data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "a897e092-2d6c-4d64-b041-e4954c0d6f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea58f40-beab-4f62-8403-82320a43734b",
   "metadata": {},
   "source": [
    "# 1. reomove or fill missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "34dd3267-e7b7-4575-a514-6917e47685c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"car_price.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "794aff81-91e5-40e8-82a3-6077fb0f3760",
   "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>Unnamed: 0</th>\n",
       "      <th>car_name</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>engine</th>\n",
       "      <th>Seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Jeep Compass 2.0 Longitude Option BSIV</td>\n",
       "      <td>10.03 Lakh</td>\n",
       "      <td>86,226 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1956 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault Duster RXZ Turbo CVT</td>\n",
       "      <td>12.83 Lakh</td>\n",
       "      <td>13,248 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>1330 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Toyota Camry 2.5 G</td>\n",
       "      <td>16.40 Lakh</td>\n",
       "      <td>60,343 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>2494 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Honda Jazz VX CVT</td>\n",
       "      <td>7.77 Lakh</td>\n",
       "      <td>26,696 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Volkswagen Polo 1.2 MPI Highline</td>\n",
       "      <td>5.15 Lakh</td>\n",
       "      <td>69,414 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                car_name car_prices_in_rupee  \\\n",
       "0           0  Jeep Compass 2.0 Longitude Option BSIV          10.03 Lakh   \n",
       "1           1            Renault Duster RXZ Turbo CVT          12.83 Lakh   \n",
       "2           2                      Toyota Camry 2.5 G          16.40 Lakh   \n",
       "3           3                       Honda Jazz VX CVT           7.77 Lakh   \n",
       "4           4        Volkswagen Polo 1.2 MPI Highline           5.15 Lakh   \n",
       "\n",
       "   kms_driven fuel_type transmission  ownership  manufacture   engine    Seats  \n",
       "0  86,226 kms    Diesel       Manual  1st Owner         2017  1956 cc  5 Seats  \n",
       "1  13,248 kms    Petrol    Automatic  1st Owner         2021  1330 cc  5 Seats  \n",
       "2  60,343 kms    Petrol    Automatic  1st Owner         2016  2494 cc  5 Seats  \n",
       "3  26,696 kms    Petrol    Automatic  1st Owner         2018  1199 cc  5 Seats  \n",
       "4  69,414 kms    Petrol       Manual  1st Owner         2016  1199 cc  5 Seats  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "2daed624-d58f-4d78-a31d-c6de40bd4c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5512, 10)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "f5efa6e4-d42d-4049-bc57-7e5a6d27adf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             0\n",
       "car_name               0\n",
       "car_prices_in_rupee    0\n",
       "kms_driven             0\n",
       "fuel_type              0\n",
       "transmission           0\n",
       "ownership              0\n",
       "manufacture            0\n",
       "engine                 0\n",
       "Seats                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "c551922b-23f0-450e-b25b-741a59904905",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)  ## to remove missing data (missing data can be 1%-5% of all data) in this braket we can declare a column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "c612d679-51ef-416b-ba03-8e493d84feaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5512 entries, 0 to 5511\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Unnamed: 0           5512 non-null   int64 \n",
      " 1   car_name             5512 non-null   object\n",
      " 2   car_prices_in_rupee  5512 non-null   object\n",
      " 3   kms_driven           5512 non-null   object\n",
      " 4   fuel_type            5512 non-null   object\n",
      " 5   transmission         5512 non-null   object\n",
      " 6   ownership            5512 non-null   object\n",
      " 7   manufacture          5512 non-null   int64 \n",
      " 8   engine               5512 non-null   object\n",
      " 9   Seats                5512 non-null   object\n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 430.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75372c1c-9521-4e05-bc42-c0b61ebe0065",
   "metadata": {},
   "source": [
    "# seperate the car name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "cfa2b34b-effb-4299-9fd0-01178b4c1142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jeep Compass 2.0 Longitude Option BSIV',\n",
       "       'Renault Duster RXZ Turbo CVT', 'Toyota Camry 2.5 G', ...,\n",
       "       'Volvo XC 90 D5 Momentum BSIV',\n",
       "       'Mercedes-Benz E-Class E250 Edition E', 'BMW M Series M4 Coupe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['car_name'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e0dbe0-1ee7-46be-8458-f36ea24f167d",
   "metadata": {},
   "source": [
    "# - Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "076c5ffd-204a-4e4b-ae8c-0432d2701765",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['car_name'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "9d762f0d-ef53-4b01-9e2e-e509a02edbde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jeep'"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:x.index(\" \")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "93ec7cf2-8887-4c4c-9788-2c59ae4e37c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def company_name(x):\n",
    "    return x[:x.index(\" \")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "2c02cb1b-8870-4834-9598-e3ca6989b7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_company_name']=df['car_name'].apply(company_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "82b383b6-359d-4130-b61b-f3ef7d5c7823",
   "metadata": {},
   "outputs": [],
   "source": [
    "def car_name(x):\n",
    "    return x[:x.index(\" \")+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "6f9cb4e3-4017-4e81-9a80-9efbf5291095",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_Name']=df['car_name'].apply(car_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "cb54c912-afba-487e-a462-cd533b6408db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('car_name',axis=1,inplace=True)\n",
