diff --git a/.ipynb_checkpoints/Quikr Analysis-checkpoint.ipynb b/.ipynb_checkpoints/Quikr Analysis-checkpoint.ipynb
new file mode 100644
index 0000000..a55c2c3
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+++ b/.ipynb_checkpoints/Quikr Analysis-checkpoint.ipynb	
@@ -0,0 +1,1592 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "%matplotlib inline\n",
+    "mpl.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=pd.read_csv('quikr_car.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80,000</td>\n",
+       "      <td>45,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550 MDI</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>4,25,000</td>\n",
+       "      <td>40 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Ask For Price</td>\n",
+       "      <td>22,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3,25,000</td>\n",
+       "      <td>28,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>5,75,000</td>\n",
+       "      <td>36,000 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     name   company  year          Price  \\\n",
+       "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n",
+       "1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n",
+       "2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n",
+       "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n",
+       "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n",
+       "\n",
+       "   kms_driven fuel_type  \n",
+       "0  45,000 kms    Petrol  \n",
+       "1      40 kms    Diesel  \n",
+       "2  22,000 kms    Petrol  \n",
+       "3  28,000 kms    Petrol  \n",
+       "4  36,000 kms    Diesel  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(892, 6)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 892 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        892 non-null    object\n",
+      " 1   company     892 non-null    object\n",
+      " 2   year        892 non-null    object\n",
+      " 3   Price       892 non-null    object\n",
+      " 4   kms_driven  840 non-null    object\n",
+      " 5   fuel_type   837 non-null    object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 41.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Creating backup copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backup=car.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quality\n",
+    "\n",
+    "- names are pretty inconsistent\n",
+    "- names have company names attached to it\n",
+    "- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'\n",
+    "- company: many of the names are not of any company like 'Used', 'URJENT', and so on.\n",
+    "- year has many non-year values\n",
+    "- year is in object. Change to integer\n",
+    "- Price has Ask for Price\n",
+    "- Price has commas in its prices and is in object\n",
+    "- kms_driven has object values with kms at last.\n",
+    "- It has nan values and two rows have 'Petrol' in them\n",
+    "- fuel_type has nan values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning Data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### year has many non-year values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car[car['year'].str.isnumeric()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### year is in object. Change to integer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['year']=car['year'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Price has Ask for Price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car[car['Price']!='Ask For Price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Price has commas in its prices and is in object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['Price']=car['Price'].str.replace(',','').astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  kms_driven has object values with kms at last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### It has nan values and two rows have 'Petrol' in them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car[car['kms_driven'].str.isnumeric()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven']=car['kms_driven'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### fuel_type has nan values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car[~car['fuel_type'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(816, 6)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### name and company had spammed data...but with the previous cleaning, those rows got removed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Company does not need any cleaning now. Changing car names. Keeping only the first three words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Resetting the index of the final cleaned data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>816 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[816 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car.to_csv('Cleaned_Car_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 816 entries, 0 to 815\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        816 non-null    object\n",
+      " 1   company     816 non-null    object\n",
+      " 2   year        816 non-null    int32 \n",
+      " 3   Price       816 non-null    int32 \n",
+      " 4   kms_driven  816 non-null    int32 \n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(3), object(3)\n",
+      "memory usage: 28.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816.000000</td>\n",
+       "      <td>8.160000e+02</td>\n",
+       "      <td>816.000000</td>\n",
+       "      <td>816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>254</td>\n",
+       "      <td>25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Maruti Suzuki Swift</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>51</td>\n",
+       "      <td>221</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.444853</td>\n",
+       "      <td>4.117176e+05</td>\n",
+       "      <td>46275.531863</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.002992</td>\n",
+       "      <td>4.751844e+05</td>\n",
+       "      <td>34297.428044</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1995.000000</td>\n",
+       "      <td>3.000000e+04</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>1.750000e+05</td>\n",
+       "      <td>27000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013.000000</td>\n",
+       "      <td>2.999990e+05</td>\n",
+       "      <td>41000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>4.912500e+05</td>\n",
+       "      <td>56818.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>8.500003e+06</td>\n",
+       "      <td>400000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name company         year         Price     kms_driven  \\\n",
+       "count                   816     816   816.000000  8.160000e+02     816.000000   \n",
+       "unique                  254      25          NaN           NaN            NaN   \n",
+       "top     Maruti Suzuki Swift  Maruti          NaN           NaN            NaN   \n",
+       "freq                     51     221          NaN           NaN            NaN   \n",
+       "mean                    NaN     NaN  2012.444853  4.117176e+05   46275.531863   \n",
+       "std                     NaN     NaN     4.002992  4.751844e+05   34297.428044   \n",
+       "min                     NaN     NaN  1995.000000  3.000000e+04       0.000000   \n",
+       "25%                     NaN     NaN  2010.000000  1.750000e+05   27000.000000   \n",
+       "50%                     NaN     NaN  2013.000000  2.999990e+05   41000.000000   \n",
+       "75%                     NaN     NaN  2015.000000  4.912500e+05   56818.500000   \n",
+       "max                     NaN     NaN  2019.000000  8.500003e+06  400000.000000   \n",
+       "\n",
+       "       fuel_type  \n",
+       "count        816  \n",
+       "unique         3  \n",
+       "top       Petrol  \n",
+       "freq         428  \n",
+       "mean         NaN  \n",
+       "std          NaN  \n",
+       "min          NaN  \n",
+       "25%          NaN  \n",
+       "50%          NaN  \n",
+       "75%          NaN  \n",
+       "max          NaN  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car=car[car['Price']<6000000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking relationship of Company with Price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Hyundai', 'Mahindra', 'Ford', 'Maruti', 'Skoda', 'Audi', 'Toyota',\n",
+       "       'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n",
+       "       'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat', 'Force',\n",
+       "       'Mercedes', 'Land', 'Jaguar', 'Jeep', 'Volvo'], dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['company'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(15,7))\n",
+    "ax=sns.boxplot(x='company',y='Price',data=car)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking relationship of Year with Price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 9.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "c:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 6.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "c:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 10.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "c:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 5.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(20,10))\n",
+    "ax=sns.swarmplot(x='year',y='Price',data=car)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking relationship of kms_driven with Price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x19df247b788>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 756x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking relationship of Fuel Type with Price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='fuel_type', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(14,7))\n",
+    "sns.boxplot(x='fuel_type',y='Price',data=car)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relationship of Price with FuelType, Year and Company mixed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x19df2fcf0c8>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1075x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)\n",
+    "ax.set_xticklabels(rotation=40,ha='right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extracting Training Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=car[['name','company','year','kms_driven','fuel_type']]\n",
+    "y=car['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>815 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012       41000    Diesel\n",
+       "..                      ...       ...   ...         ...       ...\n",
+       "811      Maruti Suzuki Ritz    Maruti  2011       50000    Petrol\n",
+       "812          Tata Indica V2      Tata  2009       30000    Diesel\n",
+       "813    Toyota Corolla Altis    Toyota  2009      132000    Petrol\n",
+       "814            Tata Zest XM      Tata  2018       27000    Diesel\n",
+       "815      Mahindra Quanto C8  Mahindra  2013       40000    Diesel\n",
+       "\n",
+       "[815 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(815,)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Applying Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating an OneHotEncoder object to contain all the possible categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OneHotEncoder()"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe=OneHotEncoder()\n",
+    "ohe.fit(X[['name','company','fuel_type']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating a column transformer to transform categorical columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n",
+    "                                    remainder='passthrough')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Linear Regression Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr=LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Making a pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe=make_pipeline(column_trans,lr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fitting the  model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('onehotencoder',\n",
+       "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+       "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+       "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+       "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
+       "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+       "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+       "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+       "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+       "      dtype=object),\n",
+       "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
+       "                                                  ['name', 'company',\n",
+       "                                                   'fuel_type'])])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred=pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Checking R2 Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6655226435284546"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Finding the model with a random state of TrainTestSplit where the model was found to give almost 0.92 as r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scores=[]\n",
+    "# for i in range(1000):\n",
+    "#     X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)\n",
+    "#     lr=LinearRegression()\n",
+    "#     pipe=make_pipeline(column_trans,lr)\n",
+    "#     pipe.fit(X_train,y_train)\n",
+    "#     y_pred=pipe.predict(X_test)\n",
+    "#     scores.append(r2_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8807751763571077"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[np.argmax(scores)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The best model is found at a certain random state "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8807751763571077"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))\n",
+    "lr=LinearRegression()\n",
+    "pipe=make_pipeline(column_trans,lr)\n",
+    "pipe.fit(X_train,y_train)\n",
+    "y_pred=pipe.predict(X_test)\n",
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pipe.steps[0][1].transformers[0][1].categories[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# using polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "poly = PolynomialFeatures()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/Quikr-Car-Price-Predictor-checkpoint.ipynb b/.ipynb_checkpoints/Quikr-Car-Price-Predictor-checkpoint.ipynb
new file mode 100644
index 0000000..3921b08
--- /dev/null
+++ b/.ipynb_checkpoints/Quikr-Car-Price-Predictor-checkpoint.ipynb
@@ -0,0 +1,1829 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28dc9f47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fe39eacb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(892, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80,000</td>\n",
+       "      <td>45,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550 MDI</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>4,25,000</td>\n",
+       "      <td>40 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Ask For Price</td>\n",
+       "      <td>22,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3,25,000</td>\n",
+       "      <td>28,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>5,75,000</td>\n",
+       "      <td>36,000 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     name   company  year          Price  \\\n",
+       "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n",
+       "1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n",
+       "2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n",
+       "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n",
+       "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n",
+       "\n",
+       "   kms_driven fuel_type  \n",
+       "0  45,000 kms    Petrol  \n",
+       "1      40 kms    Diesel  \n",
+       "2  22,000 kms    Petrol  \n",
+       "3  28,000 kms    Petrol  \n",
+       "4  36,000 kms    Diesel  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car = pd.read_csv('quikr_car.csv')\n",
+    "print(car.shape)\n",
+    "car.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "00d95862",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 892 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        892 non-null    object\n",
+      " 1   company     892 non-null    object\n",
+      " 2   year        892 non-null    object\n",
+      " 3   Price       892 non-null    object\n",
+      " 4   kms_driven  840 non-null    object\n",
+      " 5   fuel_type   837 non-null    object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 41.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "08c44a8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n",
+       "       '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n",
+       "       '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n",
+       "       'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n",
+       "       '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n",
+       "       'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n",
+       "       ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n",
+       "       't xe', 'EV2', 'r...', 'zest'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['year'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "536d0a84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['80,000', '4,25,000', 'Ask For Price', '3,25,000', '5,75,000',\n",
+       "       '1,75,000', '1,90,000', '8,30,000', '2,50,000', '1,82,000',\n",
+       "       '3,15,000', '4,15,000', '3,20,000', '10,00,000', '5,00,000',\n",
+       "       '3,50,000', '1,60,000', '3,10,000', '75,000', '1,00,000',\n",
+       "       '2,90,000', '95,000', '1,80,000', '3,85,000', '1,05,000',\n",
+       "       '6,50,000', '6,89,999', '4,48,000', '5,49,000', '5,01,000',\n",
+       "       '4,89,999', '2,80,000', '3,49,999', '2,84,999', '3,45,000',\n",
+       "       '4,99,999', '2,35,000', '2,49,999', '14,75,000', '3,95,000',\n",
+       "       '2,20,000', '1,70,000', '85,000', '2,00,000', '5,70,000',\n",
+       "       '1,10,000', '4,48,999', '18,91,111', '1,59,500', '3,44,999',\n",
+       "       '4,49,999', '8,65,000', '6,99,000', '3,75,000', '2,24,999',\n",
+       "       '12,00,000', '1,95,000', '3,51,000', '2,40,000', '90,000',\n",
+       "       '1,55,000', '6,00,000', '1,89,500', '2,10,000', '3,90,000',\n",
+       "       '1,35,000', '16,00,000', '7,01,000', '2,65,000', '5,25,000',\n",
+       "       '3,72,000', '6,35,000', '5,50,000', '4,85,000', '3,29,500',\n",
+       "       '2,51,111', '5,69,999', '69,999', '2,99,999', '3,99,999',\n",
+       "       '4,50,000', '2,70,000', '1,58,400', '1,79,000', '1,25,000',\n",
+       "       '2,99,000', '1,50,000', '2,75,000', '2,85,000', '3,40,000',\n",
+       "       '70,000', '2,89,999', '8,49,999', '7,49,999', '2,74,999',\n",
+       "       '9,84,999', '5,99,999', '2,44,999', '4,74,999', '2,45,000',\n",
+       "       '1,69,500', '3,70,000', '1,68,000', '1,45,000', '98,500',\n",
+       "       '2,09,000', '1,85,000', '9,00,000', '6,99,999', '1,99,999',\n",
+       "       '5,44,999', '1,99,000', '5,40,000', '49,000', '7,00,000', '55,000',\n",
+       "       '8,95,000', '3,55,000', '5,65,000', '3,65,000', '40,000',\n",
+       "       '4,00,000', '3,30,000', '5,80,000', '3,79,000', '2,19,000',\n",
+       "       '5,19,000', '7,30,000', '20,00,000', '21,00,000', '14,00,000',\n",
+       "       '3,11,000', '8,55,000', '5,35,000', '1,78,000', '3,00,000',\n",
+       "       '2,55,000', '5,49,999', '3,80,000', '57,000', '4,10,000',\n",
+       "       '2,25,000', '1,20,000', '59,000', '5,99,000', '6,75,000', '72,500',\n",
+       "       '6,10,000', '2,30,000', '5,20,000', '5,24,999', '4,24,999',\n",
+       "       '6,44,999', '5,84,999', '7,99,999', '4,44,999', '6,49,999',\n",
+       "       '9,44,999', '5,74,999', '3,74,999', '1,30,000', '4,01,000',\n",
+       "       '13,50,000', '1,74,999', '2,39,999', '99,999', '3,24,999',\n",
+       "       '10,74,999', '11,30,000', '1,49,000', '7,70,000', '30,000',\n",
+       "       '3,35,000', '3,99,000', '65,000', '1,69,999', '1,65,000',\n",
+       "       '5,60,000', '9,50,000', '7,15,000', '45,000', '9,40,000',\n",
+       "       '1,55,555', '15,00,000', '4,95,000', '8,00,000', '12,99,000',\n",
+       "       '5,30,000', '14,99,000', '32,000', '4,05,000', '7,60,000',\n",
+       "       '7,50,000', '4,19,000', '1,40,000', '15,40,000', '1,23,000',\n",
+       "       '4,98,000', '4,80,000', '4,88,000', '15,25,000', '5,48,900',\n",
+       "       '7,25,000', '99,000', '52,000', '28,00,000', '4,99,000',\n",
+       "       '3,81,000', '2,78,000', '6,90,000', '2,60,000', '90,001',\n",
+       "       '1,15,000', '15,99,000', '1,59,000', '51,999', '2,15,000',\n",
+       "       '35,000', '11,50,000', '2,69,000', '60,000', '4,30,000',\n",
+       "       '85,00,003', '4,01,919', '4,90,000', '4,24,000', '2,05,000',\n",
+       "       '5,49,900', '3,71,500', '4,35,000', '1,89,700', '3,89,700',\n",
+       "       '3,60,000', '2,95,000', '1,14,990', '10,65,000', '4,70,000',\n",
