Car_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "CarDekho_DS_Practice.ipynb",
      "provenance": [],
      "mount_file_id": "19F4KE6qSYEcE5iG-Vo5sVqDWocX-2Ygr",
      "authorship_tag": "ABX9TyO+YNI3lZ9qmyE7GLrsmAOK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amangiri99/CarDekho_Car_Prediction/blob/master/Car_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": "AIe5n1-OIaUt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "plt.style.use(['dark_background'])\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set(color_codes = True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bluGheYSJkFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/Data/car data.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0cQNDqCJ0V1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "b86d57d3-6c2a-4db0-ec31-a1b875bab05e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 3,
      "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",
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              "    }\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>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": "markdown",
      "metadata": {
        "id": "vBnhodh0KIag",
        "colab_type": "text"
      },
      "source": [
        "#Investigating the data\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4lqmuCJu8io",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0b377bdf-c44d-4188-a323-ff8f3bf4f4b7"
      },
      "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": {
        "id": "IywpICJZJ14g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6d7935f8-1f53-49bf-eb7f-8ab96d2ed86c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    object \n",
            " 6   Seller_Type    301 non-null    object \n",
            " 7   Transmission   301 non-null    object \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 21.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lF0J_bbvi1jG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "40d5e60b-3271-4465-f8b8-8ca81d857ead"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    object \n",
            " 6   Seller_Type    301 non-null    object \n",
            " 7   Transmission   301 non-null    object \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 21.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtzbrm21vK83",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "487ff8c8-de2a-4900-9ef9-0e82116f977a"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>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": {
        "id": "tHLL7PCuJ9rY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "5e66917f-1194-4764-8d94-deeca57c2d07"
      },
      "source": [
        "def checkUnique(row, column):\n",
        "    print(column +\":\")\n",
        "    print(np.unique(row))\n",
        "    print()\n",
        "\n",
        "checkUnique(df.Fuel_Type , \"Fuel\")\n",
        "checkUnique(df.Seller_Type , \"Seller Type\")\n",
        "checkUnique(df.Transmission , \"Transmission\")\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fuel:\n",
            "['CNG' 'Diesel' 'Petrol']\n",
            "\n",
            "Seller Type:\n",
            "['Dealer' 'Individual']\n",
            "\n",
            "Transmission:\n",
            "['Automatic' 'Manual']\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29-YN_zGg5Bg",
        "colab_type": "text"
      },
      "source": [
        "#Visualization\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1hnkyd_bOuC",
        "colab_type": "text"
      },
      "source": [
        "Distribution of Selling price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzUdOQtFFIwA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "e4eb40e2-f1fe-4d54-d548-f66df0cf16a0"
      },
      "source": [
        "sns.distplot(df['Selling_Price'] , kde = True);"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f/vhIocsRkQJR+M/S6YERhhKpBTmsw3TWN1WRzsBLe3MbL0hElh6F/yyNHextWGTdPuCM+Lm8poTnX28nreGeRTxJ4T9LY6d5LrZunzEbmqo5dWaYfcd6Cl2KiBSAwn+W2mNxSsJBqspChS5lVppXlFNeUsQzO9sKXYqIFIDCf5baY4M01JXi8/kKXcqsBPx+rr54ObsOnaK3P1HockTkPFP4z9JiGNBtOm+7tJ50JsOzr2nrX8RrFP6zMDA0Sl98dFGe6ZMtWl3Cxatr+OmuNlLpdKHLEZHzSOE/C2fP9JnlsA4LyTsua6S3P8HrR3TgV8RLFP6zcPZMn0W+5Q+weV0dVeUhntl1stCliMh5pPCfhfZYnFDQT6SquNClzFkw4Of6TQ28fiTGqTNDhS5HRM4Thf8stMUGWREpxb9Iz/QZ7/rNDeCDn+3WgV8Rr1D4z0L7qfiiP9ibLVJVzKYLIvxsdzvJlA78iniBwn+GhkeSxPqGF91ontN5x2WN9A2OsOvQqUKXIiLngcJ/hjp64gCL4taNM3HpBREilWEd+BXxCIX/DLWfcsJ/KZzpk83v93H9lkb2Heul013BicjSpfCfobbYIAG/j2U1JYUuJe+u21RPwO/T1r+IByj8Z6jt1CDLakoIBpbeoqsuD3PZhijP7m5neCRZ6HJEZB4tvQSbZ+2xpXWmz3g3bWsinkjy7GvthS5FROaRwn8Gkqk0Xb1D1C+BYR0ms7axinWNVfxweyvptG70IrJUKfxnoLMnTjqTWdJb/gA3XdnMqTPD7DjYXehSRGSeBHNpZIzZADwCRIAYcLe19tC4NgHgfuBmIAPcZ6192J33GeATwNglpM9baz+Zjz/gfGqPLc0zfca7bH0dy6pLePrlFi430UV7zwIRmVyuW/4PAg9YazcADwAPTdDmLmAdsB64BviMMWZ11vyvWGu3uD+LLvjBOdjrA1YssQu8xvP7fdx4ZRNvtPVhW04XuhwRmQfThr8xZhmwFXjMnfQYsNUYEx3X9Hbgi9batLW2G3gcuC2fxRZaW2yQSFUx4aJAoUuZd9deWk9VeYjvPH+00KWIyDzIZcu/CThprU0BuI9t7vRszcDxrOct49rcYYx5zRjzA2PMNXOouWDaY/Eld2XvZEJFAW65ehUHWk5z4HhvocsRkTzLqc8/Dx4EPmetHTXG3AA8YYzZaK2N5foGkUj5/FWXg1Q6Q2dPnMs3LicarZi0XWlpmIry3IZ6LioK5tQ213YzbVtaGiZaO3kX1vvebXj6pRa+93Ir113RnNN7TmWq5SZaPlPRspnabJZPLuHfCjQaYwLW2pR7YLfBnZ6tBVgFbHefn90TsNZ2jDWy1v7QGNMKXAL8NNdCY7GBgp562NUbZySZpqa0iO7u/gnbRKMVxOMJ+geGc3rP0dFkTm1zbTfTtvF4gu5Uaso2N1/ZzGM/OsSzr7Rw4aqanN53ItFoxaTLTbR8pqJlM7XJlo/f75tyo3nabh9rbRewC7jTnXQnsNPt18/2LeDDxhi/ezzgVuDbAMaYxrFGxpgtwGrATvfZC0nb2Jg+S6jbx+f3MZhITvlzxUXLqCoL8e2fHiGT0Xn/IktFrt0+HwMeMcbcA/QCdwMYY54C7rHWvgI8ClwFjJ0C+llr7djRwnuNMZcDKWAE+ED23sBi0NrlrFkbl1D4J0ZT7M7hXP6L1tTywp4Oth/o4sqNy89DZSIy33IKf2vtAZxgHz/9lqzfU8DHJ3n9B2db4ELR2jXAsuoSSsLn6zDJwrG2sZLjHf18+5kjXLY+SlFQ1waKLHb6X5yj1q4BmpYV9qBzofh9Pm69fg2nzgzzo1dPFLocEckDhX8OEiMpunqHPBv+ABtX1XLJBbU8+fNjnBkcKXQ5IjJHCv8cnOgeIAOeDn+AO39xPSOjKb7540PTNxaRBU3hn4PWrgFA4V8fKeM9V6/ihb2d7D/WU+hyRGQOFP45aO0aoCQcJFKV28VTS9l7r1lFtLqYR39wkNFkutDliMgsKfxz0No1QFO0TKNb4gz7cNcNho6eON/9+bFClyMis6Twn0Y6k6G1e4CmZbq8fMymtRGuuXg5T714nOMduvJSZDFS+E/j1OkhEiMpmpZ7u79/vDvfvYHykiK+9NR+kil1/4gsNgr/aehg78TKS4q4+2ZDa9eAun9EFiHvXa46Q61dA/h8eGYo58mMjQOUbUNzDdsuXMZ3f36MdU3VrKmvBCBcFEQXAYssbAr/abR0DrCittQTN3CZymTjAK1trGT/8V7+6Ym9vPftqwgFA2zbuJygB4fBEFlMtH02hUwmw+GTZ7igobLQpSxYoaIA125aweDQKC/v6yp0OSKSI4X/FDp7hxgYGmX9yupCl7KgLasp5dK1Ed5o6+PIyTOFLkdEcqDwn8LhE06QrW2sKnAlC9+mtRGW15Tw0r5O2k8NFrocEZmGwn8Kh0+epqw4SH1k8lsdisPv93Hd5gaCAT///B/7GB5JTv8iESkYhf8UDp04w9rGKvy6sjcnpcVBrt/cQFfvEP/8H/tJ685fIguWwn8SA0OjtMfirFOXz4ysiJTyK9ddwKu2myefP1bockRkEjofbxJjBy7Xr1T4z9S7tjbS3RvnieeO0lhXxhUXLit0SSIyjsJ/EodPniHg97G6Xqd5zpTP5+Pumy6koyfOw9/dR3V5mGhUYyOJLCTq9pnE4RNnaF5e7vmLu2arKOjnU+/bRE1FmH/49m6Od/QVuiQRyaLwn0AyleZoex/rGnV+/1xUlob4/du3EAz6+dN/eoHu00OFLklEXAr/CdjW04wk01zYrPCfq7rqEn7//VtIjKS472s76OyJF7okEUHhP6FdB08RCvq5aE1toUtZElYuK+feT7ydZCrNfV/bwUldBCZScAr/cTKZDDsPd3Pxmlr19+fRmoYq/vA3tgLwl4++yj7dA1ikoBT+47R0DtDTl+Cy9dFCl7LkNNaV8T8/cDk1FWH+/pu7eWbXyUKXJOJZCv9xdhzsxueDzesihS5l0Rob+z/7p6snzmAiSUlJEb/7/s1saK7mK09bvvikhoIQKQSd5z/OzkOnWL+ymorSUKFLWbQmGvu/oryY/oHhs88v3xClKODnxX0dvNHex0d++aKzN4MRkfmnLf8s3aeHONE9wGXr6wpdypLn9/vYsr6O/3rbZhIjSf7iK6/wtR8epHcg8Za9hrGfpG4VLJI32vLP8soB52YkCv/zp3lFBTdf1cyrtpsfvXqCl/Z1crmJ0ry8HN+4AfV0hzCR/NGWvyuZSvOjHScwTdUsq9EQzudTqCjANZes4MZtTQQDPn66q42nX2rVRWEi80jh79p+oIuevgQ3X9Vc6FI8a0WklPe+fTVXX7yc/vgI33uxhZ/taqM/PlLo0kSWHO1D45zb//RLLdRHnNsRSuH4fT42NFWzpr6SPUd72He0h+Od/aypr6RpWQVrdT9lkbxQ+AP7jvfS2jXAb73nQt24ZYEoCvq5bH0dpqmavUd7OHTiNJ/7yitsXFXDOy9rZMv6OoIB7biKzJbnwz+TyfDUC8epKgtx9cUrCl2OjFNaHGTbxmVcuraW+HCKF/a084XH91ASDrJlXR2Xra9jQ1M1lWXnTs1NpiExmtu1A+GiIMEluA7RMpDpeD78n9l5kv3He7nrhg0U6X/AglUcCnLd5kZuvXYNe47G2H6gi50HT/HC3g4AlteWsjJaRn2kjIrSIjp64pSEAhSHghSHA5PuJSzVM4gSo0m27+/Mqe1SXQYyNU//i5/oHuDrPz7MJRfU8s6tjYUuR3Lg9/vYtLaOTWvrSN6c5lh7PwdPnObIyTOc6Bpgx8FuJrp1cDDgc1YEoQDFYeexrDhIKp2hobaUSFUxNRXF2gAQz/Bs+CdGUjz0nb2UhAL8zi9dpL7+RSgY8LNuZRXrsm61OZpM09Eb56W9HQyPpBgaSTKcSDE8kmJ4JMnwSIrBoVFOnR5ieCTF7sOxN71nVVmI2spiIpVh97GY2spiaivDhIsChIJ+QkUBioJ+ggE/qXSaVDpDKpUhlc6QTKUZTWb/pBgdPy2Vxu/3EfT7CQadx0j3IIn4COFQgOJQgHCR+xgKEPA7K6RMxvmMsc8ZGk4yOJwknkgSH04ST4wSH04ylEgykkzTHhskGPBTEg5QEg5SVRaiJBx8y/UT4k2eDP9Tp4f4/L+9Tlv3IL/3/s1UlWkoh8VgbMyg6VRXFFNXXTJtu1QqzdqV1QwNjRLrS9DTN0ysb5ievmFOdA/y2pEYIwvgsuJgwEcmA6n0BLs0M1QU9BOpLCZaU8Ky6hKiNcV5qFAWo5zC3xizAXgEiAAx4G5r7aFxbQLA/cDNQAa4z1r78HTzzqd0JsPOg9088rQllc7wu7dt5tILdGrnYjHRmEET2bwhtxFZAwE/0eoSypZPfH/hTCbDwNAoPX0JevsT9A2NcLDlNMl0mlQqQzqdwef34fc5p6j6/T78Ph+BgI91K6tp6egnEPAR9DvTAn4/fr+PgH8szNOk07CuuZrjHQP09Q8zmkqTdPcOkmd/z+BzP6NpeTkloSBBv4+S4iBlxUWUhoOUFgfdxyKKwwHiiSQv7+8kmUw7ezvDo5wZHOF0/winzgyx50iMDOADnt3dzoamatY3VrGusYpIVbH2Djwg1y3/B4EHrLVfNcb8JvAQ8K5xbe4C1gHrcVYSO40x/89ae2yaefMqnc5wonuAA8d7+cnOk3T2DlEfKeVT79vEilpdyet10+1N+AN+6mpKqKspIZ1xvk+5uGRthFQqt72GhroyhkfSlIamP96wbeNyynI4OOvzOSuiUFGAUFGAyrIQ9ZGys/NHk2m6Tw/R7XZ//XxPBz/Z4QyxXV0eYm1jFfWRMuprS1kRKWV5TSmlxQu/oyCdzjA0kjzbJdY/5Kz0hhKjxBMphobdR/f5yGiKZMpZCY8mUyRTzr/v2MrW53OWZajIT7jI6Y4rCQcpCQcpLnK65UrCQcqKnRVwWXHRmx5DQf+CXZFO+69pjFkGbAVucCc9BvyjMSZqrc3eDLsd+KK1Ng10G2MeB24D/maaedMJgHOgb6aOd/bz8JP7SIymAGhaVsFv3LCBSy6oPduPmm/BgJ/S4qK8tp2P9zzfbUvCQVLJt76+0Msrlc6w/2huN5bZuKZ23pbXZMtnora5/F/I5fOrysOsW1nN5nV1hIM+2mNDHO/s51hHP62d/Ww/0Pmmg+el4SAlxUFKw0WUFAcoCxdRFPQTCPjOHsMI+H34/Th7OePKzH6vzFS/Zc49pNMZQqEgfYMJ0qkMo6k06XSG0VSG0WTWsZxEiqFE8uz/9cn4gGCRn1AwQCjgJxT0UxwKUFUWYnA46Rz78zkfnsHZ+xvbS0um0gyNpEiMpug+PUQimWJ0dOoVfCDgoyRcRKl73KU4FCQU8OMP+AgGnL3AQMBPwO8n6O4ROnuWaZLpDH4fvPuKJqrLw1N+zkTfiaxpE96VKpdVeRNw0lqbArDWpowxbe707PBvBo5nPW9x20w3bzr1ADU1ZdO1e4tIpJwvXFQ/49fNxcr6KlbWV03f0HXBypq8tlPbhfH589k2VzP5HgJEo5VsunB53uuQ+ReJlE81ux44Mn7iwt+Pg+3AdUA7MPVqXURExgRwgn/7RDNzCf9WoNEYE3C3+gNAgzs9WwuwKuuDsrf2p5o3nQTwXI5tRUTknLds8Y+ZtuPbWtsF7ALudCfdCewc198P8C3gw8YYvzEmCtwKfDuHeSIicp7letTzY8CnjDEHgU+5zzHGPGWMucJt8yjwBnAIeBH4rLX2aA7zRETkPPNlJroWXkREljQNZCIi4kEKfxERD1L4i4h4kMJfRMSDFsNFXgteLgPfeYkx5m+B9wGrgUuttXvc6Z5fTsaYCM7Zb2uBEZwz4D5qre02xlyNM25WCXAM+E33VGvPcId+WQOkgQHgU9baXfruvJkx5k+Bz+D+/5rNd0db/vkxNvDdBuABnH8EL3scuJ63Xsin5eQMGfPX1lpjrb0U5yKc+4wxfuCrwCfd5fMz4L4C1lkoH7TWbrbWXgb8LfAld7q+Oy5jzFbgatz/X7P97ij85yhr4LvH3EmPAVvdi9k8yVr7nLX2TVeAazk5rLU91p1iYRIAAAVUSURBVNpnsia9iHP1++XAsLV27Gr2B4H3n+fyCs5aeybraRWQ1nfnHGNMGGfl9/GsybP67ij85+4tA98BYwPfyTlaTuO4W2wfB77DuCFPrLWnAL8xprZA5RWMMeZhY0wL8Dngg+i7k+2zwFfHDYc/q++Owl+kcD6P06/9j4UuZCGx1n7IWtsM/DG5DfvuCcaYa4ArgC/k4/0U/nN3duA7OHvXsokGvvM6Lacs7kHx9cDt7n0uxgY/HJtfB6SttbndbGAJstY+CrwTOIG+OwC/AGwEjhpjjgErge/j3Chrxt8dhf8czWDgO0/TcjrHGHMvTj/trdbahDv5VaDEGHOt+/xjOAMieoYxptwY05T1/JeBHkDfHcBae5+1tsFau9pauxpnpXgTzt7RjL87GtsnD4wxF+KchlYD9OKchmYLW1XhGGPuB34NWAGcAmLW2ou1nMAYczGwBzgIDLmTj1prf9UY8zacs1iKOXe6XmdBCi0AY8xy4AmgDOfeHT3Ap621O/TdeSt36/+97qmeM/7uKPxFRDxI3T4iIh6k8BcR8SCFv4iIByn8RUQ8SOEvIuJBCn/xDGNMxhizzv39y8aYv3B/v84Ys6BOGzTGfM8Y88FC1yFLl4Z0lkXHvZjlr4GLcc4H3w/8nrV2+2zez1r7LGDyV+FbGWO+DPwGzjDOIzgXdX3KWntgkpreM5/1iGjLXxYVY0wl8F2ccXFqgUbgz4DEVK9bIP7aWluOc1l+F/Dl8Q2MMT53wDeReaUtf1lsNgBYa8eG9x0CfjA20xjz28Af4Fxd/DLwEWvt+PsKvIkx5h04IyWudJ8fwxls7W6cMVOexhlnftid/4fAf8MZm/8e4IvAemvt4Vz+AGtt3Bjzr8A33Pd7BngeeAfO0MWXGmMedmt62G3zYeC/46w4WnGu4NxhjGnAWRFejzNI3N9ba+/PpQ7xNm1hyGJzEEgZYx4xxrzHGFMzNsMY8ys4I0H+GhAFnuXcGPAz9X7gZpy7Sm0C/rP7GTfjhPC7cQbUesdM39gYUw7cBezMmvwB4CNABeNugmOMuQ3nrk13A5XAfwJi7h7Ck8BunD2gXwR+zxhz00xrEu9R+MuiYq3tA67F2er+ItBtjPmOOy7Mx4C/tNbut9YmgXuBLcaYVZO/46Tut9a2uSMjPglscae/H/gXa+1ea20cJ5Rz9WljzGngMFCOu0Jxfdl9z6S1dnTc6z6E02W03VqbsdYedvdmtgFRa+1nrbUj1to3cJbJHTP9Y8V71O0ji461dj/ntsQvxLmF3f/B6aL5B2PM/85q7sPZKp6y62cCHVm/x3GGEMZ9fCVr3kyGFf5ba+3/mmTeVO/ThHO7x/FWAQ3uCmVMAGePR2RKCn9Z1Ky1B9wzaT6KE6Cfs9Z+bR4/sh2n331Mvu4mNdUIi604N3yfaPpRa+36PNUgHqLwl0XF3dL/JeAb1toT7vjvd+LcC/cp4M+NMbustXuNMVXAjdbafI6L/03gS8aYR3H2Jv4kj+89mYeBvzPGPAfswFkRjOIc0O43xvwRcD/OKaQbgZLZnvYq3qE+f1ls+oGrgJeMMYM4ob8H+H1r7b8DfwV83RjT507P6/ny1trv4QTtT3D67l90Z83bqabuyutzwL/i/P2PA7XuvWzfi3M84ijOvRMexrnxuciUNJ6/yBwYYzbirGTC7kFmkUVB4S8yQ8aYX8XpYirFubtU2lp7a2GrEpkZ9fmLzNxHca7OTQE/BT4BYIzZS9aNtLPbz/NBaJEZ05a/iIgH6YCviIgHKfxFRDxI4S8i4kEKfxERD1L4i4h4kMJfRMSD/j/9Rxs6jUHkRgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_Yp6r49bXBH",
        "colab_type": "text"
      },
      "source": [
        "As the data is a bit right skewed we apply log function to get it into a normal distribution form\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nREDL1TWE3e_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "48e2af7a-34ee-4c7c-c05b-188e4a3a497a"
      },
      "source": [
        "sns.distplot(np.log(df['Selling_Price']) ,kde = True);"
      ],
      "execution_count": 10,
      "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": "markdown",
      "metadata": {
        "id": "-9wIqLVybk1B",
        "colab_type": "text"
      },
      "source": [
        "Changing the Selling price data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqCBHrbfFNJz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "6fe6d0d4-ada6-460c-c492-ef455e06695d"
      },
      "source": [
        "df['Selling_Price'] = np.log(df['Selling_Price'])\n",
        "df.head()"
      ],
      "execution_count": 11,
      "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>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>1.208960</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>1.558145</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>1.981001</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>1.047319</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>1.526056</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       1.208960  ...       Dealer        Manual     0\n",
              "1      sx4  2013       1.558145  ...       Dealer        Manual     0\n",
              "2     ciaz  2017       1.981001  ...       Dealer        Manual     0\n",
              "3  wagon r  2011       1.047319  ...       Dealer        Manual     0\n",
              "4    swift  2014       1.526056  ...       Dealer        Manual     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onofPGi6rOcE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "086cb626-1f79-4e33-aabe-79c16bcf41b4"
      },
      "source": [
        "#Seeing the trend over time between Selling price and Year.\n",
        "\n",
        "sns.lineplot(df.Year , df.Selling_Price);\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(10,6);"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "we12-YFObsvf",
        "colab_type": "text"
      },
      "source": [
        "Distribution of Kms_Driven by cars over the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWLKN_o-GHLw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "862af3c9-3c6b-4663-8b51-94750b130d73"
      },
      "source": [
        "sns.distplot(df['Kms_Driven'] , kde = True);"
      ],
      "execution_count": 13,
      "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": "markdown",
      "metadata": {
        "id": "dKh1Q4ncb1YI",
        "colab_type": "text"
      },
      "source": [
        "Applying log function to get it into Normal-Distribution form. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uC8qkorGSH2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "8bcf90ef-f079-428e-b4af-b6aa43731ae1"
      },
      "source": [
        "sns.distplot(np.log(df['Kms_Driven']) , kde = True);"
      ],
      "execution_count": 14,
      "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": "_I3sH0jXGX79",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Kms_Driven'] = np.log(df['Kms_Driven'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMjJq9x4nTiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "6c6a9d89-966e-47ee-ed3e-4d8db9a5d49a"
      },
      "source": [
        "#Bar plot showing the number of vechiles depending upon the type of fuel used.\n",
        "\n",
        "c = df.groupby('Fuel_Type')['Fuel_Type'].count()\n",
        "sns.barplot(x =  c.index , y = c.values);\n",
        "print(c)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fuel_Type\n",
            "CNG         2\n",
            "Diesel     60\n",
            "Petrol    239\n",
            "Name: Fuel_Type, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "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": "Zdz0frH_nXU7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "409dfd67-7152-48c6-efe9-3857dacc4a87"
