cpp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import pickle\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "car=pd.read_csv('quikr_car.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                                     name   company  year          Price  \\\n0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n\n   kms_driven fuel_type  \n0  45,000 kms    Petrol  \n1      40 kms    Diesel  \n2  22,000 kms    Petrol  \n3  28,000 kms    Petrol  \n4  36,000 kms    Diesel  ",
      "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>name</th>\n      <th>company</th>\n      <th>year</th>\n      <th>Price</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n      <td>Hyundai</td>\n      <td>2007</td>\n      <td>80,000</td>\n      <td>45,000 kms</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Mahindra Jeep CL550 MDI</td>\n      <td>Mahindra</td>\n      <td>2006</td>\n      <td>4,25,000</td>\n      <td>40 kms</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Maruti Suzuki Alto 800 Vxi</td>\n      <td>Maruti</td>\n      <td>2018</td>\n      <td>Ask For Price</td>\n      <td>22,000 kms</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n      <td>Hyundai</td>\n      <td>2014</td>\n      <td>3,25,000</td>\n      <td>28,000 kms</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n      <td>Ford</td>\n      <td>2014</td>\n      <td>5,75,000</td>\n      <td>36,000 kms</td>\n      <td>Diesel</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "carprice=pd.read_csv('carPrice_Assignment.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n0       1          3        alfa-romero giulia      gas        std        two   \n1       2          3       alfa-romero stelvio      gas        std        two   \n2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n3       4          2               audi 100 ls      gas        std       four   \n4       5          2                audi 100ls      gas        std       four   \n\n       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n0  convertible        rwd          front       88.6  ...         130   \n1  convertible        rwd          front       88.6  ...         130   \n2    hatchback        rwd          front       94.5  ...         152   \n3        sedan        fwd          front       99.8  ...         109   \n4        sedan        4wd          front       99.4  ...         136   \n\n   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n0        mpfi       3.47    2.68              9.0        111     5000      21   \n1        mpfi       3.47    2.68              9.0        111     5000      21   \n2        mpfi       2.68    3.47              9.0        154     5000      19   \n3        mpfi       3.19    3.40             10.0        102     5500      24   \n4        mpfi       3.19    3.40              8.0        115     5500      18   \n\n   highwaympg    price  \n0          27  13495.0  \n1          27  16500.0  \n2          26  16500.0  \n3          30  13950.0  \n4          22  17450.0  \n\n[5 rows x 26 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>car_ID</th>\n      <th>symboling</th>\n      <th>CarName</th>\n      <th>fueltype</th>\n      <th>aspiration</th>\n      <th>doornumber</th>\n      <th>carbody</th>\n      <th>drivewheel</th>\n      <th>enginelocation</th>\n      <th>wheelbase</th>\n      <th>...</th>\n      <th>enginesize</th>\n      <th>fuelsystem</th>\n      <th>boreratio</th>\n      <th>stroke</th>\n      <th>compressionratio</th>\n      <th>horsepower</th>\n      <th>peakrpm</th>\n      <th>citympg</th>\n      <th>highwaympg</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>3</td>\n      <td>alfa-romero giulia</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>...</td>\n      <td>130</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>alfa-romero stelvio</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>...</td>\n      <td>130</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>alfa-romero Quadrifoglio</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>94.5</td>\n      <td>...</td>\n      <td>152</td>\n      <td>mpfi</td>\n      <td>2.68</td>\n      <td>3.47</td>\n      <td>9.0</td>\n      <td>154</td>\n      <td>5000</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2</td>\n      <td>audi 100 ls</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>...</td>\n      <td>109</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>10.0</td>\n      <td>102</td>\n      <td>5500</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2</td>\n      <td>audi 100ls</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>4wd</td>\n      <td>front</td>\n      <td>99.4</td>\n      <td>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.0</td>\n      <td>115</td>\n      <td>5500</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carprice.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "creating backup"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "backup=car.copy()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Quality\n",
    "\n",
    "- names are pretty inconsistent\n",
    "- names have company names attached to it\n",
    "- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'\n",
    "- company: many of the names are not of any company like 'Used', 'URJENT', and so on.\n",
    "- year has many non-year values\n",
    "- year is in object. Change to integer\n",
    "- Price has Ask for Price\n",
    "- Price has commas in its prices and is in object\n",
    "- kms_driven has object values with kms at last.\n",
    "- It has nan values and two rows have 'Petrol' in them\n",
    "- fuel_type has nan values"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 892 entries, 0 to 891\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   name        892 non-null    object\n",
      " 1   company     892 non-null    object\n",
      " 2   year        892 non-null    object\n",
      " 3   Price       892 non-null    object\n",
      " 4   kms_driven  840 non-null    object\n",
      " 5   fuel_type   837 non-null    object\n",
      "dtypes: object(6)\n",
      "memory usage: 41.9+ KB\n"
     ]
    }
   ],
   "source": [
    "car.info()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' nan 'LPG']\n"
     ]
    }
   ],
   "source": [
    "print(car['fuel_type'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hyundai' 'Mahindra' 'Maruti' 'Ford' 'Skoda' 'Audi' 'Toyota' 'Renault'\n",
      " 'Honda' 'Datsun' 'Mitsubishi' 'Tata' 'Volkswagen' 'I' 'Chevrolet' 'Mini'\n",
      " 'BMW' 'Nissan' 'Hindustan' 'Fiat' 'Commercial' 'MARUTI' 'Force'\n",
      " 'Mercedes' 'Land' 'Yamaha' 'selling' 'URJENT' 'Swift' 'Used' 'Jaguar'\n",
      " 'Jeep' 'tata' 'Sale' 'very' 'Volvo' 'i' '2012' 'Well' 'all' '7' '9'\n",
      " 'scratch' 'urgent' 'sell' 'TATA' 'Any' 'Tara']\n"
     ]
    }
   ],
   "source": [
    "print(car['company'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hyundai Santro Xing XO eRLX Euro III' 'Mahindra Jeep CL550 MDI'\n",
      " 'Maruti Suzuki Alto 800 Vxi' 'Hyundai Grand i10 Magna 1.2 Kappa VTVT'\n",
      " 'Ford EcoSport Titanium 1.5L TDCi' 'Ford Figo' 'Hyundai Eon'\n",
      " 'Ford EcoSport Ambiente 1.5L TDCi' 'Maruti Suzuki Alto K10 VXi AMT'\n",
      " 'Skoda Fabia Classic 1.2 MPI' 'Maruti Suzuki Stingray VXi'\n",
      " 'Hyundai Elite i20 Magna 1.2' 'Mahindra Scorpio SLE BS IV' 'Audi A8'\n",
      " 'Audi Q7' 'Mahindra Scorpio S10' 'Maruti Suzuki Alto 800'\n",
      " 'Hyundai i20 Sportz 1.2' 'Maruti Suzuki Alto 800 Lx'\n",
      " 'Maruti Suzuki Vitara Brezza ZDi' 'Maruti Suzuki Alto LX'\n",
      " 'Mahindra Bolero DI' 'Maruti Suzuki Swift Dzire ZDi'\n",
      " 'Mahindra Scorpio S10 4WD' 'Maruti Suzuki Swift Vdi BSIII'\n",
      " 'Maruti Suzuki Wagon R VXi BS III' 'Maruti Suzuki Wagon R VXi Minor'\n",
      " 'Toyota Innova 2.0 G 8 STR BS IV' 'Renault Lodgy 85 PS RXL'\n",
      " 'Skoda Yeti Ambition 2.0 TDI CR 4x2' 'Maruti Suzuki Baleno Delta 1.2'\n",
      " 'Renault Duster 110 PS RxZ Diesel Plus' 'Renault Duster 85 PS RxE Diesel'\n",
      " 'Honda City 1.5 S MT' 'Maruti Suzuki Dzire' 'Honda Amaze'\n",
      " 'Honda Amaze 1.5 SX i DTEC' 'Honda City' 'Datsun Redi GO S'\n",
      " 'Maruti Suzuki SX4 ZXI MT' 'Mitsubishi Pajero Sport Limited Edition'\n",
      " 'Maruti Suzuki Swift VXi 1.2 ABS BS IV' 'Honda City ZX CVT'\n",
      " 'Maruti Suzuki Wagon R LX BS IV' 'Tata Indigo eCS LS CR4 BS IV'\n",
      " 'Volkswagen Polo Highline Exquisite P' 'I want to sell my car Tata Zest'\n",
      " 'Chevrolet Spark LS 1.0' 'Renault Duster 110PS Diesel RxZ'\n",
      " 'Mini Cooper S 1.6' 'Skoda Fabia 1.2L Diesel Ambiente' 'Renault Duster'\n",
      " 'Mahindra Scorpio S4' 'Mahindra Scorpio VLX 2WD BS IV'\n",
      " 'Mahindra Quanto C8' 'Ford EcoSport' 'Honda Brio'\n",
      " 'Volkswagen Vento Highline Plus 1.5 Diesel AT' 'Hyundai i20 Magna'\n",
      " 'Toyota Corolla Altis Diesel D4DG' 'Hyundai Verna Transform SX VTVT'\n",
      " 'Toyota Corolla Altis Petrol Ltd' 'Honda City 1.5 EXi New'\n",
      " 'Skoda Fabia 1.2L Diesel Elegance' 'BMW 3 Series 320i'\n",
      " 'Maruti Suzuki A Star Lxi' 'Toyota Etios GD'\n",
      " 'Ford Figo Diesel EXI Option' 'Maruti Suzuki Swift Dzire VXi 1.2 BS IV'\n",
      " 'Chevrolet Beat LT Diesel' 'BMW 7 Series 740Li Sedan'\n",
      " 'Mahindra XUV500 W8 AWD 2013' 'Hyundai i10 Magna 1.2'\n",
      " 'Hyundai Verna Fluidic New' 'Maruti Suzuki Swift VXi 1.2 BS IV'\n",
      " 'Maruti Suzuki Ertiga ZXI Plus' 'Maruti Suzuki Ertiga Vxi'\n",
      " 'Maruti Suzuki Ertiga VDi' 'Maruti Suzuki Alto LXi BS III'\n",
      " 'Hyundai Grand i10 Asta 1.1 CRDi' 'Honda Amaze 1.2 S i VTEC'\n",
      " 'Hyundai i20 Asta 1.4 CRDI 6 Speed' 'Ford Figo Diesel EXI'\n",
      " 'Maruti Suzuki Eeco 5 STR WITH AC HTR' 'Maruti Suzuki Ertiga ZXi'\n",
      " 'Maruti Suzuki Esteem LXi BS III' 'Maruti Suzuki Ritz VXI'\n",
      " 'Maruti Suzuki Ritz LDi' 'Maruti Suzuki Dzire VDI' 'Toyota Etios Liva G'\n",
      " 'Hyundai i20 Sportz 1.4 CRDI' 'Chevrolet Spark' 'Nissan Micra XV'\n",
      " 'Maruti Suzuki Swift' 'Honda Amaze 1.5 S i DTEC' 'Chevrolet Beat'\n",
      " 'Toyota Corolla' 'Honda City 1.5 V MT' 'Ford EcoSport Trend 1.5L TDCi'\n",
      " 'Hyundai i20 Asta 1.2' 'Tata Indica V2 eLS' 'Maruti Suzuki Alto 800 Lxi'\n",
      " 'Hindustan Motors Ambassador' 'Toyota Corolla Altis 1.8 GL'\n",
      " 'Toyota Corolla Altis 1.8 J' 'Toyota Innova 2.5 GX BS IV 7 STR'\n",
      " 'Volkswagen Jetta Highline TDI AT' 'Volkswagen Polo Comfortline 1.2L P'\n",
      " 'Volkswagen Polo' 'Mahindra Scorpio' 'Nissan Sunny' 'Hyundai Elite i20'\n",
      " 'Renault Kwid' 'Mahindra Scorpio VLX Airbag' 'Chevrolet Spark LT 1.0'\n",
      " 'Datsun Redi GO T O' 'Maruti Suzuki Swift RS VDI'\n",
      " 'Fiat Punto Emotion 1.2' 'Hyundai i10 Sportz 1.2'\n",
      " 'Chevrolet Beat LT Opt Diesel' 'Chevrolet Beat LS Diesel'\n",
