Cx_Proj1_UsedCarPrice(1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 618,
   "id": "beginning-arbor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>New_Price</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>NaN</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>NaN</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>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   New_Price  Price  \n",
       "0        NaN   1.75  \n",
       "1        NaN  12.50  \n",
       "2  8.61 Lakh   4.50  \n",
       "3        NaN   6.00  \n",
       "4        NaN  17.74  "
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('/Users/bipulkumar/Downloads/train-data.csv',index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "id": "refined-morning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 13)"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "id": "smoking-record",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  object\n",
       "Location              object\n",
       "Year                   int64\n",
       "Kilometers_Driven      int64\n",
       "Fuel_Type             object\n",
       "Transmission          object\n",
       "Owner_Type            object\n",
       "Mileage               object\n",
       "Engine                object\n",
       "Power                 object\n",
       "Seats                float64\n",
       "New_Price             object\n",
       "Price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "id": "cooked-benchmark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                    0\n",
       "Location                0\n",
       "Year                    0\n",
       "Kilometers_Driven       0\n",
       "Fuel_Type               0\n",
       "Transmission            0\n",
       "Owner_Type              0\n",
       "Mileage                 2\n",
       "Engine                 36\n",
       "Power                  36\n",
       "Seats                  42\n",
       "New_Price            5195\n",
       "Price                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "id": "horizontal-petersburg",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  \n",
       "0   1.75  \n",
       "1  12.50  \n",
       "2   4.50  \n",
       "3   6.00  \n",
       "4  17.74  "
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop('New_Price' , axis='columns')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "detected-religion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.6"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = '26.6 km/kg'\n",
    "float(m.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "id": "mysterious-queen",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to convert data type of mileage column => if cell value is of type string \n",
    "#=> split from space and return index 0 value as float \n",
    "#=> in case split function not applicable (like on numerical or null values) \n",
    "# => return x as it is\n",
    "\n",
    "def fix_mileage_engine_power(x):\n",
    "    try:\n",
    "        return float(x.split(' ')[0])\n",
    "    except:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "emotional-cookbook",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Mileage'] = df1['Mileage'].apply(fix_mileage_engine_power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "id": "tracked-antique",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </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>26.60</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</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>18.20</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage   Engine      Power  Seats  Price  \n",
       "0       CNG       Manual      First    26.60   998 CC  58.16 bhp    5.0   1.75  \n",
       "1    Diesel       Manual      First    19.67  1582 CC  126.2 bhp    5.0  12.50  \n",
       "2    Petrol       Manual      First    18.20  1199 CC   88.7 bhp    5.0   4.50  \n",
       "3    Diesel       Manual      First    20.77  1248 CC  88.76 bhp    7.0   6.00  \n",
       "4    Diesel    Automatic     Second    15.20  1968 CC  140.8 bhp    5.0  17.74  "
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "id": "concrete-israeli",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Engine'] = df1['Engine'].apply(fix_mileage_engine_power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "id": "political-surgeon",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Power'] = df1['Power'].apply(fix_mileage_engine_power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "id": "deadly-oxford",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</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</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.2</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>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine  Power  Seats  Price  \n",
       "0       CNG       Manual      First    26.60   998.0  58.16    5.0   1.75  \n",
       "1    Diesel       Manual      First    19.67  1582.0  126.2    5.0  12.50  \n",
       "2    Petrol       Manual      First    18.20  1199.0   88.7    5.0   4.50  \n",
       "3    Diesel       Manual      First    20.77  1248.0  88.76    7.0   6.00  \n",
       "4    Diesel    Automatic     Second    15.20  1968.0  140.8    5.0  17.74  "
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "id": "balanced-kernel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  object\n",
       "Location              object\n",
       "Year                   int64\n",
       "Kilometers_Driven      int64\n",
       "Fuel_Type             object\n",
       "Transmission          object\n",
       "Owner_Type            object\n",
       "Mileage              float64\n",
       "Engine               float64\n",
       "Power                 object\n",
       "Seats                float64\n",
       "Price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "id": "explicit-pound",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1['Power'].astype(float) => returns exception mentioning  -\n",
    "# some values are given as 'null bhp' in string form which can't be converted into float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "id": "metric-blues",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1['Power'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "corporate-brisbane",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76      null bhp\n",
       "79      null bhp\n",
       "89      null bhp\n",
       "120     null bhp\n",
       "143     null bhp\n",
       "          ...   \n",
       "5873    null bhp\n",
       "5893    null bhp\n",
       "5925    null bhp\n",
       "5943    null bhp\n",
       "5985    null bhp\n",
       "Name: Power, Length: 107, dtype: object"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Power'][df1['Power'] == 'null bhp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "id": "collaborative-apartment",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#Filter the power value when it was mentioned as 'null bhp'\n",
    "#Update the power value as missing value in case 'null bhp' is mentioned\n",
    "\n",
    "df1['Power'][df1['Power'] == 'null bhp'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "id": "divine-seven",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  object\n",
       "Location              object\n",
       "Year                   int64\n",
       "Kilometers_Driven      int64\n",
       "Fuel_Type             object\n",
       "Transmission          object\n",
       "Owner_Type            object\n",
       "Mileage              float64\n",
       "Engine               float64\n",
       "Power                 object\n",
       "Seats                float64\n",
       "Price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "id": "soviet-niger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Location               0\n",
       "Year                   0\n",
       "Kilometers_Driven      0\n",
       "Fuel_Type              0\n",
       "Transmission           0\n",
       "Owner_Type             0\n",
       "Mileage                2\n",
       "Engine                36\n",
       "Power                143\n",
       "Seats                 42\n",
       "Price                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "id": "resident-lightweight",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the data types of power column into float\n",
    "df1['Power'] = df1['Power'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "id": "viral-cincinnati",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  object\n",
       "Location              object\n",
       "Year                   int64\n",
       "Kilometers_Driven      int64\n",
       "Fuel_Type             object\n",
       "Transmission          object\n",
       "Owner_Type            object\n",
       "Mileage              float64\n",
       "Engine               float64\n",
       "Power                float64\n",
       "Seats                float64\n",
       "Price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "satellite-barcelona",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</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</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</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>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "0       CNG       Manual      First    26.60   998.0   58.16    5.0   1.75  \n",
       "1    Diesel       Manual      First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2    Petrol       Manual      First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3    Diesel       Manual      First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4    Diesel    Automatic     Second    15.20  1968.0  140.80    5.0  17.74  "
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "id": "bearing-community",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate age of car using purchase year of car\n",
    "df1['Year'] = 2023 - df1['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "id": "moderate-pioneer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>13</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai    13              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune     8              41000   \n",
       "2                      Honda Jazz V     Chennai    12              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai    11              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore    10              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "0       CNG       Manual      First    26.60   998.0   58.16    5.0   1.75  \n",
       "1    Diesel       Manual      First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2    Petrol       Manual      First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3    Diesel       Manual      First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4    Diesel    Automatic     Second    15.20  1968.0  140.80    5.0  17.74  "
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "id": "green-mongolia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Location               0\n",
       "Year                   0\n",
       "Kilometers_Driven      0\n",
       "Fuel_Type              0\n",
       "Transmission           0\n",
       "Owner_Type             0\n",
       "Mileage                2\n",
       "Engine                36\n",
       "Power                143\n",
       "Seats                 42\n",
       "Price                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "juvenile-wellington",
   "metadata": {},
   "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": 643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "id": "consistent-earth",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Number of samples for CNG, LPG and Electric cars are very less \n",
    "#i.e 1-2% of number of Diesel and Petrol cars\n",
    "#To avoid biasness we will drop 56 samples of CNG, 10 samples of LPG and 2 of ELectric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "id": "demonstrated-horizontal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>13</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hyundai EON LPG Era Plus Option</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>11</td>\n",
       "      <td>75000</td>\n",
       "      <td>LPG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>21.10</td>\n",
       "      <td>814.0</td>\n",
       "      <td>55.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Pune</td>\n",
       "      <td>10</td>\n",
       "      <td>89900</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Maruti Zen Estilo LXI Green (CNG)</td>\n",
       "      <td>Pune</td>\n",
       "      <td>15</td>\n",
       "      <td>42496</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.30</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Maruti Eeco 5 STR With AC Plus HTR CNG</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>6</td>\n",
       "      <td>31841</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>15.10</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.70</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5876</th>\n",
       "      <td>Maruti Ertiga VXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>6</td>\n",
       "      <td>23700</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>80.46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5881</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>55000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5902</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>11</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.20</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5935</th>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>59525</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>Maruti Wagon R Duo Lxi</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>13</td>\n",
       "      <td>70000</td>\n",
       "      <td>LPG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.20</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name   Location  Year  \\\n",
       "0                     Maruti Wagon R LXI CNG     Mumbai    13   \n",
       "5            Hyundai EON LPG Era Plus Option  Hyderabad    11   \n",
       "127                   Maruti Wagon R LXI CNG       Pune    10   \n",
       "328        Maruti Zen Estilo LXI Green (CNG)       Pune    15   \n",
       "440   Maruti Eeco 5 STR With AC Plus HTR CNG      Kochi     6   \n",
       "...                                      ...        ...   ...   \n",
       "5876                   Maruti Ertiga VXI CNG     Mumbai     6   \n",
       "5881                  Maruti Wagon R LXI CNG       Pune     8   \n",
       "5902                  Maruti Wagon R LXI CNG     Mumbai    11   \n",
       "5935                 Maruti Alto K10 LXI CNG       Pune     8   \n",
       "5997                  Maruti Wagon R Duo Lxi     Jaipur    13   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission Owner_Type  Mileage  Engine  \\\n",
       "0                 72000       CNG       Manual      First    26.60   998.0   \n",
       "5                 75000       LPG       Manual      First    21.10   814.0   \n",
       "127               89900       CNG       Manual      First    26.60   998.0   \n",
       "328               42496       CNG       Manual      First    26.30   998.0   \n",
       "440               31841       CNG       Manual      First    15.10  1196.0   \n",
       "...                 ...       ...          ...        ...      ...     ...   \n",
       "5876              23700       CNG       Manual      First    17.50  1373.0   \n",
       "5881              55000       CNG       Manual      First    26.60   998.0   \n",
       "5902              72000       CNG       Manual      First    26.20   998.0   \n",
       "5935              59525       CNG       Manual     Second    32.26   998.0   \n",
       "5997              70000       LPG       Manual      First    26.20   998.0   \n",
       "\n",
       "      Power  Seats  Price  \n",
       "0     58.16    5.0   1.75  \n",
       "5     55.20    5.0   2.35  \n",
       "127   58.16    5.0   3.25  \n",
       "328   67.10    5.0   1.40  \n",
       "440   73.00    5.0   4.70  \n",
       "...     ...    ...    ...  \n",
       "5876  80.46    7.0   8.25  \n",
       "5881  58.16    5.0   3.70  \n",
       "5902  58.20    5.0   2.25  \n",
       "5935  58.20    4.0   3.10  \n",
       "5997  58.20    5.0   2.27  \n",
       "\n",
       "[68 rows x 12 columns]"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Fuel_Type'].isin(['CNG','LPG','Electric'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "id": "meaningful-opinion",
   "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>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nissan Micra Diesel XV</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune     8              41000   \n",
       "2                      Honda Jazz V     Chennai    12              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai    11              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore    10              40670   \n",