    "#OR\n",
    "\n",
    "#df.drop(columns=['car_name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "020f74fa-f569-4e1f-b2d0-665347ad4935",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>engine</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10.03 Lakh</td>\n",
       "      <td>86,226 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1956 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Jeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12.83 Lakh</td>\n",
       "      <td>13,248 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>1330 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16.40 Lakh</td>\n",
       "      <td>60,343 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>2494 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.77 Lakh</td>\n",
       "      <td>26,696 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.15 Lakh</td>\n",
       "      <td>69,414 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>5507</td>\n",
       "      <td>28.90 Lakh</td>\n",
       "      <td>45,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>2995 cc</td>\n",
       "      <td>7 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5508</th>\n",
       "      <td>5508</td>\n",
       "      <td>64.90 Lakh</td>\n",
       "      <td>29,000 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>1968 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5509</th>\n",
       "      <td>5509</td>\n",
       "      <td>13.75 Lakh</td>\n",
       "      <td>90,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2013</td>\n",
       "      <td>2755 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>Jaguar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>5510</td>\n",
       "      <td>29.90 Lakh</td>\n",
       "      <td>79,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3rd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>2967 cc</td>\n",
       "      <td>6 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5511</th>\n",
       "      <td>5511</td>\n",
       "      <td>31.90 Lakh</td>\n",
       "      <td>42,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1991 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5512 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0              0          10.03 Lakh  86,226 kms    Diesel       Manual   \n",
       "1              1          12.83 Lakh  13,248 kms    Petrol    Automatic   \n",
       "2              2          16.40 Lakh  60,343 kms    Petrol    Automatic   \n",
       "3              3           7.77 Lakh  26,696 kms    Petrol    Automatic   \n",
       "4              4           5.15 Lakh  69,414 kms    Petrol       Manual   \n",
       "...          ...                 ...         ...       ...          ...   \n",
       "5507        5507          28.90 Lakh  45,000 kms    Diesel    Automatic   \n",
       "5508        5508          64.90 Lakh  29,000 kms    Petrol    Automatic   \n",
       "5509        5509          13.75 Lakh  90,000 kms    Diesel    Automatic   \n",
       "5510        5510          29.90 Lakh  79,000 kms    Diesel    Automatic   \n",
       "5511        5511          31.90 Lakh  42,000 kms    Diesel    Automatic   \n",
       "\n",
       "      ownership  manufacture   engine    Seats car_company_name     car_Name  \n",
       "0     1st Owner         2017  1956 cc  5 Seats             Jeep        Jeep   \n",
       "1     1st Owner         2021  1330 cc  5 Seats          Renault     Renault   \n",
       "2     1st Owner         2016  2494 cc  5 Seats           Toyota      Toyota   \n",
       "3     1st Owner         2018  1199 cc  5 Seats            Honda       Honda   \n",
       "4     1st Owner         2016  1199 cc  5 Seats       Volkswagen  Volkswagen   \n",
       "...         ...          ...      ...      ...              ...          ...  \n",
       "5507  1st Owner         2018  2995 cc  7 Seats              BMW         BMW   \n",
       "5508  2nd Owner         2015  1968 cc  5 Seats              BMW         BMW   \n",
       "5509  2nd Owner         2013  2755 cc  5 Seats           Jaguar      Jaguar   \n",
       "5510  3rd Owner         2015  2967 cc  6 Seats              BMW         BMW   \n",
       "5511  2nd Owner         2017  1991 cc  5 Seats              BMW         BMW   \n",
       "\n",
       "[5512 rows x 11 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204eb12c-9401-45f5-9b7e-4394d9a55bc7",
   "metadata": {},
   "source": [
    "# Car_prices_in_rupee\n",
    "## problem== in car price = 35,000, 23.40 lakh ,1.02 crore\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "66f0ffda-9bc0-4697-9f5c-6aef429fba65",
   "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>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>engine</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10.03 Lakh</td>\n",
       "      <td>86,226 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1956 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Jeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12.83 Lakh</td>\n",
       "      <td>13,248 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>1330 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16.40 Lakh</td>\n",
       "      <td>60,343 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>2494 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.77 Lakh</td>\n",
       "      <td>26,696 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.15 Lakh</td>\n",
       "      <td>69,414 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0           0          10.03 Lakh  86,226 kms    Diesel       Manual   \n",
       "1           1          12.83 Lakh  13,248 kms    Petrol    Automatic   \n",
       "2           2          16.40 Lakh  60,343 kms    Petrol    Automatic   \n",
       "3           3           7.77 Lakh  26,696 kms    Petrol    Automatic   \n",
       "4           4           5.15 Lakh  69,414 kms    Petrol       Manual   \n",
       "\n",
       "   ownership  manufacture   engine    Seats car_company_name     car_Name  \n",
       "0  1st Owner         2017  1956 cc  5 Seats             Jeep        Jeep   \n",
       "1  1st Owner         2021  1330 cc  5 Seats          Renault     Renault   \n",
       "2  1st Owner         2016  2494 cc  5 Seats           Toyota      Toyota   \n",
       "3  1st Owner         2018  1199 cc  5 Seats            Honda       Honda   \n",
