Quikr Analysis.ipynb

{
 "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": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>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,
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     "output_type": "execute_result"
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       "  <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"
    },
    {
     "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",
      "<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"
    },
    {
     "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": [
    {
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       "  <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"
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       "    .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",
      "<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": [
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       "  <thead>\n",
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       "      <th>count</th>\n",
       "      <td>816</td>\n",
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       "    </tr>\n",
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       "      <th>unique</th>\n",
       "      <td>254</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <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",
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       "      <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"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <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",
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       "      <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",
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       "      <td>221</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <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",
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       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <th>50%</th>\n",
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       "    <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"
    },
    {
     "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": {
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AiDK6fwIAAAAAAAARoqUaANiEy+VSbm5ug+t5vV5NmjRJkjRz5swGW6G5XK6o1AcAAAAA+AWhGgDYhGEYEXfTdLvddO0EADTINM1fblc1PH5nSwmtJbRGAABiAaEaAAAA0MqFToSzZ/YjFlZSP5/Pp4SEBKvLAAAgbIypBgAAAAAAAESIlmoAAABAKxc6vma78X+WEW+P8TbNKl+w5RxjgAIAYg2hGgAAANDKGYbxy+14l21CtVChNQIAEAvo/tnMVqxYocmTJ2vFihVWlwKbY1sBAAAAACB22Kql2tq1a/XWW2/p+++/V3l5uW688UYNGzbsgM9Zs2aN8vLy9OOPP6pr164677zzNHLkyJYpuAFer1d5eXkqLy9XXl6eBg0axCx9qBPbCgAAAAAAscVWLdW8Xq969+6tyy+/PKz1t27dqvvvv1+DBw/WAw88oKysLD399NP68ssvm7fQMOXn58vj8UiSPB6PCgoKrC0ItsW2AgAAAABAbLFVS7WMjAxlZGSEvf7ChQuVnJysnJwcSVKPHj303//+VwUFBTr66KObqcrwlJSUqLCwUKZpSpJM01RBQYGGDx+ulJQUS2uDvbCtAAAAAAAQe2wVqkXq66+/1pFHHllr2VFHHaUXXnih3udUVVWpqqoqeN8wDLVr1y54OxpM09Ts2bODIcm+y2+88UZbD8RaayBbw7B1rbEu1reVlhYLn0VLfGf4jgIAIhULxwqOaWjL7L7t8/0E6hbToZrH41GnTp1qLevUqZP27Nkjn89X57Tc8+fP17x584L3+/Tpo+nTp6tbt25Rq2vjxo0qKirab3kgEFBRUZH8fr969uwZtfeLtj179gRvp6SkBENHRF+sbystLXTbtKuW+M7wHQUARIpjKGBvdv+O8v0E6hbToVpjnHvuucrOzg7er0nbS0tL5ff7o/IeTqdT6enpWrt2rQKBQHC5w+HQ4MGD5XQ6VVxcHJX3ag5erzd4u6SkhAHzm1HNtrJmzZpardUMw1B6errtt5WWFrpt2lVLfGf4jgIAIsUxFLA3u39H+X6irXE6nWE1vorpUC0pKUk7duyotWzHjh1q165dna3UJCk+Pl7x8fF1PrZvF7ymGD9+vKZMmVJrmWEYGj9+fNTfK9pCazNN09a1tgbjx4/XLbfcUudyyd7bSkuLhc+iJb4zfEcBAJGKhWMFxzS0ZXbf9vl+AnWz1eyfkerfv79Wr15da9mqVas0YMAAiyr6RUpKijIzM2sty8rKUnJyskUVIdZw0AIAAAAAwL5sFapVVlZqw4YN2rBhgyRp69at2rBhg8rKyiRJc+bM0axZs4Lrn3766dq6dateeuklbdq0Se+++64+/fRTZWVlWVH+fk4//fRg91LDMHTaaadZXBHspmZCgn0H/TQMo84JDAAAAAAAgD3Yqvvnt99+q6lTpwbv5+XlSZJGjBihiRMnqry8PBiwSVJycrJuueUWvfjiiyosLFTXrl119dVX6+ijj27p0uu0cOHCYChimqYWLVqkMWPGWFwV7KS4uPiAExUUFxcrLS3NgsoAAAAAAMCB2CpUGzx4sObOnVvv4xMnTqzzOQ888EBzltUoJSUlKiwsrLWsoKBAw4cPV0pKikVVwW5SU1PrndRi0KBBSk1NtbA6AAAAAICdmKYpn8/X4OQWgUBAu3fvjup7d+jQQQ7HgTs8ut1uuVyu/XpjtVa2CtVai5oufft23atZPnny5DazgeHAaiavqGtSi5ycHLYTAAAAAECQz+fThAkTrC7jgHJzc9vMbLG2GlOttajp0hfa8kiq3aUPqFEzqUXo+HtMagEAAAAAgL3RUq0Z0KUPkcrOztaSJUtUXl6upKQk20y2AQAAAACwD5fLpdzcXNt3/2wrCNWaAV36ECm3262cnBy99NJLGjduXJtpKgsAAAAACJ9hGHK73WFdMyYlJTV/QW0coVozqenSt2DBguAyuvThQDIyMpSRkWF1GQAAAAAAIAyEas1o5MiRtUK1ESNGWFgNAAAAIMlfJbPhteplmqbkr9p7xxnftF4YNa8DAEAMIlRrRk899dR+92+77TaLqgEAAACkiryHrS4BAIBWgdk/m8maNWv0zTff1Fr2zTffaM2aNRZVBAAAAAAAgGihpVozCAQCeuKJJ+p87IknntCsWbManDEDAAAAiJaa2eKiwev1atKkSZKkmTNnRm