0.738549      -0.928985       2.481511   \n",
       "133        -0.150188          -0.738549       1.076443      -0.402980   \n",
       "\n",
       "     drivewheel_fwd  drivewheel_rwd  Brand_audi  Brand_bmw  Brand_buick  \\\n",
       "3          0.786796       -0.679975    4.654747  -0.174078    -0.150188   \n",
       "195       -1.270978        1.470643   -0.214834  -0.174078    -0.150188   \n",
       "29         0.786796       -0.679975   -0.214834  -0.174078    -0.150188   \n",
       "130        0.786796       -0.679975   -0.214834  -0.174078    -0.150188   \n",
       "133        0.786796       -0.679975   -0.214834  -0.174078    -0.150188   \n",
       "\n",
       "     Brand_dodge  Brand_honda  Brand_mazda  Brand_mitsubishi  Brand_nissan  \\\n",
       "3      -0.266207    -0.266207    -0.352089         -0.266207     -0.281718   \n",
       "195    -0.266207    -0.266207    -0.352089         -0.266207     -0.281718   \n",
       "29      3.756476    -0.266207    -0.352089         -0.266207     -0.281718   \n",
       "130    -0.266207    -0.266207    -0.352089         -0.266207     -0.281718   \n",
       "133    -0.266207    -0.266207    -0.352089         -0.266207     -0.281718   \n",
       "\n",
       "     Brand_peugeot  Brand_plymouth  Brand_porsche  Brand_saab  Brand_subaru  \\\n",
       "3        -0.266207       -0.214834      -0.122169   -0.122169     -0.281718   \n",
       "195      -0.266207       -0.214834      -0.122169   -0.122169     -0.281718   \n",
       "29       -0.266207       -0.214834      -0.122169   -0.122169     -0.281718   \n",
       "130      -0.266207       -0.214834      -0.122169   -0.122169     -0.281718   \n",
       "133      -0.266207       -0.214834      -0.122169    8.185353     -0.281718   \n",
       "\n",
       "     Brand_toyota  Brand_volvo  curbtype_light  curbtype_moderate  \\\n",
       "3       -0.377964    -0.232945        -0.57735           0.985401   \n",
       "195     -0.377964     4.292851        -0.57735          -1.014816   \n",
       "29      -0.377964    -0.232945        -0.57735           0.985401   \n",
       "130     -0.377964    -0.232945        -0.57735           0.985401   \n",
       "133     -0.377964    -0.232945        -0.57735           0.985401   \n",
       "\n",
       "     engsize_medium  engsize_small  hptype_low  hptype_medium  \n",
       "3          1.045135        -0.6227   -0.611341            1.0  \n",
       "195       -0.956814        -0.6227   -0.611341            1.0  \n",
       "29        -0.956814        -0.6227   -0.611341           -1.0  \n",
       "130        1.045135        -0.6227   -0.611341            1.0  \n",
       "133        1.045135        -0.6227   -0.611341            1.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset the features selected by rfe1\n",
    "col1 = x_train.columns[rfe.support_]\n",
    "\n",
    "# subsetting training data for 38 columns\n",
    "x_train_rfe = x_train[col1]\n",
    "\n",
    "# add constant to model\n",
    "x_train_rfe = sm.add_constant(x_train_rfe)\n",
    "x_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.965\n",
      "Model:                            OLS   Adj. R-squared:                  0.951\n",
      "Method:                 Least Squares   F-statistic:                     69.99\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.35e-55\n",
      "Time:                        19:15:22   Log-Likelihood:                -1144.1\n",
      "No. Observations:                 136   AIC:                             2366.\n",
      "Df Residuals:                      97   BIC:                             2480.\n",
      "Df Model:                          38                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    110.648    107.593      0.000    1.17e+04    1.21e+04\n",
      "symboling           421.2260    229.002      1.839      0.069     -33.279     875.731\n",
      "doornumber          482.4057    215.775      2.236      0.028      54.153     910.659\n",
      "wheelbase          1917.2527    459.458      4.173      0.000    1005.355    2829.150\n",
      "carlength         -1701.2500    488.280     -3.484      0.001   -2670.351    -732.149\n",
      "carwidth            996.4727    359.723      2.770      0.007     282.522    1710.423\n",
      "carheight          -598.9992    259.734     -2.306      0.023   -1114.500     -83.499\n",
      "curbweight         3069.7186    653.006      4.701      0.000    1773.681    4365.756\n",
      "cylindernumber    -1321.3011    715.382     -1.847      0.068   -2741.136      98.534\n",
      "enginesize         1954.5856   1235.095      1.583      0.117    -496.736    4405.907\n",
      "boreratio         -1779.7945    643.128     -2.767      0.007   -3056.226    -503.363\n",
      "horsepower         1269.3820    464.391      2.733      0.007     347.694    2191.070\n",
      "carbody_hardtop    -312.2936    172.325     -1.812      0.073    -654.311      29.724\n",
      "carbody_hatchback -1550.5805    464.850     -3.336      0.001   -2473.179    -627.982\n",
      "carbody_sedan     -1436.7830    514.772     -2.791      0.006   -2458.462    -415.104\n",
      "carbody_wagon     -1085.9827    389.431     -2.789      0.006   -1858.895    -313.070\n",
      "drivewheel_fwd       97.1118    338.798      0.287      0.775    -575.308     769.532\n",
      "drivewheel_rwd      636.9031    406.053      1.569      0.120    -169.000    1442.806\n",
      "Brand_audi          494.3428    170.920      2.892      0.005     155.114     833.571\n",
      "Brand_bmw          1484.8099    196.286      7.565      0.000    1095.236    1874.383\n",
      "Brand_buick         837.6318    207.435      4.038      0.000     425.931    1249.332\n",
      "Brand_dodge        -317.5175    160.779     -1.975      0.051    -636.620       1.585\n",
      "Brand_honda         165.5062    158.598      1.044      0.299    -149.267     480.279\n",
      "Brand_mazda         294.3736    169.412      1.738      0.085     -41.863     630.610\n",
      "Brand_mitsubishi   -468.4088    162.597     -2.881      0.005    -791.119    -145.699\n",
      "Brand_nissan        310.0194    170.955      1.813      0.073     -29.278     649.317\n",
      "Brand_peugeot      -285.6133    300.571     -0.950      0.344    -882.164     310.937\n",
      "Brand_plymouth     -364.9853    144.981     -2.517      0.013    -652.733     -77.237\n",
      "Brand_porsche      2453.5770    206.924     11.857      0.000    2042.890    2864.264\n",
      "Brand_saab          444.7427    154.616      2.876      0.005     137.872     751.613\n",
      "Brand_subaru        715.7035    355.777      2.012      0.047       9.585    1421.822\n",
      "Brand_toyota       -160.5783    207.374     -0.774      0.441    -572.159     251.002\n",
      "Brand_volvo        1126.3574    312.514      3.604      0.000     506.103    1746.612\n",
      "curbtype_light      431.1882    493.168      0.874      0.384    -547.614    1409.991\n",
      "curbtype_moderate   568.6424    388.837      1.462      0.147    -203.092    1340.377\n",
      "engsize_medium     1111.8585    499.897      2.224      0.028     119.702    2104.015\n",
      "engsize_small       682.1815    608.116      1.122      0.265    -524.760    1889.123\n",
      "hptype_low          656.6755    516.129      1.272      0.206    -367.697    1681.048\n",
      "hptype_medium      -187.6790    333.294     -0.563      0.575    -849.175     473.817\n",
      "==============================================================================\n",
      "Omnibus:                        1.520   Durbin-Watson:                   2.080\n",
      "Prob(Omnibus):                  0.468   Jarque-Bera (JB):                1.070\n",
      "Skew:                           0.120   Prob(JB):                        0.586\n",
      "Kurtosis:                       3.362   Cond. No.                         45.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# fitting the model with 38 variables\n",
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "\n",
    "# Performing a summary operation lists out all the different parameters of the regression line fitted\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ##### The coefficients should have very low p value (<0.05)\n",
    "The coefficient is statistically significant. So the association is not purely by chance. \n",
    "- ##### R-squared is 96.5%\n",
    "Meaning that 96.5% of the variance in `price` is explained by these variables.\n",
    "This is a very good `R-squared` value.\n",
    "- ##### F statistic should have a very low p value (practically low)\n",
    "Meaning that the model fit is statistically significant, and the explained variance isn't purely by chance.\n",
    "\n",
    "Now we need to find out VIF values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>enginesize</td>\n",
       "      <td>124.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>41.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>curbweight</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>33.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>hptype_low</td>\n",
       "      <td>21.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>carbody_sedan</td>\n",
       "      <td>21.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>20.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>19.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carlength</td>\n",
       "      <td>19.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>17.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>17.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>17.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>12.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>10.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>9.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures     VIF\n",
       "9          enginesize  124.60\n",
       "8      cylindernumber   41.80\n",
       "7          curbweight   34.83\n",
       "10          boreratio   33.78\n",
       "36      engsize_small   30.21\n",
       "37         hptype_low   21.76\n",
       "14      carbody_sedan   21.64\n",
       "35     engsize_medium   20.41\n",
       "33     curbtype_light   19.87\n",
       "4           carlength   19.47\n",
       "13  carbody_hatchback   17.65\n",
       "11         horsepower   17.62\n",
       "3           wheelbase   17.24\n",
       "17     drivewheel_rwd   13.47\n",
       "15      carbody_wagon   12.39\n",
       "34  curbtype_moderate   12.35\n",
       "5            carwidth   10.57\n",
       "30       Brand_subaru   10.34\n",
       "16     drivewheel_fwd    9.38\n",
       "38      hptype_medium    9.07\n",
       "32        Brand_volvo    7.98\n",
       "26      Brand_peugeot    7.38\n",
       "6           carheight    5.51\n",
       "1           symboling    4.28\n",
       "2          doornumber    3.80\n",
       "20        Brand_buick    3.51\n",
       "31       Brand_toyota    3.51\n",
       "28      Brand_porsche    3.50\n",
       "19          Brand_bmw    3.15\n",
       "12    carbody_hardtop    2.43\n",
       "25       Brand_nissan    2.39\n",
       "18         Brand_audi    2.39\n",
       "23        Brand_mazda    2.34\n",
       "24   Brand_mitsubishi    2.16\n",
       "21        Brand_dodge    2.11\n",
       "22        Brand_honda    2.05\n",
       "29         Brand_saab    1.95\n",
       "27     Brand_plymouth    1.72\n",
       "0               const    1.00"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### Notice that the highest value VIF is 124.60 which is much greater than the optimal cut-off of 5. These variables isn't of use. Hence we'll drop this column and rebuild our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('enginesize',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.964\n",
      "Model:                            OLS   Adj. R-squared:                  0.950\n",
      "Method:                 Least Squares   F-statistic:                     70.73\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           5.38e-56\n",
      "Time:                        19:15:22   Log-Likelihood:                -1145.9\n",
      "No. Observations:                 136   AIC:                             2368.\n",
      "Df Residuals:                      98   BIC:                             2478.\n",
      "Df Model:                          37                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    111.494    106.777      0.000    1.17e+04    1.21e+04\n",
      "symboling           408.1098    230.602      1.770      0.080     -49.511     865.731\n",
      "doornumber          476.9044    217.396      2.194      0.031      45.488     908.320\n",
      "wheelbase          1930.1311    462.899      4.170      0.000    1011.524    2848.739\n",
      "carlength         -1796.5673    488.256     -3.680      0.000   -2765.496    -827.638\n",
      "carwidth           1130.9335    352.217      3.211      0.002     431.970    1829.897\n",
      "carheight          -575.2583    261.283     -2.202      0.030   -1093.767     -56.750\n",
      "curbweight         3439.1536    614.517      5.597      0.000    2219.666    4658.642\n",
      "cylindernumber     -275.9940    276.832     -0.997      0.321    -825.359     273.371\n",
      "boreratio          -884.2333    307.901     -2.872      0.005   -1495.252    -273.215\n",
      "horsepower         1151.9772    461.932      2.494      0.014     235.287    2068.667\n",
      "carbody_hardtop    -339.3470    172.786     -1.964      0.052    -682.236       3.542\n",
      "carbody_hatchback -1608.8594    466.932     -3.446      0.001   -2535.471    -682.248\n",
      "carbody_sedan     -1529.2248    515.357     -2.967      0.004   -2551.935    -506.515\n",
      "carbody_wagon     -1177.8076    388.029     -3.035      0.003   -1947.838    -407.778\n",
      "drivewheel_fwd      133.3877    340.606      0.392      0.696    -542.534     809.310\n",
      "drivewheel_rwd      605.1322    408.658      1.481      0.142    -205.835    1416.100\n",
      "Brand_audi          501.0463    172.174      2.910      0.004     159.373     842.720\n",
      "Brand_bmw          1328.9315    171.081      7.768      0.000     989.427    1668.436\n",
      "Brand_buick         870.7830    207.952      4.187      0.000     458.108    1283.458\n",
      "Brand_dodge        -306.9096    161.868     -1.896      0.061    -628.131      14.312\n",
      "Brand_honda         210.3505    157.239      1.338      0.184    -101.685     522.386\n",
      "Brand_mazda         285.5414    170.615      1.674      0.097     -53.038     624.121\n",
      "Brand_mitsubishi   -439.5077    162.803     -2.700      0.008    -762.586    -116.430\n",
      "Brand_nissan        311.1872    172.260      1.806      0.074     -30.658     653.032\n",
      "Brand_peugeot      -390.2219    295.454     -1.321      0.190    -976.542     196.098\n",
      "Brand_plymouth     -345.7934    145.578     -2.375      0.019    -634.688     -56.898\n",
      "Brand_porsche      2429.5935    207.947     11.684      0.000    2016.930    2842.257\n",
      "Brand_saab          377.1051    149.728      2.519      0.013      79.975     674.235\n",
      "Brand_subaru        276.9497    224.665      1.233      0.221    -168.891     722.790\n",
      "Brand_toyota       -264.9266    198.115     -1.337      0.184    -658.080     128.227\n",
      "Brand_volvo         873.8433    270.764      3.227      0.002     336.520    1411.167\n",
      "curbtype_light      612.4960    483.344      1.267      0.208    -346.684    1571.676\n",
      "curbtype_moderate   790.3704    365.491      2.162      0.033      65.066    1515.675\n",
      "engsize_medium      525.0827    337.851      1.554      0.123    -145.373    1195.538\n",
      "engsize_small        27.9704    449.404      0.062      0.950    -863.858     919.799\n",
      "hptype_low          502.7246    510.754      0.984      0.327    -510.850    1516.299\n",
      "hptype_medium      -284.4420    330.143     -0.862      0.391    -939.599     370.715\n",
      "==============================================================================\n",
      "Omnibus:                        1.181   Durbin-Watson:                   2.124\n",
      "Prob(Omnibus):                  0.554   Jarque-Bera (JB):                0.752\n",
      "Skew:                           0.088   Prob(JB):                        0.687\n",
      "Kurtosis:                       3.319   Cond. No.                         21.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>curbweight</td>\n",
       "      <td>30.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>carbody_sedan</td>\n",
       "      <td>21.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>hptype_low</td>\n",
       "      <td>20.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carlength</td>\n",
       "      <td>19.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>18.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>17.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>17.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>17.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>9.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>7.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "7          curbweight  30.38\n",
       "13      carbody_sedan  21.37\n",
       "36         hptype_low  20.99\n",
       "4           carlength  19.18\n",
       "32     curbtype_light  18.79\n",
       "12  carbody_hatchback  17.54\n",