+       "       '48,000', '1,88,000', '4,65,000', '1,79,999', '21,90,000',\n",
+       "       '23,90,000', '10,75,000', '4,75,000', '10,25,000', '6,15,000',\n",
+       "       '19,00,000', '14,90,000', '15,10,000', '18,50,000', '7,90,000',\n",
+       "       '17,25,000', '12,25,000', '68,000', '9,70,000', '31,00,000',\n",
+       "       '8,99,000', '88,000', '53,000', '5,68,500', '71,000', '5,90,000',\n",
+       "       '7,95,000', '42,000', '1,89,000', '1,62,000', '35,999',\n",
+       "       '29,00,000', '39,999', '50,500', '5,10,000', '8,60,000',\n",
+       "       '5,00,001'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['Price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8bf9b37d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['45,000 kms', '40 kms', '22,000 kms', '28,000 kms', '36,000 kms',\n",
+       "       '59,000 kms', '41,000 kms', '25,000 kms', '24,530 kms',\n",
+       "       '60,000 kms', '30,000 kms', '32,000 kms', '48,660 kms',\n",
+       "       '4,000 kms', '16,934 kms', '43,000 kms', '35,550 kms',\n",
+       "       '39,522 kms', '39,000 kms', '55,000 kms', '72,000 kms',\n",
+       "       '15,975 kms', '70,000 kms', '23,452 kms', '35,522 kms',\n",
+       "       '48,508 kms', '15,487 kms', '82,000 kms', '20,000 kms',\n",
+       "       '68,000 kms', '38,000 kms', '27,000 kms', '33,000 kms',\n",
+       "       '46,000 kms', '16,000 kms', '47,000 kms', '35,000 kms',\n",
+       "       '30,874 kms', '15,000 kms', '29,685 kms', '1,30,000 kms',\n",
+       "       '19,000 kms', nan, '54,000 kms', '13,000 kms', '38,200 kms',\n",
+       "       '50,000 kms', '13,500 kms', '3,600 kms', '45,863 kms',\n",
+       "       '60,500 kms', '12,500 kms', '18,000 kms', '13,349 kms',\n",
+       "       '29,000 kms', '44,000 kms', '42,000 kms', '14,000 kms',\n",
+       "       '49,000 kms', '36,200 kms', '51,000 kms', '1,04,000 kms',\n",
+       "       '33,333 kms', '33,600 kms', '5,600 kms', '7,500 kms', '26,000 kms',\n",
+       "       '24,330 kms', '65,480 kms', '28,028 kms', '2,00,000 kms',\n",
+       "       '99,000 kms', '2,800 kms', '21,000 kms', '11,000 kms',\n",
+       "       '66,000 kms', '3,000 kms', '7,000 kms', '38,500 kms', '37,200 kms',\n",
+       "       '43,200 kms', '24,800 kms', '45,872 kms', '40,000 kms',\n",
+       "       '11,400 kms', '97,200 kms', '52,000 kms', '31,000 kms',\n",
+       "       '1,75,430 kms', '37,000 kms', '65,000 kms', '3,350 kms',\n",
+       "       '75,000 kms', '62,000 kms', '73,000 kms', '2,200 kms',\n",
+       "       '54,870 kms', '34,580 kms', '97,000 kms', '60 kms', '80,200 kms',\n",
+       "       '3,200 kms', '0,000 kms', '5,000 kms', '588 kms', '71,200 kms',\n",
+       "       '1,75,400 kms', '9,300 kms', '56,758 kms', '10,000 kms',\n",
+       "       '56,450 kms', '56,000 kms', '32,700 kms', '9,000 kms', '73 kms',\n",
+       "       '1,60,000 kms', '84,000 kms', '58,559 kms', '57,000 kms',\n",
+       "       '1,70,000 kms', '80,000 kms', '6,821 kms', '23,000 kms',\n",
+       "       '34,000 kms', '1,800 kms', '4,00,000 kms', '48,000 kms',\n",
+       "       '90,000 kms', '12,000 kms', '69,900 kms', '1,66,000 kms',\n",
+       "       '122 kms', '0 kms', '24,000 kms', '36,469 kms', '7,800 kms',\n",
+       "       '24,695 kms', '15,141 kms', '59,910 kms', '1,00,000 kms',\n",
+       "       '4,500 kms', '1,29,000 kms', '300 kms', '1,31,000 kms',\n",
+       "       '1,11,111 kms', '59,466 kms', '25,500 kms', '44,005 kms',\n",
+       "       '2,110 kms', '43,222 kms', '1,00,200 kms', '65 kms',\n",
+       "       '1,40,000 kms', '1,03,553 kms', '58,000 kms', '1,20,000 kms',\n",
+       "       '49,800 kms', '100 kms', '81,876 kms', '6,020 kms', '55,700 kms',\n",
+       "       '18,500 kms', '1,80,000 kms', '53,000 kms', '35,500 kms',\n",
+       "       '22,134 kms', '1,000 kms', '8,500 kms', '87,000 kms', '6,000 kms',\n",
+       "       '15,574 kms', '8,000 kms', '55,800 kms', '56,400 kms',\n",
+       "       '72,160 kms', '11,500 kms', '1,33,000 kms', '2,000 kms',\n",
+       "       '88,000 kms', '65,422 kms', '1,17,000 kms', '1,50,000 kms',\n",
+       "       '10,750 kms', '6,800 kms', '5 kms', '9,800 kms', '57,923 kms',\n",
+       "       '30,201 kms', '6,200 kms', '37,518 kms', '24,652 kms', '383 kms',\n",
+       "       '95,000 kms', '3,528 kms', '52,500 kms', '47,900 kms',\n",
+       "       '52,800 kms', '1,95,000 kms', '48,008 kms', '48,247 kms',\n",
+       "       '9,400 kms', '64,000 kms', '2,137 kms', '10,544 kms', '49,500 kms',\n",
+       "       '1,47,000 kms', '90,001 kms', '48,006 kms', '74,000 kms',\n",
+       "       '85,000 kms', '29,500 kms', '39,700 kms', '67,000 kms',\n",
+       "       '19,336 kms', '60,105 kms', '45,933 kms', '1,02,563 kms',\n",
+       "       '28,600 kms', '41,800 kms', '1,16,000 kms', '42,590 kms',\n",
+       "       '7,400 kms', '54,500 kms', '76,000 kms', '00 kms', '11,523 kms',\n",
+       "       '38,600 kms', '95,500 kms', '37,458 kms', '85,960 kms',\n",
+       "       '12,516 kms', '30,600 kms', '2,550 kms', '62,500 kms',\n",
+       "       '69,000 kms', '28,400 kms', '68,485 kms', '3,500 kms',\n",
+       "       '85,455 kms', '63,000 kms', '1,600 kms', '77,000 kms',\n",
+       "       '26,500 kms', '2,875 kms', '13,900 kms', '1,500 kms', '2,450 kms',\n",
+       "       '1,625 kms', '33,400 kms', '60,123 kms', '38,900 kms',\n",
+       "       '1,37,495 kms', '91,200 kms', '1,46,000 kms', '1,00,800 kms',\n",
+       "       '2,100 kms', '2,500 kms', '1,32,000 kms', 'Petrol'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['kms_driven'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6b1e60ef",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['fuel_type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "31a78278",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Hyundai Santro Xing XO eRLX Euro III', 'Mahindra Jeep CL550 MDI',\n",
+       "       'Maruti Suzuki Alto 800 Vxi',\n",
+       "       'Hyundai Grand i10 Magna 1.2 Kappa VTVT',\n",
+       "       'Ford EcoSport Titanium 1.5L TDCi', 'Ford Figo', 'Hyundai Eon',\n",
+       "       'Ford EcoSport Ambiente 1.5L TDCi',\n",
+       "       'Maruti Suzuki Alto K10 VXi AMT', 'Skoda Fabia Classic 1.2 MPI',\n",
+       "       'Maruti Suzuki Stingray VXi', 'Hyundai Elite i20 Magna 1.2',\n",
+       "       'Mahindra Scorpio SLE BS IV', 'Audi A8', 'Audi Q7',\n",
+       "       'Mahindra Scorpio S10', 'Maruti Suzuki Alto 800',\n",
+       "       'Hyundai i20 Sportz 1.2', 'Maruti Suzuki Alto 800 Lx',\n",
+       "       'Maruti Suzuki Vitara Brezza ZDi', 'Maruti Suzuki Alto LX',\n",
+       "       'Mahindra Bolero DI', 'Maruti Suzuki Swift Dzire ZDi',\n",
+       "       'Mahindra Scorpio S10 4WD', 'Maruti Suzuki Swift Vdi BSIII',\n",
+       "       'Maruti Suzuki Wagon R VXi BS III',\n",
+       "       'Maruti Suzuki Wagon R VXi Minor',\n",
+       "       'Toyota Innova 2.0 G 8 STR BS IV', 'Renault Lodgy 85 PS RXL',\n",
+       "       'Skoda Yeti Ambition 2.0 TDI CR 4x2',\n",
+       "       'Maruti Suzuki Baleno Delta 1.2',\n",
+       "       'Renault Duster 110 PS RxZ Diesel Plus',\n",
+       "       'Renault Duster 85 PS RxE Diesel', 'Honda City 1.5 S MT',\n",
+       "       'Maruti Suzuki Dzire', 'Honda Amaze', 'Honda Amaze 1.5 SX i DTEC',\n",
+       "       'Honda City', 'Datsun Redi GO S', 'Maruti Suzuki SX4 ZXI MT',\n",
+       "       'Mitsubishi Pajero Sport Limited Edition',\n",
+       "       'Maruti Suzuki Swift VXi 1.2 ABS BS IV', 'Honda City ZX CVT',\n",
+       "       'Maruti Suzuki Wagon R LX BS IV', 'Tata Indigo eCS LS CR4 BS IV',\n",
+       "       'Volkswagen Polo Highline Exquisite P',\n",
+       "       'I want to sell my car Tata Zest', 'Chevrolet Spark LS 1.0',\n",
+       "       'Renault Duster 110PS Diesel RxZ', 'Mini Cooper S 1.6',\n",
+       "       'Skoda Fabia 1.2L Diesel Ambiente', 'Renault Duster',\n",
+       "       'Mahindra Scorpio S4', 'Mahindra Scorpio VLX 2WD BS IV',\n",
+       "       'Mahindra Quanto C8', 'Ford EcoSport', 'Honda Brio',\n",
+       "       'Volkswagen Vento Highline Plus 1.5 Diesel AT',\n",
+       "       'Hyundai i20 Magna', 'Toyota Corolla Altis Diesel D4DG',\n",
+       "       'Hyundai Verna Transform SX VTVT',\n",
+       "       'Toyota Corolla Altis Petrol Ltd', 'Honda City 1.5 EXi New',\n",
+       "       'Skoda Fabia 1.2L Diesel Elegance', 'BMW 3 Series 320i',\n",
+       "       'Maruti Suzuki A Star Lxi', 'Toyota Etios GD',\n",
+       "       'Ford Figo Diesel EXI Option',\n",
+       "       'Maruti Suzuki Swift Dzire VXi 1.2 BS IV',\n",
+       "       'Chevrolet Beat LT Diesel', 'BMW 7 Series 740Li Sedan',\n",
+       "       'Mahindra XUV500 W8 AWD 2013', 'Hyundai i10 Magna 1.2',\n",
+       "       'Hyundai Verna Fluidic New', 'Maruti Suzuki Swift VXi 1.2 BS IV',\n",
+       "       'Maruti Suzuki Ertiga ZXI Plus', 'Maruti Suzuki Ertiga Vxi',\n",
+       "       'Maruti Suzuki Ertiga VDi', 'Maruti Suzuki Alto LXi BS III',\n",
+       "       'Hyundai Grand i10 Asta 1.1 CRDi', 'Honda Amaze 1.2 S i VTEC',\n",
+       "       'Hyundai i20 Asta 1.4 CRDI 6 Speed', 'Ford Figo Diesel EXI',\n",
+       "       'Maruti Suzuki Eeco 5 STR WITH AC HTR', 'Maruti Suzuki Ertiga ZXi',\n",
+       "       'Maruti Suzuki Esteem LXi BS III', 'Maruti Suzuki Ritz VXI',\n",
+       "       'Maruti Suzuki Ritz LDi', 'Maruti Suzuki Dzire VDI',\n",
+       "       'Toyota Etios Liva G', 'Hyundai i20 Sportz 1.4 CRDI',\n",
+       "       'Chevrolet Spark', 'Nissan Micra XV', 'Maruti Suzuki Swift',\n",
+       "       'Honda Amaze 1.5 S i DTEC', 'Chevrolet Beat', 'Toyota Corolla',\n",
+       "       'Honda City 1.5 V MT', 'Ford EcoSport Trend 1.5L TDCi',\n",
+       "       'Hyundai i20 Asta 1.2', 'Tata Indica V2 eLS',\n",
+       "       'Maruti Suzuki Alto 800 Lxi', 'Hindustan Motors Ambassador',\n",
+       "       'Toyota Corolla Altis 1.8 GL', 'Toyota Corolla Altis 1.8 J',\n",
+       "       'Toyota Innova 2.5 GX BS IV 7 STR',\n",
+       "       'Volkswagen Jetta Highline TDI AT',\n",
+       "       'Volkswagen Polo Comfortline 1.2L P', 'Volkswagen Polo',\n",
+       "       'Mahindra Scorpio', 'Nissan Sunny', 'Hyundai Elite i20',\n",
+       "       'Renault Kwid', 'Mahindra Scorpio VLX Airbag',\n",
+       "       'Chevrolet Spark LT 1.0', 'Datsun Redi GO T O',\n",
+       "       'Maruti Suzuki Swift RS VDI', 'Fiat Punto Emotion 1.2',\n",
+       "       'Hyundai i10 Sportz 1.2', 'Chevrolet Beat LT Opt Diesel',\n",
+       "       'Chevrolet Beat LS Diesel', 'Tata Indigo CS',\n",
+       "       'Maruti Suzuki Swift VDi', 'Hyundai Eon Era Plus',\n",
+       "       'Mahindra XUV500', 'Ford Fiesta', 'Maruti Suzuki Wagon R',\n",
+       "       'Hyundai i20', 'Tata Indigo eCS LX TDI BS III',\n",
+       "       'Hyundai Fluidic Verna 1.6 CRDi SX',\n",
+       "       'Commercial , DZire LDI, 2016, for sale', 'Fiat Petra ELX 1.2 PS',\n",
+       "       'Hyundai Santro Xing XS', 'Maruti Suzuki Ciaz VXi Plus',\n",
+       "       'Maruti Suzuki Zen VX', 'Hyundai Creta 1.6 SX Plus Petrol',\n",
+       "       'Tata indigo ecs LX, 201', 'Mahindra Scorpio SLX',\n",
+       "       'Toyota Innova 2.5 G BS III 8 STR',\n",
+       "       'Maruti Suzuki Wagon R LXI BS IV', 'Tata Nano Cx BSIV',\n",
+       "       'Maruti Suzuki Alto Std BS IV', 'Maruti Suzuki Wagon R LXi BS III',\n",
+       "       'Maruti Suzuki Swift VXI BSIII',\n",
+       "       'Tata Sumo Victa EX 10 by 7 Str BSIII', 'MARUTI SUZUKI DESI',\n",
+       "       'Volkswagen Passat Diesel Comfortline AT',\n",
+       "       'Renault Scala RxL Diesel Travelogue',\n",
+       "       'Hyundai Grand i10 Sportz O 1.2 Kappa VTVT',\n",
+       "       'Hyundai i20 Active 1.2 SX', 'Mahindra Xylo E4',\n",
+       "       'Mahindra Jeep MM 550 XDB', 'Mahindra Bolero SLE BS IV',\n",
+       "       'Force Motors Force One LX ABS 7 STR', 'Maruti Suzuki SX4',\n",
+       "       'Toyota Etios', 'Honda City ZX VTEC',\n",
+       "       'Maruti Suzuki Wagon R LX BS III', 'Honda City VX O MT Diesel',\n",
+       "       'Mahindra Thar CRDe 4x4 AC',\n",
+       "       'Audi A4 1.8 TFSI Multitronic Premium Plus',\n",
+       "       'Mercedes Benz GLA Class 200 CDI Sport',\n",
+       "       'Land Rover Freelander 2 SE', 'Renault Kwid RXT',\n",
+       "       'Tata Aria Pleasure 4X2', 'Mercedes Benz B Class B180 Sports',\n",
+       "       'Datsun GO T O', 'Honda Jazz VX MT',\n",
+       "       'Hyundai i20 Active 1.4L SX O', 'Mini Cooper S',\n",
+       "       'Maruti Suzuki Ciaz ZXI Plus', 'Chevrolet Tavera Neo',\n",
+       "       'Hyundai Eon Sportz', 'Tata Sumo Gold Select Variant',\n",
+       "       'Maruti Suzuki Wagon R 1.0', 'Maruti Suzuki Esteem VXi BS III',\n",
+       "       'Chevrolet Enjoy 1.4 LS 8 STR', 'Maruti Suzuki Wagon R 1.0 VXi',\n",
+       "       'Nissan Terrano XL D Plus', 'Renault Duster 85 PS RxL Diesel',\n",
+       "       'Maruti Suzuki Dzire ZXI', 'Renault Kwid RXT Opt',\n",
+       "       'Maruti Suzuki Maruti 800 Std', 'Renault Kwid 1.0 RXT AMT',\n",
+       "       'Renault Scala RxL Diesel',\n",
+       "       'Hyundai Grand i10 Asta 1.2 Kappa VTVT O',\n",
+       "       'Chevrolet Beat LS Petrol', 'Hyundai Accent GLX', 'Yama',\n",
+       "       'Maruti Suzuki Swift LDi', 'Mahindra TUV300 T4 Plus',\n",
+       "       'Tata Indica V2 Xeta e GLE', 'Tata Indigo CS LS DiCOR',\n",
+       "       'Mahindra Scorpio VLX Special Edition BS III',\n",
+       "       'Tata Indica eV2 LS', 'Honda Accord',\n",
+       "       'Ford EcoSport Titanium 1.5 TDCi', 'Maruti Suzuki Ertiga',\n",
+       "       'Mahindra Scorpio 2.6 CRDe', 'Honda Mobilio',\n",
+       "       'Toyota Corolla Altis', 'Skoda Laura', 'Hyundai Verna Fluidic',\n",
+       "       'Maruti Suzuki Vitara Brezza', 'Tata Manza Aura Quadrajet',\n",
+       "       'Chevrolet Sail UVA Petrol LT ABS',\n",
+       "       'Hyundai Verna Fluidic 1.6 VTVT SX',\n",
+       "       'Audi A4 2.0 TDI 177bhp Premium', 'Hyundai Elantra SX',\n",
+       "       'Mahindra Scorpio VLX 4WD Airbag', 'Mahindra KUV100 K8 D 6 STR',\n",
+       "       'Hyundai Grand i10', 'Hyundai i10', 'Hyundai i20 Active',\n",
+       "       'Datsun Redi GO', 'Toyota Etios Liva', 'Hyundai Accent',\n",
+       "       'Hyundai Verna', 'Toyota Fortuner', 'Hyundai i10 Sportz',\n",
+       "       'Mahindra Bolero Power Plus SLE', 'selling car Ta',\n",
+       "       'Honda City 1.5 V MT Exclusive', 'Chevrolet Spark LT 1.0 Airbag',\n",
+       "       'Tata Indigo eCS VX CR4 BS IV', 'Tata Zest 90',\n",
+       "       'Skoda Rapid Elegance 1.6 TDI CR MT', 'Tata Vista Quadrajet VX',\n",
+       "       'Maruti Suzuki Alto K10 VXi AT', 'Maruti Suzuki Zen LXi BS III',\n",
+       "       'Maruti Suzuki Swift Dzire Tour LDi', 'Honda City ZX EXi',\n",
+       "       'Chevrolet Beat Diesel', 'Maruti Suzuki Swift Dzire car',\n",
+       "       'Hyundai Verna 1.4 VTVT', 'Toyota Innova 2.5 E MS 7 STR BS IV',\n",
+       "       'Maruti Suzuki Maruti 800 Std – Befo',\n",
+       "       'Hyundai Elite i20 Asta 1.4 CRDI',\n",
+       "       'Maruti Suzuki Swift Dzire Tour (Gat',\n",
+       "       'Maruti Suzuki Versa DX2 8 SEATER BSIII',\n",
+       "       'Tata Indigo LX TDI BS III',\n",
+       "       'Volkswagen Vento Konekt Diesel Highline',\n",
+       "       'Mercedes Benz C Class 200 CDI Classic', 'URJE',\n",
+       "       'Hyundai Santro Xing GLS', 'Maruti Suzuki Omni Limited Edition',\n",
+       "       'Hyundai Sonata Transform 2.4 GDi MT',\n",
+       "       'Hyundai Elite i20 Sportz 1.2', 'Honda Jazz S MT',\n",
+       "       'Hyundai Grand i10 Sportz 1.2 Kappa VTVT',\n",
+       "       'Maruti Suzuki Zen LXi BSII',\n",
+       "       'Mahindra Scorpio W Turbo 2.6DX 9 Seater',\n",
+       "       'Swift Dzire Tour 27 Dec 2016 Regis', 'Maruti Suzuki Alto K10 VXi',\n",
+       "       'Hyundai Grand i10 Asta 1.2 Kappa VTVT', 'Mahindra XUV500 W8',\n",
+       "       'Hyundai i20 Magna O 1.2', 'Renault Duster 85 PS RxL Explore LE',\n",
+       "       'Honda Brio V MT', 'Mahindra TUV300 T8',\n",
+       "       'Nissan X Trail Select Variant', 'Ford Ikon 1.3 CLXi NXt Finesse',\n",
+       "       'Toyota Fortuner 3.0 4x4 MT', 'Tata Manza ELAN Quadrajet',\n",
+       "       'Tata zest x', 'Mahindra xyl',\n",
+       "       'Mercedes Benz A Class A 180 Sport Petrol', 'Tata Indigo LS',\n",
+       "       'Hyundai i20 Magna 1.2', 'Used Commercial Maruti Omn',\n",
+       "       'Honda Amaze 1.5 E i DTEC', 'Hyundai Verna 1.6 EX VTVT',\n",
+       "       'BMW 5 Series 520d Sedan', 'Skoda Superb 1.8 TFSI AT',\n",
+       "       'Audi Q3 2.0 TDI quattro Premium', 'Mahindra Bolero DI BSII',\n",
+       "       'Maruti Suzuki Zen Estilo LXI Green CNG',\n",
+       "       'Ford Figo Duratorq Diesel Titanium 1.4',\n",
+       "       'Maruti Suzuki Wagon R VXI BS IV', 'Mahindra Logan Diesel 1.5 DLS',\n",
+       "       'Tata Nano GenX XMA', 'Honda City SV', 'Ford Figo Petrol LXI',\n",
+       "       'Hyundai i10 Magna 1.2 Kappa2', 'Toyota Corolla H2',\n",
+       "       'Maruti Suzuki Swift Dzire Tour VXi', 'Tata Indigo CS eLS BS IV',\n",
+       "       'Hyundai Xcent Base 1.1 CRDi', 'Hyundai Accent Executive Edition',\n",
+       "       'Tata Zest XE 75 PS Diesel', 'Maruti Suzuki Dzire LDI',\n",
+       "       'Tata Sumo Gold LX BS IV', 'Toyota Corolla Altis GL Petrol',\n",
+       "       'Maruti Suzuki Eeco 7 STR', 'Toyota Fortuner 3.0 4x2 MT',\n",
+       "       'Mahindra XUV500 W6', 'Tata Tigor Revotron XZ',\n",
+       "       'Maruti Suzuki 800', 'Honda Mobilio S i DTEC',\n",
+       "       'Hyundai Verna 1.6 CRDI E', 'Maruti Suzuki Omni Select Variant',\n",
+       "       'Tata Indica', 'Hyundai Santro Xing', 'Maruti Suzuki Zen Estilo',\n",
+       "       'Honda Brio VX AT', 'Maruti Suzuki Wagon R Select Variant',\n",
+       "       'Tata Nano Lx BSIV', 'Jaguar XE XE Portfolio',\n",
+       "       'Hyundai Xcent S 1.2', 'Hyundai Eon Magna Plus',\n",
+       "       'Maruti Suzuki Ritz GENUS VXI',\n",
+       "       'Hyundai Grand i10 Magna AT 1.2 Kappa VTVT',\n",
+       "       'Hyundai Eon D Lite Plus', 'Honda Amaze 1.2 VX i VTEC',\n",
+       "       'Maruti Suzuki Estilo VXi ABS BS IV',\n",
+       "       'Maruti Suzuki Vitara Brezza LDi O', 'Toyota Innova 2.0 V',\n",
+       "       'Hyundai Creta 1.6 SX Plus Petrol AT', 'Mahindra Scorpio Vlx BSIV',\n",
+       "       'Mitsubishi Lancer 1.8 LXi', 'Maruti Suzuki Maruti 800 AC',\n",
+       "       'Maruti Suzuki Alto 800 LXI CNG O', 'Ford Fiesta SXi 1.6 ABS',\n",
+       "       'Maruti Suzuki Ritz VDi', 'Maruti Suzuki Estilo LX BS IV',\n",
+       "       'Audi A6 2.0 TDI Premium', 'Maruti Suzuki Alto',\n",
+       "       'Maruti Suzuki Baleno Sigma 1.2', 'Hyundai Verna 1.6 SX VTVT AT',\n",
+       "       'Maruti Suzuki Swift GLAM', 'Hyundai Getz Prime 1.3 GVS',\n",
+       "       'Hyundai Santro', 'Hyundai Getz Prime 1.3 GLX',\n",
+       "       'Chevrolet Beat PS Diesel', 'Ford EcoSport Trend 1.5 Ti VCT',\n",
+       "       'Tata Indica V2 DLG', 'BMW X1 xDrive20d xLine',\n",
+       "       'Honda City 1.5 V AT', 'Tata Nano', 'Chevrolet Cruze LTZ AT',\n",
+       "       'Hyun', 'Maruti Suzuki Swift Dzire VDi', 'Mahindra XUV500 W10',\n",
+       "       'Maruti Suzuki Alto K10 LXi CNG', 'Hyundai Accent GLE',\n",
+       "       'Force Motors One SUV', 'Datsun Go Plus T O',\n",
+       "       'Chevrolet Spark 1.0 LT', 'Toyota Etios Liva GD',\n",
+       "       'Renault Duster 85PS Diesel RxL Optional with Nav',\n",
+       "       'Chevrolet Enjoy', 'BMW 5 Series 530i', 'Chevrolet Cruze LTZ',\n",
+       "       'Jeep Wrangler Unlimited 4x4 Diesel',\n",
+       "       'Hyundai Verna VGT CRDi SX ABS', 'Maruti Suzuki Omni',\n",
+       "       'Maruti Suzuki Celerio VDi', 'Tata Zest Quadrajet 1.3',\n",
+       "       'Tata Indigo CS eLX BS IV', 'Hyundai i10 Era',\n",
+       "       'Tata Indigo eCS LX CR4 BS IV', 'Tata Indigo Marina LS',\n",
+       "       'Commercial Chevrolet Sail Hatchback ca', 'Hyundai Xcent SX 1.2',\n",
+       "       'Tata Nano LX Special Edition', 'Commercial Car Ta',\n",
+       "       'Renault Duster 110 PS RxZ Diesel',\n",
+       "       'Maruti Suzuki Wagon R AX BSIV', 'Maruti Suzuki Alto K10 New',\n",
+       "       'tata Indica', 'Mahindra Xylo E8', 'Tata Manza Aqua Quadrajet',\n",
+       "       'Used bt new conditions ta', 'Renault Kwid 1.0', 'Sale tata',\n",
+       "       'Tata Venture EX 8 STR', 'Maruti Suzuki Swift Dzire Tour LXi',\n",
+       "       'Maruti Suzuki Alto LX BSII', 'Skoda Octavia Classic 1.9 TDI MT',\n",
+       "       'Maruti Suzuki Omni LPG BS IV', 'Tata Sumo Gold EX BS IV',\n",
+       "       'Tata indigo 2017 top model..', 'Hyundai Verna 1.6 CRDI SX',\n",
+       "       'Mahindra Scorpio SLX 2.6 Turbo 8 Str', 'Ford Ikon 1.6 Nxt',\n",
+       "       'Tata indigo', 'Toyota Innova 2.5 V 7 STR', 'Nissan Sunny XL',\n",
+       "       'Maruti Suzuki Swift VDi BS IV',\n",
+       "       'very good condition tata bolts are av', 'Toyota Innova 2.0 G4',\n",
+       "       'Sale Hyundai xcent commerc', 'Maruti Suzuki Swift VDi ABS',\n",
+       "       'Hyundai Elite i20 Asta 1.2', 'Volkswagen Polo Trendline 1.5L D',\n",