      " 'Tata Indigo CS' 'Maruti Suzuki Swift VDi' 'Hyundai Eon Era Plus'\n",
      " 'Mahindra XUV500' 'Ford Fiesta' 'Maruti Suzuki Wagon R' 'Hyundai i20'\n",
      " 'Tata Indigo eCS LX TDI BS III' 'Hyundai Fluidic Verna 1.6 CRDi SX'\n",
      " 'Commercial , DZire LDI, 2016, for sale' 'Fiat Petra ELX 1.2 PS'\n",
      " 'Hyundai Santro Xing XS' 'Maruti Suzuki Ciaz VXi Plus'\n",
      " 'Maruti Suzuki Zen VX' 'Hyundai Creta 1.6 SX Plus Petrol'\n",
      " 'Tata indigo ecs LX, 201' 'Mahindra Scorpio SLX'\n",
      " 'Toyota Innova 2.5 G BS III 8 STR' 'Maruti Suzuki Wagon R LXI BS IV'\n",
      " 'Tata Nano Cx BSIV' 'Maruti Suzuki Alto Std BS IV'\n",
      " 'Maruti Suzuki Wagon R LXi BS III' 'Maruti Suzuki Swift VXI BSIII'\n",
      " 'Tata Sumo Victa EX 10 by 7 Str BSIII' 'MARUTI SUZUKI DESI'\n",
      " 'Volkswagen Passat Diesel Comfortline AT'\n",
      " 'Renault Scala RxL Diesel Travelogue'\n",
      " 'Hyundai Grand i10 Sportz O 1.2 Kappa VTVT' 'Hyundai i20 Active 1.2 SX'\n",
      " 'Mahindra Xylo E4' 'Mahindra Jeep MM 550 XDB' 'Mahindra Bolero SLE BS IV'\n",
      " 'Force Motors Force One LX ABS 7 STR' 'Maruti Suzuki SX4' 'Toyota Etios'\n",
      " 'Honda City ZX VTEC' 'Maruti Suzuki Wagon R LX BS III'\n",
      " 'Honda City VX O MT Diesel' 'Mahindra Thar CRDe 4x4 AC'\n",
      " 'Audi A4 1.8 TFSI Multitronic Premium Plus'\n",
      " 'Mercedes Benz GLA Class 200 CDI Sport' 'Land Rover Freelander 2 SE'\n",
      " 'Renault Kwid RXT' 'Tata Aria Pleasure 4X2'\n",
      " 'Mercedes Benz B Class B180 Sports' 'Datsun GO T O' 'Honda Jazz VX MT'\n",
      " 'Hyundai i20 Active 1.4L SX O' 'Mini Cooper S'\n",
      " 'Maruti Suzuki Ciaz ZXI Plus' 'Chevrolet Tavera Neo' 'Hyundai Eon Sportz'\n",
      " 'Tata Sumo Gold Select Variant' 'Maruti Suzuki Wagon R 1.0'\n",
      " 'Maruti Suzuki Esteem VXi BS III' 'Chevrolet Enjoy 1.4 LS 8 STR'\n",
      " 'Maruti Suzuki Wagon R 1.0 VXi' 'Nissan Terrano XL D Plus'\n",
      " 'Renault Duster 85 PS RxL Diesel' 'Maruti Suzuki Dzire ZXI'\n",
      " 'Renault Kwid RXT Opt' 'Maruti Suzuki Maruti 800 Std'\n",
      " 'Renault Kwid 1.0 RXT AMT' 'Renault Scala RxL Diesel'\n",
      " 'Hyundai Grand i10 Asta 1.2 Kappa VTVT O' 'Chevrolet Beat LS Petrol'\n",
      " 'Hyundai Accent GLX' 'Yama' 'Maruti Suzuki Swift LDi'\n",
      " 'Mahindra TUV300 T4 Plus' 'Tata Indica V2 Xeta e GLE'\n",
      " 'Tata Indigo CS LS DiCOR' 'Mahindra Scorpio VLX Special Edition BS III'\n",
      " 'Tata Indica eV2 LS' 'Honda Accord' 'Ford EcoSport Titanium 1.5 TDCi'\n",
      " 'Maruti Suzuki Ertiga' 'Mahindra Scorpio 2.6 CRDe' 'Honda Mobilio'\n",
      " 'Toyota Corolla Altis' 'Skoda Laura' 'Hyundai Verna Fluidic'\n",
      " 'Maruti Suzuki Vitara Brezza' 'Tata Manza Aura Quadrajet'\n",
      " 'Chevrolet Sail UVA Petrol LT ABS' 'Hyundai Verna Fluidic 1.6 VTVT SX'\n",
      " 'Audi A4 2.0 TDI 177bhp Premium' 'Hyundai Elantra SX'\n",
      " 'Mahindra Scorpio VLX 4WD Airbag' 'Mahindra KUV100 K8 D 6 STR'\n",
      " 'Hyundai Grand i10' 'Hyundai i10' 'Hyundai i20 Active' 'Datsun Redi GO'\n",
      " 'Toyota Etios Liva' 'Hyundai Accent' 'Hyundai Verna' 'Toyota Fortuner'\n",
      " 'Hyundai i10 Sportz' 'Mahindra Bolero Power Plus SLE' 'selling car Ta'\n",
      " 'Honda City 1.5 V MT Exclusive' 'Chevrolet Spark LT 1.0 Airbag'\n",
      " 'Tata Indigo eCS VX CR4 BS IV' 'Tata Zest 90'\n",
      " 'Skoda Rapid Elegance 1.6 TDI CR MT' 'Tata Vista Quadrajet VX'\n",
      " 'Maruti Suzuki Alto K10 VXi AT' 'Maruti Suzuki Zen LXi BS III'\n",
      " 'Maruti Suzuki Swift Dzire Tour LDi' 'Honda City ZX EXi'\n",
      " 'Chevrolet Beat Diesel' 'Maruti Suzuki Swift Dzire car'\n",
      " 'Hyundai Verna 1.4 VTVT' 'Toyota Innova 2.5 E MS 7 STR BS IV'\n",
      " 'Maruti Suzuki Maruti 800 Std – Befo' 'Hyundai Elite i20 Asta 1.4 CRDI'\n",
      " 'Maruti Suzuki Swift Dzire Tour (Gat'\n",
      " 'Maruti Suzuki Versa DX2 8 SEATER BSIII' 'Tata Indigo LX TDI BS III'\n",
      " 'Volkswagen Vento Konekt Diesel Highline'\n",
      " 'Mercedes Benz C Class 200 CDI Classic' 'URJE' 'Hyundai Santro Xing GLS'\n",
      " 'Maruti Suzuki Omni Limited Edition'\n",
      " 'Hyundai Sonata Transform 2.4 GDi MT' 'Hyundai Elite i20 Sportz 1.2'\n",
      " 'Honda Jazz S MT' 'Hyundai Grand i10 Sportz 1.2 Kappa VTVT'\n",
      " 'Maruti Suzuki Zen LXi BSII' 'Mahindra Scorpio W Turbo 2.6DX 9 Seater'\n",
      " 'Swift Dzire Tour 27 Dec 2016 Regis' 'Maruti Suzuki Alto K10 VXi'\n",
      " 'Hyundai Grand i10 Asta 1.2 Kappa VTVT' 'Mahindra XUV500 W8'\n",
      " 'Hyundai i20 Magna O 1.2' 'Renault Duster 85 PS RxL Explore LE'\n",
      " 'Honda Brio V MT' 'Mahindra TUV300 T8' 'Nissan X Trail Select Variant'\n",
      " 'Ford Ikon 1.3 CLXi NXt Finesse' 'Toyota Fortuner 3.0 4x4 MT'\n",
      " 'Tata Manza ELAN Quadrajet' 'Tata zest x' 'Mahindra xyl'\n",
      " 'Mercedes Benz A Class A 180 Sport Petrol' 'Tata Indigo LS'\n",
      " 'Hyundai i20 Magna 1.2' 'Used Commercial Maruti Omn'\n",
      " 'Honda Amaze 1.5 E i DTEC' 'Hyundai Verna 1.6 EX VTVT'\n",
      " 'BMW 5 Series 520d Sedan' 'Skoda Superb 1.8 TFSI AT'\n",
      " 'Audi Q3 2.0 TDI quattro Premium' 'Mahindra Bolero DI BSII'\n",
      " 'Maruti Suzuki Zen Estilo LXI Green CNG'\n",
      " 'Ford Figo Duratorq Diesel Titanium 1.4'\n",
      " 'Maruti Suzuki Wagon R VXI BS IV' 'Mahindra Logan Diesel 1.5 DLS'\n",
      " 'Tata Nano GenX XMA' 'Honda City SV' 'Ford Figo Petrol LXI'\n",
      " 'Hyundai i10 Magna 1.2 Kappa2' 'Toyota Corolla H2'\n",
      " 'Maruti Suzuki Swift Dzire Tour VXi' 'Tata Indigo CS eLS BS IV'\n",
      " 'Hyundai Xcent Base 1.1 CRDi' 'Hyundai Accent Executive Edition'\n",
      " 'Tata Zest XE 75 PS Diesel' 'Maruti Suzuki Dzire LDI'\n",
      " 'Tata Sumo Gold LX BS IV' 'Toyota Corolla Altis GL Petrol'\n",
      " 'Maruti Suzuki Eeco 7 STR' 'Toyota Fortuner 3.0 4x2 MT'\n",
      " 'Mahindra XUV500 W6' 'Tata Tigor Revotron XZ' 'Maruti Suzuki 800'\n",
      " 'Honda Mobilio S i DTEC' 'Hyundai Verna 1.6 CRDI E'\n",
      " 'Maruti Suzuki Omni Select Variant' 'Tata Indica' 'Hyundai Santro Xing'\n",
      " 'Maruti Suzuki Zen Estilo' 'Honda Brio VX AT'\n",
      " 'Maruti Suzuki Wagon R Select Variant' 'Tata Nano Lx BSIV'\n",
      " 'Jaguar XE XE Portfolio' 'Hyundai Xcent S 1.2' 'Hyundai Eon Magna Plus'\n",
      " 'Maruti Suzuki Ritz GENUS VXI'\n",
      " 'Hyundai Grand i10 Magna AT 1.2 Kappa VTVT' 'Hyundai Eon D Lite Plus'\n",
      " 'Honda Amaze 1.2 VX i VTEC' 'Maruti Suzuki Estilo VXi ABS BS IV'\n",
      " 'Maruti Suzuki Vitara Brezza LDi O' 'Toyota Innova 2.0 V'\n",
      " 'Hyundai Creta 1.6 SX Plus Petrol AT' 'Mahindra Scorpio Vlx BSIV'\n",
      " 'Mitsubishi Lancer 1.8 LXi' 'Maruti Suzuki Maruti 800 AC'\n",
      " 'Maruti Suzuki Alto 800 LXI CNG O' 'Ford Fiesta SXi 1.6 ABS'\n",
      " 'Maruti Suzuki Ritz VDi' 'Maruti Suzuki Estilo LX BS IV'\n",
      " 'Audi A6 2.0 TDI Premium' 'Maruti Suzuki Alto'\n",
      " 'Maruti Suzuki Baleno Sigma 1.2' 'Hyundai Verna 1.6 SX VTVT AT'\n",
      " 'Maruti Suzuki Swift GLAM' 'Hyundai Getz Prime 1.3 GVS' 'Hyundai Santro'\n",
      " 'Hyundai Getz Prime 1.3 GLX' 'Chevrolet Beat PS Diesel'\n",
      " 'Ford EcoSport Trend 1.5 Ti VCT' 'Tata Indica V2 DLG'\n",
      " 'BMW X1 xDrive20d xLine' 'Honda City 1.5 V AT' 'Tata Nano'\n",
      " 'Chevrolet Cruze LTZ AT' 'Hyun' 'Maruti Suzuki Swift Dzire VDi'\n",
      " 'Mahindra XUV500 W10' 'Maruti Suzuki Alto K10 LXi CNG'\n",
      " 'Hyundai Accent GLE' 'Force Motors One SUV' 'Datsun Go Plus T O'\n",
      " 'Chevrolet Spark 1.0 LT' 'Toyota Etios Liva GD'\n",
      " 'Renault Duster 85PS Diesel RxL Optional with Nav' 'Chevrolet Enjoy'\n",
      " 'BMW 5 Series 530i' 'Chevrolet Cruze LTZ'\n",
      " 'Jeep Wrangler Unlimited 4x4 Diesel' 'Hyundai Verna VGT CRDi SX ABS'\n",
      " 'Maruti Suzuki Omni' 'Maruti Suzuki Celerio VDi'\n",
      " 'Tata Zest Quadrajet 1.3' 'Tata Indigo CS eLX BS IV' 'Hyundai i10 Era'\n",
      " 'Tata Indigo eCS LX CR4 BS IV' 'Tata Indigo Marina LS'\n",
      " 'Commercial Chevrolet Sail Hatchback ca' 'Hyundai Xcent SX 1.2'\n",
      " 'Tata Nano LX Special Edition' 'Commercial Car Ta'\n",
      " 'Renault Duster 110 PS RxZ Diesel' 'Maruti Suzuki Wagon R AX BSIV'\n",
      " 'Maruti Suzuki Alto K10 New' 'tata Indica' 'Mahindra Xylo E8'\n",
      " 'Tata Manza Aqua Quadrajet' 'Used bt new conditions ta'\n",
      " 'Renault Kwid 1.0' 'Sale tata' 'Tata Venture EX 8 STR'\n",
      " 'Maruti Suzuki Swift Dzire Tour LXi' 'Maruti Suzuki Alto LX BSII'\n",
      " 'Skoda Octavia Classic 1.9 TDI MT' 'Maruti Suzuki Omni LPG BS IV'\n",
      " 'Tata Sumo Gold EX BS IV' 'Tata indigo 2017 top model..'\n",
      " 'Hyundai Verna 1.6 CRDI SX' 'Mahindra Scorpio SLX 2.6 Turbo 8 Str'\n",
      " 'Ford Ikon 1.6 Nxt' 'Tata indigo' 'Toyota Innova 2.5 V 7 STR'\n",
      " 'Nissan Sunny XL' 'Maruti Suzuki Swift VDi BS IV'\n",
      " 'very good condition tata bolts are av' 'Toyota Innova 2.0 G4'\n",
      " 'Sale Hyundai xcent commerc' 'Maruti Suzuki Swift VDi ABS'\n",
      " 'Hyundai Elite i20 Asta 1.2' 'Volkswagen Polo Trendline 1.5L D'\n",
      " 'Toyota Etios Liva Diesel' 'Maruti Suzuki Ciaz ZXi Plus RS'\n",
      " 'Hyundai Elantra 1.8 S' 'Ford EcoSport Trend 1.5L Ti VCT'\n",
      " 'Jaguar XF 2.2 Diesel Luxury' 'Audi Q5 2.0 TDI quattro Premium Plus'\n",
      " 'BMW 3 Series 320d Sedan' 'Maruti Suzuki Swift ZXi 1.2 BS IV'\n",
      " 'BMW X1 sDrive20d' 'Maruti Suzuki S Cross Sigma 1.3'\n",
      " 'Maruti Suzuki Ertiga LDi' 'Volkswagen Vento Comfortline Petrol'\n",
      " 'Mahindra KUV100' 'Maruti Suzuki Swift Dzire Tour VDi'\n",
      " 'Mahindra Scorpio 2.6 SLX' 'Maruti Suzuki Omni 8 STR BS III'\n",
      " 'Volkswagen Jetta Comfortline 1.9 TDI AT' 'Volvo S80 Summum D4'\n",
      " 'Toyota Corolla Altis VL AT Petrol' 'Mitsubishi Pajero Sport 2.5 AT'\n",
      " 'Chevrolet Beat LT Petrol' 'BMW X1'\n",
      " 'Mercedes Benz C Class C 220 CDI Avantgarde'\n",
      " 'Volkswagen Vento Comfortline Diesel' 'Tata Indigo CS GLS'\n",
      " 'Ford Figo Petrol Titanium' 'Honda City ZX GXi'\n",
      " 'Maruti Suzuki Wagon R Duo Lxi' 'Maruti Suzuki Zen LX BSII'\n",
      " 'Renault Duster RxL Petrol' 'Maruti Suzuki Baleno Zeta 1.2'\n",
      " 'Honda WR V S MT Petrol' 'Renault Duster 110 PS RxL Diesel'\n",
      " 'Mahindra Scorpio LX BS III' 'Maruti Suzuki SX4 Celebration Diesel'\n",
      " 'Audi A3 Cabriolet 40 TFSI' 'I want to sell my commercial car due t'\n",
      " 'Hyundai Santro AE GLS Audio' 'i want sale my car.no emi....uber atta'\n",
      " 'Tata ZEST 6 month old' 'Mahindra Xylo D2 BS IV' 'Hyundai Getz GLE'\n",
      " 'Hyundai Creta 1.6 SX' 'Hyundai Santro Xing XL AT eRLX Euro III'\n",
      " 'Hyundai Santro Xing XL eRLX Euro III' 'Tata Indica V2 DLS BS III'\n",
      " 'Honda City 1.5 E MT' 'Nissan Micra XL' 'Honda City 1.5 S Inspire'\n",