       "3           wheelbase  17.24\n",
       "10         horsepower  17.17\n",
       "35      engsize_small  16.25\n",
       "16     drivewheel_rwd  13.43\n",
       "14      carbody_wagon  12.11\n",
       "33  curbtype_moderate  10.75\n",
       "5            carwidth   9.98\n",
       "15     drivewheel_fwd   9.33\n",
       "34     engsize_medium   9.18\n",
       "37      hptype_medium   8.77\n",
       "9           boreratio   7.63\n",
       "25      Brand_peugeot   7.02\n",
       "8      cylindernumber   6.17\n",
       "31        Brand_volvo   5.90\n",
       "6           carheight   5.49\n",
       "1           symboling   4.28\n",
       "29       Brand_subaru   4.06\n",
       "2          doornumber   3.80\n",
       "27      Brand_porsche   3.48\n",
       "19        Brand_buick   3.48\n",
       "30       Brand_toyota   3.16\n",
       "11    carbody_hardtop   2.40\n",
       "24       Brand_nissan   2.39\n",
       "17         Brand_audi   2.38\n",
       "18          Brand_bmw   2.35\n",
       "22        Brand_mazda   2.34\n",
       "23   Brand_mitsubishi   2.13\n",
       "20        Brand_dodge   2.11\n",
       "21        Brand_honda   1.99\n",
       "28         Brand_saab   1.80\n",
       "26     Brand_plymouth   1.70\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('curbweight',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.952\n",
      "Model:                            OLS   Adj. R-squared:                  0.935\n",
      "Method:                 Least Squares   F-statistic:                     54.98\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.66e-51\n",
      "Time:                        19:15:22   Log-Likelihood:                -1164.7\n",
      "No. Observations:                 136   AIC:                             2403.\n",
      "Df Residuals:                      99   BIC:                             2511.\n",
      "Df Model:                          36                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    127.429     93.424      0.000    1.17e+04    1.22e+04\n",
      "symboling           332.1420    263.103      1.262      0.210    -189.911     854.195\n",
      "doornumber          670.8572    245.290      2.735      0.007     184.149    1157.566\n",
      "wheelbase          2240.8200    525.239      4.266      0.000    1198.631    3283.009\n",
      "carlength         -1187.7109    544.010     -2.183      0.031   -2267.146    -108.276\n",
      "carwidth           1297.1871    401.123      3.234      0.002     501.272    2093.102\n",
      "carheight          -443.6041    297.414     -1.492      0.139   -1033.738     146.530\n",
      "cylindernumber      -45.3566    312.872     -0.145      0.885    -666.164     575.450\n",
      "boreratio          -736.7572    350.616     -2.101      0.038   -1432.454     -41.060\n",
      "horsepower         1570.5098    520.988      3.014      0.003     536.756    2604.264\n",
      "carbody_hardtop    -407.3524    196.993     -2.068      0.041    -798.228     -16.476\n",
      "carbody_hatchback -1726.2383    533.129     -3.238      0.002   -2784.082    -668.395\n",
      "carbody_sedan     -1991.6465    581.394     -3.426      0.001   -3145.259    -838.034\n",
      "carbody_wagon     -1304.4104    442.732     -2.946      0.004   -2182.888    -425.933\n",
      "drivewheel_fwd     -644.8073    355.370     -1.814      0.073   -1349.938      60.323\n",
      "drivewheel_rwd      505.1194    466.617      1.083      0.282    -420.751    1430.989\n",
      "Brand_audi          524.3705    196.724      2.666      0.009     134.028     914.713\n",
      "Brand_bmw          1155.9748    192.316      6.011      0.000     774.379    1537.571\n",
      "Brand_buick        1151.0921    230.677      4.990      0.000     693.379    1608.805\n",
      "Brand_dodge        -238.7435    184.478     -1.294      0.199    -604.788     127.301\n",
      "Brand_honda         169.9745    179.523      0.947      0.346    -186.238     526.187\n",
      "Brand_mazda         184.4820    193.905      0.951      0.344    -200.267     569.231\n",
      "Brand_mitsubishi   -411.3627    185.983     -2.212      0.029    -780.393     -42.332\n",
      "Brand_nissan        287.3348    196.820      1.460      0.147    -103.198     677.868\n",
      "Brand_peugeot      -353.2095    337.597     -1.046      0.298   -1023.075     316.656\n",
      "Brand_plymouth     -333.4195    166.365     -2.004      0.048    -663.524      -3.315\n",
      "Brand_porsche      2327.3719    236.748      9.831      0.000    1857.612    2797.132\n",
      "Brand_saab          440.1488    170.642      2.579      0.011     101.557     778.741\n",
      "Brand_subaru         54.5545    252.726      0.216      0.830    -446.909     556.018\n",
      "Brand_toyota       -488.7166    221.771     -2.204      0.030    -928.757     -48.676\n",
      "Brand_volvo         449.5714    297.086      1.513      0.133    -139.911    1039.054\n",
      "curbtype_light     -880.1730    460.706     -1.910      0.059   -1794.313      33.967\n",
      "curbtype_moderate  -144.1168    371.586     -0.388      0.699    -881.424     593.191\n",
      "engsize_medium        0.0515    370.953      0.000      1.000    -736.000     736.103\n",
      "engsize_small      -513.3578    501.597     -1.023      0.309   -1508.634     481.919\n",
      "hptype_low          626.2940    583.207      1.074      0.285    -530.914    1783.502\n",
      "hptype_medium      -196.3538    376.898     -0.521      0.604    -944.202     551.494\n",
      "==============================================================================\n",
      "Omnibus:                        1.267   Durbin-Watson:                   2.098\n",
      "Prob(Omnibus):                  0.531   Jarque-Bera (JB):                0.860\n",
      "Skew:                           0.160   Prob(JB):                        0.650\n",
      "Kurtosis:                       3.223   Cond. No.                         19.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>hptype_low</td>\n",
       "      <td>20.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_sedan</td>\n",
       "      <td>20.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carlength</td>\n",
       "      <td>18.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>16.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>15.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>13.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "35         hptype_low  20.95\n",
       "12      carbody_sedan  20.82\n",
       "4           carlength  18.23\n",
       "11  carbody_hatchback  17.50\n",
       "3           wheelbase  16.99\n",
       "9          horsepower  16.72\n",
       "34      engsize_small  15.49\n",
       "15     drivewheel_rwd  13.41\n",
       "31     curbtype_light  13.07\n",
       "13      carbody_wagon  12.07\n",
       "5            carwidth   9.91\n",
       "36      hptype_medium   8.75\n",
       "32  curbtype_moderate   8.50\n",
       "33     engsize_medium   8.47\n",
       "14     drivewheel_fwd   7.78\n",
       "8           boreratio   7.57\n",
       "24      Brand_peugeot   7.02\n",
       "7      cylindernumber   6.03\n",
       "6           carheight   5.45\n",
       "30        Brand_volvo   5.44\n",
       "1           symboling   4.26\n",
       "28       Brand_subaru   3.93\n",
       "2          doornumber   3.71\n",
       "26      Brand_porsche   3.45\n",
       "18        Brand_buick   3.28\n",
       "29       Brand_toyota   3.03\n",
       "23       Brand_nissan   2.39\n",
       "10    carbody_hardtop   2.39\n",
       "16         Brand_audi   2.38\n",
       "21        Brand_mazda   2.32\n",
       "17          Brand_bmw   2.28\n",
       "22   Brand_mitsubishi   2.13\n",
       "19        Brand_dodge   2.10\n",
       "20        Brand_honda   1.98\n",
       "27         Brand_saab   1.79\n",
       "25     Brand_plymouth   1.70\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('hptype_low',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.952\n",
      "Model:                            OLS   Adj. R-squared:                  0.935\n",
      "Method:                 Least Squares   F-statistic:                     56.43\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.08e-51\n",
      "Time:                        19:15:23   Log-Likelihood:                -1165.5\n",
      "No. Observations:                 136   AIC:                             2403.\n",
      "Df Residuals:                     100   BIC:                             2508.\n",
      "Df Model:                          35                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    127.526     93.353      0.000    1.17e+04    1.22e+04\n",
      "symboling           311.6586    262.611      1.187      0.238    -209.355     832.672\n",
      "doornumber          653.3753    244.937      2.668      0.009     167.428    1139.323\n",
      "wheelbase          2062.3922    498.648      4.136      0.000    1073.088    3051.696\n",
      "carlength         -1096.1386    537.697     -2.039      0.044   -2162.915     -29.363\n",
      "carwidth           1299.4904    401.424      3.237      0.002     503.076    2095.905\n",
      "carheight          -497.8012    293.325     -1.697      0.093   -1079.750      84.148\n",
      "cylindernumber       11.9987    308.516      0.039      0.969    -600.089     624.086\n",
      "boreratio          -737.0252    350.884     -2.100      0.038   -1433.169     -40.882\n",
      "horsepower         1144.3324    337.798      3.388      0.001     474.150    1814.514\n",
      "carbody_hardtop    -427.8667    196.214     -2.181      0.032    -817.150     -38.583\n",
      "carbody_hatchback -1720.8532    533.514     -3.226      0.002   -2779.329    -662.378\n",
      "carbody_sedan     -1943.9679    580.140     -3.351      0.001   -3094.950    -792.986\n",
      "carbody_wagon     -1273.6115    442.141     -2.881      0.005   -2150.806    -396.417\n",
      "drivewheel_fwd     -666.6678    355.058     -1.878      0.063   -1371.092      37.757\n",
      "drivewheel_rwd      451.2670    464.270      0.972      0.333    -469.832    1372.366\n",
      "Brand_audi          544.1084    196.013      2.776      0.007     155.224     932.993\n",
      "Brand_bmw          1136.3768    191.594      5.931      0.000     756.259    1516.495\n",
      "Brand_buick        1157.6894    230.771      5.017      0.000     699.845    1615.533\n",
      "Brand_dodge        -230.8422    184.473     -1.251      0.214    -596.830     135.146\n",
      "Brand_honda         119.1861    173.314      0.688      0.493    -224.664     463.036\n",
      "Brand_mazda         175.6820    193.880      0.906      0.367    -208.970     560.334\n",
      "Brand_mitsubishi   -454.5435    181.723     -2.501      0.014    -815.077     -94.010\n",
      "Brand_nissan        299.8343    196.626      1.525      0.130     -90.266     689.934\n",
      "Brand_peugeot      -281.5219    331.185     -0.850      0.397    -938.583     375.539\n",
      "Brand_plymouth     -310.9836    165.175     -1.883      0.063    -638.685      16.718\n",
      "Brand_porsche      2422.0554    219.886     11.015      0.000    1985.808    2858.303\n",
      "Brand_saab          460.0493    169.763      2.710      0.008     123.244     796.855\n",
      "Brand_subaru         23.0368    251.209      0.092      0.927    -475.354     521.428\n",
      "Brand_toyota       -452.0465    219.294     -2.061      0.042    -887.119     -16.974\n",
      "Brand_volvo         527.5795    288.289      1.830      0.070     -44.377    1099.536\n",
      "curbtype_light     -894.2087    460.873     -1.940      0.055   -1808.568      20.150\n",
      "curbtype_moderate  -209.7589    366.805     -0.572      0.569    -937.489     517.971\n",
      "engsize_medium       44.5752    368.911      0.121      0.904    -687.334     776.484\n",
      "engsize_small      -354.6911    479.709     -0.739      0.461   -1306.420     597.038\n",
      "hptype_medium      -509.6692    238.780     -2.134      0.035    -983.401     -35.937\n",
      "==============================================================================\n",
      "Omnibus:                        1.554   Durbin-Watson:                   2.114\n",
      "Prob(Omnibus):                  0.460   Jarque-Bera (JB):                1.118\n",
      "Skew:                           0.187   Prob(JB):                        0.572\n",
      "Kurtosis:                       3.239   Cond. No.                         18.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_sedan</td>\n",
       "      <td>20.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carlength</td>\n",
       "      <td>17.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>15.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>14.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>13.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>8.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>8.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "12      carbody_sedan  20.70\n",
       "4           carlength  17.78\n",
       "11  carbody_hatchback  17.50\n",
       "3           wheelbase  15.29\n",
       "34      engsize_small  14.15\n",
       "15     drivewheel_rwd  13.25\n",
       "31     curbtype_light  13.06\n",
       "13      carbody_wagon  12.02\n",
       "5            carwidth   9.91\n",
       "33     engsize_medium   8.37\n",
       "32  curbtype_moderate   8.27\n",
       "14     drivewheel_fwd   7.75\n",
       "8           boreratio   7.57\n",
       "9          horsepower   7.02\n",
       "24      Brand_peugeot   6.74\n",
       "7      cylindernumber   5.85\n",
       "6           carheight   5.29\n",
       "30        Brand_volvo   5.11\n",
       "1           symboling   4.24\n",
       "28       Brand_subaru   3.88\n",
       "2          doornumber   3.69\n",
       "35      hptype_medium   3.51\n",
       "18        Brand_buick   3.27\n",
       "26      Brand_porsche   2.97\n",
       "29       Brand_toyota   2.96\n",
       "23       Brand_nissan   2.38\n",
       "10    carbody_hardtop   2.37\n",
       "16         Brand_audi   2.36\n",
       "21        Brand_mazda   2.31\n",
       "17          Brand_bmw   2.26\n",
       "19        Brand_dodge   2.09\n",
       "22   Brand_mitsubishi   2.03\n",
       "20        Brand_honda   1.85\n",
       "27         Brand_saab   1.77\n",
       "25     Brand_plymouth   1.68\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carbody_sedan',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.946\n",
      "Model:                            OLS   Adj. R-squared:                  0.928\n",
      "Method:                 Least Squares   F-statistic:                     52.45\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.75e-50\n",
      "Time:                        19:15:23   Log-Likelihood:                -1172.7\n",
      "No. Observations:                 136   AIC:                             2415.\n",
      "Df Residuals:                     101   BIC:                             2517.\n",
      "Df Model:                          34                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    133.828     88.957      0.000    1.16e+04    1.22e+04\n",
      "symboling           417.5345    273.586      1.526      0.130    -125.187     960.256\n",
      "doornumber          502.0686    252.634      1.987      0.050       0.910    1003.227\n",
      "wheelbase          1897.4567    520.733      3.644      0.000     864.463    2930.450\n",
      "carlength         -1204.5966    563.244     -2.139      0.035   -2321.921     -87.272\n",
      "carwidth           1230.9048    420.712      2.926      0.004     396.324    2065.485\n",
      "carheight          -496.4577    307.819     -1.613      0.110   -1107.088     114.173\n",
      "cylindernumber      150.6529    320.836      0.470      0.640    -485.799     787.105\n",
      "boreratio          -517.3179    361.737     -1.430      0.156   -1234.906     200.270\n",
      "horsepower         1124.7661    354.437      3.173      0.002     421.658    1827.874\n",
      "carbody_hardtop     -59.4693    170.551     -0.349      0.728    -397.797     278.859\n",
      "carbody_hatchback  -116.8146    247.198     -0.473      0.638    -607.190     373.560\n",
      "carbody_wagon        78.8957    189.393      0.417      0.678    -296.809     454.600\n",
      "drivewheel_fwd     -650.7675    372.569     -1.747      0.084   -1389.845      88.310\n",
      "drivewheel_rwd      541.6868    486.388      1.114      0.268    -423.176    1506.550\n",
      "Brand_audi          495.7558    205.141      2.417      0.017      88.811     902.700\n",
      "Brand_bmw          1025.9939    198.068      5.180      0.000     633.081    1418.907\n",
      "Brand_buick        1206.3532    241.695      4.991      0.000     726.896    1685.811\n",
      "Brand_dodge        -303.8116    192.234     -1.580      0.117    -685.153      77.530\n",
      "Brand_honda          45.5546    180.410      0.253      0.801    -312.331     403.440\n",
      "Brand_mazda         105.5641    202.272      0.522      0.603    -295.688     506.816\n",
      "Brand_mitsubishi   -533.3451    189.099     -2.820      0.006    -908.467    -158.223\n",
      "Brand_nissan        139.5451    200.143      0.697      0.487    -257.484     536.574\n",
      "Brand_peugeot      -325.0163    347.283     -0.936      0.352   -1013.932     363.899\n",