+       "       'Toyota Etios Liva Diesel', 'Maruti Suzuki Ciaz ZXi Plus RS',\n",
+       "       'Hyundai Elantra 1.8 S', 'Ford EcoSport Trend 1.5L Ti VCT',\n",
+       "       'Jaguar XF 2.2 Diesel Luxury',\n",
+       "       'Audi Q5 2.0 TDI quattro Premium Plus', 'BMW 3 Series 320d Sedan',\n",
+       "       'Maruti Suzuki Swift ZXi 1.2 BS IV', 'BMW X1 sDrive20d',\n",
+       "       'Maruti Suzuki S Cross Sigma 1.3', 'Maruti Suzuki Ertiga LDi',\n",
+       "       'Volkswagen Vento Comfortline Petrol', 'Mahindra KUV100',\n",
+       "       'Maruti Suzuki Swift Dzire Tour VDi', 'Mahindra Scorpio 2.6 SLX',\n",
+       "       'Maruti Suzuki Omni 8 STR BS III',\n",
+       "       'Volkswagen Jetta Comfortline 1.9 TDI AT', 'Volvo S80 Summum D4',\n",
+       "       'Toyota Corolla Altis VL AT Petrol',\n",
+       "       'Mitsubishi Pajero Sport 2.5 AT', 'Chevrolet Beat LT Petrol',\n",
+       "       'BMW X1', 'Mercedes Benz C Class C 220 CDI Avantgarde',\n",
+       "       'Volkswagen Vento Comfortline Diesel', 'Tata Indigo CS GLS',\n",
+       "       'Ford Figo Petrol Titanium', 'Honda City ZX GXi',\n",
+       "       'Maruti Suzuki Wagon R Duo Lxi', 'Maruti Suzuki Zen LX BSII',\n",
+       "       'Renault Duster RxL Petrol', 'Maruti Suzuki Baleno Zeta 1.2',\n",
+       "       'Honda WR V S MT Petrol', 'Renault Duster 110 PS RxL Diesel',\n",
+       "       'Mahindra Scorpio LX BS III',\n",
+       "       'Maruti Suzuki SX4 Celebration Diesel',\n",
+       "       'Audi A3 Cabriolet 40 TFSI',\n",
+       "       'I want to sell my commercial car due t',\n",
+       "       'Hyundai Santro AE GLS Audio',\n",
+       "       'i want sale my car.no emi....uber atta', 'Tata ZEST 6 month old',\n",
+       "       'Mahindra Xylo D2 BS IV', 'Hyundai Getz GLE',\n",
+       "       'Hyundai Creta 1.6 SX', 'Hyundai Santro Xing XL AT eRLX Euro III',\n",
+       "       'Hyundai Santro Xing XL eRLX Euro III',\n",
+       "       'Tata Indica V2 DLS BS III', 'Honda City 1.5 E MT',\n",
+       "       'Nissan Micra XL', 'Honda City 1.5 S Inspire',\n",
+       "       'Tata Indica eV2 eXeta eGLX', 'Maruti Suzuki Omni E 8 STR BS IV',\n",
+       "       'MARUTI SUZUKI ERTIGA F', 'Hyundai Verna 1.6 CRDI SX Plus AT',\n",
+       "       'Chevrolet Tavera LS B3 10 Seats BSII', 'Tata Tiago Revotron XM',\n",
+       "       'Tata Tiago Revotorq XZ', 'Tata Nexon', 'Tata',\n",
+       "       'Hindustan Motors Ambassador Classic Mark 4 – Befo',\n",
+       "       'Ford Fusion 1.4 TDCi Diesel',\n",
+       "       'Fiat Linea Emotion 1.4 L T Jet Petrol',\n",
+       "       'Ford Ikon 1.3 Flair Josh 100', 'Tata Indica V2 LS',\n",
+       "       'Mahindra Xylo D2', 'Hyundai Eon Magna',\n",
+       "       'Tata Sumo Grande MKII GX', 'Volkswagen Polo Highline1.2L P',\n",
+       "       'Tata Tiago Revotron XZ', 'Tata Indigo eCS',\n",
+       "       '2012 Tata Sumo Gold f', 'Mahindra Xylo E8 BS IV',\n",
+       "       'Well mentained Tata Sumo',\n",
+       "       'all paper updated tata indica v2 and u',\n",
+       "       'Maruti Ertiga showroom condition with',\n",
+       "       '7 SEATER MAHINDRA BOLERO IN VERY GOOD', '9 SEATER MAHINDRA BOL',\n",
+       "       'scratch less Tata I', 'Maruti Suzuki swift dzire for sale in',\n",
+       "       'Commercial Chevrolet beat for sale in',\n",
+       "       'urgent sell my Mahindra qu', 'Tata Sumo Gold FX BSIII',\n",
+       "       'sell my car Maruti Suzuki Swif',\n",
+       "       'Maruti Suzuki Swift Dzire good car fo', 'Hyunda',\n",
+       "       'Commercial Maruti Suzuki Alto Lxi 800', 'urgent sale Ta',\n",
+       "       'Maruti Suzuki Alto vxi t', 'tata', 'TATA INDI', 'Hyundai Creta',\n",
+       "       'Tata Bolt XM Petrol', 'Hyundai Venue', 'Maruti Suzuki Ritz',\n",
+       "       'Renault Lodgy', 'Hyundai i20 Asta',\n",
+       "       'Maruti Suzuki Swift Select Variant', 'Tata Indica V2 DLX BS III',\n",
+       "       'Mahindra Scorpio VLX 2.2 mHawk Airbag BSIV',\n",
+       "       'Toyota Innova 2.5 E 8 STR', 'Mahindra KUV100 K8 6 STR',\n",
+       "       'Datsun Go Plus', 'Ford Endeavor 4x4 Thunder Plus',\n",
+       "       'Tata Indica V2', 'Hyundai Santro Xing GL',\n",
+       "       'Toyota Innova 2.5 Z Diesel 7 Seater',\n",
+       "       'Any type car avaiabel hare...comercica', 'Maruti Suzuki Alto AX',\n",
+       "       'Mahindra Logan', 'Maruti Suzuki 800 Std BS III',\n",
+       "       'Chevrolet Sail 1.2 LS',\n",
+       "       'Volkswagen Vento Highline Plus 1.5 Diesel', 'Tata Manza',\n",
+       "       'Toyota Innova 2.0 G1 Petrol 8seater', 'Toyota Etios G',\n",
+       "       'Toyota Qualis', 'Mahindra Quanto C4', 'Maruti Suzuki Swift Dzire',\n",
+       "       'Hyundai i20 Select Variant', 'Honda City VX Petrol',\n",
+       "       'Hyundai Getz', 'Mercedes Benz C Class 200 K MT', 'Skoda Fabia',\n",
+       "       'Maruti Suzuki Alto 800 Select Variant',\n",
+       "       'Maruti Suzuki Ritz VXI ABS', 'tata zest 2017 f',\n",
+       "       'Tata Indica V2 DLE BS III', 'Ta', 'Tata Zest XM Diesel',\n",
+       "       'Honda Amaze 1.2 E i VTEC', 'Chevrolet Sail 1.2 LT ABS'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "281d8fdf",
+   "metadata": {},
+   "source": [
+    "# Quality\n",
+    "- year has many non-year values\n",
+    "- year object to integer\n",
+    "- price has Ask for Price\n",
+    "- price object to integer\n",
+    "- kms driven has kmn extra part\n",
+    "- kmn driven object to integer\n",
+    "- kms driven has nan values\n",
+    "- fuel type has some nan values\n",
+    "- keep first 3 words of name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38ffa263",
+   "metadata": {},
+   "source": [
+    "# Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "68e7ca19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backup = car.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3058c6db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['year'].str.isnumeric()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "29ca1f81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['year'] = car['year'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7bcb2fc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 842 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        842 non-null    object\n",
+      " 1   company     842 non-null    object\n",
+      " 2   year        842 non-null    int32 \n",
+      " 3   Price       842 non-null    object\n",
+      " 4   kms_driven  840 non-null    object\n",
+      " 5   fuel_type   837 non-null    object\n",
+      "dtypes: int32(1), object(5)\n",
+      "memory usage: 42.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f2c38d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['Price'] != 'Ask For Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3f2942f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['Price'] = car['Price'].str.replace(',','').astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fef0524a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 819 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        819 non-null    object\n",
+      " 1   company     819 non-null    object\n",
+      " 2   year        819 non-null    int32 \n",
+      " 3   Price       819 non-null    int32 \n",
+      " 4   kms_driven  819 non-null    object\n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(2), object(4)\n",
+      "memory usage: 38.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "66236fb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven'] = car['kms_driven'].str.replace(' kms','').str.replace(',','')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fd31c8cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['kms_driven'] != 'Petrol']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fd74b021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven'] = car['kms_driven'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2cda9273",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 817 entries, 0 to 889\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        817 non-null    object\n",
+      " 1   company     817 non-null    object\n",
+      " 2   year        817 non-null    int32 \n",
+      " 3   Price       817 non-null    int32 \n",
+      " 4   kms_driven  817 non-null    int32 \n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(3), object(3)\n",
+      "memory usage: 35.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6ff9ab7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6afc1ee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 816 entries, 0 to 889\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        816 non-null    object\n",
+      " 1   company     816 non-null    object\n",
+      " 2   year        816 non-null    int32 \n",
+      " 3   Price       816 non-null    int32 \n",
+      " 4   kms_driven  816 non-null    int32 \n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(3), object(3)\n",
+      "memory usage: 35.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f7321418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['name'] = car['name'].str.split().str.slice(0,3).str.join(' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ce740488",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9341d5ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>816 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[816 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46b239e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>816.000000</td>\n",
+       "      <td>8.160000e+02</td>\n",
+       "      <td>816.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2012.444853</td>\n",
+       "      <td>4.117176e+05</td>\n",
+       "      <td>46275.531863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.002992</td>\n",
+       "      <td>4.751844e+05</td>\n",
+       "      <td>34297.428044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1995.000000</td>\n",
+       "      <td>3.000000e+04</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>1.750000e+05</td>\n",
+       "      <td>27000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2013.000000</td>\n",
+       "      <td>2.999990e+05</td>\n",
+       "      <td>41000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>4.912500e+05</td>\n",
+       "      <td>56818.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>8.500003e+06</td>\n",
+       "      <td>400000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year         Price     kms_driven\n",
+       "count   816.000000  8.160000e+02     816.000000\n",
+       "mean   2012.444853  4.117176e+05   46275.531863\n",
+       "std       4.002992  4.751844e+05   34297.428044\n",
+       "min    1995.000000  3.000000e+04       0.000000\n",
+       "25%    2010.000000  1.750000e+05   27000.000000\n",
+       "50%    2013.000000  2.999990e+05   41000.000000\n",
+       "75%    2015.000000  4.912500e+05   56818.500000\n",
+       "max    2019.000000  8.500003e+06  400000.000000"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "928e6a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['Price']<6e6].reset_index(drop = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6aab88e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>815 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "810      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "811          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "812    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "813            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "814      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[815 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "12240c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car.to_csv('cleaned_car.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360ebe84",
+   "metadata": {},
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "db83323f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = car.drop(columns = 'Price')\n",
+    "y = car['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8b93ba4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7fea309a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OneHotEncoder()"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe = OneHotEncoder()\n",
+    "ohe.fit(x[['name','company','fuel_type']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "1e7f6dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+       "        'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+       "        'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+       "        'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n",
+       "        'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n",
+       "        'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n",
+       "        'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n",
+       "        'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n",
+       "        'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n",
+       "        'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n",
+       "        'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n",
+       "        'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n",
+       "        'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n",
+       "        'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n",
+       "        'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n",
+       "        'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n",
+       "        'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n",
+       "        'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n",
+       "        'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n",
+       "        'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n",
+       "        'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n",
+       "        'Hyundai Accent Executive', 'Hyundai Accent GLE',\n",
+       "        'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n",
+       "        'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n",
+       "        'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n",
+       "        'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n",
+       "        'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n",
+       "        'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n",
+       "        'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n",
+       "        'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n",
+       "        'Hyundai Verna Transform', 'Hyundai Verna VGT',\n",
+       "        'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n",
+       "        'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n",
+       "        'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n",
+       "        'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n",
+       "        'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n",
+       "        'Land Rover Freelander', 'Mahindra Bolero DI',\n",
+       "        'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n",
+       "        'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n",
+       "        'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n",
+       "        'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n",
+       "        'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n",
+       "        'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n",
+       "        'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n",
+       "        'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n",
+       "        'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n",
+       "        'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n",
+       "        'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n",
+       "        'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n",
+       "        'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n",
+       "        'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n",
+       "        'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n",
+       "        'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n",
+       "        'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n",
+       "        'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n",
+       "        'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n",
+       "        'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n",
+       "        'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n",
+       "        'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n",
+       "        'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n",
+       "        'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n",
+       "        'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n",
+       "        'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n",
+       "        'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n",
+       "        'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n",
+       "        'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n",
+       "        'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n",
+       "        'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n",
+       "        'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n",
+       "        'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n",
+       "        'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n",
+       "        'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n",
+       "        'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n",
+       "        'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n",
+       "        'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n",
+       "        'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n",
+       "        'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n",
+       "        'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n",
+       "        'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n",
+       "        'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n",
+       "        'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n",
+       "        'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n",
+       "        'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n",
+       "        'Volkswagen Passat Diesel', 'Volkswagen Polo',\n",
+       "        'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n",
+       "        'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n",
+       "        'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n",
+       "        'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object),\n",
+       " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+       "        'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+       "        'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+       "        'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+       "       dtype=object),\n",
+       " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "29cfad01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nmake_column_transformer((OneHotEncoder(),['name','company','fuel_type']),\\n                       (OrdinalEncoder(),['seller_type','owner']),\\n                       (LabelEncoder(),['','']),\\n                       remainder='passthrough')\\n                       \\n\""
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is a comment\n",
+    "\"\"\"\n",
+    "make_column_transformer((OneHotEncoder(),['name','company','fuel_type']),\n",
+    "                       (OrdinalEncoder(),['seller_type','owner']),\n",
+    "                       (LabelEncoder(),['','']),\n",
+    "                       remainder='passthrough')\n",
+    "                       \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b86a18c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_trans = make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n",
+    "                                      remainder='passthrough')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9f4f5ebb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "16af36e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe = make_pipeline(column_trans,lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "78dbfb3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('onehotencoder',\n",
+       "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+       "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+       "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+       "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
+       "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+       "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+       "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+       "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+       "      dtype=object),\n",
+       "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
+       "                                                  ['name', 'company',\n",
+       "                                                   'fuel_type'])])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "81bca84a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = pipe.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "eb5efa15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.40850876022444604"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f67048dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8897767222258609\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_random_state = []\n",
+    "for i in range(1):\n",
+    "    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=661)\n",
+    "    lr = LinearRegression()\n",
+    "    pipe = make_pipeline(column_trans,lr)\n",
+    "    pipe.fit(x_train,y_train)\n",
+    "    y_pred = pipe.predict(x_test)\n",
+    "#     best_random_state.append(r2_score(y_test,y_pred))\n",
+    "    print(r2_score(y_test,y_pred))\n",
+    "#     print(f\"~~~~~~~~~~~~~~~ iteration - {i+1} ~~~~~~~~~~~~~~~~~~~~~~~~\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9085f2b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "attempt to get argmax of an empty sequence",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mC:\\Users\\RIJUDA~1\\AppData\\Local\\Temp/ipykernel_14744/4122977660.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbest_random_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36margmax\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36margmax\u001b[1;34m(a, axis, out)\u001b[0m\n\u001b[0;32m   1186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m     \"\"\"\n\u001b[1;32m-> 1188\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'argmax'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mbound\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\riju dasgupta\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: attempt to get argmax of an empty sequence"
+     ]
+    }
+   ],
+   "source": [
+    "# np.argmax(best_random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b99754ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=661)\n",
+    "lr = LinearRegression()\n",
+    "pipe = make_pipeline(column_trans,lr)\n",
+    "pipe.fit(x_train,y_train)\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "print(r2_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64bc0c1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(pipe,open('CarPricePredictorModel.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "3e0d09f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car_name = list(car['name'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1244ad44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car_name.insert()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25da1f96",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CarPricePredictorModel.pkl b/CarPricePredictorModel.pkl
new file mode 100644
index 0000000..0fe802c
Binary files /dev/null and b/CarPricePredictorModel.pkl differ
diff --git a/Quikr-Car-Price-Predictor.ipynb b/Quikr-Car-Price-Predictor.ipynb
new file mode 100644
index 0000000..f4f926a
--- /dev/null
+++ b/Quikr-Car-Price-Predictor.ipynb
@@ -0,0 +1,2252 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28dc9f47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fe39eacb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(892, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80,000</td>\n",
+       "      <td>45,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550 MDI</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>4,25,000</td>\n",
+       "      <td>40 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Ask For Price</td>\n",
+       "      <td>22,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3,25,000</td>\n",
+       "      <td>28,000 kms</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>5,75,000</td>\n",
+       "      <td>36,000 kms</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     name   company  year          Price  \\\n",
+       "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n",
+       "1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n",
+       "2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n",
+       "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n",
+       "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n",
+       "\n",
+       "   kms_driven fuel_type  \n",
+       "0  45,000 kms    Petrol  \n",
+       "1      40 kms    Diesel  \n",
+       "2  22,000 kms    Petrol  \n",
+       "3  28,000 kms    Petrol  \n",
+       "4  36,000 kms    Diesel  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car = pd.read_csv('quikr_car.csv')\n",
+    "print(car.shape)\n",
+    "car.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "00d95862",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 892 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        892 non-null    object\n",
+      " 1   company     892 non-null    object\n",
+      " 2   year        892 non-null    object\n",
+      " 3   Price       892 non-null    object\n",
+      " 4   kms_driven  840 non-null    object\n",
+      " 5   fuel_type   837 non-null    object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 41.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "08c44a8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n",
+       "       '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n",
+       "       '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n",
+       "       'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n",
+       "       '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n",
+       "       'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n",
+       "       ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n",
+       "       't xe', 'EV2', 'r...', 'zest'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['year'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "536d0a84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['80,000', '4,25,000', 'Ask For Price', '3,25,000', '5,75,000',\n",
+       "       '1,75,000', '1,90,000', '8,30,000', '2,50,000', '1,82,000',\n",
+       "       '3,15,000', '4,15,000', '3,20,000', '10,00,000', '5,00,000',\n",
+       "       '3,50,000', '1,60,000', '3,10,000', '75,000', '1,00,000',\n",
+       "       '2,90,000', '95,000', '1,80,000', '3,85,000', '1,05,000',\n",
+       "       '6,50,000', '6,89,999', '4,48,000', '5,49,000', '5,01,000',\n",
+       "       '4,89,999', '2,80,000', '3,49,999', '2,84,999', '3,45,000',\n",
+       "       '4,99,999', '2,35,000', '2,49,999', '14,75,000', '3,95,000',\n",
+       "       '2,20,000', '1,70,000', '85,000', '2,00,000', '5,70,000',\n",
+       "       '1,10,000', '4,48,999', '18,91,111', '1,59,500', '3,44,999',\n",
+       "       '4,49,999', '8,65,000', '6,99,000', '3,75,000', '2,24,999',\n",
+       "       '12,00,000', '1,95,000', '3,51,000', '2,40,000', '90,000',\n",
+       "       '1,55,000', '6,00,000', '1,89,500', '2,10,000', '3,90,000',\n",
+       "       '1,35,000', '16,00,000', '7,01,000', '2,65,000', '5,25,000',\n",
+       "       '3,72,000', '6,35,000', '5,50,000', '4,85,000', '3,29,500',\n",
+       "       '2,51,111', '5,69,999', '69,999', '2,99,999', '3,99,999',\n",
+       "       '4,50,000', '2,70,000', '1,58,400', '1,79,000', '1,25,000',\n",
+       "       '2,99,000', '1,50,000', '2,75,000', '2,85,000', '3,40,000',\n",
+       "       '70,000', '2,89,999', '8,49,999', '7,49,999', '2,74,999',\n",
+       "       '9,84,999', '5,99,999', '2,44,999', '4,74,999', '2,45,000',\n",
+       "       '1,69,500', '3,70,000', '1,68,000', '1,45,000', '98,500',\n",
+       "       '2,09,000', '1,85,000', '9,00,000', '6,99,999', '1,99,999',\n",
+       "       '5,44,999', '1,99,000', '5,40,000', '49,000', '7,00,000', '55,000',\n",
+       "       '8,95,000', '3,55,000', '5,65,000', '3,65,000', '40,000',\n",
+       "       '4,00,000', '3,30,000', '5,80,000', '3,79,000', '2,19,000',\n",
+       "       '5,19,000', '7,30,000', '20,00,000', '21,00,000', '14,00,000',\n",
+       "       '3,11,000', '8,55,000', '5,35,000', '1,78,000', '3,00,000',\n",
+       "       '2,55,000', '5,49,999', '3,80,000', '57,000', '4,10,000',\n",
+       "       '2,25,000', '1,20,000', '59,000', '5,99,000', '6,75,000', '72,500',\n",
+       "       '6,10,000', '2,30,000', '5,20,000', '5,24,999', '4,24,999',\n",
+       "       '6,44,999', '5,84,999', '7,99,999', '4,44,999', '6,49,999',\n",
+       "       '9,44,999', '5,74,999', '3,74,999', '1,30,000', '4,01,000',\n",
+       "       '13,50,000', '1,74,999', '2,39,999', '99,999', '3,24,999',\n",
+       "       '10,74,999', '11,30,000', '1,49,000', '7,70,000', '30,000',\n",
+       "       '3,35,000', '3,99,000', '65,000', '1,69,999', '1,65,000',\n",
+       "       '5,60,000', '9,50,000', '7,15,000', '45,000', '9,40,000',\n",
+       "       '1,55,555', '15,00,000', '4,95,000', '8,00,000', '12,99,000',\n",
+       "       '5,30,000', '14,99,000', '32,000', '4,05,000', '7,60,000',\n",
+       "       '7,50,000', '4,19,000', '1,40,000', '15,40,000', '1,23,000',\n",
+       "       '4,98,000', '4,80,000', '4,88,000', '15,25,000', '5,48,900',\n",
+       "       '7,25,000', '99,000', '52,000', '28,00,000', '4,99,000',\n",
+       "       '3,81,000', '2,78,000', '6,90,000', '2,60,000', '90,001',\n",
+       "       '1,15,000', '15,99,000', '1,59,000', '51,999', '2,15,000',\n",
+       "       '35,000', '11,50,000', '2,69,000', '60,000', '4,30,000',\n",
+       "       '85,00,003', '4,01,919', '4,90,000', '4,24,000', '2,05,000',\n",
+       "       '5,49,900', '3,71,500', '4,35,000', '1,89,700', '3,89,700',\n",
+       "       '3,60,000', '2,95,000', '1,14,990', '10,65,000', '4,70,000',\n",
+       "       '48,000', '1,88,000', '4,65,000', '1,79,999', '21,90,000',\n",
+       "       '23,90,000', '10,75,000', '4,75,000', '10,25,000', '6,15,000',\n",
+       "       '19,00,000', '14,90,000', '15,10,000', '18,50,000', '7,90,000',\n",
+       "       '17,25,000', '12,25,000', '68,000', '9,70,000', '31,00,000',\n",
+       "       '8,99,000', '88,000', '53,000', '5,68,500', '71,000', '5,90,000',\n",
+       "       '7,95,000', '42,000', '1,89,000', '1,62,000', '35,999',\n",
+       "       '29,00,000', '39,999', '50,500', '5,10,000', '8,60,000',\n",
+       "       '5,00,001'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['Price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8bf9b37d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['45,000 kms', '40 kms', '22,000 kms', '28,000 kms', '36,000 kms',\n",
+       "       '59,000 kms', '41,000 kms', '25,000 kms', '24,530 kms',\n",
+       "       '60,000 kms', '30,000 kms', '32,000 kms', '48,660 kms',\n",
+       "       '4,000 kms', '16,934 kms', '43,000 kms', '35,550 kms',\n",
+       "       '39,522 kms', '39,000 kms', '55,000 kms', '72,000 kms',\n",
+       "       '15,975 kms', '70,000 kms', '23,452 kms', '35,522 kms',\n",
+       "       '48,508 kms', '15,487 kms', '82,000 kms', '20,000 kms',\n",
+       "       '68,000 kms', '38,000 kms', '27,000 kms', '33,000 kms',\n",
+       "       '46,000 kms', '16,000 kms', '47,000 kms', '35,000 kms',\n",
+       "       '30,874 kms', '15,000 kms', '29,685 kms', '1,30,000 kms',\n",
+       "       '19,000 kms', nan, '54,000 kms', '13,000 kms', '38,200 kms',\n",
+       "       '50,000 kms', '13,500 kms', '3,600 kms', '45,863 kms',\n",
+       "       '60,500 kms', '12,500 kms', '18,000 kms', '13,349 kms',\n",
+       "       '29,000 kms', '44,000 kms', '42,000 kms', '14,000 kms',\n",
+       "       '49,000 kms', '36,200 kms', '51,000 kms', '1,04,000 kms',\n",
+       "       '33,333 kms', '33,600 kms', '5,600 kms', '7,500 kms', '26,000 kms',\n",
+       "       '24,330 kms', '65,480 kms', '28,028 kms', '2,00,000 kms',\n",
+       "       '99,000 kms', '2,800 kms', '21,000 kms', '11,000 kms',\n",
+       "       '66,000 kms', '3,000 kms', '7,000 kms', '38,500 kms', '37,200 kms',\n",
+       "       '43,200 kms', '24,800 kms', '45,872 kms', '40,000 kms',\n",
+       "       '11,400 kms', '97,200 kms', '52,000 kms', '31,000 kms',\n",
+       "       '1,75,430 kms', '37,000 kms', '65,000 kms', '3,350 kms',\n",
+       "       '75,000 kms', '62,000 kms', '73,000 kms', '2,200 kms',\n",
+       "       '54,870 kms', '34,580 kms', '97,000 kms', '60 kms', '80,200 kms',\n",
+       "       '3,200 kms', '0,000 kms', '5,000 kms', '588 kms', '71,200 kms',\n",
+       "       '1,75,400 kms', '9,300 kms', '56,758 kms', '10,000 kms',\n",
+       "       '56,450 kms', '56,000 kms', '32,700 kms', '9,000 kms', '73 kms',\n",
+       "       '1,60,000 kms', '84,000 kms', '58,559 kms', '57,000 kms',\n",
+       "       '1,70,000 kms', '80,000 kms', '6,821 kms', '23,000 kms',\n",
+       "       '34,000 kms', '1,800 kms', '4,00,000 kms', '48,000 kms',\n",
+       "       '90,000 kms', '12,000 kms', '69,900 kms', '1,66,000 kms',\n",
+       "       '122 kms', '0 kms', '24,000 kms', '36,469 kms', '7,800 kms',\n",
+       "       '24,695 kms', '15,141 kms', '59,910 kms', '1,00,000 kms',\n",
+       "       '4,500 kms', '1,29,000 kms', '300 kms', '1,31,000 kms',\n",
+       "       '1,11,111 kms', '59,466 kms', '25,500 kms', '44,005 kms',\n",
+       "       '2,110 kms', '43,222 kms', '1,00,200 kms', '65 kms',\n",
+       "       '1,40,000 kms', '1,03,553 kms', '58,000 kms', '1,20,000 kms',\n",
+       "       '49,800 kms', '100 kms', '81,876 kms', '6,020 kms', '55,700 kms',\n",
+       "       '18,500 kms', '1,80,000 kms', '53,000 kms', '35,500 kms',\n",
+       "       '22,134 kms', '1,000 kms', '8,500 kms', '87,000 kms', '6,000 kms',\n",
+       "       '15,574 kms', '8,000 kms', '55,800 kms', '56,400 kms',\n",
+       "       '72,160 kms', '11,500 kms', '1,33,000 kms', '2,000 kms',\n",
+       "       '88,000 kms', '65,422 kms', '1,17,000 kms', '1,50,000 kms',\n",
+       "       '10,750 kms', '6,800 kms', '5 kms', '9,800 kms', '57,923 kms',\n",
+       "       '30,201 kms', '6,200 kms', '37,518 kms', '24,652 kms', '383 kms',\n",
+       "       '95,000 kms', '3,528 kms', '52,500 kms', '47,900 kms',\n",
+       "       '52,800 kms', '1,95,000 kms', '48,008 kms', '48,247 kms',\n",
+       "       '9,400 kms', '64,000 kms', '2,137 kms', '10,544 kms', '49,500 kms',\n",
+       "       '1,47,000 kms', '90,001 kms', '48,006 kms', '74,000 kms',\n",
+       "       '85,000 kms', '29,500 kms', '39,700 kms', '67,000 kms',\n",
+       "       '19,336 kms', '60,105 kms', '45,933 kms', '1,02,563 kms',\n",
+       "       '28,600 kms', '41,800 kms', '1,16,000 kms', '42,590 kms',\n",
+       "       '7,400 kms', '54,500 kms', '76,000 kms', '00 kms', '11,523 kms',\n",
+       "       '38,600 kms', '95,500 kms', '37,458 kms', '85,960 kms',\n",
+       "       '12,516 kms', '30,600 kms', '2,550 kms', '62,500 kms',\n",
+       "       '69,000 kms', '28,400 kms', '68,485 kms', '3,500 kms',\n",
+       "       '85,455 kms', '63,000 kms', '1,600 kms', '77,000 kms',\n",
+       "       '26,500 kms', '2,875 kms', '13,900 kms', '1,500 kms', '2,450 kms',\n",
+       "       '1,625 kms', '33,400 kms', '60,123 kms', '38,900 kms',\n",
+       "       '1,37,495 kms', '91,200 kms', '1,46,000 kms', '1,00,800 kms',\n",
+       "       '2,100 kms', '2,500 kms', '1,32,000 kms', 'Petrol'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['kms_driven'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6b1e60ef",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['fuel_type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "31a78278",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Hyundai Santro Xing XO eRLX Euro III', 'Mahindra Jeep CL550 MDI',\n",
+       "       'Maruti Suzuki Alto 800 Vxi',\n",
+       "       'Hyundai Grand i10 Magna 1.2 Kappa VTVT',\n",
+       "       'Ford EcoSport Titanium 1.5L TDCi', 'Ford Figo', 'Hyundai Eon',\n",
+       "       'Ford EcoSport Ambiente 1.5L TDCi',\n",
+       "       'Maruti Suzuki Alto K10 VXi AMT', 'Skoda Fabia Classic 1.2 MPI',\n",
+       "       'Maruti Suzuki Stingray VXi', 'Hyundai Elite i20 Magna 1.2',\n",
+       "       'Mahindra Scorpio SLE BS IV', 'Audi A8', 'Audi Q7',\n",
+       "       'Mahindra Scorpio S10', 'Maruti Suzuki Alto 800',\n",
+       "       'Hyundai i20 Sportz 1.2', 'Maruti Suzuki Alto 800 Lx',\n",
+       "       'Maruti Suzuki Vitara Brezza ZDi', 'Maruti Suzuki Alto LX',\n",
+       "       'Mahindra Bolero DI', 'Maruti Suzuki Swift Dzire ZDi',\n",
+       "       'Mahindra Scorpio S10 4WD', 'Maruti Suzuki Swift Vdi BSIII',\n",
+       "       'Maruti Suzuki Wagon R VXi BS III',\n",
+       "       'Maruti Suzuki Wagon R VXi Minor',\n",
+       "       'Toyota Innova 2.0 G 8 STR BS IV', 'Renault Lodgy 85 PS RXL',\n",
+       "       'Skoda Yeti Ambition 2.0 TDI CR 4x2',\n",
+       "       'Maruti Suzuki Baleno Delta 1.2',\n",
+       "       'Renault Duster 110 PS RxZ Diesel Plus',\n",
+       "       'Renault Duster 85 PS RxE Diesel', 'Honda City 1.5 S MT',\n",
+       "       'Maruti Suzuki Dzire', 'Honda Amaze', 'Honda Amaze 1.5 SX i DTEC',\n",
+       "       'Honda City', 'Datsun Redi GO S', 'Maruti Suzuki SX4 ZXI MT',\n",
+       "       'Mitsubishi Pajero Sport Limited Edition',\n",
+       "       'Maruti Suzuki Swift VXi 1.2 ABS BS IV', 'Honda City ZX CVT',\n",
+       "       'Maruti Suzuki Wagon R LX BS IV', 'Tata Indigo eCS LS CR4 BS IV',\n",
+       "       'Volkswagen Polo Highline Exquisite P',\n",
+       "       'I want to sell my car Tata Zest', 'Chevrolet Spark LS 1.0',\n",
+       "       'Renault Duster 110PS Diesel RxZ', 'Mini Cooper S 1.6',\n",
+       "       'Skoda Fabia 1.2L Diesel Ambiente', 'Renault Duster',\n",
+       "       'Mahindra Scorpio S4', 'Mahindra Scorpio VLX 2WD BS IV',\n",
+       "       'Mahindra Quanto C8', 'Ford EcoSport', 'Honda Brio',\n",
+       "       'Volkswagen Vento Highline Plus 1.5 Diesel AT',\n",
+       "       'Hyundai i20 Magna', 'Toyota Corolla Altis Diesel D4DG',\n",
+       "       'Hyundai Verna Transform SX VTVT',\n",
+       "       'Toyota Corolla Altis Petrol Ltd', 'Honda City 1.5 EXi New',\n",
+       "       'Skoda Fabia 1.2L Diesel Elegance', 'BMW 3 Series 320i',\n",
+       "       'Maruti Suzuki A Star Lxi', 'Toyota Etios GD',\n",
+       "       'Ford Figo Diesel EXI Option',\n",
+       "       'Maruti Suzuki Swift Dzire VXi 1.2 BS IV',\n",
+       "       'Chevrolet Beat LT Diesel', 'BMW 7 Series 740Li Sedan',\n",
+       "       'Mahindra XUV500 W8 AWD 2013', 'Hyundai i10 Magna 1.2',\n",
+       "       'Hyundai Verna Fluidic New', 'Maruti Suzuki Swift VXi 1.2 BS IV',\n",
+       "       'Maruti Suzuki Ertiga ZXI Plus', 'Maruti Suzuki Ertiga Vxi',\n",
+       "       'Maruti Suzuki Ertiga VDi', 'Maruti Suzuki Alto LXi BS III',\n",
+       "       'Hyundai Grand i10 Asta 1.1 CRDi', 'Honda Amaze 1.2 S i VTEC',\n",
+       "       'Hyundai i20 Asta 1.4 CRDI 6 Speed', 'Ford Figo Diesel EXI',\n",
+       "       'Maruti Suzuki Eeco 5 STR WITH AC HTR', 'Maruti Suzuki Ertiga ZXi',\n",
+       "       'Maruti Suzuki Esteem LXi BS III', 'Maruti Suzuki Ritz VXI',\n",
+       "       'Maruti Suzuki Ritz LDi', 'Maruti Suzuki Dzire VDI',\n",
+       "       'Toyota Etios Liva G', 'Hyundai i20 Sportz 1.4 CRDI',\n",
+       "       'Chevrolet Spark', 'Nissan Micra XV', 'Maruti Suzuki Swift',\n",
+       "       'Honda Amaze 1.5 S i DTEC', 'Chevrolet Beat', 'Toyota Corolla',\n",
+       "       'Honda City 1.5 V MT', 'Ford EcoSport Trend 1.5L TDCi',\n",
+       "       'Hyundai i20 Asta 1.2', 'Tata Indica V2 eLS',\n",
+       "       'Maruti Suzuki Alto 800 Lxi', 'Hindustan Motors Ambassador',\n",
+       "       'Toyota Corolla Altis 1.8 GL', 'Toyota Corolla Altis 1.8 J',\n",
+       "       'Toyota Innova 2.5 GX BS IV 7 STR',\n",
+       "       'Volkswagen Jetta Highline TDI AT',\n",
+       "       'Volkswagen Polo Comfortline 1.2L P', 'Volkswagen Polo',\n",
+       "       'Mahindra Scorpio', 'Nissan Sunny', 'Hyundai Elite i20',\n",
+       "       'Renault Kwid', 'Mahindra Scorpio VLX Airbag',\n",
+       "       'Chevrolet Spark LT 1.0', 'Datsun Redi GO T O',\n",
+       "       'Maruti Suzuki Swift RS VDI', 'Fiat Punto Emotion 1.2',\n",
+       "       'Hyundai i10 Sportz 1.2', 'Chevrolet Beat LT Opt Diesel',\n",
+       "       'Chevrolet Beat LS Diesel', 'Tata Indigo CS',\n",
+       "       'Maruti Suzuki Swift VDi', 'Hyundai Eon Era Plus',\n",
+       "       'Mahindra XUV500', 'Ford Fiesta', 'Maruti Suzuki Wagon R',\n",
+       "       'Hyundai i20', 'Tata Indigo eCS LX TDI BS III',\n",
+       "       'Hyundai Fluidic Verna 1.6 CRDi SX',\n",
+       "       'Commercial , DZire LDI, 2016, for sale', 'Fiat Petra ELX 1.2 PS',\n",
+       "       'Hyundai Santro Xing XS', 'Maruti Suzuki Ciaz VXi Plus',\n",
+       "       'Maruti Suzuki Zen VX', 'Hyundai Creta 1.6 SX Plus Petrol',\n",
+       "       'Tata indigo ecs LX, 201', 'Mahindra Scorpio SLX',\n",
+       "       'Toyota Innova 2.5 G BS III 8 STR',\n",
+       "       'Maruti Suzuki Wagon R LXI BS IV', 'Tata Nano Cx BSIV',\n",
+       "       'Maruti Suzuki Alto Std BS IV', 'Maruti Suzuki Wagon R LXi BS III',\n",
+       "       'Maruti Suzuki Swift VXI BSIII',\n",
+       "       'Tata Sumo Victa EX 10 by 7 Str BSIII', 'MARUTI SUZUKI DESI',\n",
+       "       'Volkswagen Passat Diesel Comfortline AT',\n",
