deepak_kharol_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=pd.read_excel('Data_Train.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Location', 'Year', 'Kilometers_Driven', 'Fuel_Type',\n",
       "       'Transmission', 'Owner_Type', 'Mileage', 'Engine', 'Power', 'Seats',\n",
       "       'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>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",
       "  </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",
       "\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",
       "\n",
       "   Price  \n",
       "0   1.75  \n",
       "1  12.50  \n",
       "2   4.50  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 12)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_name=[]\n",
    "model_name=[]\n",
    "for index, rows in data_train.iterrows():\n",
    "    brand_name.append(rows['Name'].split()[0].lower())\n",
    "    model_name.append(rows['Name'].split()[1].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data_train.Year.value_counts().sort_index().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Univariate Analysis</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Location</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mumbai', 'Pune', 'Chennai', 'Coimbatore', 'Hyderabad', 'Jaipur',\n",
       "       'Kochi', 'Kolkata', 'Delhi', 'Bangalore', 'Ahmedabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Location.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Distribution among the cities</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data_train.Location.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Brand Name</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_name=[]\n",
    "model_name=[]\n",
    "for index, rows in data_train.iterrows():\n",
    "    #print(rows['Name'].split())\n",
    "    brand_name.append(rows['Name'].split()[0].lower().replace('-',''))\n",
    "    model_name.append(rows['Name'].split()[1].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['Brand']=brand_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10,5]\n",
    "data_train.Brand.value_counts().plot(kind='bar')\n",
    "plt.xlabel('Brand Name')\n",
    "plt.ylabel('No of cars')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Maruti and hyundai are the highest no of cars in the dataset</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Year</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean     2013.358199\n",
       "std         3.269742\n",
       "min      1998.000000\n",
       "25%      2011.000000\n",
       "50%      2014.000000\n",
       "75%      2016.000000\n",
       "max      2019.000000\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>We have data from 1998 to 2019</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train.Year.value_counts().plot(kind='bar')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('No of cars')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>More data is available from 2010-2018</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#a = np.array([1,2,3,4,5])\n",
    "p=[]\n",
    "for i in range(1,101,1):\n",
    "    p.append(np.percentile(data_train.Kilometers_Driven, i)) # return 50th percentile, e.g median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Kilometers Driven')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(90,101,1):\n",
    "    p.append(np.percentile(data_train.Kilometers_Driven, i)) # return 50th percentile, e.g median.\n",
    "\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Kilometers Driven')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Only one value is around 60L, which could be an outlier</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>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>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Skoda Octavia Ambition Plus 2.0 TDI AT</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013</td>\n",
       "      <td>775000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>19.3 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>141 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>skoda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Hyundai i10 Magna 1.2</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2009</td>\n",
       "      <td>620000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.36 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>78.9 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>Volkswagen Vento Diesel Highline</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2013</td>\n",
       "      <td>720000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.54 kmpl</td>\n",
       "      <td>1598 CC</td>\n",
       "      <td>103.6 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>BMW X5 xDrive 30d M Sport</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2017</td>\n",
       "      <td>6500000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>15.97 kmpl</td>\n",
       "      <td>2993 CC</td>\n",
       "      <td>258 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>bmw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name Location  Year  \\\n",
       "340   Skoda Octavia Ambition Plus 2.0 TDI AT  Kolkata  2013   \n",
       "358                    Hyundai i10 Magna 1.2  Chennai  2009   \n",
       "1860        Volkswagen Vento Diesel Highline  Chennai  2013   \n",
       "2328               BMW X5 xDrive 30d M Sport  Chennai  2017   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage  \\\n",
       "340              775000    Diesel    Automatic      First   19.3 kmpl   \n",
       "358              620000    Petrol       Manual      First  20.36 kmpl   \n",
       "1860             720000    Diesel       Manual      First  20.54 kmpl   \n",
       "2328            6500000    Diesel    Automatic      First  15.97 kmpl   \n",
       "\n",
       "       Engine      Power  Seats  Price       Brand  \n",
       "340   1968 CC    141 bhp    5.0    7.5       skoda  \n",
       "358   1197 CC   78.9 bhp    5.0    2.7     hyundai  \n",
       "1860  1598 CC  103.6 bhp    5.0    5.9  volkswagen  \n",
       "2328  2993 CC    258 bhp    5.0   65.0         bmw  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train.Kilometers_Driven>=600000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Fuel_Type</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel      3205\n",
       "Petrol      2746\n",
       "CNG           56\n",
       "LPG           10\n",
       "Electric       2\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Fuel_Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>More cars are of diesel and petrol type</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Transmission</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       4299\n",
       "Automatic    1720\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Transmission.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Owner Type</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             4929\n",
       "Second             968\n",
       "Third              113\n",
       "Fourth & Above       9\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Owner_Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Null columns</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mileage  Has Null values count :  2\n",
      "Engine  Has Null values count :  36\n",
      "Power  Has Null values count :  36\n",
      "Seats  Has Null values count :  42\n"
     ]
    }
   ],
   "source": [
    "for i in data_train.columns:\n",
    "    if len(data_train[data_train[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(data_train[data_train[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Mileage</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>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>Brand</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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    Brand  \n",
       "0   1.75   maruti  \n",
       "1  12.50  hyundai  \n",
       "2   4.50    honda  \n",
       "3   6.00   maruti  \n",
       "4  17.74     audi  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>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>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4446</th>\n",
       "      <td>Mahindra E Verito D4</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2016</td>\n",
       "      <td>50000</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72 CC</td>\n",
       "      <td>41 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>mahindra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>Toyota Prius 2009-2016 Z4</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2011</td>\n",
       "      <td>44000</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1798 CC</td>\n",
       "      <td>73 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.75</td>\n",
