Car_Price_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Car Price Prediction",
      "provenance": [],
      "authorship_tag": "ABX9TyO8qkrhVrU5UEiGgzl3yYXS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sagnik220/CarPrice-Prediction-PyWebIO/blob/main/Car_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzewFsGz38ae"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAgoaMrJ4S5D"
      },
      "source": [
        "df=pd.read_csv('/content/car data.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "76370uQ84S7H",
        "outputId": "4c335c15-792f-4922-999b-f5a6e1ba6dca"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car_Name  Year  Selling_Price  ...  Seller_Type  Transmission Owner\n",
              "0     ritz  2014           3.35  ...       Dealer        Manual     0\n",
              "1      sx4  2013           4.75  ...       Dealer        Manual     0\n",
              "2     ciaz  2017           7.25  ...       Dealer        Manual     0\n",
              "3  wagon r  2011           2.85  ...       Dealer        Manual     0\n",
              "4    swift  2014           4.60  ...       Dealer        Manual     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dt-1NpCV4S8_",
        "outputId": "7bf745cf-d170-4895-d231-70915750f607"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(301, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ljwoqLyl4S--",
        "outputId": "ae82aa13-3c56-4535-a9c2-618e9916fa98"
      },
      "source": [
        "print(df['Seller_Type'].unique())\n",
        "print(df['Transmission'].unique())\n",
        "print(df['Owner'].unique())\n",
        "print(df['Transmission'].unique())\n",
        "print(df['Fuel_Type'].unique())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Dealer' 'Individual']\n",
            "['Manual' 'Automatic']\n",
            "[0 1 3]\n",
            "['Manual' 'Automatic']\n",
            "['Petrol' 'Diesel' 'CNG']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilPspIV14TBF",
        "outputId": "01360bdf-0636-4c75-a9d8-efebceeeb1f4"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "V_BYClp54TDZ",
        "outputId": "aaaed122-fbb6-4b5c-ced0-081857695a58"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.627907</td>\n",
              "      <td>4.661296</td>\n",
              "      <td>7.628472</td>\n",
              "      <td>36947.205980</td>\n",
              "      <td>0.043189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.891554</td>\n",
              "      <td>5.082812</td>\n",
              "      <td>8.644115</td>\n",
              "      <td>38886.883882</td>\n",
              "      <td>0.247915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2003.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>32000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>48767.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>92.600000</td>\n",
              "      <td>500000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
              "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
              "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
              "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
              "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
              "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
              "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
              "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
              "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxCx8okW6WBQ",
        "outputId": "13992e6b-d808-4559-942b-935a9a66b346"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
              "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p5zOKAe6WGj"
      },
      "source": [
        "final_dataset=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
        "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YlQ2pG_f6WLq",
        "outputId": "0c59ea2d-9d55-4bf1-eb80-b8f44d15ea2f"
      },
      "source": [
        "final_dataset.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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  ...  Seller_Type Transmission Owner\n",
              "0  2014           3.35           5.59  ...       Dealer       Manual     0\n",
              "1  2013           4.75           9.54  ...       Dealer       Manual     0\n",
              "2  2017           7.25           9.85  ...       Dealer       Manual     0\n",
              "3  2011           2.85           4.15  ...       Dealer       Manual     0\n",
              "4  2014           4.60           6.87  ...       Dealer       Manual     0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRkyr5Us7Woi"
      },
      "source": [
        "final_dataset['Current_Year']=2021"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-g_scPzo7Wyp",
        "outputId": "e2113858-b3b1-44ba-ca36-a2287fea1745"
      },
      "source": [
        "final_dataset.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Current_Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Selling_Price  Present_Price  ...  Transmission Owner Current_Year\n",
              "0  2014           3.35           5.59  ...        Manual     0         2021\n",
              "1  2013           4.75           9.54  ...        Manual     0         2021\n",
              "2  2017           7.25           9.85  ...        Manual     0         2021\n",
              "3  2011           2.85           4.15  ...        Manual     0         2021\n",
              "4  2014           4.60           6.87  ...        Manual     0         2021\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "260mMTxr7W1L"
      },
      "source": [
        "final_dataset['no_year']=final_dataset['Current_Year']-final_dataset['Year']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
