Car Price Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', None)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('CarPrice_Assignment.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   CarName           205 non-null    object \n",
      " 2   fueltype          205 non-null    object \n",
      " 3   aspiration        205 non-null    object \n",
      " 4   carbody           205 non-null    object \n",
      " 5   drivewheel        205 non-null    object \n",
      " 6   enginelocation    205 non-null    object \n",
      " 7   wheelbase         205 non-null    float64\n",
      " 8   carlength         205 non-null    float64\n",
      " 9   carwidth          205 non-null    float64\n",
      " 10  carheight         205 non-null    float64\n",
      " 11  curbweight        205 non-null    int64  \n",
      " 12  enginetype        205 non-null    object \n",
      " 13  cylindernumber    205 non-null    object \n",
      " 14  enginesize        205 non-null    int64  \n",
      " 15  fuelsystem        205 non-null    object \n",
      " 16  boreratio         205 non-null    float64\n",
      " 17  stroke            205 non-null    float64\n",
      " 18  compressionratio  205 non-null    float64\n",
      " 19  horsepower        205 non-null    int64  \n",
      " 20  peakrpm           205 non-null    int64  \n",
      " 21  citympg           205 non-null    int64  \n",
      " 22  highwaympg        205 non-null    int64  \n",
      " 23  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(7), object(9)\n",
      "memory usage: 38.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero giulia', 'alfa-romero stelvio',\n",
       "       'alfa-romero Quadrifoglio', 'audi 100 ls', 'audi 100ls',\n",
       "       'audi fox', 'audi 5000', 'audi 4000', 'audi 5000s (diesel)',\n",
       "       'bmw 320i', 'bmw x1', 'bmw x3', 'bmw z4', 'bmw x4', 'bmw x5',\n",
       "       'chevrolet impala', 'chevrolet monte carlo', 'chevrolet vega 2300',\n",
       "       'dodge rampage', 'dodge challenger se', 'dodge d200',\n",
       "       'dodge monaco (sw)', 'dodge colt hardtop', 'dodge colt (sw)',\n",
       "       'dodge coronet custom', 'dodge dart custom',\n",
       "       'dodge coronet custom (sw)', 'honda civic', 'honda civic cvcc',\n",
       "       'honda accord cvcc', 'honda accord lx', 'honda civic 1500 gl',\n",
       "       'honda accord', 'honda civic 1300', 'honda prelude',\n",
       "       'honda civic (auto)', 'isuzu MU-X', 'isuzu D-Max ',\n",
       "       'isuzu D-Max V-Cross', 'jaguar xj', 'jaguar xf', 'jaguar xk',\n",
       "       'maxda rx3', 'maxda glc deluxe', 'mazda rx2 coupe', 'mazda rx-4',\n",
       "       'mazda glc deluxe', 'mazda 626', 'mazda glc', 'mazda rx-7 gs',\n",
       "       'mazda glc 4', 'mazda glc custom l', 'mazda glc custom',\n",
       "       'buick electra 225 custom', 'buick century luxus (sw)',\n",
       "       'buick century', 'buick skyhawk', 'buick opel isuzu deluxe',\n",
       "       'buick skylark', 'buick century special',\n",
       "       'buick regal sport coupe (turbo)', 'mercury cougar',\n",
       "       'mitsubishi mirage', 'mitsubishi lancer', 'mitsubishi outlander',\n",
       "       'mitsubishi g4', 'mitsubishi mirage g4', 'mitsubishi montero',\n",
       "       'mitsubishi pajero', 'Nissan versa', 'nissan gt-r', 'nissan rogue',\n",
       "       'nissan latio', 'nissan titan', 'nissan leaf', 'nissan juke',\n",
       "       'nissan note', 'nissan clipper', 'nissan nv200', 'nissan dayz',\n",
       "       'nissan fuga', 'nissan otti', 'nissan teana', 'nissan kicks',\n",
       "       'peugeot 504', 'peugeot 304', 'peugeot 504 (sw)', 'peugeot 604sl',\n",
       "       'peugeot 505s turbo diesel', 'plymouth fury iii',\n",
       "       'plymouth cricket', 'plymouth satellite custom (sw)',\n",
       "       'plymouth fury gran sedan', 'plymouth valiant', 'plymouth duster',\n",
       "       'porsche macan', 'porcshce panamera', 'porsche cayenne',\n",
       "       'porsche boxter', 'renault 12tl', 'renault 5 gtl', 'saab 99e',\n",
       "       'saab 99le', 'saab 99gle', 'subaru', 'subaru dl', 'subaru brz',\n",
       "       'subaru baja', 'subaru r1', 'subaru r2', 'subaru trezia',\n",
       "       'subaru tribeca', 'toyota corona mark ii', 'toyota corona',\n",
       "       'toyota corolla 1200', 'toyota corona hardtop',\n",
       "       'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii',\n",
       "       'toyota corolla', 'toyota corolla liftback',\n",
       "       'toyota celica gt liftback', 'toyota corolla tercel',\n",
       "       'toyota corona liftback', 'toyota starlet', 'toyota tercel',\n",
       "       'toyota cressida', 'toyota celica gt', 'toyouta tercel',\n",
       "       'vokswagen rabbit', 'volkswagen 1131 deluxe sedan',\n",
       "       'volkswagen model 111', 'volkswagen type 3', 'volkswagen 411 (sw)',\n",
       "       'volkswagen super beetle', 'volkswagen dasher', 'vw dasher',\n",
       "       'vw rabbit', 'volkswagen rabbit', 'volkswagen rabbit custom',\n",
       "       'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245',\n",
       "       'volvo 264gl', 'volvo diesel', 'volvo 246'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CarName'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['CarName'] = data['CarName'].replace(['alfa-romero giulia'],'Alfa')\n",
    "data['CarName'] = data['CarName'].replace(['alfa-romero stelvio'],'Alfa')\n",
    "data['CarName'] = data['CarName'].replace(['alfa-romero Quadrifoglio'],'Alfa')\n",
    "data['CarName'] = data['CarName'].replace(['audi 100 ls'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['audi 100ls'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['audi fox'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['audi 5000'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['audi 4000'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['audi 5000s (diesel)'],'Audi')\n",
    "data['CarName'] = data['CarName'].replace(['bmw 320i'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['bmw x1'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['bmw x3'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['bmw z4'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['bmw x4'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['bmw x5'],'BMW')\n",
    "data['CarName'] = data['CarName'].replace(['chevrolet impala'],'Chevrolet')\n",
    "data['CarName'] = data['CarName'].replace(['chevrolet monte carlo'],'Chevrolet')\n",
    "data['CarName'] = data['CarName'].replace(['chevrolet vega 2300'],'Chevrolet')\n",
    "data['CarName'] = data['CarName'].replace(['dodge rampage'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge challenger se'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge d200'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge monaco (sw)'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge colt hardtop'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge colt (sw)'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge dart custom'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge coronet custom (sw)'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['dodge coronet custom'],'Dodge')\n",
    "data['CarName'] = data['CarName'].replace(['honda accord lx'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda civic 1500 gl'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda civic 1300'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda prelude'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda civic (auto)'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda civic'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda civic cvcc'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda accord cvcc'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['honda accord'],'Honda')\n",
    "data['CarName'] = data['CarName'].replace(['isuzu MU-X'],'Isuzu')\n",
    "data['CarName'] = data['CarName'].replace(['isuzu D-Max '],'Isuzu')\n",
    "data['CarName'] = data['CarName'].replace(['isuzu D-Max V-Cross'],'Isuzu')\n",
    "data['CarName'] = data['CarName'].replace(['jaguar xj'],'Jaguar')\n",
    "data['CarName'] = data['CarName'].replace(['jaguar xf'],'Jaguar')\n",
    "data['CarName'] = data['CarName'].replace(['jaguar xk'],'Jaguar')\n",
    "data['CarName'] = data['CarName'].replace(['maxda rx3'],'Maxda')\n",
    "data['CarName'] = data['CarName'].replace(['maxda glc deluxe'],'Maxda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda rx2 coupe'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda rx-4'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda glc deluxe'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda 626'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda glc'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda rx-7 gs'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda glc 4'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda glc custom l'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['mazda glc custom'],'Mazda')\n",
    "data['CarName'] = data['CarName'].replace(['buick electra 225 custom'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick century luxus (sw)'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick century'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick skyhawk'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick opel isuzu deluxe'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick skylark'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick century special'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['buick regal sport coupe (turbo)'],'Buick')\n",
    "data['CarName'] = data['CarName'].replace(['mercury cougar'],'Mercury')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi mirage'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi lancer'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi outlander'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi g4'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi mirage g4'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi montero'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['mitsubishi pajero'],'Mitsubishi')\n",