      "Brand_plymouth     -356.3080    172.754     -2.063      0.042    -699.006     -13.610\n",
      "Brand_porsche      2530.3120    228.247     11.086      0.000    2077.531    2983.093\n",
      "Brand_saab          393.3901    176.924      2.223      0.028      42.419     744.361\n",
      "Brand_subaru       -162.2774    257.154     -0.631      0.529    -672.402     347.847\n",
      "Brand_toyota       -504.7098    229.538     -2.199      0.030    -960.051     -49.368\n",
      "Brand_volvo         489.1569    302.295      1.618      0.109    -110.514    1088.828\n",
      "curbtype_light     -966.0777    483.123     -2.000      0.048   -1924.463      -7.693\n",
      "curbtype_moderate  -304.5547    383.783     -0.794      0.429   -1065.878     456.768\n",
      "engsize_medium      180.2465    384.802      0.468      0.640    -583.097     943.590\n",
      "engsize_small      -263.2277    502.598     -0.524      0.602   -1260.247     733.791\n",
      "hptype_medium      -487.3950    250.482     -1.946      0.054    -984.283       9.493\n",
      "==============================================================================\n",
      "Omnibus:                        1.341   Durbin-Watson:                   1.975\n",
      "Prob(Omnibus):                  0.512   Jarque-Bera (JB):                1.024\n",
      "Skew:                           0.205   Prob(JB):                        0.599\n",
      "Kurtosis:                       3.115   Cond. No.                         15.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carlength</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>15.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>13.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>8.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>8.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "4           carlength  17.71\n",
       "3           wheelbase  15.14\n",
       "33      engsize_small  14.10\n",
       "14     drivewheel_rwd  13.21\n",
       "30     curbtype_light  13.03\n",
       "5            carwidth   9.88\n",
       "32     engsize_medium   8.27\n",
       "31  curbtype_moderate   8.22\n",
       "13     drivewheel_fwd   7.75\n",
       "8           boreratio   7.31\n",
       "9          horsepower   7.01\n",
       "23      Brand_peugeot   6.73\n",
       "7      cylindernumber   5.75\n",
       "6           carheight   5.29\n",
       "29        Brand_volvo   5.10\n",
       "1           symboling   4.18\n",
       "27       Brand_subaru   3.69\n",
       "2          doornumber   3.56\n",
       "34      hptype_medium   3.50\n",
       "11  carbody_hatchback   3.41\n",
       "17        Brand_buick   3.26\n",
       "28       Brand_toyota   2.94\n",
       "25      Brand_porsche   2.91\n",
       "15         Brand_audi   2.35\n",
       "20        Brand_mazda   2.28\n",
       "22       Brand_nissan   2.24\n",
       "16          Brand_bmw   2.19\n",
       "18        Brand_dodge   2.06\n",
       "21   Brand_mitsubishi   2.00\n",
       "12      carbody_wagon   2.00\n",
       "19        Brand_honda   1.82\n",
       "26         Brand_saab   1.75\n",
       "24     Brand_plymouth   1.67\n",
       "10    carbody_hardtop   1.62\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carlength',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.944\n",
      "Model:                            OLS   Adj. R-squared:                  0.926\n",
      "Method:                 Least Squares   F-statistic:                     52.08\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.39e-50\n",
      "Time:                        19:15:23   Log-Likelihood:                -1175.7\n",
      "No. Observations:                 136   AIC:                             2419.\n",
      "Df Residuals:                     102   BIC:                             2519.\n",
      "Df Model:                          33                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    136.152     87.438      0.000    1.16e+04    1.22e+04\n",
      "symboling           444.6593    278.039      1.599      0.113    -106.829     996.148\n",
      "doornumber          413.6044    253.554      1.631      0.106     -89.318     916.527\n",
      "wheelbase          1328.8428    455.521      2.917      0.004     425.319    2232.366\n",
      "carwidth            982.1923    411.343      2.388      0.019     166.296    1798.089\n",
      "carheight          -383.1195    308.490     -1.242      0.217    -995.007     228.768\n",
      "cylindernumber      148.8096    326.407      0.456      0.649    -498.618     796.237\n",
      "boreratio          -652.1380    362.388     -1.800      0.075   -1370.933      66.657\n",
      "horsepower         1166.7410    360.040      3.241      0.002     452.603    1880.879\n",
      "carbody_hardtop     -69.3446    173.450     -0.400      0.690    -413.382     274.693\n",
      "carbody_hatchback   -59.4194    250.005     -0.238      0.813    -555.304     436.465\n",
      "carbody_wagon       -60.8035    180.860     -0.336      0.737    -419.538     297.931\n",
      "drivewheel_fwd     -674.9668    378.865     -1.782      0.078   -1426.445      76.511\n",
      "drivewheel_rwd      526.8609    494.786      1.065      0.289    -454.544    1508.266\n",
      "Brand_audi          462.0815    208.088      2.221      0.029      49.339     874.824\n",
      "Brand_bmw          1010.7751    201.378      5.019      0.000     611.343    1410.207\n",
      "Brand_buick        1302.2841    241.621      5.390      0.000     823.030    1781.538\n",
      "Brand_dodge        -194.0200    188.471     -1.029      0.306    -567.851     179.810\n",
      "Brand_honda          94.2852    182.074      0.518      0.606    -266.858     455.428\n",
      "Brand_mazda          65.4960    204.900      0.320      0.750    -340.922     471.914\n",
      "Brand_mitsubishi   -488.1259    191.177     -2.553      0.012    -867.325    -108.927\n",
      "Brand_nissan         62.6713    200.308      0.313      0.755    -334.639     459.981\n",
      "Brand_peugeot      -257.1202    351.835     -0.731      0.467    -954.984     440.743\n",
      "Brand_plymouth     -286.5316    172.592     -1.660      0.100    -628.867      55.804\n",
      "Brand_porsche      2517.2983    232.129     10.844      0.000    2056.872    2977.725\n",
      "Brand_saab          279.2409    171.611      1.627      0.107     -61.150     619.631\n",
      "Brand_subaru        -29.4943    253.881     -0.116      0.908    -533.066     474.077\n",
      "Brand_toyota       -462.0105    232.640     -1.986      0.050    -923.450      -0.571\n",
      "Brand_volvo         509.9389    307.386      1.659      0.100     -99.760    1119.638\n",
      "curbtype_light     -654.0616    468.567     -1.396      0.166   -1583.463     275.340\n",
      "curbtype_moderate  -252.4646    389.662     -0.648      0.519   -1025.357     520.428\n",
      "engsize_medium      215.5883    391.124      0.551      0.583    -560.204     991.381\n",
      "engsize_small       -99.5319    505.363     -0.197      0.844   -1101.917     902.853\n",
      "hptype_medium      -396.8679    251.167     -1.580      0.117    -895.057     101.321\n",
      "==============================================================================\n",
      "Omnibus:                        3.437   Durbin-Watson:                   1.965\n",
      "Prob(Omnibus):                  0.179   Jarque-Bera (JB):                3.007\n",
      "Skew:                           0.254   Prob(JB):                        0.222\n",
      "Kurtosis:                       3.522   Cond. No.                         13.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>engsize_small</td>\n",
       "      <td>13.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>11.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>8.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>7.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "32      engsize_small  13.78\n",
       "13     drivewheel_rwd  13.21\n",
       "29     curbtype_light  11.84\n",
       "3           wheelbase  11.19\n",
       "4            carwidth   9.13\n",
       "31     engsize_medium   8.25\n",
       "30  curbtype_moderate   8.19\n",
       "12     drivewheel_fwd   7.74\n",
       "7           boreratio   7.08\n",
       "8          horsepower   6.99\n",
       "22      Brand_peugeot   6.68\n",
       "6      cylindernumber   5.75\n",
       "5           carheight   5.13\n",
       "28        Brand_volvo   5.10\n",
       "1           symboling   4.17\n",
       "26       Brand_subaru   3.48\n",
       "2          doornumber   3.47\n",
       "33      hptype_medium   3.40\n",
       "10  carbody_hatchback   3.37\n",
       "16        Brand_buick   3.15\n",
       "27       Brand_toyota   2.92\n",
       "24      Brand_porsche   2.91\n",
       "14         Brand_audi   2.34\n",
       "19        Brand_mazda   2.26\n",
       "15          Brand_bmw   2.19\n",
       "21       Brand_nissan   2.16\n",
       "20   Brand_mitsubishi   1.97\n",
       "17        Brand_dodge   1.92\n",
       "18        Brand_honda   1.79\n",
       "11      carbody_wagon   1.76\n",
       "9     carbody_hardtop   1.62\n",
       "23     Brand_plymouth   1.61\n",
       "25         Brand_saab   1.59\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('engsize_small',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.944\n",
      "Model:                            OLS   Adj. R-squared:                  0.927\n",
      "Method:                 Least Squares   F-statistic:                     54.21\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.64e-51\n",
      "Time:                        19:15:24   Log-Likelihood:                -1175.8\n",
      "No. Observations:                 136   AIC:                             2418.\n",
      "Df Residuals:                     103   BIC:                             2514.\n",
      "Df Model:                          32                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    135.515     87.849      0.000    1.16e+04    1.22e+04\n",
      "symboling           445.4151    276.712      1.610      0.111    -103.378     994.208\n",
      "doornumber          411.7406    252.192      1.633      0.106     -88.423     911.904\n",
      "wheelbase          1329.3007    453.384      2.932      0.004     430.120    2228.482\n",
      "carwidth            979.0623    409.113      2.393      0.019     167.682    1790.442\n",
      "carheight          -382.4401    307.028     -1.246      0.216    -991.357     226.477\n",
      "cylindernumber      188.3756    256.059      0.736      0.464    -319.458     696.209\n",
      "boreratio          -611.9525    298.088     -2.053      0.043   -1203.141     -20.765\n",
      "horsepower         1186.5605    344.074      3.449      0.001     504.171    1868.950\n",
      "carbody_hardtop     -66.7783    172.151     -0.388      0.699    -408.199     274.643\n",
      "carbody_hatchback   -59.0626    248.830     -0.237      0.813    -552.557     434.432\n",
      "carbody_wagon       -64.6779    178.946     -0.361      0.719    -419.575     290.219\n",
      "drivewheel_fwd     -666.1880    374.475     -1.779      0.078   -1408.870      76.494\n",
      "drivewheel_rwd      535.5178    490.524      1.092      0.278    -437.321    1508.357\n",
      "Brand_audi          457.6933    205.924      2.223      0.028      49.291     866.096\n",
      "Brand_bmw          1010.1979    200.415      5.041      0.000     612.723    1407.673\n",
      "Brand_buick        1307.5641    239.006      5.471      0.000     833.552    1781.576\n",
      "Brand_dodge        -188.4487    185.464     -1.016      0.312    -556.273     179.376\n",
      "Brand_honda          98.7921    179.785      0.549      0.584    -257.770     455.354\n",
      "Brand_mazda          63.1249    203.589      0.310      0.757    -340.647     466.897\n",
      "Brand_mitsubishi   -484.5931    189.444     -2.558      0.012    -860.310    -108.876\n",
      "Brand_nissan         61.0098    199.194      0.306      0.760    -334.045     456.065\n",
      "Brand_peugeot      -250.6357    348.653     -0.719      0.474    -942.107     440.835\n",
      "Brand_plymouth     -279.1562    167.693     -1.665      0.099    -611.735      53.422\n",
      "Brand_porsche      2501.5930    216.983     11.529      0.000    2071.259    2931.927\n",
      "Brand_saab          273.1435    168.006      1.626      0.107     -60.058     606.345\n",
      "Brand_subaru        -39.4362    247.648     -0.159      0.874    -530.588     451.716\n",
      "Brand_toyota       -456.4443    229.837     -1.986      0.050    -912.271      -0.618\n",
      "Brand_volvo         519.8625    301.810      1.722      0.088     -78.707    1118.432\n",
      "curbtype_light     -662.6014    464.375     -1.427      0.157   -1583.579     258.376\n",
      "curbtype_moderate  -243.6617    385.280     -0.632      0.529   -1007.773     520.450\n",
      "engsize_medium      274.9269    248.243      1.107      0.271    -217.404     767.257\n",
      "hptype_medium      -390.1938    247.707     -1.575      0.118    -881.461     101.074\n",
      "==============================================================================\n",
      "Omnibus:                        3.623   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.163   Jarque-Bera (JB):                3.221\n",
      "Skew:                           0.260   Prob(JB):                        0.200\n",
      "Kurtosis:                       3.547   Cond. No.                         12.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>11.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carheight</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>4.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "13     drivewheel_rwd  13.10\n",
       "29     curbtype_light  11.74\n",
       "3           wheelbase  11.19\n",
       "4            carwidth   9.11\n",
       "30  curbtype_moderate   8.08\n",
       "12     drivewheel_fwd   7.64\n",
       "22      Brand_peugeot   6.62\n",
       "8          horsepower   6.45\n",
       "5           carheight   5.13\n",
       "28        Brand_volvo   4.96\n",
       "7           boreratio   4.84\n",
       "1           symboling   4.17\n",
       "6      cylindernumber   3.57\n",
       "2          doornumber   3.46\n",
       "10  carbody_hatchback   3.37\n",
       "31     engsize_medium   3.36\n",
       "32      hptype_medium   3.34\n",
       "26       Brand_subaru   3.34\n",
       "16        Brand_buick   3.11\n",
       "27       Brand_toyota   2.88\n",
       "24      Brand_porsche   2.56\n",
       "14         Brand_audi   2.31\n",
       "19        Brand_mazda   2.26\n",
       "15          Brand_bmw   2.19\n",
       "21       Brand_nissan   2.16\n",
       "20   Brand_mitsubishi   1.95\n",
       "17        Brand_dodge   1.87\n",
       "18        Brand_honda   1.76\n",
       "11      carbody_wagon   1.74\n",
       "9     carbody_hardtop   1.61\n",
       "25         Brand_saab   1.54\n",
       "23     Brand_plymouth   1.53\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('drivewheel_rwd',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.943\n",
      "Model:                            OLS   Adj. R-squared:                  0.926\n",
      "Method:                 Least Squares   F-statistic:                     55.82\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.10e-51\n",
      "Time:                        19:15:24   Log-Likelihood:                -1176.6\n",
      "No. Observations:                 136   AIC:                             2417.\n",
      "Df Residuals:                     104   BIC:                             2510.\n",
      "Df Model:                          31                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    135.640     87.768      0.000    1.16e+04    1.22e+04\n",
      "symboling           512.1034    270.134      1.896      0.061     -23.583    1047.789\n",
      "doornumber          383.0851    251.054      1.526      0.130    -114.764     880.934\n",
      "wheelbase          1463.9898    436.678      3.353      0.001     598.041    2329.939\n",
      "carwidth            996.6207    409.174      2.436      0.017     185.213    1808.028\n",
      "carheight          -517.6765    281.188     -1.841      0.068   -1075.283      39.930\n",
      "cylindernumber      202.6685    255.960      0.792      0.430    -304.910     710.247\n",
      "boreratio          -578.8304    296.814     -1.950      0.054   -1167.423       9.763\n",
      "horsepower         1272.9071    335.169      3.798      0.000     608.255    1937.560\n",
      "carbody_hardtop     -74.5182    172.164     -0.433      0.666    -415.925     266.888\n",
      "carbody_hatchback  -116.6530    243.398     -0.479      0.633    -599.320     366.014\n",
      "carbody_wagon       -48.1771    178.471     -0.270      0.788    -402.091     305.737\n",
      "drivewheel_fwd     -956.6883    263.731     -3.628      0.000   -1479.676    -433.700\n",
      "Brand_audi          369.7314    189.682      1.949      0.054      -6.416     745.878\n",
      "Brand_bmw          1058.5348    195.643      5.411      0.000     670.567    1446.502\n",
      "Brand_buick        1350.4771    235.969      5.723      0.000     882.542    1818.412\n",
      "Brand_dodge        -226.8930    182.258     -1.245      0.216    -588.318     134.532\n",
      "Brand_honda          98.9936    179.951      0.550      0.583    -257.856     455.843\n",
