Used_Car_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb46b1c2",
   "metadata": {},
   "source": [
    "## 1. Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe7784b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3565b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Dataset/car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f91e03f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f6740ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6edda752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "717ea77f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e40e262",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check null value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef4ce971",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46f4aec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96308850",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c30f4e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current_Year']= 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8117d3fd",
   "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>2023</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>2023</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>2023</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>2023</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>2023</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          2023  \n",
       "1       Manual      0          2023  \n",
       "2       Manual      0          2023  \n",
       "3       Manual      0          2023  \n",
       "4       Manual      0          2023  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ef5a095",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_of_year'] = final_dataset['Current_Year']- final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71f23b12",
   "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",
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       "      <th>Seller_Type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>9</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>2023</td>\n",
       "      <td>10</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>2023</td>\n",
       "      <td>6</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>2023</td>\n",
       "      <td>12</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>2023</td>\n",
       "      <td>9</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_of_year  \n",
       "0       Manual      0          2023           9  \n",
       "1       Manual      0          2023          10  \n",
       "2       Manual      0          2023           6  \n",
       "3       Manual      0          2023          12  \n",
       "4       Manual      0          2023           9  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b2697aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ae6ebf7",
   "metadata": {},
   "outputs": [
    {
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       "   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_of_year  \n",
       "0       Manual      0          2023           9  \n",
       "1       Manual      0          2023          10  \n",
       "2       Manual      0          2023           6  \n",
       "3       Manual      0          2023          12  \n",
       "4       Manual      0          2023           9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63b27849",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Current_Year'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2413343f",
   "metadata": {},
   "outputs": [
    {
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       "   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_of_year  \n",
       "0       Manual      0           9  \n",
       "1       Manual      0          10  \n",
       "2       Manual      0           6  \n",
       "3       Manual      0          12  \n",
       "4       Manual      0           9  "
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ee3ac5ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = pd.get_dummies(final_dataset, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5fd53ca8",
   "metadata": {},
   "outputs": [
    {
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       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_of_year  \\\n",
       "0           3.35           5.59       27000      0           9   \n",
       "1           4.75           9.54       43000      0          10   \n",
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       "3           2.85           4.15        5200      0          12   \n",
       "4           4.60           6.87       42450      0           9   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0             False              True                   False   \n",
       "1              True             False                   False   \n",
       "2             False              True                   False   \n",
       "3             False              True                   False   \n",