       "4  1st Owner         2016  1199 cc  5 Seats       Volkswagen  Volkswagen   "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['car_prices_in_rupee']=df['car_prices_in_rupee'].str.replace(\",\",\" \")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "7927f914-4b74-4369-8203-15da79b740e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ruppee_change(x):\n",
    "    pi=x.split(\" \") ## it is used to make list of data because we use index to manipulate the data-  ['28.90' , 'Lakh' ]\n",
    "    try:\n",
    "        if pi[1] =='Lakh':\n",
    "            return str(round(float(pi[0])*10000,1))\n",
    "    \n",
    "        elif pi[1] =='crore':\n",
    "            return str(round(float(pi[0])*1000000,1))\n",
    "     \n",
    "    except :\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "f86add2b-b938-4964-81e5-042d5ea76ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10.03 Lakh', '12.83 Lakh', '16.40 Lakh', ..., '28.90 Lakh',\n",
       "       '64.90 Lakh', '31.90 Lakh'], dtype=object)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['car_prices_in_rupee'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "fccec1ff-d6a7-413b-b336-7568f440222a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_prices_in_rupee']=df['car_prices_in_rupee'].apply(ruppee_change)  ## HERE ERROR = LIST OUT OF INDEX SO USE TRY AND EXCEPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "a42ffa0c-216f-4390-8dc7-db3c3ff86f28",
   "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>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>engine</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100300.0</td>\n",
       "      <td>86,226 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1956 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Jeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>13,248 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>1330 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>60,343 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>2494 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26,696 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69,414 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>5507</td>\n",
       "      <td>289000.0</td>\n",
       "      <td>45,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>2995 cc</td>\n",
       "      <td>7 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5508</th>\n",
       "      <td>5508</td>\n",
       "      <td>649000.0</td>\n",
       "      <td>29,000 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>1968 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5509</th>\n",
       "      <td>5509</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>90,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2013</td>\n",
       "      <td>2755 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>Jaguar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>5510</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>79,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3rd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>2967 cc</td>\n",
       "      <td>6 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5511</th>\n",
       "      <td>5511</td>\n",
       "      <td>319000.0</td>\n",
       "      <td>42,000 kms</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1991 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5512 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0              0            100300.0  86,226 kms    Diesel       Manual   \n",
       "1              1            128300.0  13,248 kms    Petrol    Automatic   \n",
       "2              2            164000.0  60,343 kms    Petrol    Automatic   \n",
       "3              3             77700.0  26,696 kms    Petrol    Automatic   \n",
       "4              4             51500.0  69,414 kms    Petrol       Manual   \n",
       "...          ...                 ...         ...       ...          ...   \n",
       "5507        5507            289000.0  45,000 kms    Diesel    Automatic   \n",
       "5508        5508            649000.0  29,000 kms    Petrol    Automatic   \n",
       "5509        5509            137500.0  90,000 kms    Diesel    Automatic   \n",
       "5510        5510            299000.0  79,000 kms    Diesel    Automatic   \n",
       "5511        5511            319000.0  42,000 kms    Diesel    Automatic   \n",
       "\n",
       "      ownership  manufacture   engine    Seats car_company_name     car_Name  \n",
       "0     1st Owner         2017  1956 cc  5 Seats             Jeep        Jeep   \n",
       "1     1st Owner         2021  1330 cc  5 Seats          Renault     Renault   \n",
       "2     1st Owner         2016  2494 cc  5 Seats           Toyota      Toyota   \n",
       "3     1st Owner         2018  1199 cc  5 Seats            Honda       Honda   \n",
       "4     1st Owner         2016  1199 cc  5 Seats       Volkswagen  Volkswagen   \n",
       "...         ...          ...      ...      ...              ...          ...  \n",
       "5507  1st Owner         2018  2995 cc  7 Seats              BMW         BMW   \n",
       "5508  2nd Owner         2015  1968 cc  5 Seats              BMW         BMW   \n",
       "5509  2nd Owner         2013  2755 cc  5 Seats           Jaguar      Jaguar   \n",
       "5510  3rd Owner         2015  2967 cc  6 Seats              BMW         BMW   \n",
       "5511  2nd Owner         2017  1991 cc  5 Seats              BMW         BMW   \n",
       "\n",
       "[5512 rows x 11 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "3d2d0f16-cbd4-49ba-809b-14e71adc0539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5512 entries, 0 to 5511\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Unnamed: 0           5512 non-null   int64 \n",
      " 1   car_prices_in_rupee  5333 non-null   object\n",
      " 2   kms_driven           5512 non-null   object\n",
      " 3   fuel_type            5512 non-null   object\n",
      " 4   transmission         5512 non-null   object\n",
      " 5   ownership            5512 non-null   object\n",
      " 6   manufacture          5512 non-null   int64 \n",
      " 7   engine               5512 non-null   object\n",
      " 8   Seats                5512 non-null   object\n",
      " 9   car_company_name     5512 non-null   object\n",
      " 10  car_Name             5512 non-null   object\n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 473.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6e5d03-0290-4a85-9020-d3b0bf853a65",
   "metadata": {},
   "source": [