      "Brand_mazda          91.0297    202.165      0.450      0.653    -309.871     491.930\n",
      "Brand_mitsubishi   -539.5084    182.812     -2.951      0.004    -902.032    -176.985\n",
      "Brand_nissan         50.4410    199.142      0.253      0.801    -344.466     445.348\n",
      "Brand_peugeot      -161.7944    339.336     -0.477      0.635    -834.710     511.121\n",
      "Brand_plymouth     -283.6508    167.797     -1.690      0.094    -616.398      49.096\n",
      "Brand_porsche      2497.5674    217.152     11.501      0.000    2066.948    2928.187\n",
      "Brand_saab          256.5013    167.468      1.532      0.129     -75.593     588.596\n",
      "Brand_subaru       -134.5462    232.033     -0.580      0.563    -594.675     325.583\n",
      "Brand_toyota       -422.3399    227.914     -1.853      0.067    -874.301      29.622\n",
      "Brand_volvo         600.4948    292.904      2.050      0.043      19.656    1181.333\n",
      "curbtype_light     -564.8059    456.073     -1.238      0.218   -1469.216     339.604\n",
      "curbtype_moderate  -186.3126    382.034     -0.488      0.627    -943.900     571.275\n",
      "engsize_medium      229.7624    244.997      0.938      0.351    -256.075     715.600\n",
      "hptype_medium      -360.8225    246.468     -1.464      0.146    -849.578     127.933\n",
      "==============================================================================\n",
      "Omnibus:                        5.001   Durbin-Watson:                   1.966\n",
      "Prob(Omnibus):                  0.082   Jarque-Bera (JB):                4.792\n",
      "Skew:                           0.321   Prob(JB):                       0.0911\n",
      "Kurtosis:                       3.658   Cond. No.                         11.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>curbtype_light</td>\n",
       "      <td>11.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>7.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carheight</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "28     curbtype_light  11.31\n",
       "3           wheelbase  10.36\n",
       "4            carwidth   9.10\n",
       "29  curbtype_moderate   7.93\n",
       "21      Brand_peugeot   6.26\n",
       "8          horsepower   6.11\n",
       "7           boreratio   4.79\n",
       "27        Brand_volvo   4.66\n",
       "5           carheight   4.30\n",
       "1           symboling   3.97\n",
       "12     drivewheel_fwd   3.78\n",
       "6      cylindernumber   3.56\n",
       "2          doornumber   3.43\n",
       "31      hptype_medium   3.30\n",
       "30     engsize_medium   3.26\n",
       "10  carbody_hatchback   3.22\n",
       "15        Brand_buick   3.03\n",
       "25       Brand_subaru   2.93\n",
       "26       Brand_toyota   2.82\n",
       "23      Brand_porsche   2.56\n",
       "18        Brand_mazda   2.22\n",
       "20       Brand_nissan   2.16\n",
       "14          Brand_bmw   2.08\n",
       "13         Brand_audi   1.96\n",
       "19   Brand_mitsubishi   1.82\n",
       "16        Brand_dodge   1.81\n",
       "17        Brand_honda   1.76\n",
       "11      carbody_wagon   1.73\n",
       "9     carbody_hardtop   1.61\n",
       "22     Brand_plymouth   1.53\n",
       "24         Brand_saab   1.52\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('curbtype_light',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.942\n",
      "Model:                            OLS   Adj. R-squared:                  0.926\n",
      "Method:                 Least Squares   F-statistic:                     57.34\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.03e-52\n",
      "Time:                        19:15:24   Log-Likelihood:                -1177.6\n",
      "No. Observations:                 136   AIC:                             2417.\n",
      "Df Residuals:                     105   BIC:                             2507.\n",
      "Df Model:                          30                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    135.985     87.546      0.000    1.16e+04    1.22e+04\n",
      "symboling           548.0525    269.252      2.035      0.044      14.176    1081.929\n",
      "doornumber          398.5685    251.378      1.586      0.116     -99.868     897.006\n",
      "wheelbase          1442.9925    437.456      3.299      0.001     575.598    2310.387\n",
      "carwidth           1199.9947    375.724      3.194      0.002     455.004    1944.986\n",
      "carheight          -434.0151    273.645     -1.586      0.116    -976.603     108.573\n",
      "cylindernumber      232.9744    255.434      0.912      0.364    -273.505     739.454\n",
      "boreratio          -530.1210    294.943     -1.797      0.075   -1114.938      54.696\n",
      "horsepower         1433.1165    309.981      4.623      0.000     818.481    2047.752\n",
      "carbody_hardtop     -90.8000    172.096     -0.528      0.599    -432.035     250.436\n",
      "carbody_hatchback  -157.4352    241.772     -0.651      0.516    -636.824     321.954\n",
      "carbody_wagon       -36.3290    178.666     -0.203      0.839    -390.592     317.934\n",
      "drivewheel_fwd    -1032.5344    257.172     -4.015      0.000   -1542.459    -522.610\n",
      "Brand_audi          341.8683    188.821      1.811      0.073     -32.529     716.266\n",
      "Brand_bmw          1046.7057    195.906      5.343      0.000     658.261    1435.150\n",
      "Brand_buick        1334.9679    236.234      5.651      0.000     866.559    1803.377\n",
      "Brand_dodge        -221.8469    182.675     -1.214      0.227    -584.058     140.364\n",
      "Brand_honda         126.4074    179.038      0.706      0.482    -228.591     481.406\n",
      "Brand_mazda          82.8719    202.570      0.409      0.683    -318.788     484.531\n",
      "Brand_mitsubishi   -510.8962    181.806     -2.810      0.006    -871.385    -150.408\n",
      "Brand_nissan         42.8026    199.552      0.214      0.831    -352.872     438.477\n",
      "Brand_peugeot      -120.3049    338.535     -0.355      0.723    -791.557     550.947\n",
      "Brand_plymouth     -279.6917    168.192     -1.663      0.099    -613.185      53.802\n",
      "Brand_porsche      2412.7915    206.602     11.678      0.000    2003.137    2822.446\n",
      "Brand_saab          243.9723    167.586      1.456      0.148     -88.320     576.265\n",
      "Brand_subaru       -162.2718    231.536     -0.701      0.485    -621.365     296.822\n",
      "Brand_toyota       -389.3110    226.922     -1.716      0.089    -839.256      60.634\n",
      "Brand_volvo         643.2090    291.604      2.206      0.030      65.012    1221.406\n",
      "curbtype_moderate   178.3383    244.028      0.731      0.467    -305.524     662.201\n",
      "engsize_medium      269.1407    243.541      1.105      0.272    -213.756     752.038\n",
      "hptype_medium      -367.6879    247.031     -1.488      0.140    -857.505     122.129\n",
      "==============================================================================\n",
      "Omnibus:                        5.026   Durbin-Watson:                   1.985\n",
      "Prob(Omnibus):                  0.081   Jarque-Bera (JB):                4.618\n",
      "Skew:                           0.355   Prob(JB):                       0.0994\n",
      "Kurtosis:                       3.558   Cond. No.                         9.94\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wheelbase</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>7.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>carheight</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures    VIF\n",
       "3           wheelbase  10.35\n",
       "4            carwidth   7.63\n",
       "21      Brand_peugeot   6.20\n",
       "8          horsepower   5.20\n",
       "7           boreratio   4.70\n",
       "27        Brand_volvo   4.60\n",
       "5           carheight   4.05\n",
       "1           symboling   3.92\n",
       "12     drivewheel_fwd   3.58\n",
       "6      cylindernumber   3.53\n",
       "2          doornumber   3.42\n",
       "30      hptype_medium   3.30\n",
       "28  curbtype_moderate   3.22\n",
       "29     engsize_medium   3.21\n",
       "10  carbody_hatchback   3.16\n",
       "15        Brand_buick   3.02\n",
       "25       Brand_subaru   2.90\n",
       "26       Brand_toyota   2.78\n",
       "23      Brand_porsche   2.31\n",
       "18        Brand_mazda   2.22\n",
       "20       Brand_nissan   2.15\n",
       "14          Brand_bmw   2.08\n",
       "13         Brand_audi   1.93\n",
       "16        Brand_dodge   1.80\n",
       "19   Brand_mitsubishi   1.79\n",
       "17        Brand_honda   1.73\n",
       "11      carbody_wagon   1.73\n",
       "9     carbody_hardtop   1.60\n",
       "22     Brand_plymouth   1.53\n",
       "24         Brand_saab   1.52\n",
       "0               const   1.00"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('wheelbase',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.937\n",
      "Model:                            OLS   Adj. R-squared:                  0.919\n",
      "Method:                 Least Squares   F-statistic:                     53.91\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           6.63e-51\n",
      "Time:                        19:15:25   Log-Likelihood:                -1184.3\n",
      "No. Observations:                 136   AIC:                             2429.\n",
      "Df Residuals:                     106   BIC:                             2516.\n",
      "Df Model:                          29                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    142.181     83.730      0.000    1.16e+04    1.22e+04\n",
      "symboling           294.2174    269.779      1.091      0.278    -240.645     829.080\n",
      "doornumber          390.6758    262.822      1.486      0.140    -130.394     911.745\n",
      "carwidth           1846.8453    335.094      5.511      0.000    1182.488    2511.202\n",
      "carheight          -150.9775    271.685     -0.556      0.580    -689.620     387.665\n",
      "cylindernumber      273.2779    266.769      1.024      0.308    -255.617     802.173\n",
      "boreratio          -468.2508    307.759     -1.521      0.131   -1078.413     141.911\n",
      "horsepower         1462.4912    323.973      4.514      0.000     820.183    2104.799\n",
      "carbody_hardtop     -93.7214    179.936     -0.521      0.604    -450.463     263.020\n",
      "carbody_hatchback  -180.4243    252.684     -0.714      0.477    -681.395     320.547\n",
      "carbody_wagon       -37.6080    186.808     -0.201      0.841    -407.972     332.756\n",
      "drivewheel_fwd     -984.4940    268.460     -3.667      0.000   -1516.741    -452.247\n",
      "Brand_audi          421.8923    195.789      2.155      0.033      33.721     810.064\n",
      "Brand_bmw          1260.4049    193.309      6.520      0.000     877.152    1643.658\n",
      "Brand_buick        1484.4659    242.411      6.124      0.000    1003.862    1965.070\n",
      "Brand_dodge        -133.6003    188.940     -0.707      0.481    -508.193     240.992\n",
      "Brand_honda          66.4897    186.230      0.357      0.722    -302.730     435.709\n",
      "Brand_mazda         106.3361    211.671      0.502      0.616    -313.321     525.994\n",
      "Brand_mitsubishi   -418.9451    187.843     -2.230      0.028    -791.363     -46.527\n",
      "Brand_nissan          5.5375    208.311      0.027      0.979    -407.459     418.534\n",
      "Brand_peugeot       336.8105    322.947      1.043      0.299    -303.463     977.084\n",
      "Brand_plymouth     -193.7465    173.733     -1.115      0.267    -538.190     150.697\n",
      "Brand_porsche      2246.4840    209.486     10.724      0.000    1831.157    2661.811\n",
      "Brand_saab          267.3506    175.066      1.527      0.130     -79.735     614.436\n",
      "Brand_subaru       -215.6840    241.494     -0.893      0.374    -694.470     263.102\n",
      "Brand_toyota       -199.6231    229.518     -0.870      0.386    -654.665     255.419\n",
      "Brand_volvo         834.7817    298.784      2.794      0.006     242.414    1427.150\n",
      "curbtype_moderate   242.3831    254.339      0.953      0.343    -261.869     746.636\n",
      "engsize_medium      201.1514    253.725      0.793      0.430    -301.884     704.187\n",
      "hptype_medium      -228.5647    254.496     -0.898      0.371    -733.128     275.998\n",
      "==============================================================================\n",
      "Omnibus:                       18.361   Durbin-Watson:                   2.058\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.474\n",
      "Skew:                           0.609   Prob(JB):                     5.39e-08\n",
      "Kurtosis:                       5.103   Cond. No.                         8.74\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>5.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carheight</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures   VIF\n",
       "3            carwidth  5.55\n",
       "7          horsepower  5.19\n",
       "20      Brand_peugeot  5.16\n",
       "6           boreratio  4.69\n",
       "26        Brand_volvo  4.42\n",
       "4           carheight  3.65\n",
       "1           symboling  3.60\n",
       "11     drivewheel_fwd  3.57\n",
       "5      cylindernumber  3.52\n",
       "2          doornumber  3.42\n",
       "29      hptype_medium  3.20\n",
       "27  curbtype_moderate  3.20\n",
       "28     engsize_medium  3.18\n",
       "9   carbody_hatchback  3.16\n",
       "14        Brand_buick  2.91\n",
       "24       Brand_subaru  2.88\n",
       "25       Brand_toyota  2.61\n",
       "17        Brand_mazda  2.22\n",
       "22      Brand_porsche  2.17\n",
       "19       Brand_nissan  2.15\n",
       "12         Brand_audi  1.90\n",
       "13          Brand_bmw  1.85\n",
       "15        Brand_dodge  1.77\n",
       "18   Brand_mitsubishi  1.75\n",
       "10      carbody_wagon  1.73\n",
       "16        Brand_honda  1.72\n",
       "8     carbody_hardtop  1.60\n",
       "23         Brand_saab  1.52\n",
       "21     Brand_plymouth  1.49\n",
       "0               const  1.00"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carwidth',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.897\n",
      "Method:                 Least Squares   F-statistic:                     42.96\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.86e-46\n",
      "Time:                        19:15:25   Log-Likelihood:                -1201.4\n",
      "No. Observations:                 136   AIC:                             2461.\n",
      "Df Residuals:                     107   BIC:                             2545.\n",
      "Df Model:                          28                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    160.516     74.166      0.000    1.16e+04    1.22e+04\n",
      "symboling           317.6893    304.530      1.043      0.299    -286.006     921.384\n",
      "doornumber          739.7463    287.969      2.569      0.012     168.881    1310.612\n",
      "carheight          -160.1293    306.715     -0.522      0.603    -768.156     447.897\n",
      "cylindernumber      652.0438    291.004      2.241      0.027      75.161    1228.926\n",
      "boreratio          -100.0044    339.159     -0.295      0.769    -772.348     572.339\n",
      "horsepower         2099.8251    341.658      6.146      0.000    1422.528    2777.122\n",
      "carbody_hardtop     -24.6412    202.647     -0.122      0.903    -426.364     377.082\n",
      "carbody_hatchback    47.7657    281.414      0.170      0.866    -510.104     605.635\n",
      "carbody_wagon       -44.4934    210.893     -0.211      0.833    -462.564     373.577\n",
      "drivewheel_fwd     -817.7660    301.148     -2.715      0.008   -1414.758    -220.774\n",
      "Brand_audi          858.4427    202.141      4.247      0.000     457.721    1259.164\n",
      "Brand_bmw          1118.5980    216.295      5.172      0.000     689.819    1547.377\n",
      "Brand_buick        2081.5397    244.822      8.502      0.000    1596.209    2566.870\n",
      "Brand_dodge        -330.4300    209.460     -1.578      0.118    -745.660      84.800\n",
      "Brand_honda         -45.6663    208.987     -0.219      0.827    -459.958     368.625\n",
      "Brand_mazda         316.2435    235.066      1.345      0.181    -149.748     782.235\n",
      "Brand_mitsubishi   -459.0025    211.908     -2.166      0.033    -879.086     -38.920\n",
      "Brand_nissan       -101.9715    234.140     -0.436      0.664    -566.127     362.184\n",
      "Brand_peugeot      1016.1111    336.995      3.015      0.003     348.058    1684.165\n",
      "Brand_plymouth     -365.1818    192.968     -1.892      0.061    -747.718      17.354\n",
      "Brand_porsche      1792.6434    217.463      8.243      0.000    1361.549    2223.738\n",
      "Brand_saab          231.4731    197.505      1.172      0.244    -160.058     623.004\n",
      "Brand_subaru       -470.8288    267.580     -1.760      0.081   -1001.275      59.617\n",
      "Brand_toyota       -342.8903    257.448     -1.332      0.186    -853.252     167.471\n",
      "Brand_volvo        1199.1249    328.954      3.645      0.000     547.013    1851.237\n",