       "6            Nissan Micra Diesel XV      Jaipur    10              86999   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1    Diesel       Manual      First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2    Petrol       Manual      First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3    Diesel       Manual      First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4    Diesel    Automatic     Second    15.20  1968.0  140.80    5.0  17.74  \n",
       "6    Diesel       Manual      First    23.08  1461.0   63.10    5.0   3.50  "
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1[~df1['Fuel_Type'].isin(['CNG','LPG','Electric'])]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "id": "opened-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Location               0\n",
       "Year                   0\n",
       "Kilometers_Driven      0\n",
       "Fuel_Type              0\n",
       "Transmission           0\n",
       "Owner_Type             0\n",
       "Mileage                0\n",
       "Engine                36\n",
       "Power                142\n",
       "Seats                 42\n",
       "Price                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "id": "sapphire-yellow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       4233\n",
       "Automatic    1718\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "id": "hindu-handy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             4874\n",
       "Second             956\n",
       "Third              112\n",
       "Fourth & Above       9\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Owner_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "id": "thrown-refund",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py:990: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._where(~key, value, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#Combine Second, Third and Fourth & Above category into one => \"Second & Above\"\n",
    "\n",
    "df2['Owner_Type'][df2['Owner_Type'].isin(['Second',\n",
    "                                          'Third',\n",
    "                                          'Fourth & Above'])] = \"Second & Above\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "id": "particular-blast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             4874\n",
       "Second & Above    1077\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Owner_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "id": "dental-region",
   "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>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nissan Micra Diesel XV</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune     8              41000   \n",
       "2                      Honda Jazz V     Chennai    12              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai    11              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore    10              40670   \n",
       "6            Nissan Micra Diesel XV      Jaipur    10              86999   \n",
       "\n",
       "  Fuel_Type Transmission      Owner_Type  Mileage  Engine   Power  Seats  \\\n",
       "1    Diesel       Manual           First    19.67  1582.0  126.20    5.0   \n",
       "2    Petrol       Manual           First    18.20  1199.0   88.70    5.0   \n",
       "3    Diesel       Manual           First    20.77  1248.0   88.76    7.0   \n",
       "4    Diesel    Automatic  Second & Above    15.20  1968.0  140.80    5.0   \n",
       "6    Diesel       Manual           First    23.08  1461.0   63.10    5.0   \n",
       "\n",
       "   Price  \n",
       "1  12.50  \n",
       "2   4.50  \n",
       "3   6.00  \n",
       "4  17.74  \n",
       "6   3.50  "
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "id": "fourth-entertainment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai        771\n",
       "Hyderabad     733\n",
       "Kochi         643\n",
       "Coimbatore    636\n",
       "Pune          608\n",
       "Delhi         544\n",
       "Kolkata       535\n",
       "Chennai       491\n",
       "Jaipur        411\n",
       "Bangalore     358\n",
       "Ahmedabad     221\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "id": "satisfactory-stack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5951, 12)"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "id": "italic-parcel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1847"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "id": "oriental-declaration",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#convert all names into lower case\n",
    "df2['Name'] = df2['Name'].str.lower()\n",
    "\n",
    "#rempve any extra leading or lagging space in the name mentioned\n",
    "df2['Name'] = df2['Name'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "id": "accompanied-dispatch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1823"
      ]
     },
     "execution_count": 657,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "id": "removable-kenya",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "maruti swift dzire vdi                     50\n",
       "mahindra xuv500 w8 2wd                     49\n",
       "maruti swift vdi                           45\n",
       "honda city 1.5 s mt                        34\n",
       "maruti swift vdi bsiv                      31\n",
       "                                           ..\n",
       "mahindra kuv 100 mfalcon d75 k6             1\n",
       "honda mobilio rs option i dtec              1\n",
       "toyota platinum etios 1.4 gxd               1\n",
       "hyundai sonata transform 2.4 gdi at         1\n",
       "mahindra kuv 100 mfalcon g80 k6 5str aw     1\n",
       "Name: Name, Length: 1823, dtype: int64"
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "id": "liked-windows",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['maruti', 'swift', 'dzire', 'vdi']\n",
      "['maruti', 'swift']\n",
      "maruti swift\n"
     ]
    }
   ],
   "source": [
    "car = 'maruti swift dzire vdi'\n",
    "print(car.split(' '))\n",
    "print(car.split(' ')[:2])\n",
    "print(' '. join(car.split(' ')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "id": "modern-barbados",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Use first two words in car names => increase the sample size for a particular type of car\n",
    "df2['Name'] = df2['Name'].apply(lambda x : ' '. join(x.split(' ')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "id": "educated-christmas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "id": "brazilian-spider",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "2     honda jazz     Chennai    12              46000    Petrol       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2           First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  "
      ]
     },
     "execution_count": 662,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "id": "suited-humanity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "maruti swift            353\n",
       "honda city              270\n",
       "hyundai i20             247\n",
       "hyundai verna           170\n",
       "toyota innova           164\n",
       "                       ... \n",
       "toyota platinum           1\n",
       "mahindra xuv300           1\n",
       "lamborghini gallardo      1\n",
       "maruti versa              1\n",
       "bentley continental       1\n",
       "Name: Name, Length: 213, dtype: int64"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_count = df2['Name'].value_counts()\n",
    "car_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "id": "labeled-sister",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If a type of a car having sample size lesser then 30 => we will remove such car samples\n",
    "#As lesser sample will generate lesser information for a machine learning model to be trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "id": "sweet-performance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "maruti swift             353\n",
       "honda city               270\n",
       "hyundai i20              247\n",
       "hyundai verna            170\n",
       "toyota innova            164\n",
       "hyundai i10              155\n",
       "hyundai grand            152\n",
       "volkswagen polo          151\n",
       "maruti alto              134\n",
       "maruti wagon             126\n",
       "mahindra xuv500          113\n",
       "volkswagen vento         107\n",
       "honda amaze              106\n",
       "toyota fortuner          101\n",
       "ford figo                101\n",
       "bmw 3                     98\n",
       "ford ecosport             95\n",
       "mercedes-benz new         94\n",
       "hyundai creta             93\n",
       "mercedes-benz e-class     88\n",
       "renault duster            83\n",
       "audi a4                   78\n",
       "hyundai santro            74\n",
       "maruti ciaz               71\n",
       "maruti ertiga             71\n",
       "bmw 5                     68\n",
       "toyota corolla            67\n",
       "maruti ritz               64\n",
       "maruti baleno             63\n",
       "toyota etios              61\n",
       "mahindra scorpio          61\n",
       "hyundai eon               60\n",
       "honda brio                60\n",
       "land rover                60\n",
       "honda jazz                58\n",
       "hyundai xcent             57\n",
       "maruti celerio            56\n",
       "skoda superb              49\n",
       "audi a6                   49\n",
       "skoda rapid               47\n",
       "chevrolet beat            46\n",
       "maruti vitara             43\n",
       "renault kwid              40\n",
       "tata indica               40\n",
       "ford fiesta               40\n",
       "ford endeavour            39\n",
       "audi q7                   38\n",
       "maruti sx4                34\n",
       "nissan micra              33\n",
       "honda civic               32\n",
       "bmw x1                    32\n",
       "skoda octavia             31\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_count_more_30 = car_count[car_count > 30]\n",
    "car_count_more_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "id": "military-slovenia",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "2     honda jazz     Chennai    12              46000    Petrol       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2           First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  "
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter the data to get those cars info whose sample size is more then 30\n",
    "df3 = df2[df2['Name'].apply(lambda x : x in car_count_more_30)]\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "id": "twenty-maple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "id": "perfect-reach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Location               0\n",
       "Year                   0\n",
       "Kilometers_Driven      0\n",
       "Fuel_Type              0\n",
       "Transmission           0\n",
       "Owner_Type             0\n",
       "Mileage                0\n",
       "Engine                32\n",
       "Power                116\n",
       "Seats                 35\n",
       "Price                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "id": "short-orange",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify Outliers for Engine & Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "id": "greatest-prague",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q98wkRw6fU5O8I0mq6uAkpyV5YpJjk5xWVQ/vWDcAMEY2bdqUu+++O6eddtqoS4F56Ra027Tbh+aBw6ft5ZCTkpw3HPfJJA+rqkcmeUaSLa21W1trtyXZkmRdr7oBgPExOTmZ22+fjhO33367WW2WlK5rtKtqWVVdmeTmTIflTw273jwsD3lbVT1w6Fud5IZZh9849O2pHwDYz23atOlebbPaLCVdg3ZrbWdr7egkhyY5tqoel+R1SR6d5MeTHJzkNQtxrao6taomq2rylltuWYhTAgAjNjObvac2jLNFeepIa+3rST6WZF1r7aZhechdSd6T6XXXSbItyWGzDjt06NtT/67XOLu1NtFam1i1alWHbwEALLYVK1bstQ3jrOdTR1ZV1cOG7YOSnJDkumHddaqqkjwnyReGQy5Kcsrw9JEnJflGa+2mJJcmeXpVPXy4CfLpQx8AsJ/bdenI6aefPppCYB/0fGHNI5NsrqplmQ70F7TWPlxVl1fVqiSV5MokLx3GX5zkxCRTSe5I8sIkaa3dWlVvTPKZYdwbWmu3dqwbABgTExMTWbFiRW6//fasWLEixxxzzKhLgjmr1vb2IJClaWJiok1OTo66DABgAUxOTubVr351/uiP/kjQZuxU1RWttYnd7fMKdgBgrE1MTOTyyy8fdRkwb17BDgAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gDAWNu+fXte/vKXZ/v27aMuBeZF0AYAxtrmzZtz9dVX57zzzht1KTAvgjYAMLa2b9+eSy65JK21XHLJJWa1WVKWj7oA9n9nnXVWpqamRl3GWNi2bVuSZPXq1SOuZDysXbs2GzZsGHUZwBjbvHlz7r777iTJzp07c9555+W3fuu3RlwVzI0ZbVhEd955Z+68885RlwGwZFx22WXZsWNHkmTHjh3ZsmXLiCuCuTOjTXdmLL9j48aNSZIzzjhjxJUALA1Pe9rTcvHFF2fHjh1Zvnx5TjjhhFGXBHNmRhsAGFvr16/PAQdMx5Vly5bllFNOGXFFMHeCNgAwtlauXJl169alqrJu3bqsXLly1CXBnFk6AgCMtfXr12fr1q1ms1lyBG0AYKytXLkyZ5555qjLgHmzdAQAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKCDbkG7qh5UVZ+uqs9X1TVVdfrQf0RVfaqqpqrqA1X1gKH/gUN7ati/Zta5Xjf0f6mqntGrZgBg/Fx++eU57rjj8rGPfWzUpcC89JzRvivJ8a21H01ydJJ1VfWkJH+Q5G2ttbVJbkvyomH8i5LcNvS/bRiXqjoqyclJHptkXZI/raplHesGAMbIW97yliTJm9/85hFXAvPTLWi3abcPzQOHT0tyfJIPDv2bkzxn2D5paGfY/9SqqqH//NbaXa2165NMJTm2V90AwPi4/PLLs2PHjiTJjh07zGqzpHRdo11Vy6rqyiQ3J9mS5H8l+Xprbccw5MYkq4ft1UluSJJh/zeSrJzdv5tjAID92Mxs9gyz2iwlXYN2a21na+3oJIdmehb60b2uVVWnVtVkVU3ecsstvS4DACyimdnsPbVhnC3KU0daa19P8rEkT07ysKpaPuw6NMm2YXtbksOSZNj//Um2z+7fzTGzr3F2a22itTaxatWqHl8DAFhk06tI99yGcdbzqSOrquphw/ZBSU5Icm2mA/cvDMPWJ7lw2L5oaGfYf3lrrQ39Jw9PJTkiyZFJPt2rbgBgfBx44IH3aj/gAQ8YUSUwfz1ntB+Z5GNVdVWSzyTZ0lr7cJLXJHllVU1leg32OcP4c5KsHPpfmeS1SdJauybJBUm+mOSSJC9rre3sWDcAMCbWrVu31zaMs+X3PWTftNauSvKE3fR/Jbt5akhr7f8k+cU9nOvNSdz9AADfY9avX5+LL744O3bsyPLly3PKKaeMuiSYM2+GBADG1sqVK3PiiSemqvKsZz0rK1euHHVJMGfdZrQBABbC+vXrs3XrVrPZLDmCNgAw1lauXJkzzzxz1GXAvFk6AgAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAMBYm5yczPHHH58rrrhi1KXAvAjaAMBY27RpU+6+++6cdtppoy4F5kXQBgDG1uTkZG6//fYkye23325WmyVF0AYAxtamTZvu1TarzVIiaAMAY2tmNntPbRhngjYAMLZWrFix1zaMM0EbABhbuy4dOf3000dTCOwDQRsAGFsTExP3zGKvWLEixxxzzIgrgrkTtAGAsbZp06YccMABZrNZcgRtAGCsHXHEEXnc4x6XNWvWjLoUmBdBGwAYa5s3b87VV1+d8847b9SlwLzMKWhX1SOq6pyq+n+H9lFV9aK+pQEA3+u2b9+eSy65JK21XHLJJdm+ffuoS4I5m+uM9nuTXJrkUUP7n5O8okM9AAD32Lx5c+6+++4kyc6dO81qs6TMNWgf0lq7IMndSdJa25FkZ7eqAACSXHbZZdmxY0eSZMeOHdmyZcuIK4K5m2vQ/nZVrUzSkqSqnpTkG92qAgBI8rSnPS3Lly9PkixfvjwnnHDCiCuCuZtr0H5lkouS/EhVfSLJeUk2dKsKACDJ+vXrc8AB03Fl2bJlOeWUU0ZcEczdnIJ2a+2zSZ6S5CeS/HqSx7bWrupZGADAypUrs27dulRV1q1bl5UrV466JJiz5fMYe2ySNcMxP1ZVaa25IwEA6Gr9+vXZunWr2WyWnDkF7ap6X5IfSXJlvnMTZMv0EhIAgG5WrlyZM888c9RlwLzNdUZ7IslRrbXWsxgAANhfzPVmyC8k+cGehQAAwP5krjPahyT5YlV9OsldM52ttZ/pUhUAACxxcw3am3oWAQAA+5s5Be3W2v/oXQgAAOxP9hq0q+rjrbX/VFXfyvBWyJldSVpr7aFdqwMAgCVqr0G7tfafhj+/b3HKAQCA/cNcn6N98G66v9Va+/cFrgcAAPYLc32832eT3JLkn5N8edjeWlWfrapjehUHAABL1VyD9pYkJ7bWDmmtrUzyzCQfTvIbSf60V3EAALBUzTVoP6m1dulMo7X2kSRPbq19MskDu1QGAABL2FyD9k1V9Zqq+qHh8+okX6uqZUnu3t0BVXVYVX2sqr5YVddU1cahf1NVbauqK4fPibOOeV1VTVXVl6rqGbP61w19U1X12vvxfQEAYFHM9YU1v5zktCR/O7Q/MfQtS/LcPRyzI8lvt9Y+W1Xfl+SKqtoy7Htba+2/zR5cVUclOTnJY5M8KsllVfUfht1/kuSEJDcm+UxVXdRa++IcawcAlrAXv/jFmZqayqMf/ei8853vHHU5MGdzfWHNvybZsIfdU3s45qYkNw3b36qqa5Os3stlTkpyfmvtriTXV9VUkmNnrtFa+0qSVNX5w1hBGwC+B0xNTUeN6667bsSVwPzMaelIVf2Hqjq7qj5SVZfPfOZ6kapak+QJST41dP1mVV1VVedW1cOHvtVJbph12I1D3576AYD93Itf/OJ7tV/60peOqBKYv7kuHfnLJO9M8u4kO+dzgapakeSvkryitfbNqnpHkjdm+k2Tb0zyx0l+bT7n3MN1Tk1yapIcfvjh9/d0AMAYmJnNnmFWm6VkrkF7R2vtHfM9eVUdmOmQ/f7W2l8nSWvta7P2vyvTjwlMkm1JDpt1+KFDX/bSf4/W2tlJzk6SiYmJtut+AABYTHN96siHquo3quqRVXXwzGdvB1RVJTknybWttbfO6n/krGE/m+QLw/ZFSU6uqgdW1RFJjkzy6SSfSXJkVR1RVQ/I9A2TF82xbgAAGIm5zmivH/78nVl9LckP7+WYn0zygiRXV9WVQ9/vJnleVR09HL81ya8nSWvtmqq6INM3Oe5I8rLW2s4kqarfTHJppp9ycm5r7Zo51g0ALGFr16691/KRRz/60SOsBuZnrk8dOWK+J26tfTxJ7WbXxXs55s1J3ryb/ov3dhwAsH9697vfneOOO+6etsf7sZTsdenI8GKame1f3GXfW3oVBQAwY+3atUnMZrP03NeM9slJ/nDYfl2mnz4yY12ml4IAAHTz7ne/e9QlwD65r5shaw/bu2sDAACD+wrabQ/bu2sDAACD+1o68qNV9c1Mz14fNGxnaD+oa2UAALCE7TVot9aWLVYhAACwP5nrC2sAAIB5ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgg+WjLmB/ddZZZ2VqamrUZTBmZv6b2Lhx44grYdysXbs2GzZsGHUZACwgQbuTqampXPmFa7PzwQePuhTGyAH/1pIkV3zlayOuhHGy7I5bR10CAB0I2h3tfPDBufPRJ466DGDMHXTdxaMuAYAOrNEGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOigW9CuqsOq6mNV9cWquqaqNg79B1fVlqr68vDnw4f+qqozq2qqqq6qqh+bda71w/gvV9X6XjUDAOPnVa96VY477ri89rWvHXUpMC89Z7R3JPnt1tpRSZ6U5GVVdVSS1yb5aGvtyCQfHdpJ8swkRw6fU5O8I5kO5klOS/LEJMcmOW0mnAMA+7/JyckkySc/+ckRVwLz0y1ot9Zuaq19dtj+VpJrk6xOclKSzcOwzUmeM2yflOS8Nu2TSR5WVY9M8owkW1prt7bWbkuyJcm6XnUDAOPjVa961b3aZrVZShZljXZVrUnyhCSfSvKI1tpNw65/SfKIYXt1khtmHXbj0LenfgBgPzczmz3DrDZLSfegXVUrkvxVkle01r45e19rrSVpC3SdU6tqsqomb7nlloU4JQAA7LOuQbuqDsx0yH5/a+2vh+6vDUtCMvx589C/Lclhsw4/dOjbU/+9tNbObq1NtNYmVq1atbBfBAAA5qnnU0cqyTlJrm2tvXXWrouSzDw5ZH2SC2f1nzI8feRJSb4xLDG5NMnTq+rhw02QTx/6AABgbC3veO6fTPKCJFdX1ZVD3+8m+f0kF1TVi5J8Nclzh30XJzkxyVSSO5K8MElaa7dW1RuTfGYY94bW2q0d6wYAxsRjHvOYXHvttfe0H//4x4+wGpifbkG7tfbxJLWH3U/dzfiW5GV7ONe5Sc5duOoAgKXghhtuuFf7+uuvH1ElMH/eDAkAjK3bb799r20YZ4I2ADC2VqxYsdc2jDNBGwAYW5s2bbpX+/TTTx9NIbAPBG0AYGxNTEzcM4u9YsWKHHPMMSOuCOZO0AYAxtqmTZtywAEHmM1myen5eD8AgPttYmIil19++ajLgHkzow0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgVewA8AYOuusszI1NTXqMsbCtm3bkiSrV68ecSXjYe3atdmwYcOoy2AOBG0AYKzdeeedoy4B9omgDQBjyIzld2zcuDFJcsYZZ4y4Epgfa7QBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoANBGwAAOhC0AQCgA0EbAAA6ELQBAKADQRsAADoQtAEAoIPloy5gf7Vt27Ysu+MbOei6i0ddCjDmlt2xPdu27Rh1GQAsMDPaAADQgRntTlavXp1/uWt57nz0iaMuBRhzB113cVavfsSoywBggZnRBgCADgRtAADoQNAGAIAOugXtqjq3qm6uqi/M6ttUVduq6srhc+Ksfa+rqqmq+lJVPWNW/7qhb6qqXturXgAAWEg9Z7Tfm2Tdbvrf1lo7evhcnCRVdVSSk5M8djjmT6tqWVUtS/InSZ6Z5KgkzxvGAgDAWOv21JHW2j9U1Zo5Dj8pyfmttbuSXF9VU0mOHfZNtda+kiRVdf4w9osLXS8AACykUTze7zer6pQkk0l+u7V2W5LVST45a8yNQ1+S3LBL/xMXpUoAFt1ZZ52VqampUZfBmJn5b2Ljxo0jroRxs3bt2mzYsGHUZezRYgftdyR5Y5I2/PnHSX5tIU5cVacmOTVJDj/88IU4JQCLbGpqKl++5nM5fMXOUZfCGHnAv0+vdL3rq5MjroRx8r9vXzbqEu7Togbt1trXZrar6l1JPjw0tyU5bNbQQ4e+7KV/13OfneTsJJmYmGgLVDIAi+zwFTvzuz/2zVGXAYy5t3z2oaMu4T4t6uP9quqRs5o/m2TmiSQXJTm5qh5YVUckOTLJp5N8JsmRVXVEVT0g0zdMXrSYNQMAwL7oNqNdVX+R5Lgkh1TVjUlOS3JcVR2d6aUjW5P8epK01q6pqgsyfZPjjiQva63tHM7zm0kuTbIsybmttWt61QwAAAul51NHnreb7nP2Mv7NSd68m/6Lk1y8gKUBAEB33gwJAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB10ezMkybI7bs1B13mpJd9xwP/5ZpLk7gc9dMSVME6W3XFrkkeMugwAFpig3cnatWtHXQJjaGrqW0mStT8sVDHbI/xmAOyHBO1ONmzYMOoSGEMbN25MkpxxxhkjrgQA6M0abQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoYPmoCwCAGdu2bcu3v7Usb/nsQ0ddCjDmvvqtZXnItm2jLmOvBG0AxspdOytf/dayUZfBGPn3uytJcuABbcSVME7u2ll5yKiLuA+CNgBj4ylPeUqmpqZGXQZjZua/ibVr1464EsbNuP83IWgDMDY2bNgw6hIYQxs3bkySnHHGGSOuBObHzZAAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0EG3oF1V51bVzVX1hVl9B1fVlqr68vDnw4f+qqozq2qqqq6qqh+bdcz6YfyXq2p9r3oBAGAh9ZzRfm+Sdbv0vTbJR1trRyb56NBOkmcmOXL4nJrkHcl0ME9yWpInJjk2yWkz4RwAAMZZt6DdWvuHJLfu0n1Sks3D9uYkz5nVf16b9skkD6uqRyZ5RpItrbVbW2u3JdmS7w7vAAAwdhZ7jfYjWms3Ddv/kuQRw/bqJDfMGnfj0LenfgAAGGsjuxmytdaStIU6X1WdWlWTVTV5yy23LNRpAQBgnyx20P7asCQkw583D/3bkhw2a9yhQ9+e+r9La+3s1tpEa21i1apVC144AADMx2IH7YuSzDw5ZH2SC2f1nzI8feRJSb4xLDG5NMnTq+rhw02QTx/6AABgrC3vdeKq+oskxyU5pKpuzPTTQ34/yQVV9aIkX03y3GH4xUlOTDKV5I4kL0yS1tqtVfXGJJ8Zxr2htbbrDZYAADB2ugXt1trz9rDrqbsZ25K8bA/nOTfJuQtYGgAAdOfNkAAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANCBoA0AAB0I2gAA0IGgDQAAHQjaAADQgaANAAAdCNoAANBBtdZGXcOCm5iYaJOTk6Mug8FZZ52VqampUZcxFmb+HtauXTviSsbD2rVrs2HDhlGXAWPJb+d3+O28N7+d46WqrmitTexu3/LFLga+lx100EGjLgFgyfHbyVJlRhsAAPbR3ma0rdEGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADoQNAGAIAOBG0AAOhA0AYAgA4EbQAA6EDQBgCADgRtAADooFpro65hwVXVLUm+Ouo6YA8OSfKvoy4CYInx28m4+qHW2qrd7dgvgzaMs6qabK1NjLoOgKXEbydLkaUjAADQgaANAAAdCNqw+M4edQEAS5DfTpYca7QBAKADM9oAANCBoA37oKp2VtWVVXVNVX2+qn67qg4Y9k1U1ZkLfL2tVXXIQp4TYBRm/X5+oar+sqoevJexR1fViftwjU1V9ar7Vyncf4I27Js7W2tHt9Yem+SEJM9MclqStNYmW2svH2l1AONr5vfzcUn+LclL9zL26CS7DdpVtbxDbbCgBG24n1prNyc5Nclv1rTjqurDSVJVD6mqc6vq01X1uao6aeh/7NB3ZVVdVVVHDv2/Mqv//6mqZaP7ZgDd/WOStbv7rayqByR5Q5JfGn4Tf2mYqX5fVX0iyfuqak1VXT78jn60qg4f7deBexO0YQG01r6SZFmSH9hl1+8luby1dmySn0ryR1X1kEzP4JzRWjs6yUSSG6vqMUl+KclPDv07kzx/cb4BwOIaZqSfmeTq7Oa3MsmBSf5rkg8MM+AfGA49KsnTWmvPS3JWks2ttf+Y5P1JFnTZHtxf/tkF+np6kp+ZtVbwQUkOT/I/k/xeVR2a5K9ba1+uqqcmOSbJZ6oqSQ5KcvMIagbo6aCqunLY/sck5yT5p+z+t3J3Lmqt3TlsPznJzw3b70vyhwtfLuw7QRsWQFX9cKZnoG9O8pjZu5L8fGvtS7sccm1VfSrJs5JcXFW/Pozd3Fp73WLUDDAidw7/anePmp5d+K7fyqp64m6O/3bH2mBBWToC91NVrUryziT/vX33g+kvTbJh+J9IquoJw58/nOQrrbUzk1yY5D8m+WiSX6iqHxjGHFxVP7RIXwNglHb7W5nkW0m+by/H/VOSk4ft52d6hhzGhqAN++agmcf7JbksyUeSnL6bcW/M9DrDq4axbxz6n5vkC8M/nz4uyXmttS8meX2Sj1TVVUm2JHlk368BMBb29Fv5sSRHzdwMuZvjNiR54fCb+YIkGxelWpgjb4YEAIAOzGgDAEAHgjYAAHQgaAMAQAeCNgAAdCBoAwBAB4I2AAB0IGgDLCFVtXN4pvDMZ80+nOO4qvrwHva9cNa5/62qrh62f/9+Fw/wPcYr2AGWlu96ffVCaq29J8l7kqSqtib5qdbav/a6HsD+zIw2wBJXVVur6pBhe6Kq/n7YfkhVnVtVn66qz1XVSft4/l+rqrfPar+kqt5WVWuq6rqqen9VXVtVH6yqBw9jjqmq/1FVV1TVpVXlLafA9xxBG2BpOWjW0o6/uY+xv5fk8tbasUl+KskfVdVD9uGaFyR5dlUdOLRfmOTcYfv/SvKnrbXHJPlmkt8Yxp2V5Bdaa8cMY9+8D9cFWNIsHQFYWuazdOTpSX6mql41tB+U5PD5XrC1dntVXZ7kp6vq2iQHttauHtaH39Ba+8Qw9M+SvDzJJUkel2RLVSXJsiQ3zfe6AEudoA2w9O3Id/6F8kGz+ivJz7fWvjR7cFU9Yh+u8e4kv5vkugxruAdtl3FtuO41rbUn78N1APYblo4ALH1bkxwzbP/8rP5Lk2yoYVq5qp6wrxdorX0qyWFJfjnJX8zadXhVzQTqX07y8SRfSrJqpr+qDqyqx+7rtQGWKkEbYOk7PckZVTWZZOes/jcmOTDJVVV1zdC+Py5I8onW2m2z+r6U5GXDkpKHJ3lHa+3fkvxCkj+oqs8nuTLJT9zPawMsOdXarv/qBwDfbXj29ttaax8d2muSfLi19riRFgYwpsxoA7BXVfWwqvrnTN+I+dFR1wOwVJjRBvgeVVUvTLJxl+5PtNZeNop6APY3gjYAAHRg6QgAAHQgaAMAQAeCNgAAdCBoAwBAB4I2AAB08P8DdCfGECHf/50AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Fuel_Type' , y = 'Engine' , data = df3)\n",
    "plt.title('Engine vs Fuel Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "id": "spatial-subcommittee",
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Diesel</th>\n",
       "      <td>1796.109583</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petrol</th>\n",
       "      <td>1293.685532</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>796.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean  median     max    min\n",
       "Fuel_Type                                    \n",
       "Diesel     1796.109583  1582.0  4367.0  793.0\n",
       "Petrol     1293.685532  1197.0  4134.0  796.0"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('Fuel_Type')['Engine'].agg(['mean','median','max','min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "id": "enclosed-immune",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel    2407\n",
       "Petrol    2216\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "id": "changed-potato",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Transmission' , y = 'Engine' , data = df3)\n",
    "plt.title('Transmission vs Engine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "id": "alike-steal",
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Automatic</th>\n",
       "      <td>1972.126188</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manual</th>\n",
       "      <td>1416.242865</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>2982.0</td>\n",
       "      <td>793.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean  median     max    min\n",
       "Transmission                                    \n",
       "Automatic     1972.126188  1968.0  4367.0  998.0\n",
       "Manual        1416.242865  1248.0  2982.0  793.0"
      ]
     },
     "execution_count": 674,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('Transmission')['Engine'].agg(['mean','median','max','min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "id": "gothic-bronze",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       3459\n",
       "Automatic    1164\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 675,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "id": "other-import",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Owner_Type' , y = 'Engine' , data = df3)\n",
    "plt.title('Owner_Type vs Engine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "id": "tender-thinking",
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>1543.204028</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second &amp; Above</th>\n",
       "      <td>1621.691406</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>796.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean  median     max    min\n",
       "Owner_Type                                        \n",
       "First           1543.204028  1399.0  4367.0  793.0\n",
       "Second & Above  1621.691406  1497.0  4367.0  796.0"
      ]
     },
     "execution_count": 677,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('Owner_Type')['Engine'].agg(['mean','median','max','min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "id": "french-driver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             3841\n",
       "Second & Above     782\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 678,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Owner_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "id": "intensive-polish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Year' , y = 'Engine' , data = df3)\n",
    "plt.title('Year vs Engine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "optimum-reverse",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "first-president",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unsigned-component",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "id": "christian-elder",
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1728.862745</td>\n",
       "      <td>1582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1566.140940</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1556.146758</td>\n",
       "      <td>1395.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1536.596491</td>\n",
       "      <td>1396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1612.925876</td>\n",
       "      <td>1461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1616.129560</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1636.442901</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1645.577661</td>\n",
       "      <td>1461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1708.331169</td>\n",
       "      <td>1582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1686.538235</td>\n",
       "      <td>1399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1754.969388</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1679.368421</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1606.528455</td>\n",
       "      <td>1399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1682.880000</td>\n",
       "      <td>1405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1665.446429</td>\n",
       "      <td>1494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1525.413793</td>\n",
       "      <td>1396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1466.066667</td>\n",
       "      <td>1298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1336.714286</td>\n",
       "      <td>1073.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1625.428571</td>\n",
       "      <td>1493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1817.000000</td>\n",
       "      <td>1758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1188.000000</td>\n",
       "      <td>993.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean  median\n",
       "Year                     \n",
       "4     1728.862745  1582.0\n",
       "5     1566.140940  1364.0\n",
       "6     1556.146758  1395.5\n",
       "7     1536.596491  1396.0\n",
       "8     1612.925876  1461.0\n",
       "9     1616.129560  1497.0\n",
       "10    1636.442901  1497.0\n",
       "11    1645.577661  1461.0\n",
       "12    1708.331169  1582.0\n",
       "13    1686.538235  1399.0\n",
       "14    1754.969388  1497.0\n",
       "15    1679.368421  1497.0\n",
       "16    1606.528455  1399.0\n",
       "17    1682.880000  1405.0\n",
       "18    1665.446429  1494.0\n",
       "19    1525.413793  1396.0\n",
       "20    1466.066667  1298.0\n",
       "21    1336.714286  1073.5\n",
       "22    1625.428571  1493.0\n",
       "23    1817.000000  1758.0\n",
       "24    1168.000000  1168.0\n",
       "25    1188.000000   993.0"
      ]
     },
     "execution_count": 680,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('Year')['Engine'].agg(['mean','median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "id": "modern-browser",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 681,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9re6+Z7p8dZLPJDkoyR35zmkbq6dtm9Td53f32u5eu2rVqvl2EQAAFmxegbmqTk7yq0le2N3fmNO+qqpWTpd/MJOT+z7b3Xcm+VpVHTe9OsbLk1y84N4DAMCMbXFKRlW9J8kJSZ5UVeuTvCGTq2LsluSy6dXhrpheEeNZSX6rqh5I8nCSn+/uDScMviqTK258byZznufOewYAgG3SFgNzd5+6ieY/3My2FyW5aDPrrkry9EfVOwAAWGbu9AcAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAANbvNMfAABsC9ac+cHh+nXnPH8mdY0wAwDAgMAMAAADAjMAAAwIzAAAMOCkPwAAHpUtnny3+09ufuXZ9y5yb2bPCDMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADuyx3BwAAZu7sPbew/t6l6QfbJSPMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADGxVYK6qC6rqS1V1w5y276uqy6rq1unXJ07bq6reUlW3VdX1VXX0nNe8Yrr9rVX1isU/HAAAWFxbO8L8jiQnb9R2ZpK/6u4Dk/zV9HmSPDfJgdPH6UnOSyYBO8kbkvxIkmOTvGFDyAYAgG3VVgXm7v5Ykq9s1PyiJO+cLr8zyYvntL+rJ65I8oSq2ifJSUku6+6vdPc/J7ks3x3CAQBgm7KQOcx7d/ed0+UvJtl7urxfktvnbLd+2ra5dgAA2GYtykl/3d1JejH2lSRVdXpVXVVVV919992LtVsAAHjUFhKY75pOtcj065em7Xckecqc7VZP2zbX/l26+/zuXtvda1etWrWALgIAwMLssoDXXpLkFUnOmX69eE77GVX13kxO8Lu3u++sqr9M8qY5J/r9aJLXLaA+AAB829l7bmH9vfPa7VYF5qp6T5ITkjypqtZncrWLc5K8r6p+Jsnnk/z4dPNLkzwvyW1JvpHkp5Oku79SVb+d5BPT7X6ruzc+kRAAALYpWxWYu/vUzaw6cRPbdpJXb2Y/FyS5YKt7BwAAy8yd/gAAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABgQmAEAYEBgBgCAAYEZAAAGBGYAABiYd2CuqqdW1bVzHl+rqv9QVWdX1R1z2p835zWvq6rbquqWqjppcQ4BAABmZ5f5vrC7b0lyZJJU1cokdyT5QJKfTvJ73X3u3O2r6mlJTklyaJJ9k1xeVQd190Pz7QMAAMzaYk3JODHJZ7r784NtXpTkvd39re7+XJLbkhy7SPUBAGAmFiswn5LkPXOen1FV11fVBVX1xGnbfklun7PN+mkbAABssxYcmKvqe5K8MMn7p03nJfmhTKZr3JnkzfPY5+lVdVVVXXX33XcvtIsAADBvizHC/Nwkn+zuu5Kku+/q7oe6++Ekb8+3p13ckeQpc163etr2Xbr7/O5e291rV61atQhdBACA+VmMwHxq5kzHqKp95qx7SZIbpsuXJDmlqnarqgOSHJjkykWoDwAAMzPvq2QkSVU9Jsm/SfJzc5p/p6qOTNJJ1m1Y1903VtX7kvxjkgeTvNoVMgAA2NYtKDB3931J9tqo7acG278xyRsXUhMAAJaSO/0BAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwsODAXFXrqupTVXVtVV01bfu+qrqsqm6dfn3itL2q6i1VdVtVXV9VRy+0PgAAzNJijTD/6+4+srvXTp+fmeSvuvvAJH81fZ4kz01y4PRxepLzFqk+AADMxKymZLwoyTuny+9M8uI57e/qiSuSPKGq9plRHwAAYMEWIzB3kg9X1dVVdfq0be/uvnO6/MUke0+X90ty+5zXrp+2AQDANmmXRdjHM7r7jqp6cpLLqurmuSu7u6uqH80Op8H79CTZf//9F6GLAAAwPwseYe7uO6Zfv5TkA0mOTXLXhqkW069fmm5+R5KnzHn56mnbxvs8v7vXdvfaVatWLbSLAAAwbwsKzFX1mKp63IblJD+a5IYklyR5xXSzVyS5eLp8SZKXT6+WcVySe+dM3QAAgG3OQqdk7J3kA1W1YV8XdvdfVNUnkryvqn4myeeT/Ph0+0uTPC/JbUm+keSnF1gfAABmakGBubs/m+SITbTfk+TETbR3klcvpCYAACwld/oDAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgYN6BuaqeUlUfqap/rKobq+qXpu1nV9UdVXXt9PG8Oa95XVXdVlW3VNVJi3EAAAAwS7ss4LUPJnltd3+yqh6X5Oqqumy67ve6+9y5G1fV05KckuTQJPsmubyqDuruhxbQBwAAmKl5jzB3953d/cnp8v9MclOS/QYveVGS93b3t7r7c0luS3LsfOsDAMBSWJQ5zFW1JslRSf5h2nRGVV1fVRdU1ROnbfsluX3Oy9ZnHLABAGDZLTgwV9Vjk1yU5D9099eSnJfkh5IcmeTOJG+exz5Pr6qrquqqu+++e6FdBACAeVtQYK6qXTMJy3/U3X+aJN19V3c/1N0PJ3l7vj3t4o4kT5nz8tXTtu/S3ed399ruXrtq1aqFdBEAABZkIVfJqCR/mOSm7v7dOe37zNnsJUlumC5fkuSUqtqtqg5IcmCSK+dbHwAAlsJCrpJxfJKfSvKpqrp22vZrSU6tqiOTdJJ1SX4uSbr7xqp6X5J/zOQKG692hQwAALZ18w7M3f3xJLWJVZcOXvPGJG+cb00AAFhq7vQHAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAwJIH5qo6uapuqarbqurMpa4PAACPxpIG5qpameQPkjw3ydOSnFpVT1vKPgAAwKOx1CPMxya5rbs/293/K8l7k7xoifsAAABbbakD835Jbp/zfP20DQAAtknV3UtXrOqlSU7u7v99+vynkvxId5+x0XanJzl9+vSpSW6ZZ8knJfnyPF+7EMtVdzlrO+Ydv+5y1nbMO0ftna3uctZ2zDtHbcf86PxAd6/a1Ipd5t+febkjyVPmPF89bfsO3X1+kvMXWqyqrurutQvdz/ZSdzlrO+Ydv+5y1nbMO0ftna3uctZ2zDtHbce8eJZ6SsYnkhxYVQdU1fckOSXJJUvcBwAA2GpLOsLc3Q9W1RlJ/jLJyiQXdPeNS9kHAAB4NJZ6Ska6+9Ikly5RuQVP69jO6i5nbce849ddztqOeeeovbPVXc7ajnnnqO2YF8mSnvQHAADbG7fGBgCAAYEZAAAGlnwO81Kpqnd198uXoe4zMrmj4Q3d/eEZ1vmRJDd199eq6nuTnJnk6CT/mORN3X3vDGu/JskHuvv2LW68uHU3XFnln7r78qr6yST/W5Kbkpzf3Q/MuP4PJvmxTC6N+FCSTye5sLu/Nsu6AMDy2iHmMFfVxpemqyT/Osn/lyTd/cIZ1r6yu4+dLv9sklcn+UCSH03yP7r7nBnVvTHJEdMrj5yf5BtJ/iTJidP2H5tF3Wnte5Pcl+QzSd6T5P3dffes6s2p+0eZ/JG3R5KvJnlskj/N5Jiru18xw9qvSfKCJB9L8rwk10z78JIkr+ruj86qNgBsT6rqyd39peXux2LaUQLzJzMZWf2vSTqTwPyeTEYj091/PcPa13T3UdPlTyR5XnffXVWPSXJFdx82o7o3dfch0+VPdvfRc9Zd291HzqLudP/XJDkmyXOS/ESSFya5OpP3/E+7+3/OqO713X14Ve2SyQ1v9u3uh6qqklzX3YfPou609qeSHDmtt0eSS7v7hKraP8nFG74HYLHsiL9wtqSq9urue5a7HyyeqtozyeuSvDjJkzP5Hf2lJBcnOae7v7oMffpQdz93Rvt+fCbHuzrJh7r7wjnr3tbdr5pF3en+vz/JG5I8nOQ3kvxikn+Xyaewv9Tdd86o7vdt3JRJJjgqk5z5lVnUndY+ubv/Yrq8Z5LfTfLDSW5I8svdfddi1dpR5jCvzeQf5/VJ7p2O9n2zu/96lmF5akVVPbGq9srkG+PuJOnu+5I8OMO6N1TVT0+Xr6uqtUlSVQclmenUhCTd3Q9394e7+2eS7JvkbUlOTvLZGdZdMZ2W8bhMRpn3nLbvlmTXGdbdYMMUpt0yGd1Od39h1rWras+qOqeqbq6qr1TVPVV107TtCbOsPejTh2a478dX1f9VVe+eTruZu+5ts6o73f/3V9V5VfUHVbVXVZ1dVZ+qqvdV1T4zrPt9Gz32SnLl9GfLxr+MFrv2yXOW96yqP6yq66vqwqrae4Z1z6mqJ02X11bVZ5P8Q1V9vqr+1azqTut9sqp+vap+aJZ1NlF3bVV9pKr+e1U9paouq6p7q+oTVTWzP7qr6rFV9VtVdeO03t1VdUVVnTarmnO8L8k/Jzmhu7+vu/fK5BPgf56um4mqOnozj2OSHDmrukn+WyaB8aIkp1TVRVW123TdcTOsmyTvyGTw8PYkH0nyzUw+Ef2bJP9lhnW/nEkG2/C4Ksl+ST45XZ6lN81ZfnOSO5P820xulPf/LmahHWKEeYOqWp3k95LcleSF3b3/EtRcl8lfc5XJX87Hd/edVfXYJB+f1Ujv9C+p30/yzEy+WY/O5D/J7Ule093XzaLutPY1mxtRrao9uvsbM6r7y5n8xbwyk/8YL8okoB+X5E+6+zdnUXda+5eS/EySf8jkPf+/u/u/VdWqJBd197NmWPsvM5le9M7u/uK07fuTvCLJid39ozOqe/TmViX58+6eSYCsqouS3JrkiiSvzOQPwJ/s7m9t/GnKDGr/RZIPJnlMkp9M8kdJLsxkdOw53f2iGdV9OMnnN2penWR9Jn+g/uAs6k5rP/KeVtV/TfLFJG/PZL7+v+ruF8+o7qc2fAJXVR9J8qvd/YnpH/0XzvKWulX1uUwCzY9ncrzvSfLH3f1Ps6o5rXtlJiOAT0jyO5mMgP1JVZ2Y5D9197+cUd2LM5kqeHkmx/yYJO9N8utJ7ujuX5tF3WntW7r7qY923SLUfSjJX2fy82pjx3X3986o7nd8wltVr88ktL4wyWUz/vk19xPvL8zNQBv3a5HrvjbJv0nyK939qWnb57r7gFnU26j23J9fG7/3i3vM3b3DPZI8P5MT35azD3skOWAJ6jw+yRGZTJHYe4mO7aBlfF/3zWQqRjL5pfPSJMcuUe1Dp/UOXuJjvmU+6xah7kOZBPWPbOLxzRnWvXaj569P8rdJ9kryyRm/19fMWf7CqF+LXPe1Sf4iyWFz2j43y2OdU+eTc5Y3fu9necw3JdllunzFRus+tYTH/MxMPiH74vR7+/QZ1r1mzvLG31/XzLDudRs9/8T064okN8/4vf5wkl+d+/spyd5J/s8kl8+w7g1JDtzMuttnWPemJCs2ajstyY1JPj/j9/q6Ocv/aaN1s/4/tTrJ+zOZEvG4JJ+dZb05ddcn+Y/Tn6GfzXQgeLru+sWstUNeJaO7P5jJKNFy9uEbST63BHW+lmRmo8mbqfnppay3Ue1/mrP81UxOdFyq2jdm8kNvqX2+qn41kxHmu5Jk+lH5aZl8ojArNyX5ue6+deMVVTXLurtV1YrufjhJuvuNVXVHJidcPnaGdZPvnKb2ro3WrZxV0e5+c1X9cZLfm763b8jkE6ul8OSq+o+ZjMQ9vqqqp79tMttpe29LcmlVnZPkL6rq9zM5iffZSa6dYd3v0N1/k+RvquoXMxkl+4nM7g5l91fVj2Yynayr6sXd/WfTKSgPzahmktxXVc/o7o9X1QuTfCVJuvvhqtrUCOxi+olMruL019OfW53Jp8CXZDLaPStnZ/Pfv784w7r/I5Pv4cs3NHT3O6rqi0neOsO6SXJxVT22u7/e3b++obGq/kWSW2ZZuLvXJ/n30++vyzIZNFwKb88koCfJO5M8Kcnd009hr13MQjvUlAzYEVXVEzP5hfOiTE6aSb79C+ec7v7nGdV9aSajEt/1g3bDL/oZ1f2dJB/u7ss3aj85yVu7+8BZ1J3W+K0kv9PdX9+o/V9k8l6/dFa159R6YZJfS7Kmu79/Ceq9YaOmt/XkxOXvz+S9mNnlOavqhCS/kOSgTM4RuD3JnyW5oLtndg5IVb23u0+Z1f4HdY/IZCrGw0l+OZNjf0UmJzH/bHf/3YzqHp7JSfEHZvJH/yu7+9PTKWWndvdbZlF3Tv2DMxmBvGLu/62ac8LWDOvul+QftpG6z+3umZ3/sYXaS3bMmfzx90PdfcOs625ce6bHvBRD5h4eHrN5JPlpdXe82km+N8nTd6Zj3hbq7ozHPOu6SV6TyejmnyVZl+RFc9bNbIrVMtb9xeWou5y1l+u9XupjNsIM27GNT+xQd8er7Zh3jto7at2aXJLzX3b316tqTSbT6N7d3b8/OoFc3e2n9s5yzDvkHGbYkVTV9ZtblcnJM+pu57Ud89LVXc7aO1vdqRU9/Zi8u9dNp+H8SVX9QDZ9BQt1t7/aO8UxC8yw7ds7yUmZXLd0rkoykzmPO2nd5aztmJeu7nLW3tnqJsldVXVkd1+bJNORwBckuSDJTG7stZPWXc7aO8UxC8yw7fvzJI/d8ANhrqr6qLo7RG3HvHR1l7P2zlY3SV6ejW7i1ZMTOl9eVYt6Y4mdvO5y1t4pjtkcZgAAGNhRbo0NAAAzITADAMCAwAywnaiJj1fVc+e0/fuqmumNAQB2duYwA2xHqurpSd6f5KhMTty+JsnJ3f2Zeexrl57hHfUAdhQCM8B2Znr78PuSPGb69QeSPD3JrknO7u6Lpxfxf/d0myQ5o7v/bnqd0t/O5BJjB3f3QUvbe4Dtj8AMsJ2pqsck+WSS/5XJJcNu7O7/XlVPSHJlJqPPneTh7r6/qg5M8p7uXjsNzB/M5Nbbn1uO/gNsb1yHGWA70933VdUfJ/l6kh9P8m+r6v+Yrt49yf5J/inJf66qI5M8lGTuSPKVwjLA1hOYAbZPD08fleTfdfctc1dW1dlJ7kpyRCYneN8/Z/V9S9RHgB2Cq2QAbN/+MskvVlUlSVUdNW3fM8md3f1wkp9KsnKZ+gew3ROYAbZvv53JyX7XV9WN0+dJ8rYkr6iq65IcHKPKAPPmpD8AABgwwgwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMDA/w+CpqDpQpTLJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.groupby('Year')['Engine'].agg(['mean','median']).plot.bar(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disabled-symphony",
