ML-MAJOR-APR-ML042B2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_excel('Data_Train.xlsx')\n",
    "test = pd.read_excel('Data_Test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  \n",
       "0   1.75  \n",
       "1  12.50  \n",
       "2   4.50  \n",
       "3   6.00  \n",
       "4  17.74  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6019.000000</td>\n",
       "      <td>6.019000e+03</td>\n",
       "      <td>5977.000000</td>\n",
       "      <td>6019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.358199</td>\n",
       "      <td>5.873838e+04</td>\n",
       "      <td>5.278735</td>\n",
       "      <td>9.479468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.269742</td>\n",
       "      <td>9.126884e+04</td>\n",
       "      <td>0.808840</td>\n",
       "      <td>11.187917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>3.400000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>5.300000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Kilometers_Driven        Seats        Price\n",
       "count  6019.000000       6.019000e+03  5977.000000  6019.000000\n",
       "mean   2013.358199       5.873838e+04     5.278735     9.479468\n",
       "std       3.269742       9.126884e+04     0.808840    11.187917\n",
       "min    1998.000000       1.710000e+02     0.000000     0.440000\n",
       "25%    2011.000000       3.400000e+04     5.000000     3.500000\n",
       "50%    2014.000000       5.300000e+04     5.000000     5.640000\n",
       "75%    2016.000000       7.300000e+04     5.000000     9.950000\n",
       "max    2019.000000       6.500000e+06    10.000000   160.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>40929</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>32.26 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.2 bhp</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>54493</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>24.7 kmpl</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>47.3 bhp</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name    Location  Year  Kilometers_Driven  \\\n",
       "0        Maruti Alto K10 LXI CNG       Delhi  2014              40929   \n",
       "1  Maruti Alto 800 2016-2019 LXI  Coimbatore  2013              54493   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type      Mileage  Engine     Power  Seats  \n",
       "0       CNG       Manual      First  32.26 km/kg  998 CC  58.2 bhp    4.0  \n",
       "1    Petrol       Manual     Second    24.7 kmpl  796 CC  47.3 bhp    5.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6019, 12), (1234, 11))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name',\n",
       " 'Location',\n",
       " 'Year',\n",
       " 'Kilometers_Driven',\n",
       " 'Fuel_Type',\n",
       " 'Transmission',\n",
       " 'Owner_Type',\n",
       " 'Mileage',\n",
       " 'Engine',\n",
       " 'Power',\n",
       " 'Seats',\n",
       " 'Price']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name',\n",
       " 'Location',\n",
       " 'Year',\n",
       " 'Kilometers_Driven',\n",
       " 'Fuel_Type',\n",
       " 'Transmission',\n",
       " 'Owner_Type',\n",
       " 'Mileage',\n",
       " 'Engine',\n",
       " 'Power',\n",
       " 'Seats']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               6019 non-null   object \n",
      " 1   Location           6019 non-null   object \n",
      " 2   Year               6019 non-null   int64  \n",
      " 3   Kilometers_Driven  6019 non-null   int64  \n",
      " 4   Fuel_Type          6019 non-null   object \n",
      " 5   Transmission       6019 non-null   object \n",
      " 6   Owner_Type         6019 non-null   object \n",
      " 7   Mileage            6017 non-null   object \n",
      " 8   Engine             5983 non-null   object \n",
      " 9   Power              5983 non-null   object \n",
      " 10  Seats              5977 non-null   float64\n",
      " 11  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(8)\n",
      "memory usage: 564.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage               2\n",
       "Engine               36\n",
       "Power                36\n",
       "Seats                42\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info()\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with missing values:\n",
      "['Mileage', 'Engine', 'Power', 'Seats']\n"
     ]
    }
   ],
   "source": [
    "# Get names of columns with missing values\n",
    "cols_with_missing = [col for col in train.columns\n",
    "                     if train[col].isnull().any()]\n",
    "print(\"Columns with missing values:\")\n",
    "print(cols_with_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Wagon</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Creta</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Ertiga</td>\n",
       "      <td>VDI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>New</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company Car_Name Model  \n",
       "0   1.75   Maruti    Wagon     R  \n",
       "1  12.50  Hyundai    Creta   1.6  \n",
       "2   4.50    Honda     Jazz     V  \n",
       "3   6.00   Maruti   Ertiga   VDI  \n",
       "4  17.74     Audi       A4   New  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Company'] = train.Name.str.split().str.get(0)\n",
    "test['Company'] = test.Name.str.split().str.get(0)\n",
    "train['Car_Name'] = train.Name.str.split().str.get(1)\n",
    "test['Car_Name'] = test.Name.str.split().str.get(1)\n",