+       "       'Renault Scala RxL Diesel Travelogue',\n",
+       "       'Hyundai Grand i10 Sportz O 1.2 Kappa VTVT',\n",
+       "       'Hyundai i20 Active 1.2 SX', 'Mahindra Xylo E4',\n",
+       "       'Mahindra Jeep MM 550 XDB', 'Mahindra Bolero SLE BS IV',\n",
+       "       'Force Motors Force One LX ABS 7 STR', 'Maruti Suzuki SX4',\n",
+       "       'Toyota Etios', 'Honda City ZX VTEC',\n",
+       "       'Maruti Suzuki Wagon R LX BS III', 'Honda City VX O MT Diesel',\n",
+       "       'Mahindra Thar CRDe 4x4 AC',\n",
+       "       'Audi A4 1.8 TFSI Multitronic Premium Plus',\n",
+       "       'Mercedes Benz GLA Class 200 CDI Sport',\n",
+       "       'Land Rover Freelander 2 SE', 'Renault Kwid RXT',\n",
+       "       'Tata Aria Pleasure 4X2', 'Mercedes Benz B Class B180 Sports',\n",
+       "       'Datsun GO T O', 'Honda Jazz VX MT',\n",
+       "       'Hyundai i20 Active 1.4L SX O', 'Mini Cooper S',\n",
+       "       'Maruti Suzuki Ciaz ZXI Plus', 'Chevrolet Tavera Neo',\n",
+       "       'Hyundai Eon Sportz', 'Tata Sumo Gold Select Variant',\n",
+       "       'Maruti Suzuki Wagon R 1.0', 'Maruti Suzuki Esteem VXi BS III',\n",
+       "       'Chevrolet Enjoy 1.4 LS 8 STR', 'Maruti Suzuki Wagon R 1.0 VXi',\n",
+       "       'Nissan Terrano XL D Plus', 'Renault Duster 85 PS RxL Diesel',\n",
+       "       'Maruti Suzuki Dzire ZXI', 'Renault Kwid RXT Opt',\n",
+       "       'Maruti Suzuki Maruti 800 Std', 'Renault Kwid 1.0 RXT AMT',\n",
+       "       'Renault Scala RxL Diesel',\n",
+       "       'Hyundai Grand i10 Asta 1.2 Kappa VTVT O',\n",
+       "       'Chevrolet Beat LS Petrol', 'Hyundai Accent GLX', 'Yama',\n",
+       "       'Maruti Suzuki Swift LDi', 'Mahindra TUV300 T4 Plus',\n",
+       "       'Tata Indica V2 Xeta e GLE', 'Tata Indigo CS LS DiCOR',\n",
+       "       'Mahindra Scorpio VLX Special Edition BS III',\n",
+       "       'Tata Indica eV2 LS', 'Honda Accord',\n",
+       "       'Ford EcoSport Titanium 1.5 TDCi', 'Maruti Suzuki Ertiga',\n",
+       "       'Mahindra Scorpio 2.6 CRDe', 'Honda Mobilio',\n",
+       "       'Toyota Corolla Altis', 'Skoda Laura', 'Hyundai Verna Fluidic',\n",
+       "       'Maruti Suzuki Vitara Brezza', 'Tata Manza Aura Quadrajet',\n",
+       "       'Chevrolet Sail UVA Petrol LT ABS',\n",
+       "       'Hyundai Verna Fluidic 1.6 VTVT SX',\n",
+       "       'Audi A4 2.0 TDI 177bhp Premium', 'Hyundai Elantra SX',\n",
+       "       'Mahindra Scorpio VLX 4WD Airbag', 'Mahindra KUV100 K8 D 6 STR',\n",
+       "       'Hyundai Grand i10', 'Hyundai i10', 'Hyundai i20 Active',\n",
+       "       'Datsun Redi GO', 'Toyota Etios Liva', 'Hyundai Accent',\n",
+       "       'Hyundai Verna', 'Toyota Fortuner', 'Hyundai i10 Sportz',\n",
+       "       'Mahindra Bolero Power Plus SLE', 'selling car Ta',\n",
+       "       'Honda City 1.5 V MT Exclusive', 'Chevrolet Spark LT 1.0 Airbag',\n",
+       "       'Tata Indigo eCS VX CR4 BS IV', 'Tata Zest 90',\n",
+       "       'Skoda Rapid Elegance 1.6 TDI CR MT', 'Tata Vista Quadrajet VX',\n",
+       "       'Maruti Suzuki Alto K10 VXi AT', 'Maruti Suzuki Zen LXi BS III',\n",
+       "       'Maruti Suzuki Swift Dzire Tour LDi', 'Honda City ZX EXi',\n",
+       "       'Chevrolet Beat Diesel', 'Maruti Suzuki Swift Dzire car',\n",
+       "       'Hyundai Verna 1.4 VTVT', 'Toyota Innova 2.5 E MS 7 STR BS IV',\n",
+       "       'Maruti Suzuki Maruti 800 Std – Befo',\n",
+       "       'Hyundai Elite i20 Asta 1.4 CRDI',\n",
+       "       'Maruti Suzuki Swift Dzire Tour (Gat',\n",
+       "       'Maruti Suzuki Versa DX2 8 SEATER BSIII',\n",
+       "       'Tata Indigo LX TDI BS III',\n",
+       "       'Volkswagen Vento Konekt Diesel Highline',\n",
+       "       'Mercedes Benz C Class 200 CDI Classic', 'URJE',\n",
+       "       'Hyundai Santro Xing GLS', 'Maruti Suzuki Omni Limited Edition',\n",
+       "       'Hyundai Sonata Transform 2.4 GDi MT',\n",
+       "       'Hyundai Elite i20 Sportz 1.2', 'Honda Jazz S MT',\n",
+       "       'Hyundai Grand i10 Sportz 1.2 Kappa VTVT',\n",
+       "       'Maruti Suzuki Zen LXi BSII',\n",
+       "       'Mahindra Scorpio W Turbo 2.6DX 9 Seater',\n",
+       "       'Swift Dzire Tour 27 Dec 2016 Regis', 'Maruti Suzuki Alto K10 VXi',\n",
+       "       'Hyundai Grand i10 Asta 1.2 Kappa VTVT', 'Mahindra XUV500 W8',\n",
+       "       'Hyundai i20 Magna O 1.2', 'Renault Duster 85 PS RxL Explore LE',\n",
+       "       'Honda Brio V MT', 'Mahindra TUV300 T8',\n",
+       "       'Nissan X Trail Select Variant', 'Ford Ikon 1.3 CLXi NXt Finesse',\n",
+       "       'Toyota Fortuner 3.0 4x4 MT', 'Tata Manza ELAN Quadrajet',\n",
+       "       'Tata zest x', 'Mahindra xyl',\n",
+       "       'Mercedes Benz A Class A 180 Sport Petrol', 'Tata Indigo LS',\n",
+       "       'Hyundai i20 Magna 1.2', 'Used Commercial Maruti Omn',\n",
+       "       'Honda Amaze 1.5 E i DTEC', 'Hyundai Verna 1.6 EX VTVT',\n",
+       "       'BMW 5 Series 520d Sedan', 'Skoda Superb 1.8 TFSI AT',\n",
+       "       'Audi Q3 2.0 TDI quattro Premium', 'Mahindra Bolero DI BSII',\n",
+       "       'Maruti Suzuki Zen Estilo LXI Green CNG',\n",
+       "       'Ford Figo Duratorq Diesel Titanium 1.4',\n",
+       "       'Maruti Suzuki Wagon R VXI BS IV', 'Mahindra Logan Diesel 1.5 DLS',\n",
+       "       'Tata Nano GenX XMA', 'Honda City SV', 'Ford Figo Petrol LXI',\n",
+       "       'Hyundai i10 Magna 1.2 Kappa2', 'Toyota Corolla H2',\n",
+       "       'Maruti Suzuki Swift Dzire Tour VXi', 'Tata Indigo CS eLS BS IV',\n",
+       "       'Hyundai Xcent Base 1.1 CRDi', 'Hyundai Accent Executive Edition',\n",
+       "       'Tata Zest XE 75 PS Diesel', 'Maruti Suzuki Dzire LDI',\n",
+       "       'Tata Sumo Gold LX BS IV', 'Toyota Corolla Altis GL Petrol',\n",
+       "       'Maruti Suzuki Eeco 7 STR', 'Toyota Fortuner 3.0 4x2 MT',\n",
+       "       'Mahindra XUV500 W6', 'Tata Tigor Revotron XZ',\n",
+       "       'Maruti Suzuki 800', 'Honda Mobilio S i DTEC',\n",
+       "       'Hyundai Verna 1.6 CRDI E', 'Maruti Suzuki Omni Select Variant',\n",
+       "       'Tata Indica', 'Hyundai Santro Xing', 'Maruti Suzuki Zen Estilo',\n",
+       "       'Honda Brio VX AT', 'Maruti Suzuki Wagon R Select Variant',\n",
+       "       'Tata Nano Lx BSIV', 'Jaguar XE XE Portfolio',\n",
+       "       'Hyundai Xcent S 1.2', 'Hyundai Eon Magna Plus',\n",
+       "       'Maruti Suzuki Ritz GENUS VXI',\n",
+       "       'Hyundai Grand i10 Magna AT 1.2 Kappa VTVT',\n",
+       "       'Hyundai Eon D Lite Plus', 'Honda Amaze 1.2 VX i VTEC',\n",
+       "       'Maruti Suzuki Estilo VXi ABS BS IV',\n",
+       "       'Maruti Suzuki Vitara Brezza LDi O', 'Toyota Innova 2.0 V',\n",
+       "       'Hyundai Creta 1.6 SX Plus Petrol AT', 'Mahindra Scorpio Vlx BSIV',\n",
+       "       'Mitsubishi Lancer 1.8 LXi', 'Maruti Suzuki Maruti 800 AC',\n",
+       "       'Maruti Suzuki Alto 800 LXI CNG O', 'Ford Fiesta SXi 1.6 ABS',\n",
+       "       'Maruti Suzuki Ritz VDi', 'Maruti Suzuki Estilo LX BS IV',\n",
+       "       'Audi A6 2.0 TDI Premium', 'Maruti Suzuki Alto',\n",
+       "       'Maruti Suzuki Baleno Sigma 1.2', 'Hyundai Verna 1.6 SX VTVT AT',\n",
+       "       'Maruti Suzuki Swift GLAM', 'Hyundai Getz Prime 1.3 GVS',\n",
+       "       'Hyundai Santro', 'Hyundai Getz Prime 1.3 GLX',\n",
+       "       'Chevrolet Beat PS Diesel', 'Ford EcoSport Trend 1.5 Ti VCT',\n",
+       "       'Tata Indica V2 DLG', 'BMW X1 xDrive20d xLine',\n",
+       "       'Honda City 1.5 V AT', 'Tata Nano', 'Chevrolet Cruze LTZ AT',\n",
+       "       'Hyun', 'Maruti Suzuki Swift Dzire VDi', 'Mahindra XUV500 W10',\n",
+       "       'Maruti Suzuki Alto K10 LXi CNG', 'Hyundai Accent GLE',\n",
+       "       'Force Motors One SUV', 'Datsun Go Plus T O',\n",
+       "       'Chevrolet Spark 1.0 LT', 'Toyota Etios Liva GD',\n",
+       "       'Renault Duster 85PS Diesel RxL Optional with Nav',\n",
+       "       'Chevrolet Enjoy', 'BMW 5 Series 530i', 'Chevrolet Cruze LTZ',\n",
+       "       'Jeep Wrangler Unlimited 4x4 Diesel',\n",
+       "       'Hyundai Verna VGT CRDi SX ABS', 'Maruti Suzuki Omni',\n",
+       "       'Maruti Suzuki Celerio VDi', 'Tata Zest Quadrajet 1.3',\n",
+       "       'Tata Indigo CS eLX BS IV', 'Hyundai i10 Era',\n",
+       "       'Tata Indigo eCS LX CR4 BS IV', 'Tata Indigo Marina LS',\n",
+       "       'Commercial Chevrolet Sail Hatchback ca', 'Hyundai Xcent SX 1.2',\n",
+       "       'Tata Nano LX Special Edition', 'Commercial Car Ta',\n",
+       "       'Renault Duster 110 PS RxZ Diesel',\n",
+       "       'Maruti Suzuki Wagon R AX BSIV', 'Maruti Suzuki Alto K10 New',\n",
+       "       'tata Indica', 'Mahindra Xylo E8', 'Tata Manza Aqua Quadrajet',\n",
+       "       'Used bt new conditions ta', 'Renault Kwid 1.0', 'Sale tata',\n",
+       "       'Tata Venture EX 8 STR', 'Maruti Suzuki Swift Dzire Tour LXi',\n",
+       "       'Maruti Suzuki Alto LX BSII', 'Skoda Octavia Classic 1.9 TDI MT',\n",
+       "       'Maruti Suzuki Omni LPG BS IV', 'Tata Sumo Gold EX BS IV',\n",
+       "       'Tata indigo 2017 top model..', 'Hyundai Verna 1.6 CRDI SX',\n",
+       "       'Mahindra Scorpio SLX 2.6 Turbo 8 Str', 'Ford Ikon 1.6 Nxt',\n",
+       "       'Tata indigo', 'Toyota Innova 2.5 V 7 STR', 'Nissan Sunny XL',\n",
+       "       'Maruti Suzuki Swift VDi BS IV',\n",
+       "       'very good condition tata bolts are av', 'Toyota Innova 2.0 G4',\n",
+       "       'Sale Hyundai xcent commerc', 'Maruti Suzuki Swift VDi ABS',\n",
+       "       'Hyundai Elite i20 Asta 1.2', 'Volkswagen Polo Trendline 1.5L D',\n",
+       "       'Toyota Etios Liva Diesel', 'Maruti Suzuki Ciaz ZXi Plus RS',\n",
+       "       'Hyundai Elantra 1.8 S', 'Ford EcoSport Trend 1.5L Ti VCT',\n",
+       "       'Jaguar XF 2.2 Diesel Luxury',\n",
+       "       'Audi Q5 2.0 TDI quattro Premium Plus', 'BMW 3 Series 320d Sedan',\n",
+       "       'Maruti Suzuki Swift ZXi 1.2 BS IV', 'BMW X1 sDrive20d',\n",
+       "       'Maruti Suzuki S Cross Sigma 1.3', 'Maruti Suzuki Ertiga LDi',\n",
+       "       'Volkswagen Vento Comfortline Petrol', 'Mahindra KUV100',\n",
+       "       'Maruti Suzuki Swift Dzire Tour VDi', 'Mahindra Scorpio 2.6 SLX',\n",
+       "       'Maruti Suzuki Omni 8 STR BS III',\n",
+       "       'Volkswagen Jetta Comfortline 1.9 TDI AT', 'Volvo S80 Summum D4',\n",
+       "       'Toyota Corolla Altis VL AT Petrol',\n",
+       "       'Mitsubishi Pajero Sport 2.5 AT', 'Chevrolet Beat LT Petrol',\n",
+       "       'BMW X1', 'Mercedes Benz C Class C 220 CDI Avantgarde',\n",
+       "       'Volkswagen Vento Comfortline Diesel', 'Tata Indigo CS GLS',\n",
+       "       'Ford Figo Petrol Titanium', 'Honda City ZX GXi',\n",
+       "       'Maruti Suzuki Wagon R Duo Lxi', 'Maruti Suzuki Zen LX BSII',\n",
+       "       'Renault Duster RxL Petrol', 'Maruti Suzuki Baleno Zeta 1.2',\n",
+       "       'Honda WR V S MT Petrol', 'Renault Duster 110 PS RxL Diesel',\n",
+       "       'Mahindra Scorpio LX BS III',\n",
+       "       'Maruti Suzuki SX4 Celebration Diesel',\n",
+       "       'Audi A3 Cabriolet 40 TFSI',\n",
+       "       'I want to sell my commercial car due t',\n",
+       "       'Hyundai Santro AE GLS Audio',\n",
+       "       'i want sale my car.no emi....uber atta', 'Tata ZEST 6 month old',\n",
+       "       'Mahindra Xylo D2 BS IV', 'Hyundai Getz GLE',\n",
+       "       'Hyundai Creta 1.6 SX', 'Hyundai Santro Xing XL AT eRLX Euro III',\n",
+       "       'Hyundai Santro Xing XL eRLX Euro III',\n",
+       "       'Tata Indica V2 DLS BS III', 'Honda City 1.5 E MT',\n",
+       "       'Nissan Micra XL', 'Honda City 1.5 S Inspire',\n",
+       "       'Tata Indica eV2 eXeta eGLX', 'Maruti Suzuki Omni E 8 STR BS IV',\n",
+       "       'MARUTI SUZUKI ERTIGA F', 'Hyundai Verna 1.6 CRDI SX Plus AT',\n",
+       "       'Chevrolet Tavera LS B3 10 Seats BSII', 'Tata Tiago Revotron XM',\n",
+       "       'Tata Tiago Revotorq XZ', 'Tata Nexon', 'Tata',\n",
+       "       'Hindustan Motors Ambassador Classic Mark 4 – Befo',\n",
+       "       'Ford Fusion 1.4 TDCi Diesel',\n",
+       "       'Fiat Linea Emotion 1.4 L T Jet Petrol',\n",
+       "       'Ford Ikon 1.3 Flair Josh 100', 'Tata Indica V2 LS',\n",
+       "       'Mahindra Xylo D2', 'Hyundai Eon Magna',\n",
+       "       'Tata Sumo Grande MKII GX', 'Volkswagen Polo Highline1.2L P',\n",
+       "       'Tata Tiago Revotron XZ', 'Tata Indigo eCS',\n",
+       "       '2012 Tata Sumo Gold f', 'Mahindra Xylo E8 BS IV',\n",
+       "       'Well mentained Tata Sumo',\n",
+       "       'all paper updated tata indica v2 and u',\n",
+       "       'Maruti Ertiga showroom condition with',\n",
+       "       '7 SEATER MAHINDRA BOLERO IN VERY GOOD', '9 SEATER MAHINDRA BOL',\n",
+       "       'scratch less Tata I', 'Maruti Suzuki swift dzire for sale in',\n",
+       "       'Commercial Chevrolet beat for sale in',\n",
+       "       'urgent sell my Mahindra qu', 'Tata Sumo Gold FX BSIII',\n",
+       "       'sell my car Maruti Suzuki Swif',\n",
+       "       'Maruti Suzuki Swift Dzire good car fo', 'Hyunda',\n",
+       "       'Commercial Maruti Suzuki Alto Lxi 800', 'urgent sale Ta',\n",
+       "       'Maruti Suzuki Alto vxi t', 'tata', 'TATA INDI', 'Hyundai Creta',\n",
+       "       'Tata Bolt XM Petrol', 'Hyundai Venue', 'Maruti Suzuki Ritz',\n",
+       "       'Renault Lodgy', 'Hyundai i20 Asta',\n",
+       "       'Maruti Suzuki Swift Select Variant', 'Tata Indica V2 DLX BS III',\n",
+       "       'Mahindra Scorpio VLX 2.2 mHawk Airbag BSIV',\n",
+       "       'Toyota Innova 2.5 E 8 STR', 'Mahindra KUV100 K8 6 STR',\n",
+       "       'Datsun Go Plus', 'Ford Endeavor 4x4 Thunder Plus',\n",
+       "       'Tata Indica V2', 'Hyundai Santro Xing GL',\n",
+       "       'Toyota Innova 2.5 Z Diesel 7 Seater',\n",
+       "       'Any type car avaiabel hare...comercica', 'Maruti Suzuki Alto AX',\n",
+       "       'Mahindra Logan', 'Maruti Suzuki 800 Std BS III',\n",
+       "       'Chevrolet Sail 1.2 LS',\n",
+       "       'Volkswagen Vento Highline Plus 1.5 Diesel', 'Tata Manza',\n",
+       "       'Toyota Innova 2.0 G1 Petrol 8seater', 'Toyota Etios G',\n",
+       "       'Toyota Qualis', 'Mahindra Quanto C4', 'Maruti Suzuki Swift Dzire',\n",
+       "       'Hyundai i20 Select Variant', 'Honda City VX Petrol',\n",
+       "       'Hyundai Getz', 'Mercedes Benz C Class 200 K MT', 'Skoda Fabia',\n",
+       "       'Maruti Suzuki Alto 800 Select Variant',\n",
+       "       'Maruti Suzuki Ritz VXI ABS', 'tata zest 2017 f',\n",
+       "       'Tata Indica V2 DLE BS III', 'Ta', 'Tata Zest XM Diesel',\n",
+       "       'Honda Amaze 1.2 E i VTEC', 'Chevrolet Sail 1.2 LT ABS'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "281d8fdf",
+   "metadata": {},
+   "source": [
+    "# Quality\n",
+    "- year has many non-year values\n",
+    "- year object to integer\n",
+    "- price has Ask for Price\n",
+    "- price object to integer\n",
+    "- kms driven has kmn extra part\n",
+    "- kmn driven object to integer\n",
+    "- kms driven has nan values\n",
+    "- fuel type has some nan values\n",
+    "- keep first 3 words of name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38ffa263",
+   "metadata": {},
+   "source": [
+    "# Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "68e7ca19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backup = car.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3058c6db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['year'].str.isnumeric()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "29ca1f81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['year'] = car['year'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7bcb2fc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 842 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        842 non-null    object\n",
+      " 1   company     842 non-null    object\n",
+      " 2   year        842 non-null    int32 \n",
+      " 3   Price       842 non-null    object\n",
+      " 4   kms_driven  840 non-null    object\n",
+      " 5   fuel_type   837 non-null    object\n",
+      "dtypes: int32(1), object(5)\n",
+      "memory usage: 42.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f2c38d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['Price'] != 'Ask For Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3f2942f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['Price'] = car['Price'].str.replace(',','').astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fef0524a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 819 entries, 0 to 891\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        819 non-null    object\n",
+      " 1   company     819 non-null    object\n",
+      " 2   year        819 non-null    int32 \n",
+      " 3   Price       819 non-null    int32 \n",
+      " 4   kms_driven  819 non-null    object\n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(2), object(4)\n",
+      "memory usage: 38.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "66236fb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven'] = car['kms_driven'].str.replace(' kms','').str.replace(',','')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fd31c8cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['kms_driven'] != 'Petrol']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fd74b021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['kms_driven'] = car['kms_driven'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2cda9273",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 817 entries, 0 to 889\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        817 non-null    object\n",
+      " 1   company     817 non-null    object\n",
+      " 2   year        817 non-null    int32 \n",
+      " 3   Price       817 non-null    int32 \n",
+      " 4   kms_driven  817 non-null    int32 \n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(3), object(3)\n",
+      "memory usage: 35.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6ff9ab7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6afc1ee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 816 entries, 0 to 889\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   name        816 non-null    object\n",
+      " 1   company     816 non-null    object\n",
+      " 2   year        816 non-null    int32 \n",
+      " 3   Price       816 non-null    int32 \n",
+      " 4   kms_driven  816 non-null    int32 \n",
+      " 5   fuel_type   816 non-null    object\n",
+      "dtypes: int32(3), object(3)\n",
+      "memory usage: 35.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "car.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f7321418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car['name'] = car['name'].str.split().str.slice(0,3).str.join(' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ce740488",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9341d5ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>816 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[816 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46b239e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>816.000000</td>\n",
+       "      <td>8.160000e+02</td>\n",
+       "      <td>816.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2012.444853</td>\n",
+       "      <td>4.117176e+05</td>\n",
+       "      <td>46275.531863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.002992</td>\n",
+       "      <td>4.751844e+05</td>\n",
+       "      <td>34297.428044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1995.000000</td>\n",
+       "      <td>3.000000e+04</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>1.750000e+05</td>\n",
+       "      <td>27000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2013.000000</td>\n",
+       "      <td>2.999990e+05</td>\n",
+       "      <td>41000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>4.912500e+05</td>\n",
+       "      <td>56818.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>8.500003e+06</td>\n",
+       "      <td>400000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year         Price     kms_driven\n",
+       "count   816.000000  8.160000e+02     816.000000\n",
+       "mean   2012.444853  4.117176e+05   46275.531863\n",
+       "std       4.002992  4.751844e+05   34297.428044\n",
+       "min    1995.000000  3.000000e+04       0.000000\n",
+       "25%    2010.000000  1.750000e+05   27000.000000\n",
+       "50%    2013.000000  2.999990e+05   41000.000000\n",
+       "75%    2015.000000  4.912500e+05   56818.500000\n",
+       "max    2019.000000  8.500003e+06  400000.000000"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "928e6a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car = car[car['Price']<6e6].reset_index(drop = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6aab88e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>815 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "810      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "811          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "812    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "813            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "814      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[815 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "12240c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car.to_csv('cleaned_car.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360ebe84",
