Untitled2-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a10b23e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWASTI~1\\AppData\\Local\\Temp/ipykernel_8424/3278462589.py:5: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['Ex-Showroom_Price'] = df['Ex-Showroom_Price'].str.replace('Rs.','')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "df = pd.read_csv(\"cars_engage_2022.csv\")\n",
    "df['Ex-Showroom_Price'] = df['Ex-Showroom_Price'].str.replace('Rs.','')\n",
    "df['Ex-Showroom_Price'] = df['Ex-Showroom_Price'].str.replace(',','').astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71ff8969",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Emax Xm</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Tata</td>\n",
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       "      <td>Xta</td>\n",
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       "      <td>Tata</td>\n",
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       "      <td>Xm</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1271</th>\n",
       "      <td>1271</td>\n",
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       "      <td>Vx Mt Diesel</td>\n",
       "      <td>1302000</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1272</th>\n",
       "      <td>1272</td>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>1421000</td>\n",
       "      <td>1498 cc</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1273</td>\n",
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       "      <td>City</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>1431000</td>\n",
       "      <td>1497 cc</td>\n",
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       "      <td>V Cvt Petrol</td>\n",
       "      <td>1201000</td>\n",
       "      <td>1497 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FWD (Front Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Mitsubishi</td>\n",
       "      <td>Montero</td>\n",
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       "      <td>6862560</td>\n",
       "      <td>3200 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "      Unnamed: 0        Make      Model        Variant  Ex-Showroom_Price  \\\n",
       "0              0        Tata  Nano Genx             Xt             292667   \n",
       "1              1        Tata  Nano Genx             Xe             236447   \n",
       "2              2        Tata  Nano Genx        Emax Xm             296661   \n",
       "3              3        Tata  Nano Genx            Xta             334768   \n",
       "4              4        Tata  Nano Genx             Xm             272223   \n",
       "...          ...         ...        ...            ...                ...   \n",
       "1271        1271       Honda       City   Vx Mt Diesel            1302000   \n",
       "1272        1272       Honda       City   Zx Mt Diesel            1421000   \n",
       "1273        1273       Honda       City  Zx Cvt Petrol            1431000   \n",
       "1274        1274       Honda       City   V Cvt Petrol            1201000   \n",
       "1275        1275  Mitsubishi    Montero         3.2 At            6862560   \n",
       "\n",
       "     Displacement  Cylinders  Valves_Per_Cylinder               Drivetrain  \\\n",
       "0          624 cc        2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "1          624 cc        2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "2          624 cc        2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "3          624 cc        2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "4          624 cc        2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "...           ...        ...                  ...                      ...   \n",
       "1271      1498 cc        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1272      1498 cc        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1273      1497 cc        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1274      1497 cc        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1275      3200 cc        4.0                  4.0    AWD (All Wheel Drive)   \n",
       "\n",
       "     Cylinder_Configuration  ... Leather_Wrapped_Steering Automatic_Headlamps  \\\n",
       "0                   In-line  ...                      NaN                 NaN   \n",
       "1                   In-line  ...                      NaN                 NaN   \n",
       "2                   In-line  ...                      NaN                 NaN   \n",
       "3                   In-line  ...                      NaN                 NaN   \n",
       "4                   In-line  ...                      NaN                 NaN   \n",
       "...                     ...  ...                      ...                 ...   \n",
       "1271                In-line  ...                      Yes                 NaN   \n",
       "1272                In-line  ...                      Yes                 Yes   \n",
       "1273                In-line  ...                      Yes                 Yes   \n",
       "1274                In-line  ...                      NaN                 NaN   \n",
       "1275                In-line  ...                      Yes                 NaN   \n",
       "\n",
       "     Engine_Type ASR_/_Traction_Control Cruise_Control  USB_Ports  \\\n",
       "0            NaN                    NaN            NaN        NaN   \n",
       "1            NaN                    NaN            NaN        NaN   \n",
       "2            NaN                    NaN            NaN        NaN   \n",
       "3            NaN                    NaN            NaN        NaN   \n",
       "4            NaN                    NaN            NaN        NaN   \n",
       "...          ...                    ...            ...        ...   \n",
       "1271         NaN                    NaN            Yes        NaN   \n",