       "4              True             False                   False   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                 True  \n",
       "1                 True  \n",
       "2                 True  \n",
       "3                 True  \n",
       "4                 True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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  {
   "cell_type": "code",
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   "id": "3e2ed9b3",
   "metadata": {},
   "outputs": [
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       "      <td>0.039896</td>\n",
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       "      <td>-0.098643</td>\n",
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       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
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      "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_of_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_of_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_of_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_of_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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "503114ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb192635",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_stats\\counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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3hBSJxRWLx+UPRlRd7hyVP+p87oE9mFWDLAAgWX2lU6uXHK07nt6atMfw6iVHD6nN3r+jVllWqp5gRN7exI7b4Itu6eKtlF3MzdQH4CIdCkmmJLFMF7GBYlRM7Xy2YyazxniFLp+Jt/m6MaOY6kM+VbnST/tVlTEtiLFpty/w0RY47lLVlltjCxyrlgswg1VjGGNXIJlV++pWbUcAM33Q2au/vrVX4z1lCkZi6vCH9cK2D/Wpjx2gSaMwz0It2+fmm2/WI488ojfffFNut1snnHCCVq1apY9//OMDzwkEAvr2t7+tX/3qVwoGgzrllFN09913a8KECSaWPP2+2V5/KO2E356uoOFr5iKBYmdnr1Y+ukVfOGZq0n6OuZ48zOce2PuzapAFABi76+lWwz2GS2w23bl4btpzjS6ItTTWaUlzgy55aKP8oehAjBt80W3jjk41N9YZLis7kpibrg8AFJJqt0P3nHuU7vxLa1KS2D3nHqVqN5N5GJuKpZ3PZsxk5hiv0OUi8XY475WvGzOKpT7kk8ftUEtjndYb9EFbGuvkIb5iDNre3qPL12xOGJu1NNbppoWzNbWugnIBFmHVGMbYFTBmxb56bYUzbTtSW8E4AmOL1x/Su+1+PbF5Z0Kfs7mxTg31FSp32nNeZ9PfnjuG/O1vf9NFF12kv//97/rTn/6kcDisz3zmM+rp6Rl4zre+9S09/vjj+p//+R/97W9/086dO/XP//zPJpb6I9XlTh06vlJHTK3VoeMrB/5Q+if85jfVJzy/f8LvUx87IOWxkfyx9U8aTp/kSUpCkT6aPPT6Q1m/x2BD2Zc6W15/SG/v6dbG7R16e2+3YZn7g+xTl35Say88QU9d+kndsXjuqGSPAQCy19Yd0rpW43ixLkO8SHVBbH1ru+7dsE1LWxokfRTjJA3E4NXrt+m85gY1N9YlnJuLmJuqDwAUklA0qrv/Ypwkdvdf31Yoyj7bGLuKpZ2fXOPW9z93uJ78xjz999eO15PfnKfvf+7wlGOm0RzjFbtAJKa7nt5q2Kbe9XSrAhm2DByOTHMOuf57LZb6kC/BcFQrzpiplv36oC2NdVp55kwFw8RXjC27fYGkZA+pb0x3xZrN2u0LUC7AIqwawxi7AqlZra8+wePSTQtnG7YjNy2czapjGHM6/CHd8RfjuYI7nt6qjhxdsx+MFVH2efLJJxN+/+Uvf6nx48fr5Zdf1vz58+X1enXPPffowQcf1EknnSRJuvfee3XYYYfp73//u4477jgzij0kmTIRRyNLsX/ScMkJBydkBg/WP3mYi2A0WvtSD2cp6Opy7sQCAKvLFC86e1N3ttJdENvQ2q6lzQ0Dv/fHuEPHVw7E2Z5gWDctmK1QNKaeYMQSdwYAVtEdjGqdwR0qUl+SWHeQyTyg0A13m53RGuONBR09odRtamubOnpCOZ10teLdj+jT2RvWV+9/SasWzdFlp01XdyCqSpdde3xBffHnf9cvvnyU2UUE8qqjJ2S4SqXUl/SR6/ZxqKxaLsBMVo1hjF2BwuK0l2jZSU0J7UhPMCqnnXUaMPb0hKIp+5wbWtvVE8p9DCMRJQWv1ytJGjdunCTp5ZdfVjgc1sknnzzwnOnTp2vq1Kl67rnnDBNRgsGggsGPtr7x+XyjXGpjXn9Ibd0h+QJhedwO1VckTggNTqDof+47bT1Jzx28T2m1u1RlpSXy9oZV6Up+zf5Jw2CGO61yNXmYq32pB39XlWWleundDr38bkfCc1gKujhZpb4CyGwk9bXa7dCykxoN9/H1h6IKhmPy+hOTJL3+kNp7QvowQ0ZwlatUP/nXI1XuKFUkFlMwEtXG7R198bSy744AYCwZTl31Z5isy3QcwMiMdl/Y6w9p5aNbdPiUGi054eCEGHz1o1v0g88dnjS2ytUYb7Tt7OyVtzcsX29Y1W6HPG5Hxi1oM43RR8oXiGR9PNuycWNG/gynvnpcDk2udunQAyrUG46pxBZVaUmJDj2gQpOrXZapR0C+ZEpyzNR+DtdQ62u+ywUUgnzGMMauQOEYTn31+kP6rsHq1lLfTRFc48NYY0YMIxHFQCwW0ze/+U01Nzdr1qxZkqRdu3bJ6XSqpqYm4bkTJkzQrl27DF/n5ptv1rXXXjvaxU0r1V1nNy3suyPb2/vR5FJPKGr43FWL5igcjSUtEdncWKfzmhu0+OfP66hptQl3svVPGpaVps8qzFWHMRf7Uht9V82Ndbp98Vxd8tBG+QdlguVyNRdYgxXqK4ChGUl9ddpLtHF7R9I+vrcvnqsHn39Xz77Trgke10D7vrOzVysf3aIvHDNVboc97Wt3BSL69n+/qtsXz03ali7dHd9AsRpOXfW40w9LMh0HMDKj3Rdu7wnpC8dM1b0btiXF4POaG9Tekzy2ysUYb7S9296jK/YbJ7c01unGhbM1ra7C8JzhrgyTDY8rQ5ua4ng+yoaRG059ra906vbFR+rKtcl/p7cvPlK15SSiYGzJNA9ZlaH9HK6h1td8lwsoBPmMYYxdgcIxnPo6lO1eucaHscSMGMbaQwYuuugibdmyRb/61a9G9DqXX365vF7vwM+OHTtyVMKh8fpDSZNI0r4VPR7ZpEc2vq+Fdz+rT//wb/rdll267DfGz/3rP/bqSoN9Sje0tuveDdu0tKVhYJUQ7767xfsnDTfu6FTzfvuv9cvl5OFI96VO9V0N/oz7Yyno4mJ2fQUwdNnWV68/ZLjv9obWdv1ywzZ98+SPafX6bQPte39smD7Jo3s3bNNz77SnjGnNjXXauKNTS1sakpJQJCXFSWAsGE5dLXfYk/bs7dfSWKfyDIlgAEZmtPvCkVjcMD72j7eisXjSOSMd4422nZ29SUkoUt8WCleu2aydnb1J56Qdo+ewn1Bb4UzbptZWJH93+SobRm449bUrGEm6gCf1/Z1etXazuoKssoCxxeUoSds+uh25nSofan3Nd7mAQpDPGMbYFSgcw6mvbPcKJKooK00bwyrKcp+IQnrmfpYtW6YnnnhCzzzzjA466KCBxydOnKhQKKTOzs6EVVF2796tiRMnGr5WWVmZysrKRrvIKaXL9lvf2q7zmj9KrhhfVaZ1rcbP7TuWes+opfteZ3AGYf+k4dWPbhl4n/3vDM/15OFI9qVO910N/oyDsYRtcTG7vgIYumzra6a4uLQ7JH8oOtC+9z9/yQkH686nW7Vxe6duXzxXUmJMm9dYr3ObD9YlD23UHYvnJtzpPRiZ9hhrhlNXe8JRLWluUFxKWoFvSXODesIsbwyMptHuC8di8bT7EBslokgjG+ONNm9vOOVnWt/aLm9vOGkVkXzdkecqLdE1Z83SNY9t0fr97iC+5qxZchmsXMrdgoVjOPW1KxBJ+3faxXYfGGvi0oozZur6J15Lah9XnjlTceNwlLUh19c8lwsoBPmMYYxdgcIxnPpaKNu9AvnSHYykjWHdo3CjAoko+8TjcV188cVas2aN/vrXv6qhITHx4BOf+IQcDoeeeuopLVq0SJL01ltvafv27Tr++OPNKHJGmbL9gpGY4f+ne