+   "metadata": {},
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "db83323f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = car.drop(columns = 'Price')\n",
+    "y = car['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8b93ba4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7fea309a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OneHotEncoder()"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe = OneHotEncoder()\n",
+    "ohe.fit(x[['name','company','fuel_type']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "1e7f6dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+       "        'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+       "        'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+       "        'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n",
+       "        'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n",
+       "        'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n",
+       "        'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n",
+       "        'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n",
+       "        'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n",
+       "        'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n",
+       "        'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n",
+       "        'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n",
+       "        'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n",
+       "        'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n",
+       "        'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n",
+       "        'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n",
+       "        'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n",
+       "        'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n",
+       "        'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n",
+       "        'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n",
+       "        'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n",
+       "        'Hyundai Accent Executive', 'Hyundai Accent GLE',\n",
+       "        'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n",
+       "        'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n",
+       "        'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n",
+       "        'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n",
+       "        'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n",
+       "        'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n",
+       "        'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n",
+       "        'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n",
+       "        'Hyundai Verna Transform', 'Hyundai Verna VGT',\n",
+       "        'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n",
+       "        'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n",
+       "        'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n",
+       "        'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n",
+       "        'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n",
+       "        'Land Rover Freelander', 'Mahindra Bolero DI',\n",
+       "        'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n",
+       "        'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n",
+       "        'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n",
+       "        'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n",
+       "        'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n",
+       "        'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n",
+       "        'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n",
+       "        'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n",
+       "        'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n",
+       "        'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n",
+       "        'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n",
+       "        'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n",
+       "        'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n",
+       "        'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n",
+       "        'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n",
+       "        'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n",
+       "        'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n",
+       "        'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n",
+       "        'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n",
+       "        'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n",
+       "        'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n",
+       "        'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n",
+       "        'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n",
+       "        'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n",
+       "        'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n",
+       "        'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n",
+       "        'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n",
+       "        'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n",
+       "        'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n",
+       "        'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n",
+       "        'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n",
+       "        'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n",
+       "        'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n",
+       "        'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n",
+       "        'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n",
+       "        'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n",
+       "        'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n",
+       "        'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n",
+       "        'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n",
+       "        'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n",
+       "        'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n",
+       "        'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n",
+       "        'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n",
+       "        'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n",
+       "        'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n",
+       "        'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n",
+       "        'Volkswagen Passat Diesel', 'Volkswagen Polo',\n",
+       "        'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n",
+       "        'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n",
+       "        'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n",
+       "        'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object),\n",
+       " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+       "        'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+       "        'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+       "        'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+       "       dtype=object),\n",
+       " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "29cfad01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nmake_column_transformer((OneHotEncoder(),['name','company','fuel_type']),\\n                       (OrdinalEncoder(),['seller_type','owner']),\\n                       (LabelEncoder(),['','']),\\n                       remainder='passthrough')\\n                       \\n\""
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is a comment\n",
+    "\"\"\"\n",
+    "make_column_transformer((OneHotEncoder(),['name','company','fuel_type']),\n",
+    "                       (OrdinalEncoder(),['seller_type','owner']),\n",
+    "                       (LabelEncoder(),['','']),\n",
+    "                       remainder='passthrough')\n",
+    "                       \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b86a18c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_trans = make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n",
+    "                                      remainder='passthrough')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9f4f5ebb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "16af36e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe = make_pipeline(column_trans,lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "78dbfb3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('onehotencoder',\n",
+       "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+       "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+       "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+       "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
+       "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+       "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+       "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+       "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+       "      dtype=object),\n",
+       "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
+       "                                                  ['name', 'company',\n",
+       "                                                   'fuel_type'])])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "81bca84a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = pipe.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "eb5efa15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6528783168939027"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_pred,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f67048dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8897767222258609\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_random_state = []\n",
+    "for i in range(1):\n",
+    "    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=661)\n",
+    "    lr = LinearRegression()\n",
+    "    pipe = make_pipeline(column_trans,lr)\n",
+    "    pipe.fit(x_train,y_train)\n",
+    "    y_pred = pipe.predict(x_test)\n",
+    "#     best_random_state.append(r2_score(y_test,y_pred))\n",
+    "    print(r2_score(y_test,y_pred))\n",
+    "#     print(f\"~~~~~~~~~~~~~~~ iteration - {i+1} ~~~~~~~~~~~~~~~~~~~~~~~~\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9085f2b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# np.argmax(best_random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b99754ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8897767222258609\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=661)\n",
+    "lr = LinearRegression()\n",
+    "pipe = make_pipeline(column_trans,lr)\n",
+    "pipe.fit(x_train,y_train)\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "print(r2_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "64bc0c1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(pipe,open('CarPricePredictorModel.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3e0d09f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car_name = list(car['name'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c014c7c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car_name.insert(0,'Select A Company')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72f18f92",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "ff58961a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Cleaned_Car_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "249d2ab8",
+   "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>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0                    name   company  year   Price  kms_driven  \\\n",
+       "0           0     Hyundai Santro Xing   Hyundai  2007   80000       45000   \n",
+       "1           1     Mahindra Jeep CL550  Mahindra  2006  425000          40   \n",
+       "2           2       Hyundai Grand i10   Hyundai  2014  325000       28000   \n",
+       "3           3  Ford EcoSport Titanium      Ford  2014  575000       36000   \n",
+       "4           4               Ford Figo      Ford  2012  175000       41000   \n",
+       "\n",
+       "  fuel_type  \n",
+       "0    Petrol  \n",
+       "1    Diesel  \n",
+       "2    Petrol  \n",
+       "3    Diesel  \n",
+       "4    Diesel  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "4992de4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car_name = sorted(df['name'].unique())\n",
+    "# car_name.insert(0,'Select a Company')\n",
+    "\n",
+    "# car_name = list(car_name)\n",
+    "\n",
+    "# car_name.insert(0,'Select a Car')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "daf7cefc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brand = []\n",
+    "s = \" \"\n",
+    "for i in car_name:\n",
+    "    s = \" \".join(i.split()[1:])\n",
+    "    brand.append(s)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "39a2aecc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if df['company'] in df['name'][0]:\n",
+    "    print(df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "09749617",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = \" \".join(car_name[0].split()[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "6b007dd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A3 Cabriolet'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b1425c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "609b84ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "d1e04120",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Audi',\n",
+       " 'BMW',\n",
+       " 'Chevrolet',\n",
+       " 'Datsun',\n",
+       " 'Fiat',\n",
+       " 'Force',\n",
+       " 'Ford',\n",
+       " 'Hindustan',\n",
+       " 'Honda',\n",
+       " 'Hyundai',\n",
+       " 'Jaguar',\n",
+       " 'Jeep',\n",
+       " 'Land',\n",
+       " 'Mahindra',\n",
+       " 'Maruti',\n",
+       " 'Mercedes',\n",
+       " 'Mini',\n",
+       " 'Mitsubishi',\n",
+       " 'Nissan',\n",
+       " 'Renault',\n",
+       " 'Skoda',\n",
+       " 'Tata',\n",
+       " 'Toyota',\n",
+       " 'Volkswagen',\n",
+       " 'Volvo']"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(df['company'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a541e7a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a9494819",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>company</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>kms_driven</th>\n",
+       "      <th>fuel_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hyundai Santro Xing</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mahindra Jeep CL550</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>425000</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hyundai Grand i10</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>325000</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ford EcoSport Titanium</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>575000</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Ford Figo</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>Diesel</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>Maruti Suzuki Ritz</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>270000</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>Tata Indica V2</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>110000</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>Toyota Corolla Altis</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>132000</td>\n",
+       "      <td>Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>Tata Zest XM</td>\n",
+       "      <td>Tata</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>260000</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>Mahindra Quanto C8</td>\n",
+       "      <td>Mahindra</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>390000</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Diesel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>815 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name   company  year   Price  kms_driven fuel_type\n",
+       "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+       "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+       "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+       "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+       "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+       "..                      ...       ...   ...     ...         ...       ...\n",
+       "810      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+       "811          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+       "812    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+       "813            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+       "814      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+       "\n",
+       "[815 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2679e97",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..8a8a721
--- /dev/null
+++ b/app.py
@@ -0,0 +1,45 @@
+# using streamlit we don't have to write html,css and js 
+import streamlit as st
+import numpy as np
+import pandas as pd
+import pickle
+
+model = pickle.load(open('LinearRegressionModel.pkl','rb'))
+
+
+df = pd.read_csv('cleaned_car.csv')
+
+company_name = sorted(df['company'].unique())
+
+
+brand_name = []
+
+for i in df['name']:
+    s = " ".join(i.split()[:])
+    brand_name.append(s)
+
+brand_name = sorted(brand_name)
+
+
+st.title('Welcome to Car Price Predictor')
+
+
+company_name.insert(0,'Select a Company')
+company = st.selectbox('Company',company_name)
+
+
+brand_name.insert(0,'Select a brand')
+name = st.selectbox('Brand',brand_name)
+
+year = st.number_input('Year of Pourchase')
+
+
+fuel_type = st.selectbox('Fuel Type',('Petrol','Diesel','LPG'))
+
+kms_driven = st.number_input('KMs Driven')
+
+if st.button('Predict Price'):
+    prediction=model.predict(pd.DataFrame(columns=['name', 'company', 'year', 'kms_driven', 'fuel_type'],data=np.array([name,company,year,kms_driven,fuel_type]).reshape(1, 5)))
+    st.title('Prediction: ₹'+str(np.round(prediction[0])))
+
+
diff --git a/cleaned_car.csv b/cleaned_car.csv
new file mode 100644
index 0000000..9c7299c
--- /dev/null
+++ b/cleaned_car.csv
@@ -0,0 +1,816 @@
+,name,company,year,Price,kms_driven,fuel_type
+0,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+1,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+2,Hyundai Grand i10,Hyundai,2014,325000,28000,Petrol
+3,Ford EcoSport Titanium,Ford,2014,575000,36000,Diesel
+4,Ford Figo,Ford,2012,175000,41000,Diesel
+5,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+6,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+7,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+8,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+9,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+10,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+11,Mahindra Scorpio SLE,Mahindra,2015,320000,48660,Diesel
+12,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+13,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+14,Audi A8,Audi,2017,1000000,4000,Petrol
+15,Audi Q7,Audi,2014,500000,16934,Diesel
+16,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+17,Maruti Suzuki Alto,Maruti,2014,160000,35550,Petrol
+18,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+19,Mahindra Scorpio S10,Mahindra,2016,310000,39522,Diesel
+20,Maruti Suzuki Alto,Maruti,2015,75000,39000,Petrol
+21,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+22,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+23,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+24,Maruti Suzuki Alto,Maruti,2017,190000,72000,Petrol
+25,Maruti Suzuki Vitara,Maruti,2016,290000,15975,Diesel
+26,Maruti Suzuki Alto,Maruti,2008,95000,70000,Petrol
+27,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+28,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+29,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+30,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+31,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+32,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+33,Toyota Innova 2.0,Toyota,2012,650000,82000,Diesel
+34,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+35,Skoda Yeti Ambition,Skoda,2012,448000,68000,Diesel
+36,Maruti Suzuki Baleno,Maruti,2017,549000,32000,Diesel
+37,Renault Duster 110,Renault,2012,501000,38000,Diesel
+38,Renault Duster 85,Renault,2013,489999,27000,Diesel
+39,Honda City 1.5,Honda,2011,280000,33000,Petrol
+40,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+41,Maruti Suzuki Dzire,Maruti,2013,349999,46000,Diesel
+42,Honda Amaze,Honda,2013,284999,46000,Diesel
+43,Honda Amaze 1.5,Honda,2015,345000,36000,Diesel