      },
      "source": [
        "#Distribution of selling price with respeect to fuel type.\n",
        "\n",
        "sns.boxplot(x = df['Fuel_Type'] , y = df['Selling_Price'])\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15,6);"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASRV2Ip1duWg",
        "colab_type": "text"
      },
      "source": [
        "Vehicle with fuel type diesel have selling price higher than petrol and CNG."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QgmS6djnb7m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "0278554b-281b-47d3-97ea-3825a6a60db7"
      },
      "source": [
        "#Transmission of vehicle.\n",
        "trans = df.groupby('Transmission')['Transmission'].count();\n",
        "sns.barplot(x = trans.index , y = trans.values);\n",
        "print(trans)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transmission\n",
            "Automatic     40\n",
            "Manual       261\n",
            "Name: Transmission, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eWfflv4C3Z+YzqaZkZjVxTpXlC8CHgFsys7eh/DPUI//MPAT4FY+9lne4FuvHQ17/EbEQ+CRwYn2dnkZzvxvFMewnx8uBzMwnZeZBmXkQ8JdUYX8HcDhAROwHvKih3cPAXg3b1wCviYh5EdEBnA58Z4x9GZhTHViN9K2D6jcNOmejbwBvH3hxOSL2HeO5Nc1l5l3Ae4APD6raG1hfP3N9KfDUUR6y8fp/MfCEunxP4BHg1xHRCSxttu+lMewnx2nA5Y0FmXk91f/H9cCT6hu9fAb4YcNuXwROioh1EXFqZn4LuKxuc3O9z0fG0pF6Cuh9wI0RsRb43aBdPgF8tz7n3oPqlgHrgXURsY56rl9lyczPZua6QcXvppqnXwccD9w0ysO9Fzirbvc3wN31OW4GVgO3Uf1O/LIVfS+JC6FJUgEc2UtSAQx7SSqAYS9JBTDsJakAhr0kFcCwl1okIjbXa8SMt/2tEfHCFnZJ+iPfeqkpKSI2N2w+DthK9UlLgDdn5uU7tpI0HMNeU169pMTpmXnNEHUzM/PRCe+UNM24mJCmlXqa4zLgQuAfge9ExJlUny5+DtU1/b/A0sz8Vd3mWuAHwF9Qrfh5PXBSZt4fEbOBz1GtBjqDajGuYzLzN3W7NQ3t/odqSYtPA8cCCbw6M9fX5+kHDs7MOyLiZcBy4ECqZS4+mZnL6yUlPg8cSbXo3K3ACzKzr/GPWkTMolrV8fj6W78SODsztzb8DD4JnE31jOfczLyk6R+wdlnO2Ws6eiLVKooLgTdRXceX1NsLgD8AKwa1OQl4A/B4qvXQ31mXv45q7Z8DqZacXlq3H3AC1TLSB1CtzHh9fa59qBawe/8wffw3qummecAzqZayBjiLalGwbqp1X86lWj1ysPcARwCHAM+mumnNeYN+BnvV/XojcNGgJYSlx3Bkr+moD3h/vVwzVOH85YHKiPgo1Si80SWZ+Yu6/kqqG8EAbKMK+adm5k1UN3kZ3O7Out23gKcPTCdFxGp2XABswDbg6RHx08x8EHiwoXw/YGFm3kH1jGMor6W6Ec199bk+CKziT/cr2AZ8qJ7C+mb9GkfjEsLSYziy13TUk5l/XCI3Ih4XEasiYkNEPAx8H9i78f6owK8bHv+eak10qKZ/rga+FBH3RMT5EbFbw76/aXj8hyG25zK0VwIvAzZExPci4rl1+cepVnb874i4KyLePUz7/YHGuzBtqMsG9A56raLxe5J2YNhrOho87XEW1aj2OZm5J9UdkAA6RjpQZm7LzA9m5tOpbr5xDHBqsx3MzBsz8+VU00Zfo5pzJzM3ZeZZmflkqmcX76iX8h3sHqppqQEL6jJpXJzG0a5gHtUo+7cRsQ/Dz6PvICJeRHU/09uoXkjdRjVNNG71+v6vBr6RmQ/Vzzb66rpjgJ8DdwIPUb24OtT5rgDOi4gbqf64vY/qRVlpXBzZa1dwATCHKrRvAL49hrZPpLpT18NUL7h+j2pqp1mnAOvroF9KNQcPcDDVTWc2U73Ye3FmDn59Aar7EvyIah34m6nu6TqmexVIjXyfvSQVwJG9JBXAsJekAhj2klQAw16SCmDYS1IBDHtJKoBhL0kFMOwlqQCGvSQV4P8B038vyk94RbMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4YjZwWPnjS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d977dad6-1f24-4450-dd9b-d5442585b773"
      },
      "source": [
        "#Distribution of selling price with respeect to owner type.\n",
        "\n",
        "sns.boxplot(x = df['Owner'] , y = df['Selling_Price'])\n",
        "plt.legend(['First Owner' , 'Second Owner' , 'Test Drive'])\n",
        "plt.xticks(rotation = 90);"
      ],
      "execution_count": 19,
      "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": "vfNJDpQGzMUe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "3848de10-47bd-4126-d6c5-6eada532257d"
      },
      "source": [
        "#Joint plot for relationship between km_driven and the selling price of the vehicle.\n",
        "\n",
        "sns.regplot(x = df['Selling_Price'], y = df['Kms_Driven'] );\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15,6);"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hau3wte+dxcZmAUIoUBUFjgQ2Ngv40rcNzEzE87jL8lnqF10J9gzDOAEAuq5Hup36x3YpCdsu77MXGER3F69Gd167t+njz50PAj0AUAVgO+7trQZ77dweGglaOvG7r5e1Lt+P+yfnv3v/TW3d55S/zuWlTZw5v4yxTBJjw0kePyq4vJgFIOBt4lAEYNrA1dU8Egm1oWk2unU8OXRwGucurm7JuPM7pbjhmD1qyXYpCdsu77PbGER3H69Gd1YntulcwQoCPZ8i3NsbXbfyE+Fmt4dWTqo79buvl6Xs1n68/HU28xYEBDYLNhKahdWsCdNy8DdHX8E/fegd3NcBAASEkBXvifp9dft4cursAp5++RLGRpKY1tIoWA6efvkS9u8e43dKsTIQwd6OHSO9XoXIZhoY8N4PFtcLGB3SIETxRERTBZbWCwP1XuPwPgfp8/Q9+bVTSCVVpJPuriiZUJErWHjyhQsNjRei2sLbzn0zoxgfz+Dr33sNVxazuGYqg49+8Ab8wi07e7iGg6MT2/RQOoFcwYIW2v/YUmIonYi8X/jxq5fxlSdfg6a5c9St50x85cnX8EcfPYQ//ti7qm4PlZZfbVnj45lI21Gnfve7Z0awtLqJdChLmStY2D0zgpmZ0a7tx8tfx7IlVAUwTRtLaw4EBDQFyJt2Q59bPT9+9TK+/r3XcHkxi509/F03sx57rhnG+cvrkACEAKQEJICEJpDQil1Ta31f3T6e8PjVGYN4rtNrAxHsXb26DsepfEUoTmZmRjE/v9br1WirqZEkLi9tYjNvwbIdaKqCoZSGnZNDA/Vep0aSW0qd8qaNyZFkV97nIG47AHBxfh2ZtAbTcoLbFCFwcX59IN9vL1Tadq6bzuB//dihktv4ebdHJ7bpD99xLY4+cw6W7UARgCMBCYkP33Etnnz2jUgZtq8ePw0IQFUUL/hQYAkbXz1+Gp/55Hsqbg/V9ju1lnXddKbu++nU7/6+d+/BI0+cgWXLkizlfe/eg/n5tar78YQq8L9//vttK/0rfx1NFTAtCf8sRQjAkQIJTQACwefWrmxpOqFgfimLv/zai/idD7e3RDXKenzlydcAgYbW4zeOXI+HH3sVuYIN25FQFbdseWQoWbKd1Drudvt4wuNX+w3quU43KIqomvwaiGCvkzieqLabZydw5vwyhFdrb1oOClYev3j77l6vWlux9K0zOH6MBk0ntml/XF752KD9u8cil621szFPeFnZnInVrAnLcnBlMYs/+U8nkSvYNY+XnfrdV2oSc/PsBI6dnMOXj59BOqEgm3NLX/39eDZnAVLCcmTbSv/KjxdDKQ0FKw8AEHCDdUBibDgVfAetliDGZajBsZNz0DQBVVEaWo9DB6fxqQdv2fLdNdIxtdvHEx6/qF90a+qFPwPwUQC7AHxH1/WrhmG8s9rt3VinKDieqL7Tc+6g882CDctyoGkKhpIqTs8t46Fer1wbDco8RnHDIJoGTae26YeOHNjSjOWzjz4f+QS/nSem/rJs28HiWt4NYBw3c3Xx6gamxtz7H378NMaGNORMp2Sf2cnffbhJTKWGLJASmiKCDpyaACwZfVxY1HUAiseLnZND+MXbd+P4c+eRNy0kNAVjwykMpTTkTTcwbjVYi0uX3YWVHMZHkrDsYrVV1PWo1OBn/+6xyMfdbh9PePyiftGtbpyfBvDpqLfHRVyulMXZwkoOY8NJjI8Ux0BIKQeyjTvbY7cfg2gaNI1u061UjzRygt/OE1N/WSvrBbh7fgHAHZcmhIK1rImx4STWsgWsZQtQBCrOm9bp332lYzgAjAwl8Kd/cBgA8JkvPNORIKla4OIHn0lNQd60g+/gy8fPtLQencwyNbKNTo+nsZ4zg8xeq+vRyHG328cTHr+oX7CMs4a4XCmLs+nxNC4tZrdk9nZN1R+30W9Y0tsZDKIH03b+vUTdplutHmnkBL+dJ6b+c/7i6z+F7UgIuFk92wE0VcKyJZbW8pBeckdVi/Omfe17Z4PPp9PbQ5RjeJTPsJFtudZja30H0+NzLQVrncoyNbqNPnB4Fl958jVYojeT1deag7HWd9js/orHL+oHDPZqYD12fTfPTsB4azk4qNsFGwXTxgfftae3K9ZmLOklio6/l2harR5p9AS/nSemhw5OY+dUBhevbkAIBdJxIKXbeTKhKbC8phUiNG+aLYU3n1p3RDmG1/sMo2zLfqDw9sIGcnkbI5kERjOJio+t9h20GqyFA8m3FzZg2xKaKnDs5FzJ/Y1qdBs9dHAa4+MZfPX46dhc6Kn3HXJ/RYOOwV4NrMeu78fGPAC/iAcQodtbnew3TljSSxQdfy/RtFo90vMyMunv9WVwDHBvLnaeDE8P6AZ+pfMF1tJqdjjKMbzeZ9jIRO0F04EjJdayBSS8xixRt/t2fJf+Y8Oloq0GLvW20Urf0X13XR+pK2u31PsOub8abNu5ysTHYK+Gnh9I+8DlxSwUIaCqofmfHNnVq7fdwJJe6jf+AW5xvYCpkWRX9138vUTTjuqRRrJ17T7pyZkOJkeTWNu0IKUDTXFDOduRSCXc4EcIEQR/UgK7pqK9t3ZkW6Iew2t9ho1M1O5PjSEhsLpRwFBKazh4b/U32u7ApdY2Wu07Gh/PxCrYq/cdcn81uJi1dTHYq4P12PUICFE6x2GjV2/7AUt6o6l0Mgnwgkm3hQ9wo0PdP8Dx9xJNt6pHTp1dwNe++xouXM1CUxWMe/PAtbpN+N/zrqlkcFvetDEx7F5cKJ83LZ1S8bEP3RBp2e0KWlo9hlfallc3CiiYDj7zhWewsl7A+EgCqYQKTVNgmjYkJCwbuLSYbcsY9qMnXt8y7Ua1yhk/cNnMW1jdKMCy3XLa+aVNfOYLz2zZB9e7AFBpG81umtAUgb/4+ssQApgYTUEIEXxHX3zsFaQTSmz2+fX2R9xfDS5mbV3qv/k3/6bX69CKCQD/YnOzEIwZi7Ph4RSy2UKvV6Otnnv1EtY2reDv4tXbIXzoPXt7t2JtNppJ4KXXFuBIQFVEcFL20Q8cwM4uNKPph23HDzAKloN0UsVG3sKPXr2CF87Mw5YIbnvptQXsnBzqyue2XX3x2GkULAephApVVSCEgCOB81fWcc9tnZ8Ds9e/l147dXYBXzx2Gt888QZe+Pk8RjOJiu9751QGOyeHcP7KOlY2CpgaTeGjHzjQ1pMQ/3e5uJoHvKyTHzSoqlJzm6i336n1PR86OI0908O4upqDlMC1M8P47z50Q+T39s0TbyCdVCFE8cKhqgisbBTw4Tv3NfYhtKD8Pa5uFLCaLWB4KIGRoQQ2Nk1k8zY0VQDSzXYGpBv83nPrLuizk029/tETr+PoM+eCgNmyJYy3lgEpoc9ObtnWHMfBes7Cynrem8/PHUcpJTA85E4/4e+DLy9lt+yzy/fP5dtoUhXIWxKqqmBj04SUQK7gTieR0BQUTBuXFrPu5O4x2efX2x9t9/1VnLT7XCcu+5FuEEIgk0kCwOcBLIfvY2aPWvKxD93Q0tXbfsGS3voqXUFb9MpgJsfSwW3b8apat/W6LGk7/14aLRvqdPWI/7u0HQlFuJUXjnSDlmsmh4IJvZvtROi/RrXuk61mDXudbSl/jwXTwfhwCmPDbjZzYjSFqyub7jQUQmwZu5hJay3NO3v8ufMQEFC9wY+qcLueHn/ufMlUDv62ls1ZWM+666IICdOb704RwFrWxM6pTLAPBhAp6xH+Hj/76PPBvISapsC2HSBUtrqyXkBCFVuW+eXjZ5DNvRIpO9luUbbTWvc3g+PE4iEu+5F28ONVKQEJ9wKO413RcSSgqNUr6hjsUUsOHZzGpx68ZVvs1Pq9pLfTB59KAYbtuCcCYRwL0XlxOMD1+++lWXErG/J/l/6JuYB74m/ZjpvRSShVg9P7ZkbrLr9T33OcGqSF32OlefmEcLNBPlVxgzNHApt5CxcWNpp+7VzBCgI9nyLc26vNI7ixaUJTlaCEUxEo+Tu8D270olB4Pz+WSWBxLQ8BCdNykDdtWLaDseEkLi9mYdkONFWBIoBs3g4+l7xp4+gz5wCgqwFfre20ndsxx4nFR5z2I75w0AYAjpSQjoQDQDqAAwkpJRwvmJOOhBP87XjBHgAv6POXlUqqqFa3w2CPWrZdT+r6STcOPpUCjPDEur5+varWT8IHOE0VJZM3U2f1Oqtazv9d+ifmjnSvCKuKgG070BIqLFtiLZsPTs6HUhqOnZzDfXdd35N1BuKbHQ7v5zbzFha9MtVUQkXBsiHl1ukmLLv5cSbppIa8aSN80d6R7u3VtjVFAJNjKaQSKi4tZmHbDhzpBnxA6T640YtC4fefSScAAEtreTgOcHU5B0W4YxoVRYEi3PfuB5mVspOD1LXbF7cLPv2gUw3FOr0fKc+2OQ6CQA3SDdyCYM5xgzdbSjjSgXQqB23NkjWezGCPaBvoxsGn0hW0dEoDpETe7M0Eu/2uHeV1S+sFTHa5G+d2Foesapj/u1RVBZMjSaxumLAcB9dMZvCxDx7Ew4+9io2cCSGKJ+erG3mvPK+3OnEhsZFmJ5U8cHgWf3P0FWzmraBcUwhgbDiBqys2JOAFd8UTL7VGeVU999+51xuz5waPjndieP+de3F6brnitrZzKoO8aSMPYHRIw9JaAYCD0UwaK+t5bOQsZDdNjA0nkd00AUTLepw6u4D1bAFXFouNfizbPZkdH0libDiJt+fX4UhASMebkKMyPztZS7+WQsbtgk/cdaKhWKO/c1F2MUVK6f2v+JvzyyYdKeHY0bJtccFgj2gb6MbBp9IVtE/ce8OW2/rlgN1rrWZj/RPlmZlRzM+vdXp1yRO3sqHy3+WBPWMlv0E3MBHBfHjtyEbFld/sxB8DlytY+MaJczj2ozns3zUWad907uIqsvnSIEVKIF+wqz4nqYqmAxf/BLXSiet+bx9Rvq194t6bABS/891TQ4Bwm1KEJ30vWA4gBDQBZHNWzfUK74+mxlJY3TBxdSWHhKZgLJPE+EgKAIKxgn5zGE1TYNlbPxs/O1lNP5dCxu2CT9yFL0aHu7o2ezH6W8+ew5MvvO3NdelulydevoR0SsUH37Wvcpmkl3ELsnIoZsriFLQ1i8EeUR9p9oShWwefalfi435wjiOWAvWnOJYf1sqQqaoALHfcSNBcRLSWjWq3dmV4ws1OnNBJXd50IgcTx587H4w9M20nOBFc8zJkvnCjltWs1VLg8tCRAxWzElEbj/g+++jzJccB/78jmST+9JPvqbkO4f1RKqFieCiJvGnj6nIuaFYDeGMD4X4ue68ZAQDML2WRzdtBk6BwdjLK6/nr2i/7v7hd8Ik7/2K0O55YQFMFNFXDWtYMMm6OlzYrLZN0x7mVl0k+deoSNO/qlWU7cKSEaUn8/dNzOHRwpjdvsscY7BH1iVaudPLg039YCtS/+mkc87XTw7i0mMVmwYZlOdA0pS1zw9UTNYBrZ4Yn3OzEdoqX6/0xd1GCifAy/KkQ/GX4/EBPCDdTatpOxwKXRra1VvYp1Z4LyGCaFwAYG07i6koOqiIgpXvf8FACtx/cgZfOLkYuq+vn/V/UCz79WqbaqMolksWg7eDecWxsWkgkBBQhUDAd5AuW2y14Nec1KPEW4JdJynChdKnLi1moCkqmW5BSIu9Uz74POgZ7RH2ilSudccw2UG0sBaJu8C8EpVNa1y4ENRLAtTPDE252ImVpUAZECybCy3CzEG4prIB/cimDRiiAG1QKSC8wKupF4NLKPqXac8PjA5OaAkURGBtOYjillpSGAsDSeiE4/uzfPdaxdY2DekF4P5ap1hvXtiXTJlG1RDI8tu2W2Ql888QbUFX3QtNmwW0odvc9u2CajY8d9psllTc1Cm9L2w2DPaI+0eqVzn7KNhCzsdQdnbwQVC1z0UgA1+p+z1+HCwsbyJsWbKeY1fPPO0eH3K6SUYKJ++/ci2+cOFeSGQSAXzuyHwDwjRPnSqZhAIAdo8mS7Jf/WumEgs8++nzXPvdW9inVnls+PnB6PI2P/8bNuG66mBluJrAZ9P1fpd/ASt7C3xx9BZm01rELsuGArdp8beHbpCyWRzpeF0nHKf52gg6QNTJt9dy0bxIP3QM89dIFrGRNjNfxyBUAACAASURBVGcSeP/te6DPTja1vCOHduHJ598uKxt2b9+uGOzFXLfS/NulnKCf9fuVTmpM3LKx3EcMrk5cCKp1gr+wkoMQwOW14nQPo5lExQCu1n6vfJu8eXYCp+eWS/5++uVLsGyJjZwJP5cXHk+XSamYGE01ND1J+Pn+3wCwf/cYMikt6NQpAAylNLz/9j14+uVLJYFLdtMEhMBG3kY2Z2JpLYezb6/iwbtnW5qOIEpg1WqH33rjA8ubQjWTnY3b/q/dyi9iZHOmO+5TSkxPuNv8o985AyGA2w6477k8swYUyyH9bpAylGHzM2luwCbdVv9SwoabeYtTB0l9dhL67CSmpoaxuNj8vJQAcO8d+wAAJ05dQt60kUqoOHJoV3D7dsRgL8a6lebvx3KC7WjQr3TSVnHJxnIfQY2qdYKfTqq4eHWjZLqHxdUcdu8Y3rKcavu9m2cnSrbJy0ubOHN+GWMZdwqA5Y0CHnt2DiNDCWzmLe+13BNmVVUwOZqCpgiMDCUijRv0g47F1TwAlJRl2o7E8efOY3bnCMZGkpiZHAruy5s2Ts8t43c+fFNJ4KIpAhs5C2vZAgABVVFgOxKPPTuH/bvHmv5d1QusWtmnNPvcZrKz7bi4FMcLVH7ANrtzBCvZAoaSGoRwt5Pp8TSSmoId40MQAsiZNk789BKu2zXmZdZQOj8bUBKoAYPRObId7r1jX18Gd7YjYVo2CpYD03JQMG2Y/r8tx73PdEK32cF9o5kk9AOVG9Aw2IuxbnWj6ueuV9vJoF/ppPjiPoIaVesEP51Q4OfHiienouKZarX9Xvk2uZm3ICCwWbAxPuK2b7cdiWzO9Mq5hP8qsCwHSU1BNmfhT//gcM33UX6hww9QbMeBqrgBnz9nXK33XB4ofeYLzyDrZRtLpr1wZEu/q14FVrU0WpXSjotLnbhAtaUEMih5DGXWHECKsjJIf+yaLAZsd+gzePzZN5Ev2NAUBUtreUjHna/wymLWy9JJXMxbyOZqz0dI3SGlhGXLkgDLNEOB2Za/Kz+u1n3l5eGNuCZ0kakcg70Yi7rTbnVH3c9dr7abuGR6aDBV25dwH0GNqnWCv7CSw+RoEmubVtABdHRIQ65KM4ZK+70vHz9Tsk1atuN23CvYOH9lHZqmQPFuT2gqLNsdvyPhzv0WtQS+PKj0xwA5EvDfmT9nXCNBzfR4GktruSBgBNx1S6it/a56EVjV02hVSjsuLvnL8DNnCU2Fadv47vNv4/Yb3GX41xacRhqNlJdAIjRuDdEza9fvHsc/umMfnnrpApbW8hASSKVUJDQ1WK5pO5gcTUVbIJVmxczqAVWQIauVPatwn2k5TY9LbJUAkNAUJBIqkpqChKZ4/y3+vbNGB2UGezEWZafdjh11v48Fi2OpBnUWv/P2q7Uv6fd9BLVfvd9grRP8YyfnsLxRwK6p4vxsedPGRGi+tnrKt0kBBJ3+FAHY3vxagDtubnUjD1sKQABDSTVyCXz5hY6x4SSW1wuQEiWNWO6/cy/27x6LHNQ8cHgWZ99eDZpISLj/G0qpLTVu6UVgVU+jVSnBvGvCn6QdW+ZdA0rHrYUbjEBKCCGwe0cG0gEcuFm1lK1gJVsI2vlXGrPWSqORRvnj1ADAmFvC0affQMGykVAVmLb7vb3/9j1dWpvOcqSEZTuVSxBDAVUitYTllVzXs2KtUhWBZMINvoqBmIKk93dCU5BMVL4vWel+L6jzAzpNFSVTSVSSTChV72so2NN1PQngvwfwLgAj4fsMw/j9RpZF9UXZabdjR93PY8E4liheuhGE8TvvjFr7kn7eR1D7RfkN1jvBb3V7Kt8m/dM8VXFPiqQEhJCYHE1jejwN23Zg2RKqKrBrKhN531QeVCYTakmDFr8JS3icXZR94KGD03jw7lk89uwcbEcioSoYSqlwHInVTQuWBPKmBeOtZZyeW0YqoeCXD9dv3tJsYBXWiaz97TdMBxk1QATdHf0GI4AfqAE37h3Hes4K5upzpHsxYOdUEldX85DSKWbhgIrBWr5gYX5lE0ktfIHKxthQoql2/p2mz07iISDI9E2OplrqSNko2/EzWbUDqmqZr5Lbza2PM63efebVs2KhgCtRmiUrvb364xJeMObPvRlXjWb2vgjgdgB/B+By+1eHwqLstNuxo+7nsWAcSxQf3QrC+J13Rr3xRkB/7iOo/aL+BquVnbdjeypfhgAwPpxA3nRCHT7TkBL4zCff0/R7LQ8ql9fyUBSBqbEUMml3yoa8aTfVAOWhIwewf/dYyeewvmnCciSymybWssWxWnnTwdFnzgXPq6WRdaiUtTdtZ8v4n6gZNQfA2kYBG3nLLY10imPXbH98W7UGI16gdtO+CRx92p13LZzluvue61Ewo02M/f7b97iZMqBtmTJjbqmjwVg40xfmSAkrQkBVzJRVyp45MG27ckBnFrPgveBnxZKainRKg+qV3VYMtvzSxUQx2CoPxPzHNZIVG3SNBnsPALjeMIzlTqwMbVVvp92u8qp+HQvWzbFEncpaDUpJYreCMI4f64x6+5J+3UdQ+7XrImOr21N4GZ999HksbxQwMVrcfiuVhja6vy0PKqUEJkeTQaAHtLb/qdS4JZPWcHnTBFCc1sGdykHg+HPnI0/NUH5+W6ld/0fedx0e++Gb0FT35Niy3XFPH75zH9Y2zZLGIlEyagCgJDSsbRSa+DRc7chytTtT5pdZqqqCdErD6qaJo0+/gYcAHLx2PFK3RNOqEmzVCuJMB6bdw6yYQMWSw3DAtaUcsVq2LEJWrB1TL9BWjQZ7cwA4WjRGtnt5VbfGEnUqazVIJYndCsKmx9O4tJjFZsEOmjsMJVXsqjE4merb7vsSii6OYzijbL/N7m8rBZVh7XrvQgB7Z4axumliZCjhjVdzxxkqQiChAo4U7lhBWez6KEPlkH4zEf/vetm0mfEh3P2OnXjqpQu4sFoMinZOZrDhBZy9UC3LVU21rNuN+yZKsmKXl7KRsmIFyy553LlLa7C8oCvoxCklvnjM6NRHEImmii0BVLJa5ktTSjJm1UoVw8vwy6J7odOZ1O2k0WDvvwD4pq7rn0dZGadhGP/QtrWiyLZ7eVW3TlA7lbUapJLEbp0A3jw7gTPnlyG8luWW5WDVsvHBdw3GQPZe2e77Eip16uwCnvzaKVycX9+yLcTxwkCU7bfZ/e3P3riK7/z4PBbX8hhKKhBCuCfFqoDlSKgK8Cvvuw624wTdGIsTXJcFZUBpgBaUQbp/3/XOnfjWs3PubY6ELR04ElCERNZxSxKf/dlFnHjpIhbXcpgYaf0k+KZ9k7hpX29Ooi27sbb1lbJnS+s5XFnaDILZlY0C3rhkQBGip007hIAbNIUDsSpNOirel1CrBGzFv5WYjxVrVq1MKgO+xjUa7P3P3n//bdntEkC0ugJqu+1cXtWtE9ROZa0GqSSxWyeAp+eWMT6cwmbe8tqqKxhKaTg9t4yH2vpK28923pdQkZ8BSyXVihmwuF4YKN9+y8eZFUwb0+MpCKFAwO30KOBOXp0z7S0ZMkjgzPklHDs5Bwggk9ZQsBxIx4FjO1jN2RgfTuIDh/ZiejyzZb/dzDCo2Z1j+OC7r8U3n3ody1k3s+afz0sAN+8bxzdPuCfBqWRnT4IdR8L0g7FKjTiqNOlQVBXnL6/i4tUsCqYNVVUwOpSAqiplGbPOjhWz6yxbU0Xkbok/e2MRBW8sqJtsFbAdB8NpDb925EAoKCuWOPYyK9bvnnrpAlRVCRrsJDUVBe92BnuNayjYMwzj+k6tCFGzunGC2qmsVRzLoZodQ9jNwHs0k8BYaCyOlLIvA2SiOPIzYOmkBtNyKmbA2rHfDZ8H/+yNRTz5k/NYXHX3HR98z17cct1kEIABZWPFRPH2IGPmVJ7A2i9hTCVVLK8XoCqK91yJvGljJK1heS1fcR2/9cM3sbppeiedbnMQy1uR/+mjh4LHtTNo0Wcn8ZnfuQP/8JO3cOLUJeRNG6mEiiOHduH1C6tB4xIA0BQFtiPx5E/OYzSTbCgrVjpmbGvQZtnteU+mbSNXiNZYxednxYJmGyVZrtJg7IWfzyOhqVC8qRr87cqyHHziH90YlDmGG3gkNMUtkY3ohr3jW5rGCFvgl947i+t2jTb03qi+pbU80qnSECWhupPPU+ManmdP1/UEgLsA7DEM46u6rg8DgGEYHFFJA6tTWau4lUO1OoawnwNvou2g0vlteL4xCOn9nlJIJFRYloQQCCYsD+aW8x4blCuiGHQFzTrk1vvhj3fyHuQAeO38Er51cg5CCGiagisrOTz6nTP4yOHrcMPe8dCKtjYH2ntv2Vmxy+OR91bf3zZ70hklK1Z6e+XH7ZkZDu57/swCltbd1y2PLZeQx59//afNfTBNCI8VK2+8cfFqFqblZvT8DKojJYaSKj7wrmurzzUWao3fSFbsylI2FJC7CpaNqdEUDuwZr/HM6Ho9NcJ2Mzma2vKdcpL55jU6z95tAI4CyAPYC+CrAH4RwD8G8PG2rx1RBN3oZtmprFXcyqH6YQxh3ALkQTIonWHjJGryoDzgKgZI/o2lJ/jhIApSQgpsGRcWBFtOWRMP+I+TxXFloXVJJRQsrRUwlFJhWu49edPG6JCGpdVc2yedPv6jt7CRs0pP7CwH333+PA5e256TdaB4wv6DF9/G0loe4yMp3HnzNZgaS+PCwkbFJh2KIrCyUYAiRPAZ2o4DVVHw/z7+atXsWbuyYs0Ixop5kyznC7Y7l5+mYHo8jcnRtBdchcoOg79rd1aMkhX73FdfxGgmURKsSSmRy1u48+Zr2v5+OzHNQiWNNo2h5nXrO+0FUfWPqM8X5TcAQM3xm41m9r4A4E8Mw/iSrutL3m3fB/AfG1wOUVt0s5tlp7JWcRon1Q9jCOMWIA+KuHaGrR0siSC4cSe6Lg+SAL/VfHgi7FCMFLrBDZhKn1uc8LkkY+U9qGQ5svhY/zYZeoLfPF16qa3SDFjpehUXEnpOJS1muqrxM2CW7UARIjjR+qU793Xk9cqzZ1JKqIrA1ZUcFldzFTJidTJkJUHb1kDMtNxGKssbJt68vN7kWjv4+fmVpp6ZUMOBVllApdbokJhQsbC8iefPzENVBRKqglzBxkbOwnBaw8hQApbjwHEkHrrHHXVz9Ok3kBpJBifMGzkT992xt6NBy/T4EBZXN7uWlWHWLR7Eln/Ue7wI/+H+R7hByy37p5DQFPzw5UtY2Shgx9gQ7r51F264dqJ4TBAl/3H/JYrLFWXrFNwevFZxASK8LCFC/y4uI3xfsFj/eSU7xgofQJXPRGz9COo+DmXvw6eq7Qv23gngy96/JeCWb+q6PlT9KTToepkN6IdMVD/plxLJcxdXMXd5HbmChWzOwrmLq/y+m+QfML73wtsYHU4ilVDdg6B0y/meOnUBtx7YUfKccCC19bbSsVUQMvg7CJzCwU29Uj/vgQ5CgVLEIAmoESh5j+td/iW+/JPnH75yBfOLG8HJMwD8x6Mvl2TF9u0crT4WrMp8YuX3rW+aWNkolGYrPZ/7yotdfe+KN6FzONiybQdrmyZs20EyoWL3jmFMT6Srtq4vn/g5fLtWIyvmt5q/sFA7YDmwZywIbGxbYjSTQEJTsLSeh+0F6MdOzmE4rfWkycX9h2fxyLFXW8rKlLfdP7BnDK9fWK0azMUt6xY18KkU8JT/KbyoRcAtbVXKHlAexLj/Lo2EKgYV4aAGaCKw2RJtlT4OlYnQC5b/FKYnM0h4+/iZiSHcfevu0s+hST2cM75rtmT8QhoN9s4BuAPAj/0bdF1/L4DXmlkx6n+nzi7g4cdeRc4rE1ndKODhx17Fpx68pSsn3/2QiaonTqVzDxyexcOPn8biSi4oVUqnNHzi3hsaWk4n39PRE6/j6DPnICCgKgJ508bRZ84BQOTJhjuh2QORLA9Y/H+IssCovJzPS1UVS/bc+/1yPvdxEqvreWzkLSDUYTAo6YPX0AISi2t5pJKqV37mzs3lOBIXF7LF31PZAbPbGSeqz3Fk1cYbUQKx8MTOEkAioWJpvYCv/sNrJU02WsuKNa/6xM5lEzhrakn2LBhfVjbXWHn2TFOV+ivRAY20mg8HNv/+0ecBAays5wGIYLqBK0ubyKS1kkZWQHeaXNx6cBoP3XN95ExbeWB05q1lfOukO9H72EgSq1kTT798CSOZBMZHUyjYDo4/NwdNVXDjvonay0QxWHJvL2Z/FO+BFTM+XrYnHBiFMz3hElUh/N21QKXAp2Rd4F0DqxLw+J0+/V18rePKIAYwqvf7qza2mJrTaLD3rwE8puv6XwFI6rr+LwH8jwD+SdvXjPrC1777GjZylpt6F25Hso2cha9997WuBCz9komqJpalc8EetUJNWwTh9zSc1rCSLeArT/4cQgC3HYj2nmod4J5++TLGMkkoXtc1AcCWwLOvXMGv3HP9lqzT6bklPPXiRSytF086bto3UbGEr2ScU5A9qp558p5ZXLlQ4FZcfPHBJdmoLYFTDS2U86nJBNbKJoGuRFMEltbyW5ocjA0l4PRwrqpB4GcpFldzmBhN4fA7duG6XaNVyhAby4qVd1ns5bxiqiIqZrZqTezs37+4ksOZt5aRzVkYzSTwHv0a3Lh3ovRxqjKwreybbTU/OZrCW1fWAW/eUcDdf6qKgG27TWL8ZQoApuNgx1g6CFzKiWJUFLqtuFzhRSAKRBCYlAdJw+kEfuHma/ALt+wMApaS8rgqwZH/uJ+9sYipsTQSmgoBIG86GE4noAqB0UwCUrr7ppdeW8Ddt+4KPVcEF8KKAVnt30NcAwj/Y4nr+lF/aXTqhb/Xdf0BuMHd9wFcB+CjhmH8pBMrR/F3eSnnBXrFcgAHEpeXupNZ6/dmHeVlqOmEirwAvv2j6GWoUc59SsvmsCVr5P/7xE8vYmoshZzpfo6apiCdUPHMy5fwjv1TwWHzzNwSTvz0UhBA3XPbLtx07QQggJOvXMauHcPQNBEERQXLwYmfXsL+3eOlgUvZ6we3e+tZUrrn3ZY3baheAwsnyFJJrBZsNwMVWs7P31rGY8+eg6q4V+znrqzjS98+jQfv2u8GfJE+4e1hkAfER2U7shhQNZMVq3B7NmdhI2cG2/fyholzl3pTDLMlK1atKYfX5n5iLA2zYCOpKfj2yTeRTKpQhDfPmHciXSjY+F9+63avg2JrWbGP3N2e99kPyoOtjZyFkaGEeyz1jqlDUkXBtKFpwpsT0A3owgHWg+/bj6/+w2vetAPeQgUwlknAtCXSCRWqqkBTBUzLgWU7+Mjd12FyLL016+Q9vxi8eX+j+nGmUjAyMZqCmat/gama1y+sIpPWIISbSZ5f2oTiHk6QSmre60osrqxtKYkN/ym3caQUp4oh6r1Gu3FOG4bxAoA/bvB5nwPwmwD2A7jNMIyXvdtvAvBFADsAXAXw+4Zh/LyRZVOvyWJJmX+LBITozk7W33l9++QcFrz5mR547yxua2OgFBYeKyQEKjSEqBBElf0dziQJRWD3jgyEEEGQIx2JXN4OSu8kAG01h7VNMyjBK65LMWCqFBhVWpetb6r4z9ffXsVmwQLglZt4y1pay+PqqhvAn5lbxt/98A2oinulfXktj9fOL+NX7t4PfXYSp99cQjqlbenEdnHewsamWe8jrqtQsFGwbKihzlO2I5HU1C0ZqO+98DZsx73KbdkSihCwJPD9F9+uWv6zXcW9yYGUEpYtg1LDLV0QqzXpqBW0ld0Xm6xYoqwcMcKkz1vvV5vKioXHSs1MDePud1wDfXYSL/58vmJ7+x3jaaSTDc/iFHvlH5UIZaGCbJUAhFCgeMGZEAJCKWaxio8ry2iF//aST/t2jmA1ayKpKUHX1LxpY3p8CDPjxbYI5fHLu2+cwd+deB1XlvNu6b2qYCyTwFrWwvhwAvffuQ/HTs7h4kLxpP/W/aVjcKPoZtxUXrGjaQosy0FCK15MiEsFTxyDqlhWDFFPNbqHntN1/XsAHgHwjQbm1vsGgM8DeKrs9r8C8BeGYXxZ1/XfBfDXAO5tcJ2oTeqdB2zJDgE4eO04Li9tQggBVSBo6b1zciiYZLb0ICG3LC889mjL40MledU65QlFYHw0BQlgbDgJB8BGzgyeXmwtLksCoJK/g/+VRkj+ey0Jnip+OMFaVro3eEz5vYW8hfnlzYqlc+HSu2S6PYFSPdmcVTGQgrSDz/37L74NQEBVFDgS0FQFjpRBuVGn58c5cmgXnnz+bdiO9EqH3c/1yKFdWx7LiVkb00qTA9sJdTw060/Y3Oh9pjeGrBcEUDfgCt8XDrj+4SdvIZlQi8GA99MqmDb++KO3heYV681YsbDycWMr67lg3FivM7+Vyg7DAVj47nC5oRoaoxUEXt6wA4RKwf3vJhyMKaL4ukHAFiyn+LrtCISO3LY7OEEPV6n86vv2113+r3/g4JbnmpaNX3rvwVh1e46qvGJnKKli1bIxlNIgvcZRjVbwdCIoi2tQxcZ1VK7RYG8WwG8B+GcA/krX9b8H8CiAbxmGYVV7kmEYJwBA1/XgNl3XrwHwHgAf9m76rwD+XNf1GcMw5htcr55pNDNU3j61vK13taYM1edXKt5a3pihNFCp3iY8HPgEQY8MhT6yenboyKHd+MaJczBNC3nbbZedTGp43627sbC8tZSzZjBU9gJRjp/hk5OEqmBh1R338dA918cmI1HLkZiVzqmqACw3gPIDqeB2T70AqtMnhffesQ8AcOLUJeRNG6mEiiOHdgW3h3FiVpeU4QmeHS94aqZ1vV11GX2dFSt5TvFvP2DT1OgTPJd75Y2rFTNi0+NpjGWSNZ7ZfeXjxlKaAttxL+T84a++M8j8Lq9Xz/xWG/NVKQALyv+VYiBWng1T/CX4mTR/maGSQzc7tjUI83UiK9XuZbYypcygTUdT/n52TWXwwXftwem55abeX6eCsrgGVYPQuI7aq9ExewsA/hLAX+q6fh2A3wbwfwF4GMBMg6+9D8DbhmHY3rJtXdcveLc3FOwVbAeW5QR/hwOd0H9K9s4lyaMKmaTyWu/SYKdy5OOU3x8u6wNgCYGl5c2tr4kqgU2tbFGFLFEvHNgzjgfvmt1S+nXw2vEgs9dJjQ5qL2/n3OsytbiVzu2cHMLCas7trmq7JUHppIbpsWK5TL0Aqhvv6d479lUM7sr1OhsRVZAVC435qhiUhbNelbJnFYI0y3b/7pXqWTE3oMqZNi4ubEBR3O6qjlcrfdvBHdh3zUjdoC2hqSWZ6LiJyzYYzowVOxL6AZgbXDlw250riht0JRIqJiwbedPB2HAS733HThx+566KGTCgcgBWDOqqH1Pb9wa7W25YT6PZpFaycK08N46liJXez0NNLqtTQVlcg6p+b1xH7ddKof01AHYCmAaw3J7VaY5Q3UHjcWc7wNh4pter0XZ3Tw3j7nft7clrL2dNDKfUkqvuqqJiJWtiamq45LEvn13AYz98E6oqMJJJYCNv4bEfvonR0TRu7eGBLernV/5+OuEjRw7gK8cNDKW0UCmRxEeOHAhe33+M7TjBYyBR8phebhNhd08NY3Q0jeMn53B1ZRM7xodw/+HZhr5vR8ogsMp74wVN00HetFEwbeRNu+x+N1Pm/q/4uIJZvC/v3eff3susmKYKJBMqUgkVST+jlVSR1Ly/E0rofsV7jIpU0ntsovi44jJUbxluQFYrK/Z/P/o8JsdSJScmedPG8oaJT919fTc+go5qZRsUW/5RLDksdqMNZcUqZMeENzY4aOyhuM9VhDt5cfi7mZ7IYGl1E8mECkjAtE3k8hYmRtO4bm/8KyXi5MevXsZXnnwNmiYwPpLEes7EV558DePjGfzCLTt7vXqBTq3nzMxoG9eyNYvrBYwOlY4j11SBpfVCS+u5e2YES6ubSIf2XbmChd0zIz19/x+//2b89ddPwXYcN7A1bUC6t8fpe6mmH9ax3zTaoOUdcLN5vw1gCMB/A/DrhmH8qInXfgvAtbquq15WTwWwx7u9IcvLWZhW765cRzU1NYzFxajDHCmKiUyiYonUeCax5bN+/MTrgABURfGadiiwHRuPn3gdeyaHyhcdK93advZMDuHBu6/bkpXbMzkUvH6Ux3RaOEM7MZLEXe/chet2jVUtM7x1/yQK1hhMy8HPzi7gxdNXSh9rV58Q2rR7nBVLlM8fVt5Jsdb8YiqmJjPI5woVH6dpSmeyYraNwqaNKP345hc3kE5p3vx+LkUIzC9u9O3+sjyLtndHBp/6yM1uEKYAilCQXc+5AZsfiIWCMARdFxGMKwtKHt3OSWXjj9vjyK07S8Z+OVIiX7DxG+/fg/n5tbrPj2OGqFe+evx0cLyxbOn+V9j46vHTuG668kXfXnx+zaxnPTMzo5G2l2Y08xlNjSS3ZLrypo3JkWRL63nfu/fgkSfOwLJlyTjL+94d7ffSKddNZ/CJ+27Y8jldN53p6XpF0cltZ9ApisCOHSMV72s0s/c0gL8F8EcAvmsYRtNnQYZhXNF1/UW4geOXvf++0E/j9aj3GimRYrOOaKI06YjayMORElaVCZu3lCdWur0sADMtBxs5E+t+Z1IAS+sFvNGjVvaaKuqM/4reLbGkVNH7dytjxXxxv8gU13GVtcoe3YZUws2MlQdrcB/kt8IvNmUpzoPma6bksJNliuVjpXbPjOC+d++JFHDEtVlFrzRa4terzy+OpYjVArpmP6NOTdEU57GS/diYhzqn0WBvp2EYDU+eouv6nwH4KIBdAL6j6/pVwzDeCXdC9i/quv4nAJYA/H6jy6btrZHxYXE9qezVOEIppTevWIU29g3MNRbbrJhA1WBrS4as2n2JKo/tZFZsm+nkmDZRFqwBCIIwVShBq3y/nFHxgjMlaCBSVg4ZmoMMaDzwitN4smrCJ4mNXGWPa7OKToiSXUonFFy6moXtSGiaOx2CqipVx031zr8zHAAAIABJREFU6vOL2/iuWgFds59RJ4MyBlXUD+oGe7qu/55hGF/y/vzdcEfNMMMwHq62DMMwPg3g0xVuPw3gcLRVJaosapYpLo0Swspbna9umkGr8xv3TQQBlrO8ifnFbOVArIGsWPljezhUzMuKVShPTNTOij3x3FswbaekC7uEOyH9pz92KFiGqrSeFaPOq3fBplbApgjFK4UsDdgURUDI4pxnQgi4MxuUZtfillnrZ3HMEHVClOzSqbMLWN20YDsOAAHbdrC4msdwWsMn7r2h4nJ79flVynplN01oisC/+LOn3NJOVeDa6eGuZK1qBXStfEYMymg7i5LZ+20AfrD3e1UeI+F25CSKrXZ2iYycFSu5fevj3ri4CtNyICCCCdodKfFfjhk97bZaKSvmB1xbShTLx5RV6JZYvoyEqkBpMiv25I/PQwBb5gKUEhgf2V5TKvST8qDNL4lUFAW33zCNd98445ZCKqUZtnYHbAzWOiNuGaJOiZJdOnZyDpm0hnRSxepGAZbtQFUExoaTVQOOXn1+5VmvdEIBhMBGzvLmq3Wn47m0mO15Wel22caI2q1usGcYxkcAQNd1AeAPAMzVmlOPqNsqlUHeuHcCpr11/rCEpuDI7XuC0sSltTx+8NKFYjOP8nJEyylmxcomfm5vVqyxhWmqqB5slbSpr9DcI8KYsjhnxaLMBdhpcZvCo5vKg7bXzi/j5CuXsbZZwORIGvfctgs3zU5WzbIpytaSSAZs/a9T46LiJkp2yX+MEAJD3jhxKSWyueqnTr38/MJZr88++jwsCSyv5SGE4u1jJTYLNtIpradlpdtlGyNqt8hj9gzDkLqu/xQAe6JSS/ysWPmEzTUnc64y6fPKegFXV92DrASwvFHA6xeNnr4/RaBiOeLWRh4KXj23BNN2oClKMDeV7UgMpzT8yj37g7Fh10yPILuRD5ajxDQQ64YocwF2Uq3S234J+Ko2H/E6Rb7+9jJOvnoFa1kTk6NJ3HPbbty4b2JL0Hb6zSV858dvQfHmulxcy+PrP3gdH7/3Btx2oP4JIYO2wRHnZhXtFCW71EwGKi6fnx+oWrZT7AALwLKcnpWV+gFdXD4jon7TaIOWFwDcBOB0B9aFYsIJyhNLM1kVOynWmNi5UpDm39/Lk7yEqpRkvioGY4mtJYjh8sRa2TNNjT7no75vIggc/HGEwnbwS4dncWDPePC4qYkhLDrxn16kG/yxl2PDyZ6MvXzqpQtea3r3RC6pqSh4t/ci2AsybaEJrhXFvSKvKgpG0glYw8liRs2f6DpUHhnOtL38xlUcffochCKQUAXeurKBL33bwD237sLpueWSk6xjJ+eQMx2kIGB7Uyc4EvjWs3ORgj0aLNthXFSU7FI/Z6D8QFVT3akYFOFeSNW899HtstLygG47bGNE7dZosPc9AMd0Xf/PcOfDC07ZazVoofaRUsKyqwRj1Toilre4L2ngUTamzLJL5rvqtiArFswhFgq+VDUUhLn3/ejVy0Gmy++aBwCmZeP3f+nmLfOQaTHLirVzHOF2oc9O4o75dZw4dQl500YqoeLIoV1d+8zaOYVHzfb+ioAKr+mI4mXUSjpGokKL/+J4Nv+CyvhoCoVctCbKUgKPPfMmLEcipbrzUSY0BZt5C489O4cd4+mSphS5gr2lm20zV/85Pxv1iyjZpWYyUH7jF9NysJm3sbiWx9kLL+PBu2bx0JEDnX1TIX6gOpTSsLqRh+1dHBpKqj0pKyWi1jUa7N0D4A0Av1h2Oxu0eBxHlnU/LAZi5xc3sbiUjZQV8wOxSvf1PCsWDqBqtKavNoeY/3el7FkjWTEAOH9lreKk6pMjQ9h7TeXJJeMmajdRchlzS/jJmXmMDCcwqaZg2g5+cmYe186MdOVzLJ/Cwy+9nZkYKrng4JY6FjtGKorbYMQP3sLBGlB5LBvQ/fFslcYkZXMmbEduaUph2xYKltNSwwTOz0b9Jkow0mjAcuzkHEzLwdqm6TagEu75xGM/nMP+3WMN/xaavYASDlRt2wm6ce6ayvAiDFGfaijYMwzjQ51akW4oZsXcYKtYWrg1K1Y581WaMSuUjDdzl2H3sJe9IsTWtvV+QKUW/95aqljeqKNC+/uEG4jFKSsGxHM6BeqsRssoG2mmUpJp84M2b741f162B9+3H8efeyu4UFGwbFiWg195335Mj6fdDBv8zpGufmpAUmm8kWU7SKhqyeOSmjvpu207LZWrbaf52YiqWVjJYTNvuxeJvH2PKgDbcRr+LTR7AaU8QPzUg7fwN0g0ABoK9nRdnwTwXgBTAK4CeM4wjKVOrFgjnvjxeSwsZyuPKQu1vDctp6ft7P2AKep8YluzZVU6KXrLUZXGsmKDgGWQ20+UMkr/msTPzy/jiR+/BU1TMD0xBFtK/OClCxjJJHDTvkm3BFIRbubNe2LpWLatbf7ffeMMNFXg2Mk5XLqaxfR4Gr98eBbv3D9Vsk792nyk0ngjRSgYSpUGewXLwR5v7q1WSjC3y/xsRLVMj6exuJZHuKmwIwFNbfy30MwFFGbYiQZX5GBP1/V/DeBfec9ZADADwNR1/d8ZhvF/dmj9Innx5/O4srTZ0jJURdQMqCLdVxKolTbw0FQF0ztGsLi40aZ3TT6WQXZfO6ceqLUsP9MWnlB77zUjWFovwLRsSCmR0FRk0glct2sUE6OpkrnZ/vbsVQwPJZBQFUgAqgQ2CxZ+8OIF3HHTNZHWr1LQdtuB6YFtQFJpvNFdt1yDp192x0hW6pBX6WQwahkZ587aHjgus7YHDs/i7IWX4TjSLeGUACCRSScb/i00cwGlXRn2U2cX8OTXTuHsW0vIFRw4UiKT0nD/nXs7NvYwvG2lkyogJXKmw+2sy+L0G4/TusRBpGBP1/XfAvDPAfwugG8ahmHpuq4B+HUAf67r+mnDML7awfWs6aZ9E7h2ejgItCJlxcomft6OWTGiZjQz9UC1CbVfv7CCp05dgKYpuPaaETgS+NGrV7BjPI2b9k66XSP9hiTehNq3XDeJx0++6QZhErBsE7Z0cOv+SaQTpdmncxfXkElrKIhiJ1MpJS5ezXbioxkYlQK4/bvHIh88G8kS9HPnQoqGWaP6Dh2cxoN3zeKxH87BdhxoqoJMOglNFQ3/Fpq5gNKODLv/PedNG9m8Hdy+mbdw9JlzAND2gC+8bQkBXLy6AUBgcjTJ7ayL4vQbj9O6xEXUzN4/AfC/GYbxt/4N3sTqX9N1PQXgnwLoWbD3a0euh2mxLT1RO1TLtPkB249euYyRTBKphOqVOQpYjo2fvr6I99x0TUkTknDDkXCZJOCWRz76xBnYElCke0LgOECuYOHxH76JWz+5o+L6nTp7FZqiYLPgjpXTNAUjqQROzy3jobLHMmvUPo00nGgkS8C5swYfx2VGs3/3GHZODeHyYhaOAwynVHzsQzc0/Bk1cwGlHftK/3teX3FL6gW8lu3eceD4c+fbHuyFt63L3kTwgMTapoVdU0luZ10Sp994nNYlLqIGe+8C8PEq9z0O4PPtWR0iakWU8sry0ki/a6QqFJy9uILvv3gBpuVgKJ2A5Ug8/dOL2DGexs3ecgqWg9GhBAD3QO44EooQuLSYxWgmEXldpQTOz28gk9ZgyuLFmkSdq8kLKzmMDScxPlIc3CKlrPgcZo0a067Sl0azBGy1Ptg4LrO+cDZi9/QwCpaDfJMXsZu5gNKOfaX/PTtSIjT0EFK6x5hcwWrq/UR5TQDBRPBSupPAA9zOuiVOv/E4rUtcRA32UoZhLFa6wzCMJV3Xk21cJyKKKFwe+drby/iW1y5bKAIbOQvfe+FtjI8k3WYkZe3+FWVr18jH//YcLi1uQkoJSAnb/Q/+25M/x5/+4V0A3GBscS1fcgU4b9qYGG58N9DM1eRGnsOsUXTtLH1hRpXCuD3U1+5sRKMXUNqxr/S/Z0WIkoBPeGMQ08lGZ/uK/pqphBpMBA+4k8AD3M66JU6/8TitS1xE/eUJXdevB0ou1pTc36b1IdqWKo5pC+ZoA4aSGkYyiaA7pKIICFlsRKIoAv/5WxeQL9iAAKQjUTBtrG4U8M0fvI7/41OHq752uAHJ+fksJKTX+tt9LQcSl5eKV8TamS1rZlmNPodZo2jaebLJjCqFcXuoLw7ZiFb3lf73PJLRsLphFrufS0AKifvv3NuW9az0mnkAo5kEFldzAARGhzTkTZvbWZfE6Tcep3WJi6jB3jCA11A9qOvTJuNE7VUvaFOEgKKK0qAN/ti2yhNrSwlMjadhF8yar3327bVQoOZypMSFq410qpVu8Fc23YAQxZ94O7NlzSyL2brOaOfJJr8jCuP2UN8gZCP87/PJFy6UdOMc6mA3zvJta/eO4aAb58RwkttZl8TpNx6ndYmLSMGeYRhsVUnbSt2gTfEbkFQP2vygKxy0NaLxedrqB2r17JzK4OLVDTjSy+p57b93Tg2XPK7VdvtRltXu51Bt7T7Z5HdEYdweahuUbMShg9O4767rMT+/1tXX7PW2xVb/8fgefHFalzhoewG1ruurhmGMtXu5RM2oFbSpwi2D9AOzcFbNbffvTaztB3FdC9qaEzVQq+VjHzyIhx8/jVzegu04UBUF6XQSH/vgwbrPZbvj/jYoJ5vUHTy5ba9msxH8HnqPxz6Ku/aPluX4PWozEfyfG3z5f7ot/t2gTVG8wK1O0BZuRhLXoK1ZrQRqvkMHp/Gpj9zc1MkD2x33N5a+UFQ8ue2MRrMR/B7igcc+irtOBHsxPyWmXqkVtLnlkcIrjyzO0xbuHtmOoC3uAVsr6gVqUa8AN1v+EIcGA9Qalr5QFDy5jYdWvwdmBduj18c+fo9UTyeCPdoG2lUeyaCtvWqNpev0FeC4NhjggZCovaKe3PK311mtBBnMCrZPL499/B4pCgZ7VDFw8yfZdssjvSxbBxqRMGjrjm5ciY/jmK9BPhDyRJp6JcrJ7SD/9uKilSCD2dn26eWxj98jRcExewNIlJVKKgIQigjGtgX/EwIKSudqY7ZtMHWjzCSOY74G9UDIE2nqpSgnt4P624uTVoKMXpceDpJeHvv4PVIULQV7uq4fAOAYhnEudPMvt7RGFKhYKikARak9Z1tJqaR3HxA9GGPQNni6VWYStzFfnToQ9jqrFqcT6V5/FtR9UU5ueRLaea0EGXEtu++2du2/enXs6/fvsfzz//j9N+O66UyvV2vgNBTs6br+XwH8B8MwntF1/X8A8JcAHF3XP20Yxn8CAMMwTnRgPftaMdPmB2ReMOZn20SFUklZzLi5zUq2TrQd1f/f3r1HyXmXhx3/zs5qJVsSku0VJraR7cro4VIc4pSjtlwbYyPKJYGeUrC4nZRCyElOaZrQpk3T3ICWQkIhNKFcCocIh5RDTcJFxT0NLfiAamKojil5BIqNTMCglZEvsrWrnZn+MSOxK62kndHMvJf5fs7Zc3beuewz7/vb932f9/n9fq/Jm8rYxXIcRnEgLENVrSwn0mVYFyrGuU5uq34SWhWDJhmTekxYqg77rypvx5XW/3s/sY+XXX9NZdZ/VfRb2bseeHXv918CngMcAW4BPjDEuEppNZW2syVts5svYLrTHjhpG+T1EpSzi+U4jOJAWIaqWllOpMuwLlROVT4JnQSTekxYqg77rypvx5XWf6vdrtT6r4p+k72ZzFyIiMuBizPzNoCIuHT4oY3OQN0jh1BpWzPd7P7NVb5eGqaydbEch1EcCMtQVSvLiXQZ1oXKqconoZNiEo8JS9Vl/1XV7bjS+l+7plm59V8F/SZ7X4uIXwWuBD4N0Ev8Hhh2YP04Ob0/4+0eOcjrJY3XsA+EZaiqleVEugzrQuWx0vinN910XdFhSSty/1Wsldb//PGW638E+k32/jHw28Bx4Fd6y/4OsHuYQfXroo1r6dAxaZM0cmWpqpXham5Z1oWKN8rxT04CpFFw/1WsldY/HVz/I9BXspeZB4CbTln2ceDjwwyqX1NTDdrt7u8mbZJGqSxVtTJwXeiEUY1/qsMkGion91/FWmn9OxvnaPR964WIeAbwE8CGpcsz8y3DCkqSyqwMVbWycF0IRjf+qQ6TaKi83H8V69T1v2XLRg4derDAiOqp31svvBt4KfAF4JElT1lPkyRpQo1q/FNdJtHQ8JWte2/Z4pFO6Leytwv4m5n53VEEI0mSqmdU45+cREMrKVv33rLFIy3Vb7J3DzA/ikAkVYdXMCeP21xnM6rxT06ioZWUrXtv2eIZBY8B1TXIbJzvi4ibge8vfSIz//fQopJUWl7BnDxuc63GKMY/OYmGVlK27r1li2fYPAZUW7/J3k8CzwOeyelj9ga+zBYRz6d7S4c1wH3AazLzrkE/T9LoTMIVTC3nNleRnERDpypb996yxTNsHgOqrd9k7y3ACzPzfwwrgIi4CPgw8Hczc39EvAL4A2DnsP6GpOE52xVMu3nUU92vWkuqlrJ17y1bPMPmMaDapvp8/VFg2N01rwG+n5n7e48/Azw3IjxDlEpodtO67s1Pl1hYbLNupsnuW/dz5OjCsm4e+w7MFRSphuVM27wuV60lVcu122bZdcN2Nq+f4eFji2xeP8OuG7YXdnGxbPEMm8eAauu3svfrwDsj4reAHyx9IjPbK7/lnPYDj4mIp2bm7XRn/IRut1DPEqWSOdMVzOnGlN08aqruV60lVU/ZuveWLZ5h8hhQbY1OZ/W3yIuIEwnd0jc1gHZm9n2D9iWf+xzgN4B1wGeBXwCelZn7zvHWqwDH9klj9pVvfJ9PfP5b/OC+h3n0xRfykmdfwx98Yh8bL5im0WicfF2n0+GhRxZ5/7++ocBoNQwrbfO/9YRLiw5LkjQGHgMq42rg7qULVpXsRcQzMvMLEXHlGV7y1sy86fzjg4i4FPg2cElmHj3Hy68C7jp8+CHa7fLf133Llo0cOvRg0WGogqrQdt720TtOG6A+f7zF5vUzvOmm6wqMbLJVoe2onGw7GoTtRoOy7QxuaqrBJZdsgBWSvdVW4z4ZEc/LzL2nPhER7wCedj4BRsRjMvPeiJiiOwnMH64i0ZNUInbzkCRJKpfVJns/D3wqIm7MzK+eWBgR/wl4LvCs84zjdyLiacAM8DngX57n50kas0m6H9Ykzjo6id9Zqhv/j6XJs6pkLzP/OCLWAv89In4qM++MiA8AzwCenZn3nE8Qmfna83m/pHKo8wD1E+pwc9l+T/jq8J2lSTeq/+NT9yf/6MbHc+XshcMKW9J5WvWkKpn54YhYB9waEV8GtgPPzMx7RxadpL541Xb0qn5z2UFO+Kr+nSWN5v94pf3Jez+xj5ddf437BqkkVpXsRcRP9X79JnAb8BzgDcATI+KJAJn5P0cSoaRVsfoyHlW/uewgJ3xV/86SRvN/vNL+pNVueyFIKpHVVvY+cMrjH9KdSOWEDvA3hhKRpIFYfRmP2U3rTpt1tEo3lx3khK/q31nSaP6PV9qfrF3T9EKQVCKrHbN39agDkXR+rL6MR9VnHR3khK/q31nSaP6PV9qfzB9veSFIKpGpogOQNByzm9axsNhetszqy/Bdu22WXTdsZ/P6GR4+tsjm9TPsumF7ZaqnO3dspdVqM3+8RafTYf5465wnfFX/zpJG83+80v5kcbHjhSCpRFY9QYukcitD9WVSJoip8qyj126b5e7vPcDnbv8OxxYWWTczzY1PveKc3+dM33lStrlUB8Ped610yx1n45SGZxjHWJM9qSaKvs+dE8RUw74Dc9x25708asMMs9PdavBtd97LVT/2qL63k9tc0qkJ5JYtGzl06MECI5LqYVjHWJM9qUaKrDg5QUw17Nl7kMVWhwcfnmex1Wa6OcUFa6cH2k5uc0mSRmNYx1iTPUlD4QQx1fDduaMcPXacRmOKqUaDxVaHB47O02q1z/3mU7jNpdGzq7Q0mYZ1jDXZk2qkyJMCp+evhsVWh04H2p02ix1oNKDRW94vt7k0XKfuwx+/dTO33XmvXaWlCTSsY6yzcUo1caJv95GjC8tOCvYdmBvL3x9klkeNX6fTpt2BTufEY3qP+6/suc2l4VlpH/7pLx3k+GKbtWuaNBoN1q5p0mxOsWfvwaLDlTRiwzrGWtmTaqLo8VNFTxCj1Wk0pmjQhkY30Ws0gE53eb/c5pPF7oSjtdI+vN1p88h8i00bfvQ6u0pLk2FYx1iTPakmyjB+qsq3JJgUzWaDqWaDBt3um53eT7PZGOjz3OaTwZlXR2+lffh0c4rjLe+fKk2qYRxjTfakmnD81MrGVY2oStXj8tn13Hvfwzyy0GJxsc309BQXzDR5zMXeF0tnVnTPgUmw0j78wnVreOiR48wfbxV2/1RJ1eaYPakmHD91unGNYyx6vGQ/du7YyprpKS7auJbLt6znoo1rWTM9NdHtROc2d/8xZqaXnzLYnXC4VtqHTzcbPP9vb2Xz+hkePrbI5vUz7Lphuwm2pFWzsifVhOOnTjeuakSVqh62Ew3CngOjd7b/zRcVHJuk6jLZk2rE8VPLjWscYxnGS/bDdqJ+7dyxld237mce7E44Qv5vSho2kz1JtTWuaoRVD9WdFeHiVGU8sKRyMtmTVFvjqkZY9dAksOo0fs6CKul8mexJ6ltVrjSPqxph1UPSKFRpPLCkcjLZk9SXql1pHlc1wqqHpGGr2nhgSeVjsiepL15p1rBVpVIsjZvjgSWdL5M9Scuc68TbK80apiIqxSaXqopBxwPbxiWdYLIn6aTVnHh7pVnDNO5KcdW6IWuyDTIe2DYuaSmTPUknrebE25knNUzjrhTbDVlV0+94YNu4pKVM9iSdtJoTb2ee1DCNu1JsN2TVnW1c0lIme5JOWu2JtzNPaljGXSm2G7LqzjYuaampogOQVB47d2yl1Wozf7xFp9Nh/njLLpoaqWu3zbLrhu1sXj/Dw8cW2bx+hl03bB/ZxQTbuOrONi5pKSt7kk6yi+bqONPdcI2zUmwbV1kNa79iG5e0lMmepGXsonl2znRXfbZxlc2w9ytlbuNeLJPGy26cktSHpTPdNRoN1q5p0mxOsWfvwaJDk1RRk7JfOZHUHjm6sCyp3XdgrujQpNoy2ZOkPszdf4yZ6eW7Tme6k3Q+JmW/MilJrVQmJnuS1IfZTetYWGwvW+ZMd5LOx6TsVyYlqZXKxGRPkvrgTHeShm1S9iuTktRKZVKKCVoi4gXAbwON3s9vZuYnio1Kkk7nTHeShm1S9ivjvq+mpBIkexHRAD4CPCMz74yIa4HbIuKWzGyf4+2SNHarmenOGeeqze2ncSvzDJrDMilJrVQmhSd7PW1gU+/3zcD3TPQkVZW3Z6g2t580OpOQ1EplUviYvczsAC8FPhkR3wZuAV5VbFSSNDhnnKs2t58kqS4Kr+xFxDTwq8BPZ+ZtEfE04E8i4omZ+dBqPuOSSzaMNMZh2rJlY9EhqKJsO9Vx30MLbLxgmkajcXLZdLPBDx9aKGQ72nb6U7btV6RJ+74aDtuNBmXbGb7Ckz3gKcBlmXkbQC/hOwo8Abh9NR9w+PBDtNudEYY4HFu2bOTQoQeLDkMVZNuplos3zHDk6AJr1zRPLps/3uKiDTNj3462nf6VafsVybajQdhuNCjbzuCmphpnLH4V3o0T+A5wRUQEQEQ8AbgUOFBoVJI0oEmZRr2u3H6SpLoovLKXmfdGxBuAj0fEiUlZfjYz7ysyLkkalDPOVZvbT5JUF4UnewCZuRvYXXQckjQszjhXbW4/SVIdlKEbpyRJkiRpyEpR2ZMkSaqjfQfm7BIsqTAme5IkSSOw78Acu2/dT7M5xYXrpjlydIHdt+4HMOGTNBZ245QkSRqBPXsP0mxOsXZNk0ajwdo1TZrNKfbsPVh0aJImhMmeJEnSCMzdf4yZ6eWnWjPTU8zdf6ygiCRNGpM9SZKkEZjdtI6FxfayZQuLbWY3rSsoIkmTxjF7kiTpnJxopH87d2xl9637madb0VtYbNNqtdm5Y2vRoUmaECZ7kiTprJxoZDAn1o1J8nh4QUI6ncmeJEk6q6UTjQCsXdNkvrfck+mzu3bbrOtoDLwgIa3MMXuSJOmsnGhEZefMp9LKTPYkSdJZOdGIys4LEtLK7MapyrOPviSNlhONqOxmN63jyNGFk12NwQsSEljZU8Wd6KN/5OjCsj76+w7MFR2aJNXGtdtm2XXDdjavn+HhY4tsXj/Drhu2e2FNpbFzx1ZarTbzx1t0Oh3mj7e8ICFhZU8V56QBkjQeTjSiMnPmU2llJnuqtLn7j3HhuuXN2D76kiRNHi9ISKezG6cqzUkDJEmSpJWZ7KnS7KMvSZIkrcxunKo0++hLkiRJKzPZU+XZR1+SJEk6nd04JUmSJKmGTPYkSZIkqYZM9iRJkiSphkz2JEmSJKmGTPYkSZIkqYacjVOSNPH2HZjzFi6SpNox2ZMkTbR9B+bYfet+ms0pLlw3zZGjC+y+dT+ACZ8kqdLsxilJmmh79h6k2Zxi7ZomjUaDtWuaNJtT7Nl7sOjQJEk6L1b2pBqxK1q9uD3HY+7+Y1y4bvnhcGZ6irn7jxUUkSRJw2GyJ9WEXdHqxe05PrOb1nHk6AJr1zRPLltYbDO7aV2BUUmSdP7sxinVhF3R6sXtOT47d2yl1Wozf7xFp9Nh/niLVqvNzh1biw5NkqTzYmVPqgm7otWL23N8TlRK7TIrSaobkz2pJuyKVi9uz9ONcgzjtdtmTe4kSbVjN06pJuyKVi9uz+VOjGE8cnRh2RjGfQfmig5NkqTSsrIn1YRd0erF7bnc0jGMAGvXNJnvLZ/UdSJJ0rkUnuxFxFXALUsWbQYelZkXFxORVF12RasXt+ePOIZRkqT+FZ7sZebdwFNOPI6Id1KCuCRJ5eEYRkmS+leqMXsRMQPsAj5YdCySpPJwDKMkSf0rWwXtRcBfZ+YdRQciSSoPxzBKktS/RqfTKTqGkyLiM8CezHyzXURfAAAKzUlEQVTXKt9yFXDX6CKSJEmSpEq4Grh76YLSVPYi4nLgWcAr+33v4cMP0W6XJ2k9ky1bNnLo0INFh6EKGmfbGeW9zDR+7nc0KNuOBmG70aBsO4ObmmpwySUbVnyuNMke8Grg05l5uOhApEl14l5mzebUsnuZASZ8kiRJFVOmCVpegxOzSIVaei+zRqPB2jVNms0p9uw9WHRokiRJ6lNpKnuZub3oGKRJ573MJEmS6qNMlT1JBZvdtI6FxfayZd7LTJIkqZpM9iSd5L3MJEmS6qM03TglFc97mUmSJNWHyZ6kZa7dNmtyJ0mSVAN245QkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBqaLjoASeWy78Ace/YeZO7+Y8xuWsfOHVu5