      " 'Tata Indica eV2 eXeta eGLX' 'Maruti Suzuki Omni E 8 STR BS IV'\n",
      " 'MARUTI SUZUKI ERTIGA F' 'Hyundai Verna 1.6 CRDI SX Plus AT'\n",
      " 'Chevrolet Tavera LS B3 10 Seats BSII' 'Tata Tiago Revotron XM'\n",
      " 'Tata Tiago Revotorq XZ' 'Tata Nexon' 'Tata'\n",
      " 'Hindustan Motors Ambassador Classic Mark 4 – Befo'\n",
      " 'Ford Fusion 1.4 TDCi Diesel' 'Fiat Linea Emotion 1.4 L T Jet Petrol'\n",
      " 'Ford Ikon 1.3 Flair Josh 100' 'Tata Indica V2 LS' 'Mahindra Xylo D2'\n",
      " 'Hyundai Eon Magna' 'Tata Sumo Grande MKII GX'\n",
      " 'Volkswagen Polo Highline1.2L P' 'Tata Tiago Revotron XZ'\n",
      " 'Tata Indigo eCS' '2012 Tata Sumo Gold f' 'Mahindra Xylo E8 BS IV'\n",
      " 'Well mentained Tata Sumo' 'all paper updated tata indica v2 and u'\n",
      " 'Maruti Ertiga showroom condition with'\n",
      " '7 SEATER MAHINDRA BOLERO IN VERY GOOD' '9 SEATER MAHINDRA BOL'\n",
      " 'scratch less Tata I' 'Maruti Suzuki swift dzire for sale in'\n",
      " 'Commercial Chevrolet beat for sale in' 'urgent sell my Mahindra qu'\n",
      " 'Tata Sumo Gold FX BSIII' 'sell my car Maruti Suzuki Swif'\n",
      " 'Maruti Suzuki Swift Dzire good car fo' 'Hyunda'\n",
      " 'Commercial Maruti Suzuki Alto Lxi 800' 'urgent sale Ta'\n",
      " 'Maruti Suzuki Alto vxi t' 'tata' 'TATA INDI' 'Hyundai Creta'\n",
      " 'Tata Bolt XM Petrol' 'Hyundai Venue' 'Maruti Suzuki Ritz'\n",
      " 'Renault Lodgy' 'Hyundai i20 Asta' 'Maruti Suzuki Swift Select Variant'\n",
      " 'Tata Indica V2 DLX BS III' 'Mahindra Scorpio VLX 2.2 mHawk Airbag BSIV'\n",
      " 'Toyota Innova 2.5 E 8 STR' 'Mahindra KUV100 K8 6 STR' 'Datsun Go Plus'\n",
      " 'Ford Endeavor 4x4 Thunder Plus' 'Tata Indica V2'\n",
      " 'Hyundai Santro Xing GL' 'Toyota Innova 2.5 Z Diesel 7 Seater'\n",
      " 'Any type car avaiabel hare...comercica' 'Maruti Suzuki Alto AX'\n",
      " 'Mahindra Logan' 'Maruti Suzuki 800 Std BS III' 'Chevrolet Sail 1.2 LS'\n",
      " 'Volkswagen Vento Highline Plus 1.5 Diesel' 'Tata Manza'\n",
      " 'Toyota Innova 2.0 G1 Petrol 8seater' 'Toyota Etios G' 'Toyota Qualis'\n",
      " 'Mahindra Quanto C4' 'Maruti Suzuki Swift Dzire'\n",
      " 'Hyundai i20 Select Variant' 'Honda City VX Petrol' 'Hyundai Getz'\n",
      " 'Mercedes Benz C Class 200 K MT' 'Skoda Fabia'\n",
      " 'Maruti Suzuki Alto 800 Select Variant' 'Maruti Suzuki Ritz VXI ABS'\n",
      " 'tata zest 2017 f' 'Tata Indica V2 DLE BS III' 'Ta' 'Tata Zest XM Diesel'\n",
      " 'Honda Amaze 1.2 E i VTEC' 'Chevrolet Sail 1.2 LT ABS']\n"
     ]
    }
   ],
   "source": [
    "print(car['name'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2007' '2006' '2018' '2014' '2015' '2012' '2013' '2016' '2010' '2017'\n",
      " '2008' '2011' '2019' '2009' '2005' '2000' '...' '150k' 'TOUR' '2003'\n",
      " 'r 15' '2004' 'Zest' '/-Rs' 'sale' '1995' 'ara)' '2002' 'SELL' '2001'\n",
      " 'tion' 'odel' '2 bs' 'arry' 'Eon' 'o...' 'ture' 'emi' 'car' 'able' 'no.'\n",
      " 'd...' 'SALE' 'digo' 'sell' 'd Ex' 'n...' 'e...' 'D...' ', Ac' 'go .'\n",
      " 'k...' 'o c4' 'zire' 'cent' 'Sumo' 'cab' 't xe' 'EV2' 'r...' 'zest']\n"
     ]
    }
   ],
   "source": [
    "print(car['year'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['45,000 kms' '40 kms' '22,000 kms' '28,000 kms' '36,000 kms' '59,000 kms'\n",
      " '41,000 kms' '25,000 kms' '24,530 kms' '60,000 kms' '30,000 kms'\n",
      " '32,000 kms' '48,660 kms' '4,000 kms' '16,934 kms' '43,000 kms'\n",
      " '35,550 kms' '39,522 kms' '39,000 kms' '55,000 kms' '72,000 kms'\n",
      " '15,975 kms' '70,000 kms' '23,452 kms' '35,522 kms' '48,508 kms'\n",
      " '15,487 kms' '82,000 kms' '20,000 kms' '68,000 kms' '38,000 kms'\n",
      " '27,000 kms' '33,000 kms' '46,000 kms' '16,000 kms' '47,000 kms'\n",
      " '35,000 kms' '30,874 kms' '15,000 kms' '29,685 kms' '1,30,000 kms'\n",
      " '19,000 kms' nan '54,000 kms' '13,000 kms' '38,200 kms' '50,000 kms'\n",
      " '13,500 kms' '3,600 kms' '45,863 kms' '60,500 kms' '12,500 kms'\n",
      " '18,000 kms' '13,349 kms' '29,000 kms' '44,000 kms' '42,000 kms'\n",
      " '14,000 kms' '49,000 kms' '36,200 kms' '51,000 kms' '1,04,000 kms'\n",
      " '33,333 kms' '33,600 kms' '5,600 kms' '7,500 kms' '26,000 kms'\n",
      " '24,330 kms' '65,480 kms' '28,028 kms' '2,00,000 kms' '99,000 kms'\n",
      " '2,800 kms' '21,000 kms' '11,000 kms' '66,000 kms' '3,000 kms'\n",
      " '7,000 kms' '38,500 kms' '37,200 kms' '43,200 kms' '24,800 kms'\n",
      " '45,872 kms' '40,000 kms' '11,400 kms' '97,200 kms' '52,000 kms'\n",
      " '31,000 kms' '1,75,430 kms' '37,000 kms' '65,000 kms' '3,350 kms'\n",
      " '75,000 kms' '62,000 kms' '73,000 kms' '2,200 kms' '54,870 kms'\n",
      " '34,580 kms' '97,000 kms' '60 kms' '80,200 kms' '3,200 kms' '0,000 kms'\n",
      " '5,000 kms' '588 kms' '71,200 kms' '1,75,400 kms' '9,300 kms'\n",
      " '56,758 kms' '10,000 kms' '56,450 kms' '56,000 kms' '32,700 kms'\n",
      " '9,000 kms' '73 kms' '1,60,000 kms' '84,000 kms' '58,559 kms'\n",
      " '57,000 kms' '1,70,000 kms' '80,000 kms' '6,821 kms' '23,000 kms'\n",
      " '34,000 kms' '1,800 kms' '4,00,000 kms' '48,000 kms' '90,000 kms'\n",
      " '12,000 kms' '69,900 kms' '1,66,000 kms' '122 kms' '0 kms' '24,000 kms'\n",
      " '36,469 kms' '7,800 kms' '24,695 kms' '15,141 kms' '59,910 kms'\n",
      " '1,00,000 kms' '4,500 kms' '1,29,000 kms' '300 kms' '1,31,000 kms'\n",
      " '1,11,111 kms' '59,466 kms' '25,500 kms' '44,005 kms' '2,110 kms'\n",
      " '43,222 kms' '1,00,200 kms' '65 kms' '1,40,000 kms' '1,03,553 kms'\n",
      " '58,000 kms' '1,20,000 kms' '49,800 kms' '100 kms' '81,876 kms'\n",
      " '6,020 kms' '55,700 kms' '18,500 kms' '1,80,000 kms' '53,000 kms'\n",
      " '35,500 kms' '22,134 kms' '1,000 kms' '8,500 kms' '87,000 kms'\n",
      " '6,000 kms' '15,574 kms' '8,000 kms' '55,800 kms' '56,400 kms'\n",
      " '72,160 kms' '11,500 kms' '1,33,000 kms' '2,000 kms' '88,000 kms'\n",
      " '65,422 kms' '1,17,000 kms' '1,50,000 kms' '10,750 kms' '6,800 kms'\n",
      " '5 kms' '9,800 kms' '57,923 kms' '30,201 kms' '6,200 kms' '37,518 kms'\n",
      " '24,652 kms' '383 kms' '95,000 kms' '3,528 kms' '52,500 kms' '47,900 kms'\n",
      " '52,800 kms' '1,95,000 kms' '48,008 kms' '48,247 kms' '9,400 kms'\n",
      " '64,000 kms' '2,137 kms' '10,544 kms' '49,500 kms' '1,47,000 kms'\n",
      " '90,001 kms' '48,006 kms' '74,000 kms' '85,000 kms' '29,500 kms'\n",
      " '39,700 kms' '67,000 kms' '19,336 kms' '60,105 kms' '45,933 kms'\n",
      " '1,02,563 kms' '28,600 kms' '41,800 kms' '1,16,000 kms' '42,590 kms'\n",
      " '7,400 kms' '54,500 kms' '76,000 kms' '00 kms' '11,523 kms' '38,600 kms'\n",
      " '95,500 kms' '37,458 kms' '85,960 kms' '12,516 kms' '30,600 kms'\n",
      " '2,550 kms' '62,500 kms' '69,000 kms' '28,400 kms' '68,485 kms'\n",
      " '3,500 kms' '85,455 kms' '63,000 kms' '1,600 kms' '77,000 kms'\n",
      " '26,500 kms' '2,875 kms' '13,900 kms' '1,500 kms' '2,450 kms' '1,625 kms'\n",
      " '33,400 kms' '60,123 kms' '38,900 kms' '1,37,495 kms' '91,200 kms'\n",
      " '1,46,000 kms' '1,00,800 kms' '2,100 kms' '2,500 kms' '1,32,000 kms'\n",
      " 'Petrol']\n"
     ]
    }
   ],
   "source": [
    "print(car['kms_driven'].unique())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "car=car[car['year'].str.isnumeric()]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2007' '2006' '2018' '2014' '2015' '2012' '2013' '2016' '2010' '2017'\n",
      " '2008' '2011' '2019' '2009' '2005' '2000' '2003' '2004' '1995' '2002'\n",
      " '2001']\n"
     ]
    }
   ],
   "source": [
    "print(car['year'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "car['year']=car['year'].astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "car=car[car['Price']!='Ask For Price']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "car['Price']=car['Price'].str.replace(',','').astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "car=car[car['kms_driven'].str.isnumeric()]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "car['kms_driven']=car['kms_driven'].astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "car=car[~car['fuel_type'].isna()]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "car=car.reset_index(drop=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' 'LPG']\n"
     ]
    }
   ],
   "source": [
    "print(car['fuel_type'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hyundai' 'Mahindra' 'Ford' 'Maruti' 'Skoda' 'Audi' 'Toyota' 'Renault'\n",
      " 'Honda' 'Datsun' 'Mitsubishi' 'Tata' 'Volkswagen' 'Chevrolet' 'Mini'\n",
      " 'BMW' 'Nissan' 'Hindustan' 'Fiat' 'Force' 'Mercedes' 'Land' 'Jaguar'\n",
      " 'Jeep' 'Volvo']\n"
     ]
    }
   ],
   "source": [
    "print(car['company'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hyundai Santro Xing' 'Mahindra Jeep CL550' 'Hyundai Grand i10'\n",
      " 'Ford EcoSport Titanium' 'Ford Figo' 'Hyundai Eon'\n",
      " 'Ford EcoSport Ambiente' 'Maruti Suzuki Alto' 'Skoda Fabia Classic'\n",
      " 'Maruti Suzuki Stingray' 'Hyundai Elite i20' 'Mahindra Scorpio SLE'\n",
      " 'Audi A8' 'Audi Q7' 'Mahindra Scorpio S10' 'Hyundai i20 Sportz'\n",
      " 'Maruti Suzuki Vitara' 'Mahindra Bolero DI' 'Maruti Suzuki Swift'\n",
      " 'Maruti Suzuki Wagon' 'Toyota Innova 2.0' 'Renault Lodgy 85'\n",
      " 'Skoda Yeti Ambition' 'Maruti Suzuki Baleno' 'Renault Duster 110'\n",
      " 'Renault Duster 85' 'Honda City 1.5' 'Maruti Suzuki Dzire' 'Honda Amaze'\n",
      " 'Honda Amaze 1.5' 'Honda City' 'Datsun Redi GO' 'Maruti Suzuki SX4'\n",
      " 'Mitsubishi Pajero Sport' 'Honda City ZX' 'Tata Indigo eCS'\n",
      " 'Volkswagen Polo Highline' 'Chevrolet Spark LS' 'Renault Duster 110PS'\n",
      " 'Mini Cooper S' 'Skoda Fabia 1.2L' 'Renault Duster' 'Mahindra Scorpio S4'\n",
      " 'Mahindra Scorpio VLX' 'Mahindra Quanto C8' 'Ford EcoSport' 'Honda Brio'\n",
      " 'Volkswagen Vento Highline' 'Hyundai i20 Magna' 'Toyota Corolla Altis'\n",
      " 'Hyundai Verna Transform' 'BMW 3 Series' 'Maruti Suzuki A'\n",
      " 'Toyota Etios GD' 'Ford Figo Diesel' 'Chevrolet Beat LT' 'BMW 7 Series'\n",
      " 'Mahindra XUV500 W8' 'Hyundai i10 Magna' 'Hyundai Verna Fluidic'\n",
      " 'Maruti Suzuki Ertiga' 'Honda Amaze 1.2' 'Hyundai i20 Asta'\n",
      " 'Maruti Suzuki Eeco' 'Maruti Suzuki Esteem' 'Maruti Suzuki Ritz'\n",
      " 'Toyota Etios Liva' 'Chevrolet Spark' 'Nissan Micra XV' 'Chevrolet Beat'\n",
      " 'Ford EcoSport Trend' 'Tata Indica V2' 'Hindustan Motors Ambassador'\n",
      " 'Toyota Innova 2.5' 'Volkswagen Jetta Highline'\n",
      " 'Volkswagen Polo Comfortline' 'Volkswagen Polo' 'Mahindra Scorpio'\n",
      " 'Nissan Sunny' 'Renault Kwid' 'Chevrolet Spark LT' 'Fiat Punto Emotion'\n",
      " 'Hyundai i10 Sportz' 'Chevrolet Beat LS' 'Tata Indigo CS'\n",
      " 'Hyundai Eon Era' 'Mahindra XUV500' 'Ford Fiesta' 'Hyundai i20'\n",