    "# Covert object to float or int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "aa1f909a-c77f-4bc0-8f70-db41306e2dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_prices_in_rupee']=df['car_prices_in_rupee'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "51ccdd2e-b48a-4611-a89b-1417cba00d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5512 entries, 0 to 5511\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Unnamed: 0           5512 non-null   int64  \n",
      " 1   car_prices_in_rupee  5333 non-null   float64\n",
      " 2   kms_driven           5512 non-null   object \n",
      " 3   fuel_type            5512 non-null   object \n",
      " 4   transmission         5512 non-null   object \n",
      " 5   ownership            5512 non-null   object \n",
      " 6   manufacture          5512 non-null   int64  \n",
      " 7   engine               5512 non-null   object \n",
      " 8   Seats                5512 non-null   object \n",
      " 9   car_company_name     5512 non-null   object \n",
      " 10  car_Name             5512 non-null   object \n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 473.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd971ef-aee3-4521-9b19-3f2176a85461",
   "metadata": {},
   "source": [
    "# kms_driven"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "38222ba8-c24c-4a13-a575-0ba990dedd7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['kms_driven']=df['kms_driven'].str.replace(\",\",\"\")\n",
    "df['kms_driven']=df['kms_driven'].str.replace(\"kms\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "7d0f48f4-babf-43fb-8be4-3ef16d28252c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       86226\n",
       "1       13248\n",
       "2       60343\n",
       "3       26696\n",
       "4       69414\n",
       "        ...  \n",
       "5507    45000\n",
       "5508    29000\n",
       "5509    90000\n",
       "5510    79000\n",
       "5511    42000\n",
       "Name: kms_driven, Length: 5512, dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['kms_driven']=df['kms_driven'].astype('int64')\n",
    "df['kms_driven']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "0b63139b-754c-4fc4-b51b-c9a31eecccc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5512 entries, 0 to 5511\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Unnamed: 0           5512 non-null   int64  \n",
      " 1   car_prices_in_rupee  5333 non-null   float64\n",
      " 2   kms_driven           5512 non-null   int64  \n",
      " 3   fuel_type            5512 non-null   object \n",
      " 4   transmission         5512 non-null   object \n",
      " 5   ownership            5512 non-null   object \n",
      " 6   manufacture          5512 non-null   int64  \n",
      " 7   engine               5512 non-null   object \n",
      " 8   Seats                5512 non-null   object \n",
      " 9   car_company_name     5512 non-null   object \n",
      " 10  car_Name             5512 non-null   object \n",
      "dtypes: float64(1), int64(3), object(7)\n",
      "memory usage: 473.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "6013c223-b58e-402b-8229-dae34e9d21ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>engine</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100300.0</td>\n",
       "      <td>86226</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>1956 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Jeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>13248</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>1330 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>60343</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>2494 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>1199 cc</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0           0             100300.0       86226    Diesel       Manual   \n",
       "1           1             128300.0       13248    Petrol    Automatic   \n",
       "2           2             164000.0       60343    Petrol    Automatic   \n",
       "3           3              77700.0       26696    Petrol    Automatic   \n",
       "4           4              51500.0       69414    Petrol       Manual   \n",
       "\n",
       "   ownership  manufacture   engine    Seats car_company_name     car_Name  \n",
       "0  1st Owner         2017  1956 cc  5 Seats             Jeep        Jeep   \n",
       "1  1st Owner         2021  1330 cc  5 Seats          Renault     Renault   \n",
       "2  1st Owner         2016  2494 cc  5 Seats           Toyota      Toyota   \n",
       "3  1st Owner         2018  1199 cc  5 Seats            Honda       Honda   \n",
       "4  1st Owner         2016  1199 cc  5 Seats       Volkswagen  Volkswagen   "
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7545ed9-dc18-41af-8c35-d2aa109fe82f",
   "metadata": {},
   "source": [
    "# Engine\n",
    "\n",
    "## Remove cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "84d267f8-84b3-4781-b36f-e80a8c009b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['engine']=df['engine'].str.replace(\"cc\",\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "58f61f5e-3b4e-49f9-93b6-d0126ec9e58c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['engine_in_cc']=df['engine'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "ed5def03-9a1d-458c-9172-f72abc9fa98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('engine',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "dc9e8034-8cca-4204-8155-9efd25afb37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Renault</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1st Owner</td>\n",
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       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>2494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>1199</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>5507</td>\n",
       "      <td>289000.0</td>\n",
       "      <td>45000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>7 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>2995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5508</th>\n",
       "      <td>5508</td>\n",
       "      <td>649000.0</td>\n",
       "      <td>29000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5509</th>\n",