   "metadata": {},
   "source": [
    "**Remove the data samples with outliers value in Engine**\n",
    "* We will use Fuel Type (Diesel & Petrol) infomation to remove outliers from engine vlaues\n",
    "* Because num of samples distributed as Diesel and Petrol cars are close\n",
    "* Which creates a balance data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "id": "lovely-submission",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "2     honda jazz     Chennai    12              46000    Petrol       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "2           First    18.20  1199.0   88.70    5.0   4.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  "
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-selling",
   "metadata": {},
   "source": [
    "* Seperate Data in two parts using data filtering\n",
    " * part 1 (dfd) - All Diesel cars\n",
    " * part 2 (dfp) - All Petrol cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "helpful-broadcast",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfd = df3[df3['Fuel_Type'] == 'Diesel']     #Data samples with Diesel Cars\n",
    "dfp = df3[df3['Fuel_Type'] == 'Petrol']     #Data samples with Petrol Cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "id": "lesser-peace",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "id": "violent-namibia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfd['Engine'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "id": "heard-xerox",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Engine'>"
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfd['Engine'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explicit-revolution",
   "metadata": {},
   "source": [
    "* Use describe function on Engine column to identify 25 percentile(q1) and 75 percentile(q3) values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "id": "hindu-laptop",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2400.000000\n",
       "mean     1796.109583\n",
       "std       575.627987\n",
       "min       793.000000\n",
       "25%      1364.000000\n",
       "50%      1582.000000\n",
       "75%      2179.000000\n",
       "max      4367.000000\n",
       "Name: Engine, dtype: float64"
      ]
     },
     "execution_count": 687,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd['Engine'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "embedded-interface",
   "metadata": {},
   "source": [
    "* Calculte lower fence and higher fence values of box plot to filter out outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "id": "composite-former",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1364.0 2179.0\n",
      "141.5 3401.5\n"
     ]
    }
   ],
   "source": [
    "q1 = dfd['Engine'].describe()[4]\n",
    "q3 = dfd['Engine'].describe()[6]\n",
    "print(q1,q3)\n",
    "\n",
    "iqr = q3 - q1\n",
    "lf = q1 - 1.5 * iqr\n",
    "hf = q3 + 1.5 * iqr\n",
    "print(lf,hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "id": "incident-reminder",
   "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>459</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>7</td>\n",
       "      <td>51002</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>48.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>9</td>\n",
       "      <td>79926</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>land rover</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>12</td>\n",
       "      <td>55000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.20</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>land rover</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "      <td>66538</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.20</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3242</th>\n",
       "      <td>land rover</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "      <td>63000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>11.49</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>335.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4388</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>8</td>\n",
       "      <td>50000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4559</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>13</td>\n",
       "      <td>73000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>land rover</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>11</td>\n",
       "      <td>84384</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.20</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4938</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>8</td>\n",
       "      <td>56238</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5416</th>\n",
       "      <td>land rover</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>75995</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>11.49</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>335.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "459      audi q7  Coimbatore     7              51002    Diesel    Automatic   \n",
       "586      audi q7       Kochi     9              79926    Diesel    Automatic   \n",
       "1038  land rover   Bangalore    12              55000    Diesel    Automatic   \n",
       "1093     audi q7   Bangalore     9              50000    Diesel    Automatic   \n",
       "1352  land rover   Bangalore    11              66538    Diesel    Automatic   \n",
       "3242  land rover   Bangalore    11              63000    Diesel    Automatic   \n",
       "4388     audi q7   Bangalore     8              50000    Diesel    Automatic   \n",
       "4559     audi q7       Delhi    13              73000    Diesel    Automatic   \n",
       "4869  land rover  Coimbatore    11              84384    Diesel    Automatic   \n",
       "4938     audi q7  Coimbatore     8              56238    Diesel    Automatic   \n",
       "5416  land rover  Coimbatore    10              75995    Diesel    Automatic   \n",
       "\n",
       "          Owner_Type  Mileage  Engine  Power  Seats  Price  \n",
       "459            First    11.33  4134.0  335.2    7.0  48.91  \n",
       "586            First    11.33  4134.0  335.2    7.0  29.77  \n",
       "1038           First    10.20  4367.0  308.0    7.0  67.00  \n",
       "1093           First    11.33  4134.0  335.2    7.0  51.00  \n",
       "1352           First    10.20  4367.0  308.0    7.0  70.00  \n",
       "3242  Second & Above    11.49  4367.0  335.3    5.0  65.00  \n",
       "4388           First    11.33  4134.0  335.2    7.0  48.00  \n",
       "4559           First    11.33  4134.0  335.2    7.0  17.51  \n",
       "4869           First    10.20  4367.0  308.0    7.0  52.54  \n",
       "4938           First    11.33  4134.0  335.2    7.0  51.69  \n",
       "5416  Second & Above    11.49  4367.0  335.3    5.0  65.81  "
      ]
     },
     "execution_count": 689,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For diesel cars if engine value is lesser then 141.5 and Higher then 3401.5 \n",
    "#it will be a outliers\n",
    "#View those Samples=>\n",
    "\n",
    "dfd[(dfd['Engine'] < 141.5) | (dfd['Engine'] > 3401.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "id": "miniature-plymouth",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter out car samples in the outlier range in terms of engine capability\n",
    "\n",
    "dfd = dfd[~((dfd['Engine'] < 141.5) | (dfd['Engine'] > 3401.5))]\n",
    "dfd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eight-israel",
   "metadata": {},
   "source": [
    "* Null values or Missing values inDiesel Cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "id": "complimentary-circuit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage               0\n",
       "Engine                7\n",
       "Power                44\n",
       "Seats                 9\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "major-responsibility",
   "metadata": {},
   "source": [
    "* Fill up the missing values of engine with average engine values of diesel cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "id": "thermal-infrastructure",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfd['Engine'] = dfd['Engine'].fillna(dfd['Engine'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tutorial-provincial",
   "metadata": {},
   "source": [
    "* Remove Outliers in engine of petrol cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "id": "photographic-steps",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfp['Engine'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "id": "coral-jacob",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Engine'>"
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfp['Engine'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "id": "horizontal-november",
   "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>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>maruti ciaz</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>5</td>\n",
       "      <td>25692</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>21.56</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>103.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>honda city</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>11</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>116.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>audi a6</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>8</td>\n",
       "      <td>55985</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.53</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>177.01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hyundai i20</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>45807</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "2    honda jazz  Chennai    12              46000    Petrol       Manual   \n",
       "10  maruti ciaz    Kochi     5              25692    Petrol       Manual   \n",
       "11   honda city  Kolkata    11              60000    Petrol    Automatic   \n",
       "22      audi a6   Mumbai     8              55985    Petrol    Automatic   \n",
       "23  hyundai i20  Kolkata    13              45807    Petrol       Manual   \n",
       "\n",
       "   Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "2       First    18.20  1199.0   88.70    5.0   4.50  \n",
       "10      First    21.56  1462.0  103.25    5.0   9.95  \n",
       "11      First    16.80  1497.0  116.30    5.0   4.49  \n",
       "22      First    13.53  1984.0  177.01    5.0  23.50  \n",
       "23      First    18.50  1197.0   80.00    5.0   1.87  "
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "id": "purple-latitude",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1196.0 1497.0\n",
      "744.5 1948.5\n"
     ]
    }
   ],
   "source": [
    "q1 = dfp['Engine'].describe()[4]\n",
    "q3 = dfp['Engine'].describe()[6]\n",
    "print(q1,q3)\n",
    "\n",
    "iqr = q3 - q1\n",
    "lf = q1 - 1.5 * iqr\n",
    "hf = q3 + 1.5 * iqr\n",
    "print(lf,hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "id": "human-power",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02572202166064982"
      ]
     },
     "execution_count": 697,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((dfp['Engine'] < 744.5) | (dfp['Engine'] > 1948.5)).sum()/len(dfp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-reply",
   "metadata": {},
   "source": [
    "* 2.5% of total petrol cars falls in outlier range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "id": "competitive-fruit",
   "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>22</th>\n",
       "      <td>audi a6</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>8</td>\n",
       "      <td>55985</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.53</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>177.01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "      <td>70704</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>11.25</td>\n",
       "      <td>2694.0</td>\n",
       "      <td>163.70</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>17</td>\n",
       "      <td>230000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>14</td>\n",
       "      <td>128000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>skoda octavia</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>18</td>\n",
       "      <td>100000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.45</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>227.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>5</td>\n",
       "      <td>26405</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.60</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>191.34</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>12</td>\n",
       "      <td>31000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>13</td>\n",
       "      <td>48394</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>50645</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.93</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>4</td>\n",
       "      <td>13190</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>181.43</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>audi a6</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>7</td>\n",
       "      <td>58953</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.53</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>177.01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>12</td>\n",
       "      <td>48000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>184.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>12</td>\n",
       "      <td>71703</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>181.03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>bmw x1</td>\n",
       "      <td>Pune</td>\n",
       "      <td>11</td>\n",
       "      <td>80000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.25</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>15</td>\n",
       "      <td>61000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>5</td>\n",
       "      <td>20446</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.60</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>191.34</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>13</td>\n",
       "      <td>83000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.93</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Pune</td>\n",
       "      <td>10</td>\n",
       "      <td>49000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.59</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>245.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>13</td>\n",
       "      <td>69300</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>12</td>\n",
       "      <td>29813</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>181.03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>15</td>\n",
       "      <td>71000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>8.30</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>250.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>12</td>\n",
       "      <td>75871</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>9.43</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>254.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>12</td>\n",
       "      <td>78088</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>13</td>\n",
       "      <td>77849</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.90</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>14</td>\n",
       "      <td>70521</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.90</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>13</td>\n",
       "      <td>74000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>12</td>\n",
       "      <td>38000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.80</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>13</td>\n",
       "      <td>59660</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Pune</td>\n",