      "curbtype_moderate   362.5822    286.080      1.267      0.208    -204.538     929.703\n",
      "engsize_medium      234.2956    286.364      0.818      0.415    -333.388     801.979\n",
      "hptype_medium      -118.7192    286.432     -0.414      0.679    -686.537     449.099\n",
      "==============================================================================\n",
      "Omnibus:                       27.396   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.503\n",
      "Skew:                           0.879   Prob(JB):                     2.41e-12\n",
      "Kurtosis:                       5.520   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fetures</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>boreratio</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brand_peugeot</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brand_volvo</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>carheight</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>symboling</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cylindernumber</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doornumber</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hptype_medium</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>curbtype_moderate</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>engsize_medium</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brand_subaru</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brand_toyota</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brand_buick</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brand_mazda</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Brand_nissan</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brand_porsche</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brand_bmw</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brand_mitsubishi</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brand_dodge</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brand_honda</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Brand_audi</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brand_saab</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brand_plymouth</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fetures   VIF\n",
       "6          horsepower  4.53\n",
       "5           boreratio  4.46\n",
       "19      Brand_peugeot  4.41\n",
       "25        Brand_volvo  4.20\n",
       "3           carheight  3.65\n",
       "1           symboling  3.60\n",
       "10     drivewheel_fwd  3.52\n",
       "4      cylindernumber  3.29\n",
       "2          doornumber  3.22\n",
       "28      hptype_medium  3.18\n",
       "26  curbtype_moderate  3.18\n",
       "27     engsize_medium  3.18\n",
       "8   carbody_hatchback  3.07\n",
       "23       Brand_subaru  2.78\n",
       "24       Brand_toyota  2.57\n",
       "13        Brand_buick  2.33\n",
       "16        Brand_mazda  2.14\n",
       "18       Brand_nissan  2.13\n",
       "21      Brand_porsche  1.84\n",
       "12          Brand_bmw  1.82\n",
       "17   Brand_mitsubishi  1.74\n",
       "9       carbody_wagon  1.73\n",
       "14        Brand_dodge  1.70\n",
       "15        Brand_honda  1.70\n",
       "11         Brand_audi  1.59\n",
       "7     carbody_hardtop  1.59\n",
       "22         Brand_saab  1.51\n",
       "20     Brand_plymouth  1.45\n",
       "0               const  1.00"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Fetures'] = x_train_rfe.columns\n",
    "vif['VIF'] = [variance_inflation_factor(x_train_rfe.values,i) for i in range(x_train_rfe.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'],2)\n",
    "vif = vif.sort_values(by='VIF',ascending=False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### The large VIFs have now dropped to a workable value. But from the p-value perspective, some features are still insignificant and needed to be dropped i.e. whose p-values are greater than 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.897\n",
      "Method:                 Least Squares   F-statistic:                     42.96\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.86e-46\n",
      "Time:                        19:15:25   Log-Likelihood:                -1201.4\n",
      "No. Observations:                 136   AIC:                             2461.\n",
      "Df Residuals:                     107   BIC:                             2545.\n",
      "Df Model:                          28                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    160.516     74.166      0.000    1.16e+04    1.22e+04\n",
      "symboling           317.6893    304.530      1.043      0.299    -286.006     921.384\n",
      "doornumber          739.7463    287.969      2.569      0.012     168.881    1310.612\n",
      "carheight          -160.1293    306.715     -0.522      0.603    -768.156     447.897\n",
      "cylindernumber      652.0438    291.004      2.241      0.027      75.161    1228.926\n",
      "boreratio          -100.0044    339.159     -0.295      0.769    -772.348     572.339\n",
      "horsepower         2099.8251    341.658      6.146      0.000    1422.528    2777.122\n",
      "carbody_hardtop     -24.6412    202.647     -0.122      0.903    -426.364     377.082\n",
      "carbody_hatchback    47.7657    281.414      0.170      0.866    -510.104     605.635\n",
      "carbody_wagon       -44.4934    210.893     -0.211      0.833    -462.564     373.577\n",
      "drivewheel_fwd     -817.7660    301.148     -2.715      0.008   -1414.758    -220.774\n",
      "Brand_audi          858.4427    202.141      4.247      0.000     457.721    1259.164\n",
      "Brand_bmw          1118.5980    216.295      5.172      0.000     689.819    1547.377\n",
      "Brand_buick        2081.5397    244.822      8.502      0.000    1596.209    2566.870\n",
      "Brand_dodge        -330.4300    209.460     -1.578      0.118    -745.660      84.800\n",
      "Brand_honda         -45.6663    208.987     -0.219      0.827    -459.958     368.625\n",
      "Brand_mazda         316.2435    235.066      1.345      0.181    -149.748     782.235\n",
      "Brand_mitsubishi   -459.0025    211.908     -2.166      0.033    -879.086     -38.920\n",
      "Brand_nissan       -101.9715    234.140     -0.436      0.664    -566.127     362.184\n",
      "Brand_peugeot      1016.1111    336.995      3.015      0.003     348.058    1684.165\n",
      "Brand_plymouth     -365.1818    192.968     -1.892      0.061    -747.718      17.354\n",
      "Brand_porsche      1792.6434    217.463      8.243      0.000    1361.549    2223.738\n",
      "Brand_saab          231.4731    197.505      1.172      0.244    -160.058     623.004\n",
      "Brand_subaru       -470.8288    267.580     -1.760      0.081   -1001.275      59.617\n",
      "Brand_toyota       -342.8903    257.448     -1.332      0.186    -853.252     167.471\n",
      "Brand_volvo        1199.1249    328.954      3.645      0.000     547.013    1851.237\n",
      "curbtype_moderate   362.5822    286.080      1.267      0.208    -204.538     929.703\n",
      "engsize_medium      234.2956    286.364      0.818      0.415    -333.388     801.979\n",
      "hptype_medium      -118.7192    286.432     -0.414      0.679    -686.537     449.099\n",
      "==============================================================================\n",
      "Omnibus:                       27.396   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.503\n",
      "Skew:                           0.879   Prob(JB):                     2.41e-12\n",
      "Kurtosis:                       5.520   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carbody_hardtop',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.898\n",
      "Method:                 Least Squares   F-statistic:                     44.96\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           7.21e-47\n",
      "Time:                        19:15:26   Log-Likelihood:                -1201.4\n",
      "No. Observations:                 136   AIC:                             2459.\n",
      "Df Residuals:                     108   BIC:                             2540.\n",
      "Df Model:                          27                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    159.783     74.507      0.000    1.16e+04    1.22e+04\n",
      "symboling           317.7115    303.138      1.048      0.297    -283.160     918.583\n",
      "doornumber          747.8523    278.866      2.682      0.008     195.091    1300.614\n",
      "carheight          -157.4955    304.550     -0.517      0.606    -761.167     446.176\n",
      "cylindernumber      650.4351    289.375      2.248      0.027      76.844    1224.026\n",
      "boreratio          -105.9529    334.078     -0.317      0.752    -768.154     556.248\n",
      "horsepower         2102.6155    339.328      6.196      0.000    1430.009    2775.222\n",
      "carbody_hatchback    56.8947    269.974      0.211      0.833    -478.242     592.031\n",
      "carbody_wagon       -43.1748    209.651     -0.206      0.837    -458.739     372.390\n",
      "drivewheel_fwd     -817.7139    299.771     -2.728      0.007   -1411.913    -223.515\n",
      "Brand_audi          857.7277    201.132      4.265      0.000     459.049    1256.406\n",
      "Brand_bmw          1120.8977    214.481      5.226      0.000     695.758    1546.037\n",
      "Brand_buick        2075.1032    237.938      8.721      0.000    1603.469    2546.738\n",
      "Brand_dodge        -330.7790    208.483     -1.587      0.116    -744.028      82.470\n",
      "Brand_honda         -46.8145    207.819     -0.225      0.822    -458.747     365.118\n",
      "Brand_mazda         315.8128    233.965      1.350      0.180    -147.947     779.573\n",
      "Brand_mitsubishi   -458.8291    210.935     -2.175      0.032    -876.938     -40.720\n",
      "Brand_nissan        -99.9681    232.492     -0.430      0.668    -560.808     360.871\n",
      "Brand_peugeot      1016.4037    335.446      3.030      0.003     351.492    1681.316\n",
      "Brand_plymouth     -366.1616    191.918     -1.908      0.059    -746.577      14.253\n",
      "Brand_porsche      1786.0595    209.651      8.519      0.000    1370.494    2201.625\n",
      "Brand_saab          231.3156    196.598      1.177      0.242    -158.376     621.007\n",
      "Brand_subaru       -469.7157    266.201     -1.765      0.080    -997.372      57.940\n",
      "Brand_toyota       -346.4427    254.616     -1.361      0.176    -851.136     158.251\n",
      "Brand_volvo        1200.2998    327.309      3.667      0.000     551.518    1849.082\n",
      "curbtype_moderate   361.9489    284.725      1.271      0.206    -202.426     926.323\n",
      "engsize_medium      236.4619    284.503      0.831      0.408    -327.472     800.396\n",
      "hptype_medium      -116.8818    284.725     -0.411      0.682    -681.257     447.494\n",
      "==============================================================================\n",
      "Omnibus:                       27.399   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.474\n",
      "Skew:                           0.880   Prob(JB):                     2.44e-12\n",
      "Kurtosis:                       5.518   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carbody_wagon',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.899\n",
      "Method:                 Least Squares   F-statistic:                     47.10\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.06e-47\n",
      "Time:                        19:15:26   Log-Likelihood:                -1201.4\n",
      "No. Observations:                 136   AIC:                             2457.\n",
      "Df Residuals:                     109   BIC:                             2535.\n",
      "Df Model:                          26                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    159.079     74.836      0.000    1.16e+04    1.22e+04\n",
      "symboling           324.1262    300.206      1.080      0.283    -270.872     919.124\n",
      "doornumber          744.6048    277.194      2.686      0.008     195.214    1293.995\n",
      "carheight          -184.1933    274.367     -0.671      0.503    -727.979     359.592\n",
      "cylindernumber      661.8458    282.770      2.341      0.021     101.405    1222.287\n",
      "boreratio          -104.7389    332.556     -0.315      0.753    -763.853     554.376\n",
      "horsepower         2094.4626    335.527      6.242      0.000    1429.459    2759.466\n",
      "carbody_hatchback    57.8215    268.749      0.215      0.830    -474.830     590.473\n",
      "drivewheel_fwd     -808.7700    295.303     -2.739      0.007   -1394.051    -223.489\n",
      "Brand_audi          861.6485    199.347      4.322      0.000     466.548    1256.749\n",
      "Brand_bmw          1126.2144    211.985      5.313      0.000     706.068    1546.361\n",
      "Brand_buick        2079.7566    235.820      8.819      0.000    1612.369    2547.144\n",
      "Brand_dodge        -335.3154    206.403     -1.625      0.107    -744.400      73.769\n",
      "Brand_honda         -45.6340    206.825     -0.221      0.826    -455.555     364.287\n",
      "Brand_mazda         323.4082    230.023      1.406      0.163    -132.490     779.306\n",
      "Brand_mitsubishi   -464.5154    208.199     -2.231      0.028    -877.159     -51.872\n",
      "Brand_nissan       -100.4746    231.456     -0.434      0.665    -559.212     358.263\n",
      "Brand_peugeot      1030.6467    326.794      3.154      0.002     382.952    1678.341\n",
      "Brand_plymouth     -370.0532    190.145     -1.946      0.054    -746.914       6.807\n",
      "Brand_porsche      1785.8979    208.727      8.556      0.000    1372.208    2199.588\n",
      "Brand_saab          236.5900    194.064      1.219      0.225    -148.039     621.219\n",
      "Brand_subaru       -475.4151    263.593     -1.804      0.074    -997.847      47.017\n",
      "Brand_toyota       -345.5302    253.457     -1.363      0.176    -847.873     156.813\n",
      "Brand_volvo        1212.1407    320.800      3.778      0.000     576.325    1847.956\n",
      "curbtype_moderate   363.5245    283.369      1.283      0.202    -198.104     925.153\n",
      "engsize_medium      244.4828    280.584      0.871      0.385    -311.625     800.590\n",
      "hptype_medium      -123.2089    281.817     -0.437      0.663    -681.761     435.343\n",
      "==============================================================================\n",
      "Omnibus:                       28.255   Durbin-Watson:                   2.024\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.977\n",
      "Skew:                           0.901   Prob(JB):                     6.99e-13\n",
      "Kurtosis:                       5.576   Cond. No.                         7.86\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carbody_hatchback',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.900\n",
      "Method:                 Least Squares   F-statistic:                     49.41\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.52e-48\n",
      "Time:                        19:15:26   Log-Likelihood:                -1201.5\n",
      "No. Observations:                 136   AIC:                             2455.\n",
      "Df Residuals:                     110   BIC:                             2531.\n",
      "Df Model:                          25                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    158.388     75.163      0.000    1.16e+04    1.22e+04\n",
      "symboling           327.8569    298.402      1.099      0.274    -263.507     919.220\n",
      "doornumber          714.1437    237.275      3.010      0.003     243.921    1184.367\n",
      "carheight          -195.3255    268.273     -0.728      0.468    -726.979     336.328\n",
      "cylindernumber      664.3694    281.299      2.362      0.020     106.901    1221.838\n",
      "boreratio          -100.5799    330.551     -0.304      0.761    -755.654     554.495\n",
      "horsepower         2095.5683    334.030      6.274      0.000    1433.599    2757.538\n",
      "drivewheel_fwd     -803.5675    293.033     -2.742      0.007   -1384.289    -222.846\n",
      "Brand_audi          859.4290    198.215      4.336      0.000     466.613    1252.245\n",
      "Brand_bmw          1120.3374    209.304      5.353      0.000     705.546    1535.129\n",
      "Brand_buick        2074.6325    233.595      8.881      0.000    1611.702    2537.563\n",
      "Brand_dodge        -334.3039    205.453     -1.627      0.107    -741.464      72.856\n",
      "Brand_honda         -42.9041    205.539     -0.209      0.835    -450.234     364.425\n",
      "Brand_mazda         332.9444    224.732      1.482      0.141    -112.421     778.309\n",
      "Brand_mitsubishi   -461.8248    206.920     -2.232      0.028    -871.892     -51.758\n",
      "Brand_nissan       -109.8338    226.343     -0.485      0.628    -558.393     338.726\n",
      "Brand_peugeot      1028.2314    325.182      3.162      0.002     383.797    1672.665\n",
      "Brand_plymouth     -363.8529    187.132     -1.944      0.054    -734.704       6.998\n",
      "Brand_porsche      1774.8300    201.410      8.812      0.000    1375.683    2173.976\n",
      "Brand_saab          235.2954    193.128      1.218      0.226    -147.440     618.030\n",