       "      <td>toyota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "4446       Mahindra E Verito D4  Chennai  2016              50000  Electric   \n",
       "4904  Toyota Prius 2009-2016 Z4   Mumbai  2011              44000  Electric   \n",
       "\n",
       "     Transmission Owner_Type Mileage   Engine   Power  Seats  Price     Brand  \n",
       "4446    Automatic      First     NaN    72 CC  41 bhp    5.0  13.00  mahindra  \n",
       "4904    Automatic      First     NaN  1798 CC  73 bhp    5.0  12.75    toyota  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train['Mileage'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['is_electric']=data_train['Fuel_Type'].apply(lambda x: 1 if x=='Electric' else 0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>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>Brand</th>\n",
       "      <th>is_electric</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>0</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>0</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>0</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>0</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>0</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    Brand  is_electric  \n",
       "0   1.75   maruti            0  \n",
       "1  12.50  hyundai            0  \n",
       "2   4.50    honda            0  \n",
       "3   6.00   maruti            0  \n",
       "4  17.74     audi            0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing the units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_cleaned=[]\n",
    "Engine_cleaned=[]\n",
    "power_cleaned=[]\n",
    "for index, rows in data_train.iterrows():\n",
    "    #print(rows['Name'].split())\n",
    "    try:\n",
    "        Mileage_cleaned.append(rows['Mileage'].split()[0])\n",
    "    except:\n",
    "        Mileage_cleaned.append(0)\n",
    "    try:\n",
    "        Engine_cleaned.append(rows['Engine'].split()[0])\n",
    "    except:\n",
    "        Engine_cleaned.append(0)\n",
    "    try:\n",
    "        if rows['Power'].split()[0]=='null':\n",
    "            power_cleaned.append(0)\n",
    "        else:\n",
    "            power_cleaned.append(rows['Power'].split()[0])\n",
    "    except:\n",
    "        power_cleaned.append(0)\n",
    "    #print(rows['Mileage'].split()[1].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['Mileage_cleaned']=Mileage_cleaned\n",
    "data_train.Mileage_cleaned = data_train.Mileage_cleaned.astype(float)\n",
    "data_train['Engine_cleaned']=Engine_cleaned\n",
    "data_train.Engine_cleaned = data_train.Engine_cleaned.astype(float)\n",
    "data_train['Power_cleaned']=power_cleaned\n",
    "data_train.Power_cleaned = data_train.Power_cleaned.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>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>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</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>0</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</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>0</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</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>0</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</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>0</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</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>0</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</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    Brand  is_electric  Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0   1.75   maruti            0            26.60           998.0          58.16  \n",
       "1  12.50  hyundai            0            19.67          1582.0         126.20  \n",
       "2   4.50    honda            0            18.20          1199.0          88.70  \n",
       "3   6.00   maruti            0            20.77          1248.0          88.76  \n",
       "4  17.74     audi            0            15.20          1968.0         140.80  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Engine</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.019018595041299"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[ (data_train.Engine_cleaned.between(2179,3000))]['Mileage_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_missing=[]\n",
    "for i,j in data_train[(data_train['Mileage_cleaned']==0)].iterrows():\n",
    "    if j.Engine_cleaned!=0:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data_train[(data_train.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50)) & (data_train.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(data_train[(data_train.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "    else:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data_train[(data_train.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.to_pickle('data_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data_train = pd.read_pickle('data_train')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Filling up null values for Mileage</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_train[(data_train['Mileage_cleaned']==0)]['Mileage_cleaned'].index.tolist():\n",
    "    data_train['Mileage_cleaned'].iloc[i]=Mileage_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.Mileage_cleaned[data_train.Mileage_cleaned == 0] = data_train['Mileage_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean       18.328940\n",
       "std         4.169318\n",
       "min         6.400000\n",
       "25%        15.300000\n",
       "50%        18.200000\n",
       "75%        21.100000\n",
       "max        33.540000\n",
       "Name: Mileage_cleaned, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Mileage_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mileage  Has Null values count :  2\n",
      "Engine  Has Null values count :  36\n",
      "Power  Has Null values count :  36\n",
      "Seats  Has Null values count :  42\n"
     ]
    }
   ],
   "source": [
    "for i in data_train.columns:\n",
    "    if len(data_train[data_train[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(data_train[data_train[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 17)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Filling up missing values for engine</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_M = data_train[(data_train['Fuel_Type']=='Petrol') & (data_train['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "D_M = data_train[(data_train['Fuel_Type']=='Diesel') & (data_train['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "D_A = data_train[(data_train['Fuel_Type']=='Diesel') & (data_train['Transmission']=='Automatic')].Engine_cleaned.mean()\n",
    "P_A = data_train[(data_train['Fuel_Type']=='Petrol') & (data_train['Transmission']=='Automatic')].Engine_cleaned.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.Engine_cleaned.loc[(data_train['Fuel_Type']=='Petrol') & (data_train['Transmission']=='Manual') & (data_train['Engine_cleaned']==0)] = P_M\n",
    "data_train.Engine_cleaned.loc[(data_train['Fuel_Type']=='Diesel') & (data_train['Transmission']=='Manual') & (data_train['Engine_cleaned']==0)] = D_M\n",
    "data_train.Engine_cleaned.loc[(data_train['Fuel_Type']=='Diesel') & (data_train['Transmission']=='Automatic') & (data_train['Engine_cleaned']==0)] = D_A\n",
    "data_train.Engine_cleaned.loc[(data_train['Fuel_Type']=='Petrol') & (data_train['Transmission']=='Automatic') & (data_train['Engine_cleaned']==0)] = P_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean     1620.273483\n",
       "std       600.566802\n",
       "min        72.000000\n",
       "25%      1198.000000\n",
       "50%      1493.000000\n",
       "75%      1984.000000\n",
       "max      5998.000000\n",
       "Name: Engine_cleaned, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Engine_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Filling up missing values for power</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>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>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Ford Fiesta 1.4 SXi TDCi</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2008</td>\n",
       "      <td>111111</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1399 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>ford</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Hyundai Santro Xing XL</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2005</td>\n",
       "      <td>87591</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Hyundai Santro Xing XO</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>73745</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2005</td>\n",
       "      <td>102000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2008</td>\n",