       "      <td>11</td>\n",
       "      <td>39000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>audi a6</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>15</td>\n",
       "      <td>41000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.40</td>\n",
       "      <td>2773.0</td>\n",
       "      <td>201.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>13</td>\n",
       "      <td>54000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>262.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Pune</td>\n",
       "      <td>12</td>\n",
       "      <td>43706</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.90</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3065</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>13</td>\n",
       "      <td>73000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>272.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3220</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>15</td>\n",
       "      <td>62000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>9.50</td>\n",
       "      <td>2997.0</td>\n",
       "      <td>231.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>8</td>\n",
       "      <td>7501</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>333.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3435</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>17</td>\n",
       "      <td>150000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>11</td>\n",
       "      <td>125000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.50</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>14</td>\n",
       "      <td>68001</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>272.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4202</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>9</td>\n",
       "      <td>85301</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4267</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>14</td>\n",
       "      <td>28888</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>157.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>12</td>\n",
       "      <td>87073</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.93</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4348</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>12</td>\n",
       "      <td>7000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>18</td>\n",
       "      <td>13002</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>9.30</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>193.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4493</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>24000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.60</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>258.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4507</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Pune</td>\n",
       "      <td>14</td>\n",
       "      <td>40000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4698</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>9</td>\n",
       "      <td>72000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>184.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4816</th>\n",
       "      <td>bmw 5</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>17</td>\n",
       "      <td>48000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>11.62</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>306.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4959</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>18</td>\n",
       "      <td>13000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>9.30</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>193.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4987</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>15</td>\n",
       "      <td>77000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>157.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10</td>\n",
       "      <td>50319</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>181.03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5082</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>12</td>\n",
       "      <td>31000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.90</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>215.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5195</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Pune</td>\n",
       "      <td>11</td>\n",
       "      <td>100000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>157.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5701</th>\n",
       "      <td>bmw 3</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>13</td>\n",
       "      <td>70749</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.05</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>154.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5728</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>14</td>\n",
       "      <td>43600</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>272.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5917</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "      <td>33000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>184.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5963</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10</td>\n",
       "      <td>25000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>181.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6011</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>14</td>\n",
       "      <td>53000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3597.0</td>\n",
       "      <td>262.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name    Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "22                  audi a6      Mumbai     8              55985    Petrol   \n",
       "47            toyota innova   Bangalore    11              70704    Petrol   \n",
       "77            toyota innova     Chennai    17             230000    Petrol   \n",
       "118                   bmw 5      Mumbai    14             128000    Petrol   \n",
       "351           skoda octavia      Jaipur    18             100000    Petrol   \n",
       "473   mercedes-benz e-class       Kochi     5              26405    Petrol   \n",
       "488                   bmw 5     Kolkata    12              31000    Petrol   \n",
       "531                   bmw 3  Coimbatore    13              48394    Petrol   \n",
       "536   mercedes-benz e-class     Kolkata    13              50645    Petrol   \n",
       "544       mercedes-benz new       Kochi     4              13190    Petrol   \n",
       "596                 audi a6       Kochi     7              58953    Petrol   \n",
       "887       mercedes-benz new      Mumbai    12              48000    Petrol   \n",
       "932       mercedes-benz new       Kochi    12              71703    Petrol   \n",
       "1031                 bmw x1        Pune    11              80000    Petrol   \n",
       "1035                  bmw 5   Hyderabad    15              61000    Petrol   \n",
       "1113  mercedes-benz e-class       Kochi     5              20446    Petrol   \n",
       "1134  mercedes-benz e-class   Bangalore    13              83000    Petrol   \n",
       "1227                  bmw 3        Pune    10              49000    Petrol   \n",
       "1408                  bmw 5      Mumbai    13              69300    Petrol   \n",
       "1570      mercedes-benz new       Kochi    12              29813    Petrol   \n",
       "1596                audi q7       Delhi    15              71000    Petrol   \n",
       "1762                  bmw 3  Coimbatore    12              75871    Petrol   \n",
       "1969          toyota innova     Kolkata    12              78088    Petrol   \n",
       "2104                  bmw 3  Coimbatore    13              77849    Petrol   \n",
       "2135                  bmw 3   Bangalore    14              70521    Petrol   \n",
       "2163                  bmw 5      Mumbai    13              74000    Petrol   \n",
       "2189                audi a4      Mumbai    12              38000    Petrol   \n",
       "2499                  bmw 3  Coimbatore    13              59660    Petrol   \n",
       "2544                  bmw 3        Pune    11              39000    Petrol   \n",
       "2661                audi a6   Ahmedabad    15              41000    Petrol   \n",
       "2681           skoda superb   Hyderabad    13              54000    Petrol   \n",
       "2796                  bmw 3        Pune    12              43706    Petrol   \n",
       "3065  mercedes-benz e-class      Mumbai    13              73000    Petrol   \n",
       "3220  mercedes-benz e-class      Jaipur    15              62000    Petrol   \n",
       "3266  mercedes-benz e-class     Kolkata     8               7501    Petrol   \n",
       "3435          toyota innova       Delhi    17             150000    Petrol   \n",
       "3999                audi a4   Hyderabad    11             125000    Petrol   \n",
       "4136  mercedes-benz e-class      Mumbai    14              68001    Petrol   \n",
       "4202          toyota innova  Coimbatore     9              85301    Petrol   \n",
       "4267  mercedes-benz e-class     Kolkata    14              28888    Petrol   \n",
       "4292  mercedes-benz e-class  Coimbatore    12              87073    Petrol   \n",
       "4348                  bmw 3     Kolkata    12               7000    Petrol   \n",
       "4409           skoda superb     Kolkata    18              13002    Petrol   \n",
       "4493                  bmw 5     Kolkata    13              24000    Petrol   \n",
       "4507                  bmw 3        Pune    14              40000    Petrol   \n",
       "4698  mercedes-benz e-class      Mumbai     9              72000    Petrol   \n",
       "4816                  bmw 5       Delhi    17              48000    Petrol   \n",
       "4959           skoda superb     Kolkata    18              13000    Petrol   \n",
       "4987  mercedes-benz e-class      Mumbai    15              77000    Petrol   \n",
       "5073  mercedes-benz e-class      Mumbai    10              50319    Petrol   \n",
       "5082                  bmw 3       Delhi    12              31000    Petrol   \n",
       "5195  mercedes-benz e-class        Pune    11             100000    Petrol   \n",
       "5701                  bmw 3       Kochi    13              70749    Petrol   \n",
       "5728  mercedes-benz e-class     Kolkata    14              43600    Petrol   \n",
       "5917      mercedes-benz new   Bangalore    11              33000    Petrol   \n",
       "5963      mercedes-benz new      Mumbai    10              25000    Petrol   \n",
       "6011           skoda superb   Hyderabad    14              53000    Petrol   \n",
       "\n",
       "     Transmission      Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "22      Automatic           First    13.53  1984.0  177.01    5.0  23.50  \n",
       "47         Manual           First    11.25  2694.0  163.70    7.0  10.95  \n",
       "77         Manual  Second & Above    12.40  1998.0  132.00    8.0   4.50  \n",
       "118     Automatic           First    10.80  2497.0  215.00    5.0   7.00  \n",
       "351     Automatic           First    14.45  1984.0  227.00    5.0   2.05  \n",
       "473     Automatic           First    12.60  1950.0  191.34    5.0  51.43  \n",
       "488     Automatic           First    10.80  2497.0  150.00    5.0  13.50  \n",
       "531     Automatic           First    12.05  1995.0  154.00    5.0  10.49  \n",
       "536     Automatic  Second & Above    10.93  2497.0  204.00    5.0  10.75  \n",
       "544     Automatic           First     0.00  1950.0  181.43    5.0  38.99  \n",
       "596     Automatic           First    13.53  1984.0  177.01    5.0  24.22  \n",
       "887     Automatic  Second & Above    14.74  1991.0  184.00    5.0   9.75  \n",
       "932     Automatic           First    14.74  1991.0  181.03    5.0   9.14  \n",
       "1031    Automatic           First    11.25  1995.0  150.00    5.0   9.50  \n",
       "1035    Automatic  Second & Above    10.80  2497.0  215.00    5.0  11.00  \n",
       "1113    Automatic           First    12.60  1950.0  191.34    5.0  51.35  \n",
       "1134    Automatic           First    10.93  2497.0  204.00    5.0  16.50  \n",
       "1227    Automatic           First    14.59  1997.0  245.00    5.0  19.00  \n",
       "1408    Automatic  Second & Above    10.80  2497.0  150.00    5.0  12.50  \n",
       "1570    Automatic  Second & Above    14.74  1991.0  181.03    5.0  11.50  \n",
       "1596    Automatic           First     8.30  4134.0  250.00    7.0  11.99  \n",
       "1762    Automatic           First     9.43  2996.0  254.00    5.0  14.12  \n",
       "1969       Manual           First    12.40  1998.0  132.00    8.0   6.30  \n",
       "2104    Automatic  Second & Above    10.90  2497.0  215.00    5.0  11.27  \n",
       "2135    Automatic  Second & Above    10.90  2497.0  215.00    5.0  10.30  \n",
       "2163    Automatic           First    10.80  2497.0  150.00    5.0  12.95  \n",
       "2189    Automatic           First    10.80  1984.0  132.00    5.0  12.00  \n",
       "2499    Automatic           First    12.05  1995.0  154.00    5.0  10.49  \n",
       "2544    Automatic  Second & Above    12.05  1995.0  154.00    5.0  15.00  \n",
       "2661    Automatic  Second & Above    10.40  2773.0  201.00    5.0  10.50  \n",
       "2681    Automatic           First     0.00  3597.0  262.60    5.0   6.00  \n",
       "2796    Automatic           First    10.90  2497.0  215.00    5.0   9.00  \n",
       "3065    Automatic           First    10.93  3498.0  272.00    5.0  10.40  \n",
       "3220    Automatic           First     9.50  2997.0  231.10    5.0   7.00  \n",
       "3266    Automatic           First    10.00  2996.0  333.00    4.0  48.00  \n",
       "3435       Manual           First    12.40  1998.0  132.00    8.0   4.50  \n",
       "3999    Automatic           First    10.50  3197.0     NaN    0.0  18.00  \n",
       "4136    Automatic  Second & Above    10.93  3498.0  272.00    5.0  11.75  \n",
       "4202       Manual           First    12.40  1998.0  132.00    8.0  12.36  \n",
       "4267    Automatic           First    10.00  2496.0  157.70    5.0   7.56  \n",
       "4292    Automatic           First    10.93  2497.0  204.00    5.0  17.23  \n",
       "4348    Automatic           First    12.05  1995.0  154.00    5.0   9.75  \n",
       "4409    Automatic           First     9.30  2771.0  193.10    5.0   3.25  \n",
       "4493    Automatic           First    10.60  2996.0  258.00    5.0   8.75  \n",
       "4507    Automatic  Second & Above    12.05  1995.0  154.00    5.0   9.50  \n",
       "4698    Automatic           First    12.00  1991.0  184.00    5.0  17.25  \n",
       "4816    Automatic  Second & Above    11.62  2979.0  306.00    5.0   6.99  \n",
       "4959    Automatic           First     9.30  2771.0  193.10    5.0   3.25  \n",
       "4987    Automatic  Second & Above    10.00  2496.0  157.70    5.0   6.50  \n",
       "5073    Automatic           First    12.00  1991.0  181.03    5.0  21.95  \n",
       "5082    Automatic           First    10.90  2497.0  215.00    5.0  31.00  \n",
       "5195    Automatic           First    10.00  2496.0  157.70    5.0  11.50  \n",
       "5701    Automatic           First    12.05  1995.0  154.00    5.0   9.86  \n",
       "5728    Automatic           First    10.93  3498.0  272.00    5.0  10.24  \n",
       "5917    Automatic           First    14.74  1991.0  184.00    5.0  16.00  \n",
       "5963    Automatic           First    14.74  1991.0  181.04    5.0  14.85  \n",
       "6011    Automatic           First     0.00  3597.0  262.60    5.0   4.75  "
      ]
     },
     "execution_count": 698,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp[(dfp['Engine'] < 744.5) | (dfp['Engine'] > 1948.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "id": "underlying-advertiser",
   "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>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>maruti ciaz</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>5</td>\n",
       "      <td>25692</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>21.56</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>103.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>honda city</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>11</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>116.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hyundai i20</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>45807</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>volkswagen vento</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>33000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.40</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>103.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "2         honda jazz  Chennai    12              46000    Petrol       Manual   \n",
       "10       maruti ciaz    Kochi     5              25692    Petrol       Manual   \n",