      "Brand_subaru       -472.0030    261.972     -1.802      0.074    -991.170      47.164\n",
      "Brand_toyota       -337.8734    249.856     -1.352      0.179    -833.029     157.282\n",
      "Brand_volvo        1208.3120    318.915      3.789      0.000     576.298    1840.326\n",
      "curbtype_moderate   350.2165    275.335      1.272      0.206    -195.432     895.865\n",
      "engsize_medium      239.3433    278.350      0.860      0.392    -312.282     790.968\n",
      "hptype_medium      -121.0914    280.421     -0.432      0.667    -676.821     434.638\n",
      "==============================================================================\n",
      "Omnibus:                       27.967   Durbin-Watson:                   2.017\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.097\n",
      "Skew:                           0.894   Prob(JB):                     1.09e-12\n",
      "Kurtosis:                       5.555   Cond. No.                         7.39\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_honda',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.901\n",
      "Method:                 Least Squares   F-statistic:                     51.92\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.13e-49\n",
      "Time:                        19:15:26   Log-Likelihood:                -1201.5\n",
      "No. Observations:                 136   AIC:                             2453.\n",
      "Df Residuals:                     111   BIC:                             2526.\n",
      "Df Model:                          24                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    157.704     75.489      0.000    1.16e+04    1.22e+04\n",
      "symboling           346.4934    283.504      1.222      0.224    -215.288     908.275\n",
      "doornumber          722.6548    232.736      3.105      0.002     261.473    1183.837\n",
      "carheight          -190.6834    266.195     -0.716      0.475    -718.167     336.800\n",
      "cylindernumber      672.6063    277.315      2.425      0.017     123.088    1222.125\n",
      "boreratio           -90.9649    325.913     -0.279      0.781    -736.783     554.853\n",
      "horsepower         2085.9945    329.438      6.332      0.000    1433.191    2738.798\n",
      "drivewheel_fwd     -809.5902    290.350     -2.788      0.006   -1384.937    -234.243\n",
      "Brand_audi          868.4304    192.633      4.508      0.000     486.716    1250.145\n",
      "Brand_bmw          1125.3596    207.019      5.436      0.000     715.138    1535.581\n",
      "Brand_buick        2081.3158    230.391      9.034      0.000    1624.780    2537.852\n",
      "Brand_dodge        -318.1146    189.431     -1.679      0.096    -693.484      57.255\n",
      "Brand_mazda         350.1375    208.191      1.682      0.095     -62.406     762.681\n",
      "Brand_mitsubishi   -449.5373    197.514     -2.276      0.025    -840.924     -58.150\n",
      "Brand_nissan        -97.8219    217.961     -0.449      0.654    -529.726     334.082\n",
      "Brand_peugeot      1039.8009    319.040      3.259      0.001     407.602    1672.000\n",
      "Brand_plymouth     -352.1884    177.822     -1.981      0.050    -704.555       0.178\n",
      "Brand_porsche      1776.5327    200.375      8.866      0.000    1379.475    2173.590\n",
      "Brand_saab          238.1028    191.828      1.241      0.217    -142.016     618.222\n",
      "Brand_subaru       -461.8843    256.337     -1.802      0.074    -969.832      46.064\n",
      "Brand_toyota       -322.2767    237.393     -1.358      0.177    -792.686     148.133\n",
      "Brand_volvo        1223.9659    308.634      3.966      0.000     612.387    1835.545\n",
      "curbtype_moderate   352.9918    273.826      1.289      0.200    -189.613     895.597\n",
      "engsize_medium      246.8420    274.831      0.898      0.371    -297.753     791.437\n",
      "hptype_medium      -133.2416    273.129     -0.488      0.627    -674.466     407.982\n",
      "==============================================================================\n",
      "Omnibus:                       28.177   Durbin-Watson:                   2.021\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.399\n",
      "Skew:                           0.902   Prob(JB):                     9.34e-13\n",
      "Kurtosis:                       5.554   Cond. No.                         7.23\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('boreratio',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.901\n",
      "Method:                 Least Squares   F-statistic:                     54.62\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.95e-50\n",
      "Time:                        19:15:27   Log-Likelihood:                -1201.5\n",
      "No. Observations:                 136   AIC:                             2451.\n",
      "Df Residuals:                     112   BIC:                             2521.\n",
      "Df Model:                          23                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    157.054     75.802      0.000    1.16e+04    1.22e+04\n",
      "symboling           334.8371    279.254      1.199      0.233    -218.469     888.143\n",
      "doornumber          719.2841    231.464      3.108      0.002     260.668    1177.900\n",
      "carheight          -198.1002    263.773     -0.751      0.454    -720.733     324.532\n",
      "cylindernumber      693.7743    265.643      2.612      0.010     167.438    1220.111\n",
      "horsepower         2039.7075    283.478      7.195      0.000    1478.033    2601.383\n",
      "drivewheel_fwd     -819.4816    286.990     -2.855      0.005   -1388.115    -250.848\n",
      "Brand_audi          870.1744    191.737      4.538      0.000     490.272    1250.077\n",
      "Brand_bmw          1107.8431    196.463      5.639      0.000     718.577    1497.109\n",
      "Brand_buick        2052.0062    204.222     10.048      0.000    1647.367    2456.646\n",
      "Brand_dodge        -321.0983    188.348     -1.705      0.091    -694.287      52.090\n",
      "Brand_mazda         335.4263    200.577      1.672      0.097     -61.992     732.844\n",
      "Brand_mitsubishi   -454.9347    195.754     -2.324      0.022    -842.796     -67.073\n",
      "Brand_nissan       -117.7239    205.118     -0.574      0.567    -524.138     288.690\n",
      "Brand_peugeot       995.6004    275.819      3.610      0.000     449.100    1542.100\n",
      "Brand_plymouth     -356.6094    176.385     -2.022      0.046    -706.093      -7.125\n",
      "Brand_porsche      1763.7199    194.241      9.080      0.000    1378.855    2148.584\n",
      "Brand_saab          230.0776    188.878      1.218      0.226    -144.160     604.315\n",
      "Brand_subaru       -508.3978    193.969     -2.621      0.010    -892.722    -124.074\n",
      "Brand_toyota       -343.8983    223.472     -1.539      0.127    -786.679      98.882\n",
      "Brand_volvo        1178.8665    261.871      4.502      0.000     660.003    1697.730\n",
      "curbtype_moderate   333.7134    263.879      1.265      0.209    -189.128     856.555\n",
      "engsize_medium      249.4313    273.541      0.912      0.364    -292.555     791.418\n",
      "hptype_medium      -145.6646    268.367     -0.543      0.588    -677.399     386.070\n",
      "==============================================================================\n",
      "Omnibus:                       27.582   Durbin-Watson:                   2.012\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.155\n",
      "Skew:                           0.891   Prob(JB):                     2.87e-12\n",
      "Kurtosis:                       5.490   Cond. No.                         5.47\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('hptype_medium',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.902\n",
      "Method:                 Least Squares   F-statistic:                     57.45\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.43e-51\n",
      "Time:                        19:15:27   Log-Likelihood:                -1201.7\n",
      "No. Observations:                 136   AIC:                             2449.\n",
      "Df Residuals:                     113   BIC:                             2516.\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    156.563     76.039      0.000    1.16e+04    1.22e+04\n",
      "symboling           332.9519    278.359      1.196      0.234    -218.528     884.432\n",
      "doornumber          732.7461    229.412      3.194      0.002     278.240    1187.252\n",
      "carheight          -220.7360    259.641     -0.850      0.397    -735.132     293.660\n",
      "cylindernumber      719.9581    260.409      2.765      0.007     204.041    1235.876\n",
      "horsepower         2036.4469    282.528      7.208      0.000    1476.708    2596.186\n",
      "drivewheel_fwd     -808.2497    285.348     -2.833      0.005   -1373.576    -242.924\n",
      "Brand_audi          878.5882    190.512      4.612      0.000     501.150    1256.027\n",
      "Brand_bmw          1119.2764    194.720      5.748      0.000     733.501    1505.052\n",
      "Brand_buick        2066.3265    201.877     10.236      0.000    1666.371    2466.282\n",
      "Brand_dodge        -312.3929    187.078     -1.670      0.098    -683.028      58.242\n",
      "Brand_mazda         346.7409    198.867      1.744      0.084     -47.251     740.733\n",
      "Brand_mitsubishi   -428.4133    188.965     -2.267      0.025    -802.787     -54.039\n",
      "Brand_nissan        -97.0570    200.923     -0.483      0.630    -495.121     301.007\n",
      "Brand_peugeot       981.3744    273.713      3.585      0.000     439.100    1523.648\n",
      "Brand_plymouth     -341.3448    173.584     -1.966      0.052    -685.246       2.557\n",
      "Brand_porsche      1775.6850    192.383      9.230      0.000    1394.539    2156.831\n",
      "Brand_saab          244.6181    186.384      1.312      0.192    -124.643     613.879\n",
      "Brand_subaru       -508.7700    193.361     -2.631      0.010    -891.853    -125.687\n",
      "Brand_toyota       -308.9598    213.331     -1.448      0.150    -731.608     113.688\n",
      "Brand_volvo        1145.2759    253.658      4.515      0.000     642.734    1647.817\n",
      "curbtype_moderate   308.8400    259.057      1.192      0.236    -204.398     822.078\n",
      "engsize_medium      178.6360    239.691      0.745      0.458    -296.235     653.507\n",
      "==============================================================================\n",
      "Omnibus:                       28.110   Durbin-Watson:                   1.991\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.070\n",
      "Skew:                           0.901   Prob(JB):                     1.10e-12\n",
      "Kurtosis:                       5.544   Cond. No.                         5.44\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_nissan',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.918\n",
      "Model:                            OLS   Adj. R-squared:                  0.903\n",
      "Method:                 Least Squares   F-statistic:                     60.58\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           6.29e-52\n",
      "Time:                        19:15:27   Log-Likelihood:                -1201.8\n",
      "No. Observations:                 136   AIC:                             2448.\n",
      "Df Residuals:                     114   BIC:                             2512.\n",
      "Df Model:                          21                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    156.035     76.296      0.000    1.16e+04    1.22e+04\n",
      "symboling           330.5110    277.376      1.192      0.236    -218.969     879.991\n",
      "doornumber          735.5667    228.565      3.218      0.002     282.781    1188.352\n",
      "carheight          -233.3831    257.448     -0.907      0.367    -743.385     276.619\n",
      "cylindernumber      712.7889    259.110      2.751      0.007     199.494    1226.084\n",
      "horsepower         2016.0174    278.404      7.241      0.000    1464.501    2567.534\n",
      "drivewheel_fwd     -806.1935    284.355     -2.835      0.005   -1369.499    -242.888\n",
      "Brand_audi          904.6436    182.101      4.968      0.000     543.902    1265.385\n",
      "Brand_bmw          1140.6423    188.991      6.035      0.000     766.252    1515.032\n",
      "Brand_buick        2094.4534    192.647     10.872      0.000    1712.821    2476.085\n",
      "Brand_dodge        -286.6625    178.730     -1.604      0.112    -640.726      67.401\n",
      "Brand_mazda         370.4216    192.081      1.928      0.056     -10.090     750.933\n",
      "Brand_mitsubishi   -406.4660    182.804     -2.224      0.028    -768.600     -44.332\n",
      "Brand_peugeot      1020.9424    260.289      3.922      0.000     505.312    1536.573\n",
      "Brand_plymouth     -322.0182    168.342     -1.913      0.058    -655.501      11.465\n",
      "Brand_porsche      1795.9574    187.118      9.598      0.000    1425.279    2166.636\n",
      "Brand_saab          260.8017    182.731      1.427      0.156    -101.187     622.790\n",
      "Brand_subaru       -489.9138    188.742     -2.596      0.011    -863.810    -116.017\n",
      "Brand_toyota       -271.4553    198.032     -1.371      0.173    -663.756     120.845\n",
      "Brand_volvo        1184.4683    239.522      4.945      0.000     709.976    1658.960\n",
      "curbtype_moderate   345.4349    246.898      1.399      0.164    -143.667     834.537\n",
      "engsize_medium      186.1365    238.382      0.781      0.437    -286.096     658.369\n",
      "==============================================================================\n",
      "Omnibus:                       28.536   Durbin-Watson:                   1.995\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.230\n",
      "Skew:                           0.912   Prob(JB):                     6.16e-13\n",
      "Kurtosis:                       5.568   Cond. No.                         5.04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('engsize_medium',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.917\n",
      "Model:                            OLS   Adj. R-squared:                  0.903\n",
      "Method:                 Least Squares   F-statistic:                     63.79\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.05e-52\n",
      "Time:                        19:15:27   Log-Likelihood:                -1202.2\n",
      "No. Observations:                 136   AIC:                             2446.\n",
      "Df Residuals:                     115   BIC:                             2508.\n",
      "Df Model:                          20                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    155.770     76.426      0.000    1.16e+04    1.22e+04\n",
      "symboling           282.8357    270.113      1.047      0.297    -252.206     817.878\n",
      "doornumber          718.9977    227.191      3.165      0.002     268.975    1169.020\n",
      "carheight          -237.7477    256.950     -0.925      0.357    -746.716     271.221\n",
      "cylindernumber      712.1060    258.669      2.753      0.007     199.733    1224.479\n",
      "horsepower         2033.3656    277.045      7.339      0.000    1484.593    2582.138\n",
      "drivewheel_fwd     -746.2575    273.334     -2.730      0.007   -1287.680    -204.835\n",
      "Brand_audi          949.9765    172.305      5.513      0.000     608.673    1291.281\n",
      "Brand_bmw          1130.9639    188.264      6.007      0.000     758.049    1503.879\n",
      "Brand_buick        2090.4409    192.251     10.873      0.000    1709.628    2471.254\n",
      "Brand_dodge        -295.4877    178.070     -1.659      0.100    -648.210      57.234\n",
      "Brand_mazda         362.5129    191.488      1.893      0.061     -16.789     741.814\n",
      "Brand_mitsubishi   -402.7887    182.433     -2.208      0.029    -764.154     -41.424\n",
      "Brand_peugeot      1077.7482    249.491      4.320      0.000     583.555    1571.942\n",
      "Brand_plymouth     -320.0725    168.037     -1.905      0.059    -652.922      12.777\n",
      "Brand_porsche      1772.6872    184.415      9.612      0.000    1407.396    2137.979\n",
      "Brand_saab          286.3659    179.469      1.596      0.113     -69.127     641.859\n",
      "Brand_subaru       -456.7907    183.601     -2.488      0.014    -820.469     -93.113\n",
      "Brand_toyota       -285.9466    196.826     -1.453      0.149    -675.821     103.928\n",
      "Brand_volvo        1176.2461    238.884      4.924      0.000     703.062    1649.430\n",
      "curbtype_moderate   460.9235    197.358      2.335      0.021      69.995     851.852\n",
      "==============================================================================\n",
      "Omnibus:                       28.816   Durbin-Watson:                   1.979\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.651\n",
      "Skew:                           0.922   Prob(JB):                     4.99e-13\n",
      "Kurtosis:                       5.568   Cond. No.                         4.88\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('carheight',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.917\n",
      "Model:                            OLS   Adj. R-squared:                  0.903\n",
