BigMartSales_Prediction.ipynb

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    "colab": {
      "name": "BigMartSales_Prediction.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/roshank1605A04/Big-Mart-Sales-Prediction/blob/master/BigMartSales_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "ZW_ZTAhNAu7l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Importing the libraries**"
      ]
    },
    {
      "metadata": {
        "id": "URs2bq__ALsD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ybDlFfciBBC8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Importing the datasets**"
      ]
    },
    {
      "metadata": {
        "id": "-4eIZ1ZYBAVD",
        "colab_type": "code",
        "outputId": "4ba8c8ad-fd55-4b9a-ee7d-cafabb4468aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"drive/My Drive/Projects/big mart/bm_Train.csv\")\n",
        "test = pd.read_csv(\"drive/My Drive/Projects/big mart/bm_Test.csv\")\n",
        "\n",
        "# making copies of train and test dataset\n",
        "train = train.copy()\n",
        "test = test.copy()\n",
        "\n",
        "train.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "metadata": {
        "id": "rpmEX7jcBcrL",
        "colab_type": "code",
        "outputId": "c39d0ae9-3a6c-4967-9cdb-e2439876aee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7060.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>140.992782</td>\n",
              "      <td>1997.831867</td>\n",
              "      <td>2181.288914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.643456</td>\n",
              "      <td>0.051598</td>\n",
              "      <td>62.275067</td>\n",
              "      <td>8.371760</td>\n",
              "      <td>1706.499616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.290000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>33.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.773750</td>\n",
              "      <td>0.026989</td>\n",
              "      <td>93.826500</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>834.247400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.053931</td>\n",
              "      <td>143.012800</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.850000</td>\n",
              "      <td>0.094585</td>\n",
              "      <td>185.643700</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3101.296400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>13086.964800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
              "count  7060.000000      8523.000000  8523.000000                8523.000000   \n",
              "mean     12.857645         0.066132   140.992782                1997.831867   \n",
              "std       4.643456         0.051598    62.275067                   8.371760   \n",
              "min       4.555000         0.000000    31.290000                1985.000000   \n",
              "25%       8.773750         0.026989    93.826500                1987.000000   \n",
              "50%      12.600000         0.053931   143.012800                1999.000000   \n",
              "75%      16.850000         0.094585   185.643700                2004.000000   \n",
              "max      21.350000         0.328391   266.888400                2009.000000   \n",
              "\n",
              "       Item_Outlet_Sales  \n",
              "count        8523.000000  \n",
              "mean         2181.288914  \n",
              "std          1706.499616  \n",
              "min            33.290000  \n",
              "25%           834.247400  \n",
              "50%          1794.331000  \n",
              "75%          3101.296400  \n",
              "max         13086.964800  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "metadata": {
        "id": "5i6wQeOlBiJQ",
        "colab_type": "code",
        "outputId": "d60f7142-ab61-4ebe-db06-a31269ccd19c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "test.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4705.000000</td>\n",
              "      <td>5681.000000</td>\n",
              "      <td>5681.000000</td>\n",
              "      <td>5681.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.695633</td>\n",
              "      <td>0.065684</td>\n",
              "      <td>141.023273</td>\n",
              "      <td>1997.828903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.664849</td>\n",
              "      <td>0.051252</td>\n",
              "      <td>61.809091</td>\n",
              "      <td>8.372256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.990000</td>\n",
              "      <td>1985.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.645000</td>\n",
              "      <td>0.027047</td>\n",
              "      <td>94.412000</td>\n",
              "      <td>1987.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.500000</td>\n",
              "      <td>0.054154</td>\n",
              "      <td>141.415400</td>\n",
              "      <td>1999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.700000</td>\n",
              "      <td>0.093463</td>\n",
              "      <td>186.026600</td>\n",
              "      <td>2004.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.323637</td>\n",
              "      <td>266.588400</td>\n",
              "      <td>2009.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year\n",
              "count  4705.000000      5681.000000  5681.000000                5681.000000\n",
              "mean     12.695633         0.065684   141.023273                1997.828903\n",
              "std       4.664849         0.051252    61.809091                   8.372256\n",
              "min       4.555000         0.000000    31.990000                1985.000000\n",
              "25%       8.645000         0.027047    94.412000                1987.000000\n",
              "50%      12.500000         0.054154   141.415400                1999.000000\n",
              "75%      16.700000         0.093463   186.026600                2004.000000\n",
              "max      21.350000         0.323637   266.588400                2009.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "id": "MuJpgt7yBmqT",
        "colab_type": "code",
        "outputId": "d52951f8-a6c9-40c3-a99a-ed14f7e8427a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# Checking the shape of the training and testing datasets\n",
        "\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8523, 12)\n",
            "(5681, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SUbzP0nNDhpz",
        "colab_type": "code",
        "outputId": "74501059-e92a-4703-ecd0-cede18db29e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# combining the train and test dataset\n",
        "\n",
        "data = pd.concat([train, test])\n",
        "\n",
        "print(data.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(14204, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qTC0hdM5B68_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Data Visualization**"
      ]
    },
    {
      "metadata": {
        "id": "UWUHX5ZbCBtr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "*univariate data analysis*"
      ]
    },
    {
      "metadata": {
        "id": "jFM9owPYhwYE",
        "colab_type": "code",
        "outputId": "b6779a3a-9ece-4c70-af8b-7aefe5c685ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train['Item_Outlet_Sales'], bins = 20, color = 'pink')\n",
        "plt.title('Target Variable')\n",
        "plt.xlabel('Item Outlet Sales')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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91p/lAgBgDL/N4IuLizV9+nTFxcV5t82bN0+DBw9WWlqaEhIS9MYbb0iS6tatq6VLl3r/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\nAAxEwAMAYKD/D2xI7gxkUrBHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5b0cb70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nUo2AhmiCe-f",
        "colab_type": "code",
        "outputId": "b4c4b22c-23f0-4849-c804-5e5e8929def4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the columns of the train set\n",
        "\n",
        "print(train.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
            "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
            "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
            "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "k6qkaJWcD8dm",
        "colab_type": "code",
        "outputId": "4fd65ab8-1120-42fc-cfef-04dded153f77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "cell_type": "code",
      "source": [
        "train.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier               object\n",
              "Item_Weight                  float64\n",
              "Item_Fat_Content              object\n",
              "Item_Visibility              float64\n",
              "Item_Type                     object\n",
              "Item_MRP                     float64\n",
              "Outlet_Identifier             object\n",
              "Outlet_Establishment_Year      int64\n",
              "Outlet_Size                   object\n",
              "Outlet_Location_Type          object\n",
              "Outlet_Type                   object\n",
              "Item_Outlet_Sales            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "metadata": {
        "id": "Lc8oh0ZlHOdT",
        "colab_type": "code",
        "outputId": "52c15405-a0a7-4c77-f6ce-4c35abd54e22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1165
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different items in Item Idemtifier\n",
        "\n",
        "train['Item_Identifier'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FDW13    10\n",
              "FDG33    10\n",
              "DRE49     9\n",
              "FDX04     9\n",
              "NCB18     9\n",
              "FDV38     9\n",
              "NCJ30     9\n",
              "FDG09     9\n",
              "FDV60     9\n",
              "NCL31     9\n",
              "FDW49     9\n",
              "FDF56     9\n",
              "NCQ06     9\n",
              "FDU12     9\n",
              "NCI54     9\n",
              "FDP25     9\n",