       "11        honda city  Kolkata    11              60000    Petrol    Automatic   \n",
       "23       hyundai i20  Kolkata    13              45807    Petrol       Manual   \n",
       "24  volkswagen vento  Kolkata    13              33000    Petrol    Automatic   \n",
       "\n",
       "   Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "2       First    18.20  1199.0   88.70    5.0   4.50  \n",
       "10      First    21.56  1462.0  103.25    5.0   9.95  \n",
       "11      First    16.80  1497.0  116.30    5.0   4.49  \n",
       "23      First    18.50  1197.0   80.00    5.0   1.87  \n",
       "24      First    14.40  1598.0  103.60    5.0   2.85  "
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp = dfp[~((dfp['Engine'] < 744.5) | (dfp['Engine'] > 1948.5))]\n",
    "dfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "id": "adjusted-piano",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfp['Engine'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "id": "peaceful-sigma",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Engine'>"
      ]
     },
     "execution_count": 701,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfp['Engine'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "magnetic-schema",
   "metadata": {},
   "source": [
    "* Fill up the missing values in engine of Petrol cars with average engine values of petrol cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "id": "talented-television",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp['Engine'] = dfp['Engine'].fillna(dfp['Engine'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "downtown-inside",
   "metadata": {},
   "source": [
    "* Combine the dataframes \n",
    "* pd.concat((dfd , dfp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "id": "personalized-family",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.concat((dfd , dfp))\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "id": "indie-fever",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Fuel_Type' , y = 'Engine' , data = df4)\n",
    "plt.title('Engine vs Fuel Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "id": "cooked-alpha",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Location               0\n",
       "Year                   0\n",
       "Kilometers_Driven      0\n",
       "Fuel_Type              0\n",
       "Transmission           0\n",
       "Owner_Type             0\n",
       "Mileage                0\n",
       "Engine                 0\n",
       "Power                115\n",
       "Seats                 35\n",
       "Price                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 705,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-diving",
   "metadata": {},
   "source": [
    "* Remove Outliers value in Power column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "id": "herbal-houston",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel    2396\n",
       "Petrol    2159\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 706,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "id": "humanitarian-aberdeen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       3454\n",
       "Automatic    1101\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 707,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "id": "breathing-insured",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfd = df4[df4['Fuel_Type'] == 'Diesel']\n",
    "dfp = df4[df4['Fuel_Type'] == 'Petrol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "id": "secondary-findings",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Power'>"
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfd['Power'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "republican-auditor",
   "metadata": {},
   "source": [
    "* Conclusion :- No outliers in Power values for diesel cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "id": "united-disability",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Power'>"
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfp['Power'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "id": "moderate-divorce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2088.000000\n",
       "mean       89.423870\n",
       "std        26.307936\n",
       "min        46.300000\n",
       "25%        73.900000\n",
       "50%        82.000000\n",
       "75%       103.520000\n",
       "max       189.000000\n",
       "Name: Power, dtype: float64"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp['Power'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "id": "expired-humidity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.47000000000002 147.95\n"
     ]
    }
   ],
   "source": [
    "q1 = dfp['Power'].describe()[4]\n",
    "q3 = dfp['Power'].describe()[6]\n",
    "\n",
    "iqr = q3 - q1\n",
    "\n",
    "lf = q1 - 1.5 * iqr\n",
    "hf = q3 + 1.5 * iqr\n",
    "\n",
    "print(lf , hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "id": "worst-medicaid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03195924038906901"
      ]
     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of data falls under outlier\n",
    "((dfp['Power'] < 29.47) | (dfp['Power'] > 147.95)).sum()/len(dfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "id": "greater-choir",
   "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>62</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>8</td>\n",
       "      <td>58000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.74</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>186.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>14</td>\n",
       "      <td>28352</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.70</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>178.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>11</td>\n",
       "      <td>53537</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.70</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>157.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>skoda octavia</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>9</td>\n",
       "      <td>76000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.70</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>177.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>30500</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.74</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>186.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.50</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5729</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Pune</td>\n",
       "      <td>13</td>\n",
       "      <td>71473</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>13.70</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>157.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5811</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Pune</td>\n",
       "      <td>13</td>\n",
       "      <td>35102</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>12.60</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>161.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5874</th>\n",
       "      <td>mercedes-benz e-class</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>9</td>\n",
       "      <td>40000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>157.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5984</th>\n",
       "      <td>mercedes-benz new</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>11</td>\n",
       "      <td>63720</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.74</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>186.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5992</th>\n",
       "      <td>skoda superb</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>12</td>\n",
       "      <td>42036</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.70</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>157.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "62        mercedes-benz new    Delhi     8              58000    Petrol   \n",
       "126       mercedes-benz new  Kolkata    14              28352    Petrol   \n",
       "180            skoda superb    Kochi    11              53537    Petrol   \n",
       "206           skoda octavia   Mumbai     9              76000    Petrol   \n",
       "215       mercedes-benz new  Kolkata    13              30500    Petrol   \n",
       "...                     ...      ...   ...                ...       ...   \n",
       "5729           skoda superb     Pune    13              71473    Petrol   \n",
       "5811           skoda superb     Pune    13              35102    Petrol   \n",
       "5874  mercedes-benz e-class    Delhi     9              40000    Petrol   \n",
       "5984      mercedes-benz new   Mumbai    11              63720    Petrol   \n",
       "5992           skoda superb   Mumbai    12              42036    Petrol   \n",
       "\n",
       "     Transmission      Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "62      Automatic           First    11.74  1796.0  186.00    5.0  26.70  \n",
       "126     Automatic           First    11.70  1796.0  178.00    5.0   9.35  \n",
       "180     Automatic           First    13.70  1798.0  157.75    5.0   6.89  \n",
       "206     Automatic           First    14.70  1798.0  177.50    5.0  10.00  \n",
       "215     Automatic           First    11.74  1796.0  186.00    5.0  10.50  \n",
       "...           ...             ...      ...     ...     ...    ...    ...  \n",
       "5729    Automatic  Second & Above    13.70  1798.0  157.75    5.0   5.35  \n",
       "5811    Automatic           First    12.60  1798.0  161.60    5.0   5.50  \n",
       "5874    Automatic           First    10.50  1796.0  157.70    5.0  24.00  \n",
       "5984    Automatic           First    11.74  1796.0  186.00    5.0  13.25  \n",
       "5992    Automatic           First    13.70  1798.0  157.75    5.0   6.25  \n",
       "\n",
       "[69 rows x 12 columns]"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Samples with Power values in Outliers range\n",
    "dfp[((dfp['Power'] < 29.47) | (dfp['Power'] > 147.95))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "id": "promising-generator",
   "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>2</th>\n",
       "      <td>honda jazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>maruti ciaz</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>5</td>\n",
       "      <td>25692</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>21.56</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>103.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>honda city</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>11</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>116.30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hyundai i20</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>45807</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>volkswagen vento</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>13</td>\n",
       "      <td>33000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>14.40</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>103.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "2         honda jazz  Chennai    12              46000    Petrol       Manual   \n",
       "10       maruti ciaz    Kochi     5              25692    Petrol       Manual   \n",
       "11        honda city  Kolkata    11              60000    Petrol    Automatic   \n",
       "23       hyundai i20  Kolkata    13              45807    Petrol       Manual   \n",
       "24  volkswagen vento  Kolkata    13              33000    Petrol    Automatic   \n",
       "\n",
       "   Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "2       First    18.20  1199.0   88.70    5.0   4.50  \n",
       "10      First    21.56  1462.0  103.25    5.0   9.95  \n",
       "11      First    16.80  1497.0  116.30    5.0   4.49  \n",
       "23      First    18.50  1197.0   80.00    5.0   1.87  \n",
       "24      First    14.40  1598.0  103.60    5.0   2.85  "
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removed Data Samples with Power values falls under Outliers range\n",
    "dfp = dfp[~((dfp['Power'] < 29.47) | (dfp['Power'] > 147.95))]\n",
    "dfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "id": "proud-manchester",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Power'>"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dfp['Power'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electoral-motor",
   "metadata": {},
   "source": [
    "* Fill the missing Values in Power Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "id": "reasonable-hypothesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "round(dfd['Power'].mean(),2)\n",
    "dfd['Power'] = dfd['Power'].fillna(round(dfd['Power'].mean(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "id": "eligible-collaboration",
   "metadata": {},
   "outputs": [],
   "source": [
    "round(dfp['Power'].mean(),2)\n",
    "dfp['Power'] = dfp['Power'].fillna(round(dfp['Power'].mean(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "single-spyware",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.concat((dfd,dfp))\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "id": "infrared-accountability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage               0\n",
       "Engine                0\n",
       "Power                 0\n",
       "Seats                35\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intense-administration",
   "metadata": {},
   "source": [
    "* Fix Missing values of seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "id": "frozen-central",
   "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>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>audi a4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi a6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi q7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw 3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw 5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw x1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet beat</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford ecosport</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford endeavour</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford fiesta</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford figo</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda amaze</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda brio</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda city</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda civic</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda jazz</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai creta</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai eon</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai grand</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai i10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai i20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai santro</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai verna</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai xcent</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land rover</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mahindra scorpio</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mahindra xuv500</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti alto</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti baleno</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti celerio</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti ciaz</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti ertiga</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti ritz</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti swift</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti sx4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti vitara</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maruti wagon</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz e-class</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz new</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan micra</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renault duster</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renault kwid</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skoda octavia</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skoda rapid</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skoda superb</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tata indica</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota corolla</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota etios</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota fortuner</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota innova</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen polo</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen vento</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       min  max\n",
       "Name                           \n",
