Car_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Present']=2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "      <th>Present</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",
       "      <td>2020</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",
       "      <td>2020</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",
       "      <td>2020</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",
       "      <td>2020</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",
       "      <td>2020</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  Present  \n",
       "0      Dealer       Manual      0     2020  \n",
       "1      Dealer       Manual      0     2020  \n",
       "2      Dealer       Manual      0     2020  \n",
       "3      Dealer       Manual      0     2020  \n",
       "4      Dealer       Manual      0     2020  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"]=df['Present']-df['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>Present</th>\n",
       "      <th>Age</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",
       "      <td>2020</td>\n",
       "      <td>6</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",
       "      <td>2020</td>\n",
       "      <td>7</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",
       "      <td>2020</td>\n",
       "      <td>3</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",
       "      <td>2020</td>\n",
       "      <td>9</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",
       "      <td>2020</td>\n",
       "      <td>6</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  Present  Age  \n",
       "0      Dealer       Manual      0     2020    6  \n",
       "1      Dealer       Manual      0     2020    7  \n",
       "2      Dealer       Manual      0     2020    3  \n",
       "3      Dealer       Manual      0     2020    9  \n",
       "4      Dealer       Manual      0     2020    6  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Car_Name','Present','Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>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>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>7</td>\n",
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       "    <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>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>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  Age  \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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 8 columns):\n",
      "Selling_Price    301 non-null float64\n",
      "Present_Price    301 non-null float64\n",
      "Kms_Driven       301 non-null int64\n",
      "Fuel_Type        301 non-null object\n",
      "Seller_Type      301 non-null object\n",
      "Transmission     301 non-null object\n",
      "Owner            301 non-null int64\n",
      "Age              301 non-null int64\n",
      "dtypes: float64(2), int64(3), object(3)\n",
      "memory usage: 18.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Petrol', 'Diesel', 'CNG'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Petrol    239\n",
       "Diesel     60\n",
       "CNG         2\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb=LabelEncoder()\n",
    "df['Fuel_Type']=lb.fit_transform(df['Fuel_Type'])\n",
    "df['Seller_Type']=lb.fit_transform(df['Seller_Type'])\n",
    "df['Transmission']=lb.fit_transform(df['Transmission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>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>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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          2            0   \n",
       "1           4.75           9.54       43000          1            0   \n",
       "2           7.25           9.85        6900          2            0   \n",
       "3           2.85           4.15        5200          2            0   \n",
       "4           4.60           6.87       42450          1            0   \n",
       "\n",
       "   Transmission  Owner  Age  \n",
       "0             1      0    6  \n",
       "1             1      0    7  \n",
       "2             1      0    3  \n",
       "3             1      0    9  \n",
       "4             1      0    6  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    290\n",
       "1     10\n",
       "3      1\n",
       "Name: Owner, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Owner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    195\n",
       "1    106\n",
       "Name: Seller_Type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Seller_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    261\n",
       "0     40\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    239\n",
       "1     60\n",
       "0      2\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>11.60</td>\n",
       "      <td>33988</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>11.00</td>\n",
       "      <td>87934</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>12.50</td>\n",
       "      <td>9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5.90</td>\n",
       "      <td>5464</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  Owner  \\\n",
       "0             5.59       27000          2            0             1      0   \n",
       "1             9.54       43000          1            0             1      0   \n",
       "2             9.85        6900          2            0             1      0   \n",
       "3             4.15        5200          2            0             1      0   \n",
       "4             6.87       42450          1            0             1      0   \n",
       "..             ...         ...        ...          ...           ...    ...   \n",
       "296          11.60       33988          1            0             1      0   \n",
       "297           5.90       60000          2            0             1      0   \n",
       "298          11.00       87934          2            0             1      0   \n",
       "299          12.50        9000          1            0             1      0   \n",
       "300           5.90        5464          2            0             1      0   \n",
       "\n",
       "     Age  \n",
       "0      6  \n",
       "1      7  \n",
       "2      3  \n",
       "3      9  \n",
       "4      6  \n",
       "..   ...  \n",
       "296    4  \n",
       "297    5  \n",
       "298   11  \n",
       "299    3  \n",
       "300    4  \n",
       "\n",
       "[301 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,1:]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "       ...  \n",
       "296     9.50\n",
       "297     4.00\n",
       "298     3.35\n",
       "299    11.50\n",
       "300     5.30\n",
       "Name: Selling_Price, Length: 301, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Selling_Price']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NITISH\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "                    max_features='auto', max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                    oob_score=False, random_state=None, verbose=0,\n",
       "                    warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.24222675e-01 4.23726418e-02 1.91502986e-01 2.61170545e-01\n",
      " 1.14187828e-01 2.24986524e-04 6.63183385e-02]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "import matplotlib.pyplot as plt\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": 24,
   "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,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\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",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "# 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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error',\n",
    "                           n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.9s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.5s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.4s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.8s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   2.5s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   2.6s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   2.9s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   3.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   2.5s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.8s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   1.0s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.9s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.9s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.9s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.6s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.5s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   2.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   2.4s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   2.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   2.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   2.5s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.3s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.3s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   2.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.7s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.7s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.7s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.7s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.4s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.5s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   2.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   2.3s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   2.5s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   2.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                   iid='warn', n_iter=10, 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",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.82919207,  2.80948979,  0.54809351,  2.82109634,  1.12579203,\n",
       "        5.33994598,  0.72846847,  3.20002509,  9.99452391,  0.61057781,\n",
       "        7.76412526,  3.88034413,  5.30745562,  4.39307277,  0.23760542,\n",
       "        0.36571575,  7.49835583,  5.29596328,  5.31338303,  7.49835583,\n",
       "        5.23228989,  3.6960237 , 13.19949977,  7.54863339, 17.8375705 ,\n",
       "        3.06017665,  3.63583723,  0.61860223,  0.27707732,  0.47904412,\n",
       "        0.31089731,  0.26344573,  5.10823495, 21.12916663, 20.63448098,\n",
       "        4.34441398,  3.58978498,  0.8588784 ,  0.31320711,  4.17600129,\n",
       "       10.02297097, 10.20267087,  0.31504105,  4.40701512,  4.39332785,\n",
       "        4.66611829,  6.19235789,  7.35896738,  8.08797433,  1.14635568,\n",
       "        4.83956183,  0.425945  ,  2.79478745,  4.53610204,  1.15364255,\n",
       "        0.23196328, 21.12916663,  0.6205732 ,  6.12357365,  5.94277342,\n",
       "        0.28910577])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=model.predict(X_test)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "285     7.40\n",
       "248     4.00\n",
       "150     0.50\n",
       "217     3.15\n",
       "107     1.25\n",
       "       ...  \n",
       "62     18.75\n",
       "154     0.50\n",
       "218     6.45\n",
       "286     5.65\n",
       "186     0.25\n",
       "Name: Selling_Price, Length: 61, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.491675218510828"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8685034513808573\n",
      "MSE: 5.491675218510828\n",
      "RMSE: 2.3434323584244603\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, y))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.83956183])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5.59,27000,2,0,1,0,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file=open(\"car.pkl\",'wb')\n",
    "pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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