       "      <td>5509</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>90000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2013</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>2755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>5510</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>79000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3rd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>6 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>2967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5511</th>\n",
       "      <td>5511</td>\n",
       "      <td>319000.0</td>\n",
       "      <td>42000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>5 Seats</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5512 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0              0             100300.0       86226    Diesel       Manual   \n",
       "1              1             128300.0       13248    Petrol    Automatic   \n",
       "2              2             164000.0       60343    Petrol    Automatic   \n",
       "3              3              77700.0       26696    Petrol    Automatic   \n",
       "4              4              51500.0       69414    Petrol       Manual   \n",
       "...          ...                  ...         ...       ...          ...   \n",
       "5507        5507             289000.0       45000    Diesel    Automatic   \n",
       "5508        5508             649000.0       29000    Petrol    Automatic   \n",
       "5509        5509             137500.0       90000    Diesel    Automatic   \n",
       "5510        5510             299000.0       79000    Diesel    Automatic   \n",
       "5511        5511             319000.0       42000    Diesel    Automatic   \n",
       "\n",
       "      ownership  manufacture    Seats car_company_name     car_Name  \\\n",
       "0     1st Owner         2017  5 Seats             Jeep        Jeep    \n",
       "1     1st Owner         2021  5 Seats          Renault     Renault    \n",
       "2     1st Owner         2016  5 Seats           Toyota      Toyota    \n",
       "3     1st Owner         2018  5 Seats            Honda       Honda    \n",
       "4     1st Owner         2016  5 Seats       Volkswagen  Volkswagen    \n",
       "...         ...          ...      ...              ...          ...   \n",
       "5507  1st Owner         2018  7 Seats              BMW         BMW    \n",
       "5508  2nd Owner         2015  5 Seats              BMW         BMW    \n",
       "5509  2nd Owner         2013  5 Seats           Jaguar      Jaguar    \n",
       "5510  3rd Owner         2015  6 Seats              BMW         BMW    \n",
       "5511  2nd Owner         2017  5 Seats              BMW         BMW    \n",
       "\n",
       "      engine_in_cc  \n",
       "0             1956  \n",
       "1             1330  \n",
       "2             2494  \n",
       "3             1199  \n",
       "4             1199  \n",
       "...            ...  \n",
       "5507          2995  \n",
       "5508          1968  \n",
       "5509          2755  \n",
       "5510          2967  \n",
       "5511          1991  \n",
       "\n",
       "[5512 rows x 11 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a96ad08-3f33-4e43-a844-869fe082af9b",
   "metadata": {},
   "source": [
    "# Seats\n",
    "## remove Seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "ffcebd02-2857-4e24-9625-afb1e017dce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Seats']=df['Seats'].str.replace(\"Seats\",\"\")\n",
    "df['Seats']=df['Seats'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "4fc809ef-761d-473b-a784-dbb70a9fc457",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>1st Owner</td>\n",
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       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>2494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>1199</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>5507</td>\n",
       "      <td>289000.0</td>\n",
       "      <td>45000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>2995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5508</th>\n",
       "      <td>5508</td>\n",
       "      <td>649000.0</td>\n",
       "      <td>29000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5509</th>\n",
       "      <td>5509</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>90000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>Jaguar</td>\n",
       "      <td>2755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>5510</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>79000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3rd Owner</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>2967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5511</th>\n",
       "      <td>5511</td>\n",
       "      <td>319000.0</td>\n",
       "      <td>42000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2nd Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>BMW</td>\n",
       "      <td>BMW</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5512 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0              0             100300.0       86226    Diesel       Manual   \n",
       "1              1             128300.0       13248    Petrol    Automatic   \n",
       "2              2             164000.0       60343    Petrol    Automatic   \n",
       "3              3              77700.0       26696    Petrol    Automatic   \n",
       "4              4              51500.0       69414    Petrol       Manual   \n",
       "...          ...                  ...         ...       ...          ...   \n",
       "5507        5507             289000.0       45000    Diesel    Automatic   \n",
       "5508        5508             649000.0       29000    Petrol    Automatic   \n",
       "5509        5509             137500.0       90000    Diesel    Automatic   \n",
       "5510        5510             299000.0       79000    Diesel    Automatic   \n",
       "5511        5511             319000.0       42000    Diesel    Automatic   \n",
       "\n",
       "      ownership  manufacture  Seats car_company_name     car_Name  \\\n",
       "0     1st Owner         2017      5             Jeep        Jeep    \n",
       "1     1st Owner         2021      5          Renault     Renault    \n",
       "2     1st Owner         2016      5           Toyota      Toyota    \n",
       "3     1st Owner         2018      5            Honda       Honda    \n",
       "4     1st Owner         2016      5       Volkswagen  Volkswagen    \n",
       "...         ...          ...    ...              ...          ...   \n",
       "5507  1st Owner         2018      7              BMW         BMW    \n",