    "train['Model'] = train.Name.str.split().str.get(2)\n",
    "test['Model'] = test.Name.str.split().str.get(2)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting categorical variables to numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Owner_Type']=train.Owner_Type.str.replace('First','1').str.replace('Second','2').str.replace('Third','3').str.replace('Fourth & Above','4').astype(int)\n",
    "test['Owner_Type']=test.Owner_Type.str.replace('First','1').str.replace('Second','2').str.replace('Third','3').str.replace('Fourth & Above','4').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Transmission']=train.Transmission.apply(lambda x:str(x).replace('Manual','0').replace('Automatic','1')).astype(int)\n",
    "test['Transmission']=test.Transmission.apply(lambda x:str(x).replace('Manual','0').replace('Automatic','1')).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Fuel_Type']=train.Fuel_Type.apply(lambda x:str(x).replace('CNG','0').replace('Diesel','1').replace('Petrol','2').replace('LPG','3').replace('Electric','4')).astype(int)\n",
    "test['Fuel_Type']=test.Fuel_Type.apply(lambda x:str(x).replace('CNG','0').replace('Diesel','1').replace('Petrol','2').replace('LPG','3').replace('Electric','4')).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Wagon</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Creta</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Ertiga</td>\n",
       "      <td>VDI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>New</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "   Fuel_Type  Transmission  Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          0             0           1  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1          1             0           1  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2          2             0           1   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3          1             0           1  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4          1             1           2   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company Car_Name Model  \n",
       "0   1.75   Maruti    Wagon     R  \n",
       "1  12.50  Hyundai    Creta   1.6  \n",
       "2   4.50    Honda     Jazz     V  \n",
       "3   6.00   Maruti   Ertiga   VDI  \n",
       "4  17.74     Audi       A4   New  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['age'] = 2020 - train['Year']\n",
    "test['age'] = 2020 - test['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Wagon</td>\n",
       "      <td>R</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Creta</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>V</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Ertiga</td>\n",
       "      <td>VDI</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>New</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG         9  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        10  2015              41000   \n",
       "2                      Honda Jazz V         2  2011              46000   \n",
       "3                 Maruti Ertiga VDI         2  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic         3  2013              40670   \n",
       "\n",
       "   Fuel_Type  Transmission  Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          0             0           1  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1          1             0           1  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2          2             0           1   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3          1             0           1  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4          1             1           2   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company Car_Name Model  age  \n",
       "0   1.75   Maruti    Wagon     R   10  \n",
       "1  12.50  Hyundai    Creta   1.6    5  \n",
       "2   4.50    Honda     Jazz     V    9  \n",
       "3   6.00   Maruti   Ertiga   VDI    8  \n",
       "4  17.74     Audi       A4   New    7  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "number=LabelEncoder()\n",
    "train[\"Location\"]=number.fit_transform(train[\"Location\"].astype('str'))\n",
    "test[\"Location\"]=number.fit_transform(test[\"Location\"].astype('str'))\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>18</td>\n",
       "      <td>Wagon</td>\n",
       "      <td>R</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10</td>\n",
       "      <td>Creta</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>V</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>18</td>\n",
       "      <td>Ertiga</td>\n",
       "      <td>VDI</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>A4</td>\n",
       "      <td>New</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG         9  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        10  2015              41000   \n",
       "2                      Honda Jazz V         2  2011              46000   \n",
       "3                 Maruti Ertiga VDI         2  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic         3  2013              40670   \n",
       "\n",
       "   Fuel_Type  Transmission  Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          0             0           1  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1          1             0           1  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2          2             0           1   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3          1             0           1  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4          1             1           2   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company Car_Name Model  age  \n",