dtts0WEVwnUhSZKqzGRP0kn7Dsyx+9b9NJtTXLhumiNHF9h9636AiUtyXBeSJKnq7MYp6aQ9ew/SbE6xdk2TRqPB2jVNms0p9uw9WHRoY+e6kCRJVWeyJ+mkufuPMTO9fLcwMz3F3P3HCoqoOK4LSZJUdSZ7kk6a3bSOhcX2smULi21mN60rKKLiuC4kSVLVmexJOmnnjq20Wm3mj7fodDrMH2/RarXZuWNr0aGNnetCkiRVnRO0SDrpxMQjzkDpupAkSdVnsidpmWu3zZrQ9LguJElSldmNU5IkSZJqqBSVvYhYB/we8BzgGPClzHxdsVFJkiRJUnWVItkD3kY3yduemZ2IuLTogCRJkiSpygpP9iJiA/Aq4IrM7ABk5veLjUqSJEmSqq3wZA/YBhwG/m1E/D3gIeDXMvOLxYYlSZIkSdXV6HQ6hQYQEdcBfwHsysyPRsQO4M+AazLzgXO8/SrgrhGHKEmSJElldzVw99IFZajsHQQWgZsBMnNvRMwB24GvrOYDDh9+iHa72KR1NbZs2cihQw8WHYYqyLajQdl2NCjbjgZhu9GgbDuDm5pqcMklG1Z+bsyxnCYz54A/B24AiIjtwKOBbxUZlyRJkiRVWRkqewA/B3wwIt4BHAdemZlHCo5JkiRJkiqrFMleZv4V8OwB3tqEbumyKqoUq8rFtqNB2XY0KNuOBmG70aBsO4NZst6apz5X+AQt5+npwBeKDkKSJEmSCvYMYNkdDaqe7K0Fngp8D2gVHIskSZIkjVsT+DHgdmB+6RNVT/YkSZIkSSsofDZOSZIkSdLwmexJkiRJUg2Z7EmSJElSDZnsSZIkSVINmexJkiRJUg2Z7EmSJElSDZnsSZIkSVINTRcdwKSJiPcA19O94eFDwD/NzK8UG5WqICJeAbwJeCLwxsz8/YJDUolFxHbgw8AlwGHgVZn5zWKjUtlFxNuBfwBcBTw5M+8sNiJVRURcAnwE2AYsAN8EXp+ZhwoNTJUQEbcAVwNtuufHv5iZXys2qnqwsjd+n6V7AP1x4K3AxwqOR9XxNeBlwEeLDkSV8IfAezJzO/Ae4L0Fx6NquAV4JvDtogNR5XSAt2VmZOaTgQPAvys4JlXHqzPzxzPzJ4C3Ax8sOqC6MNkbs8z8VGYe7z38EnBFRLgddE6ZeWdm/j+6V72kM4qIRwPXATf3Ft0MXBcRW4qLSlWQmV/MzHuKjkPVk5n3Zebnlyz6MnBlQeGoYjLz/iUPN+G5ztDYjbNYvwB8OjNt0JKG6bHAX2dmCyAzWxHx3d5yu1RJGqneRew3AH9adCyqjoh4P3Aj0AB2FhxObZjsDVlE3AFsPcPTl544+YqIlwE30e0uI6267UiSVHLvpjvuyrHlWrXMfC1ARLwS+A/A3y82onow2RuyzLzuXK+JiBcDbwauz8zvjz4qVcFq2o60SvcAl0dEs1fVawKX9ZZL0sj0Jvl5HPBCey5pEJn5kYj4zxFxSWYeLjqeqnOs2JhFxAuA3wWem5l3FxyOpBrKzB/QndDn5b1FLwe+6qx4kkYpIt4C/CTwM5k5X3Q8qoaI2BARj13y+IXAfb0fnadGp9MpOoaJEhGH6E5JvPSk63qvXOhcIuLldLs1XES3DR0FbuxN2iItExGPp3vrhYuAH9K99UIWG5XKLiLeBbwEeAwwBxzOzCcVG5WqICKeBNwJ7Ace6S2+KzNfXFxUqoKIuBT4JLAeaNFN8n45M+8oNLCaMNmTJEmSpBqyG6ckSZIk1ZDJniRJkiTVkMmeJEmSJNWQyZ4kSZIk1ZDJniRJkiTVkMmeJGkiREQnIq7p/f6hiPid3u/PiIhS3ZYiIj4bEa8uOg5JUrVNFx2AJEn9iIinA28DnkT3nkzfAN6YmbcP8nmZ+QUghhfh6SLiQ8BNdO+RuQD8BfCLmfmXZ4jpeaOMR5I0GazsSZIqIyIeBXwKeDdwMXA58JvAfJFxrdLbMnMDcAXwA+BDp74gIhoR4bFZkjQUVvYkSVWyHSAzb+49fgT43IknI+JngV8BHgP8H+B1mfnts31gRDwb+KPMvKL3+G7g94FXAVcCe4BXZ+ax3vNvAv4Z0AF+HXgf8LjM/NZqvkBmPhwRHwU+1vu8zwO3Ac8GrgOeHBHv78X0/t5r/gnwS3QTxXuAV2TmHRFxGd3E95nAQ8DvZea7VhOHJKn+vHooSaqS/UArIj4cEc+LiItOPBERPw38K+AlwBbgC8DNK3/MOb0U2AlcDVwLvKb3N3bSTbqeA1xDN0HrS0RsAHYBX12y+JXA64CNwLdPef0/BH6DbvL5KOBFwOFeBfDPgP9Lt8J5PfDGiHhuvzFJkurJZE+SVBmZ+QDwdLpVtfcBhyLiTyPiUuDngLdm5jcycxF4C/CUiLhygD/1rsz8bmbeRzehekpv+UuB/5KZX8/Mh+kmYav1yxFxBPgWsIFeAtnzod5nLmbm8VPe91q6XUBvz8xOZn6rV618KrAlM38rMxcy86/orpOX9ftlJUn1ZDdOSVKlZOY3+FGl7fHAHwHvpNvl8j9GxDuWvLxBt+p11q6cK7h3ye8PA5f1fr8M+MqS5+7p4zPfnpm/dobnzvY5jwUOrLD8SuCyXgJ5QpNuRVOSJJM9SVJ1ZeZf9ma6fD3dhOnNmbl7hH/ye3THzZ3w2CF9bucsz90DbDvD8rsy83FDikGSVDMme5KkyuhV8p4PfCwzvxMRjwVeDnwZ+Azw2xHxtcz8ekRsAm7MzP86xBD+BPhgRHyEbrXw3wzxs8/k/cDvRsQXgTvoJn7H6U5A82BE/AvgXXRv6fAE4IJBb0MhSaoXx+xJkqrkQWAHsDcijtJN8u4E/nlm/jfg3wN/HBEP9JYP9X51mflZuonVn9Mde/fl3lMju/VDL1l9M/BRut//FuDizGwBL6A7nvAuYI5uYrhpVLFIkqql0emcreeIJEk6k4h4At2kcm1vUhhJkkrDZE+SpD5ExIvpdhm9EPgw0M7Mnyk2KkmSTueYPUmS+vN64ENAC/hfwM8DRMTX6c6QedrrRzxpjCRJK7KyJ0mSJEk15AQtkiRJklRDJnuSJEmSVEMme5IkSZJUQyZ7kiRJklRDJnuSJEmSVEMme5IkSZJUQ/8f8tlyW8g/FQgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiMBQUsFPWxr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "504dc050-da74-4e47-c26a-b1e7464366b5"
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f72ec065d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbPVxPm-eQt0",
        "colab_type": "text"
      },
      "source": [
        "#Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "schSuWu33BE8",
        "colab_type": "text"
      },
      "source": [
        "Bringing some changes to the data set for better prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFX9SB9NW9hl",
        "colab_type": "text"
      },
      "source": [
        "Converting object into integer for fuel type\n",
        "\n",
        "---\n",
        "1. CNG - 0\n",
        "2. Diesel - 1\n",
        "3. Petrol - 2\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBxbkKu7TRdK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.Fuel_Type.replace({'CNG':0,'Diesel':1,'Petrol':2},inplace = True)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MylBmRcXy9F",
        "colab_type": "text"
      },
      "source": [
        "Converting object to integer for seller type\n",
        "\n",
        "---\n",
        "1. Dealer - 1\n",
        "2.Individual - 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvJDWX-uXxX7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.Seller_Type.replace({'Dealer':1,'Individual':2},inplace = True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxJWFz1laSw2",
        "colab_type": "text"
      },
      "source": [
        "Converting object to integer in Transmiison type\n",
        "\n",
        "---\n",
        "1. Automatic - 1\n",
        "2. Manual - 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeKmSHX_Ykpo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.Transmission.replace({'Automatic':1,'Manual':0},inplace = True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIavvYwqxjlQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c8460b35-e382-4d2b-9b8f-45f8a3d33ba2"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    float64\n",
            " 5   Fuel_Type      301 non-null    int64  \n",
            " 6   Seller_Type    301 non-null    int64  \n",
            " 7   Transmission   301 non-null    int64  \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(3), int64(5), object(1)\n",
            "memory usage: 21.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvM4BLo8748E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "e8f5b614-70a7-4f55-d730-31830496034f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 26,
      "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>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>1.208960</td>\n",
              "      <td>5.59</td>\n",
              "      <td>10.203592</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.558145</td>\n",
              "      <td>9.54</td>\n",
              "      <td>10.668955</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.981001</td>\n",
              "      <td>9.85</td>\n",
              "      <td>8.839277</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>1.047319</td>\n",
              "      <td>4.15</td>\n",
              "      <td>8.556414</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.526056</td>\n",
              "      <td>6.87</td>\n",
              "      <td>10.656082</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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       1.208960  ...            1             0      0\n",
              "1      sx4  2013       1.558145  ...            1             0      0\n",
              "2     ciaz  2017       1.981001  ...            1             0      0\n",
              "3  wagon r  2011       1.047319  ...            1             0      0\n",
              "4    swift  2014       1.526056  ...            1             0      0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0l7riuP6gdGD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "8edb83f7-0636-43d5-ac65-247bead06b1d"
      },
      "source": [
        "#Checking the correlation between different features.\n",
        "df.corr()"
      ],
      "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",
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              "    }\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>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>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.272282</td>\n",
              "      <td>-0.047584</td>\n",
              "      <td>-0.545249</td>\n",
              "      <td>-0.053643</td>\n",
              "      <td>-0.039896</td>\n",
              "      <td>-0.000394</td>\n",
              "      <td>-0.182104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Selling_Price</th>\n",