2CpbY0WxwAoHUgVGsGK1euVEVFRZ2PVVRUaOXKlQxIDwAAgBZTM1tctIU7Ax0AAK0RzaWaQbdu3Zr0OADAflasWKHJkydrxYoVVpcCAAAAwAYI1ZrBIYccol69etX5WK9evXTIIYe0cEUAgKbwer3Ky8vTtm3blJeXJ6/Xa3VJAAAAACxGqNYMDMPQtddeW+djEydObNq04wCAFpefny+PxyNJ8ng8KigosLYgAAAAAJYjVGsmKSkp6tOnT61lffv2VXJyskUVAQAao6SkRIWFhTJNU5JkmqYKCgpUUlJicWUAAAAArESo1kxKSkr0ww8/1Fr2ww8/cBEGADHENE3Nnj07GKg1tBwAAABA20Go1gxqLrbqW85FGNB0fr9U5Tcb/c9XFVBF5d5/vqpAk17L77f600BzKS4uVlFRkQKBQK3lgUBARUVFKi4utqgyAAAAAFZzWl1Aa1RzEbav0IuwtLQ0CyoDWo/Z86MdThN2Y3+pqalKT0/X2rVrawVrDodDgwYNUmpqqoXVAQAAALASLdWaQc1FmMNR++N1OBxKT0/nIgwAYoRhGBo/fvx+E8wYhqGcnBwmngEAAADaMFqqNYOai7ApU6bst5yLsMYxTVM+ny+i9VwuV4OfdTjrwD5cLpdyc3Oj8lper1eTJk2SJM2cOVNutzsqr+tyuaLyOrCPlJQUZWZmKj8/X6ZpyjAMZWVlMfEMAAAA0MYRqjUTLsKiy+fzacKECVF/3dzc3KiFKWh+hmE0y/+X2+1mO8ABZWdna8mSJSovL1dSUpKysrKsLgkAAACAxej+2Yyys7OVlJQkSVyEoUErVqzQ5MmTtWLFCqtLAZpNrG7nbrdbOTk56tq1q3JycghhAQAAANBSrTnVXIS99NJLGjduHBdhTRBut79Iu/TZpaue1+tVXl6eysvLlZeXp0GDBrG9oNWJ9e08IyNDGRkZVpcBAAAAwCYI1ZoZF2HR0Zhuf7HUpS8/P18ej0eS5PF4VFBQoDFjxlhbFBBlbOcAAAAAWhNCtUZg0HxEU0lJiQoLC2WapqS9201BQYGGDx+ulJQUi6sDooPtHAAAAEBrQ6jWCAyaj2gxTVOzZ88OBg37Lp88eTJBK2Ie2zkAAACA1oiJCgALFRcXq6ioSIFAoNbyQCCgoqIiFRcXW1QZED1s5wAAAABaI1qqNUJrHzQfLSc1NVXp6elau3ZtrcDB4XBo0KBBSk1NtbA6IDrYzgEAAAC0RrRUa4SaQfPD+VcjnHXp/tT2GIah8ePH7/d/bxiGcnJy2CbQKrCdAwAAAGiNCNUAi6WkpCgzMzMYLBiGoaysLCUnJ1tcGRA9bOdAbFixYoUmT56sFStWWF0KAACA7RGqATaQnZ2tpKQkSVJSUpKysrKsLQhoBnbczk3TlNfrbfBfZWWldu7cqZ07d6qysrLB9fedlAGIBV6vV3l5edq2bZvy8vLk9XqtLgkAAMDWGFMNsAG3262cnBy99NJLGjduHLPAolWy43bObM7AL/Lz8+XxeCRJHo9HBQUFGjNmjLVFAQAA2BihGmATGRkZysjIsLoMNBPTNOXz+aLyWqGtR6LVksTlcrXI2GZs54A9lZSUqLCwMNjK0jRNFRQUaPjw4UpJSbG4OqB1C+ccIXSdcI7ZLXVcB4C2jlANAFpAc7WIqplhuKnaassqZnMG9l6sz549e79uyzXLJ0+ezMU50Iya4xyhrR7XAaClEaoBAFrMihUrgt0/7dBirWY250jsO7szEOuKi4tVVFS03/JAIKCioiIVFxcrLS3NgsoAAADsjVANaGY06ce+zj5fcjZh72uaUnX13ttxcVJjNwW/X3pzXuPriFTNIOjl5eXKy8vToEGDCKcAG0hNTVV6errWrl2rQCAQXO5wODRo0CClpqZaWB3Q+oXTapoW0wBgT4RqQDOjST/25XRKTmfTQtH4+GhU0rIzVDIIOmBPhmFo/PjxmjJlyn7Lc3Jy+BEHaGaRtpqmxTQA2IfD6gIAAK1ffYOgl5SUWFwZAElKSUlRZmZmMEAzDENZWVlKTk62uDIAAAD7oqUa0Mxo0o+2jkHQgdiQnZ2tJUuWqLy8XElJScrKyrK6JAAAAFsjVAOaGU360dYxCDoQG9xut3JycoKTiXAsAgAAODBCtVYinMHww+X1euu83VQMrg+0TQyCDsSOjIwMW8zMCwAAEAsI1VqJ5hgMX1KwS2I0MLg+0Ho1FOxfeOGFuvPOO2stMwxDY8eOrfd5BPEAAAAA7IxQDQDQZI0J9qurq3XbbbfV+zhBPAAAAAA7I1RrhWZljpQ7Lq7RzzdNU77qvV20XHGOJrUU8VZX67rCxY1+PgAAAAC0JT5/04bgMU1TVdV7ewLExzW+5X9T6wDaAkK1VsgdF6cEZ9P+a9vFR6kYAG1CpLPcXnnllTr22GMbfE0AAIC25p63JlpdAoAwEaoBAJos0llujz32WLp2otVasWJFcAZNBv0HAABovQjVAAAAosTr9SovL0/l5eXKy8vToEGDCJABAA0Kp9V/uEJ7B8ycOTMqxyF6EAB1I1QDAACIkvz8fHk8HkmSx+NRQUGBxowZY21RAADbi7TVf7jcbjc/7gDNyGF1AQAAAK1BSUmJCgsLZZqmpL0DRRcUFKikpMTiygAAANAcCNUAAACayDRNzZ49OxioNbQcAAAAsY9QDQAAoImKi4tVVFSkQCBQa3kgEFBRUZGKi4stqgwAAADNhVANAACgiVJTU5Weni6Ho/aplcPhUHp6ulJTUy2qDAAAAM2FUA0AAKCJDMPQ+PHjZRjGfstzcnL2Ww4AAIDYR6gGAAAQBSkpKcrMzKy1LCsrS8nJyRZVBAAAgOZEqAYAABAlp59+erBVmmEYOu200yyuCAAAAM2FUA0AACBKFi5cGJzp0zRNLVq0yOKKAAAA0FycVhcAAADQGpSUlKiwsLDWsoKCAg0fPlwpKSkWVQVExjRN+Xy+A67j9XrrvF0fl8vFuIIAgFaJUA0AAKCJTNPU7Nmzg63U9l0+efJkQgXEBJ/PpwkTJoS9/qRJkxpcJzc3V263uyllAQBgS3T/BAAAaKLi4mIVFRUpEAjUWh4IBFRUVKTi4mKLKgMAAEBzoaUaAABAE6Wmpio9PV1r166tFaw5HA4NGjRIqampFlYHhM/lcik3N/eA64R2EQ2na6fL5YpafcCBBAIB7dq1q8H1wunmHKlwuzknJibK4aBtC9BaEKoBAAA0kWEYGj9+vG655ZZay03TVE5ODl0/ETMMwwirq2ZCQkILVANEZteuXWF1SbbSzJkz1bFjR6vLABAlhGqwXDR/KYp04NxwMLguAKAp9h1nDQAAAK0DoRosF+mAuOGK1q9UDK4LAGhIzYQEdWGiAgBoGbHQ1TgWagQQPkI1AACAJqqZqGBfpmkGJypIS0uzoDIAaDvcbneDYwJK4fWUKSgo0MKFC2WapgzD0Omnn66srKx61w+3dwuhGtC6EKrBVh49vavccY3/Jd80Tfmq9952xanRrQK81aZuWLit0XUAANoWO09UEO4wC40ZfJ7WdwDsJNwxAaUDjwtYUlKi9957L9h93zRNvffeexo1apRSUlKiUiuA1oFQDbbijjPkdjblBN1QQnzUygEAICw1ExVMmTJlv+VWT1TQXMMsMDwCgNaopjv/vuNh1iynOz+AUMzlCwAAEAUpKSnKzMwMXmwZhqGsrCwlJydbXBkAIFw13flDWx1LUiAQCHbnB4AatFQDUCe6CwFA5LKzs7VkyRKVl5crKSnpgOPvtBSXyxXWGENerzc4yc/MmTMbbIXGuEAAWiM7d+cHYD+EagDqRHchAIic2+1WTk6OXnrpJY0bN84W+7tIxhiq4Xa7bVE7ALQ0O3fnB2A/hGoAAABRlJGRoYyMDKvLAAA0Uk13/vz8/ODsn3TnB1AXW4Zq77zzjhYsWCCPx6NevXrpD3/4g/r161fnuosXL9aTTz5Za1l8fLz++c9/tkSpQKtFdyEAQKxYsWJFsHUggSaAaLBjd34A9mO7UO2TTz5RXl6errzySvXv318FBQWaNm2aHnvsMXXq1KnO57Rr104zZsxo4UqB1o3uQgCAWOD1epWXl6fy8nLl5eVp0KBBHIsANJkdu/MDsB/bhWr5+fk65ZRTdPLJJ0uSrrzySi1fvlwffvihzjnnnDqfYxiGkpKSWq5IALYWziQLXq+3ztv1aasTLIQ7YUU4Iv3Mw9VW/28A7JWfny+PxyNJ8ng8Kigo0JgxY6wtCi2OCZbQHOjOD6AhtgrV/H6/vvvuu1rhmcPh0JFHHqn169fX+7zKykpde+21Mk1Tffr00UUXXaRDDz20znWrqqpUVVUVvG8Yhtq1axe8HU2hr2cYRrMekGPhYF/fZ2D32pv7/67mPVry/aLJjrVHOslCTffVA3nmmWea9AulHT6XA6nv/665JqwI5zMPV1P/b8Jhx+0ciLZY3M5LSkpUWFgo0zQl7Q1MCgoK9Jvf/EYpKSkWV4eW1FzHq5Y4xoQjFr+fsA7bC9BybBWq7dy5U4FAYL