EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09fefff8-ae19-49d7-8e12-a4368c77eff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0d4ebb8-a722-480f-9a50-fb11225f644e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>Rs. 2,92,667</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>Rs. 2,36,447</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>Rs. 2,96,661</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>Rs. 3,34,768</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>Rs. 2,72,223</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Vx Mt Diesel</td>\n",
       "      <td>Rs. 13,02,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>Rs. 14,21,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>Rs. 14,31,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>V Cvt Petrol</td>\n",
       "      <td>Rs. 12,01,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Montero</td>\n",
       "      <td>3.2 At</td>\n",
       "      <td>Rs. 68,62,560</td>\n",
       "      <td>3200 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AWD (All Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make      Model        Variant Ex-Showroom_Price Displacement  \\\n",
       "0           Tata  Nano Genx             Xt      Rs. 2,92,667       624 cc   \n",
       "1           Tata  Nano Genx             Xe      Rs. 2,36,447       624 cc   \n",
       "2           Tata  Nano Genx        Emax Xm      Rs. 2,96,661       624 cc   \n",
       "3           Tata  Nano Genx            Xta      Rs. 3,34,768       624 cc   \n",
       "4           Tata  Nano Genx             Xm      Rs. 2,72,223       624 cc   \n",
       "...          ...        ...            ...               ...          ...   \n",
       "1271       Honda       City   Vx Mt Diesel     Rs. 13,02,000      1498 cc   \n",
       "1272       Honda       City   Zx Mt Diesel     Rs. 14,21,000      1498 cc   \n",
       "1273       Honda       City  Zx Cvt Petrol     Rs. 14,31,000      1497 cc   \n",
       "1274       Honda       City   V Cvt Petrol     Rs. 12,01,000      1497 cc   \n",
       "1275  Mitsubishi    Montero         3.2 At     Rs. 68,62,560      3200 cc   \n",
       "\n",
       "      Cylinders  Valves_Per_Cylinder               Drivetrain  \\\n",
       "0           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "1           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "2           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "3           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "4           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "...         ...                  ...                      ...   \n",
       "1271        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1272        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1273        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1274        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1275        4.0                  4.0    AWD (All Wheel Drive)   \n",
       "\n",
       "     Cylinder_Configuration Emission_Norm  ... Leather_Wrapped_Steering  \\\n",
       "0                   In-line         BS IV  ...                      NaN   \n",
       "1                   In-line         BS IV  ...                      NaN   \n",
       "2                   In-line         BS IV  ...                      NaN   \n",
       "3                   In-line         BS IV  ...                      NaN   \n",
       "4                   In-line         BS IV  ...                      NaN   \n",
       "...                     ...           ...  ...                      ...   \n",
       "1271                In-line         BS IV  ...                      Yes   \n",
       "1272                In-line         BS IV  ...                      Yes   \n",
       "1273                In-line          BS 6  ...                      Yes   \n",
       "1274                In-line          BS 6  ...                      NaN   \n",
       "1275                In-line         BS IV  ...                      Yes   \n",
       "\n",
       "     Automatic_Headlamps Engine_Type ASR_/_Traction_Control Cruise_Control  \\\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            Yes   \n",
       "1272                 Yes         NaN                    NaN            Yes   \n",
       "1273                 Yes         NaN                    NaN            Yes   \n",
       "1274                 NaN         NaN                    NaN            Yes   \n",
       "1275                 NaN         NaN                    Yes            Yes   \n",
       "\n",
       "     USB_Ports Heads-Up_Display Welcome_Lights  Battery Electric_Range  \n",
       "0          NaN              NaN            NaN      NaN            NaN  \n",
       "1          NaN              NaN            NaN      NaN            NaN  \n",
       "2          NaN              NaN            NaN      NaN            NaN  \n",
       "3          NaN              NaN            NaN      NaN            NaN  \n",
       "4          NaN              NaN            NaN      NaN            NaN  \n",
       "...        ...              ...            ...      ...            ...  \n",
       "1271       NaN              NaN            NaN      NaN            NaN  \n",
       "1272       NaN              NaN            NaN      NaN            NaN  \n",
       "1273       NaN              NaN            NaN      NaN            NaN  \n",
       "1274       NaN              NaN            NaN      NaN            NaN  \n",
       "1275       NaN              NaN            NaN      NaN            NaN  \n",
       "\n",
       "[1276 rows x 140 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./datas/cars.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c05aac51-cc6b-4d23-94b4-cfa4c26e3b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1276, 140)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57d80f7e-06a3-4f8e-87da-00e3ce42f0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>1210.0</td>\n",
       "      <td>4.380992</td>\n",
       "      <td>1.660957</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <td>1174.0</td>\n",
       "      <td>3.977853</td>\n",
       "      <td>0.833763</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doors</th>\n",
       "      <td>1272.0</td>\n",
       "      <td>4.550314</td>\n",
       "      <td>0.747816</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seating_Capacity</th>\n",
       "      <td>1270.0</td>\n",
       "      <td>5.270079</td>\n",
       "      <td>1.145231</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number_of_Airbags</th>\n",
       "      <td>1141.0</td>\n",
       "      <td>3.787029</td>\n",
       "      <td>2.522399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USB_Ports</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1.793103</td>\n",