    "data['CarName'] = data['CarName'].replace(['Nissan versa'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan gt-r'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan rogue'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan latio'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan titan'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan leaf'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan juke'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan note'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan clipper'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan nv200'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan dayz'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan fuga'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan otti'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan teana'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['nissan kicks'],'Nissan')\n",
    "data['CarName'] = data['CarName'].replace(['peugeot 504'],'Peugeot')\n",
    "data['CarName'] = data['CarName'].replace(['peugeot 304'],'Peugeot')\n",
    "data['CarName'] = data['CarName'].replace(['peugeot 504 (sw)'],'Peugeot')\n",
    "data['CarName'] = data['CarName'].replace(['peugeot 604sl'],'Peugeot')\n",
    "data['CarName'] = data['CarName'].replace(['peugeot 505s turbo diesel'],'Peugeot')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth fury iii'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth cricket'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth satellite custom (sw)'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth fury gran sedan'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth valiant'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['plymouth duster'],'Plymouth')\n",
    "data['CarName'] = data['CarName'].replace(['porsche macan'],'Porsche')\n",
    "data['CarName'] = data['CarName'].replace(['porcshce panamera'],'Porsche')\n",
    "data['CarName'] = data['CarName'].replace(['porsche cayenne'],'Porsche')\n",
    "data['CarName'] = data['CarName'].replace(['porsche boxter'],'Porsche')\n",
    "data['CarName'] = data['CarName'].replace(['renault 12tl'],'Renault')\n",
    "data['CarName'] = data['CarName'].replace(['renault 5 gtl'],'Renault')\n",
    "data['CarName'] = data['CarName'].replace(['saab 99e'],'Saab')\n",
    "data['CarName'] = data['CarName'].replace(['saab 99le'],'Saab')\n",
    "data['CarName'] = data['CarName'].replace(['saab 99gle'],'Saab')\n",
    "data['CarName'] = data['CarName'].replace(['subaru'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru dl'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru brz'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru baja'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru r1'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru r2'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru trezia'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['subaru tribeca'],'Subaru')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corona mark ii'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corona'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corolla 1200'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corona hardtop'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corolla 1600 (sw)'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota carina'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota mark ii'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corolla'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corolla liftback'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota celica gt liftback'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corolla tercel'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota corona liftback'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota starlet'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota tercel'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota cressida'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyota celica gt'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['toyouta tercel'],'Toyota')\n",
    "data['CarName'] = data['CarName'].replace(['vokswagen rabbit'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen 1131 deluxe sedan'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen model 111'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen type 3'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen 411 (sw)'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen super beetle'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen dasher'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['vw dasher'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['vw rabbit'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen rabbit'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volkswagen rabbit custom'],'Volkswagen')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 145e (sw)'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 144ea'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 244dl'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 245'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 264gl'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo diesel'],'Volvo')\n",
    "data['CarName'] = data['CarName'].replace(['volvo 246'],'Volvo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alfa', 'Audi', 'BMW', 'Chevrolet', 'Dodge', 'Honda', 'Isuzu',\n",
       "       'Jaguar', 'Maxda', 'Mazda', 'Buick', 'Mercury', 'Mitsubishi',\n",
       "       'Nissan', 'Peugeot', 'Plymouth', 'Porsche', 'Renault', 'Saab',\n",
       "       'Subaru', 'Toyota', 'Volkswagen', 'Volvo'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CarName'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gas', 'diesel'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fueltype'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['std', 'turbo'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['aspiration'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['convertible', 'hatchback', 'sedan', 'wagon', 'hardtop'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['carbody'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['drivewheel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['front', 'rear'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['enginelocation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dohc', 'ohcv', 'ohc', 'l', 'rotor', 'ohcf', 'dohcv'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['enginetype'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['four', 'six', 'five', 'three', 'twelve', 'two', 'eight'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cylindernumber'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mpfi', '2bbl', 'mfi', '1bbl', 'spfi', '4bbl', 'idi', 'spdi'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fuelsystem'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['fueltype'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['aspiration'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['carbody'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAOoklEQVR4nO3cf6zddX3H8efLVoTEQRGujLRkl2mzUZkT6aAGMQ4MtOAsWSApcaMhJM0YREy2uLJ/yFQS+EcMCbAw6Sw/tkKcCQ1gmo4fAxZ+XUSrpbLeFZAbmFwtMBiKK773x/dTd1Lu7T2t7T233OcjOTnf7/vz+Z77Pvn29nW/P85JVSFJmt3eM+gGJEmDZxhIkgwDSZJhIEnCMJAkAXMH3cDeOvLII2t4eHjQbUjSAePJJ5/8aVUNTTR2wIbB8PAwIyMjg25Dkg4YSZ6fbMzTRJIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJ4gD+BPJvYnj13YNu4V3ruavOHnQLkvaCRwaSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEnsQRgkmZPkqSR3tfVjkzyWZGuS25Mc1Orva+ujbXy45zUub/VnkpzZU1/aaqNJVu+7tydJ6seeHBlcBmzpWb8auKaqFgKvABe1+kXAK1X1YeCaNo8ki4AVwEeApcD1LWDmANcBy4BFwPltriRpmvQVBkkWAGcD32jrAU4DvtWmrAXOacvL2zpt/PQ2fzmwrqreqqpngVHgpPYYraptVfVLYF2bK0maJv0eGXwd+BLwq7Z+BPBqVe1o62PA/LY8H3gBoI2/1ub/ur7LNpPV3yHJqiQjSUbGx8f7bF2SNJUpwyDJZ4GXq+rJ3vIEU2uKsT2tv7NYdWNVLa6qxUNDQ7vpWpK0J+b2MecU4HNJzgIOBg6lO1KYl2Ru++t/AfBimz8GHAOMJZkLHAZs76nv1LvNZHVJ0jSY8sigqi6vqgVVNUx3Afi+qvo8cD9wbpu2ErizLa9v67Tx+6qqWn1Fu9voWGAh8DjwBLCw3Z10UPsZ6/fJu5Mk9aWfI4PJ/A2wLslXgaeAm1r9JuCWJKN0RwQrAKpqc5I7gKeBHcAlVfU2QJJLgQ3AHGBNVW3+DfqSJO2hPQqDqnoAeKAtb6O7E2jXOb8Azptk+yuBKyeo3wPcsye9SJL2HT+BLEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJoo8wSHJwkseTfD/J5iR/1+rHJnksydYktyc5qNXf19ZH2/hwz2td3urPJDmzp7601UaTrN73b1OStDv9HBm8BZxWVX8IfAxYmmQJcDVwTVUtBF4BLmrzLwJeqaoPA9e0eSRZBKwAPgIsBa5PMifJHOA6YBmwCDi/zZUkTZMpw6A6b7TV97ZHAacB32r1tcA5bXl5W6eNn54krb6uqt6qqmeBUeCk9hitqm1V9UtgXZsrSZomfV0zaH/Bfw94GdgI/CfwalXtaFPGgPlteT7wAkAbfw04ore+yzaT1SfqY1WSkSQj4+Pj/bQuSepDX2FQVW9X1ceABXR/yR830bT2nEnG9rQ+UR83VtXiqlo8NDQ0deOSpL7s0d1EVfUq8ACwBJiXZG4bWgC82JbHgGMA2vhhwPbe+i7bTFaXJE2Tfu4mGkoyry0fAnwG2ALcD5zbpq0E7mzL69s6bfy+qqpWX9HuNjoWWAg8DjwBLGx3Jx1Ed5F5/b54c5Kk/sydegpHA2vbXT/vAe6oqruSPA2sS/JV4Cngpjb/JuCWJKN0RwQrAKpqc5I7gKeBHcAlVfU2QJJLgQ3AHGBNVW3eZ+9QkjSlKcOgqjYBJ0xQ30Z3/WDX+i+A8yZ5rSuBKyeo3wPc00e/kqT9wE8gS5IMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEnA3EE3IE1lePXdg27hXeu5q84edAuaITwykCQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAk0UcYJDkmyf1JtiTZnOSyVv9Ako1Jtrbnw1s9Sa5NMppkU5KP97zWyjZ/a5KVPfUTk/ygbXNtkuyPNytJmlg/RwY7gL+qquOAJcAlSRYBq4F7q2ohcG9bB1gGLGyPVcAN0IUHcAVwMnAScMXOAGlzVvVst/Q3f2uSpH5NGQZV9VJVfbctvw5sAeYDy4G1bdpa4Jy2vBy4uTqPAvOSHA2cCWysqu1V9QqwEVjaxg6tqkeqqoCbe15LkjQN9uiaQZJh4ATgMeCoqnoJusAAPtimzQde6NlsrNV2Vx+boC5JmiZ9h0GS9wP/Anyxqv57d1MnqNVe1CfqYVWSkSQj4+PjU7UsSepTX2GQ5L10QXBbVX27lX/STvHQnl9u9THgmJ7NFwAvTlFfMEH9HarqxqpaXFWLh4aG+mldktSHfu4mCnATsKWqvtYztB7YeUfQSuDOnvoF7a6iJcBr7TTSBuCMJIe3C8dnABva2OtJlrSfdUHPa0mSpsHcPuacAvw58IMk32u1vwWuAu5IchHwY+C8NnYPcBYwCrwJXAhQVduTfAV4os37clVtb8sXA98EDgG+0x6SpGkyZRhU1cNMfF4f4PQJ5hdwySSvtQZYM0F9BDh+ql4kSfuHn0CWJBkGkiTDQJKEYSBJor+7iSRpjwyvvnvQLbxrPXfV2fvldT0ykCQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiT6CIMka5K8nOSHPbUPJNmYZGt7PrzVk+TaJKNJNiX5eM82K9v8rUlW9tRPTPKDts21SbKv36Qkaff6OTL4JrB0l9pq4N6qWgjc29YBlgEL22MVcAN04QFcAZwMnARcsTNA2pxVPdvt+rMkSfvZlGFQVQ8C23cpLwfWtuW1wDk99Zur8ygwL8nRwJnAxqraXlWvABuBpW3s0Kp6pKoKuLnntSRJ02RvrxkcVVUvAbTnD7b6fOCFnnljrba7+tgE9QklWZVkJMnI+Pj4XrYuSdrVvr6APNH5/tqL+oSq6saqWlxVi4eGhvayRUnSrvY2DH7STvHQnl9u9THgmJ55C4AXp6gvmKAuSZpGexsG64GddwStBO7sqV/Q7ipaArzWTiNtAM5Icni7cHwGsKGNvZ5kSbuL6IKe15IkTZO5U01I8s/Ap4Ejk4zR3RV0FXBHkouAHwPnten3AGcBo8CbwIUAVbU9yVeAJ9q8L1fVzovSF9PdsXQI8J32kCRNoynDoKrOn2To9AnmFnDJJK+zBlgzQX0EOH6qPiRJ+4+fQJYkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJDGDwiDJ0iTPJBlNsnrQ/UjSbDIjwiDJHOA6YBmwCDg/yaLBdiVJs8eMCAPgJGC0qrZV1S+BdcDyAfckSbPG3EE30MwHXuhZHwNO3nVSklXAqrb6RpJnpqG3QTsS+Omgm+hXrh50BzPCAbPP3F+/Nlv22e9MNjBTwiAT1OodhaobgRv3fzszR5KRqlo86D7UP/fZgcd9NnNOE40Bx/SsLwBeHFAvkjTrzJQweAJYmOTYJAcBK4D1A+5JkmaNGXGaqKp2JLkU2ADMAdZU1eYBtzVTzKrTYu8S7rMDz6zfZ6l6x6l5SdIsM1NOE0mSBsgwkCQZBgeyJJ9Octeg+5htknwhyZYkt+3hdg8kmdW3Lw5KkjlJntqb35ckw0l+uD/6mklmxAVkdZKE7jrOrwbdi3brL4FlVfXsoBtR3y4DtgCHDrqRmcojgwFrf3VsSXI9sA24qdUvS7KtLX8oycNteWmSH7X1Px1Y47NUkr8HfhdYn+T1JPPS+VmSC9qcW5J8JskhSdYl2ZTkduCQgTY/SyVZAJwNfKOtn5Tk2215eZKfJzkoycE9v3MnJvl+kkeASwbW/DQyDGaG3wNuBj4BHN9qpwI/SzIf+CTwUJKDgX8A/qSN//YAep3Vquov6D4Q+cfAbcApwEfogvzUNm0J8ChwMfBmVX0UuBI4cdobFsDXgS8BO4+4vwuc0JZPBX4I/BHdV+A81ur/CHyhqj4xjX0OlGEwMzxfVY9W1X8B70/yW3SfyP4n4FN0/2AfAn4feLaqtlZ3T/CtA+tY0O2TT7XHDcAftPDeXlVvtPqtAFW1Cdg0qEZnqySfBV6uqid31qpqBzCa5Di6L8n8Gj2/Z0kOA+ZV1b+1TW6Z5rYHwjCYGf6nZ/kR4ELgGbr/bE6lO2L49zbuB0Nmjgfp9s+pwAPAOHAu3X7byf01WKcAn0vyHN23IZ+W5Fa6fbQM+F/gX+mOvj9Jt0/DLNxvhsHM8yDw1+35KbrTEW9V1WvAj4Bjk3yozT1/MC0KoKpeoPu2y4VVtQ14mG7f7QyDB4HPAyQ5HvjoIPqczarq8qpaUFXDdF9zc19V/Rndvvki8EhVjQNH0B15b66qV4HXknyyvcznB9D6tDMMZp6H6E4RPVhVb9N9tffDAFX1C7qv8L67XUB+fmBdaqfHgP9oyw/RfR37w239BrrTfpvozlk/Pv3taRKPAUfRhQJ0p/A21f9/JcOFwHXtAvLPB9DftPPrKCRJHhlIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSgP8D/h3gknM3YuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['drivewheel'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAANv0lEQVR4nO3ca4xc5X3H8e8vNrcKESBsImS7XSocJQapIbHAKnnRQAUG2poXIBlFwSBLllJQqRSpMXkRWhIq6ItSIUEqt7gxKIqhKSpuQupYXBq14uLlUohxkLcG4hUoLLJxIQmmJv++mMftaJn1js3e7P1+pNWe85znjJ+RRv76zJxxqgpJ0tz2kZlegCRp5hkDSZIxkCQZA0kSxkCSBMyf6QUcrtNOO60GBwdnehmSdMR4+umn36yqgV7HjtgYDA4OMjQ0NNPLkKQjRpJXxzvm20SSJGMgSTIGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJ4gj+BvKHMbj2BzO9BM1Sr9x62UwvQZoRXhlIkoyBJMkYSJIwBpIkjIEkCWMgScIYSJIwBpIkjIEkCWMgScIYSJIwBpIkjIEkCWMgScIYSJIwBpIkjIEkCWMgScIYSJIwBpIkDiEGSeYleTbJ99v+GUmeTLIjyX1Jjm3jx7X94XZ8sOsxbmzjLyW5uGt8eRsbTrJ28p6eJKkfh3JlcAOwvWv/NuD2qloM7AFWt/HVwJ6qOhO4vc0jyRJgJXAWsBy4qwVmHnAncAmwBLiqzZUkTZO+YpBkIXAZ8PdtP8AFwPfalA3A5W17RdunHb+wzV8BbKyqfVX1MjAMnNt+hqtqZ1W9B2xscyVJ06TfK4O/Af4M+HXb/xjwVlXtb/sjwIK2vQDYBdCO723z/298zDnjjX9AkjVJhpIMjY6O9rl0SdJEJoxBkj8A3qiqp7uHe0ytCY4d6vgHB6vWVdXSqlo6MDBwkFVLkg7F/D7mnA/8UZJLgeOBk+hcKZycZH771/9C4LU2fwRYBIwkmQ98FNjdNX5A9znjjUuSpsGEVwZVdWNVLayqQTofAD9SVV8EHgWuaNNWAQ+27U1tn3b8kaqqNr6y3W10BrAYeArYCixudycd2/6MTZPy7CRJfennymA8XwU2Jvkm8Cxwdxu/G7g3yTCdK4KVAFW1Lcn9wIvAfuC6qnofIMn1wGZgHrC+qrZ9iHVJkg7RIcWgqh4DHmvbO+ncCTR2zrvAleOcfwtwS4/xh4CHDmUtkqTJ4zeQJUnGQJJkDCRJGANJEsZAkoQxkCRhDCRJGANJEh/uG8iSpsjg2h/M9BI0S71y62VT8rheGUiSjIEkyRhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiT6iEGS45M8leQ/k2xL8hdt/IwkTybZkeS+JMe28ePa/nA7Ptj1WDe28ZeSXNw1vryNDSdZO/lPU5J0MP1cGewDLqiq3wE+AyxPsgy4Dbi9qhYDe4DVbf5qYE9VnQnc3uaRZAmwEjgLWA7clWReknnAncAlwBLgqjZXkjRNJoxBdbzTdo9pPwVcAHyvjW8ALm/bK9o+7fiFSdLGN1bVvqp6GRgGzm0/w1W1s6reAza2uZKkadLXZwbtX/DPAW8AW4D/At6qqv1tygiwoG0vAHYBtON7gY91j485Z7zxXutYk2QoydDo6Gg/S5ck9aGvGFTV+1X1GWAhnX/Jf7rXtPY74xw71PFe61hXVUuraunAwMDEC5ck9eWQ7iaqqreAx4BlwMlJ5rdDC4HX2vYIsAigHf8osLt7fMw5441LkqZJP3cTDSQ5uW2fAPw+sB14FLiiTVsFPNi2N7V92vFHqqra+Mp2t9EZwGLgKWArsLjdnXQsnQ+ZN03Gk5Mk9Wf+xFM4HdjQ7vr5CHB/VX0/yYvAxiTfBJ4F7m7z7wbuTTJM54pgJUBVbUtyP/AisB+4rqreB0hyPbAZmAesr6ptk/YMJUkTmjAGVfU8cE6P8Z10Pj8YO/4ucOU4j3ULcEuP8YeAh/pYryRpCvgNZEmSMZAkGQNJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJGANJEsZAkkQfMUiyKMmjSbYn2ZbkhjZ+apItSXa036e08SS5I8lwkueTfLbrsVa1+TuSrOoa/1ySF9o5dyTJVDxZSVJv/VwZ7Ae+UlWfBpYB1yVZAqwFHq6qxcDDbR/gEmBx+1kDfAs68QBuAs4DzgVuOhCQNmdN13nLP/xTkyT1a8IYVNXrVfVM234b2A4sAFYAG9q0DcDlbXsFcE91PAGcnOR04GJgS1Xtrqo9wBZgeTt2UlU9XlUF3NP1WJKkaXBInxkkGQTOAZ4EPlFVr0MnGMDH27QFwK6u00ba2MHGR3qMS5KmSd8xSHIi8E/An1bVfx9sao+xOozxXmtYk2QoydDo6OhES5Yk9amvGCQ5hk4IvlNVD7Thn7e3eGi/32jjI8CirtMXAq9NML6wx/gHVNW6qlpaVUsHBgb6WbokqQ/93E0U4G5ge1X9ddehTcCBO4JWAQ92jV/d7ipaBuxtbyNtBi5Kckr74PgiYHM79naSZe3PurrrsSRJ02B+H3POB74EvJDkuTb2NeBW4P4kq4GfAVe2Yw8BlwLDwC+BawGqaneSbwBb27ybq2p32/4y8G3gBOCH7UeSNE0mjEFV/Tu939cHuLDH/AKuG+ex1gPre4wPAWdPtBZJ0tTwG8iSJGMgSTIGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJoo8YJFmf5I0kP+kaOzXJliQ72u9T2niS3JFkOMnzST7bdc6qNn9HklVd459L8kI7544kmewnKUk6uH6uDL4NLB8zthZ4uKoWAw+3fYBLgMXtZw3wLejEA7gJOA84F7jpQEDanDVd5439syRJU2zCGFTVj4HdY4ZXABva9gbg8q7xe6rjCeDkJKcDFwNbqmp3Ve0BtgDL27GTqurxqirgnq7HkiRNk8P9zOATVfU6QPv98Ta+ANjVNW+kjR1sfKTHeE9J1iQZSjI0Ojp6mEuXJI012R8g93q/vw5jvKeqWldVS6tq6cDAwGEuUZI01uHG4OftLR7a7zfa+AiwqGveQuC1CcYX9hiXJE2jw43BJuDAHUGrgAe7xq9udxUtA/a2t5E2AxclOaV9cHwRsLkdezvJsnYX0dVdjyVJmibzJ5qQ5LvA7wGnJRmhc1fQrcD9SVYDPwOubNMfAi4FhoFfAtcCVNXuJN8AtrZ5N1fVgQ+lv0znjqUTgB+2H0nSNJowBlV11TiHLuwxt4Drxnmc9cD6HuNDwNkTrUOSNHX8BrIkyRhIkoyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSmEUxSLI8yUtJhpOsnen1SNJcMitikGQecCdwCbAEuCrJkpldlSTNHbMiBsC5wHBV7ayq94CNwIoZXpMkzRnzZ3oBzQJgV9f+CHDe2ElJ1gBr2u47SV6ahrUd7U4D3pzpRcwWuW2mV6Bx+DptPuRr9LfGOzBbYpAeY/WBgap1wLqpX87ckWSoqpbO9Dqkg/F1OvVmy9tEI8Cirv2FwGsztBZJmnNmSwy2AouTnJHkWGAlsGmG1yRJc8aseJuoqvYnuR7YDMwD1lfVthle1lzh2246Evg6nWKp+sBb85KkOWa2vE0kSZpBxkCSZAyOVkn+JMn2JN+ZhMf62mSsSdLs5WcGR6kkPwUuqaqXu8bmV9X+w3isd6rqxEldoDRGktD5O+nXh3n+Yb2+1eGVwVEoyd8Cvw1sSrI3ybokPwLuSXJ8kn9I8kKSZ5N8oZ1zTZIHkvxrkh1J/qqN3wqckOS5ybjKkLolGWxXsHcBzwBfSvJ4kmeS/GOSE9u8ryfZmuQn7fWcNv5Ykr9M8m/ADTP4VI54XhkcpZK8AiwFrgf+EPh8Vf0qyVeAs6vq2iSfAn4EfJLOdzu+DpwD7ANeaufs8spAUyXJILAT+F1gGHiAzhXtL5J8FTiuqm5OcmpV7W7n3AvcX1X/kuQx4MWq+uMZeQJHEa8M5oZNVfWrtv154F6Aqvop8CqdGAA8XFV7q+pd4EUO8v+YSJPo1ap6AlhG538t/o8kzwGr+P/X4BeSPJnkBeAC4Kyu8++b1tUepWbFl8405X7Rtd3r/4E6YF/X9vv4+tD0OPD6DLClqq7qPpjkeOAuYGm7Uv1z4Pge5+tD8Mpg7vkx8EWAJJ8EfpPOW0IH8z9JjpnqhWnOewI4P8mZAEl+o71GD/zF/2b7DOGKmVrg0cwYzD13AfPa5fZ9wDVVtW+Cc9YBz/sBsqZSVY0C1wDfTfI8nTh8qqreAv4OeAH4Zzr/l5kmmR8gS5K8MpAkGQNJEsZAkoQxkCRhDCRJGANJEsZAkgT8L/iRsMNhPiG8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['enginelocation'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sebr71/h+WFW3Ar8J7KiqPe15eQ0vfR4fMN7Po3tbgzQfng8D+zvRpv8NeH+ShcBZwHWDfDAHeM7gYIf6xosDxySf58XvZ6awnanq9X+d4MW+oNPbovHGVtWzSXYApwMfAr48nQ1O4mX3rwn9rN33+72baNzPJlg2XebL82GQfyeuBS4A9gJ3VNVTU+rwELln8FLfAT6QZFF71fF+Or8A+w4c5wPOBb493gaabwF/2pKdJMcNqmE6Pa9O8uokR9E55PPvvQa2VzG7k6xufR2Z5NVt8Wbgw3Re9WwbYL9jTVf/mthtwHuSHJ/O54ecw4vP4/9N8so23ffPY0Dmw/Nh0H8ndgBvB/6YTjDMCMOgS1XdReebfw+dY3kHnoRrgc8m2QmcROd44ESuoHPcb2eSe4HfH0zHL/R8FXA7nV/4K4B9E6xyLvDn7bH8J/DLrf4tOru1N1bnMyVmxDT2rwlU1ePARcAtwL3AXVV14PDDBjrP1Wt6/Tyq6u4Z7HPOPx8G/Xeiqp4HrqdzyPb6aX8A4/DfUUiS3DOQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkAf8HqR1I23AUGg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['enginetype'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['cylindernumber'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 205 artists>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['fuelsystem'],data['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID              0\n",
       "CarName             0\n",
       "fueltype            0\n",
       "aspiration          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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2a946748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAEGCAYAAABfOZ82AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAOJ0lEQVR4nO3df5Cc9V3A8fcndwwkQay5pBFO6VFPbStIm6QdUi0DQ6gRtBFbR2fqEH9MHf9oEnEYbYdMB4dDB9sOAzdOO6iYpCiOM1rLkDQQQEtmNEKCgYQm2LWktUcM6TFDLUmBS77+8Twh2/R+ht397CXv18zNbfZ2n/3ky96bZ5/LPhelFCRJeeZkDyBJZztDLEnJDLEkJTPEkpTMEEtSst6Z3mHhwoVlYGCgDaNI0plr165d3y6lLBrvazMO8cDAADt37nzzU0nSWSQivjHR1zw0IUnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlGzGv7NOrTc8PEyj0Wj5dkdGRgDo7+9v+bY7ZXBwkDVr1mSPIbWVIe4CjUaD3Xv3cWzegpZut+fIywD876uz8z9zz5GXskeQOmJ2foeegY7NW8DRd1zX0m3O3b8FoOXb7ZQT80tnOo8RS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUrKOhnh4eJjh4eFOPqSkNvJ7ujV6O/lgjUajkw8nqc38nm4ND01IUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJTPEkpTMEEtSMkMsSckMsSQlM8SSlMwQS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLGkjmk0Glx//fU0Go3sUWZsdHSUtWvXMjo62vJtG2JJHTM0NMQrr7zC0NBQ9igztnHjRvbs2cOmTZtavm1DLKkjGo0GBw4cAODAgQOzaq94dHSUrVu3Ukph69atLd8r7m3p1qYwMjLC0aNHWbduXScftus1Gg3mvFayx+g6c773HRqN//P50sUajQZz586d1m1P3QseGhpiw4YNbZiq9TZu3Mjx48cBOHbsGJs2beKmm25q2fantUccEb8XETsjYufhw4db9uCSzh4n9oYn+nM3e+SRRxgbGwNgbGyMbdu2tXT709ojLqXcA9wDsGzZstPedevv7wfgrrvuOt1NnJHWrVvHrq8fyh6j6xw/7wIG377Y50sXm8mrlYGBge+L78DAQOsHapMVK1awZcsWxsbG6O3t5dprr23p9j1GLKkj1q9fP+mfu9nq1auZM6fKZU9PDzfeeGNLt2+IJXXE4ODgG3vBAwMDDA4O5g40A319faxcuZKIYOXKlfT19bV0+4ZYUsesX7+e+fPnz6q94RNWr17NZZdd1vK9Yejwv5qQdHYbHBxk8+bN2WOclr6+Pu6+++62bNs9YklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKVlvJx9scHCwkw8nqc38nm6NjoZ4zZo1nXw4SW3m93RreGhCkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkvdkDqNJz5CXm7t/S4m2OArR8u53Sc+QlYHH2GFLbGeIuMDg42JbtjoyMAdDfP1tjtrhtayN1E0PcBdasWZM9gqREHiOWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKZkhlqRkhliSkhliSUpmiCUpmSGWpGSGWJKSGWJJSmaIJSmZIZakZIZYkpIZYklKZoglKVmUUmZ2h4jDwDeAhcC32zFUmzhv+822mZ23/WbbzO2c922llEXjfWHGIX7jjhE7SynL3tRYHeS87TfbZnbe9pttM2fN66EJSUpmiCUp2ZsJ8T0tm6IznLf9ZtvMztt+s23mlHlP+xixJKk1PDQhSckMsSQlm1aII+KmiHg2IvZGxP0RcV5EbIiI5yNid/3x7nYPO10Rsa6e9dmI+IP6ugURsS0ivlZ//pHsOU+YYN5bI2KkaX2vS57x3oh4MSL2Nl037ppG5e6IaETEMxGxZBbMfFVEvNy03p/qknl/rX5eHI+IZafc/pP1Gj8XEb/QzfNGxEBEHG1a3893et5JZv50ROyvn6tfjIi3NH2tM2tcSpn0A+gHngfm1n/+B+C3gA3AR6a6f6c/gEuBvcA8oBd4BPhJ4M+BT9S3+QRwR/asU8x7K3Bz9nxNc14JLAH2Nl037poC1wFfBgK4AviPWTDzVcCDXbjG7wR+GvhXYFnT9e8CngbOBS4B/hvo6eJ5B5pv12Vr/EGgt758R9NzomNrPN1DE73A3IjorYPxwjTvl+GdwI5SypFSyhjwFeAGYBWwsb7NRuBXkuY71UTzdpVSyuPAS6dcPdGargI2lcoO4C0RcWFnJj1phjOnG2/eUsq+Uspz49x8FfD3pZRXSynPAw3gfR0Ys3m2mczbFSaY+eH6ew9gB/Bj9eWOrfGUIS6ljACfAb4JHAReLqU8XH/59np3/s6IOLcdA56GvcCVEdEXEfOo9s5+HFhcSjkIUH9+a+KMzSaaF+Dj9fre202HUppMtKb9wP803e5b9XXdYLLnwfKIeDoivhwRP5Mz3rR18xpP5JKI+M+I+EpEfCB7mAn8DtWrOejgGk8Z4joAq6h2zS8C5kfEbwKfBN4BvBdYAPxxOwacqVLKPqqXF9uArVQvLcYmvVOiSeb9HPATwLup/gf42awZT0OMc123/zvJp6jOBXA5MAz8c/I8U5lta3wQuLiU8h7gD4G/i4gLkmf6PhFxC9X33t+euGqcm7VljadzaGIF8Hwp5XAp5XXgn4D3l1IO1i89XwX+hg6/LJpMKeWvSylLSilXUr0M+Rpw6MTL4/rzi5kzNhtv3lLKoVLKsVLKceAv6aL1bTLRmn6Lk3v1UL3U65bDWePOXEr5Tinlu/XlLcA5EbEwb8wpdfMa/4D65f1ofXkX1fHWn8qd6qSIWA38EvDRUh8gpoNrPJ0QfxO4IiLmRUQA1wD7mp7MQXWcbe8k2+ioiHhr/fli4FeB+4EHgNX1TVYDX8qZ7geNN+8px1RvoIvWt8lEa/oAcGP9ryeuoDqcdTBjwHGMO3NE/Gj9XCYi3kf1vTGaMuH0PAD8RkScGxGXUP2A94nkmSYUEYsioqe+/Haqeb+eO1UlIlZSvaL/UCnlSNOXOrfG0/xJ458A+6li8AWqnyI+Buypr7sPOD/jp6ATzLsd+CrVy/xr6uv6gEep9o4fBRZkzznFvF+o1/eZ+glxYfKM91O9vHydak/hdydaU6qXdH9Btdezh6afnnfxzB8Hnq3/G+ygetXXDfPeUF9+FTgEPNR0+1vqNX4O+MVunhf4cNP6PgX8chc9JxpUx4J31x+f7/Qa+xZnSUrmO+skKZkhlqRkhliSkhliSUpmiCUpmSFWV4iI77ZoO1dFxIP15Vsj4uZWbFdqJ0MsSckMsToiIv4oItbWl++MiMfqy9dExH315dvrk+7siIjF9XWLIuIfI+LJ+uPn6uvn1ydDerI+kcyqCR768oh4LKrzD3+svu/5EfFoRDwVEXtO3Lfe5uZ6hr0R8ev19UvrE9XsioiHMs4kpzObIVanPA6cOOPWMuD8iDgH+HmqdxbOpzod6OX1bT9W3/Yu4M5Synup3p31V/X1twCP1ddfDXw6IuaP87g/C1wPLAc+FREXAd8DbiilLKnv+9n67c0rgRdKKZeXUi4FttYzDlOde3spcC9we2uWRKr0Zg+gs8YuYGlE/BDV21+fogryB4C1wGvAg023vba+vAJ4V30aCIAL6m18EPhQ0zHg84CLx3ncL5VSjgJHI+JfqE6etBn404i4EjhOdWrDxVRvx/5MRNxBdZL47RFxKdXJ+7fVM/RQvUVWahlDrI4opbweEQeA3wb+jeocGldTnepzH/B6Ofl++2OcfG7OAZbXMX1DvQf74XLKSchPHNJofuhx/vxRYBGwtGmu80op/xURS6nOCf1nEfEw8EXg2VLK8tP7m0tT89CEOulx4Ob683bg94HdZfITnjxMdUIeAOLk70Z8CFjTdMa090xw/1VR/Y7FPqpfh/Qk8MPAi3WErwbeVm/jIuBIKeU+ql+GsITqZC+LImJ5fZtzZsFJ4zXLGGJ10nbgQuDfSymHqI7Vbp/iPmuBZVH9ppKvUsUb4DbgHOCZqH4R5G0T3P8JqkMRO4DbSikvUJ34e1lE7KTaO95f3/Yy4ImI2E11DHqolPIa8BHgjoh4mursXO+f4d9bmpRnX5OkZO4RS1IyQyxJyQyxJCUzxJKUzBBLUjJDLEnJDLEkJft/Qd7bVDDJVtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['wheelbase'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2af0c5c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['carwidth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2af7ecc8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['curbweight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2ae7fbc8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['enginesize'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</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>15</th>\n",
       "      <td>16</td>\n",
       "      <td>BMW</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3230</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>30760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>BMW</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>193.8</td>\n",
       "      <td>67.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>3380</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>41315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>BMW</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>3505</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>113.0</td>\n",
       "      <td>199.6</td>\n",
       "      <td>69.6</td>\n",
       "      <td>52.8</td>\n",
       "      <td>4066</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>258</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.63</td>\n",
       "      <td>4.17</td>\n",
       "      <td>8.1</td>\n",
       "      <td>176</td>\n",
       "      <td>4750</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>32250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>113.0</td>\n",
       "      <td>199.6</td>\n",
       "      <td>69.6</td>\n",
       "      <td>52.8</td>\n",
       "      <td>4066</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>258</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.63</td>\n",
       "      <td>4.17</td>\n",
       "      <td>8.1</td>\n",
       "      <td>176</td>\n",
       "      <td>4750</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>35550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.0</td>\n",
       "      <td>191.7</td>\n",
       "      <td>70.6</td>\n",
       "      <td>47.8</td>\n",
       "      <td>3950</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>twelve</td>\n",
       "      <td>326</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.76</td>\n",
       "      <td>11.5</td>\n",
       "      <td>262</td>\n",
       "      <td>5000</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>36000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>Buick</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>115.6</td>\n",
       "      <td>202.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>56.5</td>\n",
       "      <td>3740</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>234</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.10</td>\n",
       "      <td>8.3</td>\n",
       "      <td>155</td>\n",
       "      <td>4750</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>34184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Buick</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>96.6</td>\n",
       "      <td>180.3</td>\n",
       "      <td>70.5</td>\n",
       "      <td>50.8</td>\n",
       "      <td>3685</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>234</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.10</td>\n",
       "      <td>8.3</td>\n",
       "      <td>155</td>\n",
       "      <td>4750</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>35056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Buick</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>120.9</td>\n",
       "      <td>208.1</td>\n",
       "      <td>71.7</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3900</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>308</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184</td>\n",
       "      <td>4500</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>40960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>Buick</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>112.0</td>\n",
       "      <td>199.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>3715</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>304</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184</td>\n",
       "      <td>4500</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    car_ID CarName fueltype aspiration      carbody drivewheel enginelocation  \\\n",
       "15      16     BMW      gas        std        sedan        rwd          front   \n",
       "16      17     BMW      gas        std        sedan        rwd          front   \n",
       "17      18     BMW      gas        std        sedan        rwd          front   \n",
       "47      48  Jaguar      gas        std        sedan        rwd          front   \n",
       "48      49  Jaguar      gas        std        sedan        rwd          front   \n",
       "49      50  Jaguar      gas        std        sedan        rwd          front   \n",
       "71      72   Buick      gas        std        sedan        rwd          front   \n",
       "72      73   Buick      gas        std  convertible        rwd          front   \n",
       "73      74   Buick      gas        std        sedan        rwd          front   \n",
       "74      75   Buick      gas        std      hardtop        rwd          front   \n",
       "\n",
       "    wheelbase  carlength  carwidth  carheight  curbweight enginetype  \\\n",
       "15      103.5      189.0      66.9       55.7        3230        ohc   \n",
       "16      103.5      193.8      67.9       53.7        3380        ohc   \n",
       "17      110.0      197.0      70.9       56.3        3505        ohc   \n",
       "47      113.0      199.6      69.6       52.8        4066       dohc   \n",
       "48      113.0      199.6      69.6       52.8        4066       dohc   \n",
       "49      102.0      191.7      70.6       47.8        3950       ohcv   \n",
       "71      115.6      202.6      71.7       56.5        3740       ohcv   \n",
       "72       96.6      180.3      70.5       50.8        3685       ohcv   \n",
       "73      120.9      208.1      71.7       56.7        3900       ohcv   \n",
       "74      112.0      199.2      72.0       55.4        3715       ohcv   \n",
       "\n",
       "   cylindernumber  enginesize fuelsystem  boreratio  stroke  compressionratio  \\\n",
       "15            six         209       mpfi       3.62    3.39               8.0   \n",
       "16            six         209       mpfi       3.62    3.39               8.0   \n",
       "17            six         209       mpfi       3.62    3.39               8.0   \n",
       "47            six         258       mpfi       3.63    4.17               8.1   \n",
       "48            six         258       mpfi       3.63    4.17               8.1   \n",
       "49         twelve         326       mpfi       3.54    2.76              11.5   \n",
       "71          eight         234       mpfi       3.46    3.10               8.3   \n",
       "72          eight         234       mpfi       3.46    3.10               8.3   \n",
       "73          eight         308       mpfi       3.80    3.35               8.0   \n",
       "74          eight         304       mpfi       3.80    3.35               8.0   \n",
       "\n",
       "    horsepower  peakrpm  citympg  highwaympg    price  \n",
       "15         182     5400       16          22  30760.0  \n",
       "16         182     5400       16          22  41315.0  \n",
       "17         182     5400       15          20  36880.0  \n",
       "47         176     4750       15          19  32250.0  \n",
       "48         176     4750       15          19  35550.0  \n",
       "49         262     5000       13          17  36000.0  \n",
       "71         155     4750       16          18  34184.0  \n",
       "72         155     4750       16          18  35056.0  \n",
       "73         184     4500       14          16  40960.0  \n",
       "74         184     4500       14          16  45400.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile25 = data['enginesize'].quantile(0.25)\n",
    "percentile75 = data['enginesize'].quantile(0.75)\n",