+44,Honda City,Honda,2015,499999,55000,Petrol
+45,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+46,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+47,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+48,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+49,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+50,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+51,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+52,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+53,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+54,Mahindra Scorpio S10,Mahindra,2015,395000,35000,Diesel
+55,Maruti Suzuki Swift,Maruti,2017,220000,30874,Petrol
+56,Honda City ZX,Honda,2017,170000,15000,Diesel
+57,Maruti Suzuki Wagon,Maruti,2013,85000,29685,Petrol
+58,Ford Figo,Ford,2012,175000,41000,Diesel
+59,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+60,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+61,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+62,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+63,Mahindra Scorpio SLE,Mahindra,2012,570000,19000,Diesel
+64,Volkswagen Polo Highline,Volkswagen,2014,315000,60000,Petrol
+65,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+66,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+67,Chevrolet Spark LS,Chevrolet,2010,110000,41000,Petrol
+68,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+69,Honda City,Honda,2015,448999,54000,Petrol
+70,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+71,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+72,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+73,Honda Amaze,Honda,2015,344999,22000,Petrol
+74,Honda Amaze,Honda,2015,344999,22000,Petrol
+75,Renault Duster,Renault,2014,449999,50000,Diesel
+76,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+77,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
+78,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+79,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+80,Ford EcoSport,Ford,2017,489999,39000,Petrol
+81,Honda Brio,Honda,2012,224999,30000,Petrol
+82,Volkswagen Vento Highline,Volkswagen,2019,1200000,3600,Diesel
+83,Hyundai i20 Magna,Hyundai,2009,195000,32000,Petrol
+84,Toyota Corolla Altis,Toyota,2010,351000,38000,Diesel
+85,Hyundai Verna Transform,Hyundai,2008,160000,45000,Petrol
+86,Toyota Corolla Altis,Toyota,2009,240000,35000,Petrol
+87,Honda City 1.5,Honda,2005,90000,50000,Petrol
+88,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+89,Skoda Fabia 1.2L,Skoda,2011,155000,45863,Diesel
+90,BMW 3 Series,BMW,2011,600000,60500,Petrol
+91,Maruti Suzuki A,Maruti,2011,189500,12500,Petrol
+92,Toyota Etios GD,Toyota,2013,350000,60000,Diesel
+93,Ford Figo Diesel,Ford,2012,210000,35000,Diesel
+94,Maruti Suzuki Swift,Maruti,2014,390000,35000,Petrol
+95,Chevrolet Beat LT,Chevrolet,2012,135000,45000,Diesel
+96,BMW 7 Series,BMW,2009,1600000,35000,Petrol
+97,Mahindra XUV500 W8,Mahindra,2013,701000,38000,Diesel
+98,Hyundai i10 Magna,Hyundai,2014,265000,18000,Petrol
+99,Hyundai Verna Fluidic,Hyundai,2015,525000,35000,Diesel
+100,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+101,Maruti Suzuki Ertiga,Maruti,2016,635000,29000,Petrol
+102,Ford EcoSport Titanium,Ford,2014,550000,44000,Diesel
+103,Maruti Suzuki Ertiga,Maruti,2016,575000,29000,Petrol
+104,Maruti Suzuki Ertiga,Maruti,2013,485000,42000,Diesel
+105,Maruti Suzuki Alto,Maruti,2012,155000,14000,Petrol
+106,Hyundai Grand i10,Hyundai,2014,345000,49000,Diesel
+107,Honda Amaze 1.2,Honda,2014,325000,42000,Petrol
+108,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+109,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+110,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+111,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+112,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+113,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+114,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+115,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+116,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+117,Maruti Suzuki Dzire,Maruti,2015,450000,104000,Diesel
+118,Toyota Etios Liva,Toyota,2014,270000,55000,Petrol
+119,Hyundai i20 Sportz,Hyundai,2011,350000,33333,Diesel
+120,Chevrolet Spark,Chevrolet,2012,158400,33600,Petrol
+121,Maruti Suzuki Alto,Maruti,2017,350000,5600,Petrol
+122,Nissan Micra XV,Nissan,2011,179000,41000,Petrol
+123,Maruti Suzuki Swift,Maruti,2007,125000,70000,Petrol
+124,Maruti Suzuki Alto,Maruti,2018,200000,7500,Petrol
+125,Honda Amaze 1.5,Honda,2013,299000,45000,Diesel
+126,Maruti Suzuki Alto,Maruti,2015,220000,38000,Petrol
+127,Chevrolet Beat,Chevrolet,2015,150000,30000,Petrol
+128,Honda City 1.5,Honda,2010,285000,35000,Petrol
+129,Ford EcoSport Trend,Ford,2016,830000,24330,Diesel
+130,Hyundai i20 Asta,Hyundai,2009,210000,65480,Petrol
+131,Maruti Suzuki Swift,Maruti,2013,340000,41000,Petrol
+132,Tata Indica V2,Tata,2006,90000,20000,Petrol
+133,Hindustan Motors Ambassador,Hindustan,2000,70000,200000,Diesel
+134,Toyota Corolla Altis,Toyota,2010,289999,70000,Petrol
+135,Toyota Corolla Altis,Toyota,2012,349999,59000,Petrol
+136,Toyota Innova 2.5,Toyota,2012,849999,99000,Diesel
+137,Volkswagen Jetta Highline,Volkswagen,2014,749999,46000,Diesel
+138,Volkswagen Polo Comfortline,Volkswagen,2015,399999,2800,Petrol
+139,Volkswagen Polo,Volkswagen,2014,274999,32000,Petrol
+140,Mahindra Scorpio,Mahindra,2015,984999,22000,Diesel
+141,Renault Duster,Renault,2014,449999,50000,Diesel
+142,Honda Amaze,Honda,2015,344999,22000,Petrol
+143,Nissan Sunny,Nissan,2012,224999,45000,Petrol
+144,Hyundai Elite i20,Hyundai,2018,599999,21000,Petrol
+145,Renault Kwid,Renault,2016,244999,11000,Petrol
+146,Renault Duster,Renault,2013,399999,41000,Diesel
+147,Ford EcoSport,Ford,2017,489999,39000,Petrol
+148,Renault Duster,Renault,2014,474999,50000,Diesel
+149,Mahindra Scorpio VLX,Mahindra,2011,499999,66000,Diesel
+150,Maruti Suzuki Alto,Maruti,2018,310000,3000,Petrol
+151,Chevrolet Spark LT,Chevrolet,2010,85000,45000,Petrol
+152,Datsun Redi GO,Datsun,2016,245000,7000,Petrol
+153,Maruti Suzuki Swift,Maruti,2010,189500,38500,Diesel
+154,Fiat Punto Emotion,Fiat,2012,169500,37200,Diesel
+155,Maruti Suzuki Swift,Maruti,2010,159500,43200,Diesel
+156,Toyota Etios GD,Toyota,2013,275000,24800,Petrol
+157,Hyundai i20 Sportz,Hyundai,2014,370000,60000,Diesel
+158,Hyundai i10 Sportz,Hyundai,2010,168000,45872,Petrol
+159,Chevrolet Beat LT,Chevrolet,2011,150000,40000,Diesel
+160,Chevrolet Beat LS,Chevrolet,2011,145000,45000,Diesel
+161,Chevrolet Beat LT,Chevrolet,2012,98500,38000,Diesel
+162,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+163,Tata Indigo CS,Tata,2011,85000,11400,Diesel
+164,Toyota Corolla Altis,Toyota,2015,575000,42000,Petrol
+165,Honda City 1.5,Honda,2014,549000,39000,Petrol
+166,Maruti Suzuki Swift,Maruti,2011,209000,47000,Diesel
+167,Hyundai Eon Era,Hyundai,2013,185000,27000,Petrol
+168,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+169,Mahindra XUV500,Mahindra,2014,699999,52000,Diesel
+170,Honda Brio,Honda,2012,224999,30000,Petrol
+171,Ford Fiesta,Ford,2011,274999,55000,Diesel
+172,Honda Amaze,Honda,2013,284999,46000,Diesel
+173,Honda City,Honda,2015,599999,30000,Diesel
+174,Maruti Suzuki Wagon,Maruti,2012,199999,44000,Petrol
+175,Honda City,Honda,2014,544999,45000,Diesel
+176,Hyundai i20,Hyundai,2009,199000,31000,Petrol
+177,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+178,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+179,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+180,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+181,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+182,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+183,Hyundai Santro Xing,Hyundai,2005,49000,7500,Petrol
+184,Maruti Suzuki Ciaz,Maruti,2016,700000,3350,Petrol
+185,Maruti Suzuki Zen,Maruti,2000,55000,60000,Petrol
+186,Honda City,Honda,2015,448999,54000,Petrol
+187,Hyundai Creta 1.6,Hyundai,2017,895000,32000,Petrol
+188,Mahindra Scorpio SLX,Mahindra,2007,355000,75000,Diesel
+189,Mahindra Scorpio SLE,Mahindra,2012,565000,62000,Diesel
+190,Toyota Innova 2.5,Toyota,2006,365000,73000,Diesel
+191,Maruti Suzuki Alto,Maruti,2011,145000,41000,Petrol
+192,Maruti Suzuki Wagon,Maruti,2011,210000,35000,Petrol
+193,Tata Nano Cx,Tata,2013,40000,2200,Petrol
+194,Maruti Suzuki Alto,Maruti,2013,125000,39000,Petrol
+195,Maruti Suzuki Wagon,Maruti,2009,135000,45000,Petrol
+196,Maruti Suzuki Swift,Maruti,2006,135000,45000,Petrol
+197,Tata Sumo Victa,Tata,2012,285000,65000,Diesel
+198,Maruti Suzuki Wagon,Maruti,2010,145000,54870,Petrol
+199,Maruti Suzuki Alto,Maruti,2010,135000,34580,Petrol
+200,Volkswagen Passat Diesel,Volkswagen,2009,450000,97000,Diesel
+201,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+202,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+203,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+204,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+205,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+206,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+207,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+208,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+209,Force Motors Force,Force,2015,580000,3200,Diesel
+210,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+211,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+212,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+213,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+214,Toyota Etios,Toyota,2011,275000,36000,Diesel
+215,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+216,Honda City ZX,Honda,2008,110000,45000,Petrol
+217,Maruti Suzuki Wagon,Maruti,2006,80000,71200,Petrol
+218,Honda City VX,Honda,2016,519000,52000,Diesel
+219,Mahindra Thar CRDe,Mahindra,2016,730000,29000,Diesel
+220,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+221,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+222,Mercedes Benz GLA,Mercedes,2015,2000000,20000,Diesel
+223,Land Rover Freelander,Land,2015,2100000,30000,Diesel
+224,Renault Kwid RXT,Renault,2017,340000,5000,Petrol
+225,Tata Aria Pleasure,Tata,2014,390000,35000,Diesel
+226,Mercedes Benz B,Mercedes,2014,1400000,31000,Petrol
+227,Datsun GO T,Datsun,2016,245000,7000,Petrol
+228,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+229,Tata Indigo eCS,Tata,2016,320000,175400,Diesel
+230,Honda Jazz VX,Honda,2016,450000,41000,Petrol
+231,Honda Amaze 1.2,Honda,2014,311000,33000,Petrol
+232,Honda Amaze,Honda,2013,284999,46000,Diesel
+233,Honda City,Honda,2012,399999,45000,Petrol
+234,Honda City,Honda,2015,599999,39000,Diesel
+235,Honda Amaze,Honda,2015,344999,22000,Petrol
+236,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+237,Force Motors Force,Force,2015,580000,3200,Diesel
+238,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+239,Hyundai i20 Active,Hyundai,2015,535000,37000,Diesel
+240,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+241,Maruti Suzuki Ciaz,Maruti,2017,699000,14000,Petrol
+242,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+243,Honda Amaze,Honda,2013,284999,46000,Diesel
+244,Hyundai Eon Sportz,Hyundai,2012,178000,30000,Petrol
+245,Tata Sumo Gold,Tata,2013,300000,50000,Diesel
+246,Maruti Suzuki Wagon,Maruti,2003,90000,45000,Petrol
+247,Maruti Suzuki Esteem,Maruti,2006,95000,45000,Petrol
+248,Maruti Suzuki Eeco,Maruti,2015,255000,9300,Petrol
+249,Chevrolet Enjoy 1.4,Chevrolet,2013,245000,55000,Diesel
+250,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+251,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+252,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+253,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+254,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+255,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+256,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+257,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+258,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+259,Maruti Suzuki Wagon,Maruti,2015,289999,22000,Petrol
+260,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+261,Nissan Terrano XL,Nissan,2015,499999,60000,Diesel
+262,Renault Duster 85,Renault,2013,489999,27000,Diesel
+263,Renault Duster 85,Renault,2014,489999,59000,Diesel
+264,Renault Duster 85,Renault,2015,549999,19000,Diesel
+265,Maruti Suzuki Dzire,Maruti,2013,380000,30000,Petrol
+266,Renault Kwid RXT,Renault,2018,325000,15000,Petrol
+267,Maruti Suzuki Maruti,Maruti,2003,57000,56758,Petrol
+268,Renault Kwid 1.0,Renault,2018,349999,10000,Petrol
+269,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+270,Renault Scala RxL,Renault,2014,349999,49000,Diesel
+271,Hyundai Grand i10,Hyundai,2014,410000,41000,Petrol
+272,Maruti Suzuki Swift,Maruti,2011,225000,45000,Petrol
+273,Chevrolet Beat LS,Chevrolet,2010,120000,43000,Petrol
+274,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+275,Hyundai Santro Xing,Hyundai,2000,59000,56450,Petrol
+276,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+277,Chevrolet Beat LS,Chevrolet,2010,80000,56000,Petrol
+278,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+279,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+280,Chevrolet Beat LS,Chevrolet,2015,220000,32700,Petrol
+281,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+282,Ford EcoSport Titanium,Ford,2016,599000,30000,Diesel
+283,Hyundai Accent GLX,Hyundai,2006,80000,56000,Petrol
+284,Mahindra TUV300 T4,Mahindra,2016,675000,9000,Diesel
+285,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+286,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+287,Tata Indica V2,Tata,2008,150000,11000,Petrol
+288,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+289,Tata Indigo CS,Tata,2009,72500,46000,Diesel
+290,Maruti Suzuki Swift,Maruti,2019,610000,73,Petrol
+291,Mahindra Scorpio VLX,Mahindra,2004,230000,160000,Diesel
+292,Honda Accord,Honda,2009,175000,58559,Petrol
+293,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+294,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+295,Ford EcoSport Titanium,Ford,2014,520000,57000,Diesel
+296,Maruti Suzuki Ertiga,Maruti,2015,524999,50000,Diesel
+297,Honda Amaze,Honda,2014,299999,37000,Petrol
+298,Maruti Suzuki Dzire,Maruti,2012,299999,40000,Petrol
+299,Honda City,Honda,2011,284999,55000,Petrol
+300,Mahindra Scorpio 2.6,Mahindra,2007,220000,170000,Diesel
+301,Maruti Suzuki Dzire,Maruti,2014,424999,55000,Diesel
+302,Honda City,Honda,2015,644999,39000,Petrol
+303,Honda Mobilio,Honda,2014,399999,44000,Petrol
+304,Toyota Corolla Altis,Toyota,2009,199999,65000,Petrol
+305,Honda City,Honda,2014,584999,39000,Petrol
+306,Skoda Laura,Skoda,2012,349999,44000,Diesel
+307,Renault Duster,Renault,2015,449999,49000,Diesel
+308,Maruti Suzuki Ertiga,Maruti,2018,799999,9000,Diesel
+309,Maruti Suzuki Dzire,Maruti,2015,444999,45000,Diesel
+310,Mahindra XUV500,Mahindra,2014,649999,47000,Diesel
+311,Hyundai Verna Fluidic,Hyundai,2012,444999,40000,Diesel
+312,Maruti Suzuki Vitara,Maruti,2016,689999,29000,Diesel
+313,Maruti Suzuki Wagon,Maruti,2016,344999,15000,Petrol
+314,Mahindra Scorpio,Mahindra,2015,944999,45000,Diesel
+315,Honda Amaze,Honda,2014,274999,35000,Petrol
+316,Mahindra XUV500,Mahindra,2013,689999,80000,Diesel
+317,Mahindra Scorpio,Mahindra,2013,574999,68000,Diesel
+318,Skoda Laura,Skoda,2013,374999,50000,Diesel
+319,Volkswagen Polo,Volkswagen,2010,199999,60000,Diesel
+320,Hyundai Elite i20,Hyundai,2016,549999,9000,Petrol
+321,Tata Manza Aura,Tata,2012,130000,72000,Diesel
+322,Chevrolet Sail UVA,Chevrolet,2013,210000,60000,Petrol
+323,Renault Duster 110,Renault,2012,501000,38000,Diesel
+324,Hyundai Verna Fluidic,Hyundai,2013,401000,45000,Diesel
+325,Audi A4 2.0,Audi,2012,1350000,40000,Diesel
+326,Hyundai Elantra SX,Hyundai,2013,600000,20000,Petrol
+327,Mahindra Scorpio VLX,Mahindra,2013,610000,35000,Diesel
+328,Mahindra KUV100 K8,Mahindra,2016,400000,20000,Diesel
+329,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+330,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+331,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+332,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+333,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+334,Hyundai Grand i10,Hyundai,2017,524999,6821,Petrol
+335,Hyundai i20,Hyundai,2014,449999,23000,Petrol
+336,Hyundai Eon,Hyundai,2014,174999,14000,Petrol
+337,Hyundai i10,Hyundai,2012,244999,38000,Petrol
+338,Hyundai i20 Active,Hyundai,2015,574999,35000,Diesel
+339,Datsun Redi GO,Datsun,2017,244999,22000,Petrol
+340,Toyota Etios Liva,Toyota,2011,239999,41000,Petrol
+341,Hyundai Accent,Hyundai,2010,99999,45000,Petrol
+342,Hyundai Verna,Hyundai,2014,489999,44000,Diesel
+343,Maruti Suzuki Swift,Maruti,2013,324999,45000,Diesel
+344,Toyota Fortuner,Toyota,2011,1074999,52000,Diesel
+345,Hyundai i10 Sportz,Hyundai,2012,230000,34000,Petrol
+346,Mahindra Bolero Power,Mahindra,2018,699000,1800,Diesel
+347,Mahindra XUV500,Mahindra,2015,1000000,15000,Diesel
+348,Honda City 1.5,Honda,2010,240000,400000,Petrol
+349,Chevrolet Spark LT,Chevrolet,2009,110000,44000,Petrol
+350,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+351,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+352,Mahindra XUV500,Mahindra,2016,1130000,72000,Diesel
+353,Tata Indigo eCS,Tata,2014,250000,40000,Diesel
+354,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+355,Force Motors Force,Force,2015,580000,3200,Diesel
+356,Skoda Rapid Elegance,Skoda,2013,340000,48000,Diesel
+357,Tata Vista Quadrajet,Tata,2011,120000,90000,Diesel
+358,Maruti Suzuki Alto,Maruti,2015,265000,12000,Petrol
+359,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+360,Maruti Suzuki Zen,Maruti,2003,85000,69900,Petrol
+361,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+362,Hyundai i10 Magna,Hyundai,2011,175000,45000,Petrol
+363,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+364,Maruti Suzuki Swift,Maruti,2016,350000,166000,Diesel
+365,Honda City ZX,Honda,2008,149000,42000,Petrol
+366,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+367,Mahindra Jeep MM,Mahindra,2006,425000,122,Diesel
+368,Chevrolet Beat Diesel,Chevrolet,2017,150000,62000,Diesel
+369,Honda City 1.5,Honda,2010,225000,70000,Petrol
+370,Hyundai Verna 1.4,Hyundai,2014,375000,36000,Petrol
+371,Toyota Innova 2.5,Toyota,2012,770000,0,Diesel
+372,Maruti Suzuki Maruti,Maruti,1995,30000,55000,Petrol
+373,Toyota Etios,Toyota,2011,275000,36000,Diesel
+374,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+375,Maruti Suzuki Swift,Maruti,2014,335000,55000,Diesel
+376,Hyundai Elite i20,Hyundai,2015,450000,20000,Diesel
+377,Maruti Suzuki Swift,Maruti,2012,225000,40000,Petrol
+378,Maruti Suzuki Versa,Maruti,2004,80000,50000,Petrol
+379,Tata Indigo LX,Tata,2016,130000,104000,Diesel
+380,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+381,Mercedes Benz C,Mercedes,2002,399000,41000,Petrol
+382,Maruti Suzuki Ertiga,Maruti,2013,450000,90000,Diesel
+383,Honda City,Honda,2000,65000,80000,Petrol
+384,Hyundai Santro Xing,Hyundai,2006,75000,46000,Petrol
+385,Maruti Suzuki Omni,Maruti,2001,70000,70000,Petrol
+386,Hyundai Sonata Transform,Hyundai,2017,190000,36469,Diesel
+387,Hyundai Elite i20,Hyundai,2018,600000,7800,Petrol
+388,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+389,Maruti Suzuki Alto,Maruti,2017,240000,60000,Petrol
+390,Maruti Suzuki Alto,Maruti,2011,155000,32000,Petrol
+391,Honda Jazz S,Honda,2009,169999,24695,Petrol
+392,Hyundai Grand i10,Hyundai,2017,450000,15141,Petrol
+393,Maruti Suzuki Zen,Maruti,2001,40000,40000,Petrol
+394,Mahindra Scorpio W,Mahindra,2012,165000,65000,Diesel
+395,Maruti Suzuki Alto,Maruti,2014,270000,22000,Petrol
+396,Hyundai Grand i10,Hyundai,2016,280000,59910,Diesel
+397,Mahindra XUV500 W8,Mahindra,2012,560000,100000,Diesel
+398,Hyundai Creta 1.6,Hyundai,2016,950000,25000,Petrol
+399,Hyundai i20 Magna,Hyundai,2013,310000,35000,Petrol
+400,Renault Duster 85,Renault,2015,715000,65000,Diesel
+401,Hyundai Grand i10,Hyundai,2014,340000,35000,Petrol
+402,Honda Brio V,Honda,2012,235000,33000,Petrol
+403,Mahindra TUV300 T4,Mahindra,2017,610000,68000,Diesel
+404,Chevrolet Spark LS,Chevrolet,2010,95000,23000,Petrol
+405,Mahindra TUV300 T8,Mahindra,2018,1000000,4500,Diesel
+406,Maruti Suzuki Swift,Maruti,2015,220000,129000,Diesel
+407,Nissan X Trail,Nissan,2019,1200000,300,Diesel
+408,Maruti Suzuki Alto,Maruti,2015,230000,5000,Petrol
+409,Ford Ikon 1.3,Ford,2001,45000,65000,Petrol
+410,Toyota Fortuner 3.0,Toyota,2010,940000,131000,Diesel
+411,Tata Manza ELAN,Tata,2010,155555,111111,Petrol
+412,Mercedes Benz A,Mercedes,2013,1500000,14000,Petrol
+413,Chevrolet Beat LS,Chevrolet,2016,210000,22000,Diesel
+414,Ford EcoSport Trend,Ford,2013,495000,38000,Diesel
+415,Tata Indigo LS,Tata,2016,125000,70000,Diesel
+416,Hyundai i20 Magna,Hyundai,2010,195000,36000,Petrol
+417,Volkswagen Vento Highline,Volkswagen,2015,550000,34000,Diesel
+418,Renault Kwid RXT,Renault,2015,270000,43000,Petrol
+419,Ford EcoSport Titanium,Ford,2014,500000,40000,Diesel
+420,Honda Amaze 1.5,Honda,2016,240000,160000,Diesel
+421,Hyundai Verna 1.6,Hyundai,2017,800000,12000,Petrol
+422,BMW 5 Series,BMW,2011,1299000,49000,Diesel
+423,Skoda Superb 1.8,Skoda,2011,530000,68000,Petrol
+424,Audi Q3 2.0,Audi,2013,1499000,37000,Diesel
+425,Mahindra Bolero DI,Mahindra,2012,220000,59466,Diesel
+426,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+427,Ford Figo Duratorq,Ford,2012,250000,99000,Diesel
+428,Maruti Suzuki Wagon,Maruti,2018,395000,25500,Petrol