      "Method:                 Least Squares   F-statistic:                     67.19\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.91e-53\n",
      "Time:                        19:15:27   Log-Likelihood:                -1202.7\n",
      "No. Observations:                 136   AIC:                             2445.\n",
      "Df Residuals:                     116   BIC:                             2504.\n",
      "Df Model:                          19                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    155.674     76.473      0.000    1.16e+04    1.22e+04\n",
      "symboling           343.6980    261.819      1.313      0.192    -174.868     862.264\n",
      "doornumber          665.8739    219.681      3.031      0.003     230.769    1100.979\n",
      "cylindernumber      676.0647    255.560      2.645      0.009     169.895    1182.234\n",
      "horsepower         2096.5289    268.336      7.813      0.000    1565.055    2628.002\n",
      "drivewheel_fwd     -762.2454    272.618     -2.796      0.006   -1302.200    -222.290\n",
      "Brand_audi          929.0630    170.711      5.442      0.000     590.949    1267.177\n",
      "Brand_bmw          1114.9443    187.350      5.951      0.000     743.874    1486.014\n",
      "Brand_buick        2049.5821    186.995     10.961      0.000    1679.215    2419.949\n",
      "Brand_dodge        -253.1954    171.997     -1.472      0.144    -593.856      87.466\n",
      "Brand_mazda         362.7741    191.369      1.896      0.060     -16.257     741.805\n",
      "Brand_mitsubishi   -353.0573    174.228     -2.026      0.045    -698.137      -7.977\n",
      "Brand_peugeot      1005.3420    236.754      4.246      0.000     536.421    1474.263\n",
      "Brand_plymouth     -294.7996    165.700     -1.779      0.078    -622.988      33.389\n",
      "Brand_porsche      1758.2847    183.643      9.574      0.000    1394.556    2122.014\n",
      "Brand_saab          246.6179    174.144      1.416      0.159     -98.295     591.531\n",
      "Brand_subaru       -455.6803    183.483     -2.484      0.014    -819.092     -92.269\n",
      "Brand_toyota       -304.5544    195.674     -1.556      0.122    -692.112      83.003\n",
      "Brand_volvo        1137.1571    234.973      4.840      0.000     671.763    1602.551\n",
      "curbtype_moderate   434.3579    195.137      2.226      0.028      47.864     820.852\n",
      "==============================================================================\n",
      "Omnibus:                       27.939   Durbin-Watson:                   1.964\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               54.666\n",
      "Skew:                           0.896   Prob(JB):                     1.35e-12\n",
      "Kurtosis:                       5.537   Cond. No.                         4.46\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('symboling',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.915\n",
      "Model:                            OLS   Adj. R-squared:                  0.902\n",
      "Method:                 Least Squares   F-statistic:                     70.39\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           5.21e-54\n",
      "Time:                        19:15:28   Log-Likelihood:                -1203.7\n",
      "No. Observations:                 136   AIC:                             2445.\n",
      "Df Residuals:                     117   BIC:                             2501.\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    156.154     76.238      0.000    1.16e+04    1.22e+04\n",
      "doornumber          506.7527    183.778      2.757      0.007     142.791     870.715\n",
      "cylindernumber      601.9146    250.009      2.408      0.018     106.785    1097.044\n",
      "horsepower         2137.0271    267.379      7.992      0.000    1607.496    2666.558\n",
      "drivewheel_fwd     -827.2077    268.917     -3.076      0.003   -1359.783    -294.633\n",
      "Brand_audi          950.7089    170.437      5.578      0.000     613.168    1288.250\n",
      "Brand_bmw          1094.7739    187.295      5.845      0.000     723.846    1465.702\n",
      "Brand_buick        1970.2746    177.513     11.099      0.000    1618.720    2321.830\n",
      "Brand_dodge        -266.4416    172.230     -1.547      0.125    -607.535      74.652\n",
      "Brand_mazda         339.6787    191.147      1.777      0.078     -38.878     718.235\n",
      "Brand_mitsubishi   -323.1232    173.262     -1.865      0.065    -666.260      20.013\n",
      "Brand_peugeot       926.9585    229.808      4.034      0.000     471.837    1382.080\n",
      "Brand_plymouth     -306.6577    165.964     -1.848      0.067    -635.340      22.025\n",
      "Brand_porsche      1792.6772    182.326      9.832      0.000    1431.590    2153.764\n",
      "Brand_saab          288.2705    171.757      1.678      0.096     -51.885     628.426\n",
      "Brand_subaru       -491.3073    182.025     -2.699      0.008    -851.798    -130.817\n",
      "Brand_toyota       -364.6704    190.827     -1.911      0.058    -742.594      13.253\n",
      "Brand_volvo         996.1448    209.627      4.752      0.000     580.989    1411.301\n",
      "curbtype_moderate   420.8506    195.467      2.153      0.033      33.738     807.963\n",
      "==============================================================================\n",
      "Omnibus:                       25.544   Durbin-Watson:                   2.024\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               47.072\n",
      "Skew:                           0.845   Prob(JB):                     6.01e-11\n",
      "Kurtosis:                       5.334   Cond. No.                         4.11\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_dodge',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.914\n",
      "Model:                            OLS   Adj. R-squared:                  0.901\n",
      "Method:                 Least Squares   F-statistic:                     73.52\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.92e-54\n",
      "Time:                        19:15:28   Log-Likelihood:                -1205.1\n",
      "No. Observations:                 136   AIC:                             2446.\n",
      "Df Residuals:                     118   BIC:                             2499.\n",
      "Df Model:                          17                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    157.073     75.792      0.000    1.16e+04    1.22e+04\n",
      "doornumber          503.4464    184.847      2.724      0.007     137.399     869.494\n",
      "cylindernumber      663.7219    248.249      2.674      0.009     172.122    1155.322\n",
      "horsepower         2103.8040    268.084      7.848      0.000    1572.924    2634.684\n",
      "drivewheel_fwd     -861.4420    269.582     -3.195      0.002   -1395.288    -327.596\n",
      "Brand_audi          988.1786    169.700      5.823      0.000     652.126    1324.231\n",
      "Brand_bmw          1103.1105    188.319      5.858      0.000     730.187    1476.034\n",
      "Brand_buick        1995.0508    177.830     11.219      0.000    1642.900    2347.202\n",
      "Brand_mazda         411.6978    186.482      2.208      0.029      42.412     780.984\n",
      "Brand_mitsubishi   -263.7141    169.947     -1.552      0.123    -600.256      72.827\n",
      "Brand_peugeot       983.8144    228.185      4.311      0.000     531.946    1435.683\n",
      "Brand_plymouth     -260.2711    164.193     -1.585      0.116    -585.419      64.877\n",
      "Brand_porsche      1802.1933    183.295      9.832      0.000    1439.220    2165.167\n",
      "Brand_saab          323.0831    171.279      1.886      0.062     -16.095     662.261\n",
      "Brand_subaru       -446.5285    180.766     -2.470      0.015    -804.495     -88.562\n",
      "Brand_toyota       -306.5441    188.193     -1.629      0.106    -679.217      66.129\n",
      "Brand_volvo        1039.9224    208.931      4.977      0.000     626.182    1453.663\n",
      "curbtype_moderate   467.4679    194.267      2.406      0.018      82.766     852.170\n",
      "==============================================================================\n",
      "Omnibus:                       24.961   Durbin-Watson:                   1.996\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               42.714\n",
      "Skew:                           0.860   Prob(JB):                     5.31e-10\n",
      "Kurtosis:                       5.139   Cond. No.                         4.07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_mitsubishi',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.912\n",
      "Model:                            OLS   Adj. R-squared:                  0.900\n",
      "Method:                 Least Squares   F-statistic:                     77.06\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           6.98e-55\n",
      "Time:                        19:15:28   Log-Likelihood:                -1206.5\n",
      "No. Observations:                 136   AIC:                             2447.\n",
      "Df Residuals:                     119   BIC:                             2496.\n",
      "Df Model:                          16                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    158.000     75.348      0.000    1.16e+04    1.22e+04\n",
      "doornumber          520.6309    185.603      2.805      0.006     153.118     888.144\n",
      "cylindernumber      717.1899    247.296      2.900      0.004     227.519    1206.860\n",
      "horsepower         2035.9455    266.054      7.652      0.000    1509.133    2562.758\n",
      "drivewheel_fwd     -926.6492    267.857     -3.459      0.001   -1457.034    -396.265\n",
      "Brand_audi         1020.2331    169.432      6.022      0.000     684.742    1355.724\n",
      "Brand_bmw          1104.6428    189.428      5.831      0.000     729.557    1479.728\n",
      "Brand_buick        2003.5636    178.793     11.206      0.000    1649.535    2357.592\n",
      "Brand_mazda         466.3473    184.207      2.532      0.013     101.599     831.095\n",
      "Brand_peugeot      1000.7905    229.267      4.365      0.000     546.819    1454.762\n",
      "Brand_plymouth     -225.1309    163.583     -1.376      0.171    -549.042      98.781\n",
      "Brand_porsche      1824.8565    183.790      9.929      0.000    1460.935    2188.778\n",
      "Brand_saab          356.1421    170.951      2.083      0.039      17.642     694.642\n",
      "Brand_subaru       -418.0209    180.891     -2.311      0.023    -776.203     -59.838\n",
      "Brand_toyota       -271.0393    187.899     -1.442      0.152    -643.097     101.019\n",
      "Brand_volvo        1053.3465    209.983      5.016      0.000     637.559    1469.134\n",
      "curbtype_moderate   470.1978    195.405      2.406      0.018      83.276     857.119\n",
      "==============================================================================\n",
      "Omnibus:                       23.029   Durbin-Watson:                   1.993\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.948\n",
      "Skew:                           0.826   Prob(JB):                     9.48e-09\n",
      "Kurtosis:                       4.947   Cond. No.                         4.05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_plymouth',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.911\n",
      "Model:                            OLS   Adj. R-squared:                  0.899\n",
      "Method:                 Least Squares   F-statistic:                     81.46\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.92e-55\n",
      "Time:                        19:15:28   Log-Likelihood:                -1207.5\n",
      "No. Observations:                 136   AIC:                             2447.\n",
      "Df Residuals:                     120   BIC:                             2494.\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    158.587     75.069      0.000    1.16e+04    1.22e+04\n",
      "doornumber          507.6324    186.052      2.728      0.007     139.262     876.002\n",
      "cylindernumber      742.2812    247.540      2.999      0.003     252.170    1232.393\n",
      "horsepower         2036.2820    267.043      7.625      0.000    1507.556    2565.008\n",
      "drivewheel_fwd     -904.3116    268.359     -3.370      0.001   -1435.644    -372.979\n",
      "Brand_audi         1041.3330    169.364      6.148      0.000     706.004    1376.662\n",
      "Brand_bmw          1121.6265    189.728      5.912      0.000     745.978    1497.275\n",
      "Brand_buick        2024.2435    178.823     11.320      0.000    1670.186    2378.301\n",
      "Brand_mazda         509.3167    182.217      2.795      0.006     148.540     870.093\n",
      "Brand_peugeot      1046.2696    227.717      4.595      0.000     595.407    1497.132\n",
      "Brand_porsche      1832.4827    184.389      9.938      0.000    1467.405    2197.560\n",
      "Brand_saab          371.0293    171.243      2.167      0.032      31.981     710.078\n",
      "Brand_subaru       -383.6872    179.829     -2.134      0.035    -739.736     -27.639\n",
      "Brand_toyota       -229.6487    186.166     -1.234      0.220    -598.244     138.946\n",
      "Brand_volvo        1089.5488    209.104      5.211      0.000     675.538    1503.559\n",
      "curbtype_moderate   483.1306    195.905      2.466      0.015      95.253     871.008\n",
      "==============================================================================\n",
      "Omnibus:                       23.260   Durbin-Watson:                   1.984\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.273\n",
      "Skew:                           0.848   Prob(JB):                     1.33e-08\n",
      "Kurtosis:                       4.877   Cond. No.                         4.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_toyota',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.909\n",
      "Model:                            OLS   Adj. R-squared:                  0.899\n",
      "Method:                 Least Squares   F-statistic:                     86.80\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.26e-56\n",
      "Time:                        19:15:28   Log-Likelihood:                -1208.4\n",
      "No. Observations:                 136   AIC:                             2447.\n",
      "Df Residuals:                     121   BIC:                             2490.\n",
      "Df Model:                          14                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    158.929     74.907      0.000    1.16e+04    1.22e+04\n",
      "doornumber          475.0911    184.569      2.574      0.011     109.688     840.494\n",
      "cylindernumber      712.9697    246.927      2.887      0.005     224.112    1201.828\n",
      "horsepower         2097.3747    262.975      7.976      0.000    1576.746    2618.004\n",
      "drivewheel_fwd     -765.4079    244.117     -3.135      0.002   -1248.702    -282.114\n",
      "Brand_audi         1075.9716    167.380      6.428      0.000     744.600    1407.343\n",
      "Brand_bmw          1186.3460    182.722      6.493      0.000     824.599    1548.093\n",
      "Brand_buick        2079.2115    173.555     11.980      0.000    1735.613    2422.810\n",
      "Brand_mazda         563.8010    177.163      3.182      0.002     213.060     914.542\n",
      "Brand_peugeot      1146.0349    213.329      5.372      0.000     723.693    1568.377\n",
      "Brand_porsche      1854.9136    183.885     10.087      0.000    1490.864    2218.963\n",
      "Brand_saab          375.7442    171.569      2.190      0.030      36.079     715.410\n",
      "Brand_subaru       -317.5385    172.017     -1.846      0.067    -658.092      23.015\n",
      "Brand_volvo        1176.5826    197.265      5.964      0.000     786.045    1567.121\n",
      "curbtype_moderate   492.0235    196.194      2.508      0.013     103.607     880.440\n",
      "==============================================================================\n",
      "Omnibus:                       25.128   Durbin-Watson:                   1.994\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.352\n",
      "Skew:                           0.885   Prob(JB):                     1.05e-09\n",
      "Kurtosis:                       5.040   Cond. No.                         3.51\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe.drop('Brand_subaru',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.907\n",
      "Model:                            OLS   Adj. R-squared:                  0.897\n",
      "Method:                 Least Squares   F-statistic:                     91.41\n",
      "Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.30e-56\n",
      "Time:                        19:15:29   Log-Likelihood:                -1210.3\n",
      "No. Observations:                 136   AIC:                             2449.\n",
      "Df Residuals:                     122   BIC:                             2489.\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               1.19e+04    160.489     74.179      0.000    1.16e+04    1.22e+04\n",
      "doornumber          432.1248    184.893      2.337      0.021      66.110     798.140\n",
      "cylindernumber      720.9316    249.314      2.892      0.005     227.390    1214.473\n",