       "      <td>80759</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Honda City 1.5 GXI</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>60006</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.95</td>\n",
       "      <td>honda</td>\n",
       "      <td>0</td>\n",
       "      <td>18.295504</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Maruti Swift 1.3 VXi</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2010</td>\n",
       "      <td>42001</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>16.1 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.11</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Maruti Swift VDI BSIV</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013</td>\n",
       "      <td>47000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2007</td>\n",
       "      <td>72000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Hyundai Santro Xing XL</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2006</td>\n",
       "      <td>99000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Hyundai Santro Xing XL</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2006</td>\n",
       "      <td>58000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Maruti Swift VDI BSIV</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2011</td>\n",
       "      <td>60020</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>50958</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Volkswagen Jetta 2007-2011 1.9 L TDI</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2010</td>\n",
       "      <td>42021</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "      <td>16.923899</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2006</td>\n",
       "      <td>108000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Hyundai Santro GLS I - Euro I</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2012</td>\n",
       "      <td>50243</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Ford Fiesta 1.6 SXI ABS Duratec</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2009</td>\n",
       "      <td>80524</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.3 kmpl</td>\n",
       "      <td>1596 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>ford</td>\n",
       "      <td>0</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>1596.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Hyundai Santro Xing XO</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2006</td>\n",
       "      <td>83072</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Hyundai Santro LS zipPlus</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2002</td>\n",
       "      <td>70000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>Hyundai Santro Xing XP</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2004</td>\n",
       "      <td>200000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Tata Indica DLS</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2009</td>\n",
       "      <td>82000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>13.5 kmpl</td>\n",
       "      <td>1405 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>tata</td>\n",
       "      <td>0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>1405.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>Maruti Swift 1.3 VXi</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2006</td>\n",
       "      <td>97800</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>16.1 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>Maruti Swift VDI BSIV</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2007</td>\n",
       "      <td>60000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>Hyundai Santro Xing XO</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>71297</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>Land Rover Range Rover 3.0 D</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2008</td>\n",
       "      <td>55001</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.50</td>\n",
       "      <td>land</td>\n",
       "      <td>0</td>\n",
       "      <td>18.295504</td>\n",
       "      <td>2277.399819</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Toyota Etios Liva G</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2012</td>\n",
       "      <td>29605</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.3 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Smart Fortwo CDI AT</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2008</td>\n",
       "      <td>103000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>799 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>smart</td>\n",
       "      <td>0</td>\n",
       "      <td>21.524298</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>Porsche Cayman 2009-2012 S</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2010</td>\n",
       "      <td>10000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>9.0 kmpl</td>\n",
       "      <td>3436 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>porsche</td>\n",
       "      <td>0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3436.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>Toyota Etios GD</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2012</td>\n",
       "      <td>60000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>23.590000</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2012</td>\n",
       "      <td>225000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>23.590000</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>Fiat Punto 1.2 Dynamic</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2017</td>\n",
       "      <td>17941</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>15.7 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.93</td>\n",
       "      <td>fiat</td>\n",
       "      <td>0</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4709</th>\n",
       "      <td>Maruti 1000 AC</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>1998</td>\n",
       "      <td>104000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.0 kmpl</td>\n",
       "      <td>970 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>970.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4712</th>\n",
       "      <td>Hyundai Santro Xing XG</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2003</td>\n",
       "      <td>80000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4714</th>\n",
       "      <td>Mercedes-Benz E-Class 220 CDI</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2011</td>\n",
       "      <td>78000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.0 kmpl</td>\n",
       "      <td>2148 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>mercedesbenz</td>\n",
       "      <td>0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2148.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4744</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2005</td>\n",
       "      <td>45000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>Maruti Esteem Vxi</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2004</td>\n",
       "      <td>95000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>15.9 kmpl</td>\n",
       "      <td>1298 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>1298.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4886</th>\n",
       "      <td>Hyundai Santro Xing XL AT eRLX Euro II</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>76000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>Hyundai Santro Xing XL AT eRLX Euro III</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>71000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4952</th>\n",
       "      <td>Fiat Punto 1.4 Emotion</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2010</td>\n",
       "      <td>47000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>14.6 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.49</td>\n",
       "      <td>fiat</td>\n",
       "      <td>0</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4954</th>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>62601</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>23.590000</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>Maruti Swift 1.3 VXi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2006</td>\n",
       "      <td>63000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>16.1 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.60</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5065</th>\n",
       "      <td>Maruti Swift VDI BSIV W ABS</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016</td>\n",
       "      <td>64000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5119</th>\n",
       "      <td>Hyundai Santro Xing XP</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2008</td>\n",
       "      <td>45500</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5185</th>\n",