16m44MzCCfXuPWDzx2u9p6QrjlzpqKxuPyhqKrdozd5mO2+1MP5riTrLAUNABi6TG19IBxNaN/7n98fA/yhqC55aKOWtjRoaXODgpGYykpLNK7CqXN+8bz8oWjGmEmmPWDM6w8b1q+NOzp1yUMb9cvzjja7iABGwB9KP5kxeBvU/WU7xhttvt7h312Xrzvy2rpD+sLPntOqRXN02WnT1R2IqtJl1x5fUF/42XP69QXHJ32n3C1YnLL5OwWK2bgKp65cs1nnNTcktY+3//kfunHhbMoFWIRVYxhjV6BwFMJ2r0A+mRHDSETZ56KLLtKDDz6oRx99VFVVVdq1a5ckqbq6Wm63W9XV1Tr//PN16aWXaty4cfJ4PLr44ot1/PHH67jjjjO59MYyZfuVDboLqszgjqihHNv/+P4ZhFadNNzfcL4rqywFDQAYnkxtfY3bkdC+9z9/cAzwh6JJK57cc+5RAxfQMsVMMu0BYx63w7B+9aPuAIWt2p1+7FTtLrw67slQZqN2K1935PkCYbV1h3T+fS8ZHje6cMPdgsUpm79ToJhVlzt1xekztPzhTQkXpcye67NquQAzWTWGMXYFCkf/zg3EV6CPGTGMRJR9fvKTn0iSPvWpTyU8fu+992rJkiWSpB//+McqKSnRokWLFAwGdcopp+juu+/Oc0mHLl22X3NjnTbu6Bz4feOOTjU31hkud7enK6h5jXWG2/MMfp2RZhB6/SG1dYfkC4TlcTtUX5G/JJZMmZGNB1Rq7YUnWGopaADA8KRr6+c11evQ8ZWa4HElPT9djJzXWK9J1S7dfc6RcjnsisfjKZ9Lpj2QWqWzb5/t9QZ1p6WxTpVO9tkGClmh3Ik2nDFptduRtt0ySq7J1/eQTVJJofwbYXiIr0CyyTVuff9zh6ujJyRfICKPu1S15c6EsSDlAsxn1Rhm1XIBMGbl7V6BfDMjhtnicXaZzBefz6fq6mp5vV55PJ68vOfOzt6kbL+WfXs9XfLQxoE7uMuddq1ecrTu/ktrUmbgrYvmKBSN6Yo1mxP+OJsb63Tevtc5alqtVi2ao0n77YE9nHJe9vCmhD2p5zfV65ZFc5L21R4tRt9Vf2Zktp8LhWs49XXBuRfK1fylhMcCG/5Ta+9LTlS76DuX6/293oTHDjygWnf94OaRFxoYo4ZTX4fb1u/s7NXVj27RvxwzVfdu2JaQYDKvsV4Xntio8+97cSCenjT9AF126mFJe3sTT4D0ddXrD6nDH9ZVaxP7my2Ndbpx4WzVuB1MEgB5NBpjV6uPt7IZk77b3qMr1xi3W9PqKlK+z2h/D15/SBc/tDFlUskdi+catqlW/zeCMeIrMDxmzkGmq69WmBsFrMTMGEZsBQqHGdddgUJlRgwjESWPzGoQ++/q6s/2czlKdM1jr+lPb+wZeE5/wonbaU+ZGbjbF0jIyneVlsjXG1ZF2cgyCL3+kJY9tDFhoDW4XKkmyUbD/t8VmZFj12glogznuQCGZrjxdbhtvdcfUntPSNFYXJFYXB09Ibkcdr21y6frf/vGQBJKv5MPG6/rzp6l3lCUeAIMkmky79rHXtOSlgaV2kvU1RtWlduhSDSmX27YpqvPnEkdAvJotMauVh1vjWRMurOzV97e8MBnqnY7Ml4wzMf3kG1SiVX/jZBapvq6t7NXgWhM3aHoQHytdNrlspfoAC5uY4wxew4yVX01u1yAVZkVwxi7AoWDRBRg6MyIYWzNU+RSLS38g88dPjC55HE7VFFWqu5ARB/4AvK4HWqor0j6Y5vgcY3KcpBt3SGt29qmcqddS1sadOTUWpWW2DSuwqlwNKZdXQFJGvYffzZb/VSXM8kGAMVucDvvC4QlmxSIxNQTjMjbmxwz+v/b1h1SIBzRAVVlKi2xGSahSNKf39ij75wSViwWVzgWV4ycXyCjtu6Qnmlt0+mHT9Z4T5licSkWi2tvV1DPbG1TW3eIPhpQBLIZb2Uzrtv/JopMWxv0j0mNZGqDKpx29YaicthL5CwtUcUQlrLNx7hzpEtQxyXJNqpFRJ4cUOPW+x1+SX3/rjZJJSU2klAwJrV1h/Tyux1adlKj5k6pUTASk8th1yvbO7R6/TbT+pxWLRdgNivGMMauQOEZ7vgQKFZt3SE9+fpuTR5XPtDn7AlF9cr2Dj352m5ddGITiSgYukxLOvb/Me3s7NV3/udV05Z+9AXCKnfadfviubp3wzbd+XTrwLH+7X9+8ORbuvbsWUMuD8tZAgBSMYoR+29bNzhmGD1/XlO9bl88N2Gbu8He7+jV+fe9lPD8VcQgICV/KKwHvnKcrnvitYQtsFoa6/TAV45TbyhsYukAmCWbcd329h5dvmZzUlty08LZmppiuxxfIH0b05XiuNXHncNNeLH650F23m3v0RUGdSLdFlJAseoOhlPOP96+eK56gub0Oa1aLsBsVoxhjF2BwpLN+BAoVmb0OUlEKVJefyhpAqncadecKTX6v7Ye7fL2qrrcqcqy0oTn9a9KMndKjd74wKeeYETjq8oyTl5lc5daP4/LoaUtDbp3w7aEYCBp4Pe5U2u1/OFNQ1qK0uizS313sg31NQAAxSlVjFjf2q64pKUtDbrz6daBmPH9zx1u+Px1W9sUi8e1tKVBq9dvG4id/Xeu1Vc6Ve60DySprBv0et2BSFbxEihm1W6nrnn8Nc2dWqulzQ0Jd4He+uQbuvrMmWYXEUCeZTOu2+0LJE0ySn1x/oo1m/XDzx9heOebx+VIGAvvfyd6lcuRk/INPne44+eRjLmHgnF0cdrZ2asrU9SJK9ds1q3/73CSjDCmVLsd+venthr2OR98/l2tPGMG5QIswqoxjLErUDiyHR8CxcrjSt/nXHF67vucJKIUqQ97QlpywsH63qkfV3cgqipXqcocJfrRH99KyHJ68CvHJiSh9GdC9V9Uk6TtH/o1pbZcEzzGCSk7O3u18tEtmj7Jo7lTavSBN6A95Q4dWOOWLS51BkLyuJ0KRWLyhyKqdjsUiMTk642o2l2qyrJSnT57oo45eJwuP+0whaN9x15890OtXt+XnLK0ue9CX3t3SB/4AvIHI6oudxpOvo1kWWUAQHEzWvLY7bDLZpMOqHLKH4zps7Mmyu3oW1a/oyekb3y6SVd89jDZJMXjfdvteHvC6gpFdHBduU4+bIK+/4c3E+LrvMZ6Pb6sRb3hiN7vDMhhL9Er2zu0bW+3vvDz5weeZ+W7jEf7ghcwmD8c1SUnNSoQie3bMrKv/9rSWK+Tpx8gfzh55SEAxa2tO6RgOKJnvvsp9YSi8u3bPq/Cadd3/ud/Dcd1HT0hhSPRlOd09IQMJxrrK526d8nRuuPprfvF8zrdu+Ro1Vcmx79sx53ZrDqSj5VKinkcPZaX4vb2hjW+0qF13/2UugfViUqnXT/+05vy9oYt2Q8FRkswGtOXjz9Y96x7J2n8dv68BgWjMcoFWIRVYxhjV6BwdPSEtHF7Z8qt71KND4FiFYrF0sawUCz3fU4SUYqUzWbT6v1WGGlprNOKM2bqxf/rUFt3SJLU2fvRMjv9q5Js3N5puDSP0USX1x/Syke36AvHTNWDz78rSZo7pUYd/rDikiZWueRyluqKNZsHXvfWP7yVVK7B2yH0b8fz+k7vwLYHkVhcty+eq6sf26J1g841KlO2yyoDAIpfquXnBsciSbrn3KN0919aE2JOf3x66Pl39b1TD9O3fvF3fen4aTqgskxLmxt0zrHTEgYzKx/botNnT9J4j0vLHnxFc6fW6Mw5kxJWSrHqXcYszY98c5dKDrtDr7/zoSZ4XApGYuoNR7Xb26tjDqlTqY3Jd2CsicTCuvX/HWG4jPKt/+8IBSOhpHNisYi+//+O0PrWtoG2pCfU15Z8//8doZ5gMOX73fn0Vq3f7065da3tks2mOxfPTXp+NuPObFYdyddKJcU6jh7rS3GX2iO65OTpWm7wHdywYLbCsdR1AihGtrj0X8/9nw6fWqsl+90F+l9/f1ffO+XjlAuwCKvGMMauQOHoDkbSbkPSHYyYWDog/9x2qdSdOoY5RiGGkYhShHb7ArpyrfFyU9c/8ZpWLzlae7uCCkZimjKufOD43Ck1uvPpVi07qdFwmxyjia627pCmT/Loweff1RePnWZ4Ye+Kzx6mjds7U26/078dwtc+eYjC0bjmTqmRJF10YpPe2uXT1z55iCZVu7TqyTeHVCaPwbLJgxktqwwAGBtq3U69ttNnmDgS10ergd35l9a028Vd/8Rr+sG+ZWCv3W9f4P7BzCUPbexb0WvDtoEtf2544o2B/+9ntbuMWZofZiix2dXm6016PC6prSugydUkQAHFYDirbZU7nEkXHqS+8eNVazfr5oWzk87xuMq0o7NXv938QVJsPri+QlNSJFPu6QomJJ8Otm5rm/Z0BZPKmc24M5tVR4xWcxvcf8lVH6IYx9EsxS257GXDrkdAUbNJ5xx3sD7wJvY7J1e79ImptZKNcgFWYdUYxtgVKBx1FU79+M//SDnHe8PZs8woFmAamwkxjESUAjOUibuOnlBSw9pvfWu7LrfZdP59L0mSlp3UqJbGOq1vbVcw0pfp1J+QYqR/ckzqmxBr7wkNJI6kSjK5+XdvDuy1nep1N27v1NVnzNS1T7yWlJl47Zkz5QuEU36m/Sfs6iudmt9Ur2cMJvjmN9UbLqsMABgbooobXpwanDgiKWW86t8u7s6nW3XNWTN17WOvpRzMLG3pu5Ot/xxJWtfapiXNBye9bjZ3GY/W1jnFvDQ/rCsSiysWl2H9XHZioyKxuImlA5ALw11tqzsUTTuu7Q4lL3seisXTJpNen2KicfBKoUa8BsezGXdms+pIqtXc+vsvPcHcrFRSjOPoTHMj6ZbiLpYtCrOpR0Axi8cll6MkZZ8zblKX06rlAsxk1RjG2BUoHOFoPGU7sqG1XeEo9RVjixkxjESUAjLUiTtfIP1yUoOPr16/TbfvW2a4rLREkgYSUlLp7A3pmsdf07qtbbrn3KMUjMRSJpmUO+06fGqNTps1Ub7eiFYvOXrgzi3