       "0   1.75       18    Wagon     R   10  \n",
       "1  12.50       10    Creta   1.6    5  \n",
       "2   4.50        9     Jazz     V    9  \n",
       "3   6.00       18   Ertiga   VDI    8  \n",
       "4  17.74        1       A4   New    7  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Company\"]=number.fit_transform(train[\"Company\"].astype('str'))\n",
    "test[\"Company\"]=number.fit_transform(test[\"Company\"].astype('str'))\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train[\"Car_Name\"]=number.fit_transform(train[\"Car_Name\"].astype('str'))\n",
    "test[\"Car_Name\"]=number.fit_transform(test[\"Car_Name\"].astype('str'))\n",
    "train[\"Model\"]=number.fit_transform(train[\"Model\"].astype('str'))\n",
    "test[\"Model\"]=number.fit_transform(test[\"Model\"].astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>18</td>\n",
       "      <td>189</td>\n",
       "      <td>231</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>280</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>286</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG         9  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        10  2015              41000   \n",
       "2                      Honda Jazz V         2  2011              46000   \n",
       "3                 Maruti Ertiga VDI         2  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic         3  2013              40670   \n",
       "\n",
       "   Fuel_Type  Transmission  Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          0             0           1  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1          1             0           1  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2          2             0           1   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3          1             0           1  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4          1             1           2   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company  Car_Name  Model  age  \n",
       "0   1.75       18       189    231   10  \n",
       "1  12.50       10        53     10    5  \n",
       "2   4.50        9        99    280    9  \n",
       "3   6.00       18        69    286    8  \n",
       "4  17.74        1        10    221    7  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>18</td>\n",
       "      <td>189</td>\n",
       "      <td>231</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>280</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>286</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG         9  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        10  2015              41000   \n",
       "2                      Honda Jazz V         2  2011              46000   \n",
       "3                 Maruti Ertiga VDI         2  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic         3  2013              40670   \n",
       "\n",
       "   Fuel_Type  Transmission  Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          0             0           1  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1          1             0           1  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2          2             0           1   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3          1             0           1  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4          1             1           2   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  Company  Car_Name  Model  age  \n",
       "0   1.75       18       189    231   10  \n",
       "1  12.50       10        53     10    5  \n",
       "2   4.50        9        99    280    9  \n",
       "3   6.00       18        69    286    8  \n",
       "4  17.74        1        10    221    7  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's deal with them one by one.\n",
    "\n",
    "train['Seats'].fillna(5,inplace=True)\n",
    "test['Seats'].fillna(5,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Year'] = train['Year'].astype('category')\n",
    "train['Location'] = train['Location'].astype('category')\n",
    "train['Fuel_Type'] = train['Fuel_Type'].astype('category')\n",
    "train['Transmission'] = train['Transmission'].astype('category')\n",
    "train['Owner_Type'] = train['Owner_Type'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Year'] = test['Year'].astype('category')\n",
    "test['Location'] = test['Location'].astype('category')\n",
    "test['Fuel_Type'] = test['Fuel_Type'].astype('category')\n",
    "test['Transmission'] = test['Transmission'].astype('category')\n",
    "test['Owner_Type'] = test['Owner_Type'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Mileage'] = train['Mileage'].apply(lambda x : str(x).split(' ')[0]).astype(float)\n",
    "\n",
    "train['Engine'] = train['Engine'].apply(lambda x : str(x).split(\" \")[0]).astype(float)\n",
    "\n",
    "train['Power'] = train['Power'].replace('null bhp','0 bhp').apply(lambda x : str(x).split(' ')[0]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Mileage'] = test['Mileage'].apply(lambda x : str(x).split(' ')[0]).astype(float)\n",
    "\n",
    "test['Engine'] = test['Engine'].apply(lambda x : str(x).split(\" \")[0]).astype(float)\n",