       "      <td>Maruti Swift 1.3 LXI</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2012</td>\n",
       "      <td>52000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>16.1 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.65</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2005</td>\n",
       "      <td>126000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5270</th>\n",
       "      <td>Honda City 1.5 GXI</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2002</td>\n",
       "      <td>53000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.85</td>\n",
       "      <td>honda</td>\n",
       "      <td>0</td>\n",
       "      <td>18.295504</td>\n",
       "      <td>1222.527154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5426</th>\n",
       "      <td>Hyundai Santro Xing XL</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2006</td>\n",
       "      <td>85000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5438</th>\n",
       "      <td>Ford Endeavour XLT TDCi 4X4</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2007</td>\n",
       "      <td>116000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>10.9 kmpl</td>\n",
       "      <td>2499 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>ford</td>\n",
       "      <td>0</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>2499.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5458</th>\n",
       "      <td>Maruti Swift VDI BSIV</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2014</td>\n",
       "      <td>81609</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5529</th>\n",
       "      <td>Hyundai Santro LP - Euro II</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2005</td>\n",
       "      <td>105000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>999 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.708319</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2007</td>\n",
       "      <td>72000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5647</th>\n",
       "      <td>Toyota Qualis Fleet A3</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2001</td>\n",
       "      <td>227000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Fourth &amp; Above</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>2446 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>12.814158</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5755</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2008</td>\n",
       "      <td>51000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2007</td>\n",
       "      <td>46707</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5861</th>\n",
       "      <td>Hyundai Santro Xing XO</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2007</td>\n",
       "      <td>79000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5873</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2006</td>\n",
       "      <td>47200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5893</th>\n",
       "      <td>Maruti Estilo LXI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2008</td>\n",
       "      <td>51000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>19.5 kmpl</td>\n",
       "      <td>1061 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5925</th>\n",
       "      <td>Skoda Laura Classic 1.8 TSI</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2010</td>\n",
       "      <td>85000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.5 kmpl</td>\n",
       "      <td>1798 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>skoda</td>\n",
       "      <td>0</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5943</th>\n",
       "      <td>Mahindra Jeep MM 540 DP</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2002</td>\n",
       "      <td>75000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>2112 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>mahindra</td>\n",
       "      <td>0</td>\n",
       "      <td>14.794087</td>\n",
       "      <td>2112.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5985</th>\n",
       "      <td>Toyota Etios GD</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013</td>\n",
       "      <td>70000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>23.590000</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Name    Location  Year  \\\n",
       "76                   Ford Fiesta 1.4 SXi TDCi      Jaipur  2008   \n",
       "79                     Hyundai Santro Xing XL   Hyderabad  2005   \n",
       "89                     Hyundai Santro Xing XO   Hyderabad  2007   \n",
       "120      Hyundai Santro Xing XL eRLX Euro III      Mumbai  2005   \n",
       "143       Hyundai Santro Xing XO eRLX Euro II       Kochi  2008   \n",
       "194                        Honda City 1.5 GXI   Ahmedabad  2007   \n",
       "208                      Maruti Swift 1.3 VXi     Kolkata  2010   \n",
       "227                     Maruti Swift VDI BSIV       Delhi  2013   \n",
       "245       Hyundai Santro Xing XO eRLX Euro II      Mumbai  2007   \n",
       "262                    Hyundai Santro Xing XL   Hyderabad  2006   \n",
       "307                    Hyundai Santro Xing XL     Chennai  2006   \n",
       "308                     Maruti Swift VDI BSIV     Kolkata  2011   \n",
       "386      Hyundai Santro Xing XL eRLX Euro III   Hyderabad  2007   \n",
       "424      Volkswagen Jetta 2007-2011 1.9 L TDI   Hyderabad  2010   \n",
       "428       Hyundai Santro Xing XO eRLX Euro II        Pune  2006   \n",
       "443             Hyundai Santro GLS I - Euro I  Coimbatore  2012   \n",
       "472           Ford Fiesta 1.6 SXI ABS Duratec     Chennai  2009   \n",
       "575                    Hyundai Santro Xing XO     Chennai  2006   \n",
       "631                 Hyundai Santro LS zipPlus     Chennai  2002   \n",
       "647                    Hyundai Santro Xing XP      Jaipur  2004   \n",
       "648                           Tata Indica DLS      Jaipur  2009   \n",
       "733                      Maruti Swift 1.3 VXi     Chennai  2006   \n",
       "739                     Maruti Swift VDI BSIV   Bangalore  2007   \n",
       "748                    Hyundai Santro Xing XO   Hyderabad  2007   \n",
       "749              Land Rover Range Rover 3.0 D      Mumbai  2008   \n",
       "829                       Toyota Etios Liva G      Mumbai  2012   \n",
       "915                       Smart Fortwo CDI AT        Pune  2008   \n",
       "926                Porsche Cayman 2009-2012 S   Hyderabad  2010   \n",
       "934                           Toyota Etios GD     Kolkata  2012   \n",
       "1068                     Toyota Etios Liva GD        Pune  2012   \n",
       "...                                       ...         ...   ...   \n",
       "4697                   Fiat Punto 1.2 Dynamic       Kochi  2017   \n",
       "4709                           Maruti 1000 AC   Hyderabad  1998   \n",
       "4712                   Hyundai Santro Xing XG        Pune  2003   \n",
       "4714            Mercedes-Benz E-Class 220 CDI   Ahmedabad  2011   \n",
       "4744     Hyundai Santro Xing XL eRLX Euro III     Kolkata  2005   \n",
       "4830                        Maruti Esteem Vxi     Chennai  2004   \n",
       "4886   Hyundai Santro Xing XL AT eRLX Euro II   Hyderabad  2007   \n",
       "4900  Hyundai Santro Xing XL AT eRLX Euro III   Hyderabad  2007   \n",
       "4952                   Fiat Punto 1.4 Emotion     Kolkata  2010   \n",
       "4954                     Toyota Etios Liva GD  Coimbatore  2013   \n",
       "5015                     Maruti Swift 1.3 VXi       Delhi  2006   \n",
       "5065              Maruti Swift VDI BSIV W ABS   Hyderabad  2016   \n",
       "5119                   Hyundai Santro Xing XP     Kolkata  2008   \n",
       "5185                     Maruti Swift 1.3 LXI       Delhi  2012   \n",
       "5228     Hyundai Santro Xing XL eRLX Euro III        Pune  2005   \n",
       "5270                       Honda City 1.5 GXI   Bangalore  2002   \n",
       "5426                   Hyundai Santro Xing XL     Chennai  2006   \n",
       "5438              Ford Endeavour XLT TDCi 4X4      Mumbai  2007   \n",
       "5458                    Maruti Swift VDI BSIV   Hyderabad  2014   \n",
       "5529              Hyundai Santro LP - Euro II     Chennai  2005   \n",
       "5533      Hyundai Santro Xing XO eRLX Euro II        Pune  2007   \n",
       "5647                   Toyota Qualis Fleet A3      Mumbai  2001   \n",
       "5755     Hyundai Santro Xing XL eRLX Euro III   Ahmedabad  2008   \n",
       "5759     Hyundai Santro Xing XL eRLX Euro III     Kolkata  2007   \n",
       "5861                   Hyundai Santro Xing XO     Chennai  2007   \n",
       "5873      Hyundai Santro Xing XO eRLX Euro II        Pune  2006   \n",
       "5893                        Maruti Estilo LXI     Chennai  2008   \n",