              "FDF52     9\n",
              "FDD38     9\n",
              "NCF42     9\n",
              "FDW26     9\n",
              "FDX31     9\n",
              "DRN47     9\n",
              "FDQ40     9\n",
              "FDX20     9\n",
              "NCY18     9\n",
              "FDO19     9\n",
              "FDT07     9\n",
              "FDO32     8\n",
              "FDH10     8\n",
              "DRK12     8\n",
              "         ..\n",
              "DRE01     2\n",
              "FDD48     2\n",
              "FDU09     2\n",
              "FDE38     2\n",
              "NCC55     2\n",
              "FDD22     2\n",
              "DRG25     2\n",
              "FDF38     2\n",
              "NCS41     2\n",
              "FDB47     2\n",
              "FDT33     2\n",
              "DRL59     2\n",
              "FDW58     2\n",
              "FDI36     2\n",
              "NCM42     2\n",
              "FDM16     2\n",
              "FDR03     2\n",
              "FDA48     2\n",
              "FDP15     2\n",
              "NCV18     2\n",
              "FDM38     2\n",
              "FDY43     1\n",
              "FDN52     1\n",
              "DRF48     1\n",
              "FDC23     1\n",
              "FDQ60     1\n",
              "FDE52     1\n",
              "FDT35     1\n",
              "FDO33     1\n",
              "FDK57     1\n",
              "Name: Item_Identifier, Length: 1559, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "metadata": {
        "id": "dUA2pZDZB24Q",
        "colab_type": "code",
        "outputId": "8510c8cb-1a14-4d2b-ebb8-348b02df575e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Item_Identifier'].value_counts(normalize = True)\n",
        "train['Item_Identifier'].value_counts().plot.hist()\n",
        "plt.title('Different types of item available in the store')\n",
        "plt.xlabel('Item Identifier')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiJiYglxERMTEHBrkeXl5dO3alffff5+TJ08SExNDdHQ0o0eP5uLFiwBs2LCBvn370r9/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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5afd240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oOYixOAUDE5p",
        "colab_type": "code",
        "outputId": "f3697842-a416-4f6b-dcfc-bf34bbbf60ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different items in Item Fat Content\n",
        "\n",
        "train['Item_Fat_Content'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5089\n",
              "Regular    2889\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "metadata": {
        "id": "i5lyo7aHa5fT",
        "colab_type": "code",
        "outputId": "3808d876-982a-46e0-d4fa-5f4ea91c22f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "cell_type": "code",
      "source": [
        "# checking different varieties of item fat content\n",
        "\n",
        "train['Item_Fat_Content'].value_counts(normalize = True)\n",
        "train['Item_Fat_Content'].value_counts().plot.bar()\n",
        "plt.title('Different varieties of fats in item in the store')\n",
        "plt.xlabel('Fat')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ta6+9tjLyAQCAc+Cx4MeNG6fo6Gg9/PDDkqTvv/9ejz/+uBYvXuz1cAAA4Nx4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\nAAb6/1AgJHg6c2TmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5b2e668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IXr6Fb_hIc-C",
        "colab_type": "code",
        "outputId": "cf0581f3-3519-4597-d81c-4ebb0b933af6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different items in Item Type\n",
        "\n",
        "train['Item_Type'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fruits and Vegetables    1232\n",
              "Snack Foods              1200\n",
              "Household                 910\n",
              "Frozen Foods              856\n",
              "Dairy                     682\n",
              "Canned                    649\n",
              "Baking Goods              648\n",
              "Health and Hygiene        520\n",
              "Soft Drinks               445\n",
              "Meat                      425\n",
              "Breads                    251\n",
              "Hard Drinks               214\n",
              "Others                    169\n",
              "Starchy Foods             148\n",
              "Breakfast                 110\n",
              "Seafood                    64\n",
              "Name: Item_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "id": "qXtPAMYaba3O",
        "colab_type": "code",
        "outputId": "7d6b5423-800e-46aa-b4bd-7b75430fa933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Item_Type'].value_counts(normalize = True)\n",
        "train['Item_Type'].value_counts().plot.bar()\n",
        "plt.title('Different types of item available in the store')\n",
        "plt.xlabel('Item')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KxIAnIiJSIAY8ERGRAjHgiYiIFIgBT0REpEAMeCIiIgViwBMRESkQA56IiEiBGPBEREQK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\nByzd88AWqPHmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5a02710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gUTx5L6xIoxU",
        "colab_type": "code",
        "outputId": "7f063cae-9633-4fb0-f9a7-8399f5ada740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different types of Outlet Identifier\n",
        "\n",
        "train['Outlet_Identifier'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OUT027    935\n",
              "OUT013    932\n",
              "OUT046    930\n",
              "OUT049    930\n",
              "OUT035    930\n",
              "OUT045    929\n",
              "OUT018    928\n",
              "OUT017    926\n",
              "OUT010    555\n",
              "OUT019    528\n",
              "Name: Outlet_Identifier, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "metadata": {
        "id": "U584tdFMc3e0",
        "colab_type": "code",
        "outputId": "49e32767-5059-4056-d5b9-8f4867ed9a90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Outlet_Identifier'].value_counts(normalize = True)\n",
        "train['Outlet_Identifier'].value_counts().plot.bar()\n",
        "plt.title('Different types of outlet identifier in the store')\n",
        "plt.xlabel('Item')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p0sRl6MTEqy4f4wnx8cnejlAstuWV7MtsW16JzJ5gW16JzJ5QnLyFlb7LIl+6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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5960a90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6aCf6VLGI1jm",
        "colab_type": "code",
        "outputId": "737b47cc-8161-42b3-cd1a-3d428ce03aca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different types of Outlet Size\n",
        "\n",
        "train['Outlet_Size'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Medium    2793\n",
              "Small     2388\n",
              "High       932\n",
              "Name: Outlet_Size, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "metadata": {
        "id": "C4T_7_kadMTV",
        "colab_type": "code",
        "outputId": "2784e334-82f9-4313-fe3d-64900e1c22ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Outlet_Size'].value_counts(normalize = True)\n",
        "train['Outlet_Size'].value_counts().plot.bar()\n",
        "plt.title('Different types of outlet sizes in the store')\n",
        "plt.xlabel('Item')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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37y5JioqK0r59++weDAAAlJzNgn/ttdc0c+ZM+fj4SJJ69OihWbNm2T0YAAAo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\n8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b59302e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8umNTuZHJByy",
        "colab_type": "code",
        "outputId": "d662de8a-c59b-422e-9d9c-04c06fe4a2ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "# checking different types of items in Outlet Location Type\n",
        "\n",
        "train['Outlet_Location_Type'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tier 3    3350\n",
              "Tier 2    2785\n",
              "Tier 1    2388\n",
              "Name: Outlet_Location_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "metadata": {
        "id": "HBuJk3wRdcZ2",
        "colab_type": "code",
        "outputId": "94e4f0ce-72b3-4eb7-9bd5-ea4cf8c25f60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Outlet_Location_Type'].value_counts(normalize = True)\n",
        "train['Outlet_Location_Type'].value_counts().plot.bar()\n",
        "plt.title('Different types of outlet location types in the store')\n",
        "plt.xlabel('Item')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAE2bNq3cmd2SdPvtt+vhhx/W73//e509e1atWrXSzJkzL7ksFotFL774ombMmKGXX35Z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\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5965f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HWBfXPNKJMJN",
        "colab_type": "code",
        "outputId": "9265f298-aa88-45d0-ac80-4f8923f8b4ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "cell_type": "code",
      "source": [
        "# checking different types of item in Outlet Type\n",
        "\n",
        "train['Outlet_Type'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Supermarket Type1    5577\n",
              "Grocery Store        1083\n",
              "Supermarket Type3     935\n",
              "Supermarket Type2     928\n",
              "Name: Outlet_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "metadata": {
        "id": "73UIcqRdJYOh",
        "colab_type": "code",
        "outputId": "6af8baa9-a4a0-4919-aed4-f11708b4c225",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "cell_type": "code",
      "source": [
        "# we will analyze only the training set\n",
        "\n",
        "train['Outlet_Type'].value_counts(normalize = True)\n",
        "train['Outlet_Type'].value_counts().plot.bar()\n",
        "plt.title('Different types of outlet types in the store')\n",
        "plt.xlabel('Item')\n",
