Car_Price_Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4792db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "072c2ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"car data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fcbd681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d397f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae18d451",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "339ee6a4",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "675b2208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51c8d550",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removed all the car name(irrelevant to predict the car price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a29fc07",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven', 'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa6f0565",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec774034",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current_Year'] = 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c00e309a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</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>2021</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>2021</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>2021</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>2021</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          2021  \n",
       "1       Manual      0          2021  \n",
       "2       Manual      0          2021  \n",
       "3       Manual      0          2021  \n",
       "4       Manual      0          2021  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11edd71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year'] = final_dataset['Current_Year'] - final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f739ec96",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current_Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</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>2021</td>\n",
       "      <td>8</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>2021</td>\n",
       "      <td>4</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>2021</td>\n",
       "      <td>10</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>2021</td>\n",
       "      <td>7</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          2021        7  \n",
       "1       Manual      0          2021        8  \n",
       "2       Manual      0          2021        4  \n",
       "3       Manual      0          2021       10  \n",
       "4       Manual      0          2021        7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdf4aff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "017c27ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Manual</td>\n",
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      "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          2021        7  \n",
       "1       Manual      0          2021        8  \n",
       "2       Manual      0          2021        4  \n",
       "3       Manual      0          2021       10  \n",
       "4       Manual      0          2021        7  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06656f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Current_Year'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16f479f4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>7</td>\n",
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      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  no_year  \n",
       "0       Manual      0        7  \n",
       "1       Manual      0        8  \n",
       "2       Manual      0        4  \n",
       "3       Manual      0       10  \n",
       "4       Manual      0        7  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0e523b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_dummies() helps to convert everything into one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "792ecb53",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset= pd.get_dummies(final_dataset, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bc29ace1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        7                 0   \n",
       "1           4.75           9.54       43000      0        8                 1   \n",
       "2           7.25           9.85        6900      0        4                 0   \n",
       "3           2.85           4.15        5200      0       10                 0   \n",
       "4           4.60           6.87       42450      0        7                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "476cb082",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.172515</td>\n",
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       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
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       "      <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85f0148b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "52a39e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e6d9cff700>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jkrWysdF1D+7frS+9cImeewNo5Gs6EOt2/LwfiHW7vq16IP8AvKI/1uXYT/tjXU2uDABq1+je1h+P6MTh8u2dODyg/nh0nVviRrbWd5h12d62qI7fv2J79w+s+i4KKBqMR50zeh29zdji//7qQcaYH5I0I+nz1tqBpWXvk/SctTZvjPlPkmSt/VWH216QNGStvVrt9oaGhuyZM6vnQebm5jWcSGk8lVVfJKTBWKR0Ip5CwerCVFoT0xlt7Q5rV09n2Yk711uPG1pNQXDKaT5f0EtjSY0lM2pva1E03Kq7YxHHEzjVO5NkftNxLadrZaOadeOpjDrbWjS/YJXKzGvnLZ26fQs9d7Or8jXd8IvslNHZuZxGEtOlz/uBWLcvTiZeRP49zbVeCtSRazlNz2U1mpgp9dP+WBcnE4dbXPnMB2pRZW9z9Th/dCypRDKjWDSs/niUk4ljXWt9h7mMa700l1vQuStJjacy6ouEtWdblJOJY02ZTF7DY8m3ByQHVwAAIABJREFUMhqPOp1MvOqM1nYa8gax1j5vjNm1YtlXl139hqQfr3cd7e2tuvf2Hsd1gYDRHb1duqPXebZ+vfWAG4LBgPbcerP23Lr+2HpnksyjkrWyUeu66xkLf2jka9rR3lbx894PyD8Ar+hsD+ne25nMALC5NLq3BYMB7b31Zu2t4jgfKFrrO8x6aGtr0dCuWxq2PfhfOBzU97qYUb9P+f6MpK9UWGclfdUY8y1jzAOV7sAY84Ax5owx5szk5GRdigSuFzmFH5BTeB0ZhR+QU/gBOYXXkVH4ATmF15FR+IFvJzeMMb8uKS/pzyoM+QFr7T2S3i/p40s/cbWKtfaz1toha+1Qb29vnaoFrg85hR+QU3gdGYUfkFP4ATmF15FR+AE5hdeRUfiBp3+WqhJjzP