       "1272         NaN                    NaN            Yes        NaN   \n",
       "1273         NaN                    NaN            Yes        NaN   \n",
       "1274         NaN                    NaN            Yes        NaN   \n",
       "1275         NaN                    Yes            Yes        NaN   \n",
       "\n",
       "      Heads-Up_Display  Welcome_Lights Battery  Electric_Range  \n",
       "0                  NaN             NaN     NaN             NaN  \n",
       "1                  NaN             NaN     NaN             NaN  \n",
       "2                  NaN             NaN     NaN             NaN  \n",
       "3                  NaN             NaN     NaN             NaN  \n",
       "4                  NaN             NaN     NaN             NaN  \n",
       "...                ...             ...     ...             ...  \n",
       "1271               NaN             NaN     NaN             NaN  \n",
       "1272               NaN             NaN     NaN             NaN  \n",
       "1273               NaN             NaN     NaN             NaN  \n",
       "1274               NaN             NaN     NaN             NaN  \n",
       "1275               NaN             NaN     NaN             NaN  \n",
       "\n",
       "[1276 rows x 141 columns]"
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     "execution_count": 46,
     "metadata": {},
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 47,
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1276 entries, 0 to 1275\n",
      "Columns: 141 entries, Unnamed: 0 to Electric_Range\n",
      "dtypes: float64(9), int32(1), int64(1), object(130)\n",
      "memory usage: 1.4+ MB\n"
     ]
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   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fed47281",
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       "      <td>150.932385</td>\n",
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       "      <td>1.145231</td>\n",
       "      <td>2.522399</td>\n",
       "      <td>0.773642</td>\n",
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       "      <th>min</th>\n",
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       "      <td>7.438760e+05</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>1495.000000</td>\n",
       "      <td>3991.750000</td>\n",
       "      <td>1695.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>637.500000</td>\n",
       "      <td>1.060064e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1557.000000</td>\n",
       "      <td>4331.000000</td>\n",
       "      <td>1770.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>956.250000</td>\n",
       "      <td>2.979828e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1666.000000</td>\n",
       "      <td>4620.000000</td>\n",
       "      <td>1852.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1275.000000</td>\n",
       "      <td>2.121554e+08</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2670.000000</td>\n",
       "      <td>6092.000000</td>\n",
       "      <td>2226.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Ex-Showroom_Price    Cylinders  Valves_Per_Cylinder  \\\n",
       "count  1276.000000       1.276000e+03  1210.000000          1174.000000   \n",
       "mean    637.500000       4.596538e+06     4.380992             3.977853   \n",
       "std     368.493781       1.214735e+07     1.660957             0.833763   \n",
       "min       0.000000       2.364470e+05     2.000000             1.000000   \n",
       "25%     318.750000       7.438760e+05     4.000000             4.000000   \n",
       "50%     637.500000       1.060064e+06     4.000000             4.000000   \n",
       "75%     956.250000       2.979828e+06     4.000000             4.000000   \n",
       "max    1275.000000       2.121554e+08    16.000000            16.000000   \n",
       "\n",
       "            Height       Length        Width        Doors  Seating_Capacity  \\\n",
       "count  1275.000000  1276.000000  1264.000000  1272.000000       1270.000000   \n",
       "mean   1591.093996  4296.883730  1788.550510     4.550314          5.270079   \n",
       "std     157.916262   476.612977   150.932385     0.747816          1.145231   \n",
       "min       1.845000     4.640000     1.845000     2.000000          2.000000   \n",
       "25%    1495.000000  3991.750000  1695.000000     4.000000          5.000000   \n",
       "50%    1557.000000  4331.000000  1770.000000     5.000000          5.000000   \n",
       "75%    1666.000000  4620.000000  1852.500000     5.000000          5.000000   \n",
       "max    2670.000000  6092.000000  2226.000000     5.000000         16.000000   \n",
       "\n",
       "       Number_of_Airbags  USB_Ports  \n",
       "count        1141.000000  29.000000  \n",
       "mean            3.787029   1.793103  \n",
       "std             2.522399   0.773642  \n",
       "min             1.000000   1.000000  \n",
       "25%             2.000000   1.000000  \n",
       "50%             2.000000   2.000000  \n",
       "75%             6.000000   2.000000  \n",
       "max            14.000000   3.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b6232528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Make', 'Model', 'Variant', 'Ex-Showroom_Price',\n",
       "       'Displacement', 'Cylinders', 'Valves_Per_Cylinder', 'Drivetrain',\n",
       "       'Cylinder_Configuration',\n",
       "       ...\n",
       "       'Leather_Wrapped_Steering', 'Automatic_Headlamps', 'Engine_Type',\n",
       "       'ASR_/_Traction_Control', 'Cruise_Control', 'USB_Ports',\n",
       "       'Heads-Up_Display', 'Welcome_Lights', 'Battery', 'Electric_Range'],\n",
       "      dtype='object', length=141)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "92ddaa55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maruti Suzuki       149\n",
       "Hyundai             130\n",