        "plt.ylabel('Number of Items')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W/Jtvvun62ul0at++fYqMjFRUVJRHgwEAgKvntuAXLFhQ7PsTJ04oISHBY4EA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\nAAAsiIIHAMCCKHgAACzo/wORZdbtzvR1bwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b58530b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FAN9qROlK8AJ",
        "colab_type": "code",
        "outputId": "feeee966-eb0a-464e-a61e-affa41191db0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        }
      },
      "cell_type": "code",
      "source": [
        "# fat content vs outlet identifier\n",
        "\n",
        "Item_Fat_Content = pd.crosstab(train['Item_Fat_Content'],train['Outlet_Identifier'])\n",
        "Item_Fat_Content.div(Item_Fat_Content.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(11, 11))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f99b57dae10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Z879SknpmCfrhEQAAIAAGixLDx8+qImeVPX7x7eT1xYV6qm8w0oJCw943aRO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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5798320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_1514nPmLDD5",
        "colab_type": "code",
        "outputId": "80d53701-03c6-4036-9013-f6e16904a272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 875
        }
      },
      "cell_type": "code",
      "source": [
        "# fat content vs item type\n",
        "\n",
        "Item_Type = pd.crosstab(train['Item_Type'], train['Item_Fat_Content'])\n",
        "Item_Type.div(Item_Type.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(13, 13))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f99b577dd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jjjp2pX3Lly/Ppz99Ti6d//MseOD5NL/y2vn0gw7dIYMO3WGl2/YZUt96edjp\n+7Rert990GsXDh6yXp4DAAIfgHW06aa9861vTcoTC59Mamqy7ZE7d/ZIAETgA7COBg0alB/+cHJO\nv+2czh4FgNep7ewBAACA9ccRfKBY3mUJgI2RI/gAAFAQgQ8AAAUR+ADk5pt/lYsv/t5bXmdp4yt5\n+MK70nj306u9zUsz5r3lxwFg9Tb4c/CdQwtsjHa9533rdb3p77p5va63OoufeTmbvX2LDHz3tqvc\n39LcksY7n8rmIzr+ff4BNhaO4AOwkmuvvTqf+MTH8olPfCxXXnl55sx5Op/97H8kSR588P4cdthB\naWlpSXNzcz7ykQ+13q/5lWWZ94fZWfhQYxrvejqLHn8xj112b2ZOvi+zfv5AWppb8uwtj2Xp3Fcy\n51d/76ynB1C8Df4IPgAd59lnn8l///ev8qMf/WeS5JRTTsjBB49OY+PcVCqVPPjg/Xn724dl1qwn\nsnz5suy884g8k1eSJHW9e6Rh/7dl6dymDNx327w0fV62O3ZEetZvkqeufyiLZr6YhlHbZfGcl7PN\nEcM682kCFE3gA9Dqscf+nhEj3pG6utd+PLzjHbtn5sxHs8MOO+bpp2fnoYdm5Mgjj8306Q/k1Vdf\nzZ57vjPP5A+rXKuud/fMueGRVFoqWbZgSfrsUN+RTwVgo+UUHQBepyaVSqV1a/ny5ampqc2ee74z\nM2ZMz6uvLs3IkXtlxowH8+CD92fkyL1Wu9LTNzycwYfvlB1PHpnNhg/oiOEBiMAH4HV22mlYpk9/\nMM3NzWlubs5DD83ITjsNy557jswtt9ycwYO3zeabb56XXnopL720IFtuOWi1a61Y2pzu/XplxZLl\naZr1UiorWpKamlRaKqu9DwBvnVN0AGi11VZb5wMfODJnnnlKWloqOeKID2bQoK2SJE8++USOOOKD\nSZK+fftmiy22WONaA961TWb++C/pucWmadhvu8y9fVb6vn2LVFa05MkpD2b7se+o+vMB2BgJfIAN\nUEe9reU/vO99R7RePvroD+Xooz/0ptv88pe3tF4+77yvrnKd/ntu1Xp50KE7ZNChO7xp3/Az3/2W\n5wVg9ZyiAwAABRH4AABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAAUBCBDwAABfE++AAboM/X91mv631z\nQdN6XQ+ADZfAByBJcvPNv8rdd9+Z+fMbs88+++buu+9ITU1t9t//oBx33L9l3ry5Oe+8L6R79+7Z\nffc9c//9f83FF1/W2WMD8AZO0QGg1dy5z+fcc7+Se++9Jz/4weRccsmP8vvf35bnn38+U6b8PIcc\nMjoXX3xZli9f1tmjArAajuDCnE/DAAAgAElEQVQD0GrnnXfJww/PyJw5T+fMMz+RJFm8+JU8//yz\nmT17Vg49dEySZNSoA/PQQzM6c1QAVkPgA9Cqrq576uq6Z999R+Wcc7600r4rrrg8tbWv/cdvTU1n\nTAdAezhFB4CVDBu2c+677y9ZunRpKpVKvve9b+fVV5dm8ODBeeSRh5Ikd999ZydPCcDqOIIPwEoG\nDRqUD33ouJx++sdTW1ubAw44KD179sqxxx6X88//Qm6//bbsssuIdOvWrbNHBWAVBD7ABqgz3tby\nfe87ovXyUUcdm6OOOnal/cuXL8+nP31Odtttj9x66y156aWXOnpEANpB4APQLptu2jvf+tak1NTU\npLa2Nl/84vmdPRIAqyDwAWiXQYMG5Yc/nNzZYwDQBn9kCwAABRH4AABQEIEPAAAFEfgAAFAQgQ8A\nAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAF\nEfgAAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAAUBCBDwAABRH4\nAABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAA\nUJB2Bf6kSZMyduzYjBs3Lg888MAqb/Od73wnH/nIR9brcAAAwNppM/DvueeezJ49O1OmTMnEiRMz\nceLEN91m5syZ+fOf/1yVAQEAgPZrM/DvuuuujB49OkkydOjQLFy4ME1NTSvd5oILLsinP/3p6kwI\nAAC0W11bN5g/f35GjBjRut2/f/80NjamT58+SZKpU6fmXe96VwYPHtyuB6yv3zR1dd3Wcdz1Z+DA\nvp09wjqp1tyPVmXV13TFr3VXnDnpmnObueN0xbnNvDLfq1dm5o7TFefemGduM/DfqFKptF5+6aWX\nMnXq1Pz0pz/N3Llz23X/BQsWr+1DVkVj46LOHmGddMW5zdxxuuLcZu44XXFuM3ecrji3mTtOV5x7\nY5h5db8QtBn4DQ0NmT9/fuv2vHnzMnDgwCTJ3XffnRdffDEf/vCHs2zZsjz11FOZNGlSxo8fv1bD\nAQBtu/D4hqqtfUnVVgY6Wpvn4I8aNSrTpk1LksyYMSMNDQ2tp+ccdthhufnmm3Pttdfm4osvzogR\nI8Q9AAB0ojaP4I8cOTIjRozIuHHjUlNTkwkTJmTq1Knp27dvxowZ0xEzAgAA7dSuc/DPPvvslbaH\nDx/+pttss802ueKKK9bPVAAAwDrxSbYAAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgA\nAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgAAFCQus4egA3Dhcc3VG3tS6q2MgAAb+QI\nPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4A\nABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAU\nROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETg\nAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMA\nQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBB\nBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+\nAAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAA\nFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE\n4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROAD\nAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBA\nQQQ+AAAUROADAEBBBD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUpK49N5o0aVLuv//+1NTUZPz4\n8dltt91a991999357ne/m9ra2gwZMiQTJ05Mba3fGwAAoDO0WeL33HNPZs+enSlTpmTixImZOHHi\nSvvPP//8fP/7388111yTV155JX/84x+rNiwAALBmbQb+XXfdldGjRydJhg4dmoULF6apqal1/9Sp\nUzNo0KAkSf/+/bNgwYIqjQoAALSlzcCfP39+6uvrW7f79++fxsbG1u0+ffokSebNm5c77rgjBx54\nYBXGBAAA2qNd5+C/XqVSedN1L7zwQj75yU9mwoQJK/0ysCr19Zumrq7b2j7sejdwYN/OHmGddMW5\nzdxxuuLcZu44XXFuM3ecrji3mTtOV5x7Y565zcBvaGjI/PnzW7fnzZuXgQMHtm43NTXl4x//eM46\n66zst99+bT7gggWL13HU9auxcVFnj7BOuuLcZu44XXFuM3ecrji3mTtOV5zbzB2nK869Mcy8ul8I\n2jxFZ9SoUZk2bVqSZMaMGWloaGg9LSdJLrjggpxwwgk54IAD1mogAABg/WvzCP7IkSMzYsSIjBs3\nLjU1NZkwYUKmTp2avn37Zr/99ssNN9yQ2bNn5xe/+EWS5PDDD8/YsWOrPjgAAPBm7ToH/+yzz15p\ne/jw4a2Xp0+fvn4nAgAA1plPpAIAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIII\nfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwA\nACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAo\niMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjA\nBwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcA\ngIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICC\nCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8\nAAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAA\nKIjABwCAggh8AAAoiMAHAICCCHwAACiIwAcAgIIIfAAAKEhdZw8AAJRryT2HVW/xQ6q3NHRlAh8A\ngE7hF8DqEPgAAG9QtfDciKOTjiPwAQBgLWzovwAKfAA2Shv6D2iAdeVddAAAoCCO4APwlvgjOYAN\niyP4AABQEIEPAAAFEfgAAFAQgQ8AAAUR+AAAUBCBDwAABRH4AABQEIEPAAAFEfgAAFAQgQ8AAAUR\n+AAAUBCBDwAABanr7AEA+Kcl9xxWvcUPqd7SAGw4BD50MAEHAFSTU3QAAKAgjuBXgSO0sGGo2r9F\n/w4B2IBt8IHvBzQAALSfU3QAAKAgAh8AAAqywZ+iA2viFK6O4e9KADZ8vlfzD47gAwBAQRzBB4Au\nwhFaoD0cwQcAgIIIfAAAKIhTdEjiv30BAErhCD4AABRE4AMAQEEEPgAAFETgAwBAQQQ+AAAUROAD\nAEBBBD4AABRE4AMAQEHaFfiTJk3K2LFjM27cuDzwwAMr7bvzzjtzzDHHZOzYsbnkkkuqMiQAANA+\nbQb+Pffck9mzZ2fKlCmZOHFiJk6cuNL+r3/967noooty9dVX54477sjMmTOrNiwAALBmbQb+XXfd\nldGjRydJhg4dmoULF6apqSlJ8vTTT6dfv37ZaqutUltbmwMPPDB33XVXdScGAABWq83Anz9/furr\n61u3+/fvn8bGxiRJY2Nj+vfvv8p9AABAx6upVCqVNd3gvPPOy4EHHth6FP+4447LpEmTMmTIkNx3\n332ZPHly67n31113XZ5++ul85jOfqf7kAADAm7R5BL+hoSHz589v3Z43b14GDhy4yn1z585NQ0ND\nFcYEAADao83AHzVqVKZNm5YkmTFjRhoaGtKnT58kyTbbbJOmpqbMmTMnzc3Nuf322zNq1KjqTgwA\nAKxWm6foJMm3v/3t3HvvvampqcmECRPy0EMPpW/fvhkzZkz+/Oc/59vf/naS5L3vfW9OPvnkqg8N\nAACsWrsCHwAA6Bp8ki0AABRE4AMAQEGKCfyWlpa8/PLLnT0GQIfwPQ+A1enSgX/ZZZflmmuuSVNT\nU4455picddZZufDCCzt7rCI9//zzb7ru8ccf74RJyrdw4cLOHmGt3XDDDbnuuuuybNmynHzyyTn6\n6KPz85//vLPHWqOHH344f/rTn5Ikl1xySU477bT85S9/6eSp1qwrf897/vnnc++99yZJli1b1snT\nlOvBBx9803V33313J0zChua5557LAw88kCT55S9/mYkTJ+aJJ57o5Kna1tTUlFmzZiVJ7rnnnlx+\n+eV58cUXO3mqNfvTn/6Um266KUkyfvz4jBs3LrfeemuHzlDXoY+2nt1222255pprcu211+bQQw/N\n6aefnhNPPLGzx2qXZcuWpUePHlm4cGGeffbZ7Lzzzp090iq9+OKLeeGFFzJ+/PhccMEF+cffZDc3\nN+dTn/pU61uobkj+/Oc/r3H/3nvv3UGTrJvjjz8+2267bT7wgQ/k0EMPTc+ePTt7pDZdffXVueqq\nq3LzzTdn2LBhOeecc3LCCSfk+OOP7+zRVusrX/lKvv3tb+eOO+7II488kgkTJuTzn/98Lr/88s4e\nbbW66ve8yy+/PLfccksWL16cG2+8Md/61rcycODAnHLKKZ092io9++yza9y/9dZbd9Ak7Td79uzM\nmjUr3/3ud/PZz3629frly5dn0qRJue222zpxujV7+OGH88ILL2S//fbLJZdckhkzZuTkk0/OO9/5\nzs4e7U0uvvjiNe4/44wzOmiStfe5z30uX/rSl/K3v/0t119/fT71qU9l4sSJmTx5cmePtkZnnXVW\nPv7xj6e5uTnf/OY3c8IJJ+SLX/xiLr300s4ebbUuuuiiTJ48Obfeemu6deuWK6+8MieddFLGjBnT\nYTN06cBvaWlJS0tLfvWrX+WrX/1qkuSVV17p5Kna9rWvfS277rprDjjggJxwwgnZY489Ultb2/oc\nNiRPPPFErr/++jz55JP58pe/3Hp9bW1tjjjiiM4bbA2uuOKKJMnLL7+cRx99NCNGjEhLS0tmzJiR\n3XbbbYMP/JtuuimPP/54fvvb3+bUU0/NwIEDc/jhh2f//ffv7NFWq7a2NnV1dZk2bVrrD7hXX321\nk6dasx49emSbbbbJj3/84xx33HHZcsst09LS0tljrVFX/Z73m9/8Jtdcc00+8pGPJPnnEa0NNfDP\nPPPM1NTUZPny5Zk1a1a23XbbrFixInPmzMkuu+ySa6+9trNHfJOlS5dm+vTpefHFF3PLLbe0Xl9T\nU7NBR2fStX7Zrq+vT5I88MADWbBgQfbee+9UKpX8z//8zwb5i9/rdevWLTvvvHNrJL/zne/MihUr\nOnusNi1btiz77LNPvv/97+fEE0/MEUcckalTp3b2WGvUo0eP9OnTJ7/5zW8yduzY1NXVdfjXuksH\n/ujRozNq1KgcdthhGTJkSC655JLsvvvunT1Wmx555JGcd955+dnPfpZjjjkmJ554Yj72sY919lir\ntNdee2WvvfbKEUcckfe85z1JkhUrVqSpqSn9+vXr5OlW7fvf/36S5PTTT8+tt96a3r17J3ntv/nO\nPffczhyt3YYOHZr+/funX79+mTp1an7yk5/kwgsvzOc+97nss88+nT3em4wYMSJjxozJkCFDsvPO\nO+eKK67Y4H/Yde/ePeeee27+9re/5bzzzssf/vCHNDc3d/ZYa9RVv+f94wdbTU1Nktd++duQv9bX\nX399kteOeF566aUZNGhQkuSZZ57JRRdd1JmjrdawYcMybNiwvPe9781OO+200r4f/OAHnTRV+3Sl\nX7Y//OEPJ3ntf9Nef+T74x//eE499dTOGqtdVqxYkR/+8Ie57bbbctZZZ+WBBx7oEgcIli1blhtv\nvDE33XRTrr/++syZMyeLFi3q7LHWaMCAATnxxBOzePHijBw5MjfeeGM22WSTjh2iUpBFixZ19gjt\ncswxx1Sef/75ylFHHVV57rnnKsuXL68cddRRnT3WGl166aWVq6++utLU1FQ58sgjKx/72Mcq3/ve\n9zp7rDX613/918qyZctat7vC17lSqVSuu+66ykknnVQ59thjKz/72c8q8+fPr1QqlcoLL7xQ+eAH\nP9jJ063eSy+91Hr5mWeeqSxfvrwTp2nbokWLKr/+9a8r8+bNq1Qqlcqdd95ZmTNnTidPtWaNjY0r\nbS9atKgyY8aMTpqm/a688srKRz7ykcoBBxxQOf/88yv/8i//Urnqqqs6e6w2HXPMMW+6buzYsZ0w\nSfv97ne/qxx55JGVQw45pHLIIYdU9t9//8qxxx7b2WOt0UknnVT50pe+VHn/+99faW5urvz+97+v\nHHfccZ091hodfvjhlb///e+t27Nmzfp/7d13VFTn1gbw5wwwYItYwWsFCyBFRAVjMBrxEy+iJjaI\nUhSxRbHHgCDYEFA/DYioMSoKGlsUFSEgkgsREfRqpNhRQRQ7VZQ28/3BmvM5NDUJc84Z9m+trMsc\nchfP5TIze97zvntLbW1tOUz0YU+ePJHu27dPeufOHalUKpWePXtWEK8fN27ckK5bt0568eJFqVRa\n/Xryxx9/cJyqYRUVFdLMzEzpu3fvpFJp9f+GoqIihWYQ5Ar+iBEj2JWgmhiGQVxcnIITfZpp06Zh\n1qxZsLW1hba2NrZu3Qpra2uuYzVIiHt/bWxsYG1tza5mPXjwAF9//TXHqT4sLS0N7u7u6N27t9z1\ntm3b8u5Wu6OjY73PRQA4cOCAAtN8Gg0NDbx79w6nT5/GzJkz0a5dO3Ts2JHrWA2aM2cOfH19oa+v\nj8rKSuzevRt//PEH729XT5s