      " 'Hyundai Fluidic Verna' 'Fiat Petra ELX' 'Maruti Suzuki Ciaz'\n",
      " 'Maruti Suzuki Zen' 'Hyundai Creta 1.6' 'Mahindra Scorpio SLX'\n",
      " 'Tata Nano Cx' 'Tata Sumo Victa' 'Volkswagen Passat Diesel'\n",
      " 'Renault Scala RxL' 'Hyundai i20 Active' 'Mahindra Xylo E4'\n",
      " 'Mahindra Jeep MM' 'Mahindra Bolero SLE' 'Force Motors Force'\n",
      " 'Toyota Etios' 'Honda City VX' 'Mahindra Thar CRDe' 'Audi A4 1.8'\n",
      " 'Mercedes Benz GLA' 'Land Rover Freelander' 'Renault Kwid RXT'\n",
      " 'Tata Aria Pleasure' 'Mercedes Benz B' 'Datsun GO T' 'Honda Jazz VX'\n",
      " 'Chevrolet Tavera Neo' 'Hyundai Eon Sportz' 'Tata Sumo Gold'\n",
      " 'Chevrolet Enjoy 1.4' 'Nissan Terrano XL' 'Maruti Suzuki Maruti'\n",
      " 'Renault Kwid 1.0' 'Hyundai Accent GLX' 'Mahindra TUV300 T4'\n",
      " 'Honda Accord' 'Mahindra Scorpio 2.6' 'Honda Mobilio' 'Skoda Laura'\n",
      " 'Tata Manza Aura' 'Chevrolet Sail UVA' 'Audi A4 2.0' 'Hyundai Elantra SX'\n",
      " 'Mahindra KUV100 K8' 'Hyundai i10' 'Hyundai Accent' 'Hyundai Verna'\n",
      " 'Toyota Fortuner' 'Mahindra Bolero Power' 'Skoda Rapid Elegance'\n",
      " 'Tata Vista Quadrajet' 'Chevrolet Beat Diesel' 'Hyundai Verna 1.4'\n",
      " 'Maruti Suzuki Versa' 'Tata Indigo LX' 'Volkswagen Vento Konekt'\n",
      " 'Mercedes Benz C' 'Maruti Suzuki Omni' 'Hyundai Sonata Transform'\n",
      " 'Honda Jazz S' 'Mahindra Scorpio W' 'Honda Brio V' 'Mahindra TUV300 T8'\n",
      " 'Nissan X Trail' 'Ford Ikon 1.3' 'Toyota Fortuner 3.0' 'Tata Manza ELAN'\n",
      " 'Mercedes Benz A' 'Tata Indigo LS' 'Hyundai Verna 1.6' 'BMW 5 Series'\n",
      " 'Skoda Superb 1.8' 'Audi Q3 2.0' 'Ford Figo Duratorq'\n",
      " 'Mahindra Logan Diesel' 'Tata Nano GenX' 'Honda City SV'\n",
      " 'Ford Figo Petrol' 'Toyota Corolla H2' 'Hyundai Xcent Base'\n",
      " 'Hyundai Accent Executive' 'Tata Zest XE' 'Mahindra XUV500 W6'\n",
      " 'Tata Tigor Revotron' 'Maruti Suzuki 800' 'Honda Mobilio S' 'Tata Indica'\n",
      " 'Honda Brio VX' 'Tata Nano Lx' 'Jaguar XE XE' 'Hyundai Eon Magna'\n",
      " 'Hyundai Eon D' 'Maruti Suzuki Estilo' 'Mahindra Scorpio Vlx'\n",
      " 'Mitsubishi Lancer 1.8' 'Ford Fiesta SXi' 'Audi A6 2.0'\n",
      " 'Hyundai Getz Prime' 'Hyundai Santro' 'Chevrolet Beat PS'\n",
      " 'BMW X1 xDrive20d' 'Tata Nano' 'Chevrolet Cruze LTZ'\n",
      " 'Mahindra XUV500 W10' 'Hyundai Accent GLE' 'Force Motors One'\n",
      " 'Chevrolet Spark 1.0' 'Renault Duster 85PS' 'Chevrolet Enjoy'\n",
      " 'Jeep Wrangler Unlimited' 'Hyundai Verna VGT' 'Maruti Suzuki Celerio'\n",
      " 'Tata Zest Quadrajet' 'Hyundai i10 Era' 'Tata Indigo Marina'\n",
      " 'Hyundai Xcent SX' 'Tata Nano LX' 'Mahindra Xylo E8' 'Tata Manza Aqua'\n",
      " 'Tata Venture EX' 'Skoda Octavia Classic' 'Ford Ikon 1.6'\n",
      " 'Nissan Sunny XL' 'Volkswagen Polo Trendline' 'Hyundai Elantra 1.8'\n",
      " 'Tata Indica eV2' 'Jaguar XF 2.2' 'Audi Q5 2.0' 'BMW X1 sDrive20d'\n",
      " 'Maruti Suzuki S' 'Volkswagen Vento Comfortline' 'Mahindra KUV100'\n",
      " 'Volkswagen Jetta Comfortline' 'Volvo S80 Summum' 'BMW X1'\n",
      " 'Renault Duster RxL' 'Honda WR V' 'Mahindra Scorpio LX'\n",
      " 'Audi A3 Cabriolet' 'Hyundai Santro AE' 'Mahindra Xylo D2'\n",
      " 'Hyundai Getz GLE' 'Nissan Micra XL' 'Chevrolet Tavera LS'\n",
      " 'Tata Tiago Revotron' 'Tata Tiago Revotorq' 'Ford Fusion 1.4'\n",
      " 'Fiat Linea Emotion' 'Toyota Corolla' 'Tata Sumo Grande'\n",
      " 'Volkswagen Polo Highline1.2L' 'Hyundai Creta' 'Tata Bolt XM'\n",
      " 'Datsun Go Plus' 'Ford Endeavor 4x4' 'Mahindra Logan'\n",
      " 'Chevrolet Sail 1.2' 'Tata Manza' 'Toyota Etios G' 'Toyota Qualis'\n",
      " 'Mahindra Quanto C4' 'Hyundai i20 Select' 'Hyundai Getz' 'Skoda Fabia'\n",
      " 'Tata Zest XM']\n"
     ]
    }
   ],
   "source": [
    "print(car['name'].unique())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2007 2006 2014 2012 2013 2016 2015 2010 2017 2008 2018 2011 2019 2009\n",
      " 2005 2000 2003 2004 1995 2002 2001]\n"
     ]
    }
   ],
   "source": [
    "print(car['year'].unique())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 45000     40  28000  36000  41000  25000  24530  60000  30000  32000\n",
      "  48660   4000  16934  43000  35550  39522  39000  55000  72000  15975\n",
      "  70000  23452  35522  48508  15487  82000  20000  68000  38000  27000\n",
      "  33000  46000  16000  47000  35000  30874  15000  29685 130000  19000\n",
      "  54000  13000  38200  22000  50000  13500   3600  45863  60500  12500\n",
      "  18000  13349  29000  44000  42000  14000  49000  36200  51000 104000\n",
      "  33333  33600   5600   7500  24330  65480 200000  59000  99000   2800\n",
      "  21000  11000  66000   3000   7000  38500  37200  43200  24800  45872\n",
      "  40000  11400  97200  52000  31000 175430  37000  65000   3350  75000\n",
      "  62000  73000   2200  54870  34580  97000     60  80200   3200      0\n",
      "   5000    588  71200 175400   9300  56758  10000  56450  56000  32700\n",
      "   9000     73 160000  58559  57000 170000  80000   6821  23000  34000\n",
      "   1800 400000  48000  90000  12000  69900 166000    122  36469   7800\n",
      "  24695  15141  59910 100000   4500 129000    300 131000 111111  59466\n",
      "  25500  44005   2110  43222 100200     65 140000 103553  58000 120000\n",
      "  49800    100  81876   6020  55700  18500  53000  35500  22134   1000\n",
      "   8500  87000   6000   8000  55800  56400  72160  11500 133000   2000\n",
      "  88000  65422 117000 150000  10750   6800   9800  57923  30201   6200\n",
      "  37518  24652    383  95000   3528  52500  47900  52800 195000  48008\n",
      "  48247   9400  64000   2137  10544 147000  90001  48006  74000  85000\n",
      "  29500  39700  67000  19336  60105  45933 102563  28600  41800 116000\n",
      "  42590   7400  54500  76000  11523  38600  95500  37458  85960  12516\n",
      "  30600   2550  62500  69000  28400  68485   3500  85455  63000   1600\n",
      "  77000  26500   2875  13900   1500   2450   1625  33400  60123 137495\n",
      "  91200 146000 100800   2100   2500 132000]\n"
     ]
    }
   ],
   "source": [
    "print(car['kms_driven'].unique())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "                       name   company  year   Price  kms_driven fuel_type\n0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n4                 Ford Figo      Ford  2012  175000       41000    Diesel\n..                      ...       ...   ...     ...         ...       ...\n811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n\n[816 rows x 6 columns]",
      "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>name</th>\n      <th>company</th>\n      <th>year</th>\n      <th>Price</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Santro Xing</td>\n      <td>Hyundai</td>\n      <td>2007</td>\n      <td>80000</td>\n      <td>45000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Mahindra Jeep CL550</td>\n      <td>Mahindra</td>\n      <td>2006</td>\n      <td>425000</td>\n      <td>40</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hyundai Grand i10</td>\n      <td>Hyundai</td>\n      <td>2014</td>\n      <td>325000</td>\n      <td>28000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Ford EcoSport Titanium</td>\n      <td>Ford</td>\n      <td>2014</td>\n      <td>575000</td>\n      <td>36000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ford Figo</td>\n      <td>Ford</td>\n      <td>2012</td>\n      <td>175000</td>\n      <td>41000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td>Maruti Suzuki Ritz</td>\n      <td>Maruti</td>\n      <td>2011</td>\n      <td>270000</td>\n      <td>50000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>812</th>\n      <td>Tata Indica V2</td>\n      <td>Tata</td>\n      <td>2009</td>\n      <td>110000</td>\n      <td>30000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>813</th>\n      <td>Toyota Corolla Altis</td>\n      <td>Toyota</td>\n      <td>2009</td>\n      <td>300000</td>\n      <td>132000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>814</th>\n      <td>Tata Zest XM</td>\n      <td>Tata</td>\n      <td>2018</td>\n      <td>260000</td>\n      <td>27000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>815</th>\n      <td>Mahindra Quanto C8</td>\n      <td>Mahindra</td>\n      <td>2013</td>\n      <td>390000</td>\n      <td>40000</td>\n      <td>Diesel</td>\n    </tr>\n  </tbody>\n</table>\n<p>816 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 816 entries, 0 to 815\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   name        816 non-null    object\n",
      " 1   company     816 non-null    object\n",
      " 2   year        816 non-null    int32 \n",
      " 3   Price       816 non-null    int32 \n",
      " 4   kms_driven  816 non-null    int32 \n",
      " 5   fuel_type   816 non-null    object\n",
      "dtypes: int32(3), object(3)\n",
      "memory usage: 28.8+ KB\n"
     ]
    }
   ],
   "source": [
    "car.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "                       name company         year         Price     kms_driven  \\\ncount                   816     816   816.000000  8.160000e+02     816.000000   \nunique                  254      25          NaN           NaN            NaN   \ntop     Maruti Suzuki Swift  Maruti          NaN           NaN            NaN   \nfreq                     51     221          NaN           NaN            NaN   \nmean                    NaN     NaN  2012.444853  4.117176e+05   46275.531863   \nstd                     NaN     NaN     4.002992  4.751844e+05   34297.428044   \nmin                     NaN     NaN  1995.000000  3.000000e+04       0.000000   \n25%                     NaN     NaN  2010.000000  1.750000e+05   27000.000000   \n50%                     NaN     NaN  2013.000000  2.999990e+05   41000.000000   \n75%                     NaN     NaN  2015.000000  4.912500e+05   56818.500000   \nmax                     NaN     NaN  2019.000000  8.500003e+06  400000.000000   \n\n       fuel_type  \ncount        816  \nunique         3  \ntop       Petrol  \nfreq         428  \nmean         NaN  \nstd          NaN  \nmin          NaN  \n25%          NaN  \n50%          NaN  \n75%          NaN  \nmax          NaN  ",