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      "source": [
        "final_dataset.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
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              "  </thead>\n",
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              "      <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",
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              "      <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",
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              "      <td>Manual</td>\n",
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              "      <td>2017</td>\n",
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              "      <td>4</td>\n",
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              "      <th>3</th>\n",
              "      <td>2011</td>\n",
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              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
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              "      <td>2021</td>\n",
              "      <td>10</td>\n",
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              "      <td>2014</td>\n",
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              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
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              "      <td>7</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Year  Selling_Price  Present_Price  ...  Owner Current_Year no_year\n",
              "0  2014           3.35           5.59  ...      0         2021       7\n",
              "1  2013           4.75           9.54  ...      0         2021       8\n",
              "2  2017           7.25           9.85  ...      0         2021       4\n",
              "3  2011           2.85           4.15  ...      0         2021      10\n",
              "4  2014           4.60           6.87  ...      0         2021       7\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqSQLMa59IAT"
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      "source": [
        "final_dataset.drop(['Year'],axis=1,inplace=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
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        "final_dataset.head()"
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      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Selling_Price  Present_Price  Kms_Driven  ... Owner Current_Year no_year\n",
              "0           3.35           5.59       27000  ...     0         2021       7\n",
              "1           4.75           9.54       43000  ...     0         2021       8\n",
              "2           7.25           9.85        6900  ...     0         2021       4\n",
              "3           2.85           4.15        5200  ...     0         2021      10\n",
              "4           4.60           6.87       42450  ...     0         2021       7\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 16
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "BsOKgqJl9iSo"
      },
      "source": [
        "final_dataset.drop(['Current_Year'],axis=1,inplace=True)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "   Selling_Price  Present_Price  Kms_Driven  ... Transmission Owner no_year\n",
              "0           3.35           5.59       27000  ...       Manual     0       7\n",
              "1           4.75           9.54       43000  ...       Manual     0       8\n",
              "2           7.25           9.85        6900  ...       Manual     0       4\n",
              "3           2.85           4.15        5200  ...       Manual     0      10\n",
              "4           4.60           6.87       42450  ...       Manual     0       7\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
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          "metadata": {
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          "execution_count": 18
        }
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      "cell_type": "code",
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        "id": "cqrCMLMv9imE"
      },
      "source": [
        "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
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      "metadata": {
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        "outputId": "c71ea58e-7cb1-4255-e382-d04f92e1f69c"
      },
      "source": [
        "final_dataset.head()"
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      "execution_count": 20,
      "outputs": [
        {
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          "data": {
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            ],
            "text/plain": [
              "   Selling_Price  Present_Price  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0           3.35           5.59  ...                       0                    1\n",
              "1           4.75           9.54  ...                       0                    1\n",
              "2           7.25           9.85  ...                       0                    1\n",
              "3           2.85           4.15  ...                       0                    1\n",
              "4           4.60           6.87  ...                       0                    1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
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          "metadata": {
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          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 351
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        "id": "pp_HbOQd-gif",
        "outputId": "bf5de22d-e3c2-439d-b793-fffa76da6f63"
      },
      "source": [