       "5925              Skoda Laura Classic 1.8 TSI        Pune  2010   \n",
       "5943                  Mahindra Jeep MM 540 DP     Chennai  2002   \n",
       "5985                          Toyota Etios GD       Delhi  2013   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission      Owner_Type     Mileage  \\\n",
       "76               111111    Diesel       Manual           First   17.8 kmpl   \n",
       "79                87591    Petrol       Manual           First    0.0 kmpl   \n",
       "89                73745    Petrol       Manual           First   17.0 kmpl   \n",
       "120              102000    Petrol       Manual          Second   17.0 kmpl   \n",
       "143               80759    Petrol       Manual           Third   17.0 kmpl   \n",
       "194               60006    Petrol       Manual           First    0.0 kmpl   \n",
       "208               42001    Petrol       Manual           First   16.1 kmpl   \n",
       "227               47000    Diesel       Manual           First   17.8 kmpl   \n",
       "245               72000    Petrol       Manual           Third   17.0 kmpl   \n",
       "262               99000    Petrol       Manual           First    0.0 kmpl   \n",
       "307               58000    Petrol       Manual          Second    0.0 kmpl   \n",
       "308               60020    Diesel       Manual           First   17.8 kmpl   \n",
       "386               50958    Petrol       Manual           First   17.0 kmpl   \n",
       "424               42021    Diesel       Manual           First    0.0 kmpl   \n",
       "428              108000    Petrol       Manual          Second   17.0 kmpl   \n",
       "443               50243    Petrol       Manual           First    0.0 kmpl   \n",
       "472               80524    Petrol       Manual          Second   15.3 kmpl   \n",
       "575               83072    Petrol       Manual           First   17.0 kmpl   \n",
       "631               70000    Petrol       Manual           Third    0.0 kmpl   \n",
       "647              200000    Petrol       Manual           First    0.0 kmpl   \n",
       "648               82000    Diesel       Manual          Second   13.5 kmpl   \n",
       "733               97800    Petrol       Manual           Third   16.1 kmpl   \n",
       "739               60000    Diesel       Manual          Second   17.8 kmpl   \n",
       "748               71297    Petrol       Manual           First   17.0 kmpl   \n",
       "749               55001    Diesel    Automatic          Second    0.0 kmpl   \n",
       "829               29605    Petrol       Manual           First   18.3 kmpl   \n",
       "915              103000    Diesel    Automatic          Second    0.0 kmpl   \n",
       "926               10000    Petrol       Manual           First    9.0 kmpl   \n",
       "934               60000    Diesel       Manual           First  23.59 kmpl   \n",
       "1068             225000    Diesel       Manual          Second  23.59 kmpl   \n",
       "...                 ...       ...          ...             ...         ...   \n",
       "4697              17941    Petrol       Manual           First   15.7 kmpl   \n",
       "4709             104000    Petrol       Manual          Second   15.0 kmpl   \n",
       "4712              80000    Petrol       Manual          Second   17.0 kmpl   \n",
       "4714              78000    Diesel       Manual          Second   15.0 kmpl   \n",
       "4744              45000    Petrol       Manual           First   17.0 kmpl   \n",
       "4830              95000    Petrol       Manual           First   15.9 kmpl   \n",
       "4886              76000    Petrol    Automatic           First   17.0 kmpl   \n",
       "4900              71000    Petrol    Automatic           First   17.0 kmpl   \n",
       "4952              47000    Petrol       Manual           First   14.6 kmpl   \n",
       "4954              62601    Diesel       Manual           First  23.59 kmpl   \n",
       "5015              63000    Petrol       Manual           First   16.1 kmpl   \n",
       "5065              64000    Diesel       Manual           First   17.8 kmpl   \n",
       "5119              45500    Petrol       Manual          Second    0.0 kmpl   \n",
       "5185              52000    Petrol       Manual           First   16.1 kmpl   \n",
       "5228             126000    Petrol       Manual          Second   17.0 kmpl   \n",
       "5270              53000    Petrol       Manual          Second    0.0 kmpl   \n",
       "5426              85000    Petrol       Manual           Third    0.0 kmpl   \n",
       "5438             116000    Diesel       Manual           Third   10.9 kmpl   \n",
       "5458              81609    Diesel       Manual           First   17.8 kmpl   \n",
       "5529             105000    Petrol       Manual           First    0.0 kmpl   \n",
       "5533              72000    Petrol       Manual          Second   17.0 kmpl   \n",
       "5647             227000    Diesel       Manual  Fourth & Above    0.0 kmpl   \n",
       "5755              51000    Petrol       Manual           First   17.0 kmpl   \n",
       "5759              46707    Petrol       Manual           First   17.0 kmpl   \n",
       "5861              79000    Petrol       Manual           First   17.0 kmpl   \n",
       "5873              47200    Petrol       Manual          Second   17.0 kmpl   \n",
       "5893              51000    Petrol       Manual          Second   19.5 kmpl   \n",
       "5925              85000    Petrol       Manual           First   17.5 kmpl   \n",
       "5943              75000    Diesel       Manual           First    0.0 kmpl   \n",
       "5985              70000    Diesel       Manual           First  23.59 kmpl   \n",
       "\n",
       "       Engine     Power  Seats  Price         Brand  is_electric  \\\n",
       "76    1399 CC  null bhp    5.0   2.00          ford            0   \n",
       "79    1086 CC  null bhp    5.0   1.30       hyundai            0   \n",
       "89    1086 CC  null bhp    5.0   2.10       hyundai            0   \n",
       "120   1086 CC  null bhp    5.0   0.85       hyundai            0   \n",
       "143   1086 CC  null bhp    5.0   1.67       hyundai            0   \n",
       "194       NaN       NaN    NaN   2.95         honda            0   \n",
       "208       NaN       NaN    NaN   2.11        maruti            0   \n",
       "227   1248 CC  null bhp    5.0   4.10        maruti            0   \n",
       "245   1086 CC  null bhp    5.0   0.95       hyundai            0   \n",
       "262   1086 CC  null bhp    5.0   1.75       hyundai            0   \n",
       "307   1086 CC  null bhp    5.0   1.50       hyundai            0   \n",
       "308   1248 CC  null bhp    5.0   2.50        maruti            0   \n",
       "386   1086 CC  null bhp    5.0   1.95       hyundai            0   \n",
       "424   1968 CC  null bhp    5.0   5.45    volkswagen            0   \n",
       "428   1086 CC  null bhp    5.0   1.20       hyundai            0   \n",
       "443   1086 CC  null bhp    5.0   3.35       hyundai            0   \n",
       "472   1596 CC  null bhp    5.0   2.50          ford            0   \n",
       "575   1086 CC  null bhp    5.0   1.40       hyundai            0   \n",
       "631   1086 CC  null bhp    5.0   0.65       hyundai            0   \n",
       "647   1086 CC  null bhp    5.0   0.80       hyundai            0   \n",
       "648   1405 CC  null bhp    5.0   0.75          tata            0   \n",
       "733       NaN       NaN    NaN   1.75        maruti            0   \n",
       "739   1248 CC  null bhp    5.0   3.40        maruti            0   \n",
       "748   1086 CC  null bhp    5.0   2.20       hyundai            0   \n",
       "749       NaN       NaN    NaN  26.50          land            0   \n",
       "829   1197 CC  null bhp    5.0   2.44        toyota            0   \n",
       "915    799 CC  null bhp    2.0   3.00         smart            0   \n",
       "926   3436 CC  null bhp    2.0  40.00       porsche            0   \n",
       "934   1364 CC  null bhp    5.0   2.95        toyota            0   \n",
       "1068  1364 CC  null bhp    5.0   2.60        toyota            0   \n",
       "...       ...       ...    ...    ...           ...          ...   \n",
       "4697      NaN       NaN    NaN   3.93          fiat            0   \n",
       "4709   970 CC  null bhp    5.0   0.85        maruti            0   \n",
       "4712      NaN       NaN    NaN   0.90       hyundai            0   \n",
       "4714  2148 CC  null bhp    5.0  14.50  mercedesbenz            0   \n",
       "4744  1086 CC  null bhp    5.0   0.95       hyundai            0   \n",
       "4830  1298 CC  null bhp    5.0   0.80        maruti            0   \n",
       "4886  1086 CC  null bhp    5.0   2.40       hyundai            0   \n",