      "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>name</th>\n      <th>company</th>\n      <th>year</th>\n      <th>Price</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>816</td>\n      <td>816</td>\n      <td>816.000000</td>\n      <td>8.160000e+02</td>\n      <td>816.000000</td>\n      <td>816</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>254</td>\n      <td>25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>Maruti Suzuki Swift</td>\n      <td>Maruti</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>51</td>\n      <td>221</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2012.444853</td>\n      <td>4.117176e+05</td>\n      <td>46275.531863</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.002992</td>\n      <td>4.751844e+05</td>\n      <td>34297.428044</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1995.000000</td>\n      <td>3.000000e+04</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2010.000000</td>\n      <td>1.750000e+05</td>\n      <td>27000.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.000000</td>\n      <td>2.999990e+05</td>\n      <td>41000.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2015.000000</td>\n      <td>4.912500e+05</td>\n      <td>56818.500000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2019.000000</td>\n      <td>8.500003e+06</td>\n      <td>400000.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.describe(include='all')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "car=car[car['Price']<6000000]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Hyundai', 'Mahindra', 'Ford', 'Maruti', 'Skoda', 'Audi', 'Toyota',\n       'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n       'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat', 'Force',\n       'Mercedes', 'Land', 'Jaguar', 'Jeep', 'Volvo'], dtype=object)"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car['company'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x700 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,7))\n",
    "ax=sns.boxplot(x='company',y='Price',data=car)\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 6.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 10.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 7.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\Harsh\\AppData\\Local\\Temp\\ipykernel_25580\\3665497750.py:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 9.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 9.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "c:\\python projects\\car price prediter\\venv\\lib\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 5.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x1000 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,10))\n",
    "ax=sns.swarmplot(x='year',y='Price',data=car)\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x18b1f91fe50>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1050x700 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='fuel_type', ylabel='Price'>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x700 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAJpCAYAAADc2OlQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABQHUlEQVR4nO39fXzddWE3/r9OUugpNwmjhRaSmiIKotwWWlvYvGFslYmDMSvjd8rdRC93gcO2fJ14W1BXFbKiF0zmvISxBmFMhcmUyoUX6kKZKYjgHRMmKdG2YhwJMFMgOb8/ehGIbYBCkk9Oz/P5eJzHI+f9+XzOeZ0QTnNeeX8+71K1Wq0GAAAAgLrRUHQAAAAAACaWQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgztR1IfTtb387b3nLW7LvvvumVCrlhhtu2O7HqFarueSSS3LAAQdk6tSpaWlpycc//vGxDwsAAAAwRqYUHaBIjz/+eA477LD8+Z//eU4++eQX9RjnnXdevvGNb+SSSy7JIYcckl//+tf59a9/PcZJAQAAAMZOqVqtVosOMRmUSqV85StfyUknnTQ8tnnz5nzgAx/IF7/4xTzyyCM5+OCD88lPfjJveMMbkiQ//vGPc+ihh+YHP/hBDjzwwGKCAwAAAGynuj5l7Pmce+65Wbt2ba699trcc889Wbx4cd70pjflpz/9aZLkq1/9al7+8pfnpptuyn777Zc5c+bk7LPPNkMIAAAAmNQUQqNYv359rrzyylx//fX5vd/7vey///45//zz87u/+7u58sorkyT/+Z//me7u7lx//fW5+uqrc9VVV+XOO+/MW9/61oLTAwAAAIyurq8h9FzuvffeDA4O5oADDhgxvnnz5kyfPj1JMjQ0lM2bN+fqq68e3u9//+//nSOPPDL33Xef08gAAACASUkhNIrHHnssjY2NufPOO9PY2Dhi22677ZYk2WeffTJlypQRpdFBBx2UZMsMI4UQAAAAMBkphEZxxBFHZHBwML/85S/ze7/3e9vc55hjjslTTz2VBx54IPvvv3+S5D/+4z+SJG1tbROWFQAAAGB71PUqY4899ljuv//+JFsKoL/5m7/JG9/4xuy555552cteliVLlqSzszPt7e054ogj8vDDD+fWW2/NoYcemje/+c0ZGhrKvHnzsttuu+XSSy/N0NBQzjnnnDQ1NeUb3/hGwa8OAAAAYNvquhC67bbb8sY3vnGr8TPOOCNXXXVVnnzyyXzsYx/L1VdfnZ///OeZMWNGFixYkAsvvDCHHHJIkuQXv/hF3v3ud+cb3/hGdt111xx//PFpb2/PnnvuOdEvBwAAAOAFqetCCAAAAKAeWXYeAAAAoM4ohAAAAADqTN2tMjY0NJRf/OIX2X333VMqlYqOAwAAADAmqtVqHn300ey7775paHjuOUB1Vwj94he/yOzZs4uOAQAAADAuHnroobS2tj7nPnVXCO2+++5JtnxzmpqaCk4DAAAAMDb6+/sze/bs4e7judRdIfT0aWJNTU0KIQAAAGCH80IukeOi0gAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQlDjOjs7s3jx4nR2dhYdBQAAgBqhEIIaNjAwkPb29mzatCnt7e0ZGBgoOhIAAAA1QCEENWz16tXp7e1NkvT29qajo6PgRAAAANQChRDUqJ6ennR0dKRarSZJqtVqOjo60tPTU3AyAAAAJjuFENSgarWaVatWjTr+dEkEAAAA26IQghrU3d2drq6uDA4OjhgfHBxMV1dXuru7C0oGAABALVAIQQ1qa2vLvHnz0tjYOGK8sbEx8+fPT1tbW0HJAAAAqAUKIahBpVIpS5cuHXW8VCoVkAoAAIBaoRCCGtXa2ppKpTJc/pRKpVQqlbS0tBScDAAAgMlOIQQ1bMmSJZk+fXqSZMaMGalUKgUnAgAAoBYohKCGlcvlLF++PDNnzsyyZctSLpeLjgQAAEANKFXrbH3q/v7+NDc3p6+vL01NTUXHAQAAABgT29N5mCEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcKLYQ++9nP5tBDD01TU1OampqycOHCfP3rX3/OY66//vq86lWvSrlcziGHHJKvfe1rE5QWAAAAYMdQaCHU2tqaT3ziE7nzzjuzbt26HHvssTnxxBPzwx/+cJv733777Tn11FPz9re/Pd/73vdy0kkn5aSTTsoPfvCDCU4OAAAAULtK1Wq1WnSIZ9tzzz1z8cUX5+1vf/tW20455ZQ8/vjjuemmm4bHFixYkMMPPzxXXHHFC3r8/v7+NDc3p6+vL01NTWOWGwAAAKBI29N5TJprCA0ODubaa6/N448/noULF25zn7Vr1+a4444bMbZo0aKsXbt21MfdvHlz+vv7R9wAAAAA6lnhhdC9996b3XbbLVOnTs273vWufOUrX8mrX/3qbe67cePGzJw5c8TYzJkzs3HjxlEff+XKlWlubh6+zZ49e0zzAwAAANSawguhAw88MHfffXf+/d//PX/xF3+RM844Iz/60Y/G7PEvuOCC9PX1Dd8eeuihMXtsAAAAgFo0pegAO++8c17xilckSY488sh0dXXl05/+dP7u7/5uq31nzZqVTZs2jRjbtGlTZs2aNerjT506NVOnTh3b0AAAAAA1rPAZQr9taGgomzdv3ua2hQsX5tZbbx0xdsstt4x6zSEAAAAAtlboDKELLrggxx9/fF72spfl0UcfzTXXXJPbbrsta9asSZKcfvrpaWlpycqVK5Mk5513Xl7/+tenvb09b37zm3Pttddm3bp1+dznPlfkywAAAACoKYUWQr/85S9z+umnZ8OGDWlubs6hhx6aNWvW5A/+4A+SJOvXr09DwzOTmI4++uhcc801+eAHP5j3v//9eeUrX5kbbrghBx98cFEvAQAAAKDmlKrVarXoEBOpv78/zc3N6evrS1NTU9FxAAAAAMbE9nQek+4aQgAAAACML4UQAAAAQJ1RCAEAAADUGYUQAAAAQJ1RCAEAjKPOzs4sXrw4nZ2dRUcBABimEAIAGCcDAwNpb2/Ppk2b0t7enoGBgaIjAQAkUQgBAIyb1atXp7e3N0nS29ubjo6OghMBAGyhEAIAGAc9PT3p6OhItVpNklSr1XR0dKSnp6fgZAAACiEAgDFXrVazatWqUcefLokAAIqiEAIAGGPd3d3p6urK4ODgiPHBwcF0dXWlu7u7oGQAAFsohAAAxlhbW1vmzZuXxsbGEeONjY2ZP39+2traCkoGALCFQggAYIyVSqUsXbp01PFSqVRAKgCAZyiEAADGQWtrayqVynD5UyqVUqlU0tLSUnAyAACFEADAuFmyZEmmT5+eJJkxY0YqlUrBiQAAtlAIAQCMk3K5nOXLl2fmzJlZtmxZyuVy0ZEAAJIkpWqdrXva39+f5ubm9PX1pampqeg4AAAAAGNiezoPM4QAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKISgxnV2dmbx4sXp7OwsOgoAAAA1QiEENWxgYCDt7e3ZtGlT2tvbMzAwUHQkAAAAaoBCCGrY6tWr09vbmyTp7e1NR0dHwYkAAACoBQohqFE9PT3p6OhItVpNklSr1XR0dKSnp6fgZAAAAEx2CiGoQdVqNatWrRp1/OmSCAAAALZFIQQ1qLu7O11dXRkcHBwxPjg4mK6urnR3dxeUDAAAgFqgEIIa1NbWlnnz5qWxsXHEeGNjY+bPn5+2traCkgEAAFALFEJQg0qlUpYuXTrqeKlUKiAVAAAAtUIhBDWqtbU1lUpluPwplUqpVCppaWkpOBkAAACTnUIIatiSJUsyffr0JMmMGTNSqVQKTgQAAEAtUAhBDSuXy1m+fHlmzpyZZcuWpVwuFx0JAACAGlCq1tn61P39/Wlubk5fX1+ampqKjgMAAAAwJran8zBDCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOKIQAAAAA6oxCCAAAAKDOFFoIrVy5MvPmzcvuu++evffeOyeddFLuu+++5zzmqquuSqlUGnErl8sTlBgAAACg9hVaCH3rW9/KOeeckzvuuCO33HJLnnzyyfzhH/5hHn/88ec8rqmpKRs2bBi+dXd3T1BiAAAAgNo3pcgnv/nmm0fcv+qqq7L33nvnzjvvzOte97pRjyuVSpk1a9Z4xwMAAADYIU2qawj19fUlSfbcc8/n3O+xxx5LW1tbZs+enRNPPDE//OEPR9138+bN6e/vH3EDAAAAqGeTphAaGhrKe97znhxzzDE5+OCDR93vwAMPzBe+8IXceOONWb16dYaGhnL00Uenp6dnm/uvXLkyzc3Nw7fZs2eP10sAAAAAqAmlarVaLTpEkvzFX/xFvv71r+ff/u3f0tra+oKPe/LJJ3PQQQfl1FNPzUc/+tGttm/evDmbN28evt/f35/Zs2enr68vTU1NY5IdAAAAoGj9/f1pbm5+QZ1HodcQetq5556bm266Kd/+9re3qwxKkp122ilHHHFE7r///m1unzp1aqZOnToWMQEAAAB2CIWeMlatVnPuuefmK1/5Sr75zW9mv/322+7HGBwczL333pt99tlnHBICAAAA7HgKnSF0zjnn5JprrsmNN96Y3XffPRs3bkySNDc3Z9q0aUmS008/PS0tLVm5cmWS5KKLLsqCBQvyile8Io888kguvvjidHd35+yzzy7sdQAAAADUkkILoc9+9rNJkje84Q0jxq+88sqceeaZSZL169enoeGZiUz/9V//lXe84x3ZuHFjfud3fidHHnlkbr/99rz61a+eqNgAAAAANW3SXFR6omzPBZYAAAAAasX2dB6TZtl5AAAAACaGQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAYBx1dnZm8eLF6ezsLDoKAMAwhRAAwDgZGBhIe3t7Nm3alPb29gwMDBQdCQAgiUIIAGDcrF69Or29vUmS3t7edHR0FJwIAGALhRAAwDjo6elJR0dHqtVqkqRaraajoyM9PT0FJwMAUAgBAIy5arWaVatWjTr+dEkEAFAUhRAAwBjr7u5OV1dXBgcHR4wPDg6mq6sr3d3dBSUDANhCIQQAMMba2toyb968NDY2jhhvbGzM/Pnz09bWVlAyAIAtFEIAAGOsVCpl6dKlo46XSqUCUgEAPEMhBAAwDlpbW1OpVIbLn1KplEqlkpaWloKTAQAohAAAxs2SJUsyffr0JMmMGTNSqVQKTgQAsIVCCABgnJTL5SxfvjwzZ87MsmXLUi6Xi44EAJAkKVXrbN3T/v7+NDc3p6+vL01NTUXHAQAAABgT29N5mCEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEAAAAUGcUQgAAAAB1RiEEADCOOjs7s3jx4nR2dhYdBQBgmEIIAGCcDAwMpL29PZs2bUp7e3sGBgaKjgQAkEQhBAAwblavXp3e3t4kSW9vbzo6OgpOBACwhUIIAGAc9PT0pKOjI9VqNUlSrVbT0dGRnp6egpMBACiEAADGXLVazapVq0Ydf7okAgAoikIIAGCMdXd3p6urK4ODgyPGBwcH09XVle7u7oKSAQBsoRACABhjbW1tmTdvXhobG0eMNzY2Zv78+WlraysoGQDAFgohAIAxViqVsnTp0lHHS6VSAakAAJ6hEAIAGAetra2pVCrD5U+pVEqlUklLS0vByQAAFEIAAONmyZIlmT59epJkxowZqVQqBScCANhCIQQAME7K5XKWL1+emTNnZtmyZSmXy0VHAgBIkpSqdbbuaX9/f5qbm9PX15empqai4wAAAACMie3pPMwQAgAAAKgzCiEAAACAOqMQAgAAAKgzCiEAgHHU2dmZxYsXp7Ozs+goAADDFEIAAONkYGAg7e3t2bRpU9rb2zMwMFB0JACAJAohAIBxs3r16vT29iZJent709HRUXAiAIAtFEIAAOOgp6cnHR0dqVarSZJqtZqOjo709PQUnAwAQCEEADDmqtVqVq1aNer40yURAEBRFEIAAGOsu7s7XV1dGRwcHDE+ODiYrq6udHd3F5QMAGALhRAAwBhra2vLvHnz0tjYOGK8sbEx8+fPT1tbW0HJAAC2UAgBAIyxUqmUpUuXjjpeKpUKSAUA8AyFEADAOGhtbU2lUhkuf0qlUiqVSlpaWgpOBgCgEAIAGDdLlizJ9OnTkyQzZsxIpVIpOBEAwBYKIQCAcVIul7N8+fLMnDkzy5YtS7lcLjoSAECSpFSts3VP+/v709zcnL6+vjQ1NRUdBwAAAGBMbE/nYYYQAAAAQJ1RCAEAAADUGYUQAAAAQJ1RCAEAAADUGYUQAMA46uzszOLFi9PZ2Vl0FACAYYUWQitXrsy8efOy++67Z++9985JJ52U++6773mPu/766/OqV70q5XI5hxxySL72ta9NQFoAgO0zMDCQ9vb2bNq0Ke3t7RkYGCg6EgBAkoILoW9961s555xzcscdd+SWW27Jk08+mT/8wz/M448/Puoxt99+e0499dS8/e1vz/e+972cdNJJOemkk/KDH/xgApMDADy/1atXp7e3N0nS29ubjo6OghMBAGxRqlar1aJDPO3hhx/O3nvvnW9961t53etet819TjnllDz++OO56aabhscWLFiQww8/PFdcccXzPkd/f3+am5vT19eXpqamMcsOAPBsPT09Oe200zI4ODg8NmXKlFx99dVpbW0tMBkAsKPans5jUl1DqK+vL0my5557jrrP2rVrc9xxx40YW7RoUdauXbvN/Tdv3pz+/v4RNwCA8VStVrNq1apRxyfR3+MAgDo1aQqhoaGhvOc978kxxxyTgw8+eNT9Nm7cmJkzZ44YmzlzZjZu3LjN/VeuXJnm5ubh2+zZs8c0NwDAb+vu7k5XV9eI2UFJMjg4mK6urnR3dxeUDABgi0lTCJ1zzjn5wQ9+kGuvvXZMH/eCCy5IX1/f8O2hhx4a08cHAPhtbW1tmTdvXhobG0eMNzY2Zv78+WlraysoGQDAFpOiEDr33HNz00035f/+3//7vOfUz5o1K5s2bRoxtmnTpsyaNWub+0+dOjVNTU0jbgAA46lUKmXp0qWjjpdKpQJSAQA8o9BCqFqt5txzz81XvvKVfPOb38x+++33vMcsXLgwt95664ixW265JQsXLhyvmAAA2621tTWVSmW4/CmVSqlUKmlpaSk4GQBAwYXQOeeck9WrV+eaa67J7rvvno0bN2bjxo35zW9+M7zP6aefngsuuGD4/nnnnZebb7457e3t+clPfpIVK1Zk3bp1Offcc4t4CQAAo1qyZEmmT5+eJJkxY0YqlUrBiQAAtii0EPrsZz+bvr6+vOENb8g+++wzfLvuuuuG91m/fn02bNgwfP/oo4/ONddck8997nM57LDD8s///M+54YYbnvNC1AAARSiXy1m+fHlmzpyZZcuWpVwuFx0JACBJUqrW2bqn/f39aW5uTl9fn+sJAQAAADuM7ek8JsVFpQEAAACYOAohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAGEednZ1ZvHhxOjs7i44CADBMIQQ1btGiRXnd616XRYsWFR0FgN8yMDCQ9vb2bNq0Ke3t7RkYGCg6EgBAEoUQ1LQ77rgjv/nNb5Ikv/nNb3LHHXcUnAiAZ1u9enV6e3uTJL29veno6Cg4EQDAFgohqGHvfe97n/M+AMXp6elJR0dHqtVqkqRaraajoyM9PT0FJwMAUAhBzTr//PO3axyAiVOtVrNq1apRx58uiQAAiqIQgho0MDCQ7373u9vc9t3vftc1KgAK1t3dna6urgwODo4YHxwcTFdXV7q7uwtKBgCwhUIIatDy5ctf0nYAxldbW1vmzZuXxsbGEeONjY2ZP39+2traCkoGALCFQghqUHt7+0vaDsD4KpVKWbp06ajjpVKpgFQAAM9QCEENKpfLmT9//ja3LVy4MOVyeYITAfDbWltbU6lURoxVKpW0tLQUlAgA4BkKIahRl1xyyTbHP/nJT05wEgBG89a3vjUNDVt+3WpoaMif/umfFpwIAGALhRDUsE996lPPeR+AYv3zP//ziGXnv/SlLxWcCABgC4UQ1LAFCxZk2rRpSZJp06ZlwYIFBScC4Gk