    "iqr=percentile75-percentile25\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "data[data['enginesize'] > upper_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['enginesize']=np.where(data['enginesize']>upper_limit,data['enginesize'].median(),data['enginesize'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2aeb0588>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['boreratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2b0d7a88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>car_ID</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</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>111</th>\n",
       "      <td>112</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3075</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>120.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.19</td>\n",
       "      <td>8.4</td>\n",
       "      <td>95</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>114.2</td>\n",
       "      <td>198.9</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3285</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>120.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.19</td>\n",
       "      <td>8.4</td>\n",
       "      <td>95</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>16695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>Saab</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.1</td>\n",
       "      <td>186.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>56.1</td>\n",
       "      <td>2707</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>121.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.07</td>\n",
       "      <td>9.3</td>\n",
       "      <td>110</td>\n",
       "      <td>5250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>15040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>156.9</td>\n",
       "      <td>63.4</td>\n",
       "      <td>53.7</td>\n",
       "      <td>2050</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69</td>\n",
       "      <td>4900</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>5118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>53.7</td>\n",
       "      <td>2120</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>8.7</td>\n",
       "      <td>73</td>\n",
       "      <td>4400</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>7053.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.3</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2240</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>8.7</td>\n",
       "      <td>73</td>\n",
       "      <td>4400</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>7603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>52.5</td>\n",
       "      <td>2145</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.5</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>7126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>52.5</td>\n",
       "      <td>2190</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.5</td>\n",
       "      <td>82</td>\n",
       "      <td>4400</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>7775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>52.5</td>\n",
       "      <td>2340</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>94</td>\n",
       "      <td>5200</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>9960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2385</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>9233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2510</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>7.7</td>\n",
       "      <td>111</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>65.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2290</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>7463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>65.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2455</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>94</td>\n",
       "      <td>5200</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>10198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>wagon</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>96.9</td>\n",
       "      <td>173.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2420</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>8013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>wagon</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>96.9</td>\n",
       "      <td>173.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2650</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>7.7</td>\n",
       "      <td>111</td>\n",
       "      <td>4800</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>11694.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  CarName fueltype aspiration    carbody drivewheel enginelocation  \\\n",
       "111     112  Peugeot      gas        std      sedan        rwd          front   \n",
       "113     114  Peugeot      gas        std      wagon        rwd          front   \n",
       "134     135     Saab      gas        std  hatchback        fwd          front   \n",
       "138     139   Subaru      gas        std  hatchback        fwd          front   \n",
       "139     140   Subaru      gas        std  hatchback        fwd          front   \n",
       "140     141   Subaru      gas        std  hatchback        4wd          front   \n",
       "141     142   Subaru      gas        std      sedan        fwd          front   \n",
       "142     143   Subaru      gas        std      sedan        fwd          front   \n",
       "143     144   Subaru      gas        std      sedan        fwd          front   \n",
       "144     145   Subaru      gas        std      sedan        4wd          front   \n",
       "145     146   Subaru      gas      turbo      sedan        4wd          front   \n",
       "146     147   Subaru      gas        std      wagon        fwd          front   \n",
       "147     148   Subaru      gas        std      wagon        fwd          front   \n",
       "148     149   Subaru      gas        std      wagon        4wd          front   \n",
       "149     150   Subaru      gas      turbo      wagon        4wd          front   \n",
       "\n",
       "     wheelbase  carlength  carwidth  carheight  curbweight enginetype  \\\n",
       "111      107.9      186.7      68.4       56.7        3075          l   \n",
       "113      114.2      198.9      68.4       56.7        3285          l   \n",
       "134       99.1      186.6      66.5       56.1        2707        ohc   \n",
       "138       93.7      156.9      63.4       53.7        2050       ohcf   \n",
       "139       93.7      157.9      63.6       53.7        2120       ohcf   \n",
       "140       93.3      157.3      63.8       55.7        2240       ohcf   \n",
       "141       97.2      172.0      65.4       52.5        2145       ohcf   \n",
       "142       97.2      172.0      65.4       52.5        2190       ohcf   \n",
       "143       97.2      172.0      65.4       52.5        2340       ohcf   \n",
       "144       97.0      172.0      65.4       54.3        2385       ohcf   \n",
       "145       97.0      172.0      65.4       54.3        2510       ohcf   \n",
       "146       97.0      173.5      65.4       53.0        2290       ohcf   \n",
       "147       97.0      173.5      65.4       53.0        2455       ohcf   \n",
       "148       96.9      173.6      65.4       54.9        2420       ohcf   \n",
       "149       96.9      173.6      65.4       54.9        2650       ohcf   \n",
       "\n",
       "    cylindernumber  enginesize fuelsystem  boreratio  stroke  \\\n",
       "111           four       120.0       mpfi       3.46    2.19   \n",
       "113           four       120.0       mpfi       3.46    2.19   \n",
       "134           four       121.0       mpfi       2.54    2.07   \n",
       "138           four        97.0       2bbl       3.62    2.36   \n",
       "139           four       108.0       2bbl       3.62    2.64   \n",
       "140           four       108.0       2bbl       3.62    2.64   \n",
       "141           four       108.0       2bbl       3.62    2.64   \n",
       "142           four       108.0       2bbl       3.62    2.64   \n",
       "143           four       108.0       mpfi       3.62    2.64   \n",
       "144           four       108.0       2bbl       3.62    2.64   \n",
       "145           four       108.0       mpfi       3.62    2.64   \n",
       "146           four       108.0       2bbl       3.62    2.64   \n",
       "147           four       108.0       mpfi       3.62    2.64   \n",
       "148           four       108.0       2bbl       3.62    2.64   \n",
       "149           four       108.0       mpfi       3.62    2.64   \n",
       "\n",
       "     compressionratio  horsepower  peakrpm  citympg  highwaympg    price  \n",
       "111               8.4          95     5000       19          24  15580.0  \n",
       "113               8.4          95     5000       19          24  16695.0  \n",
       "134               9.3         110     5250       21          28  15040.0  \n",
       "138               9.0          69     4900       31          36   5118.0  \n",
       "139               8.7          73     4400       26          31   7053.0  \n",
       "140               8.7          73     4400       26          31   7603.0  \n",
       "141               9.5          82     4800       32          37   7126.0  \n",
       "142               9.5          82     4400       28          33   7775.0  \n",
       "143               9.0          94     5200       26          32   9960.0  \n",
       "144               9.0          82     4800       24          25   9233.0  \n",
       "145               7.7         111     4800       24          29  11259.0  \n",
       "146               9.0          82     4800       28          32   7463.0  \n",
       "147               9.0          94     5200       25          31  10198.0  \n",
       "148               9.0          82     4800       23          29   8013.0  \n",