       "4900  1086 CC  null bhp    5.0   2.40       hyundai            0   \n",
       "4952      NaN       NaN    NaN   1.49          fiat            0   \n",
       "4954  1364 CC  null bhp    5.0   4.67        toyota            0   \n",
       "5015      NaN       NaN    NaN   1.60        maruti            0   \n",
       "5065  1248 CC  null bhp    5.0   5.99        maruti            0   \n",
       "5119  1086 CC  null bhp    5.0   1.17       hyundai            0   \n",
       "5185      NaN       NaN    NaN   3.65        maruti            0   \n",
       "5228  1086 CC  null bhp    5.0   1.20       hyundai            0   \n",
       "5270      NaN       NaN    NaN   1.85         honda            0   \n",
       "5426  1086 CC  null bhp    5.0   1.30       hyundai            0   \n",
       "5438  2499 CC  null bhp    7.0   3.75          ford            0   \n",
       "5458  1248 CC  null bhp    5.0   5.55        maruti            0   \n",
       "5529   999 CC  null bhp    5.0   1.75       hyundai            0   \n",
       "5533  1086 CC  null bhp    5.0   1.11       hyundai            0   \n",
       "5647  2446 CC  null bhp    8.0   2.20        toyota            0   \n",
       "5755  1086 CC  null bhp    5.0   1.75       hyundai            0   \n",
       "5759  1086 CC  null bhp    5.0   0.99       hyundai            0   \n",
       "5861  1086 CC  null bhp    5.0   1.85       hyundai            0   \n",
       "5873  1086 CC  null bhp    5.0   1.20       hyundai            0   \n",
       "5893  1061 CC  null bhp    NaN   1.75        maruti            0   \n",
       "5925  1798 CC  null bhp    5.0   2.85         skoda            0   \n",
       "5943  2112 CC  null bhp    6.0   1.70      mahindra            0   \n",
       "5985  1364 CC  null bhp    5.0   3.88        toyota            0   \n",
       "\n",
       "      Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "76          17.800000     1399.000000            0.0  \n",
       "79          19.988571     1086.000000            0.0  \n",
       "89          17.000000     1086.000000            0.0  \n",
       "120         17.000000     1086.000000            0.0  \n",
       "143         17.000000     1086.000000            0.0  \n",
       "194         18.295504     1222.527154            0.0  \n",
       "208         16.100000     1222.527154            0.0  \n",
       "227         17.800000     1248.000000            0.0  \n",
       "245         17.000000     1086.000000            0.0  \n",
       "262         19.988571     1086.000000            0.0  \n",
       "307         19.988571     1086.000000            0.0  \n",
       "308         17.800000     1248.000000            0.0  \n",
       "386         17.000000     1086.000000            0.0  \n",
       "424         16.923899     1968.000000            0.0  \n",
       "428         17.000000     1086.000000            0.0  \n",
       "443         19.988571     1086.000000            0.0  \n",
       "472         15.300000     1596.000000            0.0  \n",
       "575         17.000000     1086.000000            0.0  \n",
       "631         19.988571     1086.000000            0.0  \n",
       "647         19.988571     1086.000000            0.0  \n",
       "648         13.500000     1405.000000            0.0  \n",
       "733         16.100000     1222.527154            0.0  \n",
       "739         17.800000     1248.000000            0.0  \n",
       "748         17.000000     1086.000000            0.0  \n",
       "749         18.295504     2277.399819            0.0  \n",
       "829         18.300000     1197.000000            0.0  \n",
       "915         21.524298      799.000000            0.0  \n",
       "926          9.000000     3436.000000            0.0  \n",
       "934         23.590000     1364.000000            0.0  \n",
       "1068        23.590000     1364.000000            0.0  \n",
       "...               ...             ...            ...  \n",
       "4697        15.700000     1222.527154            0.0  \n",
       "4709        15.000000      970.000000            0.0  \n",
       "4712        17.000000     1222.527154            0.0  \n",
       "4714        15.000000     2148.000000            0.0  \n",
       "4744        17.000000     1086.000000            0.0  \n",
       "4830        15.900000     1298.000000            0.0  \n",
       "4886        17.000000     1086.000000            0.0  \n",
       "4900        17.000000     1086.000000            0.0  \n",
       "4952        14.600000     1222.527154            0.0  \n",
       "4954        23.590000     1364.000000            0.0  \n",
       "5015        16.100000     1222.527154            0.0  \n",
       "5065        17.800000     1248.000000            0.0  \n",
       "5119        19.988571     1086.000000            0.0  \n",
       "5185        16.100000     1222.527154            0.0  \n",
       "5228        17.000000     1086.000000            0.0  \n",
       "5270        18.295504     1222.527154            0.0  \n",
       "5426        19.988571     1086.000000            0.0  \n",
       "5438        10.900000     2499.000000            0.0  \n",
       "5458        17.800000     1248.000000            0.0  \n",
       "5529        19.708319      999.000000            0.0  \n",
       "5533        17.000000     1086.000000            0.0  \n",
       "5647        12.814158     2446.000000            0.0  \n",
       "5755        17.000000     1086.000000            0.0  \n",
       "5759        17.000000     1086.000000            0.0  \n",
       "5861        17.000000     1086.000000            0.0  \n",
       "5873        17.000000     1086.000000            0.0  \n",
       "5893        19.500000     1061.000000            0.0  \n",
       "5925        17.500000     1798.000000            0.0  \n",
       "5943        14.794087     2112.000000            0.0  \n",
       "5985        23.590000     1364.000000            0.0  \n",
       "\n",
       "[143 rows x 17 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train.Power_cleaned == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "Power_missing=[]\n",
    "for i,j in data_train[(data_train['Power_cleaned']==0)].iterrows():\n",
    "    Power_missing.append(data_train[(data_train.Mileage_cleaned.between(j.Mileage_cleaned-20,j.Mileage_cleaned+20)) \n",
    "                                      & (data_train.Engine_cleaned.between(j.Engine_cleaned-200,j.Engine_cleaned+200))]\n",
    "                           ['Power_cleaned'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean      110.562372\n",
       "std        55.956041\n",
       "min         0.000000\n",
       "25%        74.000000\n",
       "50%        91.100000\n",
       "75%       138.030000\n",
       "max       560.000000\n",
       "Name: Power_cleaned, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Power_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_train[(data_train['Power_cleaned']==0)]['Power_cleaned'].index.tolist():\n",
    "    data_train['Power_cleaned'].iloc[i]=Power_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Seats</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.278735151413753"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Seats.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 17)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Filling up missing values</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats_missing=[]\n",
    "for i,j in data_train[(data_train.Seats.isnull()) | (data_train.Seats== 0)].iterrows():\n",
    "    seats_missing.append(data_train[data_train['Name'].str.match(\" \".join((j['Name'].split()[0],j['Name'].split()[1])))]['Seats'].mean())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_train[(data_train.Seats.isnull()) | (data_train.Seats== 0)].index.tolist():\n",
    "    data_train['Seats'].iloc[i]=seats_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.Seats[data_train.Seats == 0] = data_train['Seats'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 17)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Same for Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.read_excel('Data_Test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_name=[]\n",
    "model_name=[]\n",
    "for index, rows in data_test.iterrows():\n",
    "    brand_name.append(rows['Name'].split()[0].lower().replace('-',''))\n",
    "    model_name.append(rows['Name'].split()[1].lower())\n",
    "\n",
    "data_test['Brand']=brand_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine  Has Null values count :  10\n",
      "Power  Has Null values count :  10\n",
      "Seats  Has Null values count :  11\n"
     ]
    }
   ],
   "source": [
    "for i in data_test.columns:\n",
    "    if len(data_test[data_test[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(data_test[data_test[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test['is_electric']=data_test['Fuel_Type'].apply(lambda x: 1 if x=='Electric' else 0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_cleaned=[]\n",