       "      <td>0.773642</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count      mean       std  min  25%  50%  75%   max\n",
       "Cylinders            1210.0  4.380992  1.660957  2.0  4.0  4.0  4.0  16.0\n",
       "Valves_Per_Cylinder  1174.0  3.977853  0.833763  1.0  4.0  4.0  4.0  16.0\n",
       "Doors                1272.0  4.550314  0.747816  2.0  4.0  5.0  5.0   5.0\n",
       "Seating_Capacity     1270.0  5.270079  1.145231  2.0  5.0  5.0  5.0  16.0\n",
       "Number_of_Airbags    1141.0  3.787029  2.522399  1.0  2.0  2.0  6.0  14.0\n",
       "USB_Ports              29.0  1.793103  0.773642  1.0  1.0  2.0  2.0   3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "281b3101-b312-4384-a01f-5e2dab6e4db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1276 entries, 0 to 1275\n",
      "Columns: 140 entries, Make to Electric_Range\n",
      "dtypes: float64(6), object(134)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7763880-0662-42ff-b3f5-830317ca595e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                   75\n",
       "Model                   0\n",
       "Variant                 0\n",
       "Ex-Showroom_Price       0\n",
       "Displacement           12\n",
       "                     ... \n",
       "USB_Ports            1247\n",
       "Heads-Up_Display     1225\n",
       "Welcome_Lights       1207\n",
       "Battery              1263\n",
       "Electric_Range       1259\n",
       "Length: 140, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70b6941e-72be-45ad-b277-33779998b452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>Rs. 2,92,667</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>Rs. 2,36,447</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>Rs. 2,96,661</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>Rs. 3,34,768</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>Rs. 2,72,223</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Vx Mt Diesel</td>\n",
       "      <td>Rs. 13,02,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>Rs. 14,21,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>Rs. 14,31,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>V Cvt Petrol</td>\n",
       "      <td>Rs. 12,01,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Montero</td>\n",
       "      <td>3.2 At</td>\n",
       "      <td>Rs. 68,62,560</td>\n",
       "      <td>3200 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AWD (All Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make      Model        Variant Ex-Showroom_Price Displacement  \\\n",
       "0           Tata  Nano Genx             Xt      Rs. 2,92,667       624 cc   \n",
       "1           Tata  Nano Genx             Xe      Rs. 2,36,447       624 cc   \n",
       "2           Tata  Nano Genx        Emax Xm      Rs. 2,96,661       624 cc   \n",
       "3           Tata  Nano Genx            Xta      Rs. 3,34,768       624 cc   \n",
       "4           Tata  Nano Genx             Xm      Rs. 2,72,223       624 cc   \n",
       "...          ...        ...            ...               ...          ...   \n",
       "1271       Honda       City   Vx Mt Diesel     Rs. 13,02,000      1498 cc   \n",
       "1272       Honda       City   Zx Mt Diesel     Rs. 14,21,000      1498 cc   \n",
       "1273       Honda       City  Zx Cvt Petrol     Rs. 14,31,000      1497 cc   \n",
       "1274       Honda       City   V Cvt Petrol     Rs. 12,01,000      1497 cc   \n",
       "1275  Mitsubishi    Montero         3.2 At     Rs. 68,62,560      3200 cc   \n",
       "\n",
       "      Cylinders  Valves_Per_Cylinder               Drivetrain  \\\n",
       "0           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "1           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "2           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "3           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "4           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "...         ...                  ...                      ...   \n",
       "1271        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1272        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1273        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1274        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1275        4.0                  4.0    AWD (All Wheel Drive)   \n",
       "\n",
       "     Cylinder_Configuration Emission_Norm  ... Leather_Wrapped_Steering  \\\n",
       "0                   In-line         BS IV  ...                      NaN   \n",
       "1                   In-line         BS IV  ...                      NaN   \n",
       "2                   In-line         BS IV  ...                      NaN   \n",
       "3                   In-line         BS IV  ...                      NaN   \n",
       "4                   In-line         BS IV  ...                      NaN   \n",
       "...                     ...           ...  ...                      ...   \n",
       "1271                In-line         BS IV  ...                      Yes   \n",
       "1272                In-line         BS IV  ...                      Yes   \n",
       "1273                In-line          BS 6  ...                      Yes   \n",
       "1274                In-line          BS 6  ...                      NaN   \n",
       "1275                In-line         BS IV  ...                      Yes   \n",
       "\n",
       "     Automatic_Headlamps Engine_Type ASR_/_Traction_Control Cruise_Control  \\\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            Yes   \n",
       "1272                 Yes         NaN                    NaN            Yes   \n",
       "1273                 Yes         NaN                    NaN            Yes   \n",
       "1274                 NaN         NaN                    NaN            Yes   \n",
       "1275                 NaN         NaN                    Yes            Yes   \n",
       "\n",
       "     USB_Ports Heads-Up_Display Welcome_Lights  Battery Electric_Range  \n",
       "0          NaN              NaN            NaN      NaN            NaN  \n",
       "1          NaN              NaN            NaN      NaN            NaN  \n",
       "2          NaN              NaN            NaN      NaN            NaN  \n",
       "3          NaN              NaN            NaN      NaN            NaN  \n",
       "4          NaN              NaN            NaN      NaN            NaN  \n",
       "...        ...              ...            ...      ...            ...  \n",
       "1271       NaN              NaN            NaN      NaN            NaN  \n",
       "1272       NaN              NaN            NaN      NaN            NaN  \n",
       "1273       NaN              NaN            NaN      NaN            NaN  \n",
       "1274       NaN              NaN            NaN      NaN            NaN  \n",
       "1275       NaN              NaN            NaN      NaN            NaN  \n",