       "Mahindra            119\n",
       "Tata                100\n",
       "Toyota               82\n",
       "Honda                64\n",
       "Skoda                43\n",
       "Ford                 43\n",
       "Bmw                  37\n",
       "Renault              36\n",
       "Volkswagen           34\n",
       "Audi                 31\n",
       "Nissan               29\n",
       "Jeep                 28\n",
       "Land Rover Rover     27\n",
       "Fiat                 23\n",
       "Jaguar               22\n",
       "Kia                  21\n",
       "Volvo                18\n",
       "Datsun               15\n",
       "Porsche              14\n",
       "Maruti Suzuki R      14\n",
       "Lamborghini          13\n",
       "Mg                   13\n",
       "Icml                 11\n",
       "Land Rover           10\n",
       "Lexus                10\n",
       "Mini                 10\n",
       "Maserati              9\n",
       "Ferrari               8\n",
       "Mitsubishi            7\n",
       "Bentley               6\n",
       "Premier               6\n",
       "Force                 6\n",
       "Isuzu                 5\n",
       "Aston Martin          3\n",
       "Bajaj                 2\n",
       "Bugatti               2\n",
       "Dc                    1\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "606ed294",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Make','Variant','Fuel_Type','Displacement','Cylinders','Valves_Per_Cylinder','Ex-Showroom_Price','Height','Width','Body_Type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cc0db71c",
   "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>Make</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Body_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Xt</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>292667</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>Hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Xe</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>236447</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>Hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>CNG</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296661</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>Hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Xta</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>334768</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>Hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Xm</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>272223</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>Hatchback</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Vx Mt Diesel</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1498 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1302000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>Sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1498 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1421000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>Sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1497 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1431000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>Sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Honda</td>\n",
       "      <td>V Cvt Petrol</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1497 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1201000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>Sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>3.2 At</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>3200 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6862560</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make        Variant Fuel_Type Displacement  Cylinders  \\\n",
       "0           Tata             Xt    Petrol       624 cc        2.0   \n",
       "1           Tata             Xe    Petrol       624 cc        2.0   \n",
       "2           Tata        Emax Xm       CNG       624 cc        2.0   \n",
       "3           Tata            Xta    Petrol       624 cc        2.0   \n",
       "4           Tata             Xm    Petrol       624 cc        2.0   \n",
       "...          ...            ...       ...          ...        ...   \n",
       "1271       Honda   Vx Mt Diesel    Diesel      1498 cc        4.0   \n",
       "1272       Honda   Zx Mt Diesel    Diesel      1498 cc        4.0   \n",
       "1273       Honda  Zx Cvt Petrol    Petrol      1497 cc        4.0   \n",
       "1274       Honda   V Cvt Petrol    Petrol      1497 cc        4.0   \n",
       "1275  Mitsubishi         3.2 At    Diesel      3200 cc        4.0   \n",
       "\n",
       "      Valves_Per_Cylinder  Ex-Showroom_Price  Height   Width  Body_Type  \n",
       "0                     2.0             292667  1652.0  1750.0  Hatchback  \n",
       "1                     2.0             236447  1652.0  1750.0  Hatchback  \n",
       "2                     2.0             296661  1652.0  1750.0  Hatchback  \n",
       "3                     2.0             334768  1652.0  1750.0  Hatchback  \n",
       "4                     2.0             272223  1652.0  1750.0  Hatchback  \n",
       "...                   ...                ...     ...     ...        ...  \n",
       "1271                  4.0            1302000  1495.0  1695.0      Sedan  \n",
       "1272                  4.0            1421000  1495.0  1695.0      Sedan  \n",
       "1273                  4.0            1431000  1495.0  1695.0      Sedan  \n",
       "1274                  4.0            1201000  1495.0  1695.0      Sedan  \n",
       "1275                  4.0            6862560  1900.0  1875.0        SUV  \n",
       "\n",
       "[1276 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1859231d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.get_dummies(x,columns=['Make','Variant','Fuel_Type','Displacement','Body_Type'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e3083d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(y.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2aa9668c",