2DcOGDUNaWhrEYjHmzp2LTp06cR3rg/r164dJkyahX79+YBgGGRkZ\ntVbH+Wbbtm0IDAyEu7s7goODERsby97F5KvAwEAkJydj0aJFUFFRgZqaGjZt2sR1rAatXLkSnp6e\nePLkCRiGgZaWFlasWMF1rDpFRESwX2tqaiIzMxOZmZkAgDt37qBv375cRfsod+/elbv7PmnSJGzd\nuhWWlpYcpqqbh4dHg9/38/NTUBKBbtGJjIyEVCrFrl27oK+vDwsLC0gkEly6dAnZ2dlcx/ugr7/+\nWq7QXLJkCYdpPo4Q9/7OmjUL9vb2yM7OhlQqRbdu3Xi7reh99+/fr1Xcy4wcOVLBaRrm7e0NADh6\n9Cg6duzIPhdTUlJ438Jx1apVaNu2LVJTUzFz5kykpqZi586d2LJlC9fR6hUcHIwVK1bAysoKp0+f\nxogRI3DkyBGuY33QzZs3ERERgeLiYkilUpw/fx6AYt/s/govLy9kZWXh3r17kEqlmDx5MvT09LiO\n1aBmzZqha9eukEgkaNOmDezs7DBjxgzY2tpyHa1ec+fORXh4OPv4888/5zDNx/n8888RGhqK7Oxs\niEQi9OjRAxoaGlzHqtPt27cBALm5ucjOzoaZmRkkEgmuXbuGPn368H7hKzExEVlZWViyZAmuXLmC\nNWvWYNy4cVzHqpNssTY+Ph4ikQjm5ubsGQ2xWKzQLIIs8Js3bw4AuHr1KpYuXcpeHzt2LG/3sgPA\n4MGD2dXOgoICaGhoQCKRoLy8HNra2vj99985Tlg/Ie79ffr0KbZv347CwkIEBQXh7NmzMDU1RefO\nnbmO1qAOHTrA3t4exsbGUFNTY6/zcXVI9kHk9u3b8PT0ZK+bmprC1dWVq1gfJS8vD35+fuzBTwcH\nB7nDiXzy9u1bANWrb0FBQfDx8cGgQYMwc+ZMVFZWyv2d8NHy5cvh6OjI7mXnu4CAgDrvTMlaDPLx\nuSijpaWFiIgI9O3bF8uXL0eXLl3w6tUrrmM1qHPnzli2bFmt1zzZfnc+OnXqFLZv346ePXuivLwc\nubm5WL58uUK7pHysH374AQAwe/ZsnDhxAqqq1aVfRUUFFi9ezGW0j7J582bs3bsXEydOhLq6OoKC\ngqCjo8N1rDoNHz4cALB//37s27ePvT5mzBjMmTNHoVkEWeDLiMVi+Pv7o3///hCJREhPT+f1iXBZ\nL+L169dj3LhxMDExAVD9QSUqKorLaB80e/ZsuY4Xzs7OCu/p+qk8PT3h5OSE3bt3A6je4uLu7s52\n2eGrL7/8kusIn6y8vBxhYWFyz0W+r+BXVFSgqKiILeSysrJ42599zJgxYBgGUqmU/c+MjAycO3cO\nDMOwK+J8pa2tDXt7e65jfDS+b8NpSEBAAAoLC2Fra4vIyEjk5+djx44dXMdqUNeuXQFUN0IQikOH\nDuHUqVPswck3b95g5syZvCzwZfLy8lBcXMx2AiorK0Nubi7Hqep38OBB9mt1dXV06tQJ+fn5uHjx\nIi5evMjrD4AFBQX4/fff2S6J6enpdc4TakyCLvCDgoJw+vRpXL58GVKplF1Z5ruMjAy5/WRmZmbY\nunUrh4k+LD09Hbt370ZBQQGA6uLo5cuX+OabbzhOVj+JRIJhw4bh559/BlB9S5XPfx/Xr19Hv379\n0LZtW66jfLLAwEAcOHCA7RGto6ODH3/8keNUDVuyZAmcnZ3x8OFDjB49GgzDYP369VzHqhOfe5h/\nDCMjIwQEBGDgwIHs6iEADBs2jMNU9ZO9rpWXlyMyMhI3btyAiooKjIyMMGbMGI7TNUwqleLixYt4\n9uwZZs6cidu3b/P+bMmCBQvw9OlT5ObmYuDAgeycGD4TiURyXVFatGgh97fNR66urpgwYQJatmwJ\noPpDyfz58zlOVb/8/Hy5x/r6+nVe56OAgACEhISwWz51dHQUviWR33+NH6Curs7ueVNRUUHr1q15\nf5gIqF7NcnNzk1vt/Oyzz7iO1aD169djyZIl2Lx5M1avXo1z587B1NSU61gNUlVVRXJyMiQSCV6+\nfIlz587xemhUSkoK+vXrV+82Eb4WQ0D1toAvv/wSHTp0AMMwMDY25n2bzIEDB+LkyZN49eoV1NTU\neP8cBKrbN4aHh7N72WX4voL//PlzAKjVAIHPf9NA9V3A1q1bw9zcHBUVFUhNTUVKSgpvPwgCtc+W\nXL58Gbt27eL12RLZILS3b9/i1KlT2LRpEzp27IhZs2ZxHa1eZmZmmDNnDtsHPzU1FQMHDuQ6VoPG\njx+P8ePH4/Xr12AYBpqamg02SeCarKmEVCrF7du3UVJSIve6x2d9+vTBsmXLcOvWLTAMA0NDQ4U3\nFmCkQvlt1eH777+v9eJbVVXF6xdfoLoX7YULF5CVlcXeeTA0NISWlhbX0erl7OyM/fv3Y+rUqTh0\n6BAAYObMmbyegPf8+XMEBgbi2rVrEIvFMDExwYIFC3i/mlVTRUUF1qxZw+u/6w0bNuDRo0dyz0VD\nQ0NeHiCfOHFig29qx48fV2CaT2NjY4Pg4OBae9ll55L4rLy8HM+fP0eXLl24jvLRHB0da23pc3Jy\n4nV3qOnTpyM0NFQuu4ODg9whVr6R5ZNllkqlsLe35/0B8itXriAjIwMMw8DIyIiXk3ffl5SUhLVr\n10JdXR0VFRXsgE2+5549ezaKiork3rsZhkFgYCCHqRq2e/duREdHw8zMDOXl5UhPT8fkyZMVOt1d\n0Cv4T58+lWulNWbMGDg5OXGY6ONIpVJIpVJ2K8arV68wbdo0Xrf3bNasGc6fP48uXbpgy5Yt6Nq1\nK/Ly8riO1aCOHTvCw8MDxcXFkEgkYBiG18N1ZI4fP47AwEDk5+dDLBZDIpGwB3f4KjMzU26/5OzZ\ns+Hg4MBhovrJBqEJUY8ePaCrq8t1jE8WFRXFDluKjIzE+vXrYWRkxPvuHRUVFXj27Bm7+PL06VPe\nv4YI6WyJjNAGoQHVk9KTk5PZ7Vtv376Fvr4+r3cRbNu2DWFhYWyhnJeXh2XLlrGLdnxVVFSEw4cP\ncx3jk5w/fx7Hjh2DiooKAKCyshIODg5U4H8sIb74AsDixYvRokULpKamYsSIEUhJSeFdf/OaNm/e\njJcvX8Lb2xuhoaG4ffs2AgICuI7VIC8vLyQmJrIvZrIDinxeoQWAw4cPIy4uDq6urggLC8P58+d5\nfRAKqH7xevfuHbtlrrS0lLcH3mVdlG7evIkdO3bgwYMHYBgGPXv2xHfffcdxuoa1bdsWdnZ2MDU1\nZd84AH53dQGA8PBwnDhxAjNnzgRQfffV0dGR9wX+kiVLMH36dIhEIkgkEnbFk89kZ0uys7Px73//\nGwDg6+vLcaqG2drawsnJCdnZ2fDx8UFKSgrvF+t++OEHmJubY8GCBexdSw8PD14vIKipqcmtgnfq\n1In35waA6u1Qd+/erbd9NF+JRCK5rxW9HYr//882oK4X33Xr1nEd64MKCwsRHBwMR0dHrFq1CkVF\nRfDx8eHlm53sdu/SpUvx008/AQDvP4zI3LhxAwkJCbzeY1gXsVjM3kKVSCSwsrKCo6MjnJ2duY5W\nL2dnZ4wbNw49evSARCJBTk4O74tODw8PLF68mO1mde3aNaxYsQInT57kOFn9BgwYwPvb6XVRUVGB\nWCxmn4t8P0ApY2Fhgf2F+9UAACAASURBVOjoaBQWFkIkEqFVq1ZcR6rX+wN29PX10b59e6ipqaF1\n69Y4duwYzMzMOEzXMCEOQnvz5o1cW25TU1PeD3/s0qUL1qxZw/Zmv3TpErp168Z1rA+Ki4vDvn37\n0LJlS3Zhg2EYJCcnc5ysfv/+978xYcIEmJqaQiKR4Pr165gyZYpCMwi6wH//xZdhGEEckgOq7zw8\nfvwYKioqePDgATp16oQHDx5wHatOGhoaMDc3R2lpqdzwEdlqOJ+fYPr6+sjPzxdcVxoTExOEh4fD\n0tISzs7O0NbWxrt377iO1SAbGxsMHz4cDx8+hEgkQvfu3eU6TPBRmzZt5LY+WVlZ4dixY9wFaoCs\nw1KbNm0E94EVqF6B+/777/Hs2TP89NNPiI+PF8QwoxMnTiAsLEwQh5rv3LmD4uJiWFpaYtiwYWje\nvLlgDiTeunULJ0+eFNQgNIlEgvT0dBgbGwOofo5KJBKOUzVs3bp1iIyMxNWrV8EwDAYNGgQbGxuu\nY31QbGws1xE+mbOzM6ysrHDz5k0wDIPZs2crfAaPoAt8oR4YWbRoEdLT0/Hdd99h1qxZKCkp4W0/\n1507dwKobvkkG5Yhw/cXs0ePHmHkyJHo3r07VFRUBLFF586dOygtLUVERASaNWsGhmHwxRdfwMfH\nh+toDbp58yY2bNiA7OxsSCQS9OnTB56enujZsyfX0WpJSEgAUN17e/Xq1bCwsADDMLhy5QpvD4DK\nOizFxMTU+X2+d6ORTaDs06cPxGIxfvjhB/Tv35/rWB+0Z88eBAcH87oBgsyvv/6KnJwcnD17Ftu2\nbYO2tjasra3x1VdfsW0R+Uo2CE0Iv2cZb29v+Pr6IisrC0B11xS+v05LpVJIJBL2vRAArxcMgoOD\nsWDBAixcuLDOnHw+ZPv48WMEBwfLtdh1c3NTaJMPQXfRsbe3R1BQkOAOjMhUVlYKYv8bUN16bc2a\nNeyesqysLHh6evL64Mvjx4/rvM7XSbbJyclYv3495s2bB0NDQ7x58wYZGRkIDQ2Fj48Pr1c8p02b\nBg8PDxgZGQEA/vzzT2zZsoWX3Ube38pQFz6vGu7cuRNz587lOsZHi4uLw8iRI+UOYL+PrwsbMgsX\nLuT1nuqG3L17F2fPnsWvv/4KQ0NDdrGGj1xdXdl5JXx38OBBTJs2DWFhYewUbKEQWufBW7duQV9f\nH8nJyXJnjmTMzc05SPVxpk+fjm+//RYWFhbs7zoiIoIdvKkIwqgu6yHUAyMpKSnw9fVFeXk5fvvt\nN2zduhUDBw7E0KFDuY5WL0NDQ8yaNQsbN27E0aNHER0djTVr1nAdq0FFRUWIiIiodXudrwXcTz/9\nhJ07d7JTHYHqAUFDhgzB8uXLeV3gy1YoZExNTXm7MlTf//+ydqR89vr1ayQlJcHY2Bhqamrsdb5u\nhyouLgYgjME07wsICADDMFBTU4O9vT369esnmEPNsr3VkZGRSElJgaWlJUaPHs11rDrJ7qb17t0b\nGzduxIABA3g/CO3AgQPIyclBbGxsnZ3k+Py3IbTOg7LBVtu3b+d1m9e6VFVVwdramn08ZswYHD16\nVKEZ+F8NN0CoB0aCgoKwf/9+LFy4EEB1X+XvvvuO1wW+vb099PT0MHnyZAwaNAjHjx/n/UE52W3f\nmj3D+aqyslKuuJfp1q2b3Gl8Pvrss8/w888/sysqly5dQuvWrTlO1TAhtiNNSEio1U6XYRhe7gkH\n/n8irLm5ea0PfCKRSK4LGp/06dMHAATVtSMtLQ2RkZG4ePEiTExMMHr0aKxevVrugyDf1BzqJ4RB\naDt27EBaWhoSExNr/X3wdVFDRqidBzt37oxly5bVWtjg8x1AsViM6OhoWFhYsPWpomsmQRf4sgMj\n//3vf8EwDAYOHMj7MeJA9YTV9w/LtWvXjrcvDDX3vmlpaSEpKQnff/89AH7vgdPW1oa9vT3XMT5a\nQ38DfP8w5e/vj/3792PHjh1gGAYmJia8vVMiI8R2pDExMWzHMCHZu3cvrly5wnYsyszMhLGxMZ4+\nfYpx48Zh9uzZHCeUJ/tgAlQXQbm5uRg4cCDKy8t5+1ycMmUKunXrBhMTE0ilUkRHRyM6Opr9Ph+f\nj7JMv//+O7766iu570VGRnIR6YN0dXWhq6uLL7/8Uq6Bg+wOIB+74ckItfOgbOGrpKSE4yQfb8OG\nDQgMDMSOHTsgEolgbGys8Ha1gi3wq6qqoKqqiq+//hra2tooKyuDWCyuc58W33Tp0oVdOYyKikJc\nXBxvV4oaGlb08uVLBSb5dEZGRggICMDAgQN5f9sXADIyMjBp0qRa16VSKR4+fKj4QB8hIiICX3/9\nNVq2bIn58+cjISGBt7/fmtTV1QXVjvTw4cM4cOAASkpKUFpaim7dumHu3LkYNWoU19E+SE1NDTEx\nMWjXrh2A6q1GGzZswO7du/Htt9/yrsCXCQ0NxW+//YbS0lKcPn0amzZtQocOHXiZl693cRqSnp6O\ntLQ0HDhwAE+ePGGvV1ZWYs+ePbC1teUwXcPi4+MFcwfw1atXaNeuHdt5sKCgACKRSDCdB4cOHYp7\n9+5BRUUFBgYG0NPT4zpSvS5cuABLS0toaWlhw4YNyMzMhKGhISdZBFngZ2ZmYunSpYiKioKKigp8\nfX1hZGSE27dvw9HRUW71hY/WrVuHM2fOYMCAAbh27RpGjBjBDiThG9mWi8rKSly4cAEFBQUAqlcr\ndu3axesWW8+fPwcgjNu+AHDmzBmuI3yyEydOyK1Y7dmzh7e/35qMjY0F04704MGDuHDhAvbt28fe\nXs/KysKGDRvw9OlTXu+jBao7Wr3fQ75169a4f/8+qqqqUFZWxmGyhsXFxeHw4cPsYcqVK1fC3t6e\nlwU+X5sHNKR9+/Zo3rw5nj17hvj4eIhEIvTo0QNt2rSBv78/1/EaJKQ7gEuWLJFreLBw4UJeNkCo\nKT8/H/PmzUOLFi3YxhO//PIL2rRpAz8/P7Rp04briLX89NNPsLS0ZB8HBARw9rsWZIHv7+8PPz8/\ndrVeU1MTfn5+ePXqFebPn8/7An/x4sUICgrC+PHjuY7y0YQ4fbfmLWm+H6IU4ht0zSZcQmrKtXz5\nckgkEojFYlhYWCA/P5/tac03UVFR+Omnn9CiRQv2Ws+ePREUFIRvv/2W9wW+jY0NRo0aBT09PTAM\ng7t378LW1hZnzpzh7eIGAHYas2z7XFlZmSD2LAtF27ZtkZycjHbt2rF9+xMTEzFo0CC4uLhwHa9B\nQroDKNTX6YCAANjZ2dWq6Y4cOYK1a9di69atHCWrH59+14Is8CUSidxUPtkqc7t27Xh9oEhGU1MT\nW7ZsgYmJiVxePq98Cmn6rowQD1EKTc1zA3w9S/K+yspKlJeXY/bs2fj555/x9u1bGBkZobKyElOn\nTuXlnRSRSCRX3Mu0aNGC19NVZYYPHw47OztkZ2cDqP4wK9uuw2e2trZwcnJCdnY2fHx8cOnSJd5P\nKxWSTZs2wcDAABs3bpS7Hhoaig0bNmDt2rUcJfswId0BFOLrNADk5OTUeSfHzs6Oty26+fS7FmSB\nX/OWrpubG/u1EA5hVFRU4MWLF4iNjYWKigoqKiqgpqbG6wJfSNN3ZYR0C1WocnJy5N6caz7mY8u4\nxMRE7Nu3D2lpaXJbzFRUVDBo0CAOk9VPKpXi3bt3da4GCeHN2t/fHwUFBRg9ejRsbW0FUdwD1V06\nhg0bhrS0NIjFYsybN08wXbmE4OrVq/Dy8qp1ffr06ZgwYQIHiT6eu7s7e+hadgdwyJAhXMeq09u3\nb5GVlcW+ftR83KtXLy7j1auhtud8Xdh49uyZ3NyPmo8V2flHkAV+3759sWfPHsycOZO9VlFRgcDA\nQF63mrx69Sq2b98ObW1tuLi4YPHixdDQ0MCLFy/g7e3NdbwGCWn6royQbqEK1aJFixp8zEcjRozA\niBEjcOrUKcFsk3vy5AnGjBkjV+AzDCM3kZLP9u7di5KSEiQkJGDLli0oLi7GV199BVtbW15PWb1w\n4QIKCwthY2MDT09P/Pzzz3B1dcXIkSO5jqYUGmqKwfdOUSUlJQgPD8erV6/g6emJS5cu8Xa6u4aG\nBlavXl3nY4ZheLsfv2ZxXPN7fDR27Fi5uR81HyuSICfZvn37Fn5+fkhOTkaPHj1QWVmJ7OxsWFlZ\nwcPDg7cvDPb29li+fDlevnyJgIAA7N27Fzo6OigoKMDcuXN5e8vpfUKavuvv748uXbqgoKAAKSkp\n0NbWxsOHD3Hs2DGuoxEeyMvLQ0hICAoLCxEUFISzZ8/C1NRUkGchhOLmzZuIjo7G5cuX0bNnT9y+\nfRvOzs687ZZiZ2eHPXv2IDk5GYmJifDx8YGLiwtvCyKhmTt3LmbMmAELCwu564mJiThy5Ai2b9/O\nUbIPW7BgAYYMGYLTp0/j8OHDiIqKwsmTJxU6qVTZBQcHN/h9vp8D5JowKrUamjVrhrVr1+LNmzd4\n9OgRGIZBt27deDvNUUYsFmPgwIEAgH379kFHRwdA9Z58vp8dENL03fLycoSEhGDZsmWQSqUQi8Uw\nNzfH+vXrcejQIa7jEZ7w8vKCk5MT+4bctm1buLu7IywsjONkyicwMBBxcXHQ0dHB+PHj4ebmBjU1\nNZSVlWHKlCm8LfDFYjFatmyJuLg42NnZQVVVlT14S/4+T09PuLm5QUdHBwYGBpBIJEhPT8fjx4+x\nZ88eruM16M2bN5g6dSo7a8DGxga//PILx6mUCxXwfw8/l7o/UosWLaCvrw89PT3eF/c1qauryz3m\n+2122fTdDh06AKievvuhT9dc2bhxI0pKStjiHgBMTU0xePBghIaGchuO8IZEIsGwYcPY597nn38u\nmO4SQtOxY0eEh4cjKCgIVlZW7IKGuro6rw9Stm/fHtOnT8eDBw9gZmaG06dPC+69hs+6du2KkydP\nYsKECVBXV0fz5s3x7bff4uTJk7w/pyGRSJCTk8O+fiQmJvJ2iw5pmgS5gi9UskFGUqkUDx48YIca\n8XmQkYyQpu9eu3YNv/76q9w1sVgMDw8PTJ06lVYFGkFpaSmSk5NRXFwsd53PXZZUVVWRnJwMiUSC\nly9f4ty5c7U+eJO/JyAggH2d2LVrV63vr1ixAv369VN0rI+2adMm3LlzBz179gQA9O7dm5et+YSM\nYRgMHTqUl3eDG+Lt7Q1vb29kZGTAwMAAQ4YM4f1U2KysLPZvWaauKcLk73Nzc8PYsWMxfPhwzqZf\nU4GvQHxsv/exhDR9t76DWwzDoKKiQsFpmoYZM2agc+fO7BAmgP93pXx9fdm/aVdXV5iYmNSancAX\nI0aMqPf3yTBMrWFufNGnTx/26927d2PWrFkcpvl0q1atqvM6X/9OSONLTk5GSEgIwsLCsGfPHsyY\nMQPPnj1DTk4OcnNzoaury3XEenl4eGDhwoWwtLREYWEh1q1bh6KiIt4X+CEhIfjuu+/krvn7+8Pd\n3Z2jRB82Y8YMnD9/Hrt370bv3r0xduxYfP755wrNIOgC38rKqtY1FRUVdO3aFUuXLuVsPHB9hHx4\nT0jTd9u0aYMrV66w5x1k/vOf/6B9+/YcpVJuampq2LJlC9cxPkllZSXmz58PAGw3GqlUColEwruD\n+pGRkZBKpdi1axf09fVhYWEBiUSCS5cusb3l+ej9ATUnTpzg/RDCmqytrdmvKysr8d///pf356VI\n49q6dSs2b94MAIiNjUVpaSl+++03FBYWYsGCBfjyyy85Tli/vXv3wt3dHQkJCbh48SJcXV15/ZyM\njY1FZGQkrly5gtu3b7PXKysrcfPmTV4X+GZmZuy8pvT0dKxduxbPnj3DlClT4OLigubNmzd6BkEX\n+FOmTEGrVq3YQj8xMRGvX7+GhYUF1q9fTwde/kFCmr67cuVKuLm5oWfPnjAwMEBVVRWuX7+OvLw8\n3h/cEqqvvvoKCQkJGDBggNwdFD7vV16yZAkyMzPZD95PnjxBr169UFBQgEWLFvFqe5HszeDq1atY\nunQpe33s2LGYMWMGV7E+Cd/v6NSl5mC8kSNHCu4uBPlnqauro1u3bgCqa45x48aBYRhoamo22PaT\nS/fu3WO/XrhwIYKDgzFgwAAYGxvj3r17vO2DP2rUKPTt2xfr1q2Ta8stEol4facEqO72GB8fj6io\nKLx8+RI2NjawsbFBUlIS5s+fj3379jV6BkEX+ImJiXI9UidPngwnJyfMmTOHw1TKSUjTd7t3746I\niAgkJSXh/v37YBgGDg4O+OKLLwRZZAjBkSNHUFlZKXeNYRicP3+eo0QfpqOjg3Xr1rHbSLKysnDg\nwAG4u7vD2dmZVwW+jFgshr+/P/r37w+RSIT09HTq6tKIEhIS5B4/f/4cjx494igN4YPy8nJIJBKU\nlZUhISFB7gNfaWkph8nqt2bNGvZr2Z3K/Px8rFmzhtd98KdPn47Q0FBIpVKYm5tzHeeTjBs3Dv/z\nP/+DhQsXQk9Pj70+YcIEXLt2TSEZBF3gq6urY8OGDTAzM2Pf7CoqKpCUlKSQ2x9NidCm74pEIkEe\n3BKq2NhYriN8snv37sntEe/Zsydu3ryJZs2a8bZoDgoKwunTp5GamgqpVAodHR1e9wofPHgwW1CU\nlJSwe1BlW6KSk5M5Ttiw3377Te5xy5Yt2e0ZpGkaN24cJkyYgPLycgwdOhS6urooLy/HqlWram0L\n5Yv3W/8+efIE//rXvwDUfeiWTzQ0NGBubo7S0lK2y9n7A/74/PoRExODO3fuoLi4GJcvX2avDxo0\nSGGHsQU56EqmpKQEERER7Mjl7t274+uvv8bbt2/RqlUr3o4yFpIPTd8dMWIE1xEJD9y5cwf+/v54\n8+YNjhw5gtDQUAwaNIh352Det379ely9ehWmpqYQiUTIyMhAjx49MHjwYGRkZMDLy4vriLUsXLgQ\nQUFBXMdoUh49eoRbt25BJBKhb9++6NSpE9eRCMceP36M4uJi6Ovrs9eOHTuGiRMn8u78zvs2bdqE\nV69ewd/fH0D1HAJNTU18//33HCdrWEBAAH744QeuY3ySuXPnoqCgAB07dmSvMQyDwMBAhWUQdIEv\nxNZ8QqMM03dJ43N0dMTq1auxevVqhIWF4d69e1i1ahXvz8HcuXOHXSDo1q0bjIyMUF5ezllbsw/x\n9vaGpqamILbKKYOff/4ZUVFRMDMzQ3l5OdLT0zF58mRMnTqV62iEfLKpU6fWGvY4bdo0ua3OfFRY\nWIiwsDDcuHEDIpEIRkZGcHR0RIsWLbiOVi87OzscOXKE0wyC3qIzY8YMdOnSpdYnJPLPEfL0XaI4\nqqqqcrd6e/XqxeuVLKD6DmB8fDxevXoFT09PXLp0CUVFRfjss8+4jlYv2Va5mmcbqMBvHHFxcTh2\n7Bh7eLKyshIODg5U4BNBkkgkuHv3LtviOi0tTRDD/dzd3TFo0CDMnz8fFRUVSE1NhYeHB6/vZlpa\nWsr9rrkg6AJfTU0N//u//8t1jCZDaNN3ieK0atUKx48fx9u3b3H9+nWcO3eO95Mo3d3dMWTIEPzn\nP/8BALx+/RrLli3D7t27uQ3WgJr91ysqKuQO0JF/3vsfVEUiEb3uEcHy8fHB6tWr8eDBA4hEIvTq\n1UsQrx9v3ryBi4sL+9jU1BTTp0/nLlAD3j93FBISglatWkFFRYWTcwOCLvCF2JpPaIQ8fZcojp+f\nH/bv3482bdpg165d6NevH7vPk6/evHmDqVOnIjo6GgBgY2PD+y1Fx48fZ4dzicViSCSSWq0c+cTR\n0bHBgpiv3TtkbGxsMHHiRPTr1w9SqRR//vknpkyZwnUsQv4SAwODWttxQkJCeDu0UkYikSA9PR3G\nxsYAgOvXr0MikXCcqm6XLl3iOgJL0AW+EFvzCY2Qp+8SxUlISMDs2bOhqvr/LykHDx6U613MNxKJ\nBDk5OWwBmpiYyNs3DZnDhw8jLi4Orq6uCAsLw/nz55Gbm8t1rHp5e3sDAI4ePYqOHTuyA7pSUlJQ\nVFTEcbr6yYogFRUVTJgwAYWFhWAYBpMnTxbElgZC6pKQkIDAwEAUFhYCqL4DqK2tXWtKLN94e3vD\n19cXWVlZAKonZPv4+HCcqmGpqak4c+YM2zHHzc0NTk5OGDRokMIyCLrAF2JrPqER8vRdojhr1qxB\nWFgYAgIC2CEwMTExvC7wvb294e3tjYyMDFhaWkJPTw9r167lOlaD1NXVoa6ujoqKCkgkElhZWcHR\n0RHOzs5cR6uTbGXw9u3b8PT0ZK+bmprC1dWVq1gflJ+fz3598uRJfPPNN2zvcEKEatu2bQgMDIS7\nuzuCg4MRGxvL64OqMn369MH+/fsBVLf5bN++PW8bIchs2bIFGzduZB/7+PhgwYIFCm1MIsgC38fH\nB2vWrMHEiRPrvP17/PhxDlIR0nT16dMHK1euxJIlS+Dg4MAWRHwUGhoKGxsb9OzZE6GhoVzH+STG\nxsYIDw+HpaUlnJ2doa2tjXfv3nEd64PKy8sRFhYmN6CLzyv4CxYsYL9OSUmRe0yIUDVr1gxdu3aF\nRCJBmzZtYGdnhxkzZsDW1pbraHVKTk5GSEgIwsLCUFVVBRcXFzx9+hRSqRReXl748ssvuY5Yr6qq\nKnaxCwDatm2r8AyCLPDd3NwAAD/++GOtTh18nSRHiLIzMDBAeHg4NmzYgD/++IO3z8XXr1/D0dER\n2trasLW1xejRowUzM8Pd3Z1t42lhYYH8/HwMGTKE61gfFBgYiAMHDiA4OBhSqRS6urr48ccfuY71\nUehQLVEWWlpaiIiIQN++fbF8+XJ06dIFr1694jpWvbZu3coOlouNjUVJSQmio6NRVFSEBQsW8LrA\nHzVqFKZMmQITExNIpVJcvXoV48ePV2gGQffBnzNnDrZs2cLeYkpKSoKfnx8iIyM5TkZI07Jt2zb2\ngzdQvT1n69attSaB8klmZiaio6MRHx8PHR0d2NrawsrKipe3fj/Up5rPW6Fkbt26heLiYrabBACF\n7kf9q5ycnHh/GJiQj1FVVYXCwkJ89tlniIyMREFBAUaNGsVOtuUbR0dHdgqvh4cHDAwM4OTkBABw\ndnZmt+3w0fPnz/H27VvcuHEDKioqMDQ0VPiWZ0Gu4MtMnToVrq6u8Pb2xsGDB/Ho0SPs2LGD61iE\nNDlubm5ITU2VG0TC5+IeAAwNDWFoaIjly5cjIyMDu3btgpeXF/773/9yHa2WuvaEC8ns2bNRVFQE\nLS0tdusWwzC8LfBl2z/r6h7GMAxtAyWC9OLFC8TGxrIftAHgxIkTvN2CVl5eDolEgrKyMiQkJGDW\nrFns9/h6h1hm6dKlCA8PR/fu3TnLIOgCf9iwYdDR0cH8+fMxcOBAXn+aI0SZbdiwATk5ObCwsMC7\nd+8QEhICQ0NDLFmyhOtoDUpPT0dUVBR+//136OnpISAggOtIdRL6nvCioiJBTb3m8wAdQv6quXPn\nYujQodDW1uY6ykcZN24cJkyYgPLycgwdOhS6urooLy/HqlWr2AGcfNWhQwfY29vD2NhYbijoihUr\nFJZBkAV+zcO1VVVVOHXqFNLT0wHQIVtCFC0zM1NuG8ns2bPh4ODAYaL63bhxA1FRUTh37hy6du0K\nW1tbLFiwQBDdJABh7gk3MzPjfKrjp6DuYUQZaWpqYtmyZVzH+GjTpk3D8OHDUVxcDH19fQCAWCzG\nwIEDMXHiRI7TNayu8wE127o3NkEW+A2trpSUlCgwCSEEqH7hevfuHTQ0NABU3z6tqqriOFXd1q5d\ni7Fjx+KXX37hpLNBUxQXF4d9+/ahZcuW7FBCRU91JKSpGzx4MA4ePIgBAwbIzSzp1asXh6kaVteH\n7cmTJ3OQ5NN88803uHv3LgoKCgBUbzfy9/dXaHZBH7ItKirCmTNn2P2pFRUViIiIQEJCAsfJCGla\noqOjsXXrVvTo0YMdILVixQqMHDmS62hKoeaecF1dXQDC3hOelJSEL774gusYhDQZjo6Ota4xDEOH\nyBuBt7c37t+/j/v378PExAQZGRlwdXXF9OnTFZZBkCv4MosWLUL//v1x9uxZ2NnZISEhAatWreI6\nFiFNTvfu3REREYGHDx+CYRj06NEDzZo14zqW0hD6nvBHjx7h0KFD7GpWRUUFLl++TIsxhCiQrCMN\nAOTl5aFdu3a87BqmDO7du4dDhw7B0dERO3fuRF5eHkJCQhSaQfThf4W/JBIJFi5ciI4dO8LFxQW7\nd+/GiRMnuI5FSJPj7+8PsViMvn37wsDAgIr7f1jnzp0b/Ifv3N3d0atXL2RmZmL48OEQiUS8nxpM\niLJITk5mV++rqqrg7OyM6dOnw9bWFomJiRynU05VVVXslvHXr1+jU6dOuHXrlkIzCHoFv6KiArdu\n3YKGhgaSkpLQtWtX5OTkcB2LkCanefPmGDVqFPT19eU6BgQGBnKYqm4eHh4Nft/Pz09BSZoOVVVV\nTJw4ESdPnoS1tTWsra0xa9YsDBs2jOtohCg9IQ+MEioHBwdER0fDwcEBY8eOhaqqqsKHEgq6wPf2\n9sbr16+xfPly+Pr6oqCggB2CQAhRHBcXF64jfDRra2sAQHx8PEQiEczNzSGVSpGSkkK3qxuJVCpF\namoqNDU1ceTIEXTr1g25ublcxyKkSVBXV0e3bt0AAImJiRg/fjxEIhE0NTXZQ+/kn9WjRw8YGxsD\nAEaMGIE3b94ofAVf0IdsZSQSCfLy8qClpSV3MpwQ0rj8/PzkVsR37dqFOXPmcJjo482YMQP79u2T\nuzZnzhzs2rWLo0TK69mzZ3j+/Dk6dOiAwMBAFBQUwM7ODsOHD+c6GiFKz87ODr/88gvKyspgZWWF\n8PBw9qD+5MmTcezYMY4TKo/s7Gw8ePAAW7ZskWtJWllZCV9fX8THxyssiyCr4atXr2L79u3Q1taG\ni4sLFi9ejGbNmuHFixfw9vbGV199xXVEQpqEmzdvyj1OSkoSTIFfUFCA33//HaamphCJREhPT8fT\np0+5jqWUtLS0Q3LMNgAACuVJREFUoKWlBYC2QBGiaEIeGCU07969Q0ZGBl6/fi03zZ1hGIUPKBRk\ngb9x40YsX74cL168gKurK/bs2QNdXV0UFBRg7ty5VOAToiA1bwAK6YZgQEAAQkJCsGXLFkilUujq\n6lLxSQhROkIeGCU0enp60NPTw6hRo9CnTx8A3HUsEmSBL/vDBIDQ0FD2VpOmpqbcAT9CSOOqOVVV\nSFNW+/Tpg/Xr16OoqIjtJ08IIcpIqAOjhCY5ORkhISEICwtDVVUVXFxc8PTpU0ilUnh5eSn0QLMg\nC/z3qauryz2mN2lCFCcjIwOTJk0CAHYI06RJkwQxgMnLywuJiYno2LEjAGEPjeK7p0+fIjY2FsXF\nxXJ3eRR9y5oQQhoTnzoWCbLAlxUV7xcUQPUb9MOHD7kNR0gTcubMGa4j/GU3btxAQkICLQoowLx5\n8zB06FB2Hz4hhCgjPnUsEmSBL+SighBlIoQhS/XR19dHfn4+2rZty3UUpde6dWssXbqU6xiEENKo\nysvLIZFIUFZWhoSEBMyaNYv9XmlpqUKzCLLAF3JRQQjhh0ePHmHkyJHo3r07VFRUaItOI7h37x4A\nwMzMDAcPHsSAAQPkWhn36tWLq2iEEPKP41PHIqXog08IIZ/q8ePHta6VlJRAT0+PgzTKydHRsd7v\nMQyDAwcOKDANIYQ0vsePH8t1LAKAY8eOYeLEiRCJRArLQQU+IaRJKioqwpkzZ5Cfnw8AqKioQERE\nBBISEjhOpnzS0tJgYmIid+3SpUsYPHgwR4kIIUS5Ke6jBCGE8MiiRYvw6tUrnDlzBs2bN8eff/6J\nVatWcR1LqWRnZyMhIQFeXl5ISEhg/zl//jxWrlzJdTxCCFFagtyDTwghf5dEIsHChQtx+fJluLi4\nwMHBAYsXL8bIkSO5jqY03r17h/T0dF5MdSSEkKaECnxCSJNUUVGBW7duQUNDA0lJSejatStycnK4\njqVUZFMdra2t0bt3b67jEEJIk0F78AkhTdKtW7fw+vVrtGvXDr6+vigoKICDgwOmTJnCdTSlMXjw\n4DrnDMg6FiUnJ3OQihBClB8V+IQQQhQuKSkJX3zxBdcxCCFEKdEWHUIIIY3q0aNHOHToEAoKCgBU\nb4+6fPkydSwihJBGQl10CCGENCp3d3f06tULmZmZGD58OEQiEdauXct1LEIIUVpU4BNCmqS8vDyk\npaUBAE6dOgVfX1/cv3+f41TKSVVVFRMnTsRnn30Ga2trbNy4EeHh4VzHIoQQpUUFPiGkSfr++++h\npqaGP//8E7/++itGjx4NX19frmMpJalUitTUVGhqauLIkSNITk5Gbm4u17EIIURpUYFPCGmSVFRU\nYGBggJiYGDg7O2PAgAGoqqriOpZS2rRpE5o1awYvLy/8+eef2L9/P3744QeuYxFCiNKiQ7aEkCap\nqqoKO3bsQHx8PBYvXoy0tDS8efOG61hKSUtLC1KpFLm5ufDz80NZWRnU1dW5jkUIIUqL2mQSQpqk\nvLw8xMTE4IsvvkDv3r0RFRUFHR0dGBgYcB1N6YSGhuK3335DaWkpTp8+DV9fX3To0AGzZ8/mOhoh\nhCgl2qJDCGmSdu/ejenTp7MTVm1sbLBr1y6OUymnuLg4HD58GK1btwYArFy5EufPn+c4FSGEKC/a\nokMIaVJiYmKwb98+3Llzh+2iAwCVlZWoqKjgMJnykp1tkE21LSsrQ2VlJZeRCCFEqdEWHUJIk1Ne\nXg5/f3/MnDmTvSYSidChQweoqtK6xz/t4MGDiImJQXZ2NoYPH46UlBQ4Ozvj22+/5ToaIYQoJSrw\nCSFNSlxcHEaOHImDBw/W+f1p06YpOFHTkJubi7S0NIjFYhgaGqJTp05cRyKEEKVFS1WEkCaluLgY\nAJCfn89xEuVX34eoZ8+eAaAPU4QQ0liowCeENCnffPMNAMDCwoLjJMrv/Q9RJ0+eZH/3hBBCGhdt\n0SGENEkLFy5kv66srMTNmzdhZGSEbdu2cZhKeTk6OiIsLIzrGIQQ0iTQCj4hpEkKCgqSe/z27Vt4\nenpylEb5yTroEEIIaXzUB58QQlDdRefevXtcxyCEEEL+NlrBJ4Q0SYMHD2ZXlaVSKUQiEbVt/IdN\nnDgRDMNAKpXiwYMHmDRpEoDq3zfDMDh+/DjHCQkhRDnRHnxCSJOUmZkJQ0NDrmMotcePHzf4/c6d\nOysoCSGENC1U4BNCmiQnJyfs3buXBlsRQghROvTORghpkpo3b45Ro0ZBX18fampq7PXAwEAOUxFC\nCCF/HxX4hJAmycXFhesIhBBCSKOgLjqEkCbFz88PAGBubg5zc3Ncu3aN/drc3JzjdIQQQsjfRwU+\nIaRJuXnzptzjpKQkjpIQQgghjYMKfEJIk1KzrwD1GSCEEKJsqMAnhDQpNSeq0oRVQgghyobaZBJC\nmhQzMzPo6uoCADuASVdXl4YvEUIIURpU4BNCmhQavkQIIUTZUYFPCCGEEEKIEqE9+IQQQgghhCgR\nKvAJIYQQQghRIjTJlhBCBEZPTw+ZmZlQVVXFqVOnMH78+Eb5OQUFBXBzcwNQfXZBKpWiS5cuAIDA\nwEC0bdu2UX4uIYSQv4f24BNCiMDICnyGYWBjY4OYmJhG/5nbtm1DZWUllixZ0ug/ixBCyN9DK/iE\nECJQK1euxOPHj+Hi4oK9e/ciKioK4eHhkEqlaNu2LdavX482bdqgf//+mDdvHuLj41FRUYG5c+fi\n6NGjePDgAVavXg1LS8tP/tlHjx7F1atX4e/vDwCIiopCTEwMhg0bhnPnzoFhGDx79gy6urrYsGED\n1NTUEBYWhujoaFRVVUFXVxc+Pj7Q0ND4p38thBDS5NEefEIIESg3Nze0bdsWe/fuRV5eHnbu3InQ\n0FD88ssvMDc3x65duwAApaWlMDIywuHDh9G8eXPEx8dj9+7d+O6773Do0KG/9LPHjBmDCxcu4M2b\nNwCA6OhoTJ48GQCQnp6OzZs34/jx43jy5AkSExORlpaGc+fO4eDBgzhy5AhatWqFY8eO/TO/CEII\nIXJoBZ8QQpTAtWvX8OLFC8ycORMAUF5ezu6XB4ABAwYAALS0tGBmZgYA0NbWRnFx8V/6eS1atICV\nlRViYmJgbW2Ne/fuYciQIYiIiICZmRmaN28OAOjfvz+ysrJw//595OTkwMnJCUD1hw5VVXoLIoSQ\nxkCvroQQogTEYjFMTEzYVfuaVFRU6vz677C3t4e/vz/EYjHGjBkDkaj6prBEImH/HdkxL7FYjBEj\nRsDb2/sf+dmEEELqR1t0CCFEoEQiESorKwEAxsbGSEtLw4sXLwBUb5mJi4tr1J9vYGCAsrIyhIeH\nY8KECez169ev4+3bt5BKpbh69Sr09PRgZmaGxMREdkvPwYMHce3atUbNRwghTRWt4BNCiEB17NgR\n7du3x4QJExAeHg5PT0/MmTMHzZo1g4aGBgICAho9w9ixYxEfH49//etf7LU+ffrAw8MDubm56N27\nNywtLaGiooJp06bB0dER6urq6Nixo9yHAkIIIf8capNJCCHkL5FKpZg3bx4cHBzYTjwnTpzAxYsX\nsXnzZo7TEUJI00Ur+IQQ0sQFBQXh8uXLta7r6+vD09Ozzv9OZmYmvLy8YGlp+ZfabBJCCGk8tIJP\nCCGEEEKIEqFDtoQQQgghhCgRKvAJIYQQQghRIlTgE0IIIYQQokSowCeEEEIIIUSJUIFPCCGEEEKI\nEqECnxBCCCGEECXyf4lb8HmLi8zwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b5748cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "N2kq7aEtfuZV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**data pre processing**"
      ]
    },
    {
      "metadata": {
        "id": "lAG7hptF9zjr",
        "colab_type": "code",
        "outputId": "847ab977-8dac-4ee9-a841-00fec715a3f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "cell_type": "code",
      "source": [
        "# checking unique values in the columns of train dataset\n",
        "\n",
        "data.apply(lambda x: len(x.unique()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Fat_Content                 5\n",
              "Item_Identifier               1559\n",
              "Item_MRP                      8052\n",
              "Item_Outlet_Sales             3494\n",
              "Item_Type                       16\n",
              "Item_Visibility              13006\n",
              "Item_Weight                    416\n",
              "Outlet_Establishment_Year        9\n",
              "Outlet_Identifier               10\n",
              "Outlet_Location_Type             3\n",
              "Outlet_Size                      4\n",
              "Outlet_Type                      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "metadata": {
        "id": "QRkNUtTk9A7Q",
        "colab_type": "code",
        "outputId": "c69c0c44-b120-4e63-9ba0-4b680bc1776b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Fat_Content                0\n",
              "Item_Identifier                 0\n",
              "Item_MRP                        0\n",
              "Item_Outlet_Sales            5681\n",