       "\n",
       "[1276 rows x 140 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = df.copy()\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5142ca26-854e-45eb-b4bf-409cde821dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make\n",
       "Maruti Suzuki       149\n",
       "Hyundai             130\n",
       "Mahindra            119\n",
       "Tata                100\n",
       "Toyota               82\n",
       "Honda                64\n",
       "Ford                 43\n",
       "Skoda                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",
       "Maruti Suzuki R      14\n",
       "Porsche              14\n",
       "Mg                   13\n",
       "Lamborghini          13\n",
       "Icml                 11\n",
       "Mini                 10\n",
       "Land Rover           10\n",
       "Lexus                10\n",
       "Maserati              9\n",
       "Ferrari               8\n",
       "Mitsubishi            7\n",
       "Premier               6\n",
       "Bentley               6\n",
       "Force                 6\n",
       "Isuzu                 5\n",
       "Aston Martin          3\n",
       "Bugatti               2\n",
       "Bajaj                 2\n",
       "Dc                    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4bc30909-3cc1-487c-89a4-6e2f9517b551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>Rs. 2,92,667</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>Rs. 2,36,447</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>Rs. 2,96,661</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>Rs. 3,34,768</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>Rs. 2,72,223</td>\n",
       "      <td>624 cc</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Vx Mt Diesel</td>\n",
       "      <td>Rs. 13,02,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>Rs. 14,21,000</td>\n",
       "      <td>1498 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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>Rs. 14,31,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>V Cvt Petrol</td>\n",
       "      <td>Rs. 12,01,000</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Montero</td>\n",
       "      <td>3.2 At</td>\n",
       "      <td>Rs. 68,62,560</td>\n",
       "      <td>3200 cc</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AWD (All Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1201 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make      Model        Variant Ex-Showroom_Price Displacement  \\\n",
       "0           Tata  Nano Genx             Xt      Rs. 2,92,667       624 cc   \n",
       "1           Tata  Nano Genx             Xe      Rs. 2,36,447       624 cc   \n",
       "2           Tata  Nano Genx        Emax Xm      Rs. 2,96,661       624 cc   \n",
       "3           Tata  Nano Genx            Xta      Rs. 3,34,768       624 cc   \n",
       "4           Tata  Nano Genx             Xm      Rs. 2,72,223       624 cc   \n",
       "...          ...        ...            ...               ...          ...   \n",
       "1271       Honda       City   Vx Mt Diesel     Rs. 13,02,000      1498 cc   \n",
       "1272       Honda       City   Zx Mt Diesel     Rs. 14,21,000      1498 cc   \n",
       "1273       Honda       City  Zx Cvt Petrol     Rs. 14,31,000      1497 cc   \n",
       "1274       Honda       City   V Cvt Petrol     Rs. 12,01,000      1497 cc   \n",
       "1275  Mitsubishi    Montero         3.2 At     Rs. 68,62,560      3200 cc   \n",
       "\n",
       "      Cylinders  Valves_Per_Cylinder               Drivetrain  \\\n",
       "0           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "1           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "2           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "3           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "4           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "...         ...                  ...                      ...   \n",
       "1271        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1272        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1273        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1274        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1275        4.0                  4.0    AWD (All Wheel Drive)   \n",
       "\n",
       "     Cylinder_Configuration Emission_Norm  ... Leather_Wrapped_Steering  \\\n",
       "0                   In-line         BS IV  ...                      NaN   \n",
       "1                   In-line         BS IV  ...                      NaN   \n",
       "2                   In-line         BS IV  ...                      NaN   \n",
       "3                   In-line         BS IV  ...                      NaN   \n",
       "4                   In-line         BS IV  ...                      NaN   \n",
       "...                     ...           ...  ...                      ...   \n",
       "1271                In-line         BS IV  ...                      Yes   \n",
       "1272                In-line         BS IV  ...                      Yes   \n",
       "1273                In-line          BS 6  ...                      Yes   \n",
       "1274                In-line          BS 6  ...                      NaN   \n",
       "1275                In-line         BS IV  ...                      Yes   \n",
       "\n",
       "     Automatic_Headlamps Engine_Type ASR_/_Traction_Control Cruise_Control  \\\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            Yes   \n",
       "1272                 Yes         NaN                    NaN            Yes   \n",
       "1273                 Yes         NaN                    NaN            Yes   \n",
       "1274                 NaN         NaN                    NaN            Yes   \n",
       "1275                 NaN         NaN                    Yes            Yes   \n",
       "\n",
       "     USB_Ports Heads-Up_Display Welcome_Lights  Battery Electric_Range  \n",
       "0          NaN              NaN            NaN      NaN            NaN  \n",
       "1          NaN              NaN            NaN      NaN            NaN  \n",
       "2          NaN              NaN            NaN      NaN            NaN  \n",
       "3          NaN              NaN            NaN      NaN            NaN  \n",
       "4          NaN              NaN            NaN      NaN            NaN  \n",
       "...        ...              ...            ...      ...            ...  \n",
       "1271       NaN              NaN            NaN      NaN            NaN  \n",
       "1272       NaN              NaN            NaN      NaN            NaN  \n",
       "1273       NaN              NaN            NaN      NaN            NaN  \n",
       "1274       NaN              NaN            NaN      NaN            NaN  \n",