       "audi a4                5.0  5.0\n",
       "audi a6                5.0  5.0\n",
       "audi q7                7.0  7.0\n",
       "bmw 3                  4.0  5.0\n",
       "bmw 5                  5.0  5.0\n",
       "bmw x1                 5.0  5.0\n",
       "chevrolet beat         5.0  5.0\n",
       "ford ecosport          5.0  5.0\n",
       "ford endeavour         7.0  7.0\n",
       "ford fiesta            5.0  5.0\n",
       "ford figo              5.0  5.0\n",
       "honda amaze            5.0  5.0\n",
       "honda brio             5.0  5.0\n",
       "honda city             5.0  5.0\n",
       "honda civic            5.0  5.0\n",
       "honda jazz             5.0  5.0\n",
       "hyundai creta          5.0  5.0\n",
       "hyundai eon            5.0  5.0\n",
       "hyundai grand          5.0  5.0\n",
       "hyundai i10            5.0  5.0\n",
       "hyundai i20            5.0  5.0\n",
       "hyundai santro         5.0  5.0\n",
       "hyundai verna          5.0  5.0\n",
       "hyundai xcent          5.0  5.0\n",
       "land rover             5.0  7.0\n",
       "mahindra scorpio       7.0  9.0\n",
       "mahindra xuv500        7.0  7.0\n",
       "maruti alto            5.0  5.0\n",
       "maruti baleno          5.0  5.0\n",
       "maruti celerio         5.0  5.0\n",
       "maruti ciaz            5.0  5.0\n",
       "maruti ertiga          7.0  7.0\n",
       "maruti ritz            5.0  5.0\n",
       "maruti swift           5.0  5.0\n",
       "maruti sx4             5.0  5.0\n",
       "maruti vitara          5.0  5.0\n",
       "maruti wagon           5.0  5.0\n",
       "mercedes-benz e-class  5.0  5.0\n",
       "mercedes-benz new      5.0  5.0\n",
       "nissan micra           5.0  5.0\n",
       "renault duster         5.0  5.0\n",
       "renault kwid           5.0  5.0\n",
       "skoda octavia          5.0  5.0\n",
       "skoda rapid            5.0  5.0\n",
       "skoda superb           5.0  5.0\n",
       "tata indica            5.0  5.0\n",
       "toyota corolla         5.0  5.0\n",
       "toyota etios           5.0  5.0\n",
       "toyota fortuner        7.0  7.0\n",
       "toyota innova          7.0  8.0\n",
       "volkswagen polo        5.0  5.0\n",
       "volkswagen vento       5.0  5.0"
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.groupby(['Name'])['Seats'].agg(['min','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "id": "expected-hepatitis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "maruti swift      10\n",
       "hyundai santro     7\n",
       "bmw 5              5\n",
       "honda city         5\n",
       "land rover         3\n",
       "ford endeavour     1\n",
       "toyota etios       1\n",
       "maruti wagon       1\n",
       "honda jazz         1\n",
       "ford figo          1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5['Seats'].isnull()]['Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "id": "completed-saint",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Most of the cars are 5 seaters , So fill the missing seats values with 5\n",
    "df5['Seats'] = df5['Seats'].fillna(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "id": "tropical-universe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 0\n",
       "Location             0\n",
       "Year                 0\n",
       "Kilometers_Driven    0\n",
       "Fuel_Type            0\n",
       "Transmission         0\n",
       "Owner_Type           0\n",
       "Mileage              0\n",
       "Engine               0\n",
       "Power                0\n",
       "Seats                0\n",
       "Price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 724,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neutral-chocolate",
   "metadata": {},
   "source": [
    "* We filled or removed all missing values in the data\n",
    "* Check if some values are 0 in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "id": "dietary-ontario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage              46\n",
       "Engine                0\n",
       "Power                 0\n",
       "Seats                 0\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df5 == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "id": "oriental-shame",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Replace 0 with null values or missing values in mileage column\n",
    "# As presence of 0 may give wrong statistical values such as mean or median\n",
    "df5['Mileage'][df5['Mileage'] == 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "id": "successful-myanmar",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "id": "identical-hypothesis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage              46\n",
       "Engine                0\n",
       "Power                 0\n",
       "Seats                 0\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "brutal-vietnamese",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                mean  median  count\n",
      "Fuel_Type                          \n",
      "Diesel     19.318312   19.87   2387\n",
      "Petrol     18.433234   18.50   2053\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df5.groupby('Fuel_Type')['Mileage'].agg(['mean','median','count']))\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Fuel_Type' , y = 'Mileage' , data = df5)\n",
    "plt.title('Engine vs  Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "id": "posted-console",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   mean  median  count\n",
      "Transmission                          \n",
      "Automatic     16.743421   16.95   1032\n",
      "Manual        19.564856   19.40   3408\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df5.groupby('Transmission')['Mileage'].agg(['mean','median','count']))\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Transmission' , y = 'Mileage' , data = df5)\n",
    "plt.title('Transmission vs  Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "id": "chronic-magnet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     mean  median  count\n",
      "Owner_Type                              \n",
      "First           19.147001    18.9   3711\n",
      "Second & Above  17.697833    17.5    729\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df5.groupby('Owner_Type')['Mileage'].agg(['mean','median','count']))\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.boxplot(x = 'Owner_Type' , y = 'Mileage' , data = df5)\n",
    "plt.title('Owner Type vs  Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "social-resident",
   "metadata": {},
   "source": [
    "**Assignment: - Fix the Missing values for mileage in the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "id": "sophisticated-semiconductor",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 732,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "id": "recorded-interference",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Average mileage of Diesel cars is close to 19 and for petrol it is close to 18\n",
    "#So we can use a value in between to fix missing values\n",
    "df5['Mileage'] = df5['Mileage'].fillna(18.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "id": "unusual-bubble",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 0\n",
       "Location             0\n",
       "Year                 0\n",
       "Kilometers_Driven    0\n",
       "Fuel_Type            0\n",
       "Transmission         0\n",
       "Owner_Type           0\n",
       "Mileage              0\n",
       "Engine               0\n",
       "Power                0\n",
       "Seats                0\n",
       "Price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "id": "mature-walnut",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 735,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "id": "neural-wildlife",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use One hot encoding for encoding non numerical values into numerical form\n",
    "\n",
    "df5_name = pd.get_dummies(df5['Name'])\n",
    "df5_location = pd.get_dummies(df5['Location'])\n",
    "df5_fuel = pd.get_dummies(df5['Fuel_Type'])\n",
    "df5_trans = pd.get_dummies(df5['Transmission'])\n",
    "df5_own = pd.get_dummies(df5['Owner_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "id": "brief-analysis",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
       "      <th>Kochi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
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       "      <td>toyota innova</td>\n",
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       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  ...  Kochi  Kolkata  Mumbai  Pune  \\\n",
       "1           First    19.67  1582.0  126.20  ...      0        0       0     1   \n",
       "3           First    20.77  1248.0   88.76  ...      0        0       0     0   \n",
       "4  Second & Above    15.20  1968.0  140.80  ...      0        0       0     0   \n",
       "6           First    23.08  1461.0   63.10  ...      0        0       0     0   \n",
       "7           First    11.36  2755.0  171.50  ...      0        0       1     0   \n",
       "\n",
       "   Diesel  Petrol  Automatic  Manual  First  Second & Above  \n",
       "1       1       0          0       1      1               0  \n",
       "3       1       0          0       1      1               0  \n",
       "4       1       0          1       0      0               1  \n",
       "6       1       0          0       1      1               0  \n",
       "7       1       0          1       0      1               0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 737,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.concat([df5,df5_name,df5_location,df5_fuel,df5_trans,df5_own],axis=1)\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "id": "architectural-junction",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Kilometers_Driven  Mileage  Engine   Power  Seats  Price  audi a4  \\\n",
       "1     8              41000    19.67  1582.0  126.20    5.0  12.50        0   \n",
       "3    11              87000    20.77  1248.0   88.76    7.0   6.00        0   \n",
       "4    10              40670    15.20  1968.0  140.80    5.0  17.74        1   \n",
       "6    10              86999    23.08  1461.0   63.10    5.0   3.50        0   \n",
       "7     7              36000    11.36  2755.0  171.50    8.0  17.50        0   \n",
       "\n",
       "   audi a6  audi q7  ...  Kochi  Kolkata  Mumbai  Pune  Diesel  Petrol  \\\n",
       "1        0        0  ...      0        0       0     1       1       0   \n",
       "3        0        0  ...      0        0       0     0       1       0   \n",
       "4        0        0  ...      0        0       0     0       1       0   \n",
       "6        0        0  ...      0        0       0     0       1       0   \n",
       "7        0        0  ...      0        0       1     0       1       0   \n",
       "\n",
       "   Automatic  Manual  First  Second & Above  \n",
       "1          0       1      1               0  \n",
       "3          0       1      1               0  \n",
       "4          1       0      0               1  \n",
       "6          0       1      1               0  \n",
       "7          1       0      1               0  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df6.drop(['Name','Location','Fuel_Type', 'Transmission','Owner_Type'] , axis=1)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "id": "legal-samoa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['Kilometers_Driven'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "powerful-wrapping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       10.621327\n",
       "3       11.373663\n",
       "4       10.613246\n",
       "6       11.373652\n",
       "7       10.491274\n",
       "          ...    \n",
       "6000    11.127410\n",
       "6002    10.545341\n",
       "6003    11.121363\n",
       "6010    10.426617\n",
       "6017    10.736397\n",
       "Name: Kilometers_Driven, Length: 4486, dtype: float64"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df7['Kilometers_Driven'].apply(lambda x : np.log(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-vitamin",
   "metadata": {},
   "source": [
    "**Apply log transformation on Kilometers driven column as it is asymetrical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "id": "basic-lotus",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 741,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['Kilometers_Driven'].apply(lambda x : np.log(x)).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "id": "chief-luxury",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['Kilometers_Driven'] = df7['Kilometers_Driven'].apply(lambda x : np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "id": "welcome-albany",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.02535169073515\n",
      "56.00000000000002\n"
     ]
    }
   ],
   "source": [
    "k = 56\n",
    "klog = np.log(56)\n",
    "print(klog)\n",
    "\n",
    "klog_exp = np.exp(klog)\n",
    "print(klog_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "binding-vocabulary",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Log transformation of Engine\n",
    "#df7['Engine'] = df7['Engine'].apply(lambda x : np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "miniature-sussex",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Log transformation of Power\n",
    "#df7['Power'] = df7['Power'].apply(lambda x : np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "id": "talented-vanilla",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 746,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['Price'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "id": "automotive-flash",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Log transformation of Price\n",
    "df7['Price'] = df7['Price'].apply(lambda x : np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "id": "vocational-bailey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 748,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['Price'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "id": "friendly-property",
   "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>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.518215</td>\n",
       "      <td>-0.265848</td>\n",
       "      <td>0.026352</td>\n",
       "      <td>-0.053177</td>\n",
       "      <td>-0.005589</td>\n",
       "      <td>-0.524946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <td>0.518215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.146321</td>\n",
       "      <td>0.211971</td>\n",
       "      <td>0.069957</td>\n",
       "      <td>0.191300</td>\n",
       "      <td>-0.166020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mileage</th>\n",
       "      <td>-0.265848</td>\n",
       "      <td>-0.146321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.636700</td>\n",
       "      <td>-0.522512</td>\n",
       "      <td>-0.471979</td>\n",
       "      <td>-0.283590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine</th>\n",
       "      <td>0.026352</td>\n",
       "      <td>0.211971</td>\n",
       "      <td>-0.636700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.842121</td>\n",
       "      <td>0.626596</td>\n",
       "      <td>0.691704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>-0.053177</td>\n",
       "      <td>0.069957</td>\n",
       "      <td>-0.522512</td>\n",
       "      <td>0.842121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295518</td>\n",
       "      <td>0.782852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seats</th>\n",
       "      <td>-0.005589</td>\n",
       "      <td>0.191300</td>\n",
       "      <td>-0.471979</td>\n",
       "      <td>0.626596</td>\n",
       "      <td>0.295518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.314770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.524946</td>\n",
       "      <td>-0.166020</td>\n",
       "      <td>-0.283590</td>\n",
       "      <td>0.691704</td>\n",
       "      <td>0.782852</td>\n",
       "      <td>0.314770</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year  Kilometers_Driven   Mileage    Engine     Power  \\\n",
       "Year               1.000000           0.518215 -0.265848  0.026352 -0.053177   \n",
       "Kilometers_Driven  0.518215           1.000000 -0.146321  0.211971  0.069957   \n",
       "Mileage           -0.265848          -0.146321  1.000000 -0.636700 -0.522512   \n",
       "Engine             0.026352           0.211971 -0.636700  1.000000  0.842121   \n",
       "Power             -0.053177           0.069957 -0.522512  0.842121  1.000000   \n",
       "Seats             -0.005589           0.191300 -0.471979  0.626596  0.295518   \n",
       "Price             -0.524946          -0.166020 -0.283590  0.691704  0.782852   \n",
       "\n",
       "                      Seats     Price  \n",
       "Year              -0.005589 -0.524946  \n",
       "Kilometers_Driven  0.191300 -0.166020  \n",
       "Mileage           -0.471979 -0.283590  \n",