    "\n",
    "test['Power'] = test['Power'].replace('null bhp','0 bhp').apply(lambda x : str(x).split(' ')[0]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
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       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>18</td>\n",
       "      <td>189</td>\n",
       "      <td>231</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>280</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>286</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG        9  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option       10  2015              41000   \n",
       "2                      Honda Jazz V        2  2011              46000   \n",
       "3                 Maruti Ertiga VDI        2  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic        3  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  \\\n",
       "0         0            0          1    26.60   998.0   58.16    5.0   1.75   \n",
       "1         1            0          1    19.67  1582.0  126.20    5.0  12.50   \n",
       "2         2            0          1    18.20  1199.0   88.70    5.0   4.50   \n",
       "3         1            0          1    20.77  1248.0   88.76    7.0   6.00   \n",
       "4         1            1          2    15.20  1968.0  140.80    5.0  17.74   \n",
       "\n",
       "   Company  Car_Name  Model  age  \n",
       "0       18       189    231   10  \n",
       "1       10        53     10    5  \n",
       "2        9        99    280    9  \n",
       "3       18        69    286    8  \n",
       "4        1        10    221    7  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>40929</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>54493</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.70</td>\n",
       "      <td>796.0</td>\n",
       "      <td>47.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota Innova Crysta Touring Sport 2.4 MT</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>34000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>147.80</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>139000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.59</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>159</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name Location  Year  \\\n",
       "0                    Maruti Alto K10 LXI CNG        4  2014   \n",
       "1              Maruti Alto 800 2016-2019 LXI        3  2013   \n",
       "2  Toyota Innova Crysta Touring Sport 2.4 MT        9  2017   \n",
       "3                       Toyota Etios Liva GD        5  2012   \n",
       "4                          Hyundai i20 Magna        9  2014   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type  Mileage  Engine  \\\n",
       "0              40929         0            0          1    32.26   998.0   \n",
       "1              54493         2            0          2    24.70   796.0   \n",
       "2              34000         1            0          1    13.68  2393.0   \n",
       "3             139000         1            0          1    23.59  1364.0   \n",
       "4              29000         2            0          1    18.50  1197.0   \n",
       "\n",
       "    Power  Seats  Company  Car_Name  Model  age  \n",
       "0   58.20    4.0       16        16    128    6  \n",
       "1   47.30    5.0       16        16     56    7  \n",
       "2  147.80    7.0       26        78     86    3  \n",
       "3    0.00    5.0       26        60    139    8  \n",
       "4   82.85    5.0        9       159    145    6  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del test[\"Year\"]\n",
    "del train[\"Year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
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       "      <th>Power</th>\n",
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       "      <th>Car_Name</th>\n",
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       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>4</td>\n",
       "      <td>40929</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>3</td>\n",
       "      <td>54493</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.70</td>\n",
       "      <td>796.0</td>\n",
       "      <td>47.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota Innova Crysta Touring Sport 2.4 MT</td>\n",
       "      <td>9</td>\n",
       "      <td>34000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>147.80</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>5</td>\n",
       "      <td>139000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.59</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>9</td>\n",
       "      <td>29000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>159</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name Location  Kilometers_Driven  \\\n",
       "0                    Maruti Alto K10 LXI CNG        4              40929   \n",
       "1              Maruti Alto 800 2016-2019 LXI        3              54493   \n",
       "2  Toyota Innova Crysta Touring Sport 2.4 MT        9              34000   \n",
       "3                       Toyota Etios Liva GD        5             139000   \n",
       "4                          Hyundai i20 Magna        9              29000   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Company  \\\n",
       "0         0            0          1    32.26   998.0   58.20    4.0       16   \n",
       "1         2            0          2    24.70   796.0   47.30    5.0       16   \n",
