Car_Price_Prediction_Using_RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Car price prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional libraries\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the data\n",
    "df_init = pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>company</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>maruti suzuki</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>maruti suzuki</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>maruti suzuki</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>maruti suzuki</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>maruti suzuki</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        company  Year  Selling_Price  Present_Price  Kms_Driven  \\\n",
       "0     ritz  maruti suzuki  2014           3.35           5.59       27000   \n",
       "1      sx4  maruti suzuki  2013           4.75           9.54       43000   \n",
       "2     ciaz  maruti suzuki  2017           7.25           9.85        6900   \n",
       "3  wagon r  maruti suzuki  2011           2.85           4.15        5200   \n",
       "4    swift  maruti suzuki  2014           4.60           6.87       42450   \n",
       "\n",
       "  Fuel_Type Seller_Type Transmission  Owner  \n",
       "0    Petrol      Dealer       Manual      0  \n",
       "1    Diesel      Dealer       Manual      0  \n",
       "2    Petrol      Dealer       Manual      0  \n",
       "3    Petrol      Dealer       Manual      0  \n",
       "4    Diesel      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# High level analaysis of data\n",
    "df_init.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_init.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_init.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   company        301 non-null    object \n",
      " 2   Year           301 non-null    int64  \n",
      " 3   Selling_Price  301 non-null    float64\n",
      " 4   Present_Price  301 non-null    float64\n",
      " 5   Kms_Driven     301 non-null    int64  \n",
      " 6   Fuel_Type      301 non-null    object \n",
      " 7   Seller_Type    301 non-null    object \n",
      " 8   Transmission   301 non-null    object \n",
      " 9   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 23.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_init.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************\n",
      "['ritz' 'sx4' 'ciaz' 'wagon r' 'swift' 'vitara brezza' 's cross'\n",
      " 'alto 800' 'ertiga' 'dzire' 'alto k10' 'ignis' '800' 'baleno' 'omni'\n",
      " 'fortuner' 'innova' 'corolla altis' 'etios cross' 'etios g' 'etios liva'\n",
      " 'corolla' 'etios gd' 'camry' 'land cruiser' 'Royal Enfield Thunder 500'\n",
      " 'UM Renegade Mojave' 'KTM RC200' 'Bajaj Dominar 400'\n",
      " 'Royal Enfield Classic 350' 'KTM RC390' 'Hyosung GT250R'\n",
      " 'Royal Enfield Thunder 350' 'KTM 390 Duke ' 'Mahindra Mojo XT300'\n",
      " 'Bajaj Pulsar RS200' 'Royal Enfield Bullet 350'\n",
      " 'Royal Enfield Classic 500' 'Bajaj Avenger 220' 'Bajaj Avenger 150'\n",
      " 'Honda CB Hornet 160R' 'Yamaha FZ S V 2.0' 'Yamaha FZ 16'\n",
      " 'TVS Apache RTR 160' 'Bajaj Pulsar 150' 'Honda CBR 150' 'Hero Extreme'\n",
      " 'Bajaj Avenger 220 dtsi' 'Bajaj Avenger 150 street' 'Yamaha FZ  v 2.0'\n",
      " 'Bajaj Pulsar  NS 200' 'Bajaj Pulsar 220 F' 'TVS Apache RTR 180'\n",
      " 'Hero Passion X pro' 'Bajaj Pulsar NS 200' 'Yamaha Fazer '\n",
      " 'Honda Activa 4G' 'TVS Sport ' 'Honda Dream Yuga '\n",
      " 'Bajaj Avenger Street 220' 'Hero Splender iSmart' 'Activa 3g'\n",
      " 'Hero Passion Pro' 'Honda CB Trigger' 'Yamaha FZ S '\n",
      " 'Bajaj Pulsar 135 LS' 'Activa 4g' 'Honda CB Unicorn'\n",
      " 'Hero Honda CBZ extreme' 'Honda Karizma' 'Honda Activa 125' 'TVS Jupyter'\n",
      " 'Hero Honda Passion Pro' 'Hero Splender Plus' 'Honda CB Shine'\n",
      " 'Bajaj Discover 100' 'Suzuki Access 125' 'TVS Wego' 'Honda CB twister'\n",
      " 'Hero Glamour' 'Hero Super Splendor' 'Bajaj Discover 125' 'Hero Hunk'\n",
      " 'Hero  Ignitor Disc' 'Hero  CBZ Xtreme' 'Bajaj  ct 100' 'i20' 'grand i10'\n",
      " 'i10' 'eon' 'xcent' 'elantra' 'creta' 'verna' 'city' 'brio' 'amaze'\n",
      " 'jazz']\n",
      "****************************************\n",
      "['maruti suzuki' 'toyota' 'other' 'bajaj' 'honda' 'yamaha' 'tvs' 'hero'\n",
      " 'hyndai']\n",
      "****************************************\n",
      "['Petrol' 'Diesel' 'CNG']\n",
      "****************************************\n",
      "['Dealer' 'Individual']\n",
      "****************************************\n",
      "['Manual' 'Automatic']\n"
     ]
    }
   ],
   "source": [
    "columns = df_init.columns\n",
    "\n",
    "#checking unique values in the category type columns(object)\n",
    "for column in columns:\n",
    "    if df_init[column].dtypes == 'object':\n",
    "        print('*'*40)\n",
    "        print(df_init[column].unique())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_init['Owner'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking some of the other columns\n",
    "df_init['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "company          0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cheking for null values\n",
    "df_init.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a new column to find the Age of car\n",
    "df_init['Age'] = df_init['Year'].apply(lambda x: 2021 - x).astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>company</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>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>maruti suzuki</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",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>maruti suzuki</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",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>maruti suzuki</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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>maruti suzuki</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",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>maruti suzuki</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",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name        company  Year  Selling_Price  Present_Price  Kms_Driven  \\\n",