        "final_dataset.corr()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>no_year</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Selling_Price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.878983</td>\n",
              "      <td>0.029187</td>\n",
              "      <td>-0.088344</td>\n",
              "      <td>-0.236141</td>\n",
              "      <td>0.552339</td>\n",
              "      <td>-0.540571</td>\n",
              "      <td>-0.550724</td>\n",
              "      <td>-0.367128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Present_Price</th>\n",
              "      <td>0.878983</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.203647</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.047584</td>\n",
              "      <td>0.473306</td>\n",
              "      <td>-0.465244</td>\n",
              "      <td>-0.512030</td>\n",
              "      <td>-0.348715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kms_Driven</th>\n",
              "      <td>0.029187</td>\n",
              "      <td>0.203647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.089216</td>\n",
              "      <td>0.524342</td>\n",
              "      <td>0.172515</td>\n",
              "      <td>-0.172874</td>\n",
              "      <td>-0.101419</td>\n",
              "      <td>-0.162510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Owner</th>\n",
              "      <td>-0.088344</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.089216</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.182104</td>\n",
              "      <td>-0.053469</td>\n",
              "      <td>0.055687</td>\n",
              "      <td>0.124269</td>\n",
              "      <td>-0.050316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_year</th>\n",
              "      <td>-0.236141</td>\n",
              "      <td>0.047584</td>\n",
              "      <td>0.524342</td>\n",
              "      <td>0.182104</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.064315</td>\n",
              "      <td>0.059959</td>\n",
              "      <td>0.039896</td>\n",
              "      <td>-0.000394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <td>0.552339</td>\n",
              "      <td>0.473306</td>\n",
              "      <td>0.172515</td>\n",
              "      <td>-0.053469</td>\n",
              "      <td>-0.064315</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.979648</td>\n",
              "      <td>-0.350467</td>\n",
              "      <td>-0.098643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <td>-0.540571</td>\n",
              "      <td>-0.465244</td>\n",
              "      <td>-0.172874</td>\n",
              "      <td>0.055687</td>\n",
              "      <td>0.059959</td>\n",
              "      <td>-0.979648</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.358321</td>\n",
              "      <td>0.091013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <td>-0.550724</td>\n",
              "      <td>-0.512030</td>\n",
              "      <td>-0.101419</td>\n",
              "      <td>0.124269</td>\n",
              "      <td>0.039896</td>\n",
              "      <td>-0.350467</td>\n",
              "      <td>0.358321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.063240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission_Manual</th>\n",
              "      <td>-0.367128</td>\n",
              "      <td>-0.348715</td>\n",
              "      <td>-0.162510</td>\n",
              "      <td>-0.050316</td>\n",
              "      <td>-0.000394</td>\n",
              "      <td>-0.098643</td>\n",
              "      <td>0.091013</td>\n",
              "      <td>0.063240</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Selling_Price  ...  Transmission_Manual\n",
              "Selling_Price                1.000000  ...            -0.367128\n",
              "Present_Price                0.878983  ...            -0.348715\n",
              "Kms_Driven                   0.029187  ...            -0.162510\n",
              "Owner                       -0.088344  ...            -0.050316\n",
              "no_year                     -0.236141  ...            -0.000394\n",
              "Fuel_Type_Diesel             0.552339  ...            -0.098643\n",
              "Fuel_Type_Petrol            -0.540571  ...             0.091013\n",
              "Seller_Type_Individual      -0.550724  ...             0.063240\n",
              "Transmission_Manual         -0.367128  ...             1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NcW_9lV-iZK"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "id": "6YitahzWAUU4",
        "outputId": "d6e4053c-bf46-4f25-daab-82f9a662ae96"
      },
      "source": [
        "sns.pairplot(final_dataset)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f52cc0e2b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B9RFNRrrrN8YpTUY647TEasLWHR9H7GvjV09M+r4otyS7bcqRG3EoZcuf3HEYd39lgSw7edOK+VhQXYhT6kpRV5aXMxe17JGoLsfTuyOZ37VGI1BXlhfzeGBsEVE20WiADSsbZXXShpWN4HIbFE8i5zzieT9csmImG0fLun0+XNZcg0feaMWm7fvxyButuKy5Bh6O3KAY0l0/sD6iyUh3/cY4pclIZ5w2VuXjhpYG2b5uaGlAY1VB0vdFuSXZbVOO3IhDKVve73BjSU0hnpsCvfTYI1Fdjqd3R7q/a8YWEWWTArMB295tx70XL8KI2wuzQYctb7bi3osXZ7poRDmB5/3ky8bRsiVWo2KP0RVNlUnfF+UOXoeQGqS7fmOc0mSkM051Og0uWFSNhvI8dA44UVlgQmNVAXTsHUZxJLttyuRGHMFseXC4TDBbXlNsDfW6ynXB9zkV3qvaRYvXRHt3pPu7ZmwRUbaoLbHi6tNmT7r+JKL4eN5PvkQ+0+NtH05EbYkVN62Yz7qUJozXIZTtMlG/MU5potIdpzqdBotmFGHRjJS8POWoZLdNmdyIg9lyUhPGKxHR5LD+JKJclc76jXUpEeUq1m+kBoxTUoNkxymTGwmYStlyv1/CoV47ugadqMhnJahGaopXxhsRZSNJynQJiKYutg1SI53tQzW1RSl7pPvYZ11Dk5Hu+o1xSpORzjhljNJkJTNOU5rcEEI8CmAlgKOSJDWNPlYMYCuAWgCHAFwqSVK/EEIAeADAeQAcAK6UJOnd0ed8A8Atoy97lyRJj48+fhKAxwCYATwHYJ0kSVK0faTyveYCv1/CC7s7I4YFrWisZOVEScd4I6JswjqJKPN4HBJNTek+9lnXkBowTinbMUYpW6R6lZfHAKwY99i/AXhZkqQGAC+P/h8AzgXQMPqzBsB/A6FkyAYAXwCwFMAGIUTR6HP+G8A/hz1vRZx9UAyHeu2hSgkIrFS//sn3cajXnuGSUS5ivBFRNmGdRJR5PA6JpqZ0H/usa0gNGKeU7RijlC1SmtyQJOk1AH3jHl4N4PHR3x8HcEHY41ukgLcBFAohqgCcA+AlSZL6RkdfvARgxejf8iVJeluSJAnAlnGvpbQPiqFr0ClbqR4IVE5Hh5wZKhHlMsYbEWUT1klEmcfjkGhqSvexz7qG1IBxStmOMUrZIhNrblRIktQx+nsngIrR36sBfBa23eHRx2I9fljh8Vj7mLCpNH9cRb4JJr1GVjmZ9BqU20wZLBUlQo1xyngjomxSkW/CzBIzVi6shhitPp/54AjrJKI0miptAzW22yYi198fJV+6j/2pUtdQ8qWzfmOc0mSlK04Zo3Q8khmnGV1QfHR9jJQu2xlvH0KINQhMg4WamhrZ3yY6f1zwi+m1u2DQauBw+1BuM0EjgIO9dlgNOlTkG1FTnJ0N/JoiCzZf3owdbX3wS4GbOjetmI/aEmumizalxYpR4PjnOfT7JbT32dE16ILd7cXMYitmFlvQ3u+YcCUzkcqptsSKTV8/ER8eHoBfArQCWDC9gPGmUvHilCgbxIrTmiILbjxnHj7pGgrVSTeeMw81RZZMFJWmqKlel06kbZDMC6JEXitZ+5tIuy1bkwTxrp9e3tuFj46MfYdN1QVYPq8iK8pO2SkV1wWx4pTXITQZqajf4sXplmua4fMJdA+5UGYzQquVGKcUU7LXwYgXoz+/fAmGRnywu7ywmnSwmbSMUYor2fVpJpIbXUKIKkmSOtC+6FIAACAASURBVEanljo6+vgRADPCtps++tgRAF8a9/iro49PV9g+1j4iSJK0GcBmAGhubpYlQaLNHzdv7bKI1dyDFcg9L+zBZc012Lh9X6giWbe8AVveakO/w411yxvQUJGHlrnZ1cD3+yW8uKdLVgHec9FCnD0/u8o5FcWKUWBicTqe3y9h+ydd2Nc1jAdeHovZuy5ows+270Nb70jCJ8PJnETdXgmbX2uVbU/qFC9OibJBrDht67Wjvc8hq5PWLW9AW68dsytsGSkvTT2sSxNrGyTzwj2R10rm/hJtt2XzIp2x4vRgzzD2Hx2OqEvrSq2YXc66lKJL9nVBvPqU1yE0Uamo32LFqdvtw6FuJ257eldof3esasLCSh9Mpoz2U6YsdrBHuZ0x94ZlmF0e+/6Qklgx6vdL6B5y45andsnuJfn9UsbbKpTdDkWpT2eXWlE3ifo01QuKK3kawDdGf/8GgD+FPX6FCDgFwMDo1FJ/BnC2EKJodCHxswH8efRvg0KIU4QQAsAV415LaR8TMpH544IXKisXVocSG8HtH3h5Hy5cMj30u9cr4d32frx1oAet3cPw+zN/7ap0oXXTtg/R3u/IcMkonsnOc+j3S/joyDE43f5QYiP43Fue2oWVC6tD/09kUaiJLibFxaeIKJt0DDoj6sIHXt6HjkHOGUuUCL9fQmv38HG1bxNtGyS6XSJlSuS1ktlmSbTdptZ2UsdAlLp0gHUpRccFxUkN0l2/fdQxEEpsBPd329O78FHHQEr2R7mhrc+u2M5o70t+/ba7YyCU2Aju55andmE3Y5Ti+DxKffr5JOvTlKZ7hRC/RWDURakQ4jCADQB+BOBJIcQ1ANoAXDq6+XMAzgOwH4ADwFUAIElSnxDiTgB/H93uDkmSgouUfwvAYwDMAJ4f/UGMfUxIovPHBYeMOz1+CAHFiiQ4f7fT48ewy4tvjzamZpaYcefqBdBrRUaHm8e60IrX+z9ctg6fT7dsnYszOAXV0SEXOgedOHB0GFqNJmbMBv8fLxaixVDXoPLzEok5xlNq8fMlGjPk9CrWSUNOb4ZKRKQewZGg46d4mehI5UTbo4m2IRIpUyKvlax2MpB4uy2Z+0wn1qU0GV2DTswpz8O1p8/GiMsLi1GHX7x2IOp1hNr2R7kh3fVb56AryvW1KyX7o9xgNegU2xkWQ/Jv/3YMKLdVOgacWDQjypOIAAy7lOvTYdfk6tOUJjckSfpalD8tV9hWAnBdlNd5FMCjCo/vANCk8Hiv0j4mqrbEivsvXRwxVVP4/NvBIeOfdA7CpA8MhFGqSCRp7Pf2fgecHj+qCky4rLkGa57YkfHh5slYCCibh8+nU7o/B6U4vf/SxRHzHEabguqnly5W/O7nVNhwfUs9gMQW1bVEPYlqFbePF3OMp9Ti50skV5FvjFInGTNYKiJ1aOu1Y1+X0tDyPMyawI3CRNujFfkmzCwxY+XC6lBnjPFtlfY+5TLVl+WhtnSsTOU25X2W5Zlk+0vWgpmJttsSeY/ZqMxmiPJ5GjJYKsp2hRYdvvaFmbjx9x+EjosN5zei0Jya2xXp3h/lhnTXb1UFym3Tyny2TSm6inwj1p81B/e/9Gmoflt/1hxUpCBuyvKUY7Q0jzFKsSX72jsT01KphkYjcPb8Cmy+vBlrl9fjmtPqcP9Ln+DFPV2hYe3BIa1P7jiMtS0NeOaDI/jOl+fIEh3rljfgD+8ehkmvwa0rT8Cre4/iujPrsf6sOXB5fSiyBE6GmRwOG7zQCi+30oVWLBzeG5CJz8GgE1hzeh2ub6nHmtPrYNBF3pxu77NHTEFVZDHgsz477ljVKPvu71jdhJ+8uBebtu/Hw6+34vozGzDi8cLrDTxPaZoHt8+HtS0NstdZ29IAj88fURYgfswxnlKLny+RnEYA68+Sn7/XnzUHWraUiOL6fGAkytDyEdl28aaJCi5MuelrJ+KeCxdg09dPxM8vXxLRHq0psuCGlgY88kZrqK1yQ0uDrANS16BLsUzje7xqNfGP/WS0k4M0GoEVjZV4bu0y/M+aL+C5tcsUOxYk8h6zkV4D3Lm6SfZZ3bm6CXrlvi5EAIDBER8e+ut+XHNa4Hrm2mV1eOiv+zHo9OXE/ig3pLt+0woR5fzEjmgU3fRCC6oKTLL7Q1UFJkwvTH77weP3YcNK+b2kDSsb4fOzLqU4JOX292RrN3ZNiKO93xEaWREUvuhfcMh4x4ATT7zdhitOnYlF0wtw38WLYHd74fb4UV5gwre/3ICqAhM6B0dw7oIqWc/5tS0NeOLtttCQruMdbj6ZqWaCF1rz1i7D0SEnym0Tn6JGrcPnky3dn8OhXjuu/817ERnP58IWpvT7Jbzbfiw0fRoAVBWY8J3l9bAY9fD5/dh8+UkYcnoxvciMO5/dLespuOmVfVi9uBp7OoawasE0/OWToxE9Dk+osmHrjnZcc1odhAAkCdi6ox0rmioVyx0v5hhPqZXpz9fn8+HQoUOh/9fW1kKr5Z0Pypw+hxuVBUZsvvwk9Ns9KLLqcWzEjWMOT6aLRpRRibQrow0tt7t8stdJZOHuRBambO934Gfb94XaHADws+37sKSmKHQOs7uVy+Rwy4e7dw+7UGjShdruVoMODpcHPcOu0AiPZLSTw2k0AnVleTHPt+39DsV5rMPfYzby+ICXPv4cP7/8JByze1Bo1ePXbx/ErJL6TBeNstiA04OvL52Jn/5lrKfxd748B4PO1JyD070/yg3prt/6R6K0TUfcKdkf5Yb2fge+//sPI+4PNU4rSHr7QUCDbe+2496LF2HE7YXZoMOWN1vx/XPmJ3U/lHuORanfBkYmdx5mciOOaDcAP+0aAhC4QWzSa1BkMeCKU2ci36zHlY/9fWx468pG3P3cx2jrHQkNd/2fv7fLLlQ2jl6cPfjK/kkPcQ86nqlmErnQiiWZQ/bVLN2fQ6y1LoJ/12s1uPmPH+HaZXWhsn3zjDr4IPC9ccOxAaBlXiU2bpcn4DQa4JandqG2xKrY4/9/b1iGm1bMjzvNQrhYMcd4Sq1Mf76HDh3CtQ8+B2tJJey9nXj4uvMwe/bstOybSEmJxYiuQTdu/P1OWZ04s4hTqdDUlWi7Mtq0BCVhU3VEGzE4L6wzRrSFKRvK87BoRlHotXrtLlzWXBPRVumzu0KvNbPYqlimmmJ5u8Ss16LX4cFtz3wsm77qhHFdcY+3nRwukYRRpjshTJbD48WZc6vwL0/slF0PjXi55gZFV2zRY+1oogEIxPpP//IpfnX10pzYH+WGdNdvpdYobdNiTvlD0aWz/WA1arF8fqVsir91yxtgNbLTIsVWbDGiM4nX3kxuxFFVYMJNK+ai2GKAxajDkWMO/PZv7fjoyCC+vfV93H/pYmy5phmDI370Drvw+bERFFkMoVEYD722H989e14oGfLQX/dj5cJqPPjK/tA+nJ7A4s0mvQZ3f2VBxM1gr9ePvV2D6Hd44PT4UFdixayyPMVkRSIXjqmS6BzCuS7dn0NFvgnNMwtwxRfrQgviPf5mKzw+CedtfB1Ojx83nzsX15xWB4NWg5999UQc7BlGqc2ETzoHZfF6+zO78cQ1S+H0+vCtL9VjVqkVR445MOL24YRpBXB6/OiMcrLsHnYmtVcj4ym1suHztZZUIq9setr2RxSLw+PDe209ePTKk9Ez5EKZzYg/vtuO2pLsngKGKJUO9drx6BsHAj3yRtsYj75xAPMqbbJ2pU+S8IPz5qN72BVavLs0zwi/NDbtVNegE0UWAy5cMj002mLbzsOyBXyjLUzZOW5hSoNWE0psBLfZuH0ftq45JbTNrFIrNn39xIgFxWeVys9zdpdPcfqqJTVFsu0mMzJaSaIJo2R3QkhW+eMx6nTY9u4+Wcw8zl6cFEef3a24wHdfikZPpnt/lBvSXb/Z3Wyb0sRFuz+Uik6MIx4fLHot1pxeB78UmObXotfC6eG0VBRbsq+9mdyIwe+XsPvzIdkUUuuWN2D9WXPw1LtH4PT4cc8Le7Bu+Rzc/MePIqaZAoDLmmtkWcy1LQ2YXmQKLdS8bedh9DvcmFthw5rT67CkplB2oeH1+vH87g4c7h+RlePeixaiqtCEEqsxa6bySfaQfbVK9+cwvcCMS0+WL4h3x6om/Ort1tCIokKLAff/Zbcsju989mP0O9yyadGKLAYc6nHIFt9c29KAp94/guoiC24+dy4qoyz8oxECh3rtqC2xJiXWGE+pla2fL6erokzRaCQ015bi6rDRl3esaoRGI8V/chZI141LmloGRty4aIm8LbthZSMGxk2JUZZnxIe+AVn74fvnzEVZ2IKSNpMO3zyjDj12dyjZ8M0z6mAzjV2OVBeaFdsYVYXyC3KH2xdlyin5xbTLI8nK9JNLFke8R0cC01cdz8jo8RLtiJTMTgjJLH88To9XMWacHLlBMZTaDIoLfJfm6XNif5Qb0l2/qb1tSpmheH9odROmF5iTvq8SqxGPvnkwNKW5zw88+uZB/PJKjoKj2JJdv3GZzBgO9drx3d/JLz4eeHkfDnTbccnJNagqMGHlwupQYiO4zcbt+3Dhkum4cMl0xV5leq0W0mhW87bzT8B9lyzEfS/uxdyK/NBQ+eCCi++292Pf0eGIHmU3bvsQr37Sg/M2vo4XdneGFmQst5lCC7IEmfQalOWlZ6qZ4JD9U+pKURdldMlUkM7PYU/nIG77k3wKh9ue3oVLm2fiyi/WAgBue3p3RBxfuGS6LF4B4JLm6bh13Gtt3L4PKxdW47Y/7cKA04fv/u4DbDhfvmjUuuUN2NnWHxGPx4vxlFrp/nx9Ph8OHDiAAwcOoK2tDZJCmASnq1r3P+/i2gefkyU6iFJJQBNRV9729G4IFTSVgjcuz9v4Or72i3eSXhfT1OX1Sbj9Wflxcfuzu+H1SRHb/fjPn8i2+/GfP5Ft5/bKEwhKjwsAN587D2uX1+P6lnqsW16Pm8+dB8245QWjtXfDeyW2dg9HtOO/+7v30do9LHtesdWo+FrFlrFh8Qd7lBMSB3vsiu8pllgdkcJpNAJnz6/A1jWn4KF/WoKta07F2fMrJnWujpZQOdQ78fLHYzboFGPGrGefOorO5wNuf2Zc3DyzGz7lakN1+6PckO76Tc1tU8ocxftDf9qFPZ2DSd9XbYkVN62Yj0feaMWm7fvxyButuGnFfM62QXElu35jKzOGrkGn4nBVi0GHvZ2DuHDJdBSYtLLFDLftPIyOASeECCQvlC5eRjw+PPLGWC+y/7hgAf5xaQ2MeoH2Pju6h134/JgTN237ELef3wi/pPw6Rp0G15xWh72dg6guNGNBdQG0GmDd8oaI0SZanv9y1ucDynFq1Gvxm7+14fozGxTjp6bYjOtb6rFt5+HQtGh1pXmK2wox9m9b7wge+ut+3HvxInzaNQSrQQufX0J5vgnXLqvDPS/siZiuggiQr7PRc+Aj5FXPgU1hO05XRZnQNehSrP+6Bl0ZKlHiMjklJalbvBE/UY+LIflx0d7vUNzus34H6isCNb3H54fd7ZONpFi3vAGesLuJR4edcPvkoy2+d/ZcHB2W3/gXkBTbuwJjyZSDvXbFMh3stYfKBABunw9rWxoi1u/w+Mee29ZnV5xSq73Pjtnl8mMs3mdakW/CzBJzqJcjADzzwZGI6SL8fgkv7ulKymiLdI7s7h5SjpnuoeyvSylzOtN8Dk73/ig3pLt+U3PblDIn2v2hjkEnFiV5X+EdMToGnKgqMKGxqoCdUimuZNdvTG7EUGjRKQ5XbSi34u+H+lFdaILbJ+H+v+ySXQxt3dGOuaMXTUpD6w+FXWw5PX784KmP8OOLF+GjwwNoPToMnxSYu+7aZXWoKjTh84ERxdepL8/D+iffR9Foz7LP+hwozzfi+Y86QgkXSQK2vNWGE2sKUVuqrhscnGIjMdMKTYpxOuL2YeXCahzudyjGT3vfCB55oxXrljegaVo+5lQsQu+wU3FbSRr7FwgkOD7tGsK2nYdx+SkzsemV/bJjIHxBz+PBGMg9wcSFvbcz00UhkqmIMuVeRX72L9qo1oWHKbP8fgnbP+mKWJOiZe7Y6IDyKMdFeZ78uLAadIrbWQxjlxoen6S4tsXDVzSHtsk3GXDfi+/JtrnvxU/wq2u+INvfgR47trzVFtHerS/PQ115oA1u1msVy2Qet1B4idWIrTvaZa+1dUc7VjRVhrYpMOlxxakzI5Ip+Sb5FDaJTP9UU2TBDS0NoYXTTXoN7rqgCTVF8jmGk5m0TPb6HbGU26LEjC3761LKnGjT3qbqHJzu/VFuSHf9pua2KWVOtPtDVQXJP+f7/RJe3Xc01Jbc0zGI7mGXrC1JpCTZ9Rv788cw7PQpDlcNjprQCE3owiT4943b9+GmFfNx93N7cPdze7C2pUE2fc+tK0/A73YcBhBYrPy6M+tx7bI6AMBT7x9BZaEZFfkmbH4tMKzr1j/tQl2ZFeuWy1/n9lWNuOeFPSiyGHD5KTOx+bVWXP/b93DFo3/DuQuq8Id3D2PbzsB+LmmeDrNep6rpKTjFRuLC4zL8/2aDFloN8OSOw4pxaDNpce2yOvzP39vxweEBrP3te9j82kH8xwVNsm3XtjTg2Q+PYMP5jXj906MAgJklZsyrsOHm8+ZHmXrt+KsWxgBJfj/a2tpw4MAB+HxclIxSy6gTuGOVfMq9O1Y1wqjL/oZ5pqekJHVq77NjX9dwqM3589dasa9rGO19Y1MVFVm0isdFoVWeIKjIN0ZMWbnh/EbZBUr0tS3G6veeYeVeXL3D8l5cJr0W/Q43HnxlPzZt348HX9mPfocbprDEhdWgjWg/r1veAKtBXvZEplTQa4ViYkavldcPiUxf1d7viLh+uOWpXWjvd8heK9HpqxIRXL8j/LOY7Pod8UiQsP6sObJ9rT9rDiSw/UbR2UzKdY3NlJp119K9P8oN6a7f1Nw2pcyJdn9ofJsiGdr77Pi8f0T22Of9I7K2JJESo07g9nH12+3HUb9x5EYMR6MMO+x3eFBmM0a96Nh/dBgdA4ELj/c/68WjV56M7kEXSvIMMBs06He4UVVgwuWnzJQNgb915Qkw6bX4jxc/Dr1uW+8I7n/pU9x70UL84opm2F1eFFsN+LRzEG29I7juzPqIm8sPvLwP65Y3QJIQ+tvm11pTtnBgPBPtfe/1+vH+4WPY2zmIa5fVhab64hQbyrqj3AjQaYD5Vfnod7jxwq4O3HvxIghI0Go0uOeFPWjrHQklLzSj98T6HW7Ullhx4zlzUZFvQqFFj2GXD984tRZDI26saKqCQSdw0Uk1+N7vP8C1y+ri3qiIxev1Y3fHwOgQRjMaq/Kh0wUKw2lWpo5gEgOAbC0OR/9R3LLtMIzG3Xj4uvMwe/bsDJaScl2fww0hgPsuXgS72wurQYcRjxf9dk+mixYXp6SkyegadCnesF9SUxQa7evyAoVWg+y4MOg1cI9bO3XQ6cG2ne249+JFGHF7YTHo8PibrWicNjb9U6HFoNhDq9A8Nvoh6ggQo/ySxagXijFvCLsgcng8qMw3Yc3pdfCPrnVXmW+CwyMvfOSUCoH2SHhb1R5lAXP7uPZOW5/yVFjh01clOtIqmaMtNBqBFY2VmLd2GY4OOVFuS91oWLvbC6NWI/vcjVpNwm1Dmpr6HV48uWOsDjEbdNjyZitmFM/Lif1Rbkh3/abmtillTrT7Qz3DyZ/OrHfYDafXL5tOdP1Zc9A77EZtadJ3Rzmkc9CFFz7qwM8vPwnHHB4UWvR47I2DKDLPBGZM/PWY3IihLMqwwwKzDg6XD3MqbIp/N+kErjuzHgUmHerL8/Dvf/gwdCP5jlWNuO+Shfi0azgiKXHnsx9jzel1uKy5Bk+83RZKkLi9Etr7RkILPZv0GvznVxZiZok5tBZCOKfHj/ryPNzw2/cibgyXXLUUZTZj2qb3SWR4fjiv14+nPjgiG6q/tqUh9Hlwio1IpXnKNwu0Gg1Meg3uu2QRPutz4Ccv7sVdqxfgn5/YEdq2yGKA0+tDQ7kN65bXo9hiwJDLA7Neh+/+LmwY48pGeLQ+3P3cXjxx9VJc/ujfQq+hPJQs/oW30nd91wVNuGBRNXQ6TdSL/65BxkCuCSYxiqf1RqzFYSmugMnEodeUelaDHj985eOIefB/fHGyZ6dNvo4Bp+IUPWqckpKSI1bngSC7S3kkhd01dvPf5fFhbVh7Egic5399zVLZ8/rsbrTMq5RNgRCYptId2mbY5cF3vjwHP/3Lp6FtvvPlObCHZUryzTrFpEW+SX7JYtHpUFkwLnFRYII1bFFXg1aHX7+zD1d8sS405/TjbwZGZYRLZG2LaImG8e2dRKbnSjRpERxtMb5ckx1todEI1JXlpbwNlWfQ49E3x+pSvwQ8+uZBVdSllDm9djd2tA1gR9t7ssfD6xA1749yQ7rrNzW3TSlzot0fKrYakr4vt8+P+1/6VHbf8f6XPsVjV56c9H1Rbimy6NHaa8ffDvaH6rfWXjsKrZNLUzC5EUNFvhF3rGoMreAeTE7otQJGgwZ7OgZw68oTcOezH4f+fu/FC2F3+XD/X8aec+vKE7D1b+348Mggbnt6Nx6/6mToNBrFC0r/6GiLa06rw4Ov7AcQmFZq0yv7ZAuX//qdg/jRhQvxWZ8D65bX48kdh0PJEJNeg3yTXvH13/usHw63D3PKbZhflY9ZpalNcky09/3ujgHFqb6uOa0Oj7zRmpK5gdUuz6BTjFPAj6ODLthdXjzwcuAz3NMxGPpsx48emllixr+fOx9ur4SBETfWLW8I9Uh86LX9uGN1E5wev6wnwLadhyMW4Uz0wlvpu77lqV1oKM/DohlFsES9QcDh4rnIUlzBtTgooxxuL649rQ7dw67Q+gPXnlaHkfFd1LNQuc2E6kIj5lbaQjdxqwuNnJZqiorXeSCo0KJXXNi6wDI2kqJ72K24kHbPsLzXqFGnVZym8pdhF7cGrRa/+Zs8Cfebv7XhPy5YENpmfkU+uoZcESNF5lfky/Y37Pbh128fCiQuwkaKfP+cscTFkMujmHAZdsnLnkhbNdFEQ0W+UTE5Ez49V6Kvlc7RFslkd3vx9aUzI5JYaqhLKXNK8wyK9VFxXvJvxmVif5Qb0l2/qbltSplj1msV7w+l4j7KkNOjeN9x0MkYpdiMOi3WnzUHB7rtofpt/VlzYNROLk6Z3IihymaGyaCV9QozGbT424FuLJhRggGnDzUlVvzX15fg3c+OodCsQ1meATf+PtAzvqrAhAuXTEfXoBPfP2cefvznvfjwyCCOOTywGnVYu7wefgmhaZeCCzY7Pf7QVBImvQYnVOXDFHbROLPEjH89vR5XPfZ32YXTlrfaYNAJ3Ll6ARxub0TSY2aJGTaTHv/3L/Ib0amcqmqii5x2DChvr9UgZXMDq51P8ivGqYAG//XqfnzrjHoUWQxompYPrUZg09dORGuPHbNKrbjvxb2hWL2suQbf3vq+LKa27TyMfocba1saMDjigUmvQal1bERTx4ATT7zdhjWn16GxqgANFXkJX3hH+647B5xYNANw+3wRiZO1LQ3w+JI/VyQRkc2ow4jHFzGsOs+Y/U0lvQ64pLlGdhP39lWN0Gd/0SkF4nUeCPJLPlx/ZoNsZPCdq5sAaWx6jQKzDt88ow49dnfowuObZ9Qh3yy/8Bh2eRWTIOGjQPKMWvzzsjocHQrcpNFpgH9eVidbA8PvlzA44olIzIxfb8vj82H14unYf3QoVK7Vi6fDE7Y+U55Rr5hw2XKVfNRJIm3VRBMNNcVWNFTkydpkDRV5qCkea79OJGmRrtEWyWTWy5NYQCCJde9FCzNbMMpqNpMO132pPuJm3PhRW2rdH+WGdNdvam6bUuZIkqR4fyg093MS2UzKHVLzuH4RxeH0+NA16Iqo3yoTmAVGCWvFGPZ0DeLG338YcaA+dtXJcHt9eKe1G4unF8Djl/DMB0fwL6fPxlutfaGbxUprahje/QzDbh/Whd1EXtvSgK072kPTUZn0GiydVYz/+vqJ8EtAoVl+cbZyYTVuf1a+QNADL+/DE1cvxecDTqwZnXYoPOnR73DjphXz076Gwfih91UFJlzSPB0Otw+t3cMRF3NVBWbFyvH0hjIsqSnK+t5qmeD2Sopx+t//eCIua67BwIgbV5w6Ez96YQ8ua66JSBY88XYbLlwyXXHtluAIomDvy/svXYyKAnmvxH6HG2a9FnMr8yKmP4m13kq077qyIFCZlViN2LqjXdbDc+uOdqxoqkz1R0pEU5DLqzys+tFvNGe4ZPF1HnNhw9PydsGGp3djy1VLUVOsnpuilBzxOg+M0YYSG8Ftbv3TLvzqmi+EtjDptLC75TdW1i1vgEknv2gtsRpwxakzI0YshE+BYNJroNdqZK91+6pGmA1jo0l2dQ4qJmZqS6xori0ObVdkMWCXazCiXIWWsf1Fm3Zr/JobiU4TlUiiQaMRaJlbgbrSvJiJCzUmLRLl9PgU25tOD9fcoOgcLl8o0QAEjtXbnt6NJ65eGueZ6tgf5YZ0129qbptS5nh8yveHUlG/5Zv0uH1VY+g6JNi2KzDp4z+ZpjSfX0pq/calJmP4PMrFYZ/dDZNehxuWz8HvdrYh36TDv62Yj65BJ4y6wDoHSjeL73z2Y3znrLn4wR8/iuhFdtfqBXhhVwf6HW7cdUETuodcWP+7D3D9b9/Dm629snJEW2dj2OXFTds+lL32Ay/vw32XLMSa0+uw/+hw1J5pqRIcem/Sa1BVYMIVp87E5tdacfVjO3Dextfxwu5OWW+8xqp83HVBE0z6QGgGe+wtnl7IxEYUPcNuxe/VpNdh4/Z9cPskPPDyPqxcWK3YgzHYy1LpNYI9UpweP4acXqxorMSMorFeide31GPN6XURvRKBsfVWztv4Or72i3civu9o33VjVQGAQOzctGI+HnmjFZu278cjbwTmSsvHmQAAIABJREFUyeboHSJKhUGn8o3QIWf235DrsUdZONCe/IUDKfsFOw+EC+88EBQtbnrD4sbu9ikuOj5+Ie1o24Uvsto97FFMwoVPcdU9pFym7iF5LA+MeBT3NzAy9lrFoyNNx38OhWb5tDPhbdXgNkqjhf1+Ca3dw3jrQA9au4cjRpMEBRMXp9SVoq4sb8q1X4Ptz/HtTROHklEMR6Mc+0eHUnMeS/f+KDeku35Tc9uUMifa/aHeFKwpNOj04r9e3Y9rTgvcG7rmtDr816v7OS0VxZXs+o2tzBhsxihDrIw69Ay7UG7T48y5VbLpof7jK02464ImtPc5FL+ofofynHR/b+vDt788Bw63DxUFRlz+yNiCzX5JedHm8f+3GnWKr60RAvMq8/FJ52DMnmmxetlPVvjQ++4hF77xy7/JGgPjR47odBpcsKgaDeV56BxworLAhMaqgohFMGlMXpShgMOjlYXT6w8lKpTiQ6uJHmPBkYuBuA/00ky0V2K8OazjfddqnWuaiNTJatAq1oPhvcqz1bQCi2LZpxWYM1gqypTGqnz8+OKF2Hd0ODRlU315XqjzQFCBWa8YN/lhve3s7iijH8YlN0YS2G446kVM2ILiUdo0tnFTxQw5lafBGg67IBp0uhWntxy/5kYi7Y1gh41Yi45TQG8CSTOi8UrSuABuJvZHuSHd9Zua26aUOdHuD1lTMJ1Z77Abbb0jofWCQ4+nIJFCuSXZ9RtrxRjyjNrA0PuwnlzrljfAZtShwmaC36+JmB7qB3/cBafbi1NmFSv2FiuxGhQf9/mBjzsGsadzCK3ddtkXvG3nYdy68oTQ85754Ag2nN8YUa4+uxszS8wRr+3xSTh7fgW+cmI17v7KAsWeafF62R+PYA82vyQlNHJEp9Ng0YwinNNUhUUzipjYiMMWJU7zLXrZY+H/Bpn0GtSX29BQbpXFWPA1/vDuYZj0Gmw4vxFGvTYUE4n0Sow1h3VQvO96qvd+pADJ70dbWxsOHDiAAwcOwOdjbyVKPrNBuS5NxeJ7yabVIKJdsOH8RmhZZ1IMDpcXa1vkMb+2pQEjYdM2FVuU261FFvl0A2U25VESpWGL85bZlF8rfJs8ky5q2ztccDRwcHTnw6+34opTZ6IybOFuo04bmt4y2Jtw6452GHWRx3S89ka0DhuHeu0RrzXVFUe51uFNY4rFrNfi9lXy89jtKVoAN+b+9Nl/zqfMSXf9pua2KWVOtPtDeSmIm4p85fZfhc0Y5RlEAcmu3zhyI4buYTfyjDrZQjx5Rh267W7YTFr0DCmPwugccuOp9z/B3V9ZgJtHp6AKflH/+dwe2XoF4WtuBNfEuHZZHWaWmLFyYXWoN5rk98vWHvB4faFySRJC62o89E8n4V9/tVP22rf+6SP88sqlqCsLTB20eEZhRM+01u7hSa/HkeiIj0TnNE7FCJJcFi1O7S4P1rY0YPveTtyxqhEPvro/ogfjd748Bz95cS8uOWkGfv1OO9acXocTqvJxsCdwsX7RSdMhScBDf92Pu1YvkMVEvO8p0e+bKB5H/1Hcsu0wiqf1wt7biYevOw+zZ8/OdLEoxxh1GkwrNMnq0mmFJhhUkGBv63Pgt++04d6LF2HE7YXZoMPDrx3AmjNmY8H0wkwXj9Jsb9cgDvePRKxHsbdrEE3VY/FQaNYrrm31k0sWhbbxSRK+f85c/PjPn4Re6/vnzIVv3KKUWg1wx6om3Pb02ELgd6xqQngewef344fnN+KHz4zNy/zD8xvhl8baCb12Fyx6+SKYFr0WvQ55r1inR3karJNqxt6fxaDFV0+uiVgHxDyJi6ZEFh2nAKtBqzj/Nm/GUSwCgesE2QK4eg1SdQUYdX+85KQY0l2/qbltSpkT7f5QTwpGUxh0AvdetAD7u+2h0cKzy6ww6FiZUmzJrt+Y3Igh36zHXf/7cSjJ4PMDD7/RinsuWojBEW8oSzn+5u2cChvyjFpUFRjx8386CU6PH7s7BrDlrTZ0DDjRPezGmtPrML3QjLa+EWzd0Y7vnT0Xbb12XLusDkVmPb69fA7+PSwxctcFTXj2w4No6x0BAFzfUo9N2wNDv6oKTKFh+TqNwLrlDfD4JNSWWvH5MQfOX1SNPrsr1BNt/AKGwZvUk7loizdMP/wGeFWBCfdfujhi2/A5jTnsf+LyTcpxevcFC7B1RztuPu8EmHQCqxdXQ6MB7r14ET4/NoLphWb4JD/+7dz5GHK4YdAJmPVaaEQgWRaMKSECi5Y73IEpII4OOVFbYg19T0UWAy5pno455TbMr8rHzGIL2vsdGBhx487VTaHFSoNxXFNkyfRHRipkKa5AXtn0TBeDctiIx4f//fAI/vGUWThm96DQqsev3z6Ia5dlfyKtwmbEp0eHsfa374UeY6+pqeuYQ3k9igXV8mmp9FoNbloxD35/YPFtq0mHxmnzYNCOXVQ4PV7ohJBdeOiEwPjFU0fcEh58dZ8sUfLgq/tw70VjiRIIDbbv7cDPLz8J/XYPioLH2Gn1oU0KzAbc/qa8TfPomwdx/yWLZfuLNs1rv2Ns1MmQ04stb7XJyrTlrTYsnjHxhB87bCRuxOPDy3sC33N4XTqrlO0/im7A6cFN2z6KOMZ+cUVqFk5O9/4oN6S7flNz25QyJ9r9IVmbLEn6HR4cc3hkHWpuPnce8s2e+E+mKW3Y7VWs364+bXL1G5MbMUh+P/719PrQ1FMmvQYbVjZiYMQDq0EHrQDuWNWI256W/33Lm61omVeJqx7bAafHj7XL67Hx5bE56DoGnNj48n7cc+ECCAH849Ka0KLP4T3LiiwGdIwuan7LU7tw/6WLcc8Le7ByYTVqisxYt7wer+w9ihVNVaHe+A/rNVh/1hyUWPTYf3QolD3tGXaHphMKF0wmxFuPY/xzggkLi0GHe17YozjiI/wGePB9bfr6ifjfG5ahe1h5TuN46zRQJI/fqxineSYd/vWMeggEsvfBGKwqMOHyU2bie7//QBZvt608Ae29DliNOty4Yi4O9djx5I7D6He4sW55A/rtrtEpH0xo77Njb+cgvvWlejSU5+FHL+xBW+9IKIHxs+2BBcyf/fCI7KbCz7bvw5KaIn6XRJR1HG4f/mF2OXa29QfOnT3AP8wux4g7+6dBM472HBzfk3D8MHGaGuxu5fUoxq+T4fb5MTDixe1hIyk2nN+IcttYW9Cs1+E/X9gb0T587KqTZa91dMilOOdy+ELgXp8PZ86twr88sVPWXvH4x8ql0wj86xn1EWXSaeXt12mFZsV2a1XBWEKvIt+EfodbViaTXoOK/IknJIKLjsfqoEMBDrcPi2eUyL7ntS0NqqhLKXMcbp9ivZWquEn3/ig3pLt+U3PblDIn2v0hbwqmdrbodbj7+b2y+3d3P78XW65amvR9UW5xe/yK9ZvLwwXFIwghVgB4AIAWwMOSJP1oIs8vs5lx34ufyKZ52PJmK/7l9HpoNYGEQVWBCb+8shndQ25ohMCPX9yLlQurQ8kGIHKx5qoCEy5pHuuB7JOAH/zxI1mF8MDLgd5vwQsyp8eP9j4H1pw+G3c++3GokvrppYvxnXHJgF+/04Y1p8+OmI6gvc+O2lL5TeVgMqHIYoiYskjpok1pZMXalgY88XZgVEqwDMF1FcYnKq7/zXt4bu0ynFJXqviZq2XYfzZNnWXQ6rDt3X0RcVpTMhcP/XU/7ljdhL4eF9Yur4dfAuZW2HDfi3sj4u3hK06C0+vHNY/viPhuH3h5H64/sx6bXtmPs+ZX4MMjA7L4Co+BW57aFUpoKN3omOh3mU2fNRHlLptRHzEdhRBAnlGv/IQskmfUY+ehHjx65cnoGXahLM+IP77bjpNrizNdNMqAQrMeV5w6M6LTTIFZ3uz3+CQ89Nf9oXM2EJiG8kcXLgxtMzCiPEJicMQre6w8ymjmsrDRQ3qtNmKtutuf3S27AO4adOHFXaO9Yh0eFFr0eOyNgygZN5/5vHIb7rqgCbc8JR8dOq8iP7RNMhMSiSw6TgFmvU423RkQmO4sFT1GKXcUWZXrrUJLam5XpHt/lBvSXb+puW1KmRPt/tCNK+YnfV99DneUkbQcuUGx2UzK9ZttkvVbzp69hRBaAA8COAvAYQB/F0I8LUnSx4m+xqxSK/7plFm4MayH++2rGjHgdKPEmoeSPA167W5UmkywGH0YcfvR1jsCIRCxIHgwcVBkMUQ0pG5deUJolEaQ0+OXfdEmvQbTCs2hsgS32dM5GFGZrFxYHUqABLd74OVAj/nxyY1gMqFjwIkn3h4bur+svhQn1xYntKDixu3yRExwxMdkEhVqGPafbVNnGXUaXHbyTFmc3rm6CYMjXrT1jmBoxAsJImoyAgh8L26vFDGNRfh36/T64fT48Vm/I7SWjOJ2YbF7vN9ltn3WRJS73D4f7G5fRMcAtwoWsK8psmBpXRmufuzvnAZwCoiX9NcIoTgt1RNXy3vRDbu8uKy5RtaxZW1LA4ZdY4mLfLNO8VyePy5REm0ecqtxbB7ynmGXYruw1z42uqPArMPSOnmvWKXEzJGBEThcXtl0WQ6XF0cGRjBrtI2ZqoTEuOVGaJxBp0cxroZcvNFB0emERrHe+s21X8iJ/VFuSHf9pua2KWVOtPtDRm3yR3TbjMrtxPD2H5ESl1e5fnNNsn7L5fkKlgLYL0lSqyRJbgD/A2D1RF9Epw3MM3x9Sz3WnF4HnVbAotdhxOuFQaeBRa/DoMsLSMCRfodspfegjgEntu5oxy+vOhn3XLQgoiF157Mfy0ZyBJ+vCbtBfOvKEyD5/REXhcFRIeG0GihePDrc8l52wFgyIVjOB1/Zj4dfb0WZzah48RctYRGsJ8N7xYW/dvj7inVzO9jLLvxzzLZh/9GmzjrUa89IeZxeD4yjC+IF49So16DMZoBJr0GBRY+f/uXTiGTEhUvGYs6kDyTqlL5bMbrAnjQaaxaDLup2wdeSpLGk3vF8l9n2WRNR7vL6oXijw+eP88Qs0N7vCPVgBxCazrK935HhklGyBZP+5218HV/7xTs4b+PreGF3J/z+sTvux6KMtjg2rhddvkkvG2kcbB/km8Z6TOk0gelOw8/l68+aA924NmLPsBNmncDmy0/CA19djM2XnwSzTqBneCxxUWFTbheWhbULdVrlG466cRfknw+M4O7n92Ljy/uxaft+bHx5P+5+fi8+HxiRbRdca+6UutLQ2nOTkcjnTgEF5ihxxZ7GFMOxKOvoDIyk5qZxuvdHuSHd9Zua26aUOU6vV/H+kNMXeT/weOWZdFi3XH7PZ93yBthMOduPnpIkWv3mnWT9lssRVw3gs7D/HwYwoa4Yh3rt+N7vPpA1fEyjlcSpdSX4rG8EXr8Eg1YDt9eHaYUmrD9rDn79TlvEFE/rz5qLf9v2Ib51Rr1iQ6qmyBLKeJr0GtyxuhHTi8zwS/WQJGDT9v246KTpEVnRZz44gh+e34gfhs1NfPLMYsXsaU1x5E3liQ7ZjzayYvm8cnxxdomsV9xkpgNQw7D/rJs6S9Lixt/vjPhOtv7zKbhzdVNCCakN5zeid9il+N1qBLC2pQFbd7Tj/ksXoyLK1BPB5EdwzY1gUm/z5c3Qa8WkppTKus+aiHLWiNsbpWNA9veOY105dSSyNplJr1U8T5sM8l50AyPKnRoGRtyh//fZPfjl/zskWz/rl//vEGpLGmXPK7KYsOHpscUrJQl49sMj2PjVE0PbaDTAhvMbI9fTCMtbuDyRHXkCo0vljw05lY/XYWdqjleuCZe4gShTVPCmMcViMmgU6y2DLjV9MdO9P8oN6a7f1Nw2pQySNLjx9x9G1G+Pp2