       "2         1            0          1    13.68  2393.0  147.80    7.0       26   \n",
       "3         1            0          1    23.59  1364.0    0.00    5.0       26   \n",
       "4         2            0          1    18.50  1197.0   82.85    5.0        9   \n",
       "\n",
       "   Car_Name  Model  age  \n",
       "0        16    128    6  \n",
       "1        16     56    7  \n",
       "2        78     86    3  \n",
       "3        60    139    8  \n",
       "4       159    145    6  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>9</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
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       "      <td>18</td>\n",
       "      <td>189</td>\n",
       "      <td>231</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>10</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>2</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>280</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>2</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>286</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>3</td>\n",
       "      <td>40670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name Location  Kilometers_Driven Fuel_Type  \\\n",
       "0            Maruti Wagon R LXI CNG        9              72000         0   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option       10              41000         1   \n",
       "2                      Honda Jazz V        2              46000         2   \n",
       "3                 Maruti Ertiga VDI        2              87000         1   \n",
       "4   Audi A4 New 2.0 TDI Multitronic        3              40670         1   \n",
       "\n",
       "  Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  Company  \\\n",
       "0            0          1    26.60   998.0   58.16    5.0   1.75       18   \n",
       "1            0          1    19.67  1582.0  126.20    5.0  12.50       10   \n",
       "2            0          1    18.20  1199.0   88.70    5.0   4.50        9   \n",
       "3            0          1    20.77  1248.0   88.76    7.0   6.00       18   \n",
       "4            1          2    15.20  1968.0  140.80    5.0  17.74        1   \n",
       "\n",
       "   Car_Name  Model  age  \n",
       "0       189    231   10  \n",
       "1        53     10    5  \n",
       "2        99    280    9  \n",
       "3        69    286    8  \n",
       "4        10    221    7  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 False\n",
       "Location             False\n",
       "Kilometers_Driven    False\n",
       "Fuel_Type            False\n",
       "Transmission         False\n",
       "Owner_Type           False\n",
       "Mileage               True\n",
       "Engine                True\n",
       "Power                 True\n",
       "Seats                False\n",
       "Price                False\n",
       "Company              False\n",
       "Car_Name             False\n",
       "Model                False\n",
       "age                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Mileage'].fillna(train['Mileage'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 False\n",
       "Location             False\n",
       "Kilometers_Driven    False\n",
       "Fuel_Type            False\n",
       "Transmission         False\n",
       "Owner_Type           False\n",
       "Mileage              False\n",
       "Engine                True\n",
       "Power                 True\n",
       "Seats                False\n",
       "Price                False\n",
       "Company              False\n",
       "Car_Name             False\n",
       "Model                False\n",
       "age                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Engine'].fillna(test['Engine'].median(),inplace=True)\n",
    "test['Power'].fillna(test['Power'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Engine'].fillna(train['Engine'].median(),inplace=True)\n",
    "train['Power'].fillna(train['Power'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 False\n",
       "Location             False\n",
       "Kilometers_Driven    False\n",
       "Fuel_Type            False\n",
       "Transmission         False\n",
       "Owner_Type           False\n",
       "Mileage              False\n",
       "Engine               False\n",
       "Power                False\n",
       "Seats                False\n",
       "Company              False\n",
       "Car_Name             False\n",
       "Model                False\n",
       "age                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2413efa3248>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(corr, vmax=1, square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065253</td>\n",
       "      <td>0.090890</td>\n",
       "      <td>0.023991</td>\n",
       "      <td>0.082782</td>\n",
       "      <td>-0.011493</td>\n",
       "      <td>0.028232</td>\n",
       "      <td>0.034198</td>\n",
       "      <td>-0.000488</td>\n",
       "      <td>0.173048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mileage</th>\n",
       "      <td>-0.065253</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.586404</td>\n",
       "      <td>-0.441802</td>\n",
       "      <td>-0.299632</td>\n",
       "      <td>-0.306588</td>\n",
       "      <td>-0.013373</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>0.161304</td>\n",