       "0     ritz  maruti suzuki  2014           3.35           5.59       27000   \n",
       "1      sx4  maruti suzuki  2013           4.75           9.54       43000   \n",
       "2     ciaz  maruti suzuki  2017           7.25           9.85        6900   \n",
       "3  wagon r  maruti suzuki  2011           2.85           4.15        5200   \n",
       "4    swift  maruti suzuki  2014           4.60           6.87       42450   \n",
       "\n",
       "  Fuel_Type Seller_Type Transmission  Owner  Age  \n",
       "0    Petrol      Dealer       Manual      0    7  \n",
       "1    Diesel      Dealer       Manual      0    8  \n",
       "2    Petrol      Dealer       Manual      0    4  \n",
       "3    Petrol      Dealer       Manual      0   10  \n",
       "4    Diesel      Dealer       Manual      0    7  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_init.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Car_Name</th>\n",
       "      <th>company</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>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>maruti suzuki</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>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>maruti suzuki</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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>maruti suzuki</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>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>maruti suzuki</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>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>maruti suzuki</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>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name        company  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  maruti suzuki           3.35           5.59       27000    Petrol   \n",
       "1      sx4  maruti suzuki           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  maruti suzuki           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  maruti suzuki           2.85           4.15        5200    Petrol   \n",
       "4    swift  maruti suzuki           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  Age  \n",
       "0      Dealer       Manual      0    7  \n",
       "1      Dealer       Manual      0    8  \n",
       "2      Dealer       Manual      0    4  \n",
       "3      Dealer       Manual      0   10  \n",
       "4      Dealer       Manual      0    7  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the column Year as it is redundant now\n",
    "df_init = df_init.drop(['Year'], axis = 1)\n",
    "df_init.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
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       "\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>company</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>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>maruti suzuki</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>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>maruti suzuki</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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>maruti suzuki</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>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>maruti suzuki</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>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>maruti suzuki</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>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         company  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0  maruti suzuki           3.35           5.59       27000    Petrol   \n",
       "1  maruti suzuki           4.75           9.54       43000    Diesel   \n",
       "2  maruti suzuki           7.25           9.85        6900    Petrol   \n",
       "3  maruti suzuki           2.85           4.15        5200    Petrol   \n",
       "4  maruti suzuki           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  Age  \n",
       "0      Dealer       Manual      0    7  \n",
       "1      Dealer       Manual      0    8  \n",
       "2      Dealer       Manual      0    4  \n",
       "3      Dealer       Manual      0   10  \n",
       "4      Dealer       Manual      0    7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the column Car_Name as it is not adding any value\n",
    "df_init = df_init.drop(['Car_Name'], axis = 1)\n",
    "df_init.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting categorical ariables into dummy variable\n",
    "df_final = pd.get_dummies(df_init, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Age</th>\n",
       "      <th>company_hero</th>\n",
       "      <th>company_honda</th>\n",
       "      <th>company_hyndai</th>\n",
       "      <th>company_maruti suzuki</th>\n",
       "      <th>company_other</th>\n",
       "      <th>company_toyota</th>\n",
       "      <th>company_tvs</th>\n",
       "      <th>company_yamaha</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>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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  Age  company_hero  \\\n",
       "0           3.35           5.59       27000      0    7             0   \n",
       "1           4.75           9.54       43000      0    8             0   \n",
       "2           7.25           9.85        6900      0    4             0   \n",
       "3           2.85           4.15        5200      0   10             0   \n",
       "4           4.60           6.87       42450      0    7             0   \n",
       "\n",
       "   company_honda  company_hyndai  company_maruti suzuki  company_other  \\\n",
       "0              0               0                      1              0   \n",
       "1              0               0                      1              0   \n",
       "2              0               0                      1              0   \n",
       "3              0               0                      1              0   \n",
       "4              0               0                      1              0   \n",
       "\n",
       "   company_toyota  company_tvs  company_yamaha  Fuel_Type_Diesel  \\\n",