       "1275       NaN              NaN            NaN      NaN            NaN  \n",
       "\n",
       "[1201 rows x 140 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = cars[cars['Make'].notna()]\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5271d993-a6b9-444e-b166-47738f234ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    0\n",
       "Model                   0\n",
       "Variant                 0\n",
       "Ex-Showroom_Price       0\n",
       "Displacement           11\n",
       "                     ... \n",
       "USB_Ports            1172\n",
       "Heads-Up_Display     1155\n",
       "Welcome_Lights       1138\n",
       "Battery              1188\n",
       "Electric_Range       1184\n",
       "Length: 140, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcf0d554-467f-455b-831f-b99a0d9fdccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1201"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(cars.Displacement)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d4809aa-60a7-40a0-a059-bd82f9d39f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "624"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(cars.Displacement[3].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6852491-0291-4292-b1f0-ac4cc6b26c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['624 cc', '799 cc', '999 cc', '1196 cc', '998 cc', '1198 cc',\n",
       "       '1086 cc', '1199 cc', '1197 cc', '1173 cc', '1489 cc', '1248 cc',\n",
       "       '1364 cc', '1193 cc', '1396 cc', '1186 cc', '1498 cc', '1194 cc',\n",
       "       '1496 cc', '1461 cc', '1368 cc', '1956 cc', '2999 cc', '2157 cc',\n",
       "       '1968 cc', '1997 cc', '1597 cc', '1798 cc', '1395 cc', '2000 cc',\n",
       "       '1998 cc', '1969 cc', '1999 cc', '1984 cc', '1995 cc', '2995 cc',\n",
       "       '2993 cc', '2998 cc', '4951 cc', '2987 cc', '6417 cc', '3604 cc',\n",
       "       '2979 cc', '1988 cc', '2982 cc', '3456 cc', '2894 cc', '6592 cc',\n",
       "       '4691 cc', '3996 cc', '4461 cc', '5204 cc', '5998 cc', '3855 cc',\n",
       "       '5950 cc', '5198 cc', '4497 cc', '6752 cc', '7993 cc', '216 cc',\n",
       "       '796 cc', '1120 cc', '1497 cc', '1994 cc', '1298 cc', '1397 cc',\n",
       "       '1493 cc', '2523 cc', '1591 cc', '1582 cc', '1598 cc', nan,\n",
       "       '2498 cc', '2596 cc', '2149 cc', '1462 cc', '2179 cc', '72 cc',\n",
       "       '2393 cc', '2694 cc', '2755 cc', '1799 cc', '2199 cc', '3198 cc',\n",
       "       '2198 cc', '2400 cc', '2967 cc', '4999 cc', '4367 cc', '3993 cc',\n",
       "       '3902 cc', '3900 cc', '6262 cc', '6498 cc', '1172 cc', '2489 cc',\n",
       "       '1353 cc', '1500 cc', '1596 cc', '2487 cc', '2494 cc', '2981 cc',\n",
       "       '3799 cc', '3982 cc', '5935 cc', '6496 cc', '2499 cc', '1898 cc',\n",
       "       '2360 cc', '1993 cc', '4395 cc', '5000 cc', '5700 cc', '1047 cc',\n",
       "       '2200 cc', '2477 cc', '3200 cc'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Displacement'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b770bc8e-3bea-46e2-a49a-acbe55f498f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1197 cc\n",
       "Name: Displacement, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_value = cars.Displacement.mode()\n",
    "mode_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90d8d865-e629-498c-9ec2-d1f4c60de258",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21857/3314136295.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  cars.Displacement.fillna(\"1197 cc\", inplace=True)\n",
      "/tmp/ipykernel_21857/3314136295.py:1: 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",
      "  cars.Displacement.fillna(\"1197 cc\", inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['624 cc', '799 cc', '999 cc', '1196 cc', '998 cc', '1198 cc',\n",
       "       '1086 cc', '1199 cc', '1197 cc', '1173 cc', '1489 cc', '1248 cc',\n",
       "       '1364 cc', '1193 cc', '1396 cc', '1186 cc', '1498 cc', '1194 cc',\n",
       "       '1496 cc', '1461 cc', '1368 cc', '1956 cc', '2999 cc', '2157 cc',\n",
       "       '1968 cc', '1997 cc', '1597 cc', '1798 cc', '1395 cc', '2000 cc',\n",
       "       '1998 cc', '1969 cc', '1999 cc', '1984 cc', '1995 cc', '2995 cc',\n",
       "       '2993 cc', '2998 cc', '4951 cc', '2987 cc', '6417 cc', '3604 cc',\n",
       "       '2979 cc', '1988 cc', '2982 cc', '3456 cc', '2894 cc', '6592 cc',\n",
       "       '4691 cc', '3996 cc', '4461 cc', '5204 cc', '5998 cc', '3855 cc',\n",
       "       '5950 cc', '5198 cc', '4497 cc', '6752 cc', '7993 cc', '216 cc',\n",
       "       '796 cc', '1120 cc', '1497 cc', '1994 cc', '1298 cc', '1397 cc',\n",
       "       '1493 cc', '2523 cc', '1591 cc', '1582 cc', '1598 cc', '2498 cc',\n",
       "       '2596 cc', '2149 cc', '1462 cc', '2179 cc', '72 cc', '2393 cc',\n",
       "       '2694 cc', '2755 cc', '1799 cc', '2199 cc', '3198 cc', '2198 cc',\n",
       "       '2400 cc', '2967 cc', '4999 cc', '4367 cc', '3993 cc', '3902 cc',\n",
       "       '3900 cc', '6262 cc', '6498 cc', '1172 cc', '2489 cc', '1353 cc',\n",
       "       '1500 cc', '1596 cc', '2487 cc', '2494 cc', '2981 cc', '3799 cc',\n",
       "       '3982 cc', '5935 cc', '6496 cc', '2499 cc', '1898 cc', '2360 cc',\n",
       "       '1993 cc', '4395 cc', '5000 cc', '5700 cc', '1047 cc', '2200 cc',\n",
       "       '2477 cc', '3200 cc'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Displacement.fillna(\"1197 cc\", inplace=True)\n",
    "cars.Displacement.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d510f769-a101-4549-964a-fd8c076c2e25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21857/1605139891.py:1: 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",
      "  cars.Displacement = cars.Displacement.str.replace(' cc','').astype(int)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>Rs. 2,92,667</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>Rs. 2,36,447</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>Rs. 2,96,661</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>Rs. 3,34,768</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>Rs. 2,72,223</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xma</td>\n",
       "      <td>Rs. 3,14,815</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Datsun</td>\n",
       "      <td>Redi-Go</td>\n",
       "      <td>D</td>\n",
       "      <td>Rs. 2,79,650</td>\n",
       "      <td>799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FWD (Front Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Datsun</td>\n",
       "      <td>Redi-Go</td>\n",
       "      <td>T</td>\n",
       "      <td>Rs. 3,51,832</td>\n",
       "      <td>799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FWD (Front Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Datsun</td>\n",
       "      <td>Redi-Go</td>\n",
       "      <td>A</td>\n",
       "      <td>Rs. 3,33,419</td>\n",