    "Engine_cleaned=[]\n",
    "power_cleaned=[]\n",
    "for index, rows in data_test.iterrows():\n",
    "    \n",
    "    try:\n",
    "        Mileage_cleaned.append(rows['Mileage'].split()[0])\n",
    "    except:\n",
    "        Mileage_cleaned.append(0)\n",
    "    try:\n",
    "        Engine_cleaned.append(rows['Engine'].split()[0])\n",
    "    except:\n",
    "        Engine_cleaned.append(0)\n",
    "    try:\n",
    "        if rows['Power'].split()[0]=='null':\n",
    "            power_cleaned.append(0)\n",
    "        else:\n",
    "            power_cleaned.append(rows['Power'].split()[0])\n",
    "    except:\n",
    "        power_cleaned.append(0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test['Mileage_cleaned']=Mileage_cleaned\n",
    "data_test.Mileage_cleaned = data_test.Mileage_cleaned.astype(float)\n",
    "data_test['Engine_cleaned']=Engine_cleaned\n",
    "data_test.Engine_cleaned = data_test.Engine_cleaned.astype(float)\n",
    "data_test['Power_cleaned']=power_cleaned\n",
    "data_test.Power_cleaned = data_test.Power_cleaned.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_missing=[]\n",
    "for i,j in data_test[(data_test['Mileage_cleaned']==0)].iterrows():\n",
    "    if j.Engine_cleaned!=0:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data_test[(data_test.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50)) & (data_test.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(data_test[(data_test.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "    else:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data_test[(data_test.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_test[(data_test['Mileage_cleaned']==0)]['Mileage_cleaned'].index.tolist():\n",
    "    data_test['Mileage_cleaned'].iloc[i]=Mileage_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.Mileage_cleaned[data_test.Mileage_cleaned == 0] = data_test['Mileage_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine  Has Null values count :  10\n",
      "Power  Has Null values count :  10\n",
      "Seats  Has Null values count :  11\n"
     ]
    }
   ],
   "source": [
    "for i in data_test.columns:\n",
    "    if len(data_test[data_test[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(data_test[data_test[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_M = data_test[(data_test['Fuel_Type']=='Petrol') & (data_test['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "D_M = data_test[(data_test['Fuel_Type']=='Diesel') & (data_test['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "D_A = data_test[(data_test['Fuel_Type']=='Diesel') & (data_test['Transmission']=='Automatic')].Engine_cleaned.mean()\n",
    "P_A = data_test[(data_test['Fuel_Type']=='Petrol') & (data_test['Transmission']=='Automatic')].Engine_cleaned.mean()\n",
    "\n",
    "data_test.Engine_cleaned.loc[(data_test['Fuel_Type']=='Petrol') & (data_test['Transmission']=='Manual') & (data_test['Engine_cleaned']==0)] = P_M\n",
    "data_test.Engine_cleaned.loc[(data_test['Fuel_Type']=='Diesel') & (data_test['Transmission']=='Manual') & (data_test['Engine_cleaned']==0)] = D_M\n",
    "data_test.Engine_cleaned.loc[(data_test['Fuel_Type']=='Diesel') & (data_test['Transmission']=='Automatic') & (data_test['Engine_cleaned']==0)] = D_A\n",
    "data_test.Engine_cleaned.loc[(data_test['Fuel_Type']=='Petrol') & (data_test['Transmission']=='Automatic') & (data_test['Engine_cleaned']==0)] = P_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "Power_missing=[]\n",
    "for i,j in data_test[(data_test['Power_cleaned']==0)].iterrows():\n",
    "    Power_missing.append(data_test[(data_test.Mileage_cleaned.between(j.Mileage_cleaned-20,j.Mileage_cleaned+20)) \n",
    "                                      & (data_test.Engine_cleaned.between(j.Engine_cleaned-200,j.Engine_cleaned+200))]\n",
    "                           ['Power_cleaned'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_test[(data_test['Power_cleaned']==0)]['Power_cleaned'].index.tolist():\n",
    "    data_test['Power_cleaned'].iloc[i]=Power_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats_missing=[]\n",
    "for i,j in data_test[(data_test.Seats.isnull()) | (data_test.Seats== 0)].iterrows():\n",
    "    seats_missing.append(round(data_test[data_test['Name'].str.match(\" \".join((j['Name'].split()[0],j['Name'].split()[1])))]['Seats'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "j=0\n",
    "for i in data_test[(data_test.Seats.isnull()) | (data_test.Seats== 0)].index.tolist():\n",
    "    data_test['Seats'].iloc[i]=seats_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.Seats[data_test.Seats == 0] = data_test['Seats'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Saving data to pickle file</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.to_pickle('data_test_after_cleanup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.to_pickle('data_train_after_cleanup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_cleaned = pd.read_pickle('data_train_after_cleanup')\n",
    "data_test_cleaned = pd.read_pickle('data_test_after_cleanup')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Converting categorical variables into one hot encoded features</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1234, 16)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 17)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_dup=data_train_cleaned.copy()\n",
    "data_test_dup=data_test_cleaned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_target = data_train_cleaned['Price']\n",
    "data_train_final = data_train_cleaned.drop(['Name','Mileage','Engine','Power','Price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019,)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_target.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'Year', 'Kilometers_Driven', 'Fuel_Type', 'Transmission',\n",
       "       'Owner_Type', 'Seats', 'Brand', 'is_electric', 'Mileage_cleaned',\n",
       "       'Engine_cleaned', 'Power_cleaned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_test_target = data_test_cleaned['Price']\n",
    "data_test_final = data_test_cleaned.drop(['Name','Mileage','Engine','Power'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>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>Seats</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>4.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>24.70</td>\n",
       "      <td>796.0</td>\n",
       "      <td>47.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2017</td>\n",
       "      <td>34000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>7.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>147.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2012</td>\n",
       "      <td>139000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>23.59</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>84.273111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
       "0       Delhi  2014              40929       CNG       Manual      First   \n",
       "1  Coimbatore  2013              54493    Petrol       Manual     Second   \n",
       "2      Mumbai  2017              34000    Diesel       Manual      First   \n",
       "3   Hyderabad  2012             139000    Diesel       Manual      First   \n",
       "4      Mumbai  2014              29000    Petrol       Manual      First   \n",
       "\n",
       "   Seats    Brand  is_electric  Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0    4.0   maruti            0            32.26           998.0      58.200000  \n",
       "1    5.0   maruti            0            24.70           796.0      47.300000  \n",
       "2    7.0   toyota            0            13.68          2393.0     147.800000  \n",
       "3    5.0   toyota            0            23.59          1364.0      84.273111  \n",
       "4    5.0  hyundai            0            18.50          1197.0      82.850000  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Normalizing train data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "for i in ['Kilometers_Driven','Mileage_cleaned','Engine_cleaned', 'Power_cleaned']:\n",
    "    data_train_final[i] = MinMaxScaler(feature_range=(0,1)).fit_transform(data_train_final[i].values.astype(float).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019,)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_target.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Normalizing test data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "for i in ['Kilometers_Driven','Mileage_cleaned','Engine_cleaned', 'Power_cleaned']:\n",