8h6aCk/bbCSUOstVeW/jthjPmypHslPX+9217+2+99kXDpRJ3FZfFoWAsF6dpsVm2BgCZnsgq1tujmjla9oy/C7yOirpzyufL31leO2Xlzhy6+MaupdFZtLQHN5hbKbrte5ittB7gelbJcXD6VzsrI6Fo6q75Iu27qCOrKm2/14IlpsomN8/s5NwDAK+inADajRve2Ks+dAJSp5nshP28P/ud2b6v75IYxpkXSb1lrf8Wl+zsg6Vf/f/buPbqN874T/vcZzACDu0iKBCjKkiyZ8gUgrbhap0lzmlSqXSeVaMVvoyTtWW9v26ZvU2urppdtbamS1W7bbdSVk7bZ9Jrs2bZRXjuy5CauHWmbnDZpt7IrUaQvomxdIpIALxIBcIjBYDDP+weJEUAMeIGAIYb6fc7hETHzzDwPMF/+gJkRZgB8kHM+U6WNH4DAOc/M/f4ogMO327dhcLw8mMD+4+eg5g3IkoCje7fBLTJ8+m/+HS0+N55830b83b9dw8e3b8BzZ4bMdvt2duPyhIIPxzrpBAdpiGr5fCwWLbsxc2mbjW1e/NKObnzuzFBFZo/u3YZH74/glTeTZev8/I+/B5rOF+yHkNtRLcvFPP7+y29W5PXg7hheGRjFw5vbcOz0EGWTLNtMVsNLA0kcODlg5udwXxy74hE6IEcIIctA9ZQQshrZXduy2TxODSQq+tsdj9IJDlLVUo4LObk/4nyNqG0NP8rOOS8A+D7G2LJTzRj7WwDfBXAvY+w6Y+xnAHweQBDAq4yxc4yxL8y1XccY+/rcohEA/8QYOw/g/wL4e875y7f7XK5MKuYfLACoeQP7j59D//UU1LyBJx5aj2Onh7Crt8s86FZsd+z0EIbGpjE4mrrdYRBiqVo+r0wqVdvs6u3C0ycGLDO7//g5DI6mKtbZf71y2vx+CLkd1bJczKNVXg+dGsRPfuBu88RG6XKUTbIUA4mM+QELmM3PgZMDGEhkVnhkhBDiLFRPCSGrkd217UIibdnfhUS6If2R1WEpx4Wc3B9xvkbUNrsuS/XvAF5kjH0VgJlwzvkLCy3EOf+kxeS/qNJ2BMBH5n5/F8CDNY+2imRaNV/8IjVvwJi7MBZjs4+L/1q1S6RUPHhXvUdGSPV8jmVUbG4PWLZZLLOjqcp1Gty6bWk/hNyOalku5rFaXm8qecomqVkynbPMTzKdW6EREUKIM1E9JYSsRnbXNqqlpBZLOS7k5P6I8zWittl1faRWAJMAdgDYPfezy6a+6yYSkiFL5S+ZLAko/aZVcX61dtGw3PBxkjtTtXx2BOVF25T+Wzq9M1zZ3sWs25b2Q8jtqJbT0jxazW/xS5RNUrNIyGOZn0jIs0IjIoQQZ6J6SghZjeyubVRLSS2WclzIyf0R52tEbbPl5Abn/Kcsfn7ajr7raVObH0f3bis7uHZ07zb0rg9DlgQ8/9p17NvZjVPnh/HUju6ydvt2dqO7I4BYZ3glnwJZxarls3gDcKs2p84P48ieuGVmj+7dhlhnuGKdPesrp83vh5DbUS3LxTxa5fXg7hj++p8uY9/OyhxTNslSxKNBHO6Ll+XncF8c8WhwhUdGCCHOQvWUELIa2V3beqIhy/56oqGG9EdWh6UcF3Jyf8T5GlHbGOe8XuOr3gljMoCfARADYJ6+a7YTHNu3b+dnz55dsI1hcFyZVDCWUdERlM0/2OK0aEhGwQBuzuQgCgImpnPwSC60+CTcFwnRzcRJqZrurrRQTq3yOf8mTvPbbGjx4drNGdxQcpBcAma0AiKhW8sulvlq/ZBVo+45XYpqWS5On1RyYGC4oWiIBD1Y45cwmrpVg8enKZt3mGVvZKuMzmQ1DCQySKZziIQ8iEeDdPNbUi8rUksJWaa65ZTqKWmgurznE1KLJda2utXSbDaPC4m02V9PNEQ3EyeLWspxIdSxli6xP0JMS6xtSw6RXffc+F8A3gLwIwAOA/gJAG/a1HddCQLD5vZAxbXjKqfRteWI/arlc7E2Cy2z9MwTUj/VcrdQxje23Zq2pYOySZbP53Xj4bvbVnoYhBDieFRPCSGrkd21zeuVqJaSZVvKcSEn90ecr961za6vEdzDOX8GgMI5/xKAHwXQY1PfhBBCCCGEEEIIIYQQQghZRew6uZGf+3eKMRYHEAawyaa+CSGEEEIIIYQQQgghhBCyith1WaovMsZaADwD4CRmr9l0wKa+b5umFdA/ksJoSkVHyIM1Xhe2rJ29f4ZhcFy7oSCZzkHJ6Vgb9ADg0HSOiekcOsNexDpvtS1eK95tcW+D5SjrV9OxsdWPu9fSde2aRXFbJ9Nqzdt4OXTdwMBICsNTWbT63Qh6RGztCEIUhbJxbGjx4frUDJLpHNJqHmGvhEjIA70AjGWWPtbS57dujYwpJY/RtIrOsBf3R4K4nsra9txJ7ezO6ULjuDyh4OoNBWFZgiwJyOQKmFbzWOOVkM7lEfRI0Ao6fJIERSsgmy9gc5sfd7cHYBgcg6OzNbq05q60Znl9ydJNZVVcTCjmtT+3Rv1Y45UXX7BJUOYIIc0ilVXxdkk9vTfqR9hB9ZTYT1V1XBhNIZHOIRryoKczDFlu3OEKu/sjq4PdtS2dVfFWSX/3Rf0IUS0li7D7Xi26bjTl/jhpXvXe77bl3Ztz/udzv34LwGY7+qwXTSvgxPkRHDg5ADVvQJYEHOqLYSSl4gfubse33xnHUHIax04PQc0b2Njmxf/7oXtw8OSg2f7Injj6etbhm2+P4fdffhMf374Bz50ZMucf3bsNj8WiSz4AYRgcZ95OlvVby3pIYxgGx8uDCew/fs6WbaPrBr52bhjPvHgrowd3xzCcyiIou/DTf/2amc3PPHovrt/MluX1Ux+8B4dODS55rKXPb2tHAJ9878ay5Q8/Hscf/58hXJ3MUi6bmN05Xco4Wnxu/MIHN0PRCmW17Zd/eCtefWMUP/HeTUikM2XzPvuxB2Fwjl/9//rLau6eB7tW9ANVs7y+ZOmmsipeGRgve78/3BfHo/F2R5zgoMwRQppFKqviHyzq6Y/E2+kEB7GkqjpOXhityExfT2dDTjjY3R9ZHeyubemsipct+nss3k4nOEhV2WwepwYSFbnZHY825ASHrhs4cX4YT58YaKr9cdK8GrHfbUvSGGMRxthfMMa+Mff4AcbYz9jR9+3qH0mZLzgAqHkDB08OwsUE9I+k0H89ZR5oA4BdvV3miY1i+6dPDKB/JIX9x89hV2+XeWKjOH//8XO4MqkseUxXJpWKfmtZD2mMK5OKeXAJaPy2GRxNmSc2iv0dOjWIvM6hF1CWzaGx6Yq8Fk9MLHWspc/vZ39wS8XyB14cwK7eLlueO6md3TldyjieeGg9JhStorb90Tcv4sn3b8blSaVi3q989TyGxqYrau7gaMrW5zFfs7y+ZOkuJpSK9/sDJwdwMeGMbUaZI4Q0i7er1NO3HVJPif0ujFbucx84OYALDfo8Z3d/ZHWwu7a9VaW/t6iWkgVcSKSt61si3ZD+BkdT5omNYn/NsD9Omlcj9rvtOo321wD+AcC6uccXAfwXm/q+LYm0ar7gRWrewM2ZPBJpFQZH2XzGYNm+uJ5q88cy6pLHlLTot5b1kMZIVslMo7bNaMq6P0XTcXMmb05jDEvO60JjLX1+2ZxuuTxj5Y8pl83H7pwuZRxWGS2OK6vpVecZHBXTEqmVzVyzvL5k6ZLpnOU2S6ZzKzSi5aHMEUKahdPrKbFfwubM2N0fWR3srm1US0kt7M5NteNRK70/TppXIzJq18mNtZzz4wAMAOCc6wAKNvV9WzpDMmSp/GWSJQEtPgnRkAwXg+X8+Y+jJeuxmt8RXPpXbyIL9Luc9ZDGiFTJTKO2TWfYa9mf3y2ixVf+tcOl5nWhsZY+P59HtFye8/LHlMvmY3dOlzqOahn1ucWq8+ZfcUeWBETDK5u5Znl9ydJFQh7LbRYJeVZoRMtDmSOENAun11Niv6jNmbG7P7I62F3bqJaSWtidm2rHo1Z6f5w0r0Zk1K6TGwpjrA0ABwDG2PcDcMR3lHrWhXG4L152YuJQXwwFbqB3XRg968PYt7PbnH/q/DAO9cXK2h/ZE0fvujCO7t2GU+eH8dSO7rL5R/duw6Y2/5LHtKnNX9FvLeshjbGpzY+je7fZtm1inSE8+3h5Rg/ujkESGUQXyrJ5T0egIq8Hd8eWNdbS5/dn336nYvnDj8fxUv+wLc+d1M7unC5lHM+/dh1tfndFbfvlH96KL33nXWxq81fM++zHHkR3R6Ci5sY6w7Y+j/ma5fUlS7c16q94vz/cF8fWqDO2GWWOENIs7q1ST+91SD0l9uvprNznPtwXR0+DPs/Z3R9ZHeyubfdV6e8+qqVkAT3RkHV9i4Ya0l+sM4Qje+JNtz9Omlcj9rsZ53zxVreJMfZ9AJ4DEAcwAKAdwI9xzvsb3vkybN++nZ89e7ZiuqYV0D+SQiKloj3owRqfC1vWhiCKAgyD49qN2Tu8z2g62vxuMAbkdI6J6Rw6wzJinWGz7ZVJBTeUHCSXgBmtgEhIxqY2/7Jv9jm/3w2tfty9dvnrIY1R3NZjGRUdwarbuKaNZZVTXTcwMJLC8FQWrX43Ah4R93YEIYpC2Tg2tPhwfWoGyXQOaTWPNV4JHSEP9AIwPr3gWKs+v86wjCll9jJt0bCM+yMhXE9lF3vupAnYndOFxnF5QsG1GwpCsgRZEjCdKyCj5hH2SpjO5RHwSNAKBfgkEYpWQDZfwN1tfmxuD8AwOAZHZ2t0tKTmrrQlvr6kPpb9wlpldCqr4mJi9r01EvJga9TviJuJF1HmmlrDaykhdVC3nKayKt4uqaf3Rv10M3GyIFXVcWE0ZWampzNc7ebedXnPX0Z/hJiWWNvqVkvTWRVvlfR3X9RPNxMni8pm87iQSN+qb9GQ1c3E61JLgdnjUc24P06a1xL3u5ecUVtObgAAY0wEcC9mB/c25zy/yCK2ox1IYjM60EGcgHJKnKBuH84JaRCqpcQJKKfECeg9nzQ7qqXECaiWkma35IzaciqNMXYWwM8BGOGcDzTjiQ1CCCGEEEIIIYQQQgghhDiDXd8T+gSALgD/xhj7O8bYjzDG6DoJhBBCCCGEEEIIIYQQQghZNlsuKsk5vwTgtxhjzwDYBeAvARiMsb8EcIxzfsNqubn5uwCMcc7jc9NaAXwFwCYAVwDs5ZzftFj2MQDHALgA/Dnn/PdqHf/8+xn43S6AAQGPZF7PWtcNvJFIIZHKwet2QRQAWXThvo4gPB4R74xP4/KEAlkSEJYl3B8Nwe121TokQsoYBsc7Y9O4PDkNWXIh4BHRHnIj4vfi7fE0RqZUeOemu10Cxqdz8EgCPC4X1gbd0AvAWEZFJDR7X45rN2eQTKvwuUVohQLa/B66djupC1038MZoCiMpFSGviM6gDJfIkJjKYVLREAl5AACTioagLMErCpicyaHF64bODah5DjVfwD1r/TDAMJYpz2lpfmu9pxG5c62We25Q/gkhK83p9ZTYz+73sOI14kdTKjrDXsQ6Q3SNeLIou2sb1VJSi5mshoFExsxNPBqEz+tuWH+0D0KWq961zbY7ZjHGegH8FICPAHgewP8G8AEAZwBsq7LYXwP4PIAvl0z7DQCnOee/xxj7jbnHvz6vLxeAPwbwCIDrmP3GyEnO+RvLHbeuG/jauWE88+IA1LwBWRJwcHcMLT4RL7x+EXveswE/fG8HTg2M4re+dsFss29nN/xuF8YyOegGx6989XzZvKs3Z/DY/VE6wUFum2FwfGNgtCJjm9f6cFabwn+1yOWffutd3JzR8Jsfvg8eyYWDJwfNNkf2xPG5M0O4OpmFLAl4akc3vnL2Gn79sfvxWCxKb1KkZrpu4MT5YTx94lY9Pdz3AACGAyUZ3LezG1/+7lXcnNGwb2c32nwSRqZUZFQdx04PocXnxpPv24hjp4fMZYo5/aUd3WX5Pbp3G+WWLMlUVsUrA+M4cLI0n3E8Gm93xE6kYXC8PJjA/uPnzPFT/gkhK8Hp9ZTYz+73MKvPpEf2xLHnwS46wUGqsru2US0ltZjJanhpIFmRm13xSENOcNA+CFmuRtQ2u+658RqAPwLwbwB6OedPcc7/lXP+WQDvVluOc/5tAPO/1fE4gC/N/f4lAHssFn0YwCXO+buccw3A380tt2yDoynzxAYAqHkDh04NQi8AP/H9d2P/8XPoH0mZJzaKbY6dHsKEoiFfuHVio3TepbFp9I+kahkSIWWuTCqWGcsXYJ7YKJ0+oWh44qH1UPMGJhTNPLFRbPP0iQHs6u0yHz93Zgi7eruw//g5XJlUVuZJklVhcDRl7kQCs/nyuSXzxEZx2rHTQ2ZGj50egs8jYSyTM09mPPHQevP34jLFnM7PL+WWLNXFhGJ+wAJm83Pg5AAuJpyRnyuTirlTAVD+CSErx+n1lNjP7vcwq8+kT58YwOAo7Z+T6uyubVRLSS0GEhnL3AwkMg3pj/ZByHI1orbZ9d8SPsY538k5/xvOea50Buf8CcbYf1rGuiKc89G5ZUcBdFi06QLwvZLH1+emVWCM/Rxj7Cxj7Oz4+HjF/NGUar7gRWregKLpmJrJQ80bSKSt2xgcUHJ61XnJtLrokyUEWDinySr5Wyh7xTveGByWbUrviFN8rOYNjGUos6S6WupptZwWM1ist6VZLebRahmr/FJuSdHCtTRnmatkuuxjS9Oq9l5A+XeexWopIc1gNddTYr9GvIctlNFq+/iJFL1nkuoaUduolpJ6q3duFvtcSvsgZLkaUdtsObnBOa/67Yw5++rcpdV3n7hVQ875Fznn2znn29vb2yvmd4a9kKXyl0mWBPjdItb4JMiSgM6QbNlGYIBfFqvOi4Toq4RkaRbKaaRK/hbKHp/7a3AxWLbhvPKxLAnoCFJmSXW11NNqOS1msFhv52e12jJW+aXckqKFa6nHMlfF+8A0u2rvBZR/51mslhLSDFZzPSX2a8R72EIZrbaPHw3TeyaprhG1jWopqbd652axz6W0D0KWqxG1rVkuKLmcC7ElGWOdADD375hFm+sA7ip5vB7ASC0Di3WG8OzjcfOFL95zQ3QB//tfLuPo3m3oWRfG73y0p6zNvp3dWOt3QxIYPvuxByvm3dMRQO+6cC1DIqTMpja/ZcYkAfhvVXL5wuvXIUsC2vxuHOqLlbU5sieOl/qHzcdP7ejGS/3DOLp3Gza1+VfmSZJVIdYZwpE95fV0JpfH4XkZ3Lez28zovp3dmMnl0R70YN/ObsiSgOdfu27+XlymmNP5+aXckqXaGvXjcF95Pg/3xbE16oz8bGrz4+jebWXjp/wTQlaC0+spsZ/d72FWn0mP7Ikj1kn756Q6u2sb1VJSi3g0aJmbeDTYkP5oH4QsVyNqG+Pc8gsNtmKMvc45f6jKvE0AXuKcx+ce/3cAkyU3FG/lnP/avGVEABcB7AQwjNl7ffw453xwoXFs376dnz17tmK6rhsYGElheCqLVr8bfrcLjAF+j4RNbX4IAoOuG3gjkUIilYPX7YIoALLown0dQXg8It4Zn8blCQWyJCAkS3ggGqKbiZOa7q5klVPD4HhnbBqXJxV4JAFBt4j2sBsRvxdvj6cxOpWDLAkIuEW4RQHj0zm4RQGy6MLaoBt6ARifVtERlLGhxYdrN2eQTKvwuV3IFwy0+j1m1skdp245BWbr6RujKYymcgjILnSGZIgiQ2IqhxszGtoDHjAGTCoagrIEWRRwYyaHFq8bOudQ8wbUfAFb1vrBwTCWKc9pMb9jmdk8U27vGMveyFYZncqquJhQkEznEAl5sDXqd9QNGw2D48qkQvlvTnWtpYQ0SN1y6vR6Suy3jPewurzn67qBwdEUEikV0bCMWGeYbiZOFrXE2ka1lKyomayGgUTGzE08GrS6mXhdailA+yBk+epdS8X6Dq9m1p9aGPtbAB8CsJYxdh3AQQC/B+A4Y+xnAFwD8LG5tusA/Dnn/COcc50x9mkA/wDABeAvFzuxsRBRFLBtQwu2bWhZsE3v+hb0rree3x0JojvSmDOlhAgCQ3c0iG6Ls/E9XS3osbzjTLktHQHz983tAWxuDyzQmpDaiKKA3rta0HtX+fQNrbXlrTS3RZRfUqs1XhkP3+3cHUZBYJR/QkhTcHo9Jfaz+z1MFAU8eFcLHrxr8baEFNld26iWklr4vG48fHebbf3RPghZrnrXtmY5ufHPVhM555+s0n6nRdsRAB8pefx1AF+vy+gIIYQQQgghhBBCCCGEENI0bPneJWMszBj7I8bY2bmfzzLGzAtacs4/bcc4CCGEEEIIIYQQQgghhBDifHZ9c+MvAQwA2Dv3+D8C+CsAT9jU/22Zf88Nn9sFzg3kdCAa9kAvAGMZFZHQ7P0KLk8quDyhwOd2we92IasZiIQ8yOoFjGVUtPo80AoGUtk8grIEryhgciaHNr+MWGdoxa71WbxO3uy9FkRohQLa6F4LjmCVUZ+bYTpnwMUYUtk8PKILmdxs5tJqHu0BGfes9WIwMW1e564nGoLXKwEoz0MkVH7dxIXm1TL2wdEURlMqOsPeFf0bII1XzE4ipUKWBOQLBrL5AvxuEVPZPFp9bng9Am5M55FRdXQEPbgv4seliRmkszoUTcfagAdpVUNHUIbBOUambmVHEFhZNkvvIXO7WSWrn9Ova1zP2kwIIbfD6fWU2E/J5sr2S2LRAPxez6rpj6wOdtc2qqWkFnbXNzqmQ5ar3rXNrpMbWzjn/0/J40OMsXM29X1bdN3A184N45kXB6DmDciSgIO7Y2j1ifi3yxPYElmDQ6cGoeYNbGzz4tM/1F3Wdt/Obnz5u1dxc0bD/ke2Yo0s4tKYgqOvXixr45NcOHTqDfzSjm7sebDL9kJgGBwvDyaw//g5c1xP7ejGV85ew68/dj8ei0XpAEmTqpbRaMiNSSWPz50Zwse3b8BzZ4bKtu2zZ9/AL36oG3/8j0O4OpmFLAk43BfH7ngUHo9YkYeje7fhsVgUAKrOW25GdN3AifPDePrErbEf2RNfkb8B0nhWdeY3P3wftALHH77yNlp8bvzCBzdD0Qo4dvpWXv/gx3qRSKlldfMzj96L6zey+N1vvFWWnfagGz//v14vm/a5M7cyXmtWyeo3lVXxysA4Dpy8VY8O98XxaLzdETuRVn9flHdCyEpwej0l9lOyOfz9wFhFZn403tGQA3J290dWB7trG9VSUgu76xsd0yHL1YjaZlfSsoyxDxQfMMZ+AEDWpr5vy+BoyjxoDABq3sChU4PIF4AfjnWZJzYAYFdvV0XbY6eH8MRD66HmDRx99SJ8Hsk8QFfaZnJGw67eLjx9YgCDoynbn+eVScU8IFIc13NnhrCrtwv7j5/DlUnF9jGRpamWUZcg4OkTA9jV22We2CjOL27bAydn5xenHzg5gAuJtGUeijlYaF4tYy++CRbXtVJ/A6TxrLIzoWj4w1fehpo38MRD6zGhaOaJjWKbS2PTFXXzD195GxOKVpGdTLZQMa0041TPSDUXE4r5AQu4VRMvJpyRl3rWZkIIuR1Or6fEfoOJacvMDCamV0V/ZHWwu7ZRLSW1sL2e0jEdskyNqG12ndz4BQB/zBi7whi7AuDzAH7epr5vy2hKNV/wIjVvQNF0jGfK5zEGy7aMlS9n1cbgt5ZPpNTGPJkFJNPWz7M4prGM/WMiS1MtozeUfNk2nD+/OJ2x8unJdK5qHsYy6oLz6jX2lfgbII1nlR2D38onY+WPrdoUFevm/GmKpldMm59xqmfESjKds8xZMp1boREtTz1rMyGE3A6n11NiP7szQxkltaCcEiewOzd0TIcsVyMyatfJjTcB/AFm773xAoATAPbY1Pdt6Qx7IUvlL5MsCfC7RbQHZct58x9zXr6cVRuBAZzP/h4N2/8Vw0jI+rkUx9QRpK89NqtqGW31S+b0hbYt5+XTIyFP1Tx0BOUF59Vr7CvxN0Aazyo7Llaez/mPq00r1s350/xusWLa/IxTPSNWIiGPZc4iIWdcnqKetZkQQm6H0+spsZ/dmaGMklpQTokT2J0bOqZDlqsRGbXr5MaLAHYDUAEMA5gG4Ijv0sU6Q3j28XjZQeKDu2OQXMA3B4dxcHfMnHfq/HBF2307u/HC69chSwL2P7IVM7k89j+ytaJNm8+Nl/qHcWRPHLHOsO3Pc1ObH0f3bisb11M7uvFS/zCO7t2GTW1+28dElqZaRguGgSN74jh1fhhP7ei23LaH++J4qX/YnH64L46eaMgyD8UcLDSvlrEf2VM+9pX6GyCNZ5WdNr8bn3n0XsiSgOdfu442vxv7dpbndUtHoKJufubRe7HW767ITtDrqphWmnGqZ6SarVE/DveV16PDfXFsjTojL/WszYQQcjucXk+J/WLRgGVmYtHAquiPrA521zaqpaQWttdTOqZDlqkRtY1xzhdvdZsYYwOc83jDO7pN27dv52fPnq2YrusGBkZSGJ7KosXvht/tArgBVQeiYQ/0AjA+raIjKGNDiw+XJxVcnlDgc7vgd7uQzRvoCHqg6gWpeT5vAAAgAElEQVSMZ1S0+DzQCgZS2TyCsgRZFHBzJodWvwexzvCK3XTHMDiuTCpIplX43C7kCwZa/R5savPTzUgbo6YX1SqnFRmVXPB5GJScAYExpNU83C4XMrnZzKXVPNr9HtzT7sNgYhrJdA6RkAc90RC8XgnArTyMZWazXZqDheYtl64bGBxNIZFSEQ3LK/o3QCzVLadAeZ1xiwL0goFsvgC/W0Qqm0eLzw2vR8CN6Twyqo72oAf3R/y4NDGDtKpjJqejLeBBWtXQEZBhgGN06lZ2BIGVZXNDiw/Xbs7UJaukqS17o1pldCqr4mJCMWvi1qjfUTdsrGdtJnVX11pKSIPULadOr6fEfko2V7ZfEosGqt38ti7v+cvojxDTEmsb1VKyopZY3+pSSwE6pkOWr9611K6TG18E8DnO+YWGd3YbaAeS2IwOdBAnoJwSJ6jbh3NCGoRqKXECyilxAnrPJ82OailxAqqlpNktOaPi4k3q4gMAfpIxdhlADrMD5JzzXpv6J4QQQgghhBBCCCGEEELIKmHXyY0P29QPIYQQQgghhBBCCCGEEEJWOVtObnDOr9rRT6Nks3lcSKTNa4GFvS7MaAXoBhDwuNDdHoIoCjAMjssTCq7eUNDmc0M3OG4oGtb4JKTUPEKyBFHgAARM53TMaAV0BD3wSAKSqRz8HhEdQQ8EAUimc0hlZ5eJhDzY0Lpy180uvUZ+JETX8G5GqqrjzWQa6ZwONT+bK1kU4BZdkESGZCqHTC4PnyQireYRlEVEgzIiQQ+GJhQoWgE3FA3r13gRX3fr+ojVtr2mFdA/kkIiraIzJKNnXRhut6umsVO+7iyGwXHthoKxzGyNC3hc8EkiMjkdWqGAsOzGpKIhKIsweAE+SUImp8PgBgJuCROKhoBHhCQySIIAtyggo+q3lR3KICly+nWNKcuEkGbh9HpK7Dd/n7v0XoCroT+yOthd26iWklrYnZt6Hh8id4Z6Z9Sub244Vjabx6mBBA6cHICaN+bu4h7DhjYZY+kcCpzh2o0sfqi7A998ewz7j5/D1o4APvnejTh0atBc5qkd3TjzVgJPvm8ThqdUHDs9ZM7b/8hW/NU/X8HNGQ37dnYjGpbxP755EVcns5AlAft2dqM7EsCOeyO2H6QwDI6XBxPYf/ycOd6je7fhsViUDpg0CVXV8cpbSQxPZcty9Tsf7UEkKCKRzuNzZ4bw8e0b8NyZW/Of/tH70bVGRiKtlWX12cfj+Oi2LggCs9z2O7rbcXJgFAdeLPmbeDyOPb3rlv0GRvm6sxgGx5m3k3h3XMHRVy+a2/zg7hief+0aHnmgE3/0zX83p//27hg0fQZ/8c+X8eMPb8QfffPWMvt2dsM/l7c//da7uDmj1ZQdyiApmsqqeGVgfN77fRyPxtsdsRNJWSaENAun11NiP+t97jh2x6MNOeFgd39kdbC7tlEtJbWwOzeaVsCJ/pG6HB8id4ZGZJRuX7+IC4m0+YIDgJo3cODkIBhcWOPz4PKEgrzO0T+SMg8o/OwPbjEPFheXee7MEJ58/2ZcGlfMA9DFeUdfvYgnHloPNW/g2OkhXJ5QsKu3y5x/7PQQ+q+ncGVSsf35X5lUzOdVHM/+4+dWZCzE