9PT3p6OgYUQh1dHSkp6en4GQAAMmUogMAL82aNWuKjgDAb6lWq1m1atWo45dccokLSwMAhTJDCABgjHV3d6erqyuDg4MjxgcHB9PV1ZXu7u6CkgEAbKEQAgAYY21tbZk3b14aGxtHjDc2Nmb+/Plpa2srKBkAwBYKIQCAMVYqlbJ06dJRx50uBgAUTSEEADAOWltbU6lUhsufUqmUSqWSlpaWgpMBACiEAADGzZIlSzJ9+vQkyYwZM1KpVApOBACwhUIIAGCclMvlLF++PDNnzsyyZctSLpeLjgQAkCQpVavVatEhJlJ/f3+am5vT19eXpqamouMAAAAAjInt6TzMEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAGEednZ1ZvHhxOjs7i44CADBMIQQAME4GBgbS3t6eTZs2pb29PQMDA0VHAgBIohACABg3q1evTm9vb5Kkt7c3HR0dBScCANhCIQQAMA56enrS0dGRarWaJKlWq+no6EhPT0/ByQAAFEIAAGOuWq1m1apVo44/XRIBABRFIQQAMMa6u7vT1dWVwcHBEeODg4Pp6upKd3d3QckAALZQCAEAjLG2trbMmzcvjY2NI8YbGxszf/78tLW1FZQMAGALhRAAwBgrlUpZunTpqOOlUqmAVAAAz1AIAQCMg9bW1lQqleHyp1QqpVKppKWlpeBkAAAKIQCAcbNkyZJMnz49STJjxoxUKpWCEwEAbKEQAgAYJ+VyOcuXL8/MmTOzbNmylMvloiMBACRJStU6W/e0v78/zc3N6evrS1NTU9FxAAAAAMbE9nQeZggBAAAA1BmFEAAAAECdUQgBAAAA1BmFEAAAAECdUQgBAAAA1JmXVAg98cQTue+++/LUU0+NVR4AAAAAxtmLKoT++7//O29/+9uzyy675DWveU3Wr1+fJHn3u9+dT3ziE2MaEAAAAICx9aIKoQsuuCDf//73c9ttt6VcLg+PH3fccbnuuuvGLBzw/Do7O7N48eJ0dnYWHQUAAIAa8aIKoRtuuCGXXXZZfvd3fzelUml4/DWveU0eeOCBMQsHPLeBgYG0t7dn06ZNaW9vz8DAQNGRAPgtinsAYDJ6UYXQww8/nL333nur8ccff3xEQQSMr9WrV6e3tzdJ0tvbm46OjoITAfBsinsAYLJ6UYXQUUcdlX/9138dvv90CfT5z38+CxcuHJtkwHPq6elJR0dHqtVqkqRaraajoyM9PT0FJwPgaYp7AGCymvJiDvrrv/7rHH/88fnRj36Up556Kp/+9Kfzox/9KLfffnu+9a1vjXVG4LdUq9WsWrVq1PFLLrnEbD2Ago1W3C9atCitra0FpwMA6t2LmiH0u7/7u7n77rvz1FNP5ZBDDsk3vvGN7L333lm7dm2OPPLIsc4I/Jbu7u50dXVlcHBwxPjg4GC6urrS3d1dUDIAkucv7p8uiQAAivKiZgglyf7775+///u/H8sswAvU1taWefPm5a677hpRCjU2NubII49MW1tbgekAeLq4/23PLu7nzJkz8cEAAP6fFzVD6Gtf+1rWrFmz1fiaNWvy9a9//SWHAp5bqVTK0qVLRx13uhhAsZ4u7hsbG0eMNzY2Zv78+Yp7AKBwL6oQet/73rfVqSrJlmnQ73vf+15yKOD5tba2plKpDJc/pVIplUolLS0tBScDQHEPAEx2L6oQ+ulPf5pXv/rVW42/6lWvyv333/+SQwEvzJIlSzJ9+vQkyYwZM1KpVApOBMDTFPcAwGT2ogqh5ubm/Od//udW4/fff3923XXXlxwKeGHK5XL+6I/+KA0NDTn++ONTLpeLjgTAsyjuAYDJ6kUVQieeeGLe85735IEHHhgeu//++7N8+fL88R//8ZiFA57bwMBAvva1r2VoaChf+9rXMjAwUHQkAJ6lXC5n+fLlmTlzZpYtW6a4BwAmjRdVCH3qU5/Krrvumle96lXZb7/9st9+++Wggw7K9OnTc8kll4x1RmAUq1evTm9vb5Kkt7c3HR0dBScC4Lf9+Mc/zsMPP5wf//jHRUcBABhWqlar1RdzYLVazS233JLvf//7mTZtWg499NC87nWvG+t8Y66/vz/Nzc3p6+tLU1NT0XHgRevp6clpp5024gLvU6ZMydVXX53W1tYCkwHwtEceeSQnnXRShoaG0tDQkBtuuCF77LFH0bEAgB3U9nQeL7oQqlUKIXYE1Wo1559/fu66664RhVBjY2Pmzp2bSy65xAo2AJPAOeeck3vvvXf4/qGHHprLLruswEQAwI5sezqPKS/0QT/zmc/kne98Z8rlcj7zmc88575/+Zd/+UIfFngRuru709XVtdX44OBgurq60t3dnTlz5kx8MACGrVu3bkQZlCT33HNP1q1bl6OOOqqgVAAAW7zgGUL77bdf1q1bl+nTp2e//fYb/QFLpW2uQDZZmCHEjuC5ZggdeeSRufjii80QAijQ0NBQ/viP/zj9/f1bbWtqasq//Mu/pKHhRV3KEQBgVOMyQ+hnP/vZNr8GJl6pVMrSpUtz2mmnbXNcGQRQrLVr126zDEq2/KK2du3aHHPMMROcCgDgGdv9p6knn3wy+++//5islPHtb387b3nLW7LvvvumVCrlhhtueM79b7vttpRKpa1uGzdufMlZoNa0trbm1a9+9YixV7/61WlpaSkoEQBPW7hwYXbbbbdtbtttt92ycOHCCU4EADDSdhdCO+20UwYGBsbkyR9//PEcdthhufzyy7fruPvuuy8bNmwYvu29995jkgdqSU9PT374wx+OGPvhD3+Ynp6eghIB8LRSqTRqQd/a2momJwBQuBd18vo555yTT37yk3nqqade0pMff/zx+djHPpY/+ZM/2a7j9t5778yaNWv49lzn4G/evDn9/f0jblDrqtVqVq1atc0PFKtWrUqdLR4IMOl0d3fnvvvu2+a2n/zkJ+nu7p7gRAAAI73gawg9W1dXV2699dZ84xvfyCGHHJJdd911xPYvf/nLYxJuNIcffng2b96cgw8+OCtWrHjOc/BXrlyZCy+8cFzzwESzyhjA5DZ79uw0NjaOuPD/0xobGzN79uwCUgEAPONFzRDaY4898qd/+qdZtGhR9t133zQ3N4+4jZd99tknV1xxRb70pS/lS1/6UmbPnp03vOENueuuu0Y95oILLkhfX9/w7aGHHhq3fDBR2traMm/evDQ2No4Yb2xszPz589PW1lZQMgCS5I477thmGZRsKe/vuOOOCU4EADDSds0QGhoaysUXX5z/+I//yBNPPJFjjz02K1asyLRp08Yr3wgHHnhgDjzwwOH7Rx99dB544IGsWrUq//iP/7jNY6ZOnZqpU6dOSD6YKFYZA5jc9tlnn5e0HQBgvG3XDKGPf/zjef/735/ddtstLS0t+cxnPpNzzjlnvLK9IPPnz8/9999faAYoQmtrayqVynD5UyqVUqlUrDIGMAm0tbWNeo3DhoYGMzkBgMJtVyF09dVX52//9m+zZs2a3HDDDfnqV7+ajo6ODA0NjVe+53X33Xf7Kxt1a8mSJZk+fXqSZMaMGalUKgUnAiDZcsrYaL8fDQ0NOWUMACjcdp0ytn79+vzRH/3R8P3jjjsupVIpv/jFL9La2rrdT/7YY4+NmN3zs5/9LHfffXf23HPPvOxlL8sFF1yQn//857n66quTJJdeemn222+/vOY1r8nAwEA+//nP55vf/Ga+8Y1vbPdzw46gXC5n+fLlufTSS/Oe97wn5XK56EgAJFm4cGGampq2ubppc3NzFi5cWEAqAIBnbFch9NRTT231gXOnnXbKk08++aKefN26dXnjG984fH/ZsmVJkjPOOCNXXXVVNmzYkPXr1w9vf+KJJ7J8+fL8/Oc/zy677JJDDz00/+f//J8RjwH15phjjnnOlfYAmHgNDQ1ZsWLF8O82z3bhhReOejoZAMBEKVWr1eoL3bmhoSHHH3/8iIs0f/WrX82xxx47Yun58V52/qXo7+9Pc3Nz+vr60tTUVHQcAGAHds455+Tee+8dvn/ooYfmsssuKzARALAj257OY7sKobPOOusF7XfllVe+0IeccAohAGCiPPLIIznppJMyNDSUhoaG3HDDDdljjz2KjgUA7KC2p/PYrlPGJnPRAwAw2eyxxx5ZsmRJVq9enSVLliiDAIBJY7tmCO0IzBACAAAAdkTb03m4oiHUuM7OzixevDidnZ1FRwEAAKBGKISghg0MDKS9vT2bNm1Ke3t7BgYGio4EAABADVAIQQ1bvXp1ent7kyS9vb3p6OgoOBEAAAC1QCEENaqnpycdHR15+jJg1Wo1HR0d6enpKTgZAAAAk51CCGpQtVrNqlWrRh2vs2vFAwAAsJ0UQlCDuru709XVlcHBwRHjg4OD6erqSnd3d0HJAAAAqAUKIahBbW1tmTdvXhobG0eMNzY2Zv78+WlraysoGQAAALVAIQQ1qFQqZenSpaOOl0qlAlIBAABQKxRCUKNaW1tTqVSGy59SqZRKpZKWlpaCkwHwbJ2dnVm8eHE6OzuLjgIAMEwhBDVsyZIlmT59epJkxowZqVQqBScC4NkGBgbS3t6eTZs2pb29PQMDA0VHAgBIohCCmlYul7N8+fLMnDkzy5YtS7lcLjoSAM+yevXq9Pb2Jkl6e3vT0dFRcCIAgC1K1Tpbn7q/vz/Nzc3p6+tLU1NT0XEAgB1UT09PTjvttBErQk6ZMiVXX311WltbC0wGAOyotqfzMEMIAGCMVavVrFq1atTxOvt7HAAwCSmEAADGWHd3d7q6ukbMDkqSwcHBdHV1pbu7u6BkAABbKIQAAMZYW1tb5s2bl8bGxhHjjY2NmT9/ftra2gpKBgCwhUIIAGCMlUqlLF26dNTxUqlUQCoAgGcohAAAxkFra2sqlcpw+VMqlVKpVNLS0lJwMgAAhRAAwLhZsmRJyuVykqRcLqdSqRScCABgC4UQAMA4GRgYyMDAwFZfAwAUTSEEADBOPvCBDwwvMV+tVvPBD36w4EQAAFsohAAAxsG6dety7733jhi75557sm7duoISAQA8QyEEADDGhoaGsmLFim1uW7FiRYaGhiY2EADAb1EIAQCMsbVr16a/v3+b2/r7+7N27doJTgQAMJJCCABgjC1cuDBNTU3b3Nbc3JyFCxdOcCIAgJEUQgAAY6yhoWHUU8YuvPDCNDT4FQwAKJbfRgAAxsFRRx2VQw45ZMTYoYcemrlz5xaUCADgGQohAIBx8vGPf3x4NlBDQ0M+9rGPFZwIAGALhRAAwDjZY489smTJkjQ0NGTJkiXZY489io4EAJBEIQQAMK4OOuig7LXXXjnooIOKjgIAMEwhBAAwTgYGBtLe3p5Nmzalvb09AwMDRUcCAEiiEAIAGDerV69Ob29vkqS3tzcdHR0FJwIA2EIhBAAwDnp6etLR0ZFqtZokqVar6ejoSE9PT8HJAAAUQlDzOjs7s3jx4nR2dhYdBYD/p1qtZtWqVaOOP10SAQAURSEENcy1KQAmp+7u7nR1dWVwcHDE+ODgYLq6utLd3V1QMgCALRRCUMNcmwJgcmpra8u8efO2uW3+/Plpa2ub4EQAACMphKBGuTYFwORVKpVy3HHHbXPbcccdl1KpNMGJAABGUghBDXJtCoDJbWhoKJdffvk2t1122WUZGhqa4EQAACMphKAGuTYFwOS2du3a9Pf3b3Nbf39/1q5dO8GJAABGUghBDXr62hSNjY0jxhsbG12bAmASWLhwYZqamra5rbm5OQsXLpzgRAAAIymEoAaVSqUsXbp01HHXpgAoVkNDQ1asWLHNbRdeeGEaGvwKBgAUy28jUKNaW1tTqVSGy59SqZRKpZKWlpaCkwGQJEcddVQOOeSQEWOHHnpo5s6dW1AiAIBnKISghi1ZsiTTp09PksyYMSOVSqXgRAA824c//OER9z/0oQ8VlAQAYCSFENSwcrmc5cuXZ+bMmVm2bFnK5XLRkQB4lq9+9asj7t90000FJQEAGKlUrbP1qfv7+9Pc3Jy+vr5RL/YIAPBS9fT05LTTThuxIuSUKVNy9dVXp7W1tcBkAMCOans6DzOEAADGWLVazapVq0Ydr7O/xwEAk5BCCABgjHV3d6erq2vE7KAkGRwcTFdXV7q7uwtKBgCwhUIIAGCMtbW1Zd68ecMrQT6tVCpl/vz5aWtrKygZAMAWCiEAgDFWKpVy6qmnbnVqWLVazamnnrpVUQQAMNEUQgAAY6xareaLX/ziNmcIXXPNNa4hBAAUTiEEADDGnr6G0LZmCLmGEAAwGSiEAADG2NPXEGpsbBwx3tjY6BpCAMCkoBACABhjpVIpS5cuHXXcNYQAgKIphAAAxkFra2sqlcqIsUqlkpaWloISAQA8QyEEADBO3vKWt4y4f8IJJxSUBABgJIUQAMA4ueiii0bc/+hHP1pQEgCAkRRCAADjYN26dbn33ntHjN1zzz1Zt25dQYkAAJ6hEAIAGGNDQ0NZsWLFNretWLEiQ0NDExsIAOC3KIQAAMbY2rVr09/fv81t/f39Wbt27QQnAgAYSSEEADDGFixYkMbGxm1ua2xszIIFCyY4EQDASAohAIAx9tBDD2VwcHCb2wYHB/PQQw9NcCIAgJEUQgAAY6ytrS3z5s3b5rb58+enra1tghMBAIykEAIAGGOlUilLly5NQ8PIX7UaGxuzdOnSlEqlgpIBAGyhEAIAGAetra1ZsmTJiLElS5akpaWloEQAAM+YUnQAAKA2VavVDAwMFB1jUjv55JPzr//6r+nt7c2MGTNy8skn5ze/+U3RsSalcrls5hQATCCFENS4z3/+81m9enWWLFmSs88+u+g4QB0ZGBjIokWLio5RM371q1/lxBNPLDrGpLVmzZpMmzat6BgAUDcKPWXs29/+dt7ylrdk3333TalUyg033PC8x9x2222ZO3dupk6dmle84hW56qqrxj0nTFaPPPJIVq9enaGhoaxevTqPPPJI0ZEAAACoAYXOEHr88cdz2GGH5c///M9z8sknP+/+P/vZz/LmN78573rXu9LR0ZFbb701Z599dvbZZx9/oaQufeADH8jQ0FCSZGhoKB/84Adz2WWXFZwKqBflcjlr1qwpOsakNjAwMDwr6MYbb0y5XC440eTlewMAE6vQQuj444/P8ccf/4L3v+KKK7Lffvulvb09SXLQQQfl3/7t37Jq1SqFEHVn3bp1uffee0eM3XPPPVm3bl2OOuqoglIB9aRUKjnFZzuUy2XfLwBg0qipVcbWrl2b4447bsTYokWLsnbt2lGP2bx5c/r7+0fcoNYNDQ1lxYoV29y2YsWK4VlDAAAAsC01VQht3LgxM2fOHDE2c+bM9Pf3j7pix8qVK9Pc3Dx8mz179kREhXG1du3aUcvN/v7+5yxJAQAAoKYKoRfjggsuSF9f3/DtoYceKjoSvGQLFy5MU1PTNrc1Nzdn4cKFE5wIAACAWlJThdCsWbOyadOmEWObNm1KU1PTqOfkT506NU1NTSNuUOsaGhpGPWXswgsvTENDTf2vDQAAwASrqU+NCxcuzK233jpi7JZbbjEbgrp01FFH5ZBDDhkxduihh2bu3LkFJQIAAKBWFFoIPfbYY7n77rtz9913J9myrPzdd9+d9evXJ9lyutfpp58+vP+73vWu/Od//mfe+9735ic/+Un+9m//Nv/0T/+UpUuXFhEfCvfhD394xP0PfehDBSUBAACglhRaCK1bty5HHHFEjjjiiCTJsmXLcsQRRwx/yN2wYcNwOZQk++23X/71X/81t9xySw477LC0t7fn85//vCXnqVtf/epXR9y/6aabCkoCAABALSlVq9Vq0SEmUn9/f5qbm9PX1+d6QtS0np6enHbaaRkcHBwemzJlSq6++uq0trYWmAyAp/3mN78Z/sPVmjVrRr3mIQDAWNiezqOmriEEbFGtVrNq1apRx+us5wUAAGA7KYSgBnV3d6erq2vE7KAkGRwcTFdXV7q7uwtKBgAAQC1QCEENamtry7x589LY2DhivLGxMfPnz09bW1tByQAAAKgFCiGoQaVSaZur6z09XiqVCkgFAABArVAIQY1qbW1NpVIZMVapVNLS0lJQIgAAAGqFQghq2Fvf+tbh2UClUil/+qd/WnAiAAAAaoFCCGrYtddeO7yiWLVazXXXXVdwIgAAAGqBQghqVE9PT774xS+OGPviF7+Ynp6eghIBAABQKxRCUIOq1Wo+8YlPDM8OetrQ0NA2xwEAAODZFEJQgx588MHcc88929x2zz335MEHH5zYQAAAANQUhRAAAABAnVEIQQ2aM2dODjnkkG1uO/TQQzNnzpyJDQQAAEBNUQhBDSqVSrngggu2ue2CCy4YXooeAAAAtkUhBDWqtbU1r3nNa0aMHXzwwWlpaSkoEQAAALVCIQQ1qqenJz/5yU9GjP3kJz+x7DwAAADPSyEENaharWbVqlWjjlt2HgAAgOeiEIIa1N3dna6urgwODo4YHxwcTFdXV7q7uwtKBgAAQC1QCEENamtry7x589LY2DhivLGxMfPnz09bW1tByQAAAKgFCiGoQaVSKUuXLh113CpjAAAAPBeFENSo1tbWVCqV4fKnVCqlUqlYZQwAAIDnpRCCGrZkyZLsvvvuSZKmpqZUKpWCEwEAAFALFEJQ455eUWxoaKjgJAAAANQKhRDUsNWrV+fRRx9Nkjz66KPp6OgoOBEAAAC1QCEENaqnpyerV68eMbZ69er09PQUlAgAAIBaoRCCGlStVrNq1arh08WeNjQ0tM1xAAAAeDaFENSg7u7udHV1bVX8VKvVdHV1pbu7u6BkAAAA1AKFENSgl73sZWlqatrmtqamprzsZS+b4EQAAADUEoUQ1KD169env79/m9v6+/uzfv36CU4EAABALVEIQQ1qa2vLvHnzUiqVRoyXSqXMnz8/bW1tBSUDAACgFiiEoAaVSqUsXbp0m9uWLl26VVEEAAAAz6YQgh2MFcYAAAB4PlOKDgDPpVqtZmBgoOgYk061Wk17e3tKpdKIAqhUKqW9vT0f//jHzRL6LeVy2fcEAADg/1EIMakNDAxk0aJFRceoGUNDQ7nzzjvzpje9qegok86aNWsybdq0omMAAABMCk4ZAwAAAKgzZggxqZXL5axZs6boGJPSz3/+87zjHe/I4ODg8NiUKVPy93//99l3330LTDY5lcvloiMAAABMGgohJrVSqeQ0n1G84hWvyCmnnJJrrrkmyZbvVaVSyf77719wMgAAACY7p4xBDTvllFOGv54+fXoqlUqBaQAAAKgVCiGoYc8+Derd736306IAAAB4QRRCsINYsGBB0REAAACoEQohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoMwohAAAAgDqjEAIAAACoM1OKDgAAk1W1Ws3AwEDRMahhz/758bPES1Eul1MqlYqOAcAORCEEAKMYGBjIokWLio7BDuLEE08sOgI1bM2aNZk2bVrRMQDYgThlDAAAAKDOmCEEAC/A5a97JFMbq0XHoMZUq8kTQ1u+3rkhccYP22PzYCnnfHuPomMAsINSCAHACzC1sZpyY9EpqEVO8uHFU0IDMH6cMgYAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZyZFIXT55Zdnzpw5KZfLee1rX5vvfve7o+571VVXpVQqjbiVy+UJTAsAAABQ2wovhK677rosW7YsH/nIR3LXXXflsMMOy6JFi/LLX/5y1GOampqyYcOG4Vt3d/cEJgYAAACobYUXQn/zN3+Td7zjHTnrrLPy6le/OldccUV22WWXfOELXxj1mFKplFmzZg3fZs6cOYGJAQAAAGpboYXQE088kTvvvDPHHXfc8FhDQ0OOO+64rF27dtTjHnvssbS1tWX27Nk58cQT88Mf/nDUfTdv3pz+/v4RNwAAAIB6Vmgh9Ktf/SqDg4NbzfCZOXNmNm7cuM1jDjzwwHzhC1/IjTfemNWrV2doaChHH310enp6trn/ypUr09zcPHybPXv2mL8OAAAAgFpS+Clj22vhwoU5/fTTc/jhh+f1r399vvzlL2evvfbK3/3d321z/wsuuCB9fX3Dt4ceemiCEwMAAABMLlOKfPIZM2aksbExmzZtGjG+adOmzJo16wU9xk477ZQjjjgi999//za3T506NVOnTn3JWQEAAAB2FIXOENp5551z5JFH5tZbbx0eGxoayq233pqFCxe+oMcYHBzMvffem3322We8YgIAAADsUAqdIZQky5YtyxlnnJGjjjoq8+fPz6WXXprHH388Z511VpLk9NNPT0tLS1auXJkkueiii7JgwYK84hWvyCOPPJKLL7443d3dOfvss4t8GQAAAAA1o/BC6JRTTsnDDz+cD3/4w9m4cWMOP/zw3HzzzcMXml6/fn0aGp6ZyPRf//Vfecc73pGNGzfmd37nd3LkkUfm9ttvz6tf/eqiXgIAAABATSm8EEqSc889N+eee+42t912220j7q9atSqrVq2agFQAAAAAO6aaW2UMAAAAgJdGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVmStEBAGCyqlarw19vHiwwCFCXnv2+8+z3IwAYCwqhAlWr1QwMDBQdgxr27J8fP0u8VOVyOaVSqegYk8rmzZuHvz7n279TYBKg3m3evDm77LJL0TEA2IEohAo0MDCQRYsWFR2DHcSJJ55YdARq3Jo1azJt2rSiYwAAABNAIQQAo5g6derw15e/7r8ytbHAMEDd2Tz4zOzEZ78fAcBYUAhNEo/PrSQN/nOwnarVZOipLV83TEmc7