       "5508  2nd Owner         2015      5              BMW         BMW    \n",
       "5509  2nd Owner         2013      5           Jaguar      Jaguar    \n",
       "5510  3rd Owner         2015      6              BMW         BMW    \n",
       "5511  2nd Owner         2017      5              BMW         BMW    \n",
       "\n",
       "      engine_in_cc  \n",
       "0             1956  \n",
       "1             1330  \n",
       "2             2494  \n",
       "3             1199  \n",
       "4             1199  \n",
       "...            ...  \n",
       "5507          2995  \n",
       "5508          1968  \n",
       "5509          2755  \n",
       "5510          2967  \n",
       "5511          1991  \n",
       "\n",
       "[5512 rows x 11 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04af804-6f68-4847-8f81-1ae5bf3ac7e1",
   "metadata": {},
   "source": [
    "# To convert the date into \n",
    "##         january 27 ,2019    to   2019-01-27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "7ed5bb43-0aee-4b12-9233-a451e48949af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['manufecture_date']=pd.to_datetime(df['manufecture_data'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5964b1-695f-4b4a-a4f8-d14a4cd97a93",
   "metadata": {},
   "source": [
    "# 2. Data cleaning for ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "f701b88c-0328-4e5d-b6d8-0e759c5beed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder ,StandardScaler\n",
    "encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "de87ce56-9be7-4294-8e04-feb80149ec2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "      <th>engine_in_cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100300.0</td>\n",
       "      <td>86226</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>13248</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Renault</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>60343</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>2494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0           0             100300.0       86226    Diesel       Manual   \n",
       "1           1             128300.0       13248    Petrol    Automatic   \n",
       "2           2             164000.0       60343    Petrol    Automatic   \n",
       "3           3              77700.0       26696    Petrol    Automatic   \n",
       "4           4              51500.0       69414    Petrol       Manual   \n",
       "\n",
       "   ownership  manufacture  Seats car_company_name     car_Name  engine_in_cc  \n",
       "0  1st Owner         2017      5             Jeep        Jeep           1956  \n",
       "1  1st Owner         2021      5          Renault     Renault           1330  \n",
       "2  1st Owner         2016      5           Toyota      Toyota           2494  \n",
       "3  1st Owner         2018      5            Honda       Honda           1199  \n",
       "4  1st Owner         2016      5       Volkswagen  Volkswagen           1199  "
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3c1359-788e-4f4a-acb8-8980ed05d994",
   "metadata": {},
   "source": [
    "# car_Name --> convert into numbers using encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "fba86aac-a5ef-48a9-9ea7-eb79bc6a1825",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_Name']=encoder.fit_transform(df['car_Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "199f4898-f35e-4513-bab3-2606564f6fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
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       "      <th>Seats</th>\n",
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       "      <td>1st Owner</td>\n",
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       "      <td>5</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>12</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>Renault</td>\n",
       "      <td>26</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>60343</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>29</td>\n",
       "      <td>2494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>Honda</td>\n",
       "      <td>8</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1st Owner</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>30</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  car_prices_in_rupee  kms_driven fuel_type transmission  \\\n",
       "0           0             100300.0       86226    Diesel       Manual   \n",
       "1           1             128300.0       13248    Petrol    Automatic   \n",
       "2           2             164000.0       60343    Petrol    Automatic   \n",
       "3           3              77700.0       26696    Petrol    Automatic   \n",
       "4           4              51500.0       69414    Petrol       Manual   \n",
       "\n",
       "   ownership  manufacture  Seats car_company_name  car_Name  engine_in_cc  \n",
       "0  1st Owner         2017      5             Jeep        12          1956  \n",
       "1  1st Owner         2021      5          Renault        26          1330  \n",
       "2  1st Owner         2016      5           Toyota        29          2494  \n",
       "3  1st Owner         2018      5            Honda         8          1199  \n",
       "4  1st Owner         2016      5       Volkswagen        30          1199  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d881d84a-f251-4006-afe2-07244ca835f1",
   "metadata": {},
   "source": [
    "# fuel-type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "b34aa1aa-b219-473d-9e96-24df7ae0e53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fuel_type']=encoder.fit_transform(df['fuel_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eb70cbf-7ae5-42d7-b15a-b7fe16206acb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "da1d6a79-73e7-4f38-8a15-e25ca1f20cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transmission']=encoder.fit_transform(df['transmission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "7334b392-af3d-45df-b5ba-5cb1ddc59479",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ownership']=encoder.fit_transform(df['ownership'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "47afc454-77db-4b49-b52b-e4712aa3bffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['car_company_name']=encoder.fit_transform(df['car_company_name'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "b23340b7-c378-4c76-ae64-a67012787ac7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>13248</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>1330</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>60343</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>2494</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>26696</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1199</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>69414</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1199</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  car_prices_in_rupee  kms_driven  fuel_type  transmission  \\\n",