              "Item_Type                       0\n",
              "Item_Visibility                 0\n",
              "Item_Weight                  2439\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Size                  4016\n",
              "Outlet_Type                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "metadata": {
        "id": "-6U66NOh9NKK",
        "colab_type": "code",
        "outputId": "87f9214b-9991-472d-efb6-bb19d1dea9d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "cell_type": "code",
      "source": [
        "# imputing missing values\n",
        "\n",
        "data['Item_Weight'] = data['Item_Weight'].replace(0, np.NaN)\n",
        "data['Item_Weight'].fillna(data['Item_Weight'].mean(), inplace = True)\n",
        "\n",
        "data['Outlet_Size'].fillna(data['Outlet_Size'].mode()[0], inplace = True)\n",
        "\n",
        "data['Item_Outlet_Sales'] = data['Item_Outlet_Sales'].replace(0, np.NaN)\n",
        "data['Item_Outlet_Sales'].fillna(data['Item_Outlet_Sales'].mode()[0], inplace = True)\n",
        "\n",
        "data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Fat_Content             0\n",
              "Item_Identifier              0\n",
              "Item_MRP                     0\n",
              "Item_Outlet_Sales            0\n",
              "Item_Type                    0\n",
              "Item_Visibility              0\n",
              "Item_Weight                  0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Size                  0\n",
              "Outlet_Type                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "metadata": {
        "id": "TPZ2FcvwfsP1",
        "colab_type": "code",
        "outputId": "ae4ca815-5010-47b1-dd7a-201cc395f20e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "# combining reg, Regular and Low Fat, low fat and, LF\n",
        "\n",
        "\n",
        "data['Item_Fat_Content'] = data['Item_Fat_Content'].replace({'LF': 'Low Fat', 'reg': 'Regular', 'low fat': 'Low Fat'})\n",
        "data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    9185\n",
              "Regular    5019\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "metadata": {
        "id": "2H6LIXAIocfB",
        "colab_type": "code",
        "outputId": "45ac1dd8-3e26-43c5-db44-cdebe24b4f60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "# Getting the first two characters of ID to separate them into different categories\n",
        "\n",
        "data['Item_Identifier'] = data['Item_Identifier'].apply(lambda x: x[0:2])\n",
        "\n",
        "data['Item_Identifier'] = data['Item_Identifier'].map({'FD':'Food', 'NC':'Non_Consumable', 'DR':'Drinks'})\n",
        "\n",
        "data['Item_Identifier'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Food              10201\n",
              "Non_Consumable     2686\n",
              "Drinks             1317\n",
              "Name: Item_Identifier, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "metadata": {
        "id": "rpmembua76pY",
        "colab_type": "code",
        "outputId": "d579e54b-fd6e-416c-d7d1-3072e84eea88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "cell_type": "code",
      "source": [
        "# determining the operation peroid of a time\n",
        "\n",
        "data['Outlet_Years'] = 2013 - data['Outlet_Establishment_Year']\n",
        "data['Outlet_Years'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28    2439\n",
              "26    1553\n",
              "14    1550\n",
              "9     1550\n",
              "16    1550\n",
              "11    1548\n",
              "4     1546\n",
              "6     1543\n",
              "15     925\n",
              "Name: Outlet_Years, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "metadata": {
        "id": "Jx3h62X6IEGN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# removing unnecassary columns from the dataset\n",
        "\n",
        "#data = data.drop('Item_Identifier', axis = 1)\n",
        "\n",
        "#print(data.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oKuTsIcZacPW",
        "colab_type": "code",
        "outputId": "7ccfec80-64f9-470d-b32e-21573b33976e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "cell_type": "code",
      "source": [
        "data['Outlet_Type'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Supermarket Type1    9294\n",
              "Grocery Store        1805\n",
              "Supermarket Type3    1559\n",
              "Supermarket Type2    1546\n",
              "Name: Outlet_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "Y9Bw93y0T_0x",
        "colab_type": "code",
        "outputId": "fb212ec2-4d73-48c9-c0ef-67c55021511a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2012
        }
      },
      "cell_type": "code",
      "source": [
        "# label encoding\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "data.apply(LabelEncoder().fit_transform)"
      ],
      "execution_count": 0,
      "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>Item_Fat_Content</th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Outlet_Years</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7567</td>\n",
              "      <td>2540</td>\n",
              "      <td>4</td>\n",
              "      <td>1097</td>\n",
              "      <td>284</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>632</td>\n",
              "      <td>422</td>\n",
              "      <td>14</td>\n",
              "      <td>1436</td>\n",
              "      <td>57</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3897</td>\n",
              "      <td>1639</td>\n",
              "      <td>10</td>\n",
              "      <td>1172</td>\n",
              "      <td>376</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5626</td>\n",
              "      <td>670</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>393</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>848</td>\n",
              "      <td>865</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>756</td>\n",
              "      <td>523</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>297</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>332</td>\n",
              "      <td>13</td>\n",
              "      <td>662</td>\n",
              "      <td>334</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2694</td>\n",
              "      <td>2658</td>\n",
              "      <td>13</td>\n",
              "      <td>11237</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2224</td>\n",
              "      <td>930</td>\n",
              "      <td>5</td>\n",
              "      <td>1162</td>\n",
              "      <td>363</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5883</td>\n",
              "      <td>2911</td>\n",
              "      <td>5</td>\n",
              "      <td>9593</td>\n",
              "      <td>393</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>517</td>\n",
              "      <td>1246</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>314</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4009</td>\n",
              "      <td>1696</td>\n",
              "      <td>4</td>\n",
              "      <td>5230</td>\n",
              "      <td>386</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4070</td>\n",
              "      <td>1299</td>\n",
              "      <td>6</td>\n",
              "      <td>9938</td>\n",
              "      <td>350</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3240</td>\n",
              "      <td>1671</td>\n",
              "      <td>13</td>\n",
              "      <td>5450</td>\n",
              "      <td>377</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6262</td>\n",
              "      <td>1552</td>\n",
              "      <td>6</td>\n",
              "      <td>7561</td>\n",
              "      <td>365</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>953</td>\n",
              "      <td>1266</td>\n",
              "      <td>2</td>\n",
              "      <td>7610</td>\n",
              "      <td>272</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3045</td>\n",
              "      <td>1322</td>\n",
              "      <td>8</td>\n",
              "      <td>218</td>\n",
              "      <td>314</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>867</td>\n",
              "      <td>660</td>\n",
              "      <td>2</td>\n",
              "      <td>7618</td>\n",
              "      <td>272</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2953</td>\n",
              "      <td>1765</td>\n",
              "      <td>7</td>\n",
              "      <td>3661</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7054</td>\n",
              "      <td>2030</td>\n",
              "      <td>4</td>\n",
              "      <td>10050</td>\n",
              "      <td>331</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7594</td>\n",
              "      <td>2558</td>\n",
              "      <td>13</td>\n",
              "      <td>11642</td>\n",
              "      <td>390</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4025</td>\n",
              "      <td>2672</td>\n",
              "      <td>0</td>\n",
              "      <td>3847</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6265</td>\n",
              "      <td>1297</td>\n",
              "      <td>9</td>\n",
              "      <td>2349</td>\n",
              "      <td>343</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2690</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>6557</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4916</td>\n",
              "      <td>2678</td>\n",
              "      <td>5</td>\n",
              "      <td>2399</td>\n",
              "      <td>336</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>531</td>\n",
              "      <td>753</td>\n",
              "      <td>9</td>\n",
              "      <td>9929</td>\n",
              "      <td>327</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>380</td>\n",
              "      <td>922</td>\n",