AdDK9PgtWglY2ktRq08PrY+YNii1a/TXZNoVxObiRECLEGwBoAqKmpkf0t2s0CvxSYg9is10KnFTDptDg65IJBp0FdqW50cUQJv7i8GQ63Fw0VNvTaAwstWqIM2+ocdOKa0+qg1QBfmFWMn770CU6ZXYaHX28Nbbtt52GsW94gm9LqsuYa/OadNjx+1VJIkFBuM6GmyKKYVJhVGplUmGgyIVrCYkF1YcRzJpuoCPayy9aLxXRPnRUrRgGgO8o0D58dG8GMIiM0GuWbHPXlNlzfUj86yqIdV3xxVkR83f2VBZhTkQenx4cVTZWhxNT4GLjnooWoLjThoiXVqCmyYElNUVKSU2qYpowC4sUpUTaIFaclVuXEbfG4uf6zEevK3BGvLk0kkWXQiojz+brlDTCMW5S72GpQjJsiy1jMVxYoL8pdmW+UvVZjVT5uaGmIWAOjsaogtE2B2YBtO9sj5oC+9+LFsv0plWn8IuCJLCieTEwgysWK06IocVVo4cgNis6o1SrWWybd5Kc2iRWnqdgf5b5U1G+52jalzIl2f6g7bDTtRMSK0c8HnPjvv7biwiXTIQTg8wP//ddW/OC85K/vQbkl2fVbLic3jgCYEfb/6aOPyUiStBnAZgBobm6WpRej3SzQiMAFoccX6P3e73DCPNob7pandofWMTDpNfjfG8J70mnwi9cO4I5VjbgtbE7idcsbsOWtNvQ73LjnooU4uaYY9168GH12FxrK83DTtkDWtd/hxqxSK9Ytb4Dd7YMkAU+8HXhemc0ou7iaSFJhIsmEiSYssj1RMRnJXKAyEbFiFAAqbMqVQoXNiD6HBzNL9BExd8eqJvzkxb1o6x0J9Jxc2Yg/7jyMU2eX4JFvNMPt9aOm2IpZpcrfbbwYSNZ3nu7PmiYvXpwmbT9+P9ra2gAAvtH5GLXaQP1bW1sb+p1ISaw4tZk0igsU55uyvxcn68rcEfecn0Aiy2LUKvais4wbuWEzaXHn6ibc+qexmL9zdRPyzWPbnVCZr3hcnBCWtAAAnU6DCxZVo6E8D50DTlQWmNBYVQBdWC/o2hIrrj5tdsw4TTSWEy1XsjCBKBcrTgvNynFVZOH5maIryTMo1lsleZO/iRsrTlOxP8p9qajfYsVpsVW5bVpizf62KWVOrPtDkxErRquidYIpmJrtI0pcSV5y67dcTm78HUCDEGIWAkmNrwL4+kReoLbEip9cshjf/d37skREdZEZbp8XPl9gMcZhtx9FFi20GoF+R2Ao//jREuEXa796uw2bvnYivH4JeSYd8gw6LJ5RKJu2J3hzeIlfwoLqgtBN5JoiC17c0xX3oi+VSYVcTFhMRLZNndVYacMdq5pw29O7ZMmLYqsWfQ4XbvzdLlx3Zh1+eeXJ6Bl2oTTPCItR4IHLTsRn/SMQADa/dgCfHh3GJSfX4NS60qwZXZNtnzVlnqP/KG7ZdhjF03rRc+AjaMwFKJ5WA3tvJx6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\n",
            "text/plain": [
              "<Figure size 1620x1620 with 90 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "pQUAPplNAUXG",
        "outputId": "6e21b554-63a6-4ea7-dcdc-1f220be51a05"
      },
      "source": [
        "corrmat=final_dataset.corr()\n",
        "plt.figure(figsize=(16,16))\n",
        "g=sns.heatmap(corrmat,annot=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6H1NpNfKAUZN",
        "outputId": "efebcc39-acc5-4eda-d4e3-a27acd01a628"
      },
      "source": [
        "final_dataset.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "      <th>no_year</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Selling_Price  Present_Price  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0           3.35           5.59  ...                       0                    1\n",
              "1           4.75           9.54  ...                       0                    1\n",
              "2           7.25           9.85  ...                       0                    1\n",
              "3           2.85           4.15  ...                       0                    1\n",
              "4           4.60           6.87  ...                       0                    1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWmMKQ8jAUbI"
      },
      "source": [
        "X=final_dataset.iloc[:,1:]\n",
        "y=final_dataset.iloc[:,0]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "HuJnA5zlAUf_",
        "outputId": "ee6f1959-e49b-41dd-dda4-cae592e2960c"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "      <th>no_year</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Present_Price  Kms_Driven  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0           5.59       27000  ...                       0                    1\n",
              "1           9.54       43000  ...                       0                    1\n",
              "2           9.85        6900  ...                       0                    1\n",
              "3           4.15        5200  ...                       0                    1\n",
              "4           6.87       42450  ...                       0                    1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rFCICJYAUh6",
        "outputId": "942b1a2b-2ec9-4867-9917-7bf90773cd93"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1M4PQ15EKXnw",
        "outputId": "efdc30f4-01b3-4f3f-e1bc-c79a683cc004"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "\n",
        "model=ExtraTreesRegressor()\n",
        "model.fit(X,y)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
              "                    max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                    max_samples=None, min_impurity_decrease=0.0,\n",
              "                    min_impurity_split=None, min_samples_leaf=1,\n",
              "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                    n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                    random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aAIKGz0KYXT",
        "outputId": "9d4e97b4-3f59-4ef5-b43b-96d6478752e9"
      },
      "source": [
        "print(model.feature_importances_)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.37057589 0.04407668 0.00149479 0.07488275 0.23545425 0.0084538\n",
            " 0.12649047 0.13857136]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Z7WFbl3nKYdG",
        "outputId": "4a9e39b3-2d53-449f-d6a5-df65cdc6da1b"
      },
      "source": [
        "#plot graph of feauture importances for better visualization\n",
        "feat_importance=pd.Series(model.feature_importances_,index=X.columns)\n",
        "feat_importance.nlargest(5).plot(kind='barh')\n",
        "plt.show()\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ca0Y3-zRlmI"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,shuffle=True)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfawAacgRlof",
        "outputId": "29b0cd74-8cd2-479d-d1d7-082c045346f4"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(210, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-N0RQWiSSwg"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_random=RandomForestRegressor()\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cdnU00zSSy4",