       "      <td>799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FWD (Front Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Datsun</td>\n",
       "      <td>Redi-Go</td>\n",
       "      <td>S</td>\n",
       "      <td>Rs. 3,62,000</td>\n",
       "      <td>799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FWD (Front Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make      Model  Variant Ex-Showroom_Price  Displacement  Cylinders  \\\n",
       "0    Tata  Nano Genx       Xt      Rs. 2,92,667           624        2.0   \n",
       "1    Tata  Nano Genx       Xe      Rs. 2,36,447           624        2.0   \n",
       "2    Tata  Nano Genx  Emax Xm      Rs. 2,96,661           624        2.0   \n",
       "3    Tata  Nano Genx      Xta      Rs. 3,34,768           624        2.0   \n",
       "4    Tata  Nano Genx       Xm      Rs. 2,72,223           624        2.0   \n",
       "5    Tata  Nano Genx      Xma      Rs. 3,14,815           624        2.0   \n",
       "6  Datsun    Redi-Go        D      Rs. 2,79,650           799        3.0   \n",
       "7  Datsun    Redi-Go        T      Rs. 3,51,832           799        3.0   \n",
       "8  Datsun    Redi-Go        A      Rs. 3,33,419           799        3.0   \n",
       "9  Datsun    Redi-Go        S      Rs. 3,62,000           799        3.0   \n",
       "\n",
       "   Valves_Per_Cylinder               Drivetrain Cylinder_Configuration  \\\n",
       "0                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "1                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "2                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "3                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "4                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "5                  2.0   RWD (Rear Wheel Drive)                In-line   \n",
       "6                  4.0  FWD (Front Wheel Drive)                In-line   \n",
       "7                  4.0  FWD (Front Wheel Drive)                In-line   \n",
       "8                  4.0  FWD (Front Wheel Drive)                In-line   \n",
       "9                  4.0  FWD (Front Wheel Drive)                In-line   \n",
       "\n",
       "  Emission_Norm  ... Leather_Wrapped_Steering Automatic_Headlamps Engine_Type  \\\n",
       "0         BS IV  ...                      NaN                 NaN         NaN   \n",
       "1         BS IV  ...                      NaN                 NaN         NaN   \n",
       "2         BS IV  ...                      NaN                 NaN         NaN   \n",
       "3         BS IV  ...                      NaN                 NaN         NaN   \n",
       "4         BS IV  ...                      NaN                 NaN         NaN   \n",
       "5         BS IV  ...                      NaN                 NaN         NaN   \n",
       "6         BS IV  ...                      NaN                 NaN         NaN   \n",
       "7         BS IV  ...                      NaN                 NaN         NaN   \n",
       "8         BS IV  ...                      NaN                 NaN         NaN   \n",
       "9         BS IV  ...                      NaN                 NaN         NaN   \n",
       "\n",
       "  ASR_/_Traction_Control Cruise_Control USB_Ports Heads-Up_Display  \\\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",
       "5                    NaN            NaN       NaN              NaN   \n",
       "6                    NaN            NaN       NaN              NaN   \n",
       "7                    NaN            NaN       NaN              NaN   \n",
       "8                    NaN            NaN       NaN              NaN   \n",
       "9                    NaN            NaN       NaN              NaN   \n",
       "\n",
       "  Welcome_Lights  Battery Electric_Range  \n",
       "0            NaN      NaN            NaN  \n",
       "1            NaN      NaN            NaN  \n",
       "2            NaN      NaN            NaN  \n",
       "3            NaN      NaN            NaN  \n",
       "4            NaN      NaN            NaN  \n",
       "5            NaN      NaN            NaN  \n",
       "6            NaN      NaN            NaN  \n",
       "7            NaN      NaN            NaN  \n",
       "8            NaN      NaN            NaN  \n",
       "9            NaN      NaN            NaN  \n",
       "\n",
       "[10 rows x 140 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Displacement = cars.Displacement.str.replace(' cc','').astype(int)\n",
    "cars.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f45cf69-1ffe-429e-adc0-be8ef46559b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21857/2240741750.py:1: 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",
      "  cars['Ex-Showroom_Price'] = cars['Ex-Showroom_Price'].str.replace('Rs. ','')\n",
      "/tmp/ipykernel_21857/2240741750.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",
      "  cars['Ex-Showroom_Price'] = cars['Ex-Showroom_Price'].str.replace(',','').astype(int)\n"
     ]
    },
    {
     "data": {
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       "      <th>Emission_Norm</th>\n",
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       "      <th>Automatic_Headlamps</th>\n",
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       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
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       "      <td>Xt</td>\n",
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       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>236447</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
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       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
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       "      <td>Emax Xm</td>\n",
       "      <td>296661</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>334768</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>272223</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Vx Mt Diesel</td>\n",
       "      <td>1302000</td>\n",
       "      <td>1498</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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Mt Diesel</td>\n",
       "      <td>1421000</td>\n",
       "      <td>1498</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>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>Zx Cvt Petrol</td>\n",
       "      <td>1431000</td>\n",
       "      <td>1497</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>V Cvt Petrol</td>\n",
       "      <td>1201000</td>\n",
       "      <td>1497</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>BS 6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Montero</td>\n",
       "      <td>3.2 At</td>\n",
       "      <td>6862560</td>\n",