              "      <td>13</td>\n",
              "      <td>7424</td>\n",
              "      <td>184</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>242</td>\n",
              "      <td>300</td>\n",
              "      <td>7</td>\n",
              "      <td>1440</td>\n",
              "      <td>313</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>516</td>\n",
              "      <td>165</td>\n",
              "      <td>4</td>\n",
              "      <td>12259</td>\n",
              "      <td>58</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>439</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>7894</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5651</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5817</td>\n",
              "      <td>835</td>\n",
              "      <td>15</td>\n",
              "      <td>5615</td>\n",
              "      <td>288</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5652</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3575</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>9122</td>\n",
              "      <td>107</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5653</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1540</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>2895</td>\n",
              "      <td>116</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5654</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4026</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5655</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3990</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>9888</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5656</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6444</td>\n",
              "      <td>835</td>\n",
              "      <td>4</td>\n",
              "      <td>12164</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5657</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7912</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>3921</td>\n",
              "      <td>363</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5658</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3433</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>4149</td>\n",
              "      <td>357</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5659</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5935</td>\n",
              "      <td>835</td>\n",
              "      <td>14</td>\n",
              "      <td>5026</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5660</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6646</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>2454</td>\n",
              "      <td>291</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5661</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4536</td>\n",
              "      <td>835</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5662</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6617</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>2484</td>\n",
              "      <td>291</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5663</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1298</td>\n",
              "      <td>835</td>\n",
              "      <td>14</td>\n",
              "      <td>3568</td>\n",
              "      <td>363</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5664</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5244</td>\n",
              "      <td>835</td>\n",
              "      <td>14</td>\n",
              "      <td>5181</td>\n",
              "      <td>327</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5665</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>539</td>\n",
              "      <td>835</td>\n",
              "      <td>4</td>\n",
              "      <td>2113</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5666</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6287</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>771</td>\n",
              "      <td>284</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5667</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1602</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>5199</td>\n",
              "      <td>286</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5668</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3036</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>12964</td>\n",
              "      <td>357</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5669</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5895</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "      <td>7921</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5670</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7022</td>\n",
              "      <td>835</td>\n",
              "      <td>10</td>\n",
              "      <td>4078</td>\n",
              "      <td>297</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5671</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1067</td>\n",
              "      <td>835</td>\n",
              "      <td>3</td>\n",
              "      <td>6205</td>\n",
              "      <td>349</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5672</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>7072</td>\n",
              "      <td>835</td>\n",
              "      <td>9</td>\n",
              "      <td>4034</td>\n",
              "      <td>124</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5673</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3078</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>9562</td>\n",
              "      <td>141</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5674</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>423</td>\n",
              "      <td>835</td>\n",
              "      <td>7</td>\n",
              "      <td>3197</td>\n",
              "      <td>358</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5675</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1210</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>7737</td>\n",
              "      <td>327</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5676</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3881</td>\n",
              "      <td>835</td>\n",
              "      <td>13</td>\n",
              "      <td>718</td>\n",
              "      <td>298</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5677</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5069</td>\n",
              "      <td>835</td>\n",
              "      <td>15</td>\n",
              "      <td>11820</td>\n",
              "      <td>180</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5678</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3196</td>\n",
              "      <td>835</td>\n",
              "      <td>8</td>\n",
              "      <td>8009</td>\n",
              "      <td>293</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5679</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6626</td>\n",
              "      <td>835</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>354</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5680</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1523</td>\n",
              "      <td>835</td>\n",
              "      <td>3</td>\n",
              "      <td>10187</td>\n",
              "      <td>288</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14204 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Identifier  Item_MRP  Item_Outlet_Sales  \\\n",
              "0                    0                1      7567               2540   \n",
              "1                    1                0       632                422   \n",
              "2                    0                1      3897               1639   \n",
              "3                    1                1      5626                670   \n",
              "4                    0                2       848                865   \n",
              "5                    1                1       756                523   \n",
              "6                    1                1       999                332   \n",
              "7                    0                1      2694               2658   \n",
              "8                    1                1      2224                930   \n",
              "9                    1                1      5883               2911   \n",
              "10                   0                1       517               1246   \n",
              "11                   1                1      4009               1696   \n",
              "12                   1                1      4070               1299   \n",
              "13                   1                1      3240               1671   \n",
              "14                   0                1      6262               1552   \n",
              "15                   1                1       953               1266   \n",
              "16                   0                2      3045               1322   \n",
              "17                   1                1       867                660   \n",
              "18                   0                0      2953               1765   \n",
              "19                   0                1      7054               2030   \n",
              "20                   1                1      7594               2558   \n",
              "21                   1                1      4025               2672   \n",
              "22                   0                2      6265               1297   \n",
              "23                   0                1      2690                207   \n",
              "24                   1                1      4916               2678   \n",
              "25                   0                2       531                753   \n",
              "26                   1                1       380                922   \n",
              "27                   0                0       242                300   \n",