/oM7i0pYGXf+E8cW8a594Xd/NsATl4Am76nKnblk0R8sf3pQwiTa/qV6rFs0pyMkjAMDIef0hrVy7JWPiiNQXv/qTKPe/87j/OcFwLOXd04OTT6TE2GoUZ4d7l/Fobp1TrEvzw9pi8fQrEV13FnepAIUsm9W2fL3htPHYKB71prlgsaG1Xb1h4wsWFU572vKXGxzPZtyZzaojNW5n0va20kft400LcnM3cDGOo4czNzJYMW1R6MuQZEW/DmONw16SVcLiaLNquQAzWTWGMXYFCkdPKP14wJ/hOFBszIhhJKIUiOFM3Hlc6f9ZK10fTaL5Q9G+O8BbGnRIfYXmNdUPJKSkEgzHBsqxcUenTjikzvAOsnKnPe2dW5c8tHEgGSXdainrtrZp+WnT05Zp/wm7yTVu3bF4rtq6Q+oKhFXlcqi+sjDvYAIA5EZbd2hIiSN2my1t/Cq19a2L3DOEu2827uiUpITYun+cHe5dxqO9dU4xLs0P6wtE0l88DkS4YxsoZNmstlVd7kgbjz3u5HjkzxCb/UHj4+VOu5ob6wzboebGOsNEFGn4485sVh0JRWNp28dQNHd7OBfbODrT3IjR8WLbotCongxGvw5jTW84Q58zRcLiaLNquQAzWTWGMXYFCkeFM/14oDzDcaDYmBHDqGUFYjgTd+MqnLp54SyN97iS7ho7cmqN9viCCef7Q1Hd+XSrPnPYeK1aNEd/+8felJNw85rq9ew7Hz2+ev02LZp7oIwW61na0mC4Xc/gO89TJZ/sr70nlLJMqSbsqssLd8IMAJB73t5Qxuds3NGpuVNqUsYvm6TzmhvU0lgntzN94uZ4T5lWr9+m5sY6bdzRqXKnXStOP0zVbofuPudIuRx27fYFdOLHDhhWvBrtrXOKcWl+WF9vhovHmY4DsLZsVtuqLCtNG49vWTQn6RyPO0PiQYrjJbJp2YmNA6/fr2952iaVyJbyNYcz7sxm1ZHuYPq79HoyHM9WXFKaj10QaiucA1sR76+lsU61Fcnfd7FtUVjptOvT0w/QYZOrk1YWemOnV5UZVgMCik2mPmWmhMbRYtVyAWayagxj7AoUDofdlvaGA4e9wAc8wDCZEcNIRCkQQ5m48/pDau/pu8j2u80fJNz13dxYp3vOPUr1VWVa/LO/G75GRZlDk2vc+uysiTr+kDqteHRLwgTMvKZ6rThjhhbctWHgMX8oqq/958v6xblHJU3wpFvlZPCd5y2NdRrvKUv7+RwlNp237/mDg0YhLxMMAMivTFnuB9a4dfFDG3XnF49Muw/xRSc2auWZM/Wn13elTtxsrNNTb+zR3Kk1Oq+5Qcsf3qTVS47WXU+3at2aLR89r6le8xrrh/U5RnvrnGJcmh/WV1GWvn5mOg7A2rJZbas7GEkbj40SNCZ6XJrXVG+YSDCvqV4TPS7D1+voDSkQjun02ZO0tLlvq76y0hLt9gUUCEfV2RvSVFWk/QxDNdxVR/K5UlkxbUkjSRM8Lt20cLauWLM5Ya6ipbFONy2crQkGfw/FtkVhIBrRVWfM0FVrtyTMz7Q01un6BbMUiLIcOcYWq/Y5rVouwExWjWHUVyA1rz+ktu6QfIGwPG6H6ivMvVm8vSdkeF2xubFO5zU3qL0npIYDzCodkH9mxDCiYoHINPnkdtr17f95VYuPmapfbtiWtPXAhtZ2ldhsuuK0w3Td2bPkcti16b1OOUttamk8QDab9KE/pNjuLnncDsXjca04Y4Zi8bi6AxGVO0sVicUUi8f13187TtGYVFJiU0dPSNFYXH9+Y5euO3uWrn7stYFJq2Ak/RLBVa5SPfnNefK4HApHYyknDOc31euQAyrVE4zomjNnKhqLyx+Kqtpd2MsEAwDyx+sPSTalTRyx2aT7lh6juNEyX4M47CWKRqM6tqFOn501SSsHxT6p70LXdWfPVHt3UKfOnCiXo0SPfP0EXblmi9a1Jsa5dVvbtPyRTVq1aI4OrC0f0mfJxwWpYluaH9bnKi1Je9e6K8PWkQDybziTjNmstuUz2P51KMevO2tmyticSmWZQ+ff95JWLZqj8Z4ydQeiqty3bct3f/Oqfn3B8SnP3e0LqKMnJF8gIo+7VLXlTsMEh8GGs4pKfaVT/3TYeH18kifpbuC3PvClXalsOP9GxbYlTT+nvUTLTmrSZadN3/fvaldPMCqn3TiuFNsWhdGoTdc8tiWp/7u+tV1Xrd2iFWfMMKlkgDlK0owJmxvrVGLSjdFWLRdgJqvGMMaugDErJrVXlpXqy6tf0NKWhoQbDjbu6NQlD23UI18/wZRyAWYxI4aRiFIgMk3cbXrPqy8cM1Vupz0pCaXfuq1t+qA5oAsfeEVS32Tc1WfO0E2/e0NPv7l34Hn9k3RdvSGNqyjTjX/+x8Brljvtuufco3T3X1oT3mdeU71Omj5B1541U9vaehSMxDRlXPrgUldRpkPHVw78virN3deZJvIAAEilfyB0fnNDyiz4c5sbFInFdNuf/zGwYlcqZY4S7fKF9Iv172jj9k4tbWnQkhMOls1m0+Qal155t0On375+YPnklsY6rThjRlISSr/1re16t92vyrLSIV3gydfWOWxxh3wKRmNa0tyguJLr55LmBgWj6ROcAeTXcCcZq8udumHBLF25NnnVzRsWzDKMN5ku+Bsd9/pDikk6bdZELTnh4IGJxj2+gGLxvuNG71Vf6dRdXzxSdzy9NWFCZl5jne764pEpY+v29h5dvmZzQrvVv9rG1LrUK6i81+FXVyAiX29Y1W6HKl2lOihFQmp1uVMrzpihy9dsTrob+KaFs1PG6uH+GxXbljRS37/3dw2Sa6S+78IouaYYtyhMtwe44T7LQDGzKe2d0aZtSWbVcgEms2IMY+wKJLNqUntthVPHHzIu4TGbrS+oHn/IOMOtOoFiZkYMIxGlQKRbJv+6s2dp7f++r7X/+76+Nv/QtK9TOmjPs3Vb23TNY6/piKm1CYko67a2aeXaLVrS3KAf/Xlrwh/j0pYG3fmX1qRO4Lqtbbpq7Rad19yg1fv28V52UmPKbH6jCRzuvsZYtXnzJi0498Kkxw88oFp3/eBmE0oEFI/BA6HDp9To9Z1ezZ1am5QF/6vn39VJh03QhtZ2zZ1aq3mNdYaJnc2Nddrynjehs9Z/Ueiec4/S9U+8bni3znsdvWnL2dkbHvIFHrbOQTHqDkR0yUMbU96lcv/SY8wuIoB9splk9PpDiseNE0TiKRJEsrpTx2bTyrWbDceg/YkbRgKRmO7cLwlFUl9fwGbTDz53uKr3O2e3L5CUhCL1xf0r1mzWDz9/hOENFe+29+gKg+SVGxfO1jSD5BWvP6Qr16a+GzjV9z3sf6PeUNJ7Jx4vrC1ppOySa4qtn2W0hdVwjgPFpqLUrl89vz3FmHC7rjnTnBUWrFouwExWjWGMXYFkVk1qd5WWaMUZM3Xl2uSk/hsWzGYFI4w5ZsQwElEKSKpEjf9r79HsA6t125+3avlp09O+Rm154l1j61vbBzLupb4VT5a2NGjulBpVux1afuphCkai2tsdVFmpXeMry+Sw2wb+QPuXBF69fpvWt7brKy2HDLze6vXbdPviuZISM6vm7dvH0SjwcPc1xqJQ3C5X85eSHn9/w3+aUBqguPQPhMqddjnsNi0/9TDd+oc3VVpiU0tjvSLRuE6dOVGL5h6o3235QOVOu1av36a1Fzbr2ideM7wb7ZKHNuqOffFtsPGespR362RSVlqirsDQL/CQvIliU1FWKn8omjAxsP9xANaQzSSjrzesa594TTMmVw8kZ9hsNr3vDei6J17TNWfOTDqnRNK1Z83SNY+9lrCq2LzGel1z1kwZTRn2hKIpY/H61nb17FutbH8d/lDalUU7/KGkpJIPe0Jp3+vDnuRz3uvwJyWh9D//yjWbdcuiOUkro2TzfWdzTrkzfTtb7rSnPW5Fvgx9q1R9r2LqZ5mxBzhgZZFYXOfP67vJbnC/s7mxTstObFIkZs4SC1YtF2Amq8Ywxq5Asmz73aPN1xvW9U+8lpDo2X9N8/oU41CgmJkRw4iKBWb/RA2vP6SyUruCkb7lciLReNo9RSPR5IFL/7nlTrtuXzxX927YNpBEcu+GfyQmkTTV68JPHarz73tpYMuB5sY63b54ri55aKP84ai+8z+vDmRTRWJxXXvWTHX6w9rbHZTTXqIDqsrkD3HXDQBg9PkCYZU77brzi3O1ev02/edz7+qeJUfrB0++qdv+vHXgeX0TfI2664tH6qIHX9G29h7Du9EueWij/KHoQOwcrDtgfHFLkjbu6NS8xnrD7XmaG+u0cUenFh5x4LA+G8mbKCY2KW0fltXIAevIZpKxJxzVF4+dpns3bEu6wHZec4N6wskxNKyYSktKdNrsiVrSnLiKir1ECseTY3FXhlU7Uk2AdvWmH58aHc/mvboCkbTJK12B5PfJ5vvO5pySElvadtheUngtsSeL7Z36ZdPP8vpDausOyRcIy+N2qL7C/L4a8RVI1BOO6vz7XjK8C/T8+17U//zb8ZQLsAirxjCrlgsw00j63aOpJxzV4mGOQ4FiZkYMIxGlgL3f4dfe7pBCkagOrivX48taFIlF0+4p6vUnTziVlZao3GnX3V88UuM9Zbrs1Oly2Et002+TtxdYt7VNsXi8b4uefQ13/3OWtjSorLQkKZvq7nOO1IUPvJLw+/iqstx9EQAApOBxOXTB/EN0776Vu5ad1KjvP/lm0tL7/bHs7MMn647FczXeU6av/efLKV+3ob5Cay48QV29EYVjMdltNtlLbFq95OiBlcL8g+66Xr1+m357cYtWProl4Y7reY31Orf5YP36he2qb2kweitgTCgpUdo+bAmrpQKWkdUkY1y6d98WroP1/77i9OQtB5wldl2xZnPKrXmMttmpcjsSVvncfxXPVBOg5WXpV/swOl7lHv734MsieSWb7zubc0pLbGnbYaskogwn2aO+0qn5TfUJW+z0M9oueCR2dvYmbYc0v6letyyao8k17py9z3ARX4FEPcFo2rtA/UFzLkhZtVyAmawaw6xaLsBM+ex3D0tcevD5dw1XRHnw+Xf1vVPS7zABFBtbhhhmG4UYRiJKgdr+oV9XPrIp4WJWc2OdrvrsYfrV828b3sX94PPvasbkxJ2tWxrrtGWnV3d98Ui5HCW68XdvaENru+4596iUSxNvaG3X0uaGpMcu/FSjnnsn+Zyy/fZZKystUXWGSTsAAHKhvtKpT318/MDqJ3On1KSc3NvQ2q7lp03Xrb9/U4dPrU2bHfzbzR9o4/YOndfcoIeef1eLj52mJfe+IH8omrhS2L5klCOn1iiquE6bPUnfO2263uvoldNeonEVTt39l1Zdd7bxlnXAWOEoKUmaHBjchzW6SF3MrHhXPdAvm0nGuJRyJZANre0y2nCgJxQ1TEKRUm+zU+G0655zjzLc2uCec49SRYrtZdwOe8qVy+Y11svtSD6v3GlXS2NdykQZo61sPFkkr1S6StOWrdKVPK2TzTl1FU7d/Ls3DNvhX7+wXT/43OFpy56Pdmu4yR7V5U7dsmiOlj+8KeHvdX5TvVYtmpOz8nn9oaRySX3bIC1/eJPuWDzXtDac+Aok8rjTT4VXZTg+WqxaLsBMVo1hVi0XYKZ89buHzaa0K3OyhBHGGqcJMYxebAF6vyM5CUXqm8D7/h/f0nc+83Hd/Ls3dOfTrQN3g51wSJ3mNdbLH4pq2UmNWr1+mz4xrVbXnDVD7d0hvb2nW09s/mBgctBoy4HBjI7bS2xavX5bwmP92w0M/n1PV1BHTavN8tMDADB01eVOhXd3D/yeKb7t8QX18vZOfeLgcVp+2nTt8QVls9kG7qSeO7VG5zU3JCSZzJ1aq3s3bBtYLWzwSmF3Pt2qlsY6XXPWLP3xtQ9065P/UHNjneZOrdWdT7fqyW/M0w8+dzgXmDHmdfhD+tJxB+sDb+/AYzabTZOrXfrE1Fp1+EMaK2sG7ezs1WW/2ZRwAXnevskbM++qB/plM8nYE0y/9Y3R8W6DbWoyHbdLuvsvrYYrr5TYbLplwSzD13KU2HT92TMUk03BSEy+3r5kirLSEpUobrgaiKPEpmvOmqlrHnstIRmlP+47DM6pcpXq09MP0GGTq5NWbHljp1dVBgkiwX1jeCm+36pqdVp2UqOCBgk5wVBUF554qGKKJ93ldOGJxudUlzu14owZumLN5oRJ2v7VZ9L1VfKxGki2yR6Ta9y6Y/FctXWH1BUIq8rlUH1lbpNk2rpDSeUaXL627pBpfT3iK5CoMkPiYaVB4mE+WLVcgJmsGsOsWi7AbPnodw+XTelX5lx5BoljGFvMiGEkohQYrz+kd9v9KVcrefrNvfrycQdr5VkzFY3FVWqz6drHX0uYSJrXVK8nLm5Rpz+kUCSuyrJSTap2JzTG+69isj+j47F4PGEbgpbGOi3Zd7FO6pv0uvikJh08rpwLbgCAvKkYtJx+pvg2sdqlB796nL7/hzcHVlGR+i72rL2wWb9/7YOEJJT+VcLufLo1YbWwDa3tuuK0w3TKzAnqCUa0s6NXdz79dsI585vqNanaRUwEJJWXlaqzN6zfbf4g4YLuvMY6ndfSoPKysTFs8fpDuuw3ryb19ddtbdNlD2/SnSbeVQ8MNtxJRqPVQTIdN1q1I9Px7nA05Vh53dY2dafaAzwWl81WoqvXbk5K9rhhwWwplrxmy+Tacu1o79Gykxp12WnT1R2IqtJlV08wojK7TZNry5POOai2XCvPnGmY7HHjwtk6yOCcnnBE/lBUp82epCWD7lja7QvIH4qqJ2yQxBOK6Pz7XtLSloaku5zOv+9F/ebfjk86x+sP6bonXtcRU2t13n7nXP/E6ykTZ/O1GshIkj2qy0d3AtxnsKXSYEZbLuUL8RVIFI5FdcOCWbrKqL1fOEvhmDlb4Fi1XICZrBrDrFouwErikiVWG4nF06/MaTDMA4qaGTGMqFhg2rpD6sywr7Q/HNVuX0ClJTbd9XSrXt7eqWUnNSbccfX3d9q1tzuo//jbO7r2zJmaPaVav7rguH1LFdvU3h1MuyXB4FVOpL4/0gOqyvTYsmb5Q1FVux3a7QvI4yrVvUuOVqWrVJXOUtWUO5g8BwDkVYWzVCdPP0DTJ1erptyhe849KmGVk/6kkubGOu3yBgwz5de1tuvaJ17T3Km1CUmX5U67xlU4dc+5R6ncWarVS44eeF1fICJ/KKKNOzoT3qefqUtTAhZTZi/R/7y0Q+c1Nwxc0K1ylWq3L6DfvLRD3x0j+/bu9gXTXkTf7QvSbsAyhnNx3+2w66TpB2iGwUogr+/0Gm59U2JT2nMMFhxRVyCi+kqnVi2ao/GesoS25LKHN6krxSorthKbrlqzOTkJrLVdV63dopsXJq+k4vWH9PN172hpS4N6wzGV2KIqLSnRRI9LP1/3jr79mY8nfT+7fQFduWZzUj9jfWu7rlyzWT/8/BGa4HElls1m0y/Wv5NybG50F58/FJU/FE25HeH+fRKpb67hz2/s0Z/f2GN4TqpEj3ytBmLlZA+PwZZKgxltuZQvxFcgUTAi/dff39b1C2apNxxTV29YVW6H3I4S/fyZt/Wvxx9MuQCLsGoMs2q5ALPlY5XE4cpmZU6gmJkRw0hEKTC+QDjj3dzlDrsOrHZrpzegl7d36vbFcw33QLvmzJk6/KAa3bPuHX334U0Jxy45qUlXnzlT1z7+WsKE17zGel10YqOW3vdiwvPPbW7Q2XdukD8U1fymel1/9izVuB2WWH4LADC2ldltuuqMmbpq7eakWHj74rm65KGNA1vu2EtsCdnAg21obdf5LYcM/F7utOv2xXP1oz++lXDhqv91g+Gozr/vJcPXOrDGrUlssQEMCMdj+ubJH9d1TyT2PVsa67TijJkKx9Nvq1UsvBkSzjMdB6wqGo/pis/O0NWPbUlaCeSas2YpGje4Fc0mXXbqYbruideSzllxxkzJlnxOlbtUD3zlOMO25IGvHGd4jpRhJZVW45VU2ntC+tT08bpq7RbDO4nae5ITMDp6QnrF4EaR/iTWjp5QUiKKMtzFZ/TVVWa4i8noeLaJHvlKELFyskd9pVPzm+oTtqrqN7+pXvWV5s2HEF+BRHFJj2zcqZqKsoF2uDsU1SvbO/TIxp0657iDKRdgEVaNYVYtF2CmfK2SOFwVGcZFmY4DxcaMGJY+owGW43E5tHFHp+Y11hkeb26sk8tZopWPbpG3N6ylLQ0p90B76f8+1D3r3kmacNvQ2q7bn96q/93eoaXNDXrwK8fqv792vB6/uFlLWw7Wy9s/1M+/fJR+82/H68GvHKu5U2sHtino3xd8Wn2Fjphaq0PHV5KEAgAwjdcf0vveXl21dnNSgsmG1nbdt2GbfnXBcQOxrMSWft3IUvtHx/tjrFEc/eWGbXI5jbtZzY11hnd+A2OZw1aSNAiS+lYJuP6J1+SwjY1hS3lZhu1LMhwHrMpeUqJrH9tiWMevffw12Q2WN6lwlur6NO1ChTN50rC81J62LSkvNa5Dvt70d8J1GRyPxGK6d/22pP7FutZ23bv+/xSJJU/gdAfDun3xXG3c3qHz73tJFz7wipb+8kVt3N6h2xfPVbfBHXk9oQx38RkcLymxqTnNnIHR951toke+EkT6kz2MmJ3sUV3u1C2L5iSVr39+xMw5EeIrkMgfjKRth/0Z2tyxVi7ATFaNYVYtF2CmoaySaAablHZcZIHdg4C8MiOGke5VYOornXrrA5/On3eIZLMlNO7NjXVadmKTKstKta61XUuaGzR3Sk3K5XjHe1wp7/ra0Nqupc0NcjvsspfYtOTeFwaW753XVK+zDz9QB40rl9ffd8fWydPHs/oJAMBy2rpDcpc5Uq5ysq61XUu6ggOxsrYi/cWS2vKPjh9/SF3KGLu+tV3LTzssaZu75sY6XXxSk2rKzbtrF7CiQCSW8o7/9a3tCkTGxl1lBpl78gABAABJREFUVWWlammsM2yzWhrrVMXdOihQwUgs7bZTQYM63h2MpIzf61vbDZM2ukPRtG1Jt8GWNJJUkUUSWCyutKuoGO03Pq6iTD/+81bDG0Uk6fqzk7cAqnCWpt1uyCghp7TEpvOaGxJeW+rrh/SvALe/bFf1yNdqIP3JHssf3pTwXlZI9pCkyTVu3bF4rtq6Q+oKhC0zP0J8BRKNq3DqR3/+R8p2+AaDdjgfrFouwExWjWFWLRdgJqtuo2mzKe24KMP9iEDRMSOGMZNZALz+kNq6Q/IFwvK4Hbr2rJl6v7NXZ86ZpG98ukmRWFxVZaWKxuP6y1t79LEJVZKkjTs6NXdKTcrXNZrs2/+4x+2Qx1Wq//7a8QqEo6otdyZMpgxnX3AAAPLNFwgrNIR41y8SjSclj/RrbqyTo6REv7rgOFWVlQ4kaKayo8OvuVNrtXTfgKfa7dDWPd06eFw5sRPYT1eGLWfMmrTIt2AkpiXNDYoreZJkSXNDxv47YFVGK4okHA8kH/dlaBeMjmc6J1VbUu6wp43/5QYrmWXzmULR1JM+G1rbFYom1/HSUulXFxyvq/dbUaalsU6/uuB4yZZ8Tl2FUzf/7o2BfkgwElNZaYk27ujUr1/Yrh987vCkc7JN9MhngsjkGre+/7nD1dETki8QkcddqtpyZ/J2Riax4vwI8RVIFMzQDgcN2uF8sGq5ADNZNYZZtVyAmay6jWa5w66Hnn/XcFz00PPv6pozZ5pSLsAsZsQwElEsbmdnb9Leajf/82z9btPOhLuv7j7nSF34wCuSpHvOPUqStHr9Nv36a8elfO2y0vRL7JSVlqiyrFQL735WR02rNW0fNwAAsuVxOQwv6gw2OB56/eG0mfLReEw/e+ZtffHYaZqY4aKH016iO59u1bymel17Vt/A5rOzJhJLAQNVbmtOWuRbdzCiSx7aqKUtDUmTJJc8tFEPfuVYs4sIZKXSlX7qodJgtZ9M9d7ouCfLtqSkJK6LT2qSlBz/Lz6pSSUlycubZPOZug2SUzIdLyuxa/mazYZL517z2BbdvHB20jnV5U6tOGOGrlizOWH1tpbGOt20cHbKvki2q3rkazUQo/mR+U31umXRHE2ucef0vYoF8RVIlE2SYz5YtVyAmawaw6xaLsBM+Volcbg6e0P63qmH6fonXksaF608c6a8vSFNUYUpZQPMYEYMIxHFwrz+0MAkS7nTrqUtfVvtHFBVpssf2Zzw3MEX0Tbu6By4m+upN/ZoXmOd4ZLBu30BzWus17rW5ODQ3FinPb6AptaV66hptZZY6hYAgOGqr3Sq0x9OudXFvMZ67fYFBn5/aXuH3tjpNcyU/9UL2/XNT39MMyZX68Hn39U5x05Leff0vKZ6jatw6oGvHKsDa1w6uL5yVD8nUOiqnPb0W9I402+bUSw8Lof8oWjKbb+Y1EShcpWWpF1xxGVwk0R5hnah3KBdqMxwTmWKtsQWL1G5s0RnzJmcEP/3dAVV4SyRLZ5cvrIMn8noxo+KDNtrGR3PZrshrz+k6554XUdMrdV5+/Vnrn/idf3gc4enXeEkm7H/aK8GMnh+ZLBntrZp+cObuHEmBeIrkCjTHduZjo+WbJIvgWJn1Rhm1XIBZqou79tK9K//2KvxVWUKRmJyOeza7QvoxI8dYFo/vdzp0L/87DmtWjRHl502Xd2BqCpddu3xBfXFn/9dv77geFPKBZjFjBhGIoqFtXWHBpJQbl88V/du2KY7n27V3eccmfTcwcknq9dv0+2L50qSfvbMO7p98VzFlHhn17ymeh3dME7HH1KnlY+9ljCZ09xYp4tPbNIBnjI5bTYmdIAsbN68SQvOvTDp8dfefEufaDahQMAYFo/Hdc1ZM3XNY68ldLKaG+t0XsvBausK6vGLm7W3K6gJnjJ9dtZE3fy7NxMuBPeviPL5nz2nI6fWaElzgy57eJNuWTRHUvLd01edfpgW3v2s/KGonvzGvPx9WKBARWJR3bhgtq5cuzmhnrY01unGhbMViaXfCqtY1Fc6Na+pPulCq9TXfzfrLiJgpDp7Q2lXHOvsDSWd09YV0IozZur6J15LahdWnjlT7d0BTatLvHutVNINC2brqhRtSaoJkFAspvbusOLxxJVP4vG42rrDcjuTz+zwZ/hM/uTPZNt3PFXyitEW5dlsN9TWHdKf39ijP7+xx/Cctu5QwY3x++dHjDyzta0gP1M+EF+BRLUVzrST77UV5rQj2SZSAsXMqjHMquUCzBaX9LtNHyTc+D6/qV6f/NgBppWpvtKpGZM8Ov++l5KOmblSC2AWM2IYiSgW5PWH1NYdUntP38TV1z55iPb4Alra3KBzjp2mKePKk84ZnHyyobV9YEnviz7VqFK7TSvPmKForG8Sq8JlV1tXUH98bZdOnjFB1501U8FITF2BiMrL7HKX2vWhP6hKh10TWN4WyEoobper+UtJjwc2LTehNMDY1dYd0pJ7X9D9S4/RspMadeXpM9QViKhq33L68Xhck6vdisZjmlTt1m5vQK++36lPHFyrJc0HKxSN6YDKMjlLSxQIR3X3OUdq2rhy3fDb19XWHRqItxd+qlHO0hL1BCN66d0OfdAZkH/fHcp+gzuVs9HfP/AFwvK4HaqvGN07j4F8CkQlf69fNy+cre5QVF29YVW5Hap02rXX51e5u8zsIubNRSc2KhaPJ13YvujERhNLBYyM21mqS37xQsptpx658ISkc8rLHDrnF39Peffaf56fvFVVIBaTw1ai686e1TfG3deWlJWWyL7vuJFYLK7//Pv/acbkak3Yt/WezWbTTm9Af35jt5afeljSOZVlpfrSPak/0xqDz2SzKW3yis0gEyWb7YZ8GfZ1Ho19n0dbIXwmK/bViK9Aogkel25aOFtXrEmefL9p4eyBGJBvHrdD15w1S9c8tiWpXNecNStjLACKkVVjmFXLBZjJ6w9p5aNbdPjUGi1pPnhgRZRXtnfo6ke3pF2RcTRVlzt1y6I5Wv7wpoRtg+Y31bMLBMYkM2IYiSgWM3jP418uOVqXnzZdn/r4AfL1RlRRZpfLYZe9xJa03Y4/FNUlD23UitMP07dO/pj2dAVVVlqi3nBUD6x/V0+/uTfpveY11euMOZMUj0nO0hKVl9nlD0ZVWmJT0/gqGmHAgi76zuV6f6836fEDD6jWXT+42YQSAdbmC4TVG45qj7dXB9aWKxKL66antiZk5y87qVEbt3do7tRabdzekfIu5blTa3Xn0606fdYELf/sYbr0nz4uXyAycFHsG7/aqLbukJob6xLOrc7BpOHg/kG/+U31umXRHE0maRRFoMJh1/ef3KbDJldr7pSavgvIwYg27ujUGzu9uubMmWYXMS/aukNa+ssXDS9sL/3li3p8WQt9dBSkEptNc6fWGG471dxYJ7tBBkaF067pE6sM715raaxThcHd4eMqynTz79/Q+fMOSXg8Go/rzr++rctPS04okaSYpC8eO21gFdLBZTuvuUExxZPOqSl36hPTag0/07ymetUY1NUql0MPPf+m4RaADz3/rq5fMDvpnGzuks+0tUS6LR6smEwhjewz5YNV+2rEVyDZ1LoK/fDzR6ijJyRfICKPq1S1FU7TklCkvgtlvaGolp3UlJB82ROMqsppt0Q7DOSbVWOYVcsFmKm9J6Tzmw/WhGq3gpGYfL19Y4l/nnugdh8yTu095q1eOLnGrTsWz1Vbd0hdgbCqXA7VV1pjjAPkmxkxjEQUCxm853G50666SqfWr9+rm3//5sBzmhvr9I1PN+n6BbO14tEtCZMcc6fW6BMHj9Mtv39jIPGk3GnXPecepXAknrQk1qpFczSJi1dAQXl/r9dwpZX3N/ynCaUBrK/G7dCvvnacHLYSPfdOu2Yf5NENC2bpqrVbBuLi6vXbdM+5R+meddvS3qV8yUMbNa+xXt/6zHT9+E9vaeGRB+neDdtSPlfKzTKPg/sHgz2ztU3LH97EFnooCi5JK86YqavWbk64qNu/NKR5lwXyyxcIyx+KGl7Ylqxxxz+QDWeJTTcumKUVjyZuCzuvqV7Xnz1LiicnesRisbTb7MRSrG7y1XmH6prHtiTcuDFv3x3l6ewf06WP+gMrzpiR9PwJHldfn2LN5qT3umHBLMMLmqFwVFedMUMr1m5JauuuXzBboXDyKmoetyPl93DDgtmGd8nXVzo1v6k+4a6/fun6JlZNppCy/0z5YOW+mjMWTRtfnWwfgDFqgsdlauKJkYk1brmddrV1hxSNxlXuKNXU2nLGehizrBrDymLxtOUqiyX3a4HiF9OkmnJduXZzwpiqf8wSi5vb56wuJ/EEkMyJrSSiWEhbd0hvfODToxeeIKfDrq7esK44fYbKnXaVSnKqb5+1n27oy1a6ccEs+cNRdQUiKrOXqMpVqk5/SFd+doa+d2pM3YGIKspKVWKTrl8wU+FoXD3BCBl/AIAxw+2wKxyNyR+Jau7UGkXj0oc9Qd24cF8M7e3bls5TVqorzzhMgXBUK06fobiknmBEla5SOUpK1OEP6Tf/doLKSm36sCeoS//p4wpFY7rys4fJZrOpOxBRudOuXb6ALnt4k/yhaM6WeWzrDiVd2Oj3zNY2tXXn964Cq94pjcIWlBSLGi8NGY74FSxN3pqyGFn9jn8gW26HXdFIVDctmJVUx+3xuOyO5FU93I5SxSNRw3bBEZdsjuTpDF9vWL3BgG4yOMfbG5Cv12YYs+Jxaa8vqD9dOl+RaHzgDr5Su00X/dcrRnky8vpDau/q1RWnH6a4bOrad44UV3t3QDVuR9J7hWJx3f/sNt2wYJZ6wx9tHeR2lOgX697WuSc0JL1PdblTvb1h4+9h33Gjc76/aI4+7A0rLg0qm1RnUK7+z2PVZArJ2stqW62vNli4xK5YJHV8DY+R+AoUCi6UAR+xagx7zx+S02bcR/X2+NUet2u8KSUDzOMssWuv169b9tWL/vFUpdOuNq9fB1TT5wSswIzYSiKKhdhtYa39+gnq2q+hdqkvASUWl0okXXBCg3Z4/eoNR1VmL1HMadfvtuzS6bMn6UN/WB/6w9q4o1Or12/TJ6bV6lZWPgEAjFGhSEyb3u/U4QfVyBeIqDsYkcftUDASU4ktrvoqp4LhmNp7gqqtKJNkU3cwrPqKMrlKnfuSVULyuEvltJcoEA5rQpVLkXhcsVhc/lBUHnep6qucau8OaVK1S2u+foJisbhcZaXqDkS0cXtHUsLGcJI5fBlWQMjnCglWvlMaw2O1hKKyWFRl9nIF9v0++Jpvlb1cGiN3bNdXOvXl46ZqaUuDesMfLWfrdpRo9fptpt7xD4xEPBqT9NH2O4l5HbZ9x5PPiac4Jy5JBufYYnFVu93qDkUUl23gnO5QRNUul2wp7lCNxSK658tHqTscSXjPSDSme758lHzBYNI5nT0h1Ve4ZCuxqTv0URtV6SxVRaldnQbLT9tscX113iH6wBdQRVmponEpGotrly+gr847RCGDVV68/pCCcenZt9s03uNSMBJTdyiq3d5enXBovbx+4ySHUDQmb29IFWWliu17n+5g2HArH2nkyRTvdfjVFYjI1xtWtduhSlepDqpNP4m12xf4aGsMd6lqy9NvjTG5xq3vf+7wYZ0jjX7Ms1JfbX/EVwBAobJqDKtyOXT2XS9oaUtD0rYGq9dv06MXNZtSLsBMZbGoDvJ8VF8HO8hjfp8zm7EKUIzMiK0kogzTXXfdpe9///vatWuXDj/8cN1xxx065phjcvLathKnLluTvHTVjQtmq1xS/7bZEUkTa8q1yxdUeZldUlxzp9TI19uXgDJ3So3mTqnRmgtP0ESPi0x6AMCY1HfRoVezDqzR8v3i66enH6AV+5bGf2NXlx74ynG6Ys1mbdzeqbu+eKTC0YDu/Etrwjnzmup149mz9IEvoNuf3pp07PyWBn159Qs6cmqNblw4W9c9/pp+u3nXwHP679iNS8NK5rDKCglWv1MaQ2fJhKISu/xx6YpUfeES4wunxaa63Knz5x1i/D0snE0dQ8GySQpJutLgb/uGBbNl1PKUSPKnOcdo2rCkxKZwPK6N2zs1YV/SRs9A0kadHCU2g7Okca4yBeNx3fDbNwzea5bGucqSznGW2BSMpy5fmcFbue12BaIx3fF0a9I515w1U