       "149               7.7         111     4800       23          23  11694.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile25 = data['stroke'].quantile(0.25)\n",
    "percentile75 = data['stroke'].quantile(0.75)\n",
    "iqr=percentile75-percentile25\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "#data[data['stroke'] > upper_limit]\n",
    "data[data['stroke'] <lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['stroke']=np.where(data['stroke']>upper_limit,data['stroke'].median(),data['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['stroke']=np.where(data['stroke']<lower_limit,data['stroke'].median(),data['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2b1361c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['compressionratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>car_ID</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</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>49</th>\n",
       "      <td>50</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.0</td>\n",
       "      <td>191.7</td>\n",
       "      <td>70.6</td>\n",
       "      <td>47.8</td>\n",
       "      <td>3950</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>twelve</td>\n",
       "      <td>120.0</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.76</td>\n",
       "      <td>11.5</td>\n",
       "      <td>262</td>\n",
       "      <td>5000</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>36000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.8</td>\n",
       "      <td>177.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2443</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>122.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.39</td>\n",
       "      <td>22.7</td>\n",
       "      <td>64</td>\n",
       "      <td>4650</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>10795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.9</td>\n",
       "      <td>175.0</td>\n",
       "      <td>66.1</td>\n",
       "      <td>54.4</td>\n",
       "      <td>2700</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>134.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.64</td>\n",
       "      <td>22.0</td>\n",
       "      <td>72</td>\n",
       "      <td>4200</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>18344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Buick</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>190.9</td>\n",
       "      <td>70.3</td>\n",
       "      <td>56.5</td>\n",
       "      <td>3515</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>25552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>Buick</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>190.9</td>\n",
       "      <td>70.3</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3750</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>Buick</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>106.7</td>\n",
       "      <td>187.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>54.9</td>\n",
       "      <td>3495</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>Buick</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>115.6</td>\n",
       "      <td>202.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>56.3</td>\n",
       "      <td>3770</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>31600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>165.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>54.5</td>\n",
       "      <td>2017</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>103.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.47</td>\n",
       "      <td>21.9</td>\n",
       "      <td>55</td>\n",
       "      <td>4800</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>7099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3197</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>152.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4150</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>13200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>114.2</td>\n",
       "      <td>198.9</td>\n",
       "      <td>68.4</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3430</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>152.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4150</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>13860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>113</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3252</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>152.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4150</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>16900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>115</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>114.2</td>\n",
       "      <td>198.9</td>\n",
       "      <td>68.4</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3485</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>152.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4150</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>17075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>3252</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>152.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4150</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>17950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>159</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.7</td>\n",
       "      <td>166.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2275</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>110.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>22.5</td>\n",
       "      <td>56</td>\n",
       "      <td>4500</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>7898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.7</td>\n",
       "      <td>166.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>52.8</td>\n",
       "      <td>2275</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>110.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>22.5</td>\n",
       "      <td>56</td>\n",
       "      <td>4500</td>\n",
       "      <td>38</td>\n",
       "      <td>47</td>\n",
       "      <td>7788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2480</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>110.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>22.5</td>\n",
       "      <td>73</td>\n",
       "      <td>4500</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>10698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2261</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2264</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>188</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2319</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>9495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>193</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2579</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97.0</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>13845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</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>145.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID     CarName fueltype aspiration    carbody drivewheel  \\\n",
       "49       50      Jaguar      gas        std      sedan        rwd   \n",
       "63       64       Mazda   diesel        std      sedan        fwd   \n",
       "66       67       Mazda   diesel        std      sedan        rwd   \n",
       "67       68       Buick   diesel      turbo      sedan        rwd   \n",
       "68       69       Buick   diesel      turbo      wagon        rwd   \n",
       "69       70       Buick   diesel      turbo    hardtop        rwd   \n",
       "70       71       Buick   diesel      turbo      sedan        rwd   \n",
       "90       91      Nissan   diesel        std      sedan        fwd   \n",
       "108     109     Peugeot   diesel      turbo      sedan        rwd   \n",
       "110     111     Peugeot   diesel      turbo      wagon        rwd   \n",
       "112     113     Peugeot   diesel      turbo      sedan        rwd   \n",
       "114     115     Peugeot   diesel      turbo      wagon        rwd   \n",
       "116     117     Peugeot   diesel      turbo      sedan        rwd   \n",
       "158     159      Toyota   diesel        std      sedan        fwd   \n",
       "159     160      Toyota   diesel        std  hatchback        fwd   \n",
       "174     175      Toyota   diesel      turbo      sedan        fwd   \n",
       "182     183  Volkswagen   diesel        std      sedan        fwd   \n",
       "184     185  Volkswagen   diesel        std      sedan        fwd   \n",
       "187     188  Volkswagen   diesel      turbo      sedan        fwd   \n",
       "192     193  Volkswagen   diesel      turbo      sedan        fwd   \n",
       "203     204       Volvo   diesel      turbo      sedan        rwd   \n",
       "\n",
       "    enginelocation  wheelbase  carlength  carwidth  carheight  curbweight  \\\n",
       "49           front      102.0      191.7      70.6       47.8        3950   \n",
       "63           front       98.8      177.8      66.5       55.5        2443   \n",
       "66           front      104.9      175.0      66.1       54.4        2700   \n",
       "67           front      110.0      190.9      70.3       56.5        3515   \n",
       "68           front      110.0      190.9      70.3       58.7        3750   \n",
       "69           front      106.7      187.5      70.3       54.9        3495   \n",
       "70           front      115.6      202.6      71.7       56.3        3770   \n",
       "90           front       94.5      165.3      63.8       54.5        2017   \n",
       "108          front      107.9      186.7      68.4       56.7        3197   \n",
       "110          front      114.2      198.9      68.4       58.7        3430   \n",
       "112          front      107.9      186.7      68.4       56.7        3252   \n",
       "114          front      114.2      198.9      68.4       58.7        3485   \n",