sP2Gnoqu97VUXSKSevZp9BNbUzKCiGgIE7pBWCsaSAmi4YpSeNORaegJu1cdAAAAABqjGXnAQAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgzkwpOgAAAABjr1qtZmBgoOgYk1a1Ws3mzZuTJFOnTk2pVCo40eRWLpd9j3YwCiEAAIAd0MDAQBYtWlR0DHYQa9asybRp04qOwRhyyhgAAABAnTFDCAAAYAdULpezZs2aomNMWgMDAznxxBOTJDfeeGPK5XLBiSY3358dj0IIAABgB1QqlZzi8wKVy2XfK+qOU8YAAAAA6owZQgDwAmweLCWpFh2DGlOtJk8Mbfl654bE4ixsjy3vOzwXq2jxUjz7Z8fPES9VLa7CphACgBfgnG/vUXQEAH6LVbQYK09fSwherFpchU0hVKBq9Vl/aR58srggQP161nvPiPckAABgh6YQKtDmzZuHv971e9cUmARgy3vSLrvsUnSMScXqLLxUVrBhrPjZeX6Dbxn06YbtU00y+P++bkxSW2f7MBk8lTR+tbHoFC+at0wAGIXVWRhLVrCBcTYlPt2w/XYqOgAUx1tmgaZOnTr89eNH/P+SRu9GwAQbfHJ4huKz35MAAIAdm0KoQCOuQN64k0IIKFStrYoAAAC8eAohAACgJo1YEOGp4nIAdepZ7zu1uECLQggAAKhJz16kpZYv7ArUvlpcoKWh6AAAAAAATCwzhAAAgJr07AURLDsPTLhnLTtfiwu0eMsEAABq0ogFESw7DxSoFhdoccoYAAAAQJ1RCAEAAADUGZMqAQCA2mfZebZXNcng//u6MUntnfFD0Wr8fUchBAAA1DzLzgNsH6eMAQAAANQZM4QAAICaVC6Xs2bNmqJjUKMGBgZy4oknJkluvPHGlMvlghNRy2rx50chBAAA1KRSqZRp06YVHYMdQLlc9rNE3VEITRZDNX41KopRrT7zs9MwJSm5Eh7byXsPAADUJYXQJLHrXR1FRwAAAADqhEIIAABgB1StVjMwMFB0jEnr2d8b36fnVy6XU3JGwg5FIVQgF8HjpXIhPMaSnx8A2LEMDAxk0aJFRceoCU//Ts3o1qxZ4zpLOxiFUIFcBI+x5EJ4AAAAvFAKIQAAgB2QMxKeW7VazebNm5MkU6dOdTrU8zCbfMejEAIAANgBOSPh+e2yyy5FR4DCNBQdAAAAAICJpRACAAAAqDMKIQAAAIA6oxACAAAAqDMKIQAAAIA6oxACAAAAqDMKIQAAAIA6oxACAAAAqDNTig4AANSmarWagYGBomNMas/+/vhePbdyuZxSqVR0DACoGwohJjUfNp6bDxovnA8aMPYGBgayaNGiomPUjBNPPLHoCJPamjVrMm3atKJjAEDdUAgxqfmw8cL5oPHcfNAAAAB4hkIIAHhRyuVy1qxZU3SMSa1arWbz5s1JkqlTp5qp+BzK5XLREQCgrkyKQujyyy/PxRdfnI0bN+awww7L//pf/yvz588fdf/rr78+H/rQh/Lggw/mla98ZT75yU/mj/7ojyYwMRPFh43n5oPGC+eDBoy9Uqlk5t0LsMsuuxQdAQBgK4UXQtddd12WLVuWK664Iq997Wtz6aWXZtGiRbnvvvuy9957b7X/7bffnlNPPTUrV67MCSeckGuuuSYnnXRS7rrrrhx88MEFvALGkw8bz88HDQAAALZXqVqtVosM8NrXvjbz5s3LZZddliQZGhrK7Nmz8+53vzvve9/7ttr/lFNOyeOPP56bbrppeGzBggU5/PDDc8UVVzzv8/X396e5uTl9fX1pamoauxcCAAAAUKDt6TwaJijTNj3xxBO58847c9xxxw2PNTQ05LjjjsvatWu3eczatWtH7J8kixYtGnX/zZs3p7+/f8QNAAAAoJ4VWgj96le/yuDgYGbOnDlifObMmdm4ceM2j9m4ceN27b9y5co0NzcP32bPnj024QEAAABqVKGF0ES44IIL0tfXN3x76KGHio4EAAAAUKhCLyo9Y8aMNDY2ZtOmTSPGN23alFmzZm3zmFmzZm3X/lOnTs3UqVPHJjAAAADADqDQGUI777xzjjzyyNx6663DY0NDQ7n11luzcOHCbR6zcOHCEfsnyS233DLq/gAAAACMVPiy88uWLcsZZ5yRo446KvPnz8+ll16axx9/PGeddVaS5PTTT09LS0tWrlyZJDnvvPPy+te/Pu3t7Xnzm9+ca6+9NuvWrcvnPve5Il8GAAAAQM0ovBA65ZRT8vDDD+fDH/5wNm7cmMMPPzw333zz8IWj169fn4aGZyYyHX300bnmmmvywQ9+MO9///vzyle+MjfccEMOPvjgol4CAAAAQE0pVavVatEhJlJ/f3+am5vT19eXpqamouMAAAAAjInt6Tx2+FXGAAAAABhJIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZxRCAAAAAHVGIQQAAABQZ6YUHWCiVavVJEl/f3/BSQAAAADGztNdx9Pdx3Opu0Lo0UcfTZLMnj274CQAAAAAY+/RRx9Nc3Pzc+5Tqr6Q2mgHMjQ0lF/84hfZfffdUyqVio4DL1l/f39mz56dhx56KE1NTUXHAeC3eJ8GmLy8R7OjqVarefTRR7PvvvumoeG5rxJUdzOEGhoa0traWnQMGHNNTU3+EQOYxLxPA0xe3qPZkTzfzKCnuag0AAAAQJ1RCAEAAADUGYUQ1LipU6fmIx/5SKZOnVp0FAC2wfs0wOTlPZp6VncXlQYAAACod2YIAQAAANQZhRAAAABAnVEIAQAAANQZhRCQJJkzZ04uvfTSomMA1JRSqZQbbrhhh30+AGDHpRCCAp155pkplUoplUrZeeed84pXvCIXXXRRnnrqqec99qqrrsoee+wx/iEB6tCz35932mmnzJw5M3/wB3+QL3zhCxkaGhreb8OGDTn++OMLTArAbzvzzDNz0kknbXPbnDlzht/fd91118ydOzfXX3/9iH36+/vzoQ99KK95zWsybdq0TJ8+PfPmzcunPvWp/Nd//dcEvAKYGAohKNib3vSmbNiwIT/96U+zfPnyrFixIhdffPGYPf4TTzwxZo8FUE+efn9+8MEH8/Wvfz1vfOMbc9555+WEE04YLu5nzZplqWKAGnPRRRdlw4YN+d73vpd58+bllFNOye23354k+fWvf50FCxbkyiuvzPnnn59///d/z1133ZWPf/zj+d73vpdrrrmm4PQwdhRCULCpU6dm1qxZaWtry1/8xV/kuOOOy7/8y79k8+bNOf/889PS0pJdd901r33ta3PbbbclSW677bacddZZ6evrG/4Lx4oVK5Js+avHRz/60Zx++ulpamrKO9/5ziTJl770pbzmNa/J1KlTM2fOnLS3txf0igFqw9Pvzy0tLZk7d27e//7358Ybb8zXv/71XHXVVUm2PoXroYceytve9rbsscce2XPPPXPiiSfmwQcfHN5+2223Zf78+dl1112zxx575Jhjjkl3d/fw9htvvDFz585NuVzOy1/+8lx44YUvaNYoAC/c7rvvnlmzZuWAAw7I5ZdfnmnTpuWrX/1qkuT9739/1q9fn+9+97s566yzcuihh6atrS1/+Id/mC9+8Yv5n//zfxacHsaOQggmmWnTpuWJJ57Iueeem7Vr1+baa6/NPffck8WLF+dNb3pTfvrTn+boo4/OpZdemqampmzYsCEbNmzI+eefP/wYl1xySQ477LB873vfy4c+9KHceeededvb3pY/+7M/y7333psVK1bkQx/60PAHGgBemGOPPTaHHXZYvvzlL2+17cknn8yiRYuy++675zvf+U46Ozuz22675U1velOeeOKJPPXUUznppJPy+te/Pvfcc0/Wrl2bd77znSmVSkmS73znOzn99NNz3nnn5Uc/+lH+7u/+LldddVU+/vGPT/TLBKgbU6ZMyU477ZQnnngiQ0NDue6667JkyZLsu+++29z/6fds2BFMKToAsEW1Ws2tt96aNWvW5NRTT82VV16Z9evXD/9jdP755+fmm2/OlVdemb/+679Oc3NzSqVSZs2atdVjHXvssVm+fPnw/Uqlkt///d/Phz70oSTJAQcckB/96Ee5+OKLc+aZZ07I6wPYUbzqVa/KPffcs9X4ddddl6GhoXz+858f/sBw5ZVXZo899shtt92Wo446Kn19fTnhhBOy//77J0kOOuig4eMvvPDCvO9978sZZ5yRJHn5y1+ej370o3nve9+bj3zkIxPwygDqyxNPPJH29vb09fXl2GOPzcMPP5xHHnkkBx544Ij9jjzyyNx3331Jkre85S354he/WERcGHNmCEHBbrrppuy2224pl8s5/vjjc8opp+Stb31rBgcHc8ABB2S33XYbvn3rW9/KAw888LyPedRRR424/+Mf/zjHHHPMiLFjjjkmP/3pTzM4ODimrwdgR1etVrf5F+Lvf//7uf/++7P77rsPv2/vueeeGRgYyAMPPJA999wzZ555ZhYtWpS3vOUt+fSnP50NGzaMOP6iiy4a8b7/jne8Ixs2bMh///d/T+RLBNih/dVf/VV222237LLLLvnkJz+ZT3ziE3nzm9886v5f+cpXcvfdd2fRokX5zW9+M4FJYXyZIQQFe+Mb35jPfvaz2XnnnbPvvvtmypQpue6669LY2Jg777wzjY2NI/bfbbfdnvcxd9111/GKC1D3fvzjH2e//fbbavyxxx7LkUcemY6Ojq227bXXXkm2zBj6y7/8y9x888257rrr8sEPfjC33HJLFixYkMceeywXXnhhTj755K2OL5fLY/9CAOrU//f//X8588wzs9tuu2XmzJnDJf9ee+2VPfbYY3g20NNe9rKXJdly7aFHHnlkouPCuFEIQcF23XXXvOIVrxgxdsQRR2RwcDC//OUv83u/93vbPG7nnXd+wbN7DjrooHR2do4Y6+zszAEHHLBV4QTA6L75zW/m3nvvzdKlS7faNnfu3Fx33XXZe++909TUNOpjHHHEETniiCNywQUXZOHChbnmmmuyYMGCzJ07N/fdd99W/yYAMLZmzJixzffahoaGvO1tb8vq1avz4Q9/eNTrCMGOQiEEk9ABBxyQSqWS008/Pe3t7TniiCPy8MMP59Zbb82hhx6aN7/5zZkzZ04ee+yx3HrrrTnssMOyyy67ZJdddtnm4y1fvjzz5s3LRz/60ZxyyilZu3ZtLrvssvzt3/7tBL8ygNqxefPmbNy4MYODg9m0aVNuvvnmrFy5MieccEJOP/30rfavVCq5+OKLc+KJJ+aiiy5Ka2truru78+Uvfznvfe978+STT+Zzn/tc/viP/zj77rtv7rvvvvz0pz8dfqwPf/jDOeGEE/Kyl70sb33rW9PQ0JDvf//7+cEPfpCPfexjE/3yAWpaX19f7r777hFj06dPf97j/vqv/3p4RciLLrooRx11VHbdddfhxQAOPvjgcUoME08hBJPUlVdemY997GNZvnx5fv7zn2fGjBlZsGBBTjjhhCTJ0UcfnXe961055ZRT0tvbm4985CPDS8//trlz5+af/umf8uEPfzgf/ehHs88+++Siiy5yQWmA53DzzTdnn332yZQpU/I7v/M7Oeyww/KZz3wmZ5xxRhoatr4M4y677JJvf/vb+au/+qucfPLJefTRR9PS0pLf//3fT1NTU37zm9/kJz/5Sf7hH/4hvb292WeffXLOOefkf/yP/5EkWbRoUW666aZcdNFF+eQnP5mddtopr3rVq3L22WdP9EsHqHm33XZbjjjiiBFjb3/725/3uOnTp+e73/1uPvnJT+biiy/Oz372szQ0NOSVr3xlTjnllLznPe8Zp8Qw8UrVarVadAgAAAAAJo5VxgAAAADqjEIIAAAAoM4ohAAAAADqjEIIAAAAoM4ohAAAAADqjEIIAAAAoM4ohAAAAADqjEIIAAAAoM4ohACAulCtVvPOd74ze+65Z0qlUu6+++6X/JgrVqzI4Ycf/pIfBwBgok0pOgAAwES4+eabc9VVV+W2227Ly1/+8syYMWNCn//MM8/MI488khtuuGFCnxcAYFsUQgBAXXjggQeyzz775Oijjy46CgBA4ZwyBgDs8M4888y8+93vzvr161MqlTJnzpzMmTMnl1566Yj9Dj/88KxYsWL4/iOPPJKzzz47e+21V5qamnLsscfm+9///nY//4oVK/IP//APufHGG1MqlVIqlXLbbbfl2GOPzbnnnjti34cffjg777xzbr311iTJnDlz8tGPfjSnnnpqdt1117S0tOTyyy8fccxY5QQA6odCCADY4X3605/ORRddlNbW1mzYsCFdXV0v6LjFixfnl7/8Zb7+9a/nzjvvzNy5c/P7v//7+fWvf71dz3/++efnbW97W970pjdlw4YN2bBhQ44++uicffbZueaaa7J58+bhfVevXp2WlpYce+yxw2MXX3xxDjvssHzve9/L+973vpx33nm55ZZbxjwnAFA/FEIAwA6vubk5u+++exobGzNr1qzstddez3vMv/3bv+W73/1urr/++hx11FF55StfmUsuuSR77LFH/vmf/3m7nn+33XbLtGnTMnXq1MyaNSuzZs3KzjvvnJNPPjlJcuONNw7ve9VVV+XMM89MqVQaHjvmmGPyvve9LwcccEDe/e53561vfWtWrVo15jkBgPqhEAIA2Ibvf//7eeyxxzJ9+vTstttuw7ef/exneeCBB8bkOcrlck477bR84QtfSJLcdddd+cEPfpAzzzxzxH4LFy7c6v6Pf/zjCcsJAOx4XFQaAKhLDQ0NqVarI8aefPLJ4a8fe+yx7LPPPrntttu2OnaPPfYYsxxnn312Dj/88PT09OTKK6/Msccem7a2thd8/ETlBAB2LAohAKAu7bXXXtmwYcPw/f7+/vzsZz8bvj937txs3LgxU6ZMyZw5c17y8+28884ZHBzcavyQQw7JUUcdlb//+7/PNddck8suu2yrfe64446t7h900EHjkhMAqA9OGQMA6tKxxx6bf/zHf8x3vvOd3HvvvTnjjDPS2Ng4vP24447LwoULc9JJJ+Ub3/hGHnzwwdx+++35wAc+kHXr1m33882ZMyf33HNP7rvvvvzqV78aMRvp7LPPzic+8YlUq9X8yZ/8yVbHdnZ25lOf+lT+4z/+I5dffnmuv/76nHfeeeOSEwCoDwohAKAuXXDBBXn961+fE044IW9+85tz0kknZf/99x/eXiqV8rWvfS2ve93rctZZZ+WAAw7In/3Zn6W7uzszZ87c7ud7xzvekQMPPDBHHXVU9tprr3R2dg5vO/XUUzNlypSceuqpKZfLWx27fPnyrFu3LkcccUQ+9rGP5W/+5m+yaNGicckJANSHUvW3T54HAGBCPfjgg9l///3T1dWVuXPnjtg2Z86cvOc978l73vOeYsIBADsk1xACACjIk08+md7e3nzwgx/MggULtiqDAADGi1PGAADGwLOXfP/t23e+851tHtPZ2Zl99tknXV1dueKKKyY4MQBQz5wyBgAwBu6///5Rt7W0tGTatGkTmAYA4LkphAAAAADqjFPGAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOqMQggAAACgziiEAAAAAOrM/x+YnHHMTHG7tQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(14,7))\n",
    "sns.boxplot(x='fuel_type',y='Price',data=car)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x18b2263b760>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1492.62x700 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)\n",
    "ax.set_xticklabels(rotation=40,ha='right')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harsh\\AppData\\Local\\Temp\\ipykernel_25580\\3148980726.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corrmat = car.corr(method='pearson')\n"
     ]
    }
   ],
   "source": [
    "corrmat = car.corr(method='pearson')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['year', 'Price', 'kms_driven'], dtype='object')"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat.index"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harsh\\AppData\\Local\\Temp\\ipykernel_25580\\77719472.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corrmat = car.corr(method='pearson')\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x2000 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = car.corr(method='pearson')\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map of the corerlation of the features\n",
    "g=sns.heatmap(car[top_corr_features].corr(method='pearson'),annot=True,cmap=\"RdYlGn\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "tranning the data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "X=car[['name','company','year','kms_driven','fuel_type']]\n",
    "y=car['Price']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "                       name   company  year  kms_driven fuel_type\n0       Hyundai Santro Xing   Hyundai  2007       45000    Petrol\n1       Mahindra Jeep CL550  Mahindra  2006          40    Diesel\n2         Hyundai Grand i10   Hyundai  2014       28000    Petrol\n3    Ford EcoSport Titanium      Ford  2014       36000    Diesel\n4                 Ford Figo      Ford  2012       41000    Diesel\n..                      ...       ...   ...         ...       ...\n811      Maruti Suzuki Ritz    Maruti  2011       50000    Petrol\n812          Tata Indica V2      Tata  2009       30000    Diesel\n813    Toyota Corolla Altis    Toyota  2009      132000    Petrol\n814            Tata Zest XM      Tata  2018       27000    Diesel\n815      Mahindra Quanto C8  Mahindra  2013       40000    Diesel\n\n[815 rows x 5 columns]",