       "0           0             100300.0       86226          1             1   \n",
       "1           1             128300.0       13248          4             0   \n",
       "2           2             164000.0       60343          4             0   \n",
       "3           3              77700.0       26696          4             0   \n",
       "4           4              51500.0       69414          4             1   \n",
       "\n",
       "   ownership  manufacture  Seats  car_company_name  car_Name  engine_in_cc  \\\n",
       "0          1         2017      5                12        12          1956   \n",
       "1          1         2021      5                26        26          1330   \n",
       "2          1         2016      5                29        29          2494   \n",
       "3          1         2018      5                 8         8          1199   \n",
       "4          1         2016      5                30        30          1199   \n",
       "\n",
       "   fuel-type  \n",
       "0          1  \n",
       "1          4  \n",
       "2          4  \n",
       "3          4  \n",
       "4          4  "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61887bd6-cb75-43d1-877d-cd1d5344df35",
   "metadata": {},
   "source": [
    "# df['year']=df['full_date'].dt.year  is used to extract year only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d395295b-ccfc-4d19-9813-0973f10d3628",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "467c5285-e18f-4761-98f8-739c0a375ffc",
   "metadata": {},
   "source": [
    "# to find outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "a0b160a9-1387-469a-a03d-b51b008ce947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "sns.boxplot(x=\"car_Name\",data=df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2261890-28b8-4eae-b983-639e085162a8",
   "metadata": {},
   "source": [
    "# no outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "2287b594-a76a-4d0e-bdf6-84a4fa831dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"kms_driven\",data=df)\n",
    "plt.show()\n",
    "sns.boxplot(x=\"car_prices_in_rupee\",data=df)\n",
    "plt.show()\n",
    "sns.boxplot(x=\"manufacture\",data=df)\n",
    "plt.show()\n",
    "sns.boxplot(x=\"engine_in_cc\",data=df)\n",
    "plt.show()\n",
    "sns.boxplot(x=\"fuel_type\",data=df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "01462427-7351-4245-84b6-356aabbd800f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feadc444-7e71-4a69-ada7-f445f1f15f9f",
   "metadata": {},
   "source": [
    "# Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "7e175091-859d-4e02-8561-526daac1162b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1197.0, 1950.0)"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.quantile(df['engine_in_cc'],0.25)\n",
    "q3 = np.quantile(df['engine_in_cc'],0.75)\n",
    "q1,q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "13b89570-cb06-49d8-ae75-2c9c1b6ecc69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "753.0"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr=q3-q1\n",
    "iqr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "3e9cfc51-6028-4e8e-a366-2bbbf51d60f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67.5, 3079.5)"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_rate=q1-(1.5*iqr)\n",
    "max_rate=q3+(1.5*iqr)\n",
    "min_rate,max_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "781a9682-946f-447c-b3af-91f9690248c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df['engine_in_cc']<=max_rate]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "368f2ddf-39cc-4dbf-b84b-8763e8599012",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "babf5446-71a1-4707-a2d6-f9ea59a8a0b0",
   "metadata": {},
   "source": [
    "# Scaling -:\n",
    "## 1.supervised ML-:\n",
    "### scaling -> input data\n",
    "### Not scaling -> output data\n",
    "## 2. unsupervised ML-:\n",
    "### scaling -> All data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "1dd41481-f4a5-4350-9563-4a98ece80418",
   "metadata": {},
   "outputs": [],
   "source": [
    "S_scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "459bced9-c42a-45e1-be3a-a158896043ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_prices_in_rupee</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ownership</th>\n",
       "      <th>manufacture</th>\n",
       "      <th>Seats</th>\n",
       "      <th>car_company_name</th>\n",
       "      <th>car_Name</th>\n",
       "      <th>engine_in_cc</th>\n",
       "      <th>fuel-type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.732986</td>\n",
       "      <td>-0.025047</td>\n",
       "      <td>0.549894</td>\n",
       "      <td>-1.071915</td>\n",
       "      <td>0.621558</td>\n",
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       "      <td>0.394400</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>-0.426950</td>\n",
       "      <td>-0.426950</td>\n",
       "      <td>0.875942</td>\n",
       "      <td>-1.071915</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.732357</td>\n",
       "      <td>0.181120</td>\n",
       "      <td>-1.195849</td>\n",
       "      <td>0.915801</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>1.412677</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>1.297643</td>\n",
       "      <td>1.297643</td>\n",
       "      <td>-0.342654</td>\n",
       "      <td>0.915801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.731728</td>\n",
       "      <td>0.443983</td>\n",
       "      <td>-0.069266</td>\n",
       "      <td>0.915801</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>0.139831</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>1.667198</td>\n",
       "      <td>1.667198</td>\n",
       "      <td>1.923235</td>\n",
       "      <td>0.915801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.731099</td>\n",
       "      <td>-0.191454</td>\n",
       "      <td>-0.874153</td>\n",