       "      <td>-0.321534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine</th>\n",
       "      <td>0.090890</td>\n",
       "      <td>-0.586404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840965</td>\n",
       "      <td>0.393368</td>\n",
       "      <td>0.657347</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>-0.074722</td>\n",
       "      <td>-0.193268</td>\n",
       "      <td>0.050086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>0.023991</td>\n",
       "      <td>-0.441802</td>\n",
       "      <td>0.840965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109676</td>\n",
       "      <td>0.757958</td>\n",
       "      <td>-0.176088</td>\n",
       "      <td>-0.109634</td>\n",
       "      <td>-0.150787</td>\n",
       "      <td>-0.073031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seats</th>\n",
       "      <td>0.082782</td>\n",
       "      <td>-0.299632</td>\n",
       "      <td>0.393368</td>\n",
       "      <td>0.109676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052811</td>\n",
       "      <td>0.189674</td>\n",
       "      <td>0.079632</td>\n",
       "      <td>-0.024161</td>\n",
       "      <td>-0.015204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.011493</td>\n",
       "      <td>-0.306588</td>\n",
       "      <td>0.657347</td>\n",
       "      <td>0.757958</td>\n",
       "      <td>0.052811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.132929</td>\n",
       "      <td>-0.088536</td>\n",
       "      <td>-0.109099</td>\n",
       "      <td>-0.305327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <td>0.028232</td>\n",
       "      <td>-0.013373</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>-0.176088</td>\n",
       "      <td>0.189674</td>\n",
       "      <td>-0.132929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168307</td>\n",
       "      <td>-0.083901</td>\n",
       "      <td>-0.018578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Name</th>\n",
       "      <td>0.034198</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>-0.074722</td>\n",
       "      <td>-0.109634</td>\n",
       "      <td>0.079632</td>\n",
       "      <td>-0.088536</td>\n",
       "      <td>0.168307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032675</td>\n",
       "      <td>0.027290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>-0.000488</td>\n",
       "      <td>0.161304</td>\n",
       "      <td>-0.193268</td>\n",
       "      <td>-0.150787</td>\n",
       "      <td>-0.024161</td>\n",
       "      <td>-0.109099</td>\n",
       "      <td>-0.083901</td>\n",
       "      <td>0.032675</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.173048</td>\n",
       "      <td>-0.321534</td>\n",
       "      <td>0.050086</td>\n",
       "      <td>-0.073031</td>\n",
       "      <td>-0.015204</td>\n",
       "      <td>-0.305327</td>\n",
       "      <td>-0.018578</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Kilometers_Driven   Mileage    Engine     Power     Seats  \\\n",
       "Kilometers_Driven           1.000000 -0.065253  0.090890  0.023991  0.082782   \n",
       "Mileage                    -0.065253  1.000000 -0.586404 -0.441802 -0.299632   \n",
       "Engine                      0.090890 -0.586404  1.000000  0.840965  0.393368   \n",
       "Power                       0.023991 -0.441802  0.840965  1.000000  0.109676   \n",
       "Seats                       0.082782 -0.299632  0.393368  0.109676  1.000000   \n",
       "Price                      -0.011493 -0.306588  0.657347  0.757958  0.052811   \n",
       "Company                     0.028232 -0.013373  0.008120 -0.176088  0.189674   \n",
       "Car_Name                    0.034198  0.002763 -0.074722 -0.109634  0.079632   \n",
       "Model                      -0.000488  0.161304 -0.193268 -0.150787 -0.024161   \n",
       "age                         0.173048 -0.321534  0.050086 -0.073031 -0.015204   \n",
       "\n",
       "                      Price   Company  Car_Name     Model       age  \n",
       "Kilometers_Driven -0.011493  0.028232  0.034198 -0.000488  0.173048  \n",
       "Mileage           -0.306588 -0.013373  0.002763  0.161304 -0.321534  \n",
       "Engine             0.657347  0.008120 -0.074722 -0.193268  0.050086  \n",
       "Power              0.757958 -0.176088 -0.109634 -0.150787 -0.073031  \n",
       "Seats              0.052811  0.189674  0.079632 -0.024161 -0.015204  \n",
       "Price              1.000000 -0.132929 -0.088536 -0.109099 -0.305327  \n",
       "Company           -0.132929  1.000000  0.168307 -0.083901 -0.018578  \n",
       "Car_Name          -0.088536  0.168307  1.000000  0.032675  0.027290  \n",
       "Model             -0.109099 -0.083901  0.032675  1.000000  0.011236  \n",
       "age               -0.305327 -0.018578  0.027290  0.011236  1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.09755744],\n",
       "       [-0.09755744,  1.        ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(train[\"Price\"],train[\"Owner_Type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.58602454],\n",
       "       [0.58602454, 1.        ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(train[\"Price\"],train[\"Transmission\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.28958456],\n",
       "       [-0.28958456,  1.        ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(train[\"Price\"],train[\"Fuel_Type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we get to know that price is most affected by power,engine,Transmission with a positive realtion\n",