       "Engine             0.626596  0.691704  \n",
       "Power              0.295518  0.782852  \n",
       "Seats              1.000000  0.314770  \n",
       "Price              0.314770  1.000000  "
      ]
     },
     "execution_count": 749,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.iloc[:,0:7].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "id": "virtual-novel",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df7.drop('Price',axis=1)\n",
    "Y = df7['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "id": "alleged-paris",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>audi a4</th>\n",
       "      <th>audi a6</th>\n",
       "      <th>audi q7</th>\n",
       "      <th>bmw 3</th>\n",
       "      <th>...</th>\n",
       "      <th>Kochi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Pune</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Manual</th>\n",
       "      <th>First</th>\n",
       "      <th>Second &amp; Above</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>10.621327</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>11.373663</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>10.613246</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>11.373652</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>10.491274</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Kilometers_Driven  Mileage  Engine   Power  Seats  audi a4  audi a6  \\\n",
       "1     8          10.621327    19.67  1582.0  126.20    5.0        0        0   \n",
       "3    11          11.373663    20.77  1248.0   88.76    7.0        0        0   \n",
       "4    10          10.613246    15.20  1968.0  140.80    5.0        1        0   \n",
       "6    10          11.373652    23.08  1461.0   63.10    5.0        0        0   \n",
       "7     7          10.491274    11.36  2755.0  171.50    8.0        0        0   \n",
       "\n",
       "   audi q7  bmw 3  ...  Kochi  Kolkata  Mumbai  Pune  Diesel  Petrol  \\\n",
       "1        0      0  ...      0        0       0     1       1       0   \n",
       "3        0      0  ...      0        0       0     0       1       0   \n",
       "4        0      0  ...      0        0       0     0       1       0   \n",
       "6        0      0  ...      0        0       0     0       1       0   \n",
       "7        0      0  ...      0        0       1     0       1       0   \n",
       "\n",
       "   Automatic  Manual  First  Second & Above  \n",
       "1          0       1      1               0  \n",
       "3          0       1      1               0  \n",
       "4          1       0      0               1  \n",
       "6          0       1      1               0  \n",
       "7          1       0      1               0  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "id": "young-theater",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Engine and Power Interaction\n",
    "#ep = X.Engine * X.Power\n",
    "#X.insert(5,'EP',ep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "id": "hired-storm",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "id": "straight-mining",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "id": "seventh-palestinian",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "id": "alleged-secretariat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "id": "acknowledged-package",
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrainPred = model1.predict(xtrain)\n",
    "ytestPred = model1.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "id": "noted-convert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11409824214397932\n",
      "0.12016689234946211\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error,r2_score\n",
    "mae_train = mean_absolute_error(ytrain,ytrainPred)\n",
    "mae_test = mean_absolute_error(ytest,ytestPred)\n",
    "\n",
    "print(mae_train)\n",
    "print(mae_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "id": "furnished-phenomenon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9590920026235192\n",
      "0.9536107867131051\n"
     ]
    }
   ],
   "source": [
    "r2_train = r2_score(ytrain,ytrainPred) \n",
    "r2_test = r2_score(ytest,ytestPred)\n",
    "\n",
    "print(r2_train)\n",
    "print(r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ruled-acting",
   "metadata": {},
   "source": [
    "**Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "id": "domestic-administrator",
   "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>1</th>\n",
       "      <td>hyundai creta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>8</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti ertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>11</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi a4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>10</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second &amp; Above</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nissan micra</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>10</td>\n",
       "      <td>86999</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota innova</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>7</td>\n",
       "      <td>36000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name    Location  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
       "1  hyundai creta        Pune     8              41000    Diesel       Manual   \n",
       "3  maruti ertiga     Chennai    11              87000    Diesel       Manual   \n",
       "4        audi a4  Coimbatore    10              40670    Diesel    Automatic   \n",
       "6   nissan micra      Jaipur    10              86999    Diesel       Manual   \n",
       "7  toyota innova      Mumbai     7              36000    Diesel    Automatic   \n",
       "\n",
       "       Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
       "1           First    19.67  1582.0  126.20    5.0  12.50  \n",
       "3           First    20.77  1248.0   88.76    7.0   6.00  \n",
       "4  Second & Above    15.20  1968.0  140.80    5.0  17.74  \n",
       "6           First    23.08  1461.0   63.10    5.0   3.50  \n",
       "7           First    11.36  2755.0  171.50    8.0  17.50  "
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "id": "personalized-conference",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5['Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "id": "communist-chapel",
   "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>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>audi a4</th>\n",
       "      <th>audi a6</th>\n",
       "      <th>audi q7</th>\n",
       "      <th>...</th>\n",
       "      <th>Kochi</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Pune</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Manual</th>\n",
       "      <th>First</th>\n",
       "      <th>Second &amp; Above</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>10.621327</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.525729</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>11.373663</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>10.613246</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.875822</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>11.373652</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.252763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>10.491274</td>\n",
       "      <td>11.36</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>171.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.862201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Kilometers_Driven  Mileage  Engine   Power  Seats     Price  audi a4  \\\n",
       "1     8          10.621327    19.67  1582.0  126.20    5.0  2.525729        0   \n",
       "3    11          11.373663    20.77  1248.0   88.76    7.0  1.791759        0   \n",
       "4    10          10.613246    15.20  1968.0  140.80    5.0  2.875822        1   \n",
       "6    10          11.373652    23.08  1461.0   63.10    5.0  1.252763        0   \n",
       "7     7          10.491274    11.36  2755.0  171.50    8.0  2.862201        0   \n",
       "\n",
       "   audi a6  audi q7  ...  Kochi  Kolkata  Mumbai  Pune  Diesel  Petrol  \\\n",
       "1        0        0  ...      0        0       0     1       1       0   \n",
       "3        0        0  ...      0        0       0     0       1       0   \n",
       "4        0        0  ...      0        0       0     0       1       0   \n",
       "6        0        0  ...      0        0       0     0       1       0   \n",
       "7        0        0  ...      0        0       1     0       1       0   \n",
       "\n",
       "   Automatic  Manual  First  Second & Above  \n",
       "1          0       1      1               0  \n",
       "3          0       1      1               0  \n",
       "4          1       0      0               1  \n",
       "6          0       1      1               0  \n",
       "7          1       0      1               0  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "id": "mounted-geneva",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'nissan micra'\n",
    "location = 'Mumbai'\n",
    "age = 5\n",
    "kms = 50000\n",
    "ft = 'Diesel'\n",
    "trans = 'Manual'\n",
    "owner = 'First'\n",
    "mileage = 20.2\n",
    "engine = 1460\n",
    "power = 62\n",
    "seats = 5\n",
    "price = '????'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "id": "speaking-species",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3364, 75)"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "id": "interim-cache",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Seats',\n",
       "       'audi a4', 'audi a6', 'audi q7', 'bmw 3', 'bmw 5', 'bmw x1',\n",
       "       'chevrolet beat', 'ford ecosport', 'ford endeavour', 'ford fiesta',\n",
       "       'ford figo', 'honda amaze', 'honda brio', 'honda city', 'honda civic',\n",
       "       'honda jazz', 'hyundai creta', 'hyundai eon', 'hyundai grand',\n",
       "       'hyundai i10', 'hyundai i20', 'hyundai santro', 'hyundai verna',\n",
       "       'hyundai xcent', 'land rover', 'mahindra scorpio', 'mahindra xuv500',\n",
       "       'maruti alto', 'maruti baleno', 'maruti celerio', 'maruti ciaz',\n",
       "       'maruti ertiga', 'maruti ritz', 'maruti swift', 'maruti sx4',\n",
       "       'maruti vitara', 'maruti wagon', 'mercedes-benz e-class',\n",
       "       'mercedes-benz new', 'nissan micra', 'renault duster', 'renault kwid',\n",
       "       'skoda octavia', 'skoda rapid', 'skoda superb', 'tata indica',\n",
       "       'toyota corolla', 'toyota etios', 'toyota fortuner', 'toyota innova',\n",
       "       'volkswagen polo', 'volkswagen vento', 'Ahmedabad', 'Bangalore',\n",
       "       'Chennai', 'Coimbatore', 'Delhi', 'Hyderabad', 'Jaipur', 'Kochi',\n",
       "       'Kolkata', 'Mumbai', 'Pune', 'Diesel', 'Petrol', 'Automatic', 'Manual',\n",
       "       'First', 'Second & Above'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 769,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "id": "located-placement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_val = np.zeros(xtrain.shape[1])\n",
    "input_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "id": "quick-revision",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_val[0] = age\n",
    "input_val[1] = np.log(kms)\n",
    "input_val[2] = mileage\n",
    "input_val[3] = engine\n",
    "input_val[4] = power\n",
    "input_val[5] = seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "id": "informative-angel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(xtrain.columns).index('honda amaze')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "id": "convenient-beverage",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_val[17] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "id": "gross-beverage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(xtrain.columns).index('Mumbai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "id": "devoted-mechanics",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_val[67] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "id": "requested-reading",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_val[list(xtrain.columns).index(ft)] = 1\n",
    "input_val[list(xtrain.columns).index(trans)] = 1\n",
    "input_val[list(xtrain.columns).index(owner)] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "id": "signed-cleveland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.00000000e+00, 1.08197783e+01, 2.02000000e+01, 1.46000000e+03,\n",
       "       6.20000000e+01, 5.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\n",
       "       0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.00000000e+00, 1.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 798,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "id": "isolated-binary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.76118596]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "print(model1.predict([input_val]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "id": "reflected-nature",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.81933482]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "#We used log transformation of price value before training\n",
    "#So to get actial predicted price use exponential transformation of predicted value\n",
    "print(np.exp(model1.predict([input_val])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "id": "defined-microphone",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.105666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <td>-0.062830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mileage</th>\n",
       "      <td>0.001949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine</th>\n",
       "      <td>-0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>0.003902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petrol</th>\n",
       "      <td>-0.043628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automatic</th>\n",
       "      <td>0.052705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manual</th>\n",
       "      <td>-0.052705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>0.034857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second &amp; Above</th>\n",
       "      <td>-0.034857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "Year              -0.105666\n",
       "Kilometers_Driven -0.062830\n",
       "Mileage            0.001949\n",
       "Engine            -0.000127\n",
       "Power              0.003902\n",
       "...                     ...\n",
       "Petrol            -0.043628\n",
       "Automatic          0.052705\n",
       "Manual            -0.052705\n",
       "First              0.034857\n",
       "Second & Above    -0.034857\n",
       "\n",
       "[75 rows x 1 columns]"
      ]
     },
     "execution_count": 804,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model1.coef_ , index = xtrain.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "id": "crazy-onion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 805,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(model1.coef_ , index = xtrain.columns).plot.bar(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reduced-hundred",
   "metadata": {},
   "source": [
    "**Prediction Function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "id": "comparative-pavilion",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(age,kms,mlg,eng,pwr,sts,car_name,loc,ft,trans,own):\n",
    "    input_val = np.zeros(xtrain.shape[1])\n",
    "    \n",
    "    input_val[0] = age\n",
    "    input_val[1] = np.log(kms)\n",
    "    input_val[2] = mlg\n",
    "    input_val[3] = eng\n",
    "    input_val[4] = pwr\n",
    "    input_val[5] = sts\n",
    "    \n",
    "    input_val[list(xtrain.columns).index(car_name.lower())] = 1\n",
    "    input_val[list(xtrain.columns).index(loc)] = 1\n",
    "    input_val[list(xtrain.columns).index(ft)] = 1\n",
    "    input_val[list(xtrain.columns).index(trans)] = 1\n",
    "    input_val[list(xtrain.columns).index(own)] = 1\n",
    "    \n",
    "    return np.exp(model1.predict([input_val])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "id": "later-torture",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.40256837]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "print(predict_price(9,70000,15,1422,110,5,\n",
    "                    'Hyundai Creta','Delhi','Diesel','Automatic','First'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offensive-rugby",
   "metadata": {},
   "source": [
    "**Save the final model and download for deployment purpose**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "id": "missing-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('car_price.pickle','wb') as f:\n",
    "    pickle.dump(model1,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "id": "structural-welding",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {'data_columns' : list(xtrain.columns)}\n",
    "\n",
    "json.dumps(columns)\n",
    "\n",
    "with open('columns.json' , 'w') as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pleasant-hungary",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}