       "116          front      107.9      186.7      68.4       56.7        3252   \n",
       "158          front       95.7      166.3      64.4       53.0        2275   \n",
       "159          front       95.7      166.3      64.4       52.8        2275   \n",
       "174          front      102.4      175.6      66.5       54.9        2480   \n",
       "182          front       97.3      171.7      65.5       55.7        2261   \n",
       "184          front       97.3      171.7      65.5       55.7        2264   \n",
       "187          front       97.3      171.7      65.5       55.7        2319   \n",
       "192          front      100.4      180.2      66.9       55.1        2579   \n",
       "203          front      109.1      188.8      68.9       55.5        3217   \n",
       "\n",
       "    enginetype cylindernumber  enginesize fuelsystem  boreratio  stroke  \\\n",
       "49        ohcv         twelve       120.0       mpfi       3.54    2.76   \n",
       "63         ohc           four       122.0        idi       3.39    3.39   \n",
       "66         ohc           four       134.0        idi       3.43    3.64   \n",
       "67         ohc           five       183.0        idi       3.58    3.64   \n",
       "68         ohc           five       183.0        idi       3.58    3.64   \n",
       "69         ohc           five       183.0        idi       3.58    3.64   \n",
       "70         ohc           five       183.0        idi       3.58    3.64   \n",
       "90         ohc           four       103.0        idi       2.99    3.47   \n",
       "108          l           four       152.0        idi       3.70    3.52   \n",
       "110          l           four       152.0        idi       3.70    3.52   \n",
       "112          l           four       152.0        idi       3.70    3.52   \n",
       "114          l           four       152.0        idi       3.70    3.52   \n",
       "116          l           four       152.0        idi       3.70    3.52   \n",
       "158        ohc           four       110.0        idi       3.27    3.35   \n",
       "159        ohc           four       110.0        idi       3.27    3.35   \n",
       "174        ohc           four       110.0        idi       3.27    3.35   \n",
       "182        ohc           four        97.0        idi       3.01    3.40   \n",
       "184        ohc           four        97.0        idi       3.01    3.40   \n",
       "187        ohc           four        97.0        idi       3.01    3.40   \n",
       "192        ohc           four        97.0        idi       3.01    3.40   \n",
       "203        ohc            six       145.0        idi       3.01    3.40   \n",
       "\n",
       "     compressionratio  horsepower  peakrpm  citympg  highwaympg    price  \n",
       "49               11.5         262     5000       13          17  36000.0  \n",
       "63               22.7          64     4650       36          42  10795.0  \n",
       "66               22.0          72     4200       31          39  18344.0  \n",
       "67               21.5         123     4350       22          25  25552.0  \n",
       "68               21.5         123     4350       22          25  28248.0  \n",
       "69               21.5         123     4350       22          25  28176.0  \n",
       "70               21.5         123     4350       22          25  31600.0  \n",
       "90               21.9          55     4800       45          50   7099.0  \n",
       "108              21.0          95     4150       28          33  13200.0  \n",
       "110              21.0          95     4150       25          25  13860.0  \n",
       "112              21.0          95     4150       28          33  16900.0  \n",
       "114              21.0          95     4150       25          25  17075.0  \n",
       "116              21.0          95     4150       28          33  17950.0  \n",
       "158              22.5          56     4500       34          36   7898.0  \n",
       "159              22.5          56     4500       38          47   7788.0  \n",
       "174              22.5          73     4500       30          33  10698.0  \n",
       "182              23.0          52     4800       37          46   7775.0  \n",
       "184              23.0          52     4800       37          46   7995.0  \n",
       "187              23.0          68     4500       37          42   9495.0  \n",
       "192              23.0          68     4500       33          38  13845.0  \n",
       "203              23.0         106     4800       26          27  22470.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile25 = data['compressionratio'].quantile(0.25)\n",
    "percentile75 = data['compressionratio'].quantile(0.75)\n",
    "iqr=percentile75-percentile25\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "data[data['compressionratio'] > upper_limit]\n",
    "#data[data['compressionratio'] <lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['compressionratio']=np.where(data['compressionratio']>upper_limit,data['compressionratio'].median(),data['compressionratio'])\n",
    "data['compressionratio']=np.where(data['compressionratio']<lower_limit,data['compressionratio'].median(),data['compressionratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2c1645c8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UkpScoZak5P4fKWafGk+1EyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['horsepower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2c1dac48>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['peakrpm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2c230388>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['citympg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da2c299f48>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['highwaympg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID             -0.109093\n",
       "wheelbase           0.577816\n",
       "carlength           0.682920\n",
       "carwidth            0.759325\n",
       "carheight           0.119336\n",
       "curbweight          0.835305\n",
       "enginesize          0.580308\n",
       "boreratio           0.553173\n",
       "stroke             -0.066613\n",
       "compressionratio   -0.240784\n",
       "horsepower          0.808139\n",
       "peakrpm            -0.085267\n",
       "citympg            -0.685751\n",
       "highwaympg         -0.697599\n",
       "price               1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = data.corr()\n",
    "df_corr['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['car_ID','carheight','wheelbase', 'boreratio', 'stroke', 'compressionratio', 'peakrpm' ],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(\"CarName\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dython.nominal import associations\n",
    "associations(data,figsize=(20,20),mark_columns=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.get_dummies(data,columns=['fueltype','cylindernumber','aspiration','carbody','drivewheel','enginelocation','enginetype','fuelsystem',],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>fueltype_gas</th>\n",
       "      <th>cylindernumber_five</th>\n",
       "      <th>cylindernumber_four</th>\n",
       "      <th>cylindernumber_six</th>\n",
       "      <th>cylindernumber_three</th>\n",
       "      <th>cylindernumber_twelve</th>\n",
       "      <th>cylindernumber_two</th>\n",
       "      <th>aspiration_turbo</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>enginelocation_rear</th>\n",
       "      <th>enginetype_dohcv</th>\n",
       "      <th>enginetype_l</th>\n",
       "      <th>enginetype_ohc</th>\n",
       "      <th>enginetype_ohcf</th>\n",
       "      <th>enginetype_ohcv</th>\n",
       "      <th>enginetype_rotor</th>\n",
       "      <th>fuelsystem_2bbl</th>\n",
       "      <th>fuelsystem_4bbl</th>\n",
       "      <th>fuelsystem_idi</th>\n",
       "      <th>fuelsystem_mfi</th>\n",
       "      <th>fuelsystem_mpfi</th>\n",
       "      <th>fuelsystem_spdi</th>\n",
       "      <th>fuelsystem_spfi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>130.0</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>130.0</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2823</td>\n",
       "      <td>152.0</td>\n",
       "      <td>154</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2337</td>\n",
       "      <td>109.0</td>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>1</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>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>2824</td>\n",
       "      <td>136.0</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>1</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>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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carlength  carwidth  curbweight  enginesize  horsepower  citympg  \\\n",
       "0      168.8      64.1        2548       130.0         111       21   \n",
       "1      168.8      64.1        2548       130.0         111       21   \n",
       "2      171.2      65.5        2823       152.0         154       19   \n",
       "3      176.6      66.2        2337       109.0         102       24   \n",
       "4      176.6      66.4        2824       136.0         115       18   \n",
       "\n",
       "   highwaympg    price  fueltype_gas  cylindernumber_five  \\\n",
       "0          27  13495.0             1                    0   \n",
       "1          27  16500.0             1                    0   \n",
       "2          26  16500.0             1                    0   \n",
       "3          30  13950.0             1                    0   \n",
       "4          22  17450.0             1                    1   \n",
       "\n",
       "   cylindernumber_four  cylindernumber_six  cylindernumber_three  \\\n",
       "0                    1                   0                     0   \n",
       "1                    1                   0                     0   \n",
       "2                    0                   1                     0   \n",