    "while mileage, age,Fuel_Type, owner type are having inverse relation with price\n",
    "\n",
    "whereas the price is least affected by seats,kilometers_driven"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shivam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2413f070388>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot of different columns with price\n",
    "import seaborn as sns\n",
    "sns.set(rc={\"figure.figsize\":(12,12)})\n",
    "plt.subplot(3,3,1)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Power\"])\n",
    "plt.subplot(3,3,2)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Transmission\"],color='g')\n",
    "plt.subplot(3,3,3)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Engine\"],color='red')\n",
    "plt.subplot(3,3,4)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Fuel_Type\"],color=\"blue\")\n",
    "plt.subplot(3,3,5)\n",
    "sns.scatterplot(train[\"Price\"],train[\"age\"],color='violet')\n",
    "plt.subplot(3,3,6)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Mileage\"],color='c')\n",
    "plt.subplot(3,3,7)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Owner_Type\"],color='orange')\n",
    "plt.subplot(3,3,7)\n",
    "sns.scatterplot(train[\"Price\"],train[\"Owner_Type\"],color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    " #power,engine,Transmission with a positive realtion while mileage,\n",
    " #   age,Fuel_Type, owner type are having inverse relation with price\n",
    "X=train[['age','Fuel_Type','Transmission','Mileage','Engine',\"Location\",\"Power\"]]\n",
    "Y=train['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size=0.20,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.24069529 -1.65016411 23.38624932 ... 11.8916471  11.90982027\n",
      "  6.90718804]\n",
      "1463     1.50\n",
      "181      1.45\n",
      "2506    25.75\n",
      "3374     8.90\n",
      "5816     3.00\n",
      "        ...  \n",
      "3041     6.35\n",
      "4348     9.75\n",
      "2112     9.95\n",
      "1437     9.20\n",
      "2802     5.50\n",
      "Name: Price, Length: 1204, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_predict=model.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666805263250855"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6888599319233908"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6725369186905636"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mae=mean_absolute_error(y_test,y_predict)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.267184980874596"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "Mse=mean_squared_error(y_test,y_predict)\n",
    "Mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6888599319233908"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2413e9ef188>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(x=y_predict, y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regr = RandomForestRegressor(n_estimators=20,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=20, n_jobs=None, oob_score=False,\n",
       "                      random_state=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833786548124606"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8622826672442867"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.58133333  1.7625     25.91416667 ... 10.7625     10.776\n",
      "  4.90591667]\n",
      "1463     1.50\n",
      "181      1.45\n",
      "2506    25.75\n",
      "3374     8.90\n",
      "5816     3.00\n",
      "        ...  \n",
      "3041     6.35\n",
      "4348     9.75\n",
      "2112     9.95\n",
      "1437     9.20\n",
      "2802     5.50\n",
      "Name: Price, Length: 1204, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_predict=regr.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4848577276382715"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae=mean_absolute_error(y_test,y_predict)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.167398226096442"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mse=mean_squared_error(y_test,y_predict)\n",
    "Mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8622826672442867"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(y_test, y_predict, edgecolors=(0, 0, 0))\n",
    "\n",
    "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "\n",
    "ax.set_xlabel('Actual')\n",
    "\n",
    "ax.set_ylabel('Predicted')\n",
    "\n",
    "ax.set_title(\"Ground Truth vs Predicted\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
      "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "reg= SVR().fit(X_train,y_train)\n",
    "print(reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.71126068  4.71441574 11.30798296 ...  7.28410109  6.64867893\n",
      "  4.26139503]\n",
      "1463     1.50\n",
      "181      1.45\n",
      "2506    25.75\n",
      "3374     8.90\n",
      "5816     3.00\n",
      "        ...  \n",
      "3041     6.35\n",
      "4348     9.75\n",
      "2112     9.95\n",
      "1437     9.20\n",
      "2802     5.50\n",
      "Name: Price, Length: 1204, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_predict=reg.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4233099899410122"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.51333460031274"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mse=mean_squared_error(y_test,y_predict)\n",