2oXRFC6NT1fk6re+dgFul4SnTwxgV2+XeWKjOP/I378JlyBUZPWZFwcwOJqquu0vjKbMN67i9AMvDqB/JLXssVO+7ixXJhX0X0+ZJzaA2W1+6NQgnnz/ZvPkRXH6b58axISiYVdvV8W8Y6eHMKFomFA0s37Wkh3KICm6mFAs3u8HcDHhjCxQlgkhzcLp9ZTYz3qfewAXEulV0R9ZHeyubVRLSS3szk3/SP2OD5E7QyMySic3FpFM58wXvEjNGxjLqLg5k4fBAUXTkUirZrtsTrdcJqvpMDgs5zF263eDw3xcOm0so9b/CS4iWfK8SsezEmMh1hLpXNVcJTOqmS+r+TeVvOX0REqtuu0TVf4mkunlZ4LydWdJptWqWc1q1nWzWA+rzSutl7VkhzJIiqq93yfTuRUa0fJQlgkhzcLp9ZTYz+7MUEZJLSinxAnszk2iyj5ILceHyJ2hERmlkxuLiIQ8kKXyl0mWBHQEZbT4JAgM8LtFREOy2c7nES2X8blFuBgs53F+63eBwXxcOq0jaP9XDyMlz6t0PCsxFmItGvJUzVXp9rOa3+KXLKdHw3LVbR+t8jcRCS0/E5SvO0skJFfNqs9tXTeLV9OpNq+0XtaSHcogKar2fh8JeVZoRMtDWSaENAun11NiP7szQxkltaCcEiewOzedVfZBajk+RO4MjcioI09uMMbuZYydK/lJM8b+y7w2H2KMpUraHKilr55oCIf74mUHiA/3xcBRwNRMDnev9UMSGXrXhXF07zbIkoA/+/Y7OLg7VrbMUzu68aXvvIst7X7s29ldNm//I1vxwuvXzevI373Wj5f6h835+3Z2o3d9GJva/LW/aDXa1OY3n1dxPEf3bluRsRBrPZ1hbGkPVOTqdz7aA03P48ieOE6dH8ZTO8rnP/2j96NgGBVZffbxOGKd4arbvqczjMOPz/ubeDyO3nXhZY+d8nVn2dTmR8/6MPY/srVsmx/cHcOXvvMufvmHy6f/9u4Y1vrdOHV+uGLevp3dWOt3Y63fbdbPWrJDGSRFW6N+i/f7OLZGnZEFyjIhpFk4vZ4S+1nvc8fREw2tiv7I6mB3baNaSmphd2561tXv+BC5MzQio4yXfkXAgRhjLgDDAN7LOb9aMv1DAD7DOd+11HVt376dnz17tmJ6NpvHhUR69i7uQQ/CPhdmtAJ0Awh4XOhuD0EUBRgGx+UJBdduKGj1uaEbHDcUDWt8ElJqHiGPBNHFwSAgk9OR1QpYG/BAdgsYS+fgc4voCHogCMBYOoepbB4hWUIk5MGGVv+K3RDUMDiuTCoYy6joCMrY1LZyY1llanoRrXKqqjreTKaRzulQNQPtQTe8kgC36IIkMiRTOWRyeXglERlVR8DjQjQkIxr0YGhCgaIVcFPR0LXGi/i6MERxtshU2/aaVkD/SArJtIpISEbvunDNN4uifDW9uuUUmN3e124oGM/M1ji/2wW/W0QmpyNfMBCUJdxUNARkEQY34JNm5xmcI+AWMaFoCHhESCKDJAhwiwIyqo5IqPbsUAZXhWVvMKuMTmVVXEwos+/3IQ+2Rv2OumEjZbmp1bWWEtIgdcup0+spsV/ZPnfIg55oqNrNvevynr+M/ggxLbG2US0lK6pROa32ubSex4fInaHeGRXrO7wVsRPAO6UnNurN65Xw8N1ti7YTBIYtHQFs6Qgsu4/YuvLHG9uWv45GEQSGze0BbG5vnjGRcrIs4j0bW6vO39Bafds9eJe76rxq297tdmH7pur9LQfl684iCAyb1gawaW3zbG/KICla45Xx8N3O3WGkLBNCmoXT6ymx31L3uZ3aH1kd7K5tVEtJLezOTT2PD5E7Q70z6sjLUs3zCQB/W2Xe+xhj5xlj32CMxawaMMZ+jjF2ljF2dnx8vHGjJOQ2UE6JE1BOSbOjjBInoJwSJ6CckmZHGSVOQDklzY4ySpzA0Sc3GGNuAH0Avmox+3UAGznnDwL4HIATVuvgnH+Rc76dc769vb29cYMl5DZQTokTUE5Js6OMEiegnBInoJySZkcZJU5AOSXNjjJKnMDpl6X6MIDXOefJ+TM45+mS37/OGPsTxthazvnEcjsxDI5LY2lcnpyBV3Ih4BHR4hNwfUqD2+VCJOTBupAXbybTGE2p6Ax7cX8kiNFMFmPpHMYyOawNeOB2MYymVbT4Zu+HoBvAeGb2mnRqvoDpXAFrfLP32NALwFhGhc8tQisU0B7woGDMTlvKteWL190uXvPOrutuW/ULYEXGciexymhQFpDOGkipGmTRhdaAhEKB48aMjmlVx9qAG0HZhVzeQCZXgKLpWBf2IuARkUirWLdGxpSSx2h6NtNrfCJGpuzfhrpuYHA0Zf5txTpD5j1BiDPpuoG3kmlM53QwBmg6x7SqozXghk8SoBU4Jqc1BGUROV2Hzy1B0fJo8bmxRpaQzGhQNB0bW3xwuRjGp3MQGMPEdA5BWURnyIuNVGdIDZx+XWOql4SQZuH0ekrsZ3dmKKOkFpRT4gTprIq3SnJzX9SPUANzo6o6LoymkEjnEA150NMZhiw7/XAzaaR61zanp+2TqHJJKsZYFECSc84ZYw9j9lsqk8vtwDA4vjEwil/56nmoeQOyJGDfzm50rZHR6hPxa88P4BP/YQO6Wrz47Ctv4+pkFrIk4NgntmFqJo+DJwfLlvvyd6/i5oyGQ30x/Mk/XoKmczz5vo04dnrIbHdwdwxf+NYlc13/9bH7kCsYOPrqRbPN0b3b8FgsannwzjA4Xh5MYP/xc0tqXy/V+nWLDJ/+m3+3dSx3koUyOpZSoBkunHkrgf/0/rtx/Wa2LGvPPh5HNOzGf/7y6+a0/Y9sxXcvTeDReCcOnRosy+Xf/utVXBybtm0b6rqBE+eH8fSJAXMcR/bEsefBLjpg51C6buAbg6O4qWgQBIaMqpuZ3NjmxS/+UDcOvHhrez+1oxtfOXsN//H7N+HlgUv4se/bgN8uyeWv/si9cLsE/M7X3yzLf3ckgB33RqjOkCWbyqp4ZWAcB07eyt/hvjgejbc7YieS6iUhpFk4vZ4S+9mdGcooqQXllDhBOqviZYvcPBZvb8gJDlXVcfLCaEV/fT2ddIKDWGpEbXPs3i5jzAfgEQAvlEz7FGPsU3MPfwzAAGPsPIDnAHyCc86X28+VScU8aAwAat7AsdNDuDSuwC1K2NXbNft4bBq7ervMNnmdmyc2Spd74qH1UPMGDp4cxK7eLjzx0HrzwF6x3aFTg2XrmpzRzBMbxWn7j5/DlUml6piLJxiW0r5eqvXbfz1l+1juJAtltPeutXjuzBCefP9mDI1NV2TtmRcHIAqusmlHX72In/zA3eaJjeL0Q6cG8bM/uMXWbTg4mjIP1BXH8fSJAQyOphreN2mMwdEUhsamMaFoGMvkyjK5q7fLPLEBzG7v584MYVdvF/7wlbfx5Ps3myc2ivP/+z+8jfHpXEX++6+nqM6QZbmYUMwPWMBslg6cHMDFhDNyRPWSENIsnF5Pif3szgxllNSCckqc4K0quXmrQbm5MJqy7O8C7YOQKhpR2xx7coNzPsM5b+Ocp0qmfYFz/oW53z/POY9xzh/knH8/5/w7tfSTTKvmC16k5g0YHEhmVDB26zEr+Q/CSk63XK7Ypvh7cflq7QDA4NZtxjLqssZcrX29LPRa2T2WO8liGVXzBrI5vWqOJqZzFdNuKnnLtllNN3+3YxuOpqyfWyJF+XGq0ZQKg8P8Kd2+C9XDYo6XWmMMDqozZFmS6ZxlvpLpXJUlmgvVS0JIs3B6PSX2szszlFFSC8opcQK7c5OgnJJlakRGHXtywy6RkAxZKn+ZZEmAwIBIUAbntx6Xfi/EL4uWyxXbzP+9WjsAcDHrNh1B66/rVBtztfb1stBrZfdY7iSLZVSWBPg8YtUcrQ14Kqa1+iXLtl63aP5uxzbsDHstxxENU36cqjPshYvB/LHavvMfF+usz2NdV61qjMBAdYYsSyTkscxXJOSpskRzoXpJCGkWTq+nxH52Z4YySmpBOSVOYHduopRTskyNyCid3FjEpjY/PvuxB80Xvng993va/dD0PF7qH5593BHAS/3DZhvJxXCoL1ax3AuvX4csCTjUF8NL/cN4/rXr2Lezu6zdwd2xsnW1+tzY/8jWsjZH924zb9ZtNeaje7ctuX29VOu3d33Y9rHcSRbKaP/3JvDUjm586Tvv4p6OQEXWnn08Dt0olE3b/8hW/NU/XcbB3bGKXP75t9+xdRvGOkM4sideNo4je+KIdYYb3jdpjFhnCPd0BNDmd6M96CnL5Knzwzj8ePn2fmpHN17qH8ZnHr0XX/rOu/jtebn81R+5F+0BT0X+e9eHqc6QZdka9eNwX3n+DvfFsTXqjBxRvSSENAun11NiP7szQxkltaCcEie4r0pu7mtQbno6w5b99dA+CKmiEbWN1XAbilVr+/bt/OzZsxXTDYPj0lgaVyaz8EgCgm4RLX4Bw1MaJJcLkZAH60JevJlMI5FSEQ3LuD8Swmgmi7F0DmOZHNYGPHC7GEbTKlp8bvgkAXkDGJ9WEQnKUPMFTOcKWOOTEAl5oBdmL6nic7uQLxhYG/CgMNe+IyhjU5t/wRvlGgbHlUkFY5mlta8Xq34BrMhYHKCmF8EqpxUZ9YgIeQWkZgyk1TzcooA2v4SCwXFzRkdG1bE24EZQdiGXN5DJFTCjFRANexD0SEhmVHSGZUwpeSTSKqIhGWv8EkZT9m9DXTcwOJoy/7ZinWG6Oa696pbTIl038FYyDSWngzEgp3MoOR2tPje8bgFagWNyWkNQFpHTC/C5RcxoOtZ4JazxSkhmNMxoOu5q8UF0MYxP5yAwhslpDX6PC+vCXmykOnOnWfbGtsroVFbFxYSCZDqHSMiDrVG/o27YSPWyqdW9lhLSAHXLqdPrKbHfMjJD7/lkxSwxN1RLyYpKZ1W8VZKb+6J+q5uJ16WWArM3Fb8wmjL76+kM083EyYLqXUvp5EYJ2oEkNqMDHcQJKKfECer24ZyQBqFaSpyAckqcgN7zSbOjWkqcgGopaXZLzij9dz5CCCGEEEIIIYQQQgghhDgKndwghBBCCCGEEEIIIYQQQoij0EXQlqB4P4PLkzPwSi4EPCLCXgHXbubgd4voXONBV/jWvSWSaRU+twitMHsPg8RUDom564h5RIabM3mEZQkz+QIyqo6gR4QoMnhcAnSD44aSxxqfCFl0YSqbx4xWwIZWL/IFjnRWx3RORyTkwQOREEYyKpJpFZFQ+X0QDIPj8oSCq5MKZLcAj8uFVr8bjAGjqcr2zaZ43fBkWkWb3wMDHG1+T1OPeSVpWgEXRlIYy+QQkEWEPCJ8HgaDAwJzYSanwS25kMkWkMzMZjFfKECWXPCKLqRUHZOKhrUBNwIeEVta/bh0YxojU6qZ+RafgLtaQnW/fntxW4+mVHSGvYh1lvdRvI+LVc6J82haAf0jKYxlVHSGZOQKBtLZPMI+N2ZyeQRkEZwzjGdy6Ah6wBnHTSWP9oAHblGAWwSmlALG5nIsuhjCXjflgtSF069rTPWSENIsnF5Pif3szgxllNSCckqcwO7cTGdVvFHS3wNRPwKUU7KAemeUTm4swjA4vjEwil/56nmoeQOyJGDfzm50rZGxvkXG7379TezdvgH3RFQkUnnsP37ObPcHP9aDy+MKDpwcNKc9+3gcbhdwMTmNo69eLFtnNCzjf3zzIq5OZrGxzYtPffAeHDo1iBafG7/4Q1uQUXUcOz1Utq7P/58hXJ3MQpYEHN27DY/FogCAlwcTZWPZt7MbfrcLAPCn33oXN2c0s32zHfjQdQMnzg/j6RMD5vif2tGNr5y9hl9/7P6mHPNK0rQCXuwfwTMvDpRt783tfii5Av7lnTH0bevCSLI8iwd3xfD869fwse0b8Cf/eMnM0e890YNLY9P4jRcuVGT+xkweD3a11e0Eh9W2PrInjj0PdkEUBRgGr8hys+aWLE7TCjjRP4IDLw6gxefGT/3AprI6eHj3A0ikNRw6VZLT3TH87b9excWxaTz3yfdgStEsc/zTH9hCuSC3ZSqr4pWBcRw4easeHe6L49F4uyN2IqleEkKahdPrKbGf3ZmhjJJaUE6JE9idm+msiq9b9PeReDud4CCWGpFRuizVIq5MKuaJDQBQ8waOnR7CpXEFeoHhyfdvxoGTg8jrzDygUGy3xus2D8IVpz3z4gBcgss8oFe6zssTCnb1dgEAdvV2mQf4nnhoPcYyOfPERum6iu3VvIH9x8/hyqSCK5NKxViOnR7ChKJhQtHwxEPry9o3m8HRlHmwG5gd/3NnhrCrt6tpx7yS+kdS5okN4Nb2zuscv/m1C9jz0Aa4BFdFFg+9NIgn378ZB08OluXo3QnFPLFRur5L4woKBQGDo6m6jd1qWz99YsDswyrLlAHn6h9J4cBcVp94aH1FHfR5JLPuFacdOjWIn/3BLVDzBrS8UTXHlAtyuy4mFPMDFjCbrwMnB3Ax4YxcUb0khDQLp9dTYj+7M0MZJbWgnBInsDs3b1Tp7w3KKamiERmlkxuLSKZV8wUvUvMGDA4kMyqymg41b2AsU9nuhpK3XFaZW8ZqnWzuP1cyBrMNY4DBYbkMY+WPx+YuU1Vt/aV9FNs3m9GU9fiLr0kzjnklJapsbyU3m7OJTA7jmZxlm2J+S3NULWvFzCdS9Xv9q23rYh/VskwZcKbSrJbWuKJiZksVc7rYfMoFuV3JtHWdTKZzKzSi5aF6SQhpFk6vp8R+dmeGMkpqQTklTkA5Jc2uEZmhkxuLiIRkyFL5yyRLAgQGRIIyvG4RsiSgI1jZrtUvWS7rn1vGap2cl08rcjFYLjO/fUdQXnDMpX0U2zebzrC36nNt1jGvpM4q29svz+asPehBe9Bj2aaY39IcVctaMfPRcP1e/2rbuthHtSxTBpxpflbnb9tiZksVc7rYfMoFuV2RkHWdjIQ8KzSi5aF6SQhpFk6vp8R+dmeGMkpqQTklTkA5Jc2uEZmhkxuL2NTmx2c/9qD5whfvP3BPux+ii+PL33kXh/tikESOo3u3lbWbymo43Bcrm/bs43EUjAL2P7K1Yp13r/Xjpf5hAMCp88M4uHt22edfu472oAf7dnZXrKvYvnht7U1tfmxq81eMZd/Obqz1u7HW78YLr18va99sYp0hHNkTLxv/Uzu68VL/cNOOeSX1rAvj2cfjFdtbcjH87kd78LXXr6FgFCqyeHBXDF/+zrs41Bcry9Hda/34vSd6LDPvchmIdYbrNnarbX1kT9zswyrLlAHn6lkXxuG5rD7/2vWKOjij5s26V5x2cHcMf/7tdyBLAtyiUDXHlAtyu7ZG/TjcV16PDvfFsTXqjFxRvSSENAun11NiP7szQxkltaCcEiewOzcPVOnvAcopqaIRGWW89L9s3+G2b9/Oz549WzHdMDgujaVxZTILjyQg6BYR9gm4djMHvySis8WDrvDsRrgyqSCZVuFzu5AvGIiEPUhM5ZBM59AR8sAjMkzN5BGSJczkC8ioOgIeEZLI4HEJ0A2Om0oeYa8IWXJhKptHVitgfasXeoEjreqYzunoCHoQi4QwklExllHREZSxqc1v3jTUMDguTyi4dkOBWxQgiy60+t1gbPbSMPPbNxtdNzA4mkIynUOr3w0Ojja/p6nHXIOanohVTjWtgAsjKYxlcgjIIoIeEX4PQ4EDLubCTE6DW3QhoxYwNp1DR8CDvFGAR3TBJ7mQUnXcUDS0BdwIuEVsafPj0o1pjE7lzMy3+AXc1RKq283Ei4rbOpFSEQ3LiHWGy/owDI4rk4plzokt6pZTYDar/SMpjGVUdIZk5AoG0lkdYa+ErKbDL7vAOcN4Jof2oAeMcdyc1tEWdMMjCnCLwJQym+NI0APRxRD2uikXZNkb3yqjU1kVFxMKkukcIiEPtkb9jrphI9XLplbXWkpIg9Qtp06vp8R+y8gMveeTFbPE3FAtJSuqUTmt9rl0OqvijZL+Hoj66WbiZEH1ziid3ChBO5DEZnSggzgB5ZQ4Qd0+nBPSII6ppV13bcDI9e8tqe269Xdh+HvXGjwiYiPH5JTc0eg9nzQ7qqXECaiWkma35IyKjRwFIYQQQgghxDlGrn8PH/+f31lS26/8/PsbPBpCCCGEEEIIqY7uuUEIIYQQQgghhBBCCCGEEEdx7Dc3GGNXAGQAFADonPPt8+YzAMcAfATADICf5Jy/Xktfum7gwsgUhqdUtPnd8LldCMoMqawBgTGILgavJGJTmx+6buDtsQymc7P3MOhq8SHeWX6fAl03MDSeRjpbwM0ZDQGPCK8koMCBdDaPoCwhGvZALwDJtAqPKGAmn4dXkqDlDYT9ImZyBaTUPNZ43dDyBvyyiLSqzd5Dwe1COqvD7xGhFQpl96ooXo87mVYRCckV0yeVHNwuATNaoWx+vVTrn9yemayGgUTGvF5di88FgQGiC0imCkhmctjQ6oWmG5iY1tAedENggKIZmFZ1rA24EfK6cEPJI6MWEPJK6AhImFTySMyts6vFg86Q9fZaaLvSNidFqqrjys1pZNTZ2hf2uZHO5hGQRbgFBo8kQNM5Mjkdar6AFp8ESRBQ4AVwLmBS0dAZ9iLWWf97vxACOP+6xlRvCSHNwun1lNjP7mu20zXiSS3srm1US0kt7M7N/ONR8WgQPq+7Yf0R56t3Rh17cmPOD3HOJ6rM+zCA7rmf9wL407l/l0XXDXzt3DCeeXEAat6ALAk4uDuGVp+I6BoPhm+qcDHg9JsJPBJbB4Nz3FDyOHRq0Gz/7ONxfHRbF0RRgK4bOP12ElMzeRw8OVi2zi986xKuTmatH++K4QvfHoSmczz5vo34u3+7ho9v34DnzgyZ63hqRze+cvYaPvEfNuDL372KmzOaOe3XH7sfj94fwStvJrH/+DlzmaN7t5nTf//lNyvWeXTvNjwWi9bl4IhhcLw8mKjov17rv1PNZDW8NJDEgZO3Mnq4L4aergBeu6rgwMkBbO0I4JPv3ViWy9KMbWzz4hc/1F22jvkZPNwXw72dOfSsay3bXgttVwC0zQmA2RMbZ783idGUVlZPizXqUx+8B5GQG28npnHs9K0a9Jsfvg9rfBJ+7fkL5rQje+LY82AXneAgdTWVVfHKwPi8WhrHo/F2R+xE0nssIaRZOL2eEvtNZ1V83SIzH4m3N+SEg939kdXB7tpGtZTUwu7cWB+PimNXPEInOIilRmR0NR8ZehzAl/msfwGwhjHWudyVDI6mzANxAKDmDRw6NYh8AchpgGEA+QKw56ENuDCcQl7n5gHkYvtnXhzA4GjKXF9e5+aJjdJ17rtgBcMAACAASURBVOrtqv74pdnHTzy0HsdOD2FXb5d5EqLY5rkzs9OPnR7CEw+tL5u2//g5DI6mzIMexWVKp1utc//xc7gyqdS4CcpdmVQs+6/X+u9UA4mMWRSA2df1wMlBZFRuTv/ZH9xSkcvSjO3q7apYx/wMHjg5iJyGiu210HalbU6KLoym4BJcFfW0WKMOnRqEKAjmiY3i/N/9xlu4NK6UTXv6xK2aSki9XEwoFrV0ABcTzqhXVG8JIc3C6fWU2O+NKpl5o0GZsbs/sjrYXduolpJa2J0b6+NRAxhIZBrSH3G+RmTUySc3OIBXGGOvMcZ+zmJ+F4DvlTy+PjetDGPs5xhjZxljZ8fHxytWMppSzRe8SM0bUDQdyYwKRdOhaDomp3MwOKDkdMv2iZRqrq9aG8YWf8zYrd+rraN02dJp1Z5LcXq1dY5l1IrXpRbJtHX/9Vr/arZQTpPpnOXrmszcer2zi2RuoTzNX+f87bXQdqVtfmdZKKeJdA7jGeusFvN3Q8lbzjc4KqYVayohy1FTLU3n7Bxizajerh6LfTatRdddG8AYW9IPIUuxmuspsV8jMkMZJfVGOSVOUO/cLPa5lHJKlqsRmXHyZal+gHM+whjrAPAqY+wtzvm3S+Zb7Z3xigmcfxHAFwFg+/btFfM7w17IklD2wsuSAL9bREfQg7HM7IvfGnDDNTYNvyxato+GZXN9usEt2/CS3qs9Zmz29+I0q3WULls6rdpz6QzLC66zI1ifr65FQnJD17+aLZTTSMhj+bqWvt4+j3Uu52dssfmRoIy1QU/Z2BbbrrTN7xwL5TQa8ixY+2RJQKtfspw//4o6pTWVkOWorZaW17xmRe+xq8din01rMXL9e/j4//zOktp+5effX48uySq3muspsV8jMkMZJfVGOSVOUO/cLPa5lHJKlqsRmXHsNzc45yNz/44B+BqAh+c1uQ7grpLH6wGMLLefWGcIzz4eLzv4f3B3DJIL8LgBQQAkF3Di9WuId4UhuRgO7o6VtX/28ThinWFzfZKL4VBfrGKdL/UPV3+8a/bx869dx76d3Th1fhhP7eguW8dTO7rxUv8w9u3sxguvXy+bdnTvNsQ6Qzi6d1vZMrPTwzi6d5vlOo/u3YZNbf7lvmyWNrX5Lfuv1/rvVPFoEIf7yjN6uC+GoIeZ0//s2+9U5LI0Y6fOD1esY34GD/fF4HGjYnsttF1pm5Oins4wCkahop4Wa9TB3THohoF9O8tr0G9++D7c0+4vm3Zkz62aSki9bI36LWppHFujzqhXVG8JIc3C6fWU2O+BKpl5oEGZsbs/sjrYXduolpJa2J0b6+NRccSjwYb0R5yvERllnNflP4TZijHmByBwzjNzv78K4DDn/OWSNj8K4NMAPoLZG4k/xzmffwKkzPbt2/nZs2crpuu6gQsjUxiZUtHid8MvuRD0MkxlDbgYg+RikCURm9r80HUDb49lMJ3TcUPR0LXGi/i6cNmNb3XdwNB4GulsATdnNAQ8IrySgAIH0tk8grKEaNgDvQCMZVRILgFqPg9ZkqDpBsJeETNaASk1jzVeNzTdgN/tQiaXh0d0wet2IaPq8LldyBcMtPo92NTmhyAwGAbHlUkFYxkVHUG5YvoNJQfJJWBGKyASujW/Xqr1f4eq6Ylb5XQmq2EgkUEynUMk6EGL3wWBAaILSKYKGMvkcFerF5puYFLRsNbvhuACZnIGMqqOtQE3Ql4Xbih5ZNQCwl4J7QEJk0oeibl1drV60Bmy3l4LbVfa5o5Xt5yqqo4rN6eRUWdrX9jrRlrNI+AR4XYxeCQBms4xPXcZtTU+EZIgwOAFGFzApKKhMywj1hmmm4mT+ZadU6uMTmVVXEwos7U05MHWqN9RN2yketvU6lZLa+qcsWV9c2M5bZ24L0GqqltOnV5Pif2msyreKMnMA1F/tZt71+U9fxn9EWJaYm2jWkpWVKNyWu1zadnxqJAH8WiQbiZOFlTvjDr1slQRAF+buy6wCOBvOOcvM8Y+BQCc8y8A+DpmT2xcAjAD4Kdq7UwUBbxnQyves2Hxtm63Cz3r1yy6vvs7F25TtKUjsKR2SyUIDJvbA9jcHljS9Hqzq587jc/rxsN3t1nO22g9eUk2L7HdQtuVtjkpkmUR9y2x9hGyEtZ4ZTx8t3N3GKneEkKahdPrKbFfwObM2N0fWR3srm1US0kt7M7NQsejCLFS74w68uQG5/xdAA9aTP9Cye8cwC/aOS5CCCGEEEIIIYQQQgghhDQeXdeDEEIIIYQQQgghhBBCCCGO4shvbqyEbDaPC4m0eT2wsNcFwwCmsnmILgEBjwvd7SEIAsOVSQWTSg5ui3tX6LqBN0ZTGEmpCHlFhGQJN2c0+CQRkotBmWu/ocWHazdnkMpqKBjAxHQOnWEvYp2zfVyeUHD1hgK/W0Qk5MGG1srrahevvZ1Mq3W9f8b89RbHatVPrWNo1NjrpdnGZ5VPgQE+j4jrN3KY0XS0+txQNB3TuQLa/G6AcegFjlQ2D1lyIeARsTboxrrQ/8/evce3cd53vv8+uJMgQVkURVI3S3EkJyaleFOtk6a7bWrlorjypWnrpO3ZnKbtK8k5Se3WbU8vJ7Fjx5v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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": 33,