   "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>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Make_Audi</th>\n",
       "      <th>Make_Bajaj</th>\n",
       "      <th>Make_Bentley</th>\n",
       "      <th>Make_Bmw</th>\n",
       "      <th>Make_Bugatti</th>\n",
       "      <th>...</th>\n",
       "      <th>Body_Type_MUV</th>\n",
       "      <th>Body_Type_Pick-up</th>\n",
       "      <th>Body_Type_SUV</th>\n",
       "      <th>Body_Type_SUV, Crossover</th>\n",
       "      <th>Body_Type_Sedan</th>\n",
       "      <th>Body_Type_Sedan, Coupe</th>\n",
       "      <th>Body_Type_Sedan, Crossover</th>\n",
       "      <th>Body_Type_Sports</th>\n",
       "      <th>Body_Type_Sports, Convertible</th>\n",
       "      <th>Body_Type_Sports, Hatchback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>292667</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>236447</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296661</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>334768</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>272223</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1302000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1421000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1431000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1201000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6862560</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 1255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cylinders  Valves_Per_Cylinder  Ex-Showroom_Price  Height   Width  \\\n",
       "0           2.0                  2.0             292667  1652.0  1750.0   \n",
       "1           2.0                  2.0             236447  1652.0  1750.0   \n",
       "2           2.0                  2.0             296661  1652.0  1750.0   \n",
       "3           2.0                  2.0             334768  1652.0  1750.0   \n",
       "4           2.0                  2.0             272223  1652.0  1750.0   \n",
       "...         ...                  ...                ...     ...     ...   \n",
       "1271        4.0                  4.0            1302000  1495.0  1695.0   \n",
       "1272        4.0                  4.0            1421000  1495.0  1695.0   \n",
       "1273        4.0                  4.0            1431000  1495.0  1695.0   \n",
       "1274        4.0                  4.0            1201000  1495.0  1695.0   \n",
       "1275        4.0                  4.0            6862560  1900.0  1875.0   \n",
       "\n",
       "      Make_Audi  Make_Bajaj  Make_Bentley  Make_Bmw  Make_Bugatti  ...  \\\n",
       "0             0           0             0         0             0  ...   \n",
       "1             0           0             0         0             0  ...   \n",
       "2             0           0             0         0             0  ...   \n",
       "3             0           0             0         0             0  ...   \n",
       "4             0           0             0         0             0  ...   \n",
       "...         ...         ...           ...       ...           ...  ...   \n",
       "1271          0           0             0         0             0  ...   \n",
       "1272          0           0             0         0             0  ...   \n",
       "1273          0           0             0         0             0  ...   \n",
       "1274          0           0             0         0             0  ...   \n",
       "1275          0           0             0         0             0  ...   \n",
       "\n",
       "      Body_Type_MUV  Body_Type_Pick-up  Body_Type_SUV  \\\n",
       "0                 0                  0              0   \n",
       "1                 0                  0              0   \n",
       "2                 0                  0              0   \n",
       "3                 0                  0              0   \n",
       "4                 0                  0              0   \n",
       "...             ...                ...            ...   \n",
       "1271              0                  0              0   \n",
       "1272              0                  0              0   \n",
       "1273              0                  0              0   \n",
       "1274              0                  0              0   \n",
       "1275              0                  0              1   \n",
       "\n",
       "      Body_Type_SUV, Crossover  Body_Type_Sedan  Body_Type_Sedan, Coupe  \\\n",
       "0                            0                0                       0   \n",
       "1                            0                0                       0   \n",
       "2                            0                0                       0   \n",
       "3                            0                0                       0   \n",
       "4                            0                0                       0   \n",
       "...                        ...              ...                     ...   \n",
       "1271                         0                1                       0   \n",
       "1272                         0                1                       0   \n",
       "1273                         0                1                       0   \n",
       "1274                         0                1                       0   \n",
       "1275                         0                0                       0   \n",
       "\n",
       "      Body_Type_Sedan, Crossover  Body_Type_Sports  \\\n",
       "0                              0                 0   \n",
       "1                              0                 0   \n",
       "2                              0                 0   \n",
       "3                              0                 0   \n",
       "4                              0                 0   \n",