      "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>name</th>\n      <th>company</th>\n      <th>year</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Santro Xing</td>\n      <td>Hyundai</td>\n      <td>2007</td>\n      <td>45000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Mahindra Jeep CL550</td>\n      <td>Mahindra</td>\n      <td>2006</td>\n      <td>40</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hyundai Grand i10</td>\n      <td>Hyundai</td>\n      <td>2014</td>\n      <td>28000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Ford EcoSport Titanium</td>\n      <td>Ford</td>\n      <td>2014</td>\n      <td>36000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ford Figo</td>\n      <td>Ford</td>\n      <td>2012</td>\n      <td>41000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td>Maruti Suzuki Ritz</td>\n      <td>Maruti</td>\n      <td>2011</td>\n      <td>50000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>812</th>\n      <td>Tata Indica V2</td>\n      <td>Tata</td>\n      <td>2009</td>\n      <td>30000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>813</th>\n      <td>Toyota Corolla Altis</td>\n      <td>Toyota</td>\n      <td>2009</td>\n      <td>132000</td>\n      <td>Petrol</td>\n    </tr>\n    <tr>\n      <th>814</th>\n      <td>Tata Zest XM</td>\n      <td>Tata</td>\n      <td>2018</td>\n      <td>27000</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>815</th>\n      <td>Mahindra Quanto C8</td>\n      <td>Mahindra</td>\n      <td>2013</td>\n      <td>40000</td>\n      <td>Diesel</td>\n    </tr>\n  </tbody>\n</table>\n<p>815 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "0       80000\n1      425000\n2      325000\n3      575000\n4      175000\n        ...  \n811    270000\n812    110000\n813    300000\n814    260000\n815    390000\nName: Price, Length: 815, dtype: int32"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor=RandomForestRegressor()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import r2_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "OneHotEncoder()",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe=OneHotEncoder()\n",
    "ohe.fit(X[['name','company','fuel_type']])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n",
    "                                    remainder='passthrough')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "pipe=make_pipeline(column_trans,lr)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('columntransformer',\n                 ColumnTransformer(remainder='passthrough',\n                                   transformers=[('onehotencoder',\n                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n      dtype=object),\n                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n                                                  ['name', 'company',\n                                                   'fuel_type'])])),\n                ('linearregression', LinearRegression())])",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n                                   transformers=[(&#x27;onehotencoder&#x27;,\n                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n      dtype=object),\n                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n                                                   &#x27;fuel_type&#x27;])])),\n                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n                                   transformers=[(&#x27;onehotencoder&#x27;,\n                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n      dtype=object),\n                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n                                                   &#x27;fuel_type&#x27;])])),\n                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n                  transformers=[(&#x27;onehotencoder&#x27;,\n                                 OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet B...\n       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n                                                           array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n      dtype=object),\n                                                           array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n                                 [&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet Beat LT&#x27;, &#x27;Chevrolet Beat PS&#x27;,\n       &#x27;Chevrolet Cruze LTZ&#x27;, &#x27;Chevrolet Enjoy&#x27;, &#x27;Chevrolet E...\n       &#x27;Volkswagen Vento Comfortline&#x27;, &#x27;Volkswagen Vento Highline&#x27;,\n       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n                          array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n      dtype=object),\n                          array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;kms_driven&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "y_pred=pipe.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Checking R2 Score"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "scores=[]\n",
    "for i in range(1000):\n",
    "    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)\n",
    "    lr=LinearRegression()\n",
    "    pipe=make_pipeline(column_trans,lr)\n",
    "    pipe.fit(X_train,y_train)\n",
    "    y_pred=pipe.predict(X_test)\n",
    "    scores.append(r2_score(y_test,y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "302"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(scores)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8991181549156528"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[np.argmax(scores)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "array([359719.57338926])"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2017,25698,'Petrol']).reshape(1,5)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### The best model is found at a certain random state"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8991181549156528"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))\n",
    "lr=LinearRegression()\n",
    "pipe=make_pipeline(column_trans,lr)\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred=pipe.predict(X_test)\n",
    "r2_score(y_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n       'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n       'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n       'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n       'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n       'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n       'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n       'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n       'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n       'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n       'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n       'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n       'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n       'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n       'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n       'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n       'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n       'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n       'Hyundai Accent Executive', 'Hyundai Accent GLE',\n       'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n       'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n       'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n       'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n       'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n       'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n       'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n       'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n       'Hyundai Verna Transform', 'Hyundai Verna VGT',\n       'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n       'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n       'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n       'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n       'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n       'Land Rover Freelander', 'Mahindra Bolero DI',\n       'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n       'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n       'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n       'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n       'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n       'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n       'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n       'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n       'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n       'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n       'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n       'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n       'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n       'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n       'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n       'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n       'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n       'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n       'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n       'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n       'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n       'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n       'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n       'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n       'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n       'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n       'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n       'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n       'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n       'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n       'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n       'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n       'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n       'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n       'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n       'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n       'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n       'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n       'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n       'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n       'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n       'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n       'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n       'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n       'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n       'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n       'Volkswagen Passat Diesel', 'Volkswagen Polo',\n       'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n       'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n       'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n       'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object)"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.steps[0][1].transformers[0][1].categories[0]\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}