       "      <td>3200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AWD (All Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1201 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make      Model        Variant  Ex-Showroom_Price  Displacement  \\\n",
       "0           Tata  Nano Genx             Xt             292667           624   \n",
       "1           Tata  Nano Genx             Xe             236447           624   \n",
       "2           Tata  Nano Genx        Emax Xm             296661           624   \n",
       "3           Tata  Nano Genx            Xta             334768           624   \n",
       "4           Tata  Nano Genx             Xm             272223           624   \n",
       "...          ...        ...            ...                ...           ...   \n",
       "1271       Honda       City   Vx Mt Diesel            1302000          1498   \n",
       "1272       Honda       City   Zx Mt Diesel            1421000          1498   \n",
       "1273       Honda       City  Zx Cvt Petrol            1431000          1497   \n",
       "1274       Honda       City   V Cvt Petrol            1201000          1497   \n",
       "1275  Mitsubishi    Montero         3.2 At            6862560          3200   \n",
       "\n",
       "      Cylinders  Valves_Per_Cylinder               Drivetrain  \\\n",
       "0           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "1           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "2           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "3           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "4           2.0                  2.0   RWD (Rear Wheel Drive)   \n",
       "...         ...                  ...                      ...   \n",
       "1271        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1272        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1273        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1274        4.0                  4.0  FWD (Front Wheel Drive)   \n",
       "1275        4.0                  4.0    AWD (All Wheel Drive)   \n",
       "\n",
       "     Cylinder_Configuration Emission_Norm  ... Leather_Wrapped_Steering  \\\n",
       "0                   In-line         BS IV  ...                      NaN   \n",
       "1                   In-line         BS IV  ...                      NaN   \n",
       "2                   In-line         BS IV  ...                      NaN   \n",
       "3                   In-line         BS IV  ...                      NaN   \n",
       "4                   In-line         BS IV  ...                      NaN   \n",
       "...                     ...           ...  ...                      ...   \n",
       "1271                In-line         BS IV  ...                      Yes   \n",
       "1272                In-line         BS IV  ...                      Yes   \n",
       "1273                In-line          BS 6  ...                      Yes   \n",
       "1274                In-line          BS 6  ...                      NaN   \n",
       "1275                In-line         BS IV  ...                      Yes   \n",
       "\n",
       "     Automatic_Headlamps Engine_Type ASR_/_Traction_Control Cruise_Control  \\\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            Yes   \n",
       "1272                 Yes         NaN                    NaN            Yes   \n",
       "1273                 Yes         NaN                    NaN            Yes   \n",
       "1274                 NaN         NaN                    NaN            Yes   \n",
       "1275                 NaN         NaN                    Yes            Yes   \n",
       "\n",
       "     USB_Ports Heads-Up_Display Welcome_Lights  Battery Electric_Range  \n",
       "0          NaN              NaN            NaN      NaN            NaN  \n",
       "1          NaN              NaN            NaN      NaN            NaN  \n",
       "2          NaN              NaN            NaN      NaN            NaN  \n",
       "3          NaN              NaN            NaN      NaN            NaN  \n",
       "4          NaN              NaN            NaN      NaN            NaN  \n",
       "...        ...              ...            ...      ...            ...  \n",
       "1271       NaN              NaN            NaN      NaN            NaN  \n",
       "1272       NaN              NaN            NaN      NaN            NaN  \n",
       "1273       NaN              NaN            NaN      NaN            NaN  \n",
       "1274       NaN              NaN            NaN      NaN            NaN  \n",
       "1275       NaN              NaN            NaN      NaN            NaN  \n",
       "\n",
       "[1201 rows x 140 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Ex-Showroom_Price'] = cars['Ex-Showroom_Price'].str.replace('Rs. ','')\n",
    "cars['Ex-Showroom_Price'] = cars['Ex-Showroom_Price'].str.replace(',','').astype(int)\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5688ecb5-d6b4-44b4-a283-065dd785c0bf",
   "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>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>292667</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>236447</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>296661</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>334768</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>272223</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RWD (Rear Wheel Drive)</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make      Model  Variant  Ex-Showroom_Price  Displacement  Cylinders  \\\n",
       "0  Tata  Nano Genx       Xt             292667           624        2.0   \n",
       "1  Tata  Nano Genx       Xe             236447           624        2.0   \n",
       "2  Tata  Nano Genx  Emax Xm             296661           624        2.0   \n",
       "3  Tata  Nano Genx      Xta             334768           624        2.0   \n",
       "4  Tata  Nano Genx       Xm             272223           624        2.0   \n",
       "\n",
       "   Valves_Per_Cylinder              Drivetrain Cylinder_Configuration  \\\n",
       "0                  2.0  RWD (Rear Wheel Drive)                In-line   \n",
       "1                  2.0  RWD (Rear Wheel Drive)                In-line   \n",
       "2                  2.0  RWD (Rear Wheel Drive)                In-line   \n",
       "3                  2.0  RWD (Rear Wheel Drive)                In-line   \n",
       "4                  2.0  RWD (Rear Wheel Drive)                In-line   \n",
       "\n",
       "  Emission_Norm  ... Leather_Wrapped_Steering Automatic_Headlamps Engine_Type  \\\n",
       "0         BS IV  ...                      NaN                 NaN         NaN   \n",
       "1         BS IV  ...                      NaN                 NaN         NaN   \n",
       "2         BS IV  ...                      NaN                 NaN         NaN   \n",