       "3                    1                   0                     0   \n",
       "4                    0                   0                     0   \n",
       "\n",
       "   cylindernumber_twelve  cylindernumber_two  aspiration_turbo  \\\n",
       "0                      0                   0                 0   \n",
       "1                      0                   0                 0   \n",
       "2                      0                   0                 0   \n",
       "3                      0                   0                 0   \n",
       "4                      0                   0                 0   \n",
       "\n",
       "   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  enginelocation_rear  enginetype_dohcv  \\\n",
       "0               0               1                    0                 0   \n",
       "1               0               1                    0                 0   \n",
       "2               0               1                    0                 0   \n",
       "3               1               0                    0                 0   \n",
       "4               0               0                    0                 0   \n",
       "\n",
       "   enginetype_l  enginetype_ohc  enginetype_ohcf  enginetype_ohcv  \\\n",
       "0             0               0                0                0   \n",
       "1             0               0                0                0   \n",
       "2             0               0                0                1   \n",
       "3             0               1                0                0   \n",
       "4             0               1                0                0   \n",
       "\n",
       "   enginetype_rotor  fuelsystem_2bbl  fuelsystem_4bbl  fuelsystem_idi  \\\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",
       "   fuelsystem_mfi  fuelsystem_mpfi  fuelsystem_spdi  fuelsystem_spfi  \n",
       "0               0                1                0                0  \n",
       "1               0                1                0                0  \n",
       "2               0                1                0                0  \n",
       "3               0                1                0                0  \n",
       "4               0                1                0                0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['carlength','carwidth','curbweight','enginesize','horsepower','citympg','highwaympg']]=StandardScaler().fit_transform(data[['carlength','carwidth','curbweight','enginesize','horsepower','citympg','highwaympg']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carlength', 'carwidth', 'curbweight', 'enginesize', 'horsepower',\n",
       "       'citympg', 'highwaympg', 'price', 'fueltype_gas', 'cylindernumber_five',\n",
       "       'cylindernumber_four', 'cylindernumber_six', 'cylindernumber_three',\n",
       "       'cylindernumber_twelve', 'cylindernumber_two', 'aspiration_turbo',\n",
       "       'carbody_hardtop', 'carbody_hatchback', 'carbody_sedan',\n",
       "       'carbody_wagon', 'drivewheel_fwd', 'drivewheel_rwd',\n",
       "       'enginelocation_rear', 'enginetype_dohcv', 'enginetype_l',\n",
       "       'enginetype_ohc', 'enginetype_ohcf', 'enginetype_ohcv',\n",
       "       'enginetype_rotor', 'fuelsystem_2bbl', 'fuelsystem_4bbl',\n",
       "       'fuelsystem_idi', 'fuelsystem_mfi', 'fuelsystem_mpfi',\n",
       "       'fuelsystem_spdi', 'fuelsystem_spfi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fuelsystem_spfi</td>\n",
       "      <td>1.153260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>fuelsystem_mfi</td>\n",
       "      <td>1.208361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cylindernumber_three</td>\n",
       "      <td>1.315802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cylindernumber_twelve</td>\n",
       "      <td>1.737783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>enginetype_ohcf</td>\n",
       "      <td>1.741192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>carbody_hardtop</td>\n",
       "      <td>1.950755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>enginetype_l</td>\n",
       "      <td>1.988355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cylindernumber_five</td>\n",
       "      <td>1.992370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>enginetype_ohcv</td>\n",
       "      <td>2.049763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>fuelsystem_spdi</td>\n",
       "      <td>2.243851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>enginetype_dohcv</td>\n",
       "      <td>2.587638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>enginelocation_rear</td>\n",
       "      <td>2.624027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cylindernumber_six</td>\n",
       "      <td>3.454665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>aspiration_turbo</td>\n",
       "      <td>3.621881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>carbody_wagon</td>\n",
       "      <td>4.236986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fuelsystem_4bbl</td>\n",
       "      <td>4.433640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>enginesize</td>\n",
       "      <td>4.434572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>enginetype_rotor</td>\n",
       "      <td>4.559945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fuelsystem_idi</td>\n",
       "      <td>4.816278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fuelsystem_2bbl</td>\n",
       "      <td>6.351672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>carwidth</td>\n",
       "      <td>7.701918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>carlength</td>\n",
       "      <td>8.383294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>carbody_hatchback</td>\n",
       "      <td>9.392666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fuelsystem_mpfi</td>\n",
       "      <td>9.986869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>drivewheel_rwd</td>\n",
       "      <td>10.596540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>carbody_sedan</td>\n",
       "      <td>13.364317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>drivewheel_fwd</td>\n",
       "      <td>13.493851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>horsepower</td>\n",
       "      <td>14.453109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Features        VIF\n",
       "27        fuelsystem_spfi   1.153260\n",
       "24         fuelsystem_mfi   1.208361\n",
       "6    cylindernumber_three   1.315802\n",
       "7   cylindernumber_twelve   1.737783\n",
       "18        enginetype_ohcf   1.741192\n",
       "9         carbody_hardtop   1.950755\n",
       "17           enginetype_l   1.988355\n",
       "4     cylindernumber_five   1.992370\n",
       "19        enginetype_ohcv   2.049763\n",
       "26        fuelsystem_spdi   2.243851\n",
       "16       enginetype_dohcv   2.587638\n",
       "15    enginelocation_rear   2.624027\n",
       "5      cylindernumber_six   3.454665\n",
       "8        aspiration_turbo   3.621881\n",
       "12          carbody_wagon   4.236986\n",
       "22        fuelsystem_4bbl   4.433640\n",
       "2              enginesize   4.434572\n",
       "20       enginetype_rotor   4.559945\n",
       "23         fuelsystem_idi   4.816278\n",
       "21        fuelsystem_2bbl   6.351672\n",
       "1                carwidth   7.701918\n",
       "0               carlength   8.383294\n",
       "10      carbody_hatchback   9.392666\n",
       "25        fuelsystem_mpfi   9.986869\n",
       "14         drivewheel_rwd  10.596540\n",
       "11          carbody_sedan  13.364317\n",
       "13         drivewheel_fwd  13.493851\n",
       "3              horsepower  14.453109"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "# most correlated numerical cols with Occupancy\n",
    "variables = data[['carlength', 'carwidth','enginesize', 'horsepower','cylindernumber_five', 'cylindernumber_six', 'cylindernumber_three',\n",
    "       'cylindernumber_twelve',  'aspiration_turbo',\n",
    "       'carbody_hardtop', 'carbody_hatchback', 'carbody_sedan',\n",
    "       'carbody_wagon', 'drivewheel_fwd', 'drivewheel_rwd',\n",
    "       'enginelocation_rear', 'enginetype_dohcv', 'enginetype_l',\n",
    "       'enginetype_ohcf', 'enginetype_ohcv',\n",
    "       'enginetype_rotor', 'fuelsystem_2bbl', 'fuelsystem_4bbl',\n",
    "       'fuelsystem_idi', 'fuelsystem_mfi', 'fuelsystem_mpfi',\n",
    "       'fuelsystem_spdi', 'fuelsystem_spfi'\n",
    "       ]]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"Features\"] = variables.columns\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif.sort_values(by=['VIF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['fueltype_gas'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['cylindernumber_four'],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['cylindernumber_two'],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['citympg'],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['highwaympg'],axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('price',axis=1)\n",
    "y=data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1234,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiplelinear = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8497943922476567"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,multiplelinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1631.738434629084"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test,multiplelinear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=1234, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(random_state=1234)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomforest=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8718098650930718"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,randomforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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