car_price_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70ddcd62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e217cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d18a83ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e754c565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n",
      "['Petrol' 'Diesel' 'CNG']\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())\n",
    "print(df['Fuel_Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2192f39c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3053ff26",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
    "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a3f717e",
   "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>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "432bcdb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current Year'] = 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c84243cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  \n",
       "0       Manual      0          2020  \n",
       "1       Manual      0          2020  \n",
       "2       Manual      0          2020  \n",
       "3       Manual      0          2020  \n",
       "4       Manual      0          2020  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "855346ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year'] = final_dataset['Current Year'] - final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab8a4eec",
   "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>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  no_year  \n",
       "0       Manual      0          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6fe992be",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77491b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  no_year  \n",
       "0       Manual      0          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53068076",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Current Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "448c19e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
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       "      <td>6</td>\n",
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       "      <th>1</th>\n",
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       "      <td>6900</td>\n",
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       "      <td>5200</td>\n",
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  no_year  \n",
       "0       Manual      0        6  \n",
       "1       Manual      0        7  \n",
       "2       Manual      0        3  \n",
       "3       Manual      0        9  \n",
       "4       Manual      0        6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79cee120",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = pd.get_dummies(final_dataset,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28f5c8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
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       "      <td>27000</td>\n",
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       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
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       "      <td>6900</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
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       "      <td>9</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
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      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        6                 0   \n",
       "1           4.75           9.54       43000      0        7                 1   \n",
       "2           7.25           9.85        6900      0        3                 0   \n",
       "3           2.85           4.15        5200      0        9                 0   \n",
       "4           4.60           6.87       42450      0        6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "298fd09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>Present_Price</th>\n",
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       "      <td>0.089216</td>\n",
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       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
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       "      <th>Owner</th>\n",
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       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_year</th>\n",
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       "      <td>0.047584</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "no_year                     -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                         no_year  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price          -0.236141          0.552339         -0.540571   \n",
       "Present_Price           0.047584          0.473306         -0.465244   \n",
       "Kms_Driven              0.524342          0.172515         -0.172874   \n",
       "Owner                   0.182104         -0.053469          0.055687   \n",
       "no_year                 1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel       -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol        0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual  0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual    -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "no_year                               0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "799bccc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df13abc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x14ff248b3a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b13453e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b2a6650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=final_dataset.corr()\n",
    "top_corr_features=corrmat.index \n",
    "plt.figure(figsize=(20,20)) \n",
    "#plot heat map\n",
    "g=sns.heatmap(final_dataset[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fee91529",
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        6                 0   \n",
       "1           4.75           9.54       43000      0        7                 1   \n",
       "2           7.25           9.85        6900      0        3                 0   \n",
       "3           2.85           4.15        5200      0        9                 0   \n",
       "4           4.60           6.87       42450      0        6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9eb7fa71",
   "metadata": {},
   "outputs": [],
   "source": [
    "##independent and dependent features\n",
    "X = final_dataset.iloc[:,1:]\n",
    "y = final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c45aaa5",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0        6                 0   \n",
       "1           9.54       43000      0        7                 1   \n",
       "2           9.85        6900      0        3                 0   \n",
       "3           4.15        5200      0        9                 0   \n",
       "4           6.87       42450      0        6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "297caa16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d01f7929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1b1e4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3631226  0.03988707 0.00039062 0.0755732  0.25591101 0.00643832\n",
      " 0.11801065 0.14066654]\n"
     ]
    }
   ],
   "source": [
    "##feature importance\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a26a01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0b37e73d",
   "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": 31,
   "id": "26a0b8df",
   "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>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>7.60</td>\n",
       "      <td>50456</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>7.50</td>\n",
       "      <td>44000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>9.40</td>\n",
       "      <td>36000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>30.61</td>\n",
       "      <td>104707</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.75</td>\n",
       "      <td>49000</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.87</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.99</td>\n",
       "      <td>13000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.46</td>\n",
       "      <td>45280</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>7.13</td>\n",
       "      <td>35866</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "281           7.60       50456      0       14                 0   \n",
       "287           7.50       44000      0        5                 0   \n",
       "219           9.40       36000      0        8                 0   \n",
       "50           30.61      104707      0        8                 1   \n",
       "0             5.59       27000      0        6                 0   \n",
       "..             ...         ...    ...      ...               ...   \n",
       "192           0.75       49000      1       13                 0   \n",
       "138           0.87       15000      0        4                 0   \n",
       "152           0.99       13000      0        8                 0   \n",
       "23            3.46       45280      0        6                 0   \n",
       "241           7.13       35866      1        5                 0   \n",
       "\n",
       "     Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "281                 1                       0                    1  \n",
       "287                 1                       0                    0  \n",
       "219                 1                       0                    1  \n",
       "50                  0                       0                    0  \n",
       "0                   1                       0                    1  \n",
       "..                ...                     ...                  ...  \n",
       "192                 1                       1                    1  \n",
       "138                 1                       1                    1  \n",
       "152                 1                       1                    1  \n",
       "23                  1                       0                    1  \n",
       "241                 1                       0                    1  \n",
       "\n",
       "[240 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5ae34fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_random = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c5024eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f85c36e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "##Hyperparameters\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2def66ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4c04bad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f2cf428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "932787bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "31628732",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2efa0d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d835573",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "02373ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.83253941,  5.31790903,  0.44619414,  5.13352417,  4.26699774,\n",
       "       10.06522114,  0.32078579,  0.55869749,  7.34658781,  5.66909731,\n",
       "       10.04690557,  1.2358851 ,  3.02860401,  2.80792416,  5.8219205 ,\n",
       "        3.47428048,  2.81171368,  3.45540741,  4.5958501 ,  3.13940353,\n",
       "        7.44306289,  7.63195248,  7.45688994,  4.79362526,  3.07271416,\n",
       "        5.81040286,  4.81112503,  4.55137108,  3.86979886,  4.29608636,\n",
       "        7.45688994,  5.44793087, 20.43194041,  6.64880589,  9.56580555,\n",
       "        1.10693066, 16.86274096,  0.41951637,  6.93715501, 10.27651027,\n",
       "        3.55840695, 10.47714539,  2.75191063,  0.48063546,  4.71413709,\n",
       "        2.79488706,  0.42297146,  2.81593879,  6.3938041 ,  0.69996946,\n",
       "        0.42297146, 20.32035017,  0.68199467,  4.29921497, 19.7648246 ,\n",
       "        4.83752135,  0.41051636,  1.28045528,  5.52943079,  0.35814618,\n",
       "        0.30561826])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0dd5cb38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python39\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "54fa09dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14ffead9fd0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54267c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89201edf",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}