      "horsepower         2170.5730    262.521      8.268      0.000    1650.886    2690.260\n",
      "drivewheel_fwd     -646.0193    237.705     -2.718      0.008   -1116.580    -175.458\n",
      "Brand_audi         1095.3998    168.689      6.494      0.000     761.464    1429.336\n",
      "Brand_bmw          1231.9517    182.822      6.739      0.000     870.037    1593.866\n",
      "Brand_buick        2111.8698    174.347     12.113      0.000    1766.733    2457.006\n",
      "Brand_mazda         625.7818    175.661      3.562      0.001     278.044     973.519\n",
      "Brand_peugeot      1224.1879    211.139      5.798      0.000     806.217    1642.159\n",
      "Brand_porsche      1857.8069    185.684     10.005      0.000    1490.226    2225.387\n",
      "Brand_saab          378.3695    173.247      2.184      0.031      35.409     721.330\n",
      "Brand_volvo        1236.8959    196.451      6.296      0.000     848.002    1625.789\n",
      "curbtype_moderate   458.1369    197.251      2.323      0.022      67.659     848.615\n",
      "==============================================================================\n",
      "Omnibus:                       26.255   Durbin-Watson:                   1.973\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               43.647\n",
      "Skew:                           0.918   Prob(JB):                     3.33e-10\n",
      "Kurtosis:                       5.080   Cond. No.                         3.47\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm1 = sm.OLS(y_train,x_train_rfe).fit()\n",
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136, 14)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_rfe.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### All the VIF values and p-values seem to be in a good range. We came down from 38 variables to 14 variables. This model is explaining most of the variance without being too complex. So let's proceed with this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Residual analysis \n",
    "To validate assumptions of the model, and hence the reliability for inference\n",
    "\n",
    "### Distribution of the error terms\n",
    "We need to check if the error terms are also normally distributed (which is infact, one of the major assumptions of linear regression), let us plot the histogram of the error terms and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = lm1.predict(x_train_rfe)\n",
    "res = y_train - y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(res,bins=15)\n",
    "plt.suptitle(\"Error Terms\")\n",
    "plt.xlabel(\"Error values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The residuals are following a normal curve and the mean is negative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Model Evaluation\n",
    "\n",
    "Now that you have fitted a regression line on your train dataset, it's time to make some predictions on the test data. For this, first need to add a constant to the `x_test` data like you did for `x_train` and then you can simply go on and predict the y values corresponding to `x_test` using the `predict` attribute of the fitted regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['const', 'doornumber', 'cylindernumber', 'horsepower', 'drivewheel_fwd',\n",
       "       'Brand_audi', 'Brand_bmw', 'Brand_buick', 'Brand_mazda',\n",
       "       'Brand_peugeot', 'Brand_porsche', 'Brand_saab', 'Brand_volvo',\n",
       "       'curbtype_moderate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col1 = x_train_rfe.columns\n",
    "col1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = car_test.drop('price',1)\n",
    "y_test = car_test['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subsetting\n",
    "x_test[x_test.columns] = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding constant to test dataset\n",
    "x_test = sm.add_constant(x_test,has_constant='add')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>symboling</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>carbody_hardtop</th>\n",
       "      <th>carbody_hatchback</th>\n",
       "      <th>carbody_sedan</th>\n",
       "      <th>carbody_wagon</th>\n",
       "      <th>drivewheel_fwd</th>\n",
       "      <th>drivewheel_rwd</th>\n",
       "      <th>Brand_audi</th>\n",
       "      <th>Brand_bmw</th>\n",
       "      <th>Brand_buick</th>\n",
       "      <th>Brand_chevrolet</th>\n",
       "      <th>Brand_dodge</th>\n",
       "      <th>Brand_honda</th>\n",
       "      <th>Brand_isuzu</th>\n",
       "      <th>Brand_mazda</th>\n",
       "      <th>Brand_mercury</th>\n",
       "      <th>Brand_mitsubishi</th>\n",
       "      <th>Brand_nissan</th>\n",
       "      <th>Brand_peugeot</th>\n",
       "      <th>Brand_plymouth</th>\n",
       "      <th>Brand_porsche</th>\n",
       "      <th>Brand_renault</th>\n",
       "      <th>Brand_saab</th>\n",
       "      <th>Brand_subaru</th>\n",
       "      <th>Brand_toyota</th>\n",
       "      <th>Brand_volkswagen</th>\n",
       "      <th>Brand_volvo</th>\n",
       "      <th>curbtype_light</th>\n",
       "      <th>curbtype_moderate</th>\n",
       "      <th>engsize_medium</th>\n",
       "      <th>engsize_small</th>\n",
       "      <th>hptype_low</th>\n",
       "      <th>hptype_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.500467</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>0.795451</td>\n",
       "      <td>0.258082</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.448988</td>\n",
       "      <td>-0.033698</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>-0.340073</td>\n",
       "      <td>-0.119442</td>\n",
       "      <td>-0.804156</td>\n",
       "      <td>0.736737</td>\n",
       "      <td>0.301528</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.40298</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.577350</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.313470</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>1.156879</td>\n",
       "      <td>1.462104</td>\n",
       "      <td>0.846708</td>\n",
       "      <td>0.965119</td>\n",
       "      <td>1.244999</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.359095</td>\n",
       "      <td>1.267245</td>\n",
       "      <td>2.056956</td>\n",
       "      <td>-1.430569</td>\n",
       "      <td>-1.474402</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.40298</td>\n",
       "      <td>-1.270978</td>\n",
       "      <td>1.470643</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>4.292851</td>\n",
       "      <td>-0.577350</td>\n",
       "      <td>-1.014816</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.687465</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>-0.479061</td>\n",
       "      <td>-0.590207</td>\n",
       "      <td>-0.708653</td>\n",
       "      <td>-0.305358</td>\n",
       "      <td>-0.936708</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>-0.759574</td>\n",
       "      <td>-0.436398</td>\n",
       "      <td>-0.900598</td>\n",
       "      <td>0.736737</td>\n",
       "      <td>0.947321</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.738549</td>\n",
       "      <td>1.076443</td>\n",
       "      <td>-0.40298</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>-1.014816</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>1.635749</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.751542</td>\n",
       "      <td>-1.159667</td>\n",
       "      <td>0.167706</td>\n",
       "      <td>1.261434</td>\n",
       "      <td>0.457868</td>\n",
       "      <td>0.925416</td>\n",
       "      <td>0.708625</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>0.044469</td>\n",
       "      <td>0.950288</td>\n",
       "      <td>1.992661</td>\n",
       "      <td>-1.097138</td>\n",
       "      <td>-0.828609</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>1.354006</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>-0.40298</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>8.185353</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>-0.577350</td>\n",
       "      <td>0.985401</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>-0.611341</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.687465</td>\n",
       "      <td>0.862316</td>\n",
       "      <td>-0.479061</td>\n",
       "      <td>-0.590207</td>\n",
       "      <td>-0.708653</td>\n",
       "      <td>-0.384763</td>\n",
       "      <td>-0.843917</td>\n",
       "      <td>-0.301113</td>\n",
       "      <td>-0.759574</td>\n",
       "      <td>-0.436398</td>\n",
       "      <td>-0.900598</td>\n",
       "      <td>0.403305</td>\n",
       "      <td>0.462977</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>1.354006</td>\n",
       "      <td>-0.928985</td>\n",
       "      <td>-0.40298</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>-0.679975</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.174078</td>\n",
       "      <td>-0.150188</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.352089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.266207</td>\n",
       "      <td>-0.214834</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.122169</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>-0.281718</td>\n",
       "      <td>-0.232945</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>-1.014816</td>\n",
       "      <td>1.045135</td>\n",
       "      <td>-0.6227</td>\n",
       "      <td>1.635749</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     const  symboling  doornumber  wheelbase  carlength  carwidth  carheight  \\\n",
       "174    1.0  -1.500467    0.862316   0.795451   0.258082  0.457868   0.448988   \n",
       "198    1.0  -2.313470    0.862316   1.156879   1.462104  0.846708   0.965119   \n",
       "156    1.0  -0.687465    0.862316  -0.479061  -0.590207 -0.708653  -0.305358   \n",
       "136    1.0   1.751542   -1.159667   0.167706   1.261434  0.457868   0.925416   \n",
       "161    1.0  -0.687465    0.862316  -0.479061  -0.590207 -0.708653  -0.384763   \n",
       "\n",
       "     curbweight  cylindernumber  enginesize  boreratio  horsepower   citympg  \\\n",
       "174   -0.033698       -0.301113   -0.340073  -0.119442   -0.804156  0.736737   \n",
       "198    1.244999       -0.301113    0.359095   1.267245    2.056956 -1.430569   \n",
       "156   -0.936708       -0.301113   -0.759574  -0.436398   -0.900598  0.736737   \n",
       "136    0.708625       -0.301113    0.044469   0.950288    1.992661 -1.097138   \n",
       "161   -0.843917       -0.301113   -0.759574  -0.436398   -0.900598  0.403305   \n",
       "\n",
       "     highwaympg  carbody_hardtop  carbody_hatchback  carbody_sedan  \\\n",
       "174    0.301528        -0.150188          -0.738549       1.076443   \n",
       "198   -1.474402        -0.150188          -0.738549       1.076443   \n",
       "156    0.947321        -0.150188          -0.738549       1.076443   \n",
       "136   -0.828609        -0.150188           1.354006      -0.928985   \n",
       "161    0.462977        -0.150188           1.354006      -0.928985   \n",
       "\n",
       "     carbody_wagon  drivewheel_fwd  drivewheel_rwd  Brand_audi  Brand_bmw  \\\n",
       "174       -0.40298        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "198       -0.40298       -1.270978        1.470643   -0.214834  -0.174078   \n",
       "156       -0.40298        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "136       -0.40298        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "161       -0.40298        0.786796       -0.679975   -0.214834  -0.174078   \n",
       "\n",
       "     Brand_buick  Brand_chevrolet  Brand_dodge  Brand_honda  Brand_isuzu  \\\n",
       "174    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "198    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "156    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "136    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "161    -0.150188        -0.122169    -0.266207    -0.266207    -0.122169   \n",
       "\n",
       "     Brand_mazda  Brand_mercury  Brand_mitsubishi  Brand_nissan  \\\n",
       "174    -0.352089            0.0         -0.266207     -0.281718   \n",
       "198    -0.352089            0.0         -0.266207     -0.281718   \n",
       "156    -0.352089            0.0         -0.266207     -0.281718   \n",
       "136    -0.352089            0.0         -0.266207     -0.281718   \n",
       "161    -0.352089            0.0         -0.266207     -0.281718   \n",
       "\n",
       "     Brand_peugeot  Brand_plymouth  Brand_porsche  Brand_renault  Brand_saab  \\\n",
       "174      -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "198      -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "156      -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "136      -0.266207       -0.214834      -0.122169      -0.122169    8.185353   \n",
       "161      -0.266207       -0.214834      -0.122169      -0.122169   -0.122169   \n",
       "\n",
       "     Brand_subaru  Brand_toyota  Brand_volkswagen  Brand_volvo  \\\n",
       "174     -0.281718      2.645751         -0.281718    -0.232945   \n",
       "198     -0.281718     -0.377964         -0.281718     4.292851   \n",
       "156     -0.281718      2.645751         -0.281718    -0.232945   \n",
       "136     -0.281718     -0.377964         -0.281718    -0.232945   \n",
       "161     -0.281718      2.645751         -0.281718    -0.232945   \n",
       "\n",
       "     curbtype_light  curbtype_moderate  engsize_medium  engsize_small  \\\n",
       "174       -0.577350           0.985401        1.045135        -0.6227   \n",
       "198       -0.577350          -1.014816        1.045135        -0.6227   \n",
       "156        1.732051          -1.014816        1.045135        -0.6227   \n",
       "136       -0.577350           0.985401        1.045135        -0.6227   \n",
       "161        1.732051          -1.014816        1.045135        -0.6227   \n",
       "\n",
       "     hptype_low  hptype_medium  \n",
       "174   -0.611341            1.0  \n",
       "198   -0.611341           -1.0  \n",
       "156    1.635749           -1.0  \n",
       "136   -0.611341           -1.0  \n",
       "161    1.635749           -1.0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_rfe = x_test[col1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 59 entries, 174 to 132\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   const              59 non-null     float64\n",
      " 1   doornumber         59 non-null     float64\n",
      " 2   cylindernumber     59 non-null     float64\n",
      " 3   horsepower         59 non-null     float64\n",
      " 4   drivewheel_fwd     59 non-null     float64\n",
      " 5   Brand_audi         59 non-null     float64\n",
      " 6   Brand_bmw          59 non-null     float64\n",
      " 7   Brand_buick        59 non-null     float64\n",
      " 8   Brand_mazda        59 non-null     float64\n",
      " 9   Brand_peugeot      59 non-null     float64\n",
      " 10  Brand_porsche      59 non-null     float64\n",
      " 11  Brand_saab         59 non-null     float64\n",
      " 12  Brand_volvo        59 non-null     float64\n",
      " 13  curbtype_moderate  59 non-null     float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 6.9 KB\n"
     ]
    }
   ],
   "source": [
    "x_test_rfe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting y values corresponding to x_test_rfe\n",
    "y_pred = lm1.predict(x_test_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174     8383.630400\n",
       "198    20604.810772\n",
       "156     7257.923240\n",
       "136    16723.890029\n",
       "161     7257.923240\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174    10698.0\n",
       "198    18420.0\n",
       "156     6938.0\n",
       "136    18150.0\n",
       "161     8358.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17455162d48>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting y_test and y_pred to understand the spread\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.scatter(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the above plot, it's evident that the model is doing good on the test set as well. Let's also check the R-squared and more importantly, the adjusted R-squared value for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2658.267222995913"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns the mean squared error; we'll take a square root\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8264312523940242"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chacking r-squared of the test set\n",
    "r_square = r2_score(y_test,y_pred)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Checking the correlations between the final predictor variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17454f91f48>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['doornumber', 'cylindernumber', 'horsepower', 'drivewheel_fwd',\n",
    "       'Brand_audi', 'Brand_bmw', 'Brand_buick', 'Brand_mazda',\n",
    "       'Brand_peugeot', 'Brand_porsche', 'Brand_saab', 'Brand_volvo',\n",
    "       'curbtype_moderate']\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(car[features].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the final predictors seems to have low correlation"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}