   "id": "4d0eef9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LJycpNS1DTRpV1cujBioo0PVWu2T5do157UdlZztUsUKo/vfh0JztHA6nrr35HcVGR+j9N28ptuMq6z6+eYT6Nu+kpPRDaj5qcEmHU+a1fnOEqvbpouxjx/X7rU/o0MoN+cbEdmuvVq8+Jr+gQB38Y72W3DFC1uFQYESYOv7vFYXWrCoT4K8/Xx2v7Z9+VwJHUTZYazXmuy2avyFFIYF+GjO4iZrWCM83bsSEjVq/O13WWtWKCdWYwY1VPjhAS7cc0v0frVH1qHKSpMtaROv+K2oX92H4LGutXnhjpuYv2qaQkEC9+HRfNW1YJd+4PfsO69GnJ7veMxrG6aVnr1ZQoL9S0zI04oUftGvvYQUH+euFEX3VoG6MJOnzL5fq62mrZK3VwH6tdMugtsV9eECJO++TGNZpteZ/W9TxXy1UrlKw5j2/QnEXRimiWvk846LqR6r9P5sXuI9Oj7VUcHhgcYRbpjmdVvM/3qR+T7VSWFSwvn5yuWq3iVal6uXzjVv8xVbVuDAq57GKVctr0Cttc9Z/evdC1W5buVjjL2scDqfefmmuxr5zjSrHhunBIZPUoXMdXVDn5O89PCJE9/27ixbN3Z5n2xq1KmrchME5+7mpz8fq1K1uscZfFsxfsEnxuw5o5tR/a/Xa3XpuzBR9/X/35xv36ps/6dbBl+jKK1rqmdGT9c3k5brp+vaSpDatauv9t27NMz4oKECffXCXyocGKyvLoZtuH6fOnRrqwhY18+0bLvMXblb87hTNnPIvrV63W8+9OFVff35fvnGvvvWzbh3cSVf2aqlnxkzRN1OW66aB7ZWWnqGRY6fqo//epqpVKijl4JE8230+cZHq1orWkaOZxXVI54VPF/+ot+d+o89vfaakQynzqvburPD6tfR9/csV1a6lLn7vOc1sf33eQcao/WdjNbvHrUrfEq/mIx9S7Vuu0fbx36j+/YOVumGb5vW7V8GVK6rvpp8V/8X3cmZllcjx+Lr5G1K0M/mYfn6qvVbvTNPzX2/Sl4+2yTfuyQH1FRbi+jgwdvIWTZi/R3f1rCVJal2ngsbd3bI4wy4z5i/epp27D2rG1/dq9fp9Gvnyz/rq49vyjXv1ndm6ZVBbXdmzqZ59abq+/X6VbhzQWu9/tkiNGsTq7ZcGanv8AT3/6gx9+vZgbd6WpK+nrdJXH9+mwAB/3fXIRHXpVE+1alQqgaMESs55P53k0PY0lY8pp/Ix5eQX4Kdq7WKUsCqlpMM6LyVtTVNkXKgiY8vJP8BP9TvGaMey5Hzj1v60R3XbxSg0ouDE0Z61BxUZV04R0eW8HXKZtml9oqrWiFSV6pEKDPRXl8sbaNG8vMmKipVC1bBpnPwDTv9SsnLZblWpFqnYKhHeDrnMmTVvg/r3vUjGGF3YoqbS0jOUlJyWZ4y1Vr8v26ZelzWTJF1z1UWaNXd9QbvLYYxR+dBgSVJ2tkPZ2Q4Z451jKCtmzdug/le2cp2L5jWVduT4ac7FdvXq4T4XfS/SrLkbJUnf/7RaPbs3VdUqFSRJUZXCcrZLSEzV3AV/6rr+eSud8Pf9tnWVDh5NO/NA/G3Vru6hHZ9PkSSlLFmtoAoRComLzjMmOKqCnJknlL4lXpKU8MtC1bz2ctdKaxUQ7vrSIiCsvE4cTJUzO7vY4i9rZq87oKsvjnO9ZtWKVFpGtpJS8ydJ/0pgWGt1PMsp3gzOjVnzN+vq3i1cv/9m1VzvGQfS84yx1ur3P+LVq1tjSVL/Pi306/zNkqRt8cnq0MZV+VKnVmXtTTisAwePaHt8ilo2rapyIYEKCPDTxa1q6td5m4r34M5nTmfZvPmgMyYxjDGPFnYrjiC96fjhEypXKThnuVzFYB0/lP9F/uC2NM15ZrkWv75GaXuP5jxujNHi19Zo7sg/FD93X7HEXFYdOZipsKiT5yIsKlhHD2bmG7N9abKaXp6/jPsvWxYmqX6nWK/Feb44kHRE0bEnS0+jY8KUknSkkC0KNm/GZnXr1fBchnbeSExKU1xchZzluNhIJSbl/UB26PAxRYSXU0CAf4FjVq3ZpX7X/0d33j9eW7Yl5jzucDh19Q1vqmOP0erYvr5aNqcKozCJSWmKi43MWY6LiVBickHnIuTkuYiJUGJyqiQpftcBpaVl6OahH2rA4Lc15YcVOduNee0HDXu4t/z8+PAA3xVaLVbHdifkLB/bk6DQannfizMPHJJfYIAqtXYl+mped4VCa8RJkja//YUiG9fVNft+U5+10/THwy9IliZ6ZyvxcKbiKoTkLMdFBheYxJCk4V9s0KVPLdCOxKP6R+fqOY+vik9V/5eWaui4Vdqyv+jv/+ezxOR0VYk9+eVNXHSEEpPzJjEOp2YoIixEAe4vguJiIpTkHtOwXqxmzv1TkrRm/V7tS0hVQlK66teN1rJVu3Uo9Zgyjmdp3uJt2p9IohbnH0+mk+SfQOcBY8xQSUMl6fJhl6jl1Y3PZjdeV+D74yl/R0ZeEKbLX2mvgBB/Ja5J0dL/rtdlY11TFy558kKVqxiszLQTWvTqGoVVCVXlhhUK2CnOqMBzkfdkLPh0szoMrnvaP/Yd2U7F/3FAHW5i6oI3mCJ+Q5OV5dDi+dt1+wMdvRRR2WYLeIHKdwoKGdO0UTXNnv64yocGa95vf+r+Rz7XzGnDJEn+/n6a+uXDSkvP0P2P/p82b01Qg3px5/oQyoyC3ivy/38o4Fy431AcDqfWb9ynT8fdoePHszTotnFq2bym4nceUKWKYWrWuJqWLN+eb3vAZxTw/lDQa9jCQY/qojeelH9wkPbPXChntkOSVKXXJTq0aqNmdR+isLo11f2XTzS9ZT9lpx/Ntw+cmQd/UuUYM7iJHE6r0d9s1k8rEjWgfVU1qRGuWc91VPngAM1bf0APfLRWM57u4NWYy5aC3pvznoCC/n/8ZeiQjnrhjZnqP+RDNagbo8YN4hTg76e6tSrrrn900B0PTVBoaJAa1YtRgP95X1iP89AZkxjW2pFns2Nr7QeSPpCkxxYOLbWp9HIVg5SR69v+jEOZCqkQnGdMYLmTv6bYFlFa/X9blJmepeDwQJWr6BobHBGkKhdV1uEd6SQxzlJYVLCOpJw8F0dSMlW+YlCeMUnb0jXzTVepfEZalnauTJGfn1Gdtq6S1Z0rUxRdO0yhFfJuh6KrHBOm5MST3xokJx1RpejyhWyR37KF8arXKEYVo4q23fnsiy8X66vvlkqSmjetroSEwznrEhJTFROdd1pOxYrllZaeoexshwIC/POMCQs7+S1cl0sbaeSLU3Tw0FFVqnjyfESEl1O7NnX026LNJDFO8cVXi/XV5OWSpOZNqikhMTVnXUJSmmIq583xV6xQXmnpx0+ei6S0nHMRFxOpihVCFVouSKHlgtTmolr6c/N+bfhzn2bP36j5Czcp80S2jhzJ1L+f+kqvjj6llwBQCtW/7ybVu8v1bzVl2dqcqgpJCq0ep4x9Sfm2OfD7Kv3a2dUzKa5nJ4U3qCVJqnPbAG0Y+4Ek6ci2XTqyY48iG9VRyrK1Xj6KsuOL3/bom8WuquBmNcOVcPh4zrqE1ExFRwSfblP5+xn1vihG42ft0oD2VXOmmUhSl6aV9fw3m3XoyAlVDOPvq9P54pvl+nraSklS88ZV81RIJCSnKaZyWJ7xFSuEKu3IcWVnOxUQ4Od+z3C9r4SVD9aLT10lyZXs6DHgHVWv6vp8cV2/C3VdvwslSa+/N0dxMWf1fTPg0zxO3RljGhhjZhlj1rmXWxhjnvJeaMWjQu0IHU3M0NHkDDmzndq7JElxuRpGStLx1BM52dJD29MkKwWFBSg706GsDNd8zexMh5LWH1J4NT6sna2YuuFK3X9MaUkZcmQ7tWVRkmq1yducc8g7HXNu9dpHq/OdDXMSGJK0ZWEiU0nOkYZNYrV392Ht35uqrCyH5s3crA6d6xRpH3NmbFa3Xg28FGHZNPiGDpr65cOa+uXDuqxbU035YYWstVq1ZpfCw0LyJTGMMWrXpq5m/LpOkjT5+xXq3rWJJCn5QHrOa9eadbvltFYVK4Tq4MEjSkvPkCQdP56lRUu2qk6tvHPXIQ2+voOmTnxQUyc+qMu6NtGUH1e6zsXaws5FHc2Y5T4XP6xQ9y6uKsQeXRtr+cp4ZWc7lJFxQmvW7Vbd2tH614O9NP+nJzT7h8f0+phBan9xHRIY8Blb3p2gn1r110+t+mvPlF9Ve0h/SVJUu5bKSk3X8YT8fa2Co10NCP2CAtXk8bu0ddwkSdKxXfsV18P1TX9ITJQiGtbWke17iulIyobBl1bX5MfaavJjbdWjebSmLktwvWbFpyo8xF8xkXmTGNZa7Uw+lnN/7roDqhMbKklKTss8+f6xM03WaVWhPE3sCzP4ujaa8vldmvL5XerRuYGm/rTG9ftft1fh5YPzJb6NMWp30QWaMcfVO2nK9DXqcWl9SVJa+nGdyHJVKX09bZUuvrCmwsq7zl/KQVd10r6EVP0yd5Ou7Nm0uA4RKDWKcnWSDyUNk/S+JFlr1xhjJkga7Y3Aioufv1GLf9TT4tfXyjqtal4Sp4hq5bVjjiuTXbtbVe1bnqz4Oftk/Iz8g/zU5p7GMsYoM/WElr7tqgqwTqtq7WIU25zuwGfLz99Pl97eQNNeWCXrtGrcraqiaoRp3cy9kqRmhfTBkKSsTId2rzmorkMbFUe4ZZ5/gJ8eGNZVwx+cIqfDqle/JqpVN0o/fLNGktT3uhY6eOCoHhgySceOnpAx0uSJq/ThV/9Q+bBgHT+epRVLd+mfI7qX8JH4ri6XNNS8BX+qZ79XVC4kUGOeG5iz7q4HPtHoZ65VbEyEhj18hR55YqL+8+5MNW5YVQPdDSJn/LpWE7/+Xf7+fgoJCdTrL94kY4ySDqTriWe+ksNpZZ1WV/Rsrm6dS+eUv9KiyyUNNW/hJvW8+jX3ubg2Z91dD32q0U8PUGx0hIY9dIUeGT5J/3n3F/e5cF0NoG7tGF3asYH6DXpLfn5G1/W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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "corrmat = final_dataset.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize = (20, 20))\n",
    "\n",
    "g = sns.heatmap(final_dataset[top_corr_features].corr(), annot = True, cmap = 'RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15fef473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>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        7                 0   \n",
       "1           4.75           9.54       43000      0        8                 1   \n",
       "2           7.25           9.85        6900      0        4                 0   \n",
       "3           2.85           4.15        5200      0       10                 0   \n",
       "4           4.60           6.87       42450      0        7                 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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2974473c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# independent and dependent features\n",
    "X = final_dataset.iloc[:, 1:]\n",