       "...                          ...               ...   \n",
       "1271                           0                 0   \n",
       "1272                           0                 0   \n",
       "1273                           0                 0   \n",
       "1274                           0                 0   \n",
       "1275                           0                 0   \n",
       "\n",
       "      Body_Type_Sports, Convertible  Body_Type_Sports, Hatchback  \n",
       "0                                 0                            0  \n",
       "1                                 0                            0  \n",
       "2                                 0                            0  \n",
       "3                                 0                            0  \n",
       "4                                 0                            0  \n",
       "...                             ...                          ...  \n",
       "1271                              0                            0  \n",
       "1272                              0                            0  \n",
       "1273                              0                            0  \n",
       "1274                              0                            0  \n",
       "1275                              0                            0  \n",
       "\n",
       "[1276 rows x 1255 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a3a1b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cylinders                         66\n",
       "Valves_Per_Cylinder              102\n",
       "Ex-Showroom_Price                  0\n",
       "Height                             1\n",
       "Width                             12\n",
       "                                ... \n",
       "Body_Type_Sedan, Coupe             0\n",
       "Body_Type_Sedan, Crossover         0\n",
       "Body_Type_Sports                   0\n",
       "Body_Type_Sports, Convertible      0\n",
       "Body_Type_Sports, Hatchback        0\n",
       "Length: 1255, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "049dbd36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(y.isnull(),yticklabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cb62571b",
   "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>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Make_Audi</th>\n",
       "      <th>Make_Bajaj</th>\n",
       "      <th>Make_Bentley</th>\n",
       "      <th>Make_Bmw</th>\n",
       "      <th>Make_Bugatti</th>\n",
       "      <th>...</th>\n",
       "      <th>Body_Type_MUV</th>\n",
       "      <th>Body_Type_Pick-up</th>\n",
       "      <th>Body_Type_SUV</th>\n",
       "      <th>Body_Type_SUV, Crossover</th>\n",
       "      <th>Body_Type_Sedan</th>\n",
       "      <th>Body_Type_Sedan, Coupe</th>\n",
       "      <th>Body_Type_Sedan, Crossover</th>\n",
       "      <th>Body_Type_Sports</th>\n",
       "      <th>Body_Type_Sports, Convertible</th>\n",
       "      <th>Body_Type_Sports, Hatchback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>292667</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>236447</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296661</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>334768</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>272223</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1302000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1421000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1431000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1201000</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6862560</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1875.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 1255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cylinders  Valves_Per_Cylinder  Ex-Showroom_Price  Height   Width  \\\n",
       "0           2.0                  2.0             292667  1652.0  1750.0   \n",
       "1           2.0                  2.0             236447  1652.0  1750.0   \n",
       "2           2.0                  2.0             296661  1652.0  1750.0   \n",
       "3           2.0                  2.0             334768  1652.0  1750.0   \n",
       "4           2.0                  2.0             272223  1652.0  1750.0   \n",
       "...         ...                  ...                ...     ...     ...   \n",
       "1271        4.0                  4.0            1302000  1495.0  1695.0   \n",
       "1272        4.0                  4.0            1421000  1495.0  1695.0   \n",
       "1273        4.0                  4.0            1431000  1495.0  1695.0   \n",
       "1274        4.0                  4.0            1201000  1495.0  1695.0   \n",
       "1275        4.0                  4.0            6862560  1900.0  1875.0   \n",
       "\n",
       "      Make_Audi  Make_Bajaj  Make_Bentley  Make_Bmw  Make_Bugatti  ...  \\\n",
       "0             0           0             0         0             0  ...   \n",
       "1             0           0             0         0             0  ...   \n",
       "2             0           0             0         0             0  ...   \n",
       "3             0           0             0         0             0  ...   \n",
       "4             0           0             0         0             0  ...   \n",
       "...         ...         ...           ...       ...           ...  ...   \n",
       "1271          0           0             0         0             0  ...   \n",
       "1272          0           0             0         0             0  ...   \n",
       "1273          0           0             0         0             0  ...   \n",
       "1274          0           0             0         0             0  ...   \n",
       "1275          0           0             0         0             0  ...   \n",
       "\n",
       "      Body_Type_MUV  Body_Type_Pick-up  Body_Type_SUV  \\\n",