    "Mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.337323345190665"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae=mean_absolute_error(y_test,y_predict)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4233099899410122"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=10, splitter='best')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor=DecisionTreeRegressor(random_state=10)\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.45  1.55 25.64 ... 10.9  13.34  4.25]\n",
      "1463     1.50\n",
      "181      1.45\n",
      "2506    25.75\n",
      "3374     8.90\n",
      "5816     3.00\n",
      "        ...  \n",
      "3041     6.35\n",
      "4348     9.75\n",
      "2112     9.95\n",
      "1437     9.20\n",
      "2802     5.50\n",
      "Name: Price, Length: 1204, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_predict=regressor.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9414652547065336"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae=mean_absolute_error(y_test,y_predict)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.892122263819676"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mse=mean_squared_error(y_test,y_predict)\n",
    "Mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7195049132410389"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the price in test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we get the best test results from Random Forest so find thee price using random forest in test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train[['age','Fuel_Type','Transmission','Mileage','Engine',\"Location\",\"Power\",\"Owner_Type\"]]\n",
    "Y=train['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size=0.20,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regr = RandomForestRegressor(n_estimators=20,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=20, n_jobs=None, oob_score=False,\n",
       "                      random_state=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.77183333  1.7525     26.3045     ... 10.7675     10.585\n",
      "  4.71425   ]\n",
      "1463     1.50\n",
      "181      1.45\n",
      "2506    25.75\n",
      "3374     8.90\n",
      "5816     3.00\n",
      "        ...  \n",
      "3041     6.35\n",
      "4348     9.75\n",
      "2112     9.95\n",
      "1437     9.20\n",
      "2802     5.50\n",
      "Name: Price, Length: 1204, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_predict=regr.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_find=test[['age','Fuel_Type','Transmission','Mileage','Engine',\"Location\",\"Power\",\"Owner_Type\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.22        2.3855     16.7825     ...  2.81629762  5.00175\n",
      " 22.53066667]\n"
     ]
    }
   ],
   "source": [
    "y_find=regr.predict(X_find)\n",
    "print(y_find)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Price\"]=y_find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Company</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Model</th>\n",
       "      <th>age</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>4</td>\n",
       "      <td>40929</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "      <td>3.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>3</td>\n",
       "      <td>54493</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.70</td>\n",
       "      <td>796.0</td>\n",
       "      <td>47.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>2.385500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota Innova Crysta Touring Sport 2.4 MT</td>\n",
       "      <td>9</td>\n",
       "      <td>34000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>147.80</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "      <td>16.782500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>5</td>\n",
       "      <td>139000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.59</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "      <td>3.544500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>9</td>\n",
       "      <td>29000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>159</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>3.757792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name Location  Kilometers_Driven  \\\n",
       "0                    Maruti Alto K10 LXI CNG        4              40929   \n",
       "1              Maruti Alto 800 2016-2019 LXI        3              54493   \n",
       "2  Toyota Innova Crysta Touring Sport 2.4 MT        9              34000   \n",
       "3                       Toyota Etios Liva GD        5             139000   \n",
       "4                          Hyundai i20 Magna        9              29000   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Company  \\\n",
       "0         0            0          1    32.26   998.0   58.20    4.0       16   \n",
       "1         2            0          2    24.70   796.0   47.30    5.0       16   \n",
       "2         1            0          1    13.68  2393.0  147.80    7.0       26   \n",
       "3         1            0          1    23.59  1364.0    0.00    5.0       26   \n",
       "4         2            0          1    18.50  1197.0   82.85    5.0        9   \n",
       "\n",
       "   Car_Name  Model  age      Price  \n",
       "0        16    128    6   3.220000  \n",
       "1        16     56    7   2.385500  \n",
       "2        78     86    3  16.782500  \n",
       "3        60    139    8   3.544500  \n",
       "4       159    145    6   3.757792  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  }
 ],
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