    "y = final_dataset.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98d4d072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>7</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>8</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>4</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>10</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>7</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        7                 0   \n",
       "1           9.54       43000      0        8                 1   \n",
       "2           9.85        6900      0        4                 0   \n",
       "3           4.15        5200      0       10                 0   \n",
       "4           6.87       42450      0        7                 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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "237ec979",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "285cb08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## feature importance\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32a8c29f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.98464683e-01 4.09448145e-02 3.25023888e-04 7.49746693e-02\n",
      " 2.16952897e-01 1.60109625e-02 1.21918553e-01 1.30408397e-01]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ddd80612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph feature importances for visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index = X.columns)\n",
    "feat_importances.nlargest(5).plot(kind = 'barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ecee0f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3544d680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "244900da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_random = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "314aaec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning\n",
    "import numpy as np\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "72f5225f",
   "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 a 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 amples required at each leaf node \n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "779e1f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds the best values of the hyperparameters\n",
    "# better than GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "31461ca3",
   "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'], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create random grid\n",
    "random_grid = {'n_estimators' : n_estimators,\n",
    "              'max_features': max_features,\n",
    "              'min_samples_split' : min_samples_split,\n",
    "              'min_samples_leaf': min_samples_leaf}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4f335a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for the best hyperparameters\n",
    "# first create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e3b56028",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, scoring = 'neg_mean_squared_error', n_iter = 10, verbose = 2, random_state = 42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "30c631cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.2s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.8s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=100, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=10, n_estimators=1100; total time=   1.2s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=200; total time=   0.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=200; total time=   0.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=200; total time=   0.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=200; total time=   0.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=200; total time=   0.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=15, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=15, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=15, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=15, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=15, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=500; total time=   0.4s\n",
      "[CV] END max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=500; total time=   0.4s\n",
      "[CV] END max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=   0.6s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   0.7s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   0.8s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   0.7s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   0.7s\n",
      "[CV] END max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time=   0.7s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=1100; total time=   1.2s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_features=sqrt, min_samples_leaf=10, min_samples_split=5, n_estimators=1100; total time=   1.4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d37d31d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "987ad4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.9730862 ,  0.56247454,  8.20278622,  0.18058385,  0.72250423,\n",
       "        7.92309854,  0.4464911 ,  3.41365126,  6.97766709,  4.47614222,\n",
       "        2.33982693,  4.2821663 , 17.21189214,  0.55687856,  0.53523983,\n",
       "        6.91330928,  4.04831035,  5.16629591,  1.54675813,  1.61333523,\n",
       "        2.77007726,  5.91675896,  0.9806501 ,  4.98854968,  0.28552774,\n",
       "        2.91233223,  0.4082723 ,  3.82089263,  5.64884138,  7.44306519,\n",
       "        4.79824504,  5.70875334,  4.53839675,  0.51427311,  2.58385971,\n",
       "        5.18296275,  3.64584124,  8.3174926 ,  0.7977194 ,  0.65599229,\n",
       "        5.30755232,  4.52143267,  5.30407089, 15.27335229,  9.24684149,\n",
       "        0.36620829,  5.73356908,  3.89978667,  5.90058932,  9.33535126,\n",
       "        1.12665782,  2.88903406,  2.41035736,  1.078125  ,  7.06307975,\n",
       "        6.17496099,  0.45188492,  0.34662113,  5.70207908,  1.1451595 ,\n",