        "outputId": "8fb97c5e-d7db-440c-88dc-60abbe48b045"
      },
      "source": [
        "#Hyperparameters\n",
        "n_estimators=[int(x) for x in np.linspace(start=100,stop=1200,num=12)]\n",
        "print(n_estimators)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2iA72FRSS2-"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a4rgy9uRlqi"
      },
      "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]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plVugu_q4XZ-",
        "outputId": "95a42137-ac52-4ca9-a02d-3ccf44e0a891"
      },
      "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)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTh8ZVFO4Xb8"
      },
      "source": [
        "# Use the random grid to search for best hyperparameters\n",
        "# First create the base model to tune\n",
        "rf = RandomForestRegressor()"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YopI_h0q4Xdv"
      },
      "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)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KPqLGAi4Xf3",
        "outputId": "c7495336-b602-4af4-8880-2039bc0ca3b1"
      },
      "source": [
        "rf_random.fit(X_train,y_train)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
            "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
            "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
            "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
            "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
            "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
            "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
            "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.1s\n",
            "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
            "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
            "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
            "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
            "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
            "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
            "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
            "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
            "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
            "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.4s\n",
            "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
            "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.4s\n",
            "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
            "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.4s\n",
            "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
            "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.3s\n",
            "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
            "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.5s\n",
            "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.4s\n",
            "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.4s\n",
            "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
            "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
            "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
            "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.0s\n",
            "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
            "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
            "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   47.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100,\n",
              "                                                   n_jobs=None, oob_score=Fals...\n",
              "                   iid='deprecated', n_iter=10, n_jobs=1,\n",
              "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
              "                                        'min_samples_split': [2, 5, 10, 15,\n",
              "                                                              100],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yqek9yB4Xh0",
        "outputId": "d04ff2f9-49c0-4551-9ad7-564ec01f3a80"
      },
      "source": [
        "rf_random.best_params_"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 25,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 1000}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nStpjAfnRlsx",
        "outputId": "3fc6eae3-d40a-4114-a567-1049802a892b"
      },
      "source": [
        "rf_random.best_score_"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-4.616899989786352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1RULrTd44XP"
      },
      "source": [
        "predictions=rf_random.predict(X_test)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "hBJnTpkk44Zd",
        "outputId": "82ec94ed-4877-468a-ab98-f9fd8b33483d"
      },
      "source": [
        "sns.distplot(y_test-predictions)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f52ba93f610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IV3ofjyH44bu"
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29BG3Jjb44fM",
        "outputId": "0169dc2d-236d-470d-aecf-e13b031e6ea1"
      },
      "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)))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 0.7623182417582471\n",
            "MSE: 1.8408132016769367\n",
            "RMSE: 1.3567657136281623\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_quwL6k5Edx"
      },
      "source": [
        "import pickle\n",
        "# open a file, where you ant to store the data\n",
        "file = open('random_forest_regression_model.pkl', 'wb')\n",
        "\n",
        "# dump information to that file\n",
        "pickle.dump(rf_random, file)"
      ],
      "execution_count": 50,
      "outputs": []
    }
  ]
}