       "3         BS IV  ...                      NaN                 NaN         NaN   \n",
       "4         BS IV  ...                      NaN                 NaN         NaN   \n",
       "\n",
       "  ASR_/_Traction_Control Cruise_Control USB_Ports Heads-Up_Display  \\\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",
       "  Welcome_Lights  Battery Electric_Range  \n",
       "0            NaN      NaN            NaN  \n",
       "1            NaN      NaN            NaN  \n",
       "2            NaN      NaN            NaN  \n",
       "3            NaN      NaN            NaN  \n",
       "4            NaN      NaN            NaN  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = cars[cars['Drivetrain'].notna()]\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "458ce2f6-0998-47ed-b224-9af71219dca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing \n",
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43ac2883-798f-4590-9ffc-4f87e1aa95d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21857/3135190005.py:1: 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",
      "  cars.Drivetrain = le.fit_transform(cars.Drivetrain)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       3\n",
       "1       3\n",
       "2       3\n",
       "3       3\n",
       "4       3\n",
       "       ..\n",
       "1271    2\n",
       "1272    2\n",
       "1273    2\n",
       "1274    2\n",
       "1275    1\n",
       "Name: Drivetrain, Length: 1195, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Drivetrain = le.fit_transform(cars.Drivetrain)\n",
    "cars.Drivetrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbce3e8d-c6c8-4344-8d08-1ef367752d16",
   "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>Model</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Ex-Showroom_Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Valves_Per_Cylinder</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Cylinder_Configuration</th>\n",
       "      <th>Emission_Norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Leather_Wrapped_Steering</th>\n",
       "      <th>Automatic_Headlamps</th>\n",
       "      <th>Engine_Type</th>\n",
       "      <th>ASR_/_Traction_Control</th>\n",
       "      <th>Cruise_Control</th>\n",
       "      <th>USB_Ports</th>\n",
       "      <th>Heads-Up_Display</th>\n",
       "      <th>Welcome_Lights</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Electric_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xt</td>\n",
       "      <td>292667</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xe</td>\n",
       "      <td>236447</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Emax Xm</td>\n",
       "      <td>296661</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xta</td>\n",
       "      <td>334768</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Nano Genx</td>\n",
       "      <td>Xm</td>\n",
       "      <td>272223</td>\n",
       "      <td>624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make      Model  Variant  Ex-Showroom_Price  Displacement  Cylinders  \\\n",
       "0  Tata  Nano Genx       Xt             292667           624        2.0   \n",
       "1  Tata  Nano Genx       Xe             236447           624        2.0   \n",
       "2  Tata  Nano Genx  Emax Xm             296661           624        2.0   \n",
       "3  Tata  Nano Genx      Xta             334768           624        2.0   \n",
       "4  Tata  Nano Genx       Xm             272223           624        2.0   \n",
       "\n",
       "   Valves_Per_Cylinder  Drivetrain Cylinder_Configuration Emission_Norm  ...  \\\n",
       "0                  2.0           3                In-line         BS IV  ...   \n",
       "1                  2.0           3                In-line         BS IV  ...   \n",
       "2                  2.0           3                In-line         BS IV  ...   \n",
       "3                  2.0           3                In-line         BS IV  ...   \n",
       "4                  2.0           3                In-line         BS IV  ...   \n",
       "\n",
       "  Leather_Wrapped_Steering Automatic_Headlamps Engine_Type  \\\n",
       "0                      NaN                 NaN         NaN   \n",
       "1                      NaN                 NaN         NaN   \n",
       "2                      NaN                 NaN         NaN   \n",
       "3                      NaN                 NaN         NaN   \n",
       "4                      NaN                 NaN         NaN   \n",
       "\n",
       "  ASR_/_Traction_Control Cruise_Control USB_Ports Heads-Up_Display  \\\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",
       "  Welcome_Lights  Battery Electric_Range  \n",
       "0            NaN      NaN            NaN  \n",
       "1            NaN      NaN            NaN  \n",
       "2            NaN      NaN            NaN  \n",
       "3            NaN      NaN            NaN  \n",
       "4            NaN      NaN            NaN  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91c5a1dc-cbdc-42e9-8065-7f449b7ec689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cars['Ex-Showroom_Price'], bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15c2501d-0511-4569-9ff2-6b747678dae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Ex-Showroom_Price'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "170d8937-4fd8-4c38-b458-0b4a3d053a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nano Genx', 'Tiago', 'Bolt', 'Altroz', 'Tigor', 'Zest', 'Nexon',\n",
       "       'Tigor Ev', 'Hexa', 'Safari Storme', 'Nexon Ev', 'Harrier',\n",
       "       'Tiago Nrg', 'Winger'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[cars['Make']=='Tata']['Model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b8cb7c55-9021-497c-afa5-afafcaacdee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BS IV', 'BS 6', nan, 'BS III', 'BS VI'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Emission_Norm.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8cec192e-67cb-4dc9-92fe-c526136906fb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
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       "      <th>Ex-Showroom_Price</th>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make      Model  Variant  Ex-Showroom_Price  Displacement  Cylinders  \\\n",
       "0  Tata  Nano Genx       Xt             292667           624        2.0   \n",
       "1  Tata  Nano Genx       Xe             236447           624        2.0   \n",
       "2  Tata  Nano Genx  Emax Xm             296661           624        2.0   \n",
       "3  Tata  Nano Genx      Xta             334768           624        2.0   \n",
       "4  Tata  Nano Genx       Xm             272223           624        2.0   \n",
       "\n",
       "   Valves_Per_Cylinder  Drivetrain Cylinder_Configuration Emission_Norm  ...  \\\n",