              "      <td>0.272282</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.705845</td>\n",
              "      <td>0.170268</td>\n",
              "      <td>-0.456973</td>\n",
              "      <td>-0.851627</td>\n",
              "      <td>0.175041</td>\n",
              "      <td>-0.117410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Present_Price</th>\n",
              "      <td>-0.047584</td>\n",
              "      <td>0.705845</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.296159</td>\n",
              "      <td>-0.440415</td>\n",
              "      <td>-0.512030</td>\n",
              "      <td>0.348715</td>\n",
              "      <td>0.008057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kms_Driven</th>\n",
              "      <td>-0.545249</td>\n",
              "      <td>0.170268</td>\n",
              "      <td>0.296159</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.260903</td>\n",
              "      <td>-0.321632</td>\n",
              "      <td>0.019486</td>\n",
              "      <td>0.074260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel_Type</th>\n",
              "      <td>-0.053643</td>\n",
              "      <td>-0.456973</td>\n",
              "      <td>-0.440415</td>\n",
              "      <td>-0.260903</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.352415</td>\n",
              "      <td>-0.080466</td>\n",
              "      <td>0.055705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seller_Type</th>\n",
              "      <td>-0.039896</td>\n",
              "      <td>-0.851627</td>\n",
              "      <td>-0.512030</td>\n",
              "      <td>-0.321632</td>\n",
              "      <td>0.352415</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.063240</td>\n",
              "      <td>0.124269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission</th>\n",
              "      <td>-0.000394</td>\n",
              "      <td>0.175041</td>\n",
              "      <td>0.348715</td>\n",
              "      <td>0.019486</td>\n",
              "      <td>-0.080466</td>\n",
              "      <td>-0.063240</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.050316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Owner</th>\n",
              "      <td>-0.182104</td>\n",
              "      <td>-0.117410</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.074260</td>\n",
              "      <td>0.055705</td>\n",
              "      <td>0.124269</td>\n",
              "      <td>0.050316</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Year  Selling_Price  ...  Transmission     Owner\n",
              "Year           1.000000       0.272282  ...     -0.000394 -0.182104\n",
              "Selling_Price  0.272282       1.000000  ...      0.175041 -0.117410\n",
              "Present_Price -0.047584       0.705845  ...      0.348715  0.008057\n",
              "Kms_Driven    -0.545249       0.170268  ...      0.019486  0.074260\n",
              "Fuel_Type     -0.053643      -0.456973  ...     -0.080466  0.055705\n",
              "Seller_Type   -0.039896      -0.851627  ...     -0.063240  0.124269\n",
              "Transmission  -0.000394       0.175041  ...      1.000000  0.050316\n",
              "Owner         -0.182104      -0.117410  ...      0.050316  1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shzlaH30czQe",
        "colab_type": "text"
      },
      "source": [
        "#Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICSQSQ5J-zjl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing,model_selection,svm\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor \n",
        "from sklearn.metrics import mean_squared_error,r2_score"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CawOY-dh_S7v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop('Car_Name' , axis = 1);"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x89H0w3e_N6-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "a2cd1e72-ad16-4022-8d24-424a7b599a97"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 30,
      "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>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>1.208960</td>\n",
              "      <td>5.59</td>\n",
              "      <td>10.203592</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>1.558145</td>\n",
              "      <td>9.54</td>\n",
              "      <td>10.668955</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>1.981001</td>\n",
              "      <td>9.85</td>\n",
              "      <td>8.839277</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1.047319</td>\n",
              "      <td>4.15</td>\n",
              "      <td>8.556414</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>1.526056</td>\n",
              "      <td>6.87</td>\n",
              "      <td>10.656082</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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       1.208960           5.59  ...            1             0      0\n",
              "1  2013       1.558145           9.54  ...            1             0      0\n",
              "2  2017       1.981001           9.85  ...            1             0      0\n",
              "3  2011       1.047319           4.15  ...            1             0      0\n",
              "4  2014       1.526056           6.87  ...            1             0      0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dh7W3RL_R1n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = pd.DataFrame(df['Selling_Price'])\n",
        "X = (df.drop(['Selling_Price'] , axis = 1))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kwcw2pd1AbdD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Preprocessing the data.\n",
        "X = preprocessing.scale(X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQtKaY00AmtI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Splitting the data.\n",
        "X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y,test_size = 0.2)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__3aQEZByN19",
        "colab_type": "text"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WSyJsbLA5US",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d15788cf-96a6-4394-ea1e-eecefcdb320c"
      },
      "source": [
        "clf = LinearRegression()\n",
        "\n",
        "#Fitting the data.\n",
        "clf.fit(X_train , y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "#Calculating Accuracy.\n",
        "print(\"The R2 Score is {}\".format(r2_score(y_test,y_pred)))\n",
        "print(\"The Mean Squared Error is {}\".format(mean_squared_error(y_test,y_pred)))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The R2 Score is 0.9130781080419725\n",
            "The Mean Squared Error is 0.1293323066073875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prl3b3KMZFb6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b2ad2235-18a8-4daa-d767-d877975c135d"
      },
      "source": [
        "# create a regressor object \n",
        "regressor = DecisionTreeRegressor(random_state = 0)  \n",
        "  \n",
        "# fit the regressor with X and Y data \n",
        "regressor.fit(X, y) \n",
        "\n",
        "y_pred = regressor.predict(X_test)\n",
        "\n",
        "#Calculating Accuracy.\n",
        "print(\"The R2 Score is {}\".format(r2_score(y_test,y_pred)))\n",
        "print(\"The Mean Squared Error is {}\".format(mean_squared_error(y_test,y_pred)))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The R2 Score is 1.0\n",
            "The Mean Squared Error is 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v1FTQJE0dLG",
        "colab_type": "text"
      },
      "source": [
        "The Linear Regression is giving an accuracy of approx 90%\n",
        "\n",
        "But it seems like there is an overfitting condition in DecisionTree "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4gjW4-o0KPW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    }
  ]
}