       "        7.33196724])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e9b8597",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python3\\envs\\maskdetection\\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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6e271d880>"
      ]
     },
     "execution_count": 55,
     "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": "raw",
   "id": "0c0c1a57",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "534e55d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.1056008549724075"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e89980f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e6e1e21e50>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IKA0NDWUsyzppob76cMr6tOW1PG++WTaZgj4ijla+HwMeAa6s2WQCuLDq/QXA0SzHtHR5zdi4UF89a9/dUxqYZdNy60bSmcCyiPhZ5fW1wH+p2WwXcLukLwG/CrwWEae1bSy7pPbGHzy0jzse2sfwItwwVDu75L/9F8M8/txU4g1KWW9gWjk4kDhJmUfimDUmS4/+POARlYfULQf+MiK+JelWgIi4D9gNbASOAMeB38tWrqVJam/M9cja3dNO+qHytb2TdWeXzHID09b1I6fNiOmROGaNaznoI+IFYHXC8vuqXgdwW6vHsMYt1MaY62nXhm0r877X65kvxsVRT2lglo2nQCiItPZGtdofBq2OZsmjZ+4pDcxa5ykQCiLpgmet2p52q6NZfPeqWW9x0BdE9Y1GcPq41qSedtoZ+OT0TN1RO7571ay3uHVTINXtjUZ67/XaPfXaOO6Zm/UWPzO2gBq9wJr0fNdafs6qWW+o98xYn9EXTDMXWKvPzOud2ZtZb3OPvmCavcC6ee0wT267OnU6AoEnEDPrcQ76HpU23UGrQx+3rh9JnZjIc8qY9TYHfQ+aa89MTs8Q/KI9Mzo+2fLQx81rh5Nnm8Nzypj1Ogd9C/KaPGxOvfZMlqGPae0bj483620O+ibVO5vulHrtmbQHdzQy9NHj482KyaNumtTpeV6SLDSbY6vTBXh8vFkxOeib1A1zoy/mbI6eU8aseNy6aVI3zPOSpT1jZkuPz+ib1C1zo/vM28wa5aBvUtY+divzvy/mfsys+Bz0LWj1bLrV+d8Xaz9mtjS4R99Brc7/Xm10fJKPfHl/5v2Y2dLhoO+grCN25s7kT6bMOOo7WM0siYO+g7KO2En6jaCV/ZjZ0tJy0Eu6UNLjkp6VdEjShxO2ea+k1yTtq3x9PFu5ve2qdww1tbxWvTN238FqZmmyXIw9AXwkIp6WdBawV9JjEfGDmu3+OiKuz3Ccwnj8uammls+ZG2GTNulYn+Rx9GaWquWgj4iXgZcrr38m6VlgGKgN+iUpafhjKz36hZ4CNdDf55A3s7ra0qOXtApYC3wvYfW7Je2X9E1Jl9XZxxZJY5LGpqbqn+F2u7SJzwZX9CduX6+3Xq8v7ztizawRmcfRS/pl4GvAHRHxes3qp4G3R8TPJW0ERoFLkvYTETuAHVB+ZmzWuvKUNozyl5YvY6C/r6m7atPO9gV+lquZNSTTGb2kfsoh/2BEPFy7PiJej4ifV17vBvolnZvlmL0gLZxfm5lteo6abphbx8x6W8tn9JIEfB54NiL+OGWbtwGvRERIupLyD5a/b/WYvaLeNMLN3lXbLXPrmFnvynJGvw74beDqquGTGyXdKunWyjbvBw5K2g98GrgpIuVunwJp5wM8PFOlmWWlbszdUqkUY2NjeZeRiScdM7NOkrQ3IkpJ6zyp2SLxNMJm1i0c9AvwmbmZ9ToHfR2NTgfsHwZm1s08qVkdjUwrnHZz1Oj4ZIerNTNL5qCvo5EpC9oxx7yZ2WJy0NfRyM1KWeeYNzNbbA76OhoZD+87V82s2/liLPMvpp490I8EPz0+S5/EyYhT34cTLrT6zlUz63ZLPuhrR9ZMz8yeWjf3yL6TEafCu3Y0zdx7j7oxs2615IP+k18/VPfxfHPmLrAmBbhvjjKzbrake/Sj45P89PjswhtW+AKrmfWiJR30zQ6B9AVWM+tFS6Z1U33BdXBFPxHz+/EL8QVWM+tVSyLoR8cn2frV/cyeLF9cbaZdAySOtjEz6xWFDfqPjR5g5/deOjVyplV+ZJ+Z9bpCBv3HRg/wF0/9uC37cl/ezHpd4S7Gjo5PthTygwP9bXsqlJlZNynMGf3o+CR37TrU1AXWOQP9fdy16TLANz6ZWfEUIuhrL7Y2o/ZCq4PdzIqmEEH/ya8fajrkf+tdF/HfNl++SBWZmXWPTD16SRskHZZ0RNK2hPWS9OnK+mckXZHleGmaHS7pkDezpaTlM3pJfcBngGuACeD7knZFxA+qNrsOuKTy9avAZyvfc3HOin4+8W8uc3vGzJaULK2bK4EjEfECgKQvATcA1UF/A/DFiAjgKUmDks6PiJczHPc0gwP9qRdhBwf6uWuTw93Mlq4srZth4KWq9xOVZc1uA4CkLZLGJI1NTU01Vchdmy6jf5nmLetfJj71gTXs+8S1DnkzW9KyBL0SltVeEW1km/LCiB0RUYqI0tDQUFOFbF47zPbfWM3w4ACiPJJm+2+sdsCbmZGtdTMBXFj1/gLgaAvbtIXnhDczS5bljP77wCWSLpZ0BnATsKtmm13A71RG37wLeK3d/XkzM6uv5TP6iDgh6XZgD9AH3B8RhyTdWll/H7Ab2AgcAY4Dv5e9ZDMza0amG6YiYjflMK9edl/V6wBuy3IMMzPLpnCTmpmZ2XwOejOzglNkfDDHYpA0Bfyowc3PBX6yiOW0g2tsn16osxdqhN6osxdqhO6o8+0RkTg2vSuDvhmSxiKilHcd9bjG9umFOnuhRuiNOnuhRuj+Ot26MTMrOAe9mVnBFSHod+RdQANcY/v0Qp29UCP0Rp29UCN0eZ0936M3M7P6inBGb2ZmdTjozcwKrmeDfqHHGHYDSRdKelzSs5IOSfpw3jWlkdQnaVzS/867liSVh9Z8VdJzlb/Pd+ddUxJJf1D5b31Q0k5J/6gLarpf0jFJB6uWvUXSY5Ker3w/J88aKzUl1bm98t/8GUmPSBrsthqr1v0nSSHp3Dxqq6cng77qMYbXAZcCN0u6NN+qEp0APhIR/xx4F3Bbl9YJ8GHg2byLqONPgW9FxDuA1XRhrZKGgf8IlCLinZQn+7sp36oAeADYULNsG/DtiLgE+Hblfd4e4PQ6HwPeGRG/AvwdcGeni6rxAKfXiKQLKT9W9cedLqgRPRn0VD3GMCLeAOYeY9hVIuLliHi68vpnlMOp6ybNl3QB8K+Bz+VdSxJJ/xh4D/B5gIh4IyKm860q1XJgQNJyYAWL9PyFZkTEE8CrNYtvAL5Qef0FYHNHi0qQVGdEPBoRJypvn6L8TIvcpPxdAvwJ8J9JebBS3no16Bt+RGG3kLQKWAt8L99KEn2K8v+kb+ZdSIp/CkwBf15pL31O0pl5F1UrIiaB/0H5rO5lys9feDTfqlKdN/dsiMr3t+ZcTyM+CHwz7yJqSdoETEbE/rxrSdOrQd/wIwq7gaRfBr4G3BERr+ddTzVJ1wPHImJv3rXUsRy4AvhsRKwF/oHuaDXMU+lz3wBcDKwEzpT0W/lWVQySPkq5Ffpg3rVUk7QC+Cjw8bxrqadXg75jjyjMSlI/5ZB/MCIezrueBOuATZJepNwCu1rSX+Rb0mkmgImImPtt6KuUg7/b/BrwfyJiKiJmgYeBf5lzTWlekXQ+QOX7sZzrSSXpFuB64Dej+278+WeUf7Dvr/wbugB4WtLbcq2qRq8GfSOPMcydJFHuKz8bEX+cdz1JIuLOiLggIlZR/nv8TkR01VloRPxf4CVJI5VF7wN+kGNJaX4MvEvSisp/+/fRhReNK3YBt1Re3wL8VY61pJK0AfhDYFNEHM+7nloRcSAi3hoRqyr/hiaAKyr/z3aNngz6ysWZuccYPgt8OSIO5VtVonXAb1M+S95X+dqYd1E96j8AD0p6BlgD/Pec6zlN5TeOrwJPAwco//vK/dZ4STuB7wIjkiYkfQi4B7hG0vOUR4vck2eNkFrnvcBZwGOVfz/31d1JPjV2PU+BYGZWcD15Rm9mZo1z0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCu7/AymlKwebZOPjAAAAAElFTkSuQmCC\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": 58,
   "id": "adbabd57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "# open a file where you want to store the data\n",
    "file = open('random_forest_regressor_model.pkl', 'wb')\n",
    "\n",
    "# dump info into the file \n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44b51ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the requirements.txt file \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8df3a13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a90c7ee4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c91ee7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5e77a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2ecb3d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03160c2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "131e0647",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d85faf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5af9454f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b17bc01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28fd6346",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1057594",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dccf78a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9d2ab68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd4720fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e97d655c",
   "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": "3.8.5"
  }
 },
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