       "0                 0                  0              0   \n",
       "1                 0                  0              0   \n",
       "2                 0                  0              0   \n",
       "3                 0                  0              0   \n",
       "4                 0                  0              0   \n",
       "...             ...                ...            ...   \n",
       "1271              0                  0              0   \n",
       "1272              0                  0              0   \n",
       "1273              0                  0              0   \n",
       "1274              0                  0              0   \n",
       "1275              0                  0              1   \n",
       "\n",
       "      Body_Type_SUV, Crossover  Body_Type_Sedan  Body_Type_Sedan, Coupe  \\\n",
       "0                            0                0                       0   \n",
       "1                            0                0                       0   \n",
       "2                            0                0                       0   \n",
       "3                            0                0                       0   \n",
       "4                            0                0                       0   \n",
       "...                        ...              ...                     ...   \n",
       "1271                         0                1                       0   \n",
       "1272                         0                1                       0   \n",
       "1273                         0                1                       0   \n",
       "1274                         0                1                       0   \n",
       "1275                         0                0                       0   \n",
       "\n",
       "      Body_Type_Sedan, Crossover  Body_Type_Sports  \\\n",
       "0                              0                 0   \n",
       "1                              0                 0   \n",
       "2                              0                 0   \n",
       "3                              0                 0   \n",
       "4                              0                 0   \n",
       "...                          ...               ...   \n",
       "1271                           0                 0   \n",
       "1272                           0                 0   \n",
       "1273                           0                 0   \n",
       "1274                           0                 0   \n",
       "1275                           0                 0   \n",
       "\n",
       "      Body_Type_Sports, Convertible  Body_Type_Sports, Hatchback  \n",
       "0                                 0                            0  \n",
       "1                                 0                            0  \n",
       "2                                 0                            0  \n",
       "3                                 0                            0  \n",
       "4                                 0                            0  \n",
       "...                             ...                          ...  \n",
       "1271                              0                            0  \n",
       "1272                              0                            0  \n",
       "1273                              0                            0  \n",
       "1274                              0                            0  \n",
       "1275                              0                            0  \n",
       "\n",
       "[1276 rows x 1255 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dropna(how='all')\n",
    "y.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "dbf3bd34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 1255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cylinders  Valves_Per_Cylinder  Ex-Showroom_Price  Height   Width  \\\n",
       "0           2.0                  2.0             292667  1652.0  1750.0   \n",
       "1           2.0                  2.0             236447  1652.0  1750.0   \n",
       "2           2.0                  2.0             296661  1652.0  1750.0   \n",
       "3           2.0                  2.0             334768  1652.0  1750.0   \n",
       "4           2.0                  2.0             272223  1652.0  1750.0   \n",
       "...         ...                  ...                ...     ...     ...   \n",
       "1271        4.0                  4.0            1302000  1495.0  1695.0   \n",
       "1272        4.0                  4.0            1421000  1495.0  1695.0   \n",
       "1273        4.0                  4.0            1431000  1495.0  1695.0   \n",
       "1274        4.0                  4.0            1201000  1495.0  1695.0   \n",
       "1275        4.0                  4.0            6862560  1900.0  1875.0   \n",
       "\n",
       "      Make_Audi  Make_Bajaj  Make_Bentley  Make_Bmw  Make_Bugatti  ...  \\\n",
       "0             0           0             0         0             0  ...   \n",
       "1             0           0             0         0             0  ...   \n",
       "2             0           0             0         0             0  ...   \n",
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       "1273          0           0             0         0             0  ...   \n",
       "1274          0           0             0         0             0  ...   \n",
       "1275          0           0             0         0             0  ...   \n",
       "\n",
       "      Body_Type_MUV  Body_Type_Pick-up  Body_Type_SUV  \\\n",
       "0                 0                  0              0   \n",
       "1                 0                  0              0   \n",
       "2                 0                  0              0   \n",
       "3                 0                  0              0   \n",
       "4                 0                  0              0   \n",