257clvX3RvWB95ePbH5g6TtABvqK1VqS14VZVdnr4Jp3mdXZ68m7tfejySZ4t32npTt1rS6CsNztrf36HKDc25aOFtTU5yTTfzKR8yzSl/NEPEVAFCoLBrDxleV6RPTag23U53XVK/xVcn9RqDoWbS+StmNVYCiZUJdLcn5KxaxX//617r00kt19dVX65VXXtHhhx+uU045RXv27Bnxa7/X4U/6h5ek9a3tunLtZgVtUkB9P6FYXN2hqMrL7PIHowpF4gpGYvK4HTp5+ngdXFeho6bV6uMTPUxWAwDGrE5/WO4yR9L+pJJ02ORqXbl2i9a1tmvVojm67onXtKG1XUtbGvSBtzcpCUWS1m1t04Z32nXHfkko/cfuWbdNS1sa+mL3ms365yMPSnjOM1vb9Nd/7NVlv0mdzOH1h5I+R32lU/Ob6g0/4/ym+rytkDCUO6VhfZkSioz+BvMhKOkKg7o60Bc2pVT5l3ZMsGaz3uvwm1QyYGRCkmE8Xt/arqvWbpZRyxPMcI5RuxCTtNMb0G83f6Dz73tJFz7wipb+8kU9sfkD7fQGlLzeSJ+IpCvXbknxXlsUSXlO6vIZnROVdPVjrxmec81jr8no3qNQLK47DPolG1rbdcdftipksMpLMBpL+z5Bg9Vksk2myKbd2u0LJCWh9J9zxZrN2u1Lvpcym/iVr5hnlb6aEeIrAKBQWTWGVZc7tWrRnKTYP7+pXreavF0gYBar1lfmWIBEZtRVElGG4Uc/+pG++tWv6rzzztOMGTP005/+VOXl5Vq9evWIX7srEEn6h++3vrVdXaGouvf99Iaj6gqE5Sq1q8pdKq+/bzmpardDR0yt1aHjK+nwAADGvJ59y/Ibxde5U2oGHh/vKRv4/7lTajTB40oZk8dXlWl9imPrWts0d0qNpL7YPd6TfBfM+KoyrWsdXjJHdblTt6SY5FiVx0kOKy87j6GzakJRVyiasS88FmQcEwSMLm0D1tedoY53G9TxbM4JxeKGyaQbWtt1519aDZM28lk+f4Zz/EbnhFOfs6G1Xf5wbt4n22SKbNqtjp5Q2nM6epJjUTbxK18xzyp9NSPEVwBAobJyDJtc49Ydi+fqqUs/qbUXnqCnLv2k7lg8V5PYshhjlFXrK3MsQCIz6ipb8wxRKBTSyy+/rMsvv3zgsZKSEp188sl67rnnDM8JBoMKDtpL2ufzpXx9X+/QL+7YZFOVyyGbTap02PWBt1ctjfXmLaUOFIHh1FcA5hpqfe0JRRVLccEpGPnoTuDuQNTw8Uz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",
      "text/plain": [
       "<Figure size 2250x2250 with 90 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0d4a4b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 8))  # Adjust the figure size if needed\n",
    "sns.heatmap(final_dataset.corr(), annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae701a36",
   "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_of_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>9</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_of_year  \\\n",
       "0           3.35           5.59       27000      0           9   \n",
       "1           4.75           9.54       43000      0          10   \n",
       "2           7.25           9.85        6900      0           6   \n",
       "3           2.85           4.15        5200      0          12   \n",
       "4           4.60           6.87       42450      0           9   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0             False              True                   False   \n",
       "1              True             False                   False   \n",
       "2             False              True                   False   \n",
       "3             False              True                   False   \n",
       "4              True             False                   False   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                 True  \n",
       "1                 True  \n",
       "2                 True  \n",
       "3                 True  \n",
       "4                 True  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a012cc",
   "metadata": {},
   "source": [
    "### Independependent and Target Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0103b8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_dataset.iloc[:,1:]\n",
    "y = final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e17bc673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_of_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>9</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_of_year  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0           9             False   \n",
       "1           9.54       43000      0          10              True   \n",
       "2           9.85        6900      0           6             False   \n",
       "3           4.15        5200      0          12             False   \n",
       "4           6.87       42450      0           9              True   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0              True                   False                 True  \n",
       "1             False                   False                 True  \n",
       "2              True                   False                 True  \n",
       "3              True                   False                 True  \n",
       "4             False                   False                 True  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c280374",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a6115551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Understanding the important features\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d2e3145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.40252834 0.03989884 0.00049489 0.07787218 0.21236538 0.01653467\n",
      " 0.11528008 0.13502562]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8ad13b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_of_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>9</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_of_year  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0           9             False   \n",