       "0                  2.0           3                In-line         BS IV  ...   \n",
       "1                  2.0           3                In-line         BS IV  ...   \n",
       "2                  2.0           3                In-line         BS IV  ...   \n",
       "3                  2.0           3                In-line         BS IV  ...   \n",
       "4                  2.0           3                In-line         BS IV  ...   \n",
       "\n",
       "  Leather_Wrapped_Steering Automatic_Headlamps Engine_Type  \\\n",
       "0                      NaN                 NaN         NaN   \n",
       "1                      NaN                 NaN         NaN   \n",
       "2                      NaN                 NaN         NaN   \n",
       "3                      NaN                 NaN         NaN   \n",
       "4                      NaN                 NaN         NaN   \n",
       "\n",
       "  ASR_/_Traction_Control Cruise_Control USB_Ports Heads-Up_Display  \\\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",
       "  Welcome_Lights  Battery Electric_Range  \n",
       "0            NaN      NaN            NaN  \n",
       "1            NaN      NaN            NaN  \n",
       "2            NaN      NaN            NaN  \n",
       "3            NaN      NaN            NaN  \n",
       "4            NaN      NaN            NaN  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = cars[cars['Cylinders'].notna()]\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b840502-5cc9-49da-98d9-e776b9256641",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>In-line</td>\n",
       "      <td>BS IV</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1133 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make      Model        Variant  Ex-Showroom_Price  Displacement  \\\n",
       "0           Tata  Nano Genx             Xt             292667           624   \n",
       "1           Tata  Nano Genx             Xe             236447           624   \n",
       "2           Tata  Nano Genx        Emax Xm             296661           624   \n",
       "3           Tata  Nano Genx            Xta             334768           624   \n",
       "4           Tata  Nano Genx             Xm             272223           624   \n",
       "...          ...        ...            ...                ...           ...   \n",
       "1271       Honda       City   Vx Mt Diesel            1302000          1498   \n",
       "1272       Honda       City   Zx Mt Diesel            1421000          1498   \n",
       "1273       Honda       City  Zx Cvt Petrol            1431000          1497   \n",
       "1274       Honda       City   V Cvt Petrol            1201000          1497   \n",
       "1275  Mitsubishi    Montero         3.2 At            6862560          3200   \n",
       "\n",
       "      Cylinders  Valves_Per_Cylinder  Drivetrain Cylinder_Configuration  \\\n",
       "0             2                  2.0           3                In-line   \n",
       "1             2                  2.0           3                In-line   \n",
       "2             2                  2.0           3                In-line   \n",
       "3             2                  2.0           3                In-line   \n",
       "4             2                  2.0           3                In-line   \n",
       "...         ...                  ...         ...                    ...   \n",
       "1271          4                  4.0           2                In-line   \n",
       "1272          4                  4.0           2                In-line   \n",
       "1273          4                  4.0           2                In-line   \n",
       "1274          4                  4.0           2                In-line   \n",
       "1275          4                  4.0           1                In-line   \n",
       "\n",
       "     Emission_Norm  ... Leather_Wrapped_Steering Automatic_Headlamps  \\\n",
       "0            BS IV  ...                      NaN                 NaN   \n",
       "1            BS IV  ...                      NaN                 NaN   \n",
       "2            BS IV  ...                      NaN                 NaN   \n",
       "3            BS IV  ...                      NaN                 NaN   \n",
       "4            BS IV  ...                      NaN                 NaN   \n",
       "...            ...  ...                      ...                 ...   \n",
       "1271         BS IV  ...                      Yes                 NaN   \n",
       "1272         BS IV  ...                      Yes                 Yes   \n",
       "1273          BS 6  ...                      Yes                 Yes   \n",
       "1274          BS 6  ...                      NaN                 NaN   \n",
       "1275         BS IV  ...                      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",
       "[1133 rows x 140 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Cylinders = cars.Cylinders.astype(int)\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ae884f8-70de-4052-b462-5ab57c92c6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  5,  6,  8, 12, 10, 16])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cylinders = cars.Cylinders.unique()\n",
    "cylinders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13ba2955-88a1-4c09-b932-0013a6f772ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in cylinders:\n",
    "    plt.scatter(cars[cars.Cylinders == i]['Cylinders'], cars[cars.Cylinders == i]['Displacement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db7a2422-e1dc-424f-a7da-6803b329b0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x75172e55e2e0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cars['Cylinders'], cars['Displacement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "77663c98-3a2b-4a45-96ac-86ffa6232cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cylinders', ylabel='Displacement'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x ='Cylinders', y = 'Displacement', data = cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72c401ec-305b-4c5b-a647-a269b11fdb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x75172db265b0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x2500 with 110 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e901e377-43de-4b5a-8e13-2a52409e0e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['In-line', 'V', 'Flat', 'W', nan], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Cylinder_Configuration.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "118393ba-f797-4e3d-98bf-b86262c96f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cylinders', ylabel='Ex-Showroom_Price'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x ='Cylinders', y = 'Ex-Showroom_Price', data = cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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