    "    data_test_final[i] = MinMaxScaler(feature_range=(0,1)).fit_transform(data_test_final[i].values.astype(float).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019,)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_target.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_train_final, data_train_target, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4032,)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4032, 12)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Converting train to one hot encoded data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(cols=['Location', 'Fuel_Type', 'Transmission', 'Owner_Type', 'Brand'],\n",
       "       drop_invariant=False, handle_missing='value',\n",
       "       handle_unknown='ignore', return_df=True, use_cat_names=True,\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = ce.OneHotEncoder(handle_unknown='ignore',use_cat_names=True)\n",
    "ohe.fit(data_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ohe=ohe.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Hyderabad</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_smart</th>\n",
       "      <th>Brand_ambassador</th>\n",
       "      <th>Brand_isuzu</th>\n",
       "      <th>Brand_force</th>\n",
       "      <th>Brand_bentley</th>\n",
       "      <th>Brand_lamborghini</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
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       "      <td>0.319946</td>\n",
       "      <td>0.215500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location_Mumbai  Location_Pune  Location_Chennai  Location_Coimbatore  \\\n",
       "956                 0              0                 0                    0   \n",
       "5753                0              0                 0                    0   \n",
       "1918                0              0                 0                    0   \n",
       "4664                0              0                 0                    0   \n",
       "5572                0              1                 0                    0   \n",
       "\n",
       "      Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "956                    0                0               0                 0   \n",
       "5753                   0                1               0                 0   \n",
       "1918                   0                0               1                 0   \n",
       "4664                   1                0               0                 0   \n",
       "5572                   0                0               0                 0   \n",
       "\n",
       "      Location_Delhi  Location_Bangalore  ...  Brand_smart  Brand_ambassador  \\\n",
       "956                1                   0  ...            0                 0   \n",
       "5753               0                   0  ...            0                 0   \n",
       "1918               0                   0  ...            0                 0   \n",
       "4664               0                   0  ...            0                 0   \n",
       "5572               0                   0  ...            0                 0   \n",
       "\n",
       "      Brand_isuzu  Brand_force  Brand_bentley  Brand_lamborghini  is_electric  \\\n",
       "956             0            0              0                  0            0   \n",
       "5753            0            0              0                  0            0   \n",
       "1918            0            0              0                  0            0   \n",
       "4664            0            0              0                  0            0   \n",
       "5572            0            0              0                  0            0   \n",
       "\n",
       "      Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "956          0.611643        0.223422       0.106124  \n",
       "5753         0.517686        0.234391       0.094332  \n",
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       "4664         0.449521        0.189841       0.098136  \n",
       "5572         0.389094        0.319946       0.197471  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_ohe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4032, 59)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_ohe.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Converting test to one hot encoded data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(cols=['Location', 'Fuel_Type', 'Transmission', 'Owner_Type', 'Brand'],\n",
       "       drop_invariant=False, handle_missing='value',\n",
       "       handle_unknown='ignore', return_df=True, use_cat_names=True,\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = ce.OneHotEncoder(handle_unknown='ignore',use_cat_names=True)\n",
    "ohe.fit(data_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_ohe=ohe.transform(data_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.434211</td>\n",
       "      <td>0.106624</td>\n",
       "      <td>0.083620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location_Mumbai  Location_Pune  Location_Chennai  Location_Coimbatore  \\\n",
       "0                0              0                 0                    0   \n",
       "1                0              0                 0                    1   \n",
       "2                1              0                 0                    0   \n",
       "3                0              0                 0                    0   \n",
       "4                1              0                 0                    0   \n",
       "\n",
       "   Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "0                   0                0               0                 0   \n",
       "1                   0                0               0                 0   \n",
       "2                   0                0               0                 0   \n",
       "3                   1                0               0                 0   \n",
       "4                   0                0               0                 0   \n",
       "\n",
       "   Location_Delhi  Location_Bangalore  ...  Brand_smart  Brand_ambassador  \\\n",
       "0               1                   0  ...          0.0               0.0   \n",
       "1               0                   0  ...          0.0               0.0   \n",
       "2               0                   0  ...          0.0               0.0   \n",
       "3               0                   0  ...          0.0               0.0   \n",
       "4               0                   0  ...          0.0               0.0   \n",
       "\n",
       "   Brand_isuzu  Brand_force  Brand_bentley  Brand_lamborghini  is_electric  \\\n",
       "0          0.0          0.0            0.0                0.0            0   \n",
       "1          0.0          0.0            0.0                0.0            0   \n",
       "2          0.0          0.0            0.0                0.0            0   \n",
       "3          0.0          0.0            0.0                0.0            0   \n",
       "4          0.0          0.0            0.0                0.0            0   \n",
       "\n",
       "   Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0         1.000000        0.069594       0.041251  \n",
       "1         0.689145        0.032006       0.022516  \n",
       "2         0.236020        0.329178       0.195256  \n",
       "3         0.643503        0.137700       0.086066  \n",
       "4         0.434211        0.106624       0.083620  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_ohe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1234, 59)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_ohe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in X_test_ohe.columns:\n",
    "    if len(X_test_ohe[X_test_ohe[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(X_test_ohe[X_test_ohe[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest regressor model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "hyperparameters = dict(n_estimators=[1, 2, 4, 8, 16, 32, 64, 100, 200],max_depth=np.linspace(1, 32, 32, endpoint=True),min_samples_split = np.linspace(0.1, 1.0, 10, endpoint=True),min_samples_leaf = np.linspace(0.1, 0.5, 5, endpoint=True))\n",
    "clf=RandomizedSearchCV(RandomForestRegressor(), hyperparameters, random_state=1, cv=5, verbose=0,scoring='neg_mean_squared_error')\n",
    "best_model = clf.fit(X_train_ohe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#best_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''''\n",
    "clf1= RandomForestRegressor(n_estimators=32,\n",
    " min_samples_split= 0.1,\n",
    " min_samples_leaf= 0.1,\n",
    " max_depth=3.0)\n",
    "clf1.fit(X_train_ohe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_predict=clf1.predict(X_test_ohe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''''\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "rms = sqrt(mean_squared_error(y_test, y_predict))\n",
    "print(rms)"
   ]
  }
 ],
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