       "1           9.54       43000      0          10              True   \n",
       "2           9.85        6900      0           6             False   \n",
       "3           4.15        5200      0          12             False   \n",
       "4           6.87       42450      0           9              True   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0              True                   False                 True  \n",
       "1             False                   False                 True  \n",
       "2              True                   False                 True  \n",
       "3              True                   False                 True  \n",
       "4             False                   False                 True  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "83b30bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot graph of feature importance for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index = X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4633e47",
   "metadata": {},
   "source": [
    "#### Splitting the data into training and testinmg data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a120648",
   "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": 36,
   "id": "e9cd7a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d250c0",
   "metadata": {},
   "source": [
    "##### Random forest Regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "20bc81c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_random = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9d61bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eb1d6741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start= 100 , stop =1200, num=12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "54b24c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a2233af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "95360732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3fa800c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1164362a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "62f0be00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "20 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "20 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [-7.11798579 -5.48733043         nan         nan         nan -3.93165322\n",
      " -8.89853109 -5.56763303 -5.95899626         nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28125d7f",
   "metadata": {},
   "source": [
    "#### Testing the given model on test data(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7014b413",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1979e55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.50526,  5.74819, 11.46593,  0.58528,  7.99079,  3.47505,\n",
       "        4.30938,  1.45777,  0.5581 ,  3.75745,  0.22236,  6.82015,\n",
       "        4.42336, 20.25525,  0.97653,  3.62986,  4.43955,  4.33582,\n",
       "        4.92512,  3.15947,  0.52602,  2.99817, 20.78775,  0.32238,\n",
       "        5.45895,  7.41626,  0.86859,  7.6029 ,  0.59048,  6.53214,\n",
       "        2.96887,  3.15399,  3.44564,  5.3953 ,  8.19143,  4.82276,\n",
       "        3.63364,  0.36615,  4.94551,  5.3779 ,  3.08166,  2.857  ,\n",
       "       20.33381,  3.30618,  0.43765,  0.9244 ,  0.27012,  5.0784 ,\n",
       "        0.85702,  5.81795,  2.93765,  0.7252 ,  2.89875,  5.07474,\n",
       "        1.06724,  8.79151,  5.427  ,  0.48408,  4.37508,  0.60377,\n",
       "        4.84343])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "239eccd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c3290caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.9316532194882456"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "00cc08bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudip\\AppData\\Local\\Temp\\ipykernel_12752\\2067326607.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-predictions)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ca9ce3c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudip\\AppData\\Local\\Temp\\ipykernel_12752\\2067326607.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-predictions)\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\sudip\\anaconda3\\envs\\env_carprediction\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f94d896e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bff0daa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03706c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8991514754098285\n",
      "MSE: 4.387483275390137\n",
      "RMSE: 2.0946320143142416\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6751488",
   "metadata": {},
   "source": [
    "### Pickling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "abc14d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('randomForestRegression_Car_Prediction.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "76a32245",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e22be9f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe8b4588",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}