       "      <td>0.915801</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>0.648970</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>-0.919691</td>\n",
       "      <td>-0.919691</td>\n",
       "      <td>-0.597664</td>\n",
       "      <td>0.915801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.730470</td>\n",
       "      <td>-0.384367</td>\n",
       "      <td>0.147726</td>\n",
       "      <td>0.915801</td>\n",
       "      <td>0.621558</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>0.139831</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>1.790383</td>\n",
       "      <td>1.790383</td>\n",
       "      <td>-0.597664</td>\n",
       "      <td>0.915801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5453</th>\n",
       "      <td>1.730751</td>\n",
       "      <td>1.364371</td>\n",
       "      <td>-0.436294</td>\n",
       "      <td>-1.071915</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>0.648970</td>\n",
       "      <td>2.453431</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>2.898501</td>\n",
       "      <td>-1.071915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5454</th>\n",
       "      <td>1.731380</td>\n",
       "      <td>4.015090</td>\n",
       "      <td>-0.819038</td>\n",
       "      <td>0.915801</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>0.821675</td>\n",
       "      <td>-0.114738</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>0.899302</td>\n",
       "      <td>0.915801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5455</th>\n",
       "      <td>1.732009</td>\n",
       "      <td>0.248860</td>\n",
       "      <td>0.640173</td>\n",
       "      <td>-1.071915</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>0.821675</td>\n",
       "      <td>-0.623877</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>-0.550135</td>\n",
       "      <td>-0.550135</td>\n",
       "      <td>2.431307</td>\n",
       "      <td>-1.071915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5456</th>\n",
       "      <td>1.732638</td>\n",
       "      <td>1.438002</td>\n",
       "      <td>0.377037</td>\n",
       "      <td>-1.071915</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>2.243134</td>\n",
       "      <td>-0.114738</td>\n",
       "      <td>1.053157</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>2.843995</td>\n",
       "      <td>-1.071915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5457</th>\n",
       "      <td>1.733267</td>\n",
       "      <td>1.585264</td>\n",
       "      <td>-0.508058</td>\n",
       "      <td>-1.071915</td>\n",
       "      <td>-1.608859</td>\n",
       "      <td>0.821675</td>\n",
       "      <td>0.394400</td>\n",
       "      <td>-0.347118</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>-1.781987</td>\n",
       "      <td>0.944075</td>\n",
       "      <td>-1.071915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5458 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  car_prices_in_rupee  kms_driven  fuel_type  transmission  \\\n",
       "0      -1.732986            -0.025047    0.549894  -1.071915      0.621558   \n",
       "1      -1.732357             0.181120   -1.195849   0.915801     -1.608859   \n",
       "2      -1.731728             0.443983   -0.069266   0.915801     -1.608859   \n",
       "3      -1.731099            -0.191454   -0.874153   0.915801     -1.608859   \n",
       "4      -1.730470            -0.384367    0.147726   0.915801      0.621558   \n",
       "...          ...                  ...         ...        ...           ...   \n",
       "5453    1.730751             1.364371   -0.436294  -1.071915     -1.608859   \n",
       "5454    1.731380             4.015090   -0.819038   0.915801     -1.608859   \n",
       "5455    1.732009             0.248860    0.640173  -1.071915     -1.608859   \n",
       "5456    1.732638             1.438002    0.377037  -1.071915     -1.608859   \n",
       "5457    1.733267             1.585264   -0.508058  -1.071915     -1.608859   \n",
       "\n",
       "      ownership  manufacture     Seats  car_company_name  car_Name  \\\n",
       "0     -0.599784     0.394400 -0.347118         -0.426950 -0.426950   \n",
       "1     -0.599784     1.412677 -0.347118          1.297643  1.297643   \n",
       "2     -0.599784     0.139831 -0.347118          1.667198  1.667198   \n",
       "3     -0.599784     0.648970 -0.347118         -0.919691 -0.919691   \n",
       "4     -0.599784     0.139831 -0.347118          1.790383  1.790383   \n",
       "...         ...          ...       ...               ...       ...   \n",
       "5453  -0.599784     0.648970  2.453431         -1.781987 -1.781987   \n",
       "5454   0.821675    -0.114738 -0.347118         -1.781987 -1.781987   \n",
       "5455   0.821675    -0.623877 -0.347118         -0.550135 -0.550135   \n",
       "5456   2.243134    -0.114738  1.053157         -1.781987 -1.781987   \n",
       "5457   0.821675     0.394400 -0.347118         -1.781987 -1.781987   \n",
       "\n",
       "      engine_in_cc  fuel-type  \n",
       "0         0.875942  -1.071915  \n",
       "1        -0.342654   0.915801  \n",
       "2         1.923235   0.915801  \n",
       "3        -0.597664   0.915801  \n",
       "4        -0.597664   0.915801  \n",
       "...            ...        ...  \n",
       "5453      2.898501  -1.071915  \n",
       "5454      0.899302   0.915801  \n",
       "5455      2.431307  -1.071915  \n",
       "5456      2.843995  -1.071915  \n",
       "5457      0.944075  -1.071915  \n",
       "\n",
       "[5458 rows x 12 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaled_df=pd.DataFrame(S_scaler.fit_transform(df))\n",
    "# or for columns \n",
    "Scaled_df=pd.DataFrame(S_scaler.fit_transform(df),columns=df.columns)\n",
    "\n",
    "Scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "1e71ef31-7246-45d7-8614-15c21916ad7d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'barplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[353], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcar_prices_in_rupee\u001b[39m\u001b[38;5;124m'\u001b[39m,y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransmission\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'barplot'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc60ed73-862d-47a3-8e0e-68360347b56f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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