       "0               0            0               0                 0   \n",
       "1               0            0               0                 1   \n",
       "2               0            0               0                 0   \n",
       "3               0            0               0                 0   \n",
       "4               0            0               0                 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": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the correlation between the variables\n",
    "\n",
    "corr = df_final.corr()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(corr, annot = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the dataset for test train split\n",
    "X, y = df_final.iloc[:, 1:], df_final.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for feature importance\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.38151525e-01 3.89987406e-02 5.94784365e-04 7.24007174e-02\n",
      " 4.01880625e-06 4.00585798e-03 2.91315523e-03 3.62026928e-03\n",
      " 7.89949058e-03 7.54844225e-03 4.88514290e-06 4.58307187e-06\n",
      " 2.36643872e-01 9.33466695e-03 1.34342049e-01 1.43532943e-01]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the graph for feature importance\n",
    "imp_features = pd.Series(data = model.feature_importances_, index = X.columns)\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.barh(imp_features.index, imp_features)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a Random Forest model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "base_rf = RandomForestRegressor()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the hyperparameters\n",
    "\n",
    "# Number of trees in Random Forest\n",
    "n_estimators = [100, 200, 400, 500, 800]\n",
    "# Maximum number of levels in the tree\n",
    "max_depth = [5, 10, 15, 20, 25, 30]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 20, 50, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [100, 200, 400, 500, 800],\n",
       " 'max_depth': [5, 10, 15, 20, 25, 30],\n",
       " 'min_samples_split': [2, 5, 10, 20, 50, 100],\n",
       " 'min_samples_leaf': [1, 2, 5, 10]}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will tune the hyperparameters using RandomGridSearch\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "random_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=15, min_samples_leaf=10, min_samples_split=10, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=15, min_samples_leaf=10, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=10, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=10, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=10, min_samples_split=10, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=2, min_samples_split=50, n_estimators=800; total time=   2.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=2, min_samples_split=50, n_estimators=800; total time=   2.0s\n",
      "[CV] END max_depth=5, min_samples_leaf=2, min_samples_split=50, n_estimators=800; total time=   1.9s\n",
      "[CV] END max_depth=5, min_samples_leaf=2, min_samples_split=50, n_estimators=800; total time=   1.9s\n",
      "[CV] END max_depth=5, min_samples_leaf=2, min_samples_split=50, n_estimators=800; total time=   2.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=5, min_samples_split=20, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=5, min_samples_split=20, n_estimators=500; total time=   1.3s\n",
      "[CV] END max_depth=10, min_samples_leaf=5, min_samples_split=20, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=5, min_samples_split=20, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=5, min_samples_split=20, n_estimators=500; total time=   1.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=10, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=10, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=10, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=10, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=10, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=5, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=2, n_estimators=500; total time=   1.3s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=2, n_estimators=500; total time=   1.3s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=2, n_estimators=500; total time=   1.3s\n",
      "[CV] END max_depth=5, min_samples_leaf=5, min_samples_split=2, n_estimators=500; total time=   1.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=100, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=100, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=100, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=100, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=100, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=400; total time=   1.0s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=400; total time=   1.0s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=400; total time=   1.0s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, min_samples_leaf=2, min_samples_split=50, n_estimators=400; total time=   1.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 20, 50,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 400, 500,\n",
       "                                                         800]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = base_rf, param_distributions = random_grid, scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the best params\n",
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.5175701773196466"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\parth\\anaconda3\\envs\\carsalepred\\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": {
      "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(predictions - y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.85904205893833\n",
      "MSE: 4.534830412289833\n",
      "RMSE: 2.129514125872339\n"
     ]
    }
   ],
   "source": [
    "# Getting an idea about the performance metrics of the model\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model as a pickle file\n",
    "import pickle\n",
    "\n",
    "file = open('Car_Price_Pred_RF_model.pkl', 'wb')\n",
    "\n",
    "# Dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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