car_prediction_jupyter_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the necessary Library and exploring the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here *Selling_Price* is our dependent feature which is we need to predict from the other independent features given in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here there are no missing values. Let us try to gain more insights from the data like the distribution of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do not need Car_name to predict the selling price hence we are dropping the car_name column\n",
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
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       "      <th>Owner</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current Year']=2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
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       "      <th>Seller_Type</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  \n",
       "0       Manual      0          2020  \n",
       "1       Manual      0          2020  \n",
       "2       Manual      0          2020  \n",
       "3       Manual      0          2020  \n",
       "4       Manual      0          2020  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are adding a column which tells us how old is that car.\n",
    "final_dataset['no_year']=final_dataset['Current Year']- final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  no_year  \n",
       "0       Manual      0          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the year column\n",
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
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       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  no_year  \n",
       "0       Manual      0          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One Hot-Encoding\n",
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True) #drop_first will prevent first column from dummy variable trap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Present_Price</th>\n",
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       "      <th>Owner</th>\n",
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       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
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       "  <tbody>\n",
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       "      <td>6900</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.85</td>\n",
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       "      <td>5200</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  Current Year  no_year  \\\n",
       "0           3.35           5.59       27000      0          2020        6   \n",
       "1           4.75           9.54       43000      0          2020        7   \n",
       "2           7.25           9.85        6900      0          2020        3   \n",
       "3           2.85           4.15        5200      0          2020        9   \n",
       "4           4.60           6.87       42450      0          2020        6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  Current Year  no_year  \\\n",
       "0           3.35           5.59       27000      0          2020        6   \n",
       "1           4.75           9.54       43000      0          2020        7   \n",
       "2           7.25           9.85        6900      0          2020        3   \n",
       "3           2.85           4.15        5200      0          2020        9   \n",
       "4           4.60           6.87       42450      0          2020        6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do not require Current Year as well, hence we will drop it\n",
    "final_dataset=final_dataset.drop(['Current Year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        6                 0   \n",
       "1           4.75           9.54       43000      0        7                 1   \n",
       "2           7.25           9.85        6900      0        3                 0   \n",
       "3           2.85           4.15        5200      0        9                 0   \n",
       "4           4.60           6.87       42450      0        6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_year</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "no_year                     -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                         no_year  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price          -0.236141          0.552339         -0.540571   \n",
       "Present_Price           0.047584          0.473306         -0.465244   \n",
       "Kms_Driven              0.524342          0.172515         -0.172874   \n",
       "Owner                   0.182104         -0.053469          0.055687   \n",
       "no_year                 1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel       -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol        0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual  0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual    -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "no_year                               0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out correlation among the independent features\n",
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x203753c2190>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#get correlations of each features in dataset\n",
    "corrmat = df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Fuel_type_petrol and fuel_type_diesel are negatively correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Independent and dependent features\n",
    "X=final_dataset.iloc[:,1:]\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0        6                 0   \n",
       "1           9.54       43000      0        7                 1   \n",
       "2           9.85        6900      0        3                 0   \n",
       "3           4.15        5200      0        9                 0   \n",
       "4           6.87       42450      0        6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Feature Importance\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37865916 0.04148139 0.00080965 0.07553466 0.22732119 0.00837473\n",
      " 0.12271479 0.14510442]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that **present price** and **fuel type** are having the highest feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "\n",
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh') #plotting 5 most important features\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 227, 354, 481, 609, 736, 863, 990, 1118, 1245, 1372, 1500]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "n_estimators=[int(x) for x in np.linspace(start=100, stop=1500,num=12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.047084148035726"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tanma\\anaconda3\\envs\\carPrediction\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Density'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2037e9ff460>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8823032967032987\n",
      "MSE: 4.043644458980226\n",
      "RMSE: 2.010881512914231\n"
     ]
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "## creating a pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}