+429,Mahindra Logan Diesel,Mahindra,2009,130000,66000,Petrol
+430,Tata Nano GenX,Tata,2010,32000,44005,Petrol
+431,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+432,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+433,Hyundai Elite i20,Hyundai,2015,405000,28000,Petrol
+434,Hyundai Elite i20,Hyundai,2015,400000,30000,Petrol
+435,Honda City SV,Honda,2017,760000,4000,Petrol
+436,Maruti Suzuki Baleno,Maruti,2016,500000,28000,Petrol
+437,Ford Figo Petrol,Ford,2011,175000,75000,Petrol
+438,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+439,Honda City,Honda,2017,750000,3000,Petrol
+440,Hyundai Elite i20,Hyundai,2015,419000,20000,Petrol
+441,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+442,Hyundai Eon Era,Hyundai,2018,140000,2110,Petrol
+443,Mitsubishi Pajero Sport,Mitsubishi,2015,1540000,43222,Petrol
+444,Hyundai i10 Magna,Hyundai,2008,275000,100200,Petrol
+445,Toyota Corolla H2,Toyota,2003,150000,100000,Petrol
+446,Maruti Suzuki Swift,Maruti,2011,230000,65,Petrol
+447,Tata Indigo CS,Tata,2015,123000,100000,Diesel
+448,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+449,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+450,Hyundai Xcent Base,Hyundai,2016,300000,140000,Diesel
+451,Honda City,Honda,2015,499999,55000,Petrol
+452,Hyundai Accent Executive,Hyundai,2009,165000,48000,Petrol
+453,Maruti Suzuki Baleno,Maruti,2016,498000,22000,Petrol
+454,Tata Zest XE,Tata,2018,480000,103553,Diesel
+455,Maruti Suzuki Dzire,Maruti,2017,488000,80000,Diesel
+456,Tata Sumo Gold,Tata,2014,250000,99000,Diesel
+457,Toyota Corolla Altis,Toyota,2010,220000,58000,Petrol
+458,Maruti Suzuki Eeco,Maruti,2013,290000,70000,LPG
+459,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+460,Mahindra XUV500 W6,Mahindra,2013,548900,49800,Diesel
+461,Tata Tigor Revotron,Tata,2019,650000,100,Diesel
+462,Maruti Suzuki 800,Maruti,2001,55000,81876,Petrol
+463,Maruti Suzuki Ertiga,Maruti,2015,550000,75000,Petrol
+464,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+465,Honda Mobilio S,Honda,2014,399000,44000,Diesel
+466,Maruti Suzuki Ertiga,Maruti,2016,730000,55000,Diesel
+467,Maruti Suzuki Vitara,Maruti,2017,725000,36000,Diesel
+468,Hyundai Verna 1.6,Hyundai,2016,195000,56000,Diesel
+469,Maruti Suzuki Swift,Maruti,2007,130000,62000,Petrol
+470,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+471,Maruti Suzuki Omni,Maruti,2014,190000,6020,Petrol
+472,Honda Amaze,Honda,2013,250000,55700,Diesel
+473,Tata Indica,Tata,2005,80000,42000,Petrol
+474,Hyundai Santro Xing,Hyundai,2003,120000,50000,Petrol
+475,Maruti Suzuki Zen,Maruti,2010,149000,35000,Petrol
+476,Maruti Suzuki Wagon,Maruti,2014,250000,18500,Petrol
+477,Maruti Suzuki Wagon,Maruti,2007,120000,7000,Petrol
+478,Honda Brio VX,Honda,2017,450000,11000,Petrol
+479,Maruti Suzuki Zen,Maruti,2003,99999,53000,Petrol
+480,Maruti Suzuki Zen,Maruti,2008,135000,23000,Petrol
+481,Maruti Suzuki Wagon,Maruti,2016,225000,35500,Diesel
+482,Maruti Suzuki Alto,Maruti,2010,99000,22134,Petrol
+483,Renault Kwid RXT,Renault,2019,370000,1000,Petrol
+484,Tata Nano Lx,Tata,2010,52000,9000,Petrol
+485,Jaguar XE XE,Jaguar,2016,2800000,8500,Petrol
+486,Hyundai Eon Magna,Hyundai,2014,190000,35000,Petrol
+487,Honda City 1.5,Honda,2014,499000,22000,Petrol
+488,Hindustan Motors Ambassador,Hindustan,2002,90000,25000,Diesel
+489,Maruti Suzuki Ritz,Maruti,2010,149000,40000,Petrol
+490,Hyundai Grand i10,Hyundai,2017,400000,20000,Petrol
+491,Hyundai Eon D,Hyundai,2016,120000,87000,Petrol
+492,Maruti Suzuki Swift,Maruti,2015,250000,55000,Petrol
+493,Maruti Suzuki Wagon,Maruti,2017,375000,23000,Petrol
+494,Honda Amaze 1.2,Honda,2014,381000,6000,Petrol
+495,Maruti Suzuki Estilo,Maruti,2013,180000,65000,Petrol
+496,Maruti Suzuki Vitara,Maruti,2016,580000,25000,Diesel
+497,Maruti Suzuki Eeco,Maruti,2015,278000,39000,Petrol
+498,Hyundai Creta 1.6,Hyundai,2016,1000000,8000,Petrol
+499,Mahindra Scorpio Vlx,Mahindra,2013,690000,75000,Diesel
+500,Maruti Suzuki Ertiga,Maruti,2012,480000,51000,Diesel
+501,Mitsubishi Lancer 1.8,Mitsubishi,2006,85000,50000,Petrol
+502,Maruti Suzuki Maruti,Maruti,2001,40000,75000,Petrol
+503,Maruti Suzuki Alto,Maruti,2015,90000,55800,Petrol
+504,Hyundai Grand i10,Hyundai,2015,340000,53000,Petrol
+505,Hyundai Eon D,Hyundai,2018,260000,25000,Petrol
+506,Ford Fiesta SXi,Ford,2009,250000,56400,Petrol
+507,Maruti Suzuki Ritz,Maruti,2010,180000,72160,Diesel
+508,Hyundai Verna Fluidic,Hyundai,2012,350000,10000,Diesel
+509,Maruti Suzuki Wagon,Maruti,2006,90001,48000,Petrol
+510,Maruti Suzuki Estilo,Maruti,2007,115000,36000,Petrol
+511,Audi A6 2.0,Audi,2012,1599000,11500,Diesel
+512,Maruti Suzuki Wagon,Maruti,2003,130000,133000,Petrol
+513,Maruti Suzuki Wagon,Maruti,2009,159000,27000,Petrol
+514,Maruti Suzuki Wagon,Maruti,2009,160000,35000,Petrol
+515,Maruti Suzuki Alto,Maruti,2010,110000,55000,Petrol
+516,Maruti Suzuki Baleno,Maruti,2016,425000,40000,Petrol
+517,Hyundai Verna 1.6,Hyundai,2019,900000,2000,Petrol
+518,Maruti Suzuki Swift,Maruti,2009,150000,45000,Petrol
+519,Hyundai Getz Prime,Hyundai,2009,110000,20000,Petrol
+520,Hyundai Santro,Hyundai,2000,51999,88000,Petrol
+521,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+522,Chevrolet Beat PS,Chevrolet,2012,215000,65422,Diesel
+523,Ford EcoSport Trend,Ford,2017,580000,10000,Petrol
+524,Maruti Suzuki Dzire,Maruti,2013,380000,35000,Petrol
+525,Hyundai Fluidic Verna,Hyundai,2013,350000,117000,Diesel
+526,Tata Indica V2,Tata,2005,35000,150000,Diesel
+527,BMW X1 xDrive20d,BMW,2011,1150000,72000,Diesel
+528,Hyundai i20 Asta,Hyundai,2010,300000,10750,Petrol
+529,Honda City 1.5,Honda,2009,269000,55000,Petrol
+530,Tata Nano,Tata,2013,60000,6800,Petrol
+531,Chevrolet Cruze LTZ,Chevrolet,2014,400000,41000,Diesel
+532,Hyundai Verna Fluidic,Hyundai,2015,430000,73000,Diesel
+533,Maruti Suzuki Swift,Maruti,2011,140000,65000,Diesel
+534,Mahindra XUV500 W10,Mahindra,2018,1299000,40000,Diesel
+535,Maruti Suzuki Alto,Maruti,2014,199000,37000,Petrol
+536,Hyundai Accent GLE,Hyundai,2006,90000,55000,Petrol
+537,Force Motors One,Force,2013,550000,140000,Diesel
+538,Maruti Suzuki Alto,Maruti,2019,265000,9800,Petrol
+539,Chevrolet Spark 1.0,Chevrolet,2011,100000,27000,Petrol
+540,Hyundai i10,Hyundai,2009,215000,27000,Petrol
+541,Toyota Etios Liva,Toyota,2012,380000,20000,Diesel
+542,Renault Duster 85PS,Renault,2013,401919,57923,Diesel
+543,Chevrolet Enjoy,Chevrolet,2014,490000,30201,Diesel
+544,Maruti Suzuki Alto,Maruti,2017,280000,6200,Petrol
+545,BMW 5 Series,BMW,2009,650000,37518,Petrol
+546,Toyota Etios Liva,Toyota,2014,160000,24652,Petrol
+547,Mahindra Jeep MM,Mahindra,2004,424000,383,Diesel
+548,Chevrolet Beat LS,Chevrolet,2016,225000,95000,Diesel
+549,Chevrolet Cruze LTZ,Chevrolet,2011,350000,35000,Diesel
+550,Jeep Wrangler Unlimited,Jeep,2015,950000,3528,Diesel
+551,Maruti Suzuki Ertiga,Maruti,2013,485000,52500,Diesel
+552,Hyundai Verna VGT,Hyundai,2010,205000,47900,Diesel
+553,Maruti Suzuki Omni,Maruti,2012,160000,14000,Petrol
+554,Maruti Suzuki Celerio,Maruti,2018,310000,37000,Petrol
+555,Tata Zest Quadrajet,Tata,2017,180000,90000,Diesel
+556,Mahindra XUV500 W6,Mahindra,2013,549900,52800,Diesel
+557,Tata Indigo CS,Tata,2016,150000,104000,Diesel
+558,Hyundai i10 Era,Hyundai,2011,175000,30000,Petrol
+559,Tata Indigo eCS,Tata,2014,95000,195000,Diesel
+560,Tata Indigo LX,Tata,2016,230000,104000,Diesel
+561,Tata Indigo eCS,Tata,2016,230000,104000,Diesel
+562,Tata Indigo Marina,Tata,2004,180000,70000,Diesel
+563,Hyundai Xcent SX,Hyundai,2015,400000,43000,Diesel
+564,Hyundai Eon Magna,Hyundai,2013,185000,23000,Petrol
+565,Renault Duster 85,Renault,2015,385000,51000,Diesel
+566,Maruti Suzuki Alto,Maruti,2009,90000,62000,Petrol
+567,Tata Nano LX,Tata,2010,32000,48008,Petrol
+568,Renault Duster 110,Renault,2013,435000,39000,Diesel
+569,Maruti Suzuki Wagon,Maruti,2010,225000,40000,Petrol
+570,Maruti Suzuki Swift,Maruti,2006,189700,48247,Petrol
+571,Maruti Suzuki Ertiga,Maruti,2012,389700,39000,Diesel
+572,Maruti Suzuki Swift,Maruti,2014,365000,23000,Petrol
+573,Maruti Suzuki Alto,Maruti,2017,360000,9400,Petrol
+574,Hyundai i20 Magna,Hyundai,2010,210000,50000,Petrol
+575,Hyundai i10 Magna,Hyundai,2009,170000,75000,Petrol
+576,Tata Zest XE,Tata,2017,380000,70000,Diesel
+577,Mahindra Xylo E8,Mahindra,2009,295000,64000,Diesel
+578,Toyota Corolla Altis,Toyota,2010,185000,55000,Petrol
+579,Tata Manza Aqua,Tata,2014,160000,200000,Diesel
+580,Renault Kwid 1.0,Renault,2018,290000,2137,Petrol
+581,Tata Venture EX,Tata,2013,100000,30000,Diesel
+582,Maruti Suzuki Swift,Maruti,2014,315000,44000,Petrol
+583,Skoda Octavia Classic,Skoda,2006,114990,65000,Diesel
+584,Maruti Suzuki Omni,Maruti,2012,120000,160000,LPG
+585,Chevrolet Beat Diesel,Chevrolet,2011,125000,56000,Diesel
+586,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+587,Hyundai Verna 1.6,Hyundai,2018,855000,42000,Diesel
+588,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+589,Mahindra Scorpio 2.6,Mahindra,2007,260000,56000,Diesel
+590,Maruti Suzuki Zen,Maruti,2002,95000,10544,Petrol
+591,Maruti Suzuki Swift,Maruti,2011,255000,64000,Petrol
+592,Mahindra Scorpio SLX,Mahindra,2008,300000,70000,Diesel
+593,Hyundai Grand i10,Hyundai,2014,340000,25000,Petrol
+594,Hyundai Elite i20,Hyundai,2017,550000,15000,Petrol
+595,Ford Ikon 1.6,Ford,2003,60000,50000,Petrol
+596,Toyota Innova 2.5,Toyota,2011,750000,147000,Diesel
+597,Nissan Sunny XL,Nissan,2011,230000,52000,Petrol
+598,Chevrolet Beat LT,Chevrolet,2012,130000,90001,Diesel
+599,Maruti Suzuki Alto,Maruti,2017,270000,21000,Petrol
+600,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+601,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+602,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+603,Toyota Innova 2.0,Toyota,2012,600000,80000,Diesel
+604,Maruti Suzuki Swift,Maruti,2010,190000,74000,Diesel
+605,Hyundai Elite i20,Hyundai,2015,500000,22000,Petrol
+606,Mahindra XUV500 W10,Mahindra,2016,1065000,41000,Diesel
+607,Volkswagen Polo Trendline,Volkswagen,2015,350000,25000,Diesel
+608,Toyota Etios Liva,Toyota,2012,350000,85000,Diesel
+609,Mahindra TUV300 T4,Mahindra,2016,540000,29500,Diesel
+610,Hyundai Elite i20,Hyundai,2015,470000,30000,Petrol
+611,Hyundai Santro Xing,Hyundai,2014,179000,57000,Petrol
+612,Maruti Suzuki Zen,Maruti,2003,48000,60000,Petrol
+613,Maruti Suzuki Ciaz,Maruti,2016,650000,50000,Petrol
+614,Hyundai Eon Era,Hyundai,2013,190000,39700,Petrol
+615,Hyundai Elantra 1.8,Hyundai,2012,500000,65000,Petrol
+616,Maruti Suzuki Swift,Maruti,2010,270000,67000,Diesel
+617,Maruti Suzuki Zen,Maruti,2008,125000,46000,Petrol
+618,Hyundai Eon Era,Hyundai,2012,188000,38000,Petrol
+619,Hyundai Grand i10,Hyundai,2016,380000,27000,Petrol
+620,Hyundai Verna Fluidic,Hyundai,2011,365000,43000,Diesel
+621,Ford EcoSport Trend,Ford,2014,465000,47000,Petrol
+622,Hyundai i20 Magna,Hyundai,2011,240000,42000,Petrol
+623,Chevrolet Beat Diesel,Chevrolet,2016,179999,19336,Diesel
+624,Tata Indica eV2,Tata,2015,140000,60105,Diesel
+625,Jaguar XF 2.2,Jaguar,2013,2190000,29000,Diesel
+626,Audi Q5 2.0,Audi,2014,2390000,34000,Diesel
+627,BMW 3 Series,BMW,2011,1075000,35000,Diesel
+628,Maruti Suzuki Swift,Maruti,2015,475000,22000,Petrol
+629,BMW X1 sDrive20d,BMW,2012,1025000,41000,Diesel
+630,Maruti Suzuki S,Maruti,2016,615000,21000,Diesel
+631,Maruti Suzuki Ertiga,Maruti,2013,475000,48000,Diesel
+632,Maruti Suzuki Alto,Maruti,2016,270000,38000,Petrol
+633,Honda City SV,Honda,2014,475000,34000,Diesel
+634,Volkswagen Vento Comfortline,Volkswagen,2011,240000,45933,Petrol
+635,Honda City 1.5,Honda,2005,120000,68000,Petrol
+636,Audi A4 2.0,Audi,2016,1900000,44000,Diesel
+637,Mahindra KUV100,Mahindra,2017,360000,35000,Diesel
+638,Tata Zest XE,Tata,2018,450000,102563,Diesel
+639,Mahindra XUV500 W8,Mahindra,2015,900000,28600,Diesel
+640,Maruti Suzuki Swift,Maruti,2017,650000,41800,Diesel
+641,Tata Sumo Gold,Tata,2014,275000,116000,Diesel
+642,Maruti Suzuki Swift,Maruti,2009,210000,59000,Petrol
+643,Mahindra Scorpio 2.6,Mahindra,2004,175000,58000,Diesel
+644,Maruti Suzuki Omni,Maruti,2009,85000,45000,Petrol
+645,Mitsubishi Pajero Sport,Mitsubishi,2015,1490000,42590,Diesel
+646,Renault Duster,Renault,2014,800000,7400,Diesel
+647,Volkswagen Jetta Comfortline,Volkswagen,2009,450000,54500,Diesel
+648,Maruti Suzuki Ertiga,Maruti,2012,1000000,200000,Diesel
+649,Audi A4 2.0,Audi,2013,1510000,27000,Diesel
+650,Volvo S80 Summum,Volvo,2015,1850000,42000,Diesel
+651,Toyota Corolla Altis,Toyota,2014,790000,29000,Petrol
+652,Mitsubishi Pajero Sport,Mitsubishi,2015,1725000,37000,Diesel
+653,Chevrolet Beat LT,Chevrolet,2012,135000,36000,Petrol
+654,BMW X1,BMW,2011,1000000,34000,Diesel
+655,Datsun Redi GO,Datsun,2018,299999,7000,Petrol
+656,Mercedes Benz C,Mercedes,2009,1225000,76000,Diesel
+657,Mahindra Scorpio SLX,Mahindra,2004,175000,60000,Diesel
+658,Volkswagen Vento Comfortline,Volkswagen,2011,200000,95000,Diesel
+659,Tata Indigo CS,Tata,2017,270000,50000,Diesel
+660,Ford Figo Petrol,Ford,2019,525000,0,Petrol
+661,Honda City ZX,Honda,2006,180000,50000,Petrol
+662,Maruti Suzuki Wagon,Maruti,2008,140000,68000,Petrol
+663,Ford EcoSport Trend,Ford,2014,400000,16000,Petrol
+664,Maruti Suzuki Swift,Maruti,2016,499000,51000,Diesel
+665,Maruti Suzuki Omni,Maruti,2009,85000,56000,Petrol
+666,Maruti Suzuki Zen,Maruti,2004,70000,100000,Petrol
+667,Renault Duster RxL,Renault,2015,550000,36000,Petrol
+668,Maruti Suzuki Swift,Maruti,2014,370000,11523,Petrol
+669,Maruti Suzuki Baleno,Maruti,2018,690000,1000,Petrol
+670,Honda WR V,Honda,2009,250000,60000,Petrol
+671,Tata Indigo CS,Tata,2016,110000,85000,Diesel
+672,Renault Duster 110,Renault,2013,490000,38600,Diesel
+673,Mahindra Scorpio LX,Mahindra,2009,320000,95500,Diesel
+674,Maruti Suzuki Zen,Maruti,2004,68000,56000,Petrol
+675,Maruti Suzuki Wagon,Maruti,2014,130000,37458,Petrol
+676,Maruti Suzuki SX4,Maruti,2016,970000,85960,Diesel
+677,Audi A3 Cabriolet,Audi,2015,3100000,12516,Petrol
+678,Hyundai Eon D,Hyundai,2018,280000,35000,Petrol
+679,Maruti Suzuki Zen,Maruti,2009,125000,0,Petrol
+680,Mahindra Scorpio SLX,Mahindra,2008,285000,80000,Diesel
+681,Hyundai Santro AE,Hyundai,2011,165000,45000,Petrol
+682,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+683,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
+684,Mahindra Xylo D2,Mahindra,2011,390000,48000,Diesel
+685,Hyundai Santro,Hyundai,2003,60000,51000,Petrol
+686,Chevrolet Beat LT,Chevrolet,2015,215000,90000,Diesel
+687,Maruti Suzuki Swift,Maruti,2015,475000,43000,Diesel
+688,Mahindra XUV500 W8,Mahindra,2015,899000,53000,Diesel
+689,Toyota Fortuner 3.0,Toyota,2013,1499000,97000,Diesel
+690,Maruti Suzuki Alto,Maruti,2013,240000,20000,Petrol
+691,Hyundai Getz GLE,Hyundai,2007,99000,55000,Petrol
+692,Maruti Suzuki Swift,Maruti,2014,260000,120000,Diesel
+693,Hyundai Creta 1.6,Hyundai,2019,1200000,0,Petrol
+694,Hyundai Santro Xing,Hyundai,2007,115000,46000,Petrol
+695,Hyundai Santro Xing,Hyundai,2009,88000,43200,Petrol
+696,Mahindra Xylo D2,Mahindra,2011,390000,56000,Diesel
+697,Hyundai Santro Xing,Hyundai,2007,135000,42000,Petrol
+698,Tata Indica V2,Tata,2009,90000,30600,Diesel
+699,Hyundai i10 Sportz,Hyundai,2011,220000,38000,Petrol
+700,Hyundai Grand i10,Hyundai,2017,424999,2550,Petrol
+701,Hyundai Santro Xing,Hyundai,2007,135000,47000,Petrol
+702,Honda City 1.5,Honda,2005,95000,41000,Petrol
+703,Nissan Micra XL,Nissan,2017,430000,62500,Diesel
+704,Honda City 1.5,Honda,2005,115000,68000,Petrol
+705,Maruti Suzuki Alto,Maruti,2015,215000,50000,Petrol
+706,Maruti Suzuki Wagon,Maruti,2004,53000,69000,Petrol
+707,Maruti Suzuki Ertiga,Maruti,2012,500000,48000,Diesel
+708,Tata Indica eV2,Tata,2012,85000,55000,Diesel
+709,Maruti Suzuki Omni,Maruti,2013,165000,25000,Petrol
+710,Hyundai Eon Era,Hyundai,2014,200000,28400,Petrol
+711,Hyundai Eon,Hyundai,2014,200000,28000,Petrol
+712,Maruti Suzuki Swift,Maruti,2015,425000,42000,Diesel
+713,Hyundai Verna 1.6,Hyundai,2012,600000,29000,Diesel
+714,Chevrolet Tavera LS,Chevrolet,2005,130000,68485,Diesel
+715,Tata Tiago Revotron,Tata,2018,430000,3500,Petrol
+716,Tata Tiago Revotorq,Tata,2019,568500,0,Petrol
+717,Maruti Suzuki Zen,Maruti,2006,71000,32000,Petrol
+718,Mahindra KUV100 K8,Mahindra,2018,560000,8000,Diesel
+719,Ford EcoSport Titanium,Ford,2014,590000,34000,Diesel
+720,Hindustan Motors Ambassador,Hindustan,1995,750000,37000,Petrol
+721,Ford Fusion 1.4,Ford,2007,125000,85455,Diesel
+722,Hyundai Santro Xing,Hyundai,2007,135000,46000,Petrol
+723,Hyundai Santro,Hyundai,2002,60000,47000,Petrol
+724,Fiat Linea Emotion,Fiat,2009,120000,64000,Petrol
+725,Ford Ikon 1.3,Ford,2008,95000,46000,Petrol
+726,Maruti Suzuki Omni,Maruti,2017,240000,8000,Petrol
+727,Tata Indica V2,Tata,2012,115000,64000,Diesel
+728,Mahindra Scorpio S4,Mahindra,2015,795000,63000,Diesel
+729,Hyundai Santro Xing,Hyundai,2007,55000,65000,Petrol
+730,Mahindra Xylo D2,Mahindra,2009,300000,62000,Diesel
+731,Hyundai Grand i10,Hyundai,2014,320000,41000,Petrol
+732,Maruti Suzuki Alto,Maruti,2015,265000,14000,Petrol
+733,Toyota Corolla,Toyota,2006,160000,40000,Petrol
+734,Hyundai Eon Magna,Hyundai,2017,300000,1600,Petrol
+735,Tata Sumo Grande,Tata,2010,130000,90000,Diesel
+736,Maruti Suzuki Swift,Maruti,2011,250000,58000,Diesel
+737,Volkswagen Polo Highline1.2L,Volkswagen,2013,380000,27000,Petrol
+738,Maruti Suzuki Alto,Maruti,2003,42000,60000,Petrol
+739,Tata Tiago Revotron,Tata,2017,400000,31000,Petrol
+740,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+741,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+742,Tata Indigo eCS,Tata,2016,130000,150000,Diesel
+743,Chevrolet Beat LS,Chevrolet,2014,189000,31000,Diesel
+744,Mahindra Xylo E8,Mahindra,2011,365000,43000,Diesel
+745,Hyundai Eon D,Hyundai,2013,170000,20000,Petrol
+746,Tata Sumo Gold,Tata,2013,215000,100000,Petrol
+747,Tata Nano,Tata,2013,60000,7000,Petrol
+748,Hyundai Elite i20,Hyundai,2017,599999,31000,Petrol
+749,Hyundai i10 Magna,Hyundai,2009,400000,33000,Petrol
+750,Hyundai Creta,Hyundai,2016,900000,60000,Diesel
+751,Volkswagen Polo,Volkswagen,2013,299999,48000,Diesel
+752,Maruti Suzuki Dzire,Maruti,2014,374999,33000,Petrol
+753,Tata Bolt XM,Tata,2015,600000,15000,Petrol
+754,Maruti Suzuki Alto,Maruti,2005,70000,47000,Petrol
+755,Maruti Suzuki Alto,Maruti,2005,100000,40000,Petrol
+756,Maruti Suzuki Ritz,Maruti,2010,150000,38000,Diesel
+757,Maruti Suzuki Alto,Maruti,2017,225000,12500,Petrol
+758,Maruti Suzuki Dzire,Maruti,2009,210000,42000,Petrol
+759,Hyundai i20 Asta,Hyundai,2014,425000,31000,Petrol
+760,Maruti Suzuki Swift,Maruti,2008,162000,60000,Diesel
+761,Tata Indica V2,Tata,2005,60000,80000,Diesel
+762,Mahindra Scorpio VLX,Mahindra,2014,650000,77000,Diesel
+763,Toyota Innova 2.5,Toyota,2012,750000,75000,Diesel
+764,Mahindra Xylo E8,Mahindra,2010,375000,40000,Diesel
+765,Hyundai i20 Magna,Hyundai,2011,230000,47000,Petrol
+766,Maruti Suzuki Omni,Maruti,2000,35999,60000,Petrol
+767,Mahindra KUV100,Mahindra,2016,380000,26500,Petrol
+768,Mahindra KUV100 K8,Mahindra,2019,560000,2875,Petrol
+769,Datsun Go Plus,Datsun,2016,285000,13900,Petrol
+770,Ford Endeavor 4x4,Ford,2019,2900000,9000,Diesel
+771,Tata Indica V2,Tata,2005,39999,80000,Diesel
+772,Hyundai Santro Xing,Hyundai,2006,85000,60000,Petrol
+773,Maruti Suzuki Wagon,Maruti,2016,395000,20000,Petrol
+774,Maruti Suzuki Swift,Maruti,2008,175000,58000,Diesel
+775,Maruti Suzuki Alto,Maruti,2019,400000,1500,Petrol
+776,Toyota Innova 2.5,Toyota,2011,750000,75000,Diesel
+777,Maruti Suzuki Alto,Maruti,2016,250000,2450,Petrol
+778,Maruti Suzuki Alto,Maruti,2019,425000,1625,Petrol
+779,Volkswagen Polo Highline1.2L,Volkswagen,2017,525000,45000,Petrol
+780,Mahindra Logan,Mahindra,2009,130000,65000,Diesel
+781,Maruti Suzuki 800,Maruti,2000,30000,33400,Petrol
+782,Mahindra Scorpio,Mahindra,2011,475000,60123,Diesel
+783,Chevrolet Sail 1.2,Chevrolet,2013,300000,28000,Petrol
+784,Hyundai Santro AE,Hyundai,2003,60000,70000,Petrol
+785,Maruti Suzuki Wagon,Maruti,2006,100000,7000,Petrol
+786,Hyundai Eon,Hyundai,2018,260000,25000,Petrol
+787,Tata Manza,Tata,2015,100000,100000,Diesel
+788,Toyota Etios G,Toyota,2013,265000,42000,Petrol
+789,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+790,Toyota Qualis,Toyota,2003,180000,100000,Diesel
+791,Hyundai Santro Xing,Hyundai,2004,45000,137495,Petrol
+792,Tata Indica eV2,Tata,2016,50500,91200,Diesel
+793,Honda City 1.5,Honda,2009,270000,55000,Petrol
+794,Tata Zest XE,Tata,2017,290000,120000,Diesel
+795,Mahindra Quanto C4,Mahindra,2013,325000,63000,Diesel
+796,Tata Indigo eCS,Tata,2016,160000,104000,Diesel
+797,Maruti Suzuki Swift,Maruti,2016,350000,146000,Diesel
+798,Hyundai Elite i20,Hyundai,2011,290000,40000,Petrol
+799,Hyundai i20 Select,Hyundai,2011,290000,40000,Petrol
+800,Chevrolet Tavera Neo,Chevrolet,2007,465000,100800,Diesel
+801,Maruti Suzuki Dzire,Maruti,2016,325000,150000,Diesel
+802,Hyundai Elite i20,Hyundai,2018,510000,2100,Petrol
+803,Honda City VX,Honda,2016,860000,95000,Petrol
+804,Maruti Suzuki Dzire,Maruti,2016,450000,2500,Diesel
+805,Hyundai Getz,Hyundai,2006,125000,80000,Petrol
+806,Mercedes Benz C,Mercedes,2006,500001,15000,Petrol
+807,Maruti Suzuki Alto,Maruti,2005,95000,65000,Petrol
+808,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+809,Skoda Fabia,Skoda,2009,110000,45000,Petrol
+810,Maruti Suzuki Ritz,Maruti,2011,270000,50000,Petrol
+811,Tata Indica V2,Tata,2009,110000,30000,Diesel
+812,Toyota Corolla Altis,Toyota,2009,300000,132000,Petrol
+813,Tata Zest XM,Tata,2018,260000,27000,Diesel
+814,Mahindra Quanto C8,Mahindra,2013,390000,40000,Diesel