       "...             ...                ...            ...   \n",
       "1271              0                  0              0   \n",
       "1272              0                  0              0   \n",
       "1273              0                  0              0   \n",
       "1274              0                  0              0   \n",
       "1275              0                  0              1   \n",
       "\n",
       "      Body_Type_SUV, Crossover  Body_Type_Sedan  Body_Type_Sedan, Coupe  \\\n",
       "0                            0                0                       0   \n",
       "1                            0                0                       0   \n",
       "2                            0                0                       0   \n",
       "3                            0                0                       0   \n",
       "4                            0                0                       0   \n",
       "...                        ...              ...                     ...   \n",
       "1271                         0                1                       0   \n",
       "1272                         0                1                       0   \n",
       "1273                         0                1                       0   \n",
       "1274                         0                1                       0   \n",
       "1275                         0                0                       0   \n",
       "\n",
       "      Body_Type_Sedan, Crossover  Body_Type_Sports  \\\n",
       "0                              0                 0   \n",
       "1                              0                 0   \n",
       "2                              0                 0   \n",
       "3                              0                 0   \n",
       "4                              0                 0   \n",
       "...                          ...               ...   \n",
       "1271                           0                 0   \n",
       "1272                           0                 0   \n",
       "1273                           0                 0   \n",
       "1274                           0                 0   \n",
       "1275                           0                 0   \n",
       "\n",
       "      Body_Type_Sports, Convertible  Body_Type_Sports, Hatchback  \n",
       "0                                 0                            0  \n",
       "1                                 0                            0  \n",
       "2                                 0                            0  \n",
       "3                                 0                            0  \n",
       "4                                 0                            0  \n",
       "...                             ...                          ...  \n",
       "1271                              0                            0  \n",
       "1272                              0                            0  \n",
       "1273                              0                            0  \n",
       "1274                              0                            0  \n",
       "1275                              0                            0  \n",
       "\n",
       "[1276 rows x 1255 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3dbeb3fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Height', ylabel='Ex-Showroom_Price'>"
      ]
     },
     "execution_count": 80,
     "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.regplot(x=\"Height\",y=\"Ex-Showroom_Price\",data=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d68a9d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Width', ylabel='Ex-Showroom_Price'>"
      ]
     },
     "execution_count": 81,
     "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.regplot(x=\"Width\",y=\"Ex-Showroom_Price\",data=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8683b024",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = y.drop('Ex-Showroom_Price',axis=1)\n",
    "z = y['Ex-Showroom_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5924ecd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1276, 1254)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0911a8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1276, 1255)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "35d0f4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "z.shape\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b2550aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(x,z,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e7dde044",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c99f6861",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8e334da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "362a14cb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\SWASTI~1\\AppData\\Local\\Temp/ipykernel_8424/1002171229.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    516\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'coo'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 518\u001b[1;33m         X, y = self._validate_data(X, y, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    519\u001b[0m                                    y_numeric=True, multi_output=True)\n\u001b[0;32m    520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    872\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 720\u001b[1;33m             _assert_all_finite(array,\n\u001b[0m\u001b[0;32m    721\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0;32m    722\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    101\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m    102\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m    104\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m                     (type_err,\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2a2bac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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