9WZ0lJSdq8eXOdz0lLS9M111yjXr16qaKiQm+99ZZuu+02PfLII+ratet+68+fP1/z5s0L3u/Tp4+mT5+ubt26RfVvkaQ9e/YEb6ekpATDu+YQ+l52Vd9nYPfam/v/TmrZbSXa7Fh7c2xTTf3bYnU7t3vdEt9RIFpibTs3TVMzZ86s87FXX31V9913HxeSrYRpmqqsrDzgOg093lidOnVSQkLCAddJSEho9m0t1r6fsBbbC9BybBWqNcaAAQM0YMCAWvdvuOEGLVq0SGPHjt1v/XPPPVfZ2dnB+zUHwNLSUvn9/qjWFtq9qaSkpFl/5YpmV6rmUt9nEHoS5PWbLVlSvULr2LJlS4MnU01+vxbcVqLNjrWbpqlnnnmmwXUi6f5RXl4e7F7UGHb/jtb3fxda94ixUlwTjhqmKQWq9952xElNuf6o9kv/fmXv7ZbY7uy4naPlhdO9LJa7lsXadr5582Z98cUX+y2vrq7WF198oeXLlystLc2CyhBtXq9XV111lSXvfeGFFza4Tku0Zou17yesxfYCNJ3T6Qyr8ZWtQrWOHTvK4XDsd+Hq8XjCHjPN6XSqT58+2rJlS52Px8fHKz4+vs7HaroOREvo65mmGfXXr++97Kq+zyB0p3/Dom0tWVJYvF5vsx+IWnJbiTa71h7OLKOR/r825W+zy+dSn/r+70KXxTklZ7w9Lv6llt3u7Lqdo2V5vd6ody/Lzc21zcVOrG3n3bt3V3p6uoqKivZ7LD09Xd27d7f934Dw2P3/keMQ7IbtBWg5tgrVnE6n+vbtq6KiIg0bNkySFAgEVFRUpDPPPDOs1wgEAtq4cSMDSgIAALRihmHorLPOqjNUy8zMtE0LQERXx5x7ZDgb/uGsPqZpSv7/ja/sjG/0dmL6fdqZd3uj6wAAtA62CtUkKTs7W0888YT69u2rfv36qbCwUF6vVyNHjpQkzZo1S126dNHFF18sSZo3b5769++v7t27a/fu3XrrrbdUWlqqU045xcK/ApEIbVH06Gld5XZafxLs9ZvBVnPhtHgCALR+LpdLubm5B1zH6/UGJ+KYOXNmg63QOMY0nmmaevvtt2UYRq1WGIZhqLCwUAMHDiRYa4UMp0tGfBMmDpIkV/MO6wEAaDtsF6qdcMIJ2rlzp+bOnSuPx6PevXtrypQpwe6fZWVltU6Qdu3apdzcXHk8HnXo0EF9+/bVvffeqx49elj0FyBSof+fbqdhi1AtFCfkAABp7/Egkq6abrfbNl07W6Pi4uI6W6mZpqmioiIVFxczphoAAGhWtgvVJOnMM8+st7vnXXfdVev+pZdeqksvvbT5iwKANii09Ue1Xwody8xK1SHzytQ3Tkg4g8qHK3Tsx2hNOmGnAeoRu9rydp6amqr09HStXbtWgUAguNzhcGjQoEFKTU1tlvcFAACoYctQDQBgD6EX6zWzbdqNz+erc4Zen88X9UHlJQW79jWVnQaoR+xqy9u5YRgaP368pkyZst/ynJwcQmsAANDsHFYXAAAAADRGSkpKrUkJDMNQVlaWkpOTLa4MAAC0BbRUAwDUK3QQ9RFjpTibHDWq/b+0nAtnoPcBOYYcTajdNE2Z/+tyajgbP9ZiwC+tz7NHF1q0PvE5YyRn4zf0vbMiVu+944xrfEsvv19VeW80uo5IZWdna8mSJSovL1dSUpKysrJa7L0BAEDbZpPLI/uI1bFJQscU8tacENtAaC31jXsEwL5C9zdxTskZb5fuVLVn+muIwyk5mlS7IUVlkkb2g2hGTqeM+Maf2u2dFTG+yWW09FbudruVk5Ojl156SePGjaNbNWzD7tcVEuN7AkBTEartI1bHJgk9YF/39uKovFe01TfuEQAAANDa2P26QmJ8TwBoKsZUAwAAQMzyer3Ky8vTtm3blJeXF9VWPAAAAAdCS7UDeDLzd3I3YQAh0zTlq97b/dEV1/ixSbzVfl1b+NoB1wkdU2jWWSPldsY16r2izeuvDracC2fcIwAAgEjk5+fL4/FIkjwejwoKCjRmzBhriwL2cezvZyouvvEtwkzTVMC/t2eKw9m0LpvVVV4tezF6rd0AoC0jVDsAd5xTCc6mjS3SrulDk4Ql9MDqdsYpoQkDFTcXxmsAAADRVFJSosLCwuC4raZpqqCgQMOHD1dKSorF1QG/iIt3NylUkyS5GEalrQt3nL5Ix+BjbD2g8eyXvAAAAAANME1Ts2fP3m8ipJrlkydP5iIRQKvSmHH6whmDj7H1gMZjTDUAAADEnOLiYhUVFSkQCNRaHggEVFRUpOLiYosqAwAAbQUt1QAAABBzUlNTlZ6erqKiov0eS09PV2pqqgVVAUDzcblcys3NbXC90G6i4XTtZOxroPEI1YBGCndMg3BEOu5BuBgfAdFU7Zcks6HV6mWaUmDv3C1yxElN2TT31gKgLTMMQ8cff3ydodrxxx/P8Q9Aq2MYRtjdNBMSGIMPaAmEakAjNWZMg3CEM+5BuBgfAdH071esrgAAfhEIBPTKK3XvmF555RWdcMIJcjgY6QQAADQfzjQAAAAQc1atWqVdu3bV+diuXbu0atWqFq4IAAC0NbRUA6LgtkyXXE34Npmmqar/dYuLj1OTuqz4/NK9hdHplgqEO3ZHOLxeb7Al5syZM6PWipJxQIC2aciQIUpMTKwzWEtMTNSQIUMsqAoAALQlhGpAFLicksvZlLFbDLnjo1VNw2Ne2X08uNY4Fpxp/vL/4m/i2GTR4g8Zlyy0vlCRjN0RCbfbTddkAA1q6Hh1xRVX6LHHHttv+ZVXXqmqqqo6n9MajzEAAMAahGqtkLe6uknPN01Tvuq909O74hxNOvFsai1oHnYfD641jgUXelH45jwLC6mHz+djQFsAttPY49Wjjz5a72Ot8RgDAACsQajWCl1XuNjqEgAAAAAAAFo1QjWgjZs4Ok7xTRwPzv+/BonOJowHV+WXnnir9bZsDB336+zzJacN9r5+/y+t5hiXDIhNoV23zSr/AdZsOaF11Ne1PFzhjOsYOl7j6aefrvPOO6/B10Rsqb2d22Pc2NA6mrqdAwBilw0u6xANDCaOxoq3zXhwrfuENDRsdDolZ5M+82j55TNnfCEgNoV2LffPfsPCSurW1K7lkY7reN5559G1sxUK3c53zr7dwkrqxhAKANB2Eaq1EgwmDgAAAAAA0HII1QAAAGJUaCtw5/gxMprSnz9KzCp/sNUcrdQRDaHbUcfx98iIt367Mqt8wVZzbOcA0HZZf+YFAACARgntum3EO20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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.6% 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\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.0% 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\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.6% 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\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: UserWarning: 13.0% 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\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: 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\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\categorical.py:3544: 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"
     ]
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=car[['name','company','year','kms_driven','fuel_type']]\n",
    "y=car['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=make_pipeline(column_trans,lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking R2 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.argmax(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores[np.argmax(scores)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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