              "28                   1                1       516                165   \n",
              "29                   1                1       439                100   \n",
              "...                ...              ...       ...                ...   \n",
              "5651                 1                1      5817                835   \n",
              "5652                 0                1      3575                835   \n",
              "5653                 1                1      1540                835   \n",
              "5654                 0                1      4026                835   \n",
              "5655                 1                1      3990                835   \n",
              "5656                 0                1      6444                835   \n",
              "5657                 0                1      7912                835   \n",
              "5658                 0                1      3433                835   \n",
              "5659                 0                0      5935                835   \n",
              "5660                 0                1      6646                835   \n",
              "5661                 0                0      4536                835   \n",
              "5662                 0                1      6617                835   \n",
              "5663                 0                0      1298                835   \n",
              "5664                 1                0      5244                835   \n",
              "5665                 0                0       539                835   \n",
              "5666                 1                1      6287                835   \n",
              "5667                 0                1      1602                835   \n",
              "5668                 0                1      3036                835   \n",
              "5669                 0                1      5895                835   \n",
              "5670                 1                1      7022                835   \n",
              "5671                 1                1      1067                835   \n",
              "5672                 0                2      7072                835   \n",
              "5673                 0                1      3078                835   \n",
              "5674                 0                0       423                835   \n",
              "5675                 1                1      1210                835   \n",
              "5676                 1                1      3881                835   \n",
              "5677                 1                1      5069                835   \n",
              "5678                 0                2      3196                835   \n",
              "5679                 1                1      6626                835   \n",
              "5680                 1                1      1523                835   \n",
              "\n",
              "      Item_Type  Item_Visibility  Item_Weight  Outlet_Establishment_Year  \\\n",
              "0             4             1097          284                          4   \n",
              "1            14             1436           57                          8   \n",
              "2            10             1172          376                          4   \n",
              "3             6                0          393                          3   \n",
              "4             9                0          265                          1   \n",
              "5             0                0          297                          8   \n",
              "6            13              662          334                          1   \n",
              "7            13            11237          324                          0   \n",
              "8             5             1162          363                          5   \n",
              "9             5             9593          393                          7   \n",
              "10            6                0          314                          4   \n",
              "11            4             5230          386                          2   \n",
              "12            6             9938          350                          4   \n",
              "13           13             5450          377                          2   \n",
              "14            6             7561          365                          1   \n",
              "15            2             7610          272                          2   \n",
              "16            8              218          314                          8   \n",
              "17            2             7618          272                          4   \n",
              "18            7             3661          324                          0   \n",
              "19            4            10050          331                          6   \n",
              "20           13            11642          390                          1   \n",
              "21            0             3847          324                          0   \n",
              "22            9             2349          343                          6   \n",
              "23            0             6557          324                          0   \n",
              "24            5             2399          336                          2   \n",
              "25            9             9929          327                          7   \n",
              "26           13             7424          184                          6   \n",
              "27            7             1440          313                          1   \n",
              "28            4            12259           58                          3   \n",
              "29            3             7894          324                          0   \n",
              "...         ...              ...          ...                        ...   \n",
              "5651         15             5615          288                          1   \n",
              "5652          6             9122          107                          5   \n",
              "5653          6             2895          116                          6   \n",
              "5654         13                0          154                          1   \n",
              "5655          6             9888          324                          0   \n",
              "5656          4            12164          324                          0   \n",
              "5657          6             3921          363                          2   \n",
              "5658         13             4149          357                          1   \n",
              "5659         14             5026           10                          4   \n",
              "5660         13             2454          291                          1   \n",
              "5661         14                0          324                          0   \n",
              "5662         13             2484          291                          7   \n",
              "5663         14             3568          363                          8   \n",
              "5664         14             5181          327                          8   \n",
              "5665          4             2113          324                          0   \n",
              "5666         13              771          284                          2   \n",
              "5667         13             5199          286                          2   \n",
              "5668          6            12964          357                          3   \n",
              "5669          6             7921          324                          0   \n",
              "5670         10             4078          297                          7   \n",
              "5671          3             6205          349                          4   \n",
              "5672          9             4034          124                          4   \n",
              "5673         13             9562          141                          8   \n",
              "5674          7             3197          358                          2   \n",
              "5675         13             7737          327                          4   \n",
              "5676         13              718          298                          2   \n",
              "5677         15            11820          180                          8   \n",
              "5678          8             8009          293                          5   \n",
              "5679          3                0          354                          7   \n",
              "5680          3            10187          288                          5   \n",
              "\n",
              "      Outlet_Identifier  Outlet_Location_Type  Outlet_Size  Outlet_Type  \\\n",
              "0                     9                     0            1            1   \n",
              "1                     3                     2            1            2   \n",
              "2                     9                     0            1            1   \n",
              "3                     0                     2            1            0   \n",
              "4                     1                     2            0            1   \n",
              "5                     3                     2            1            2   \n",
              "6                     1                     2            0            1   \n",
              "7                     5                     2            1            3   \n",
              "8                     7                     1            1            1   \n",
              "9                     2                     1            1            1   \n",
              "10                    9                     0            1            1   \n",
              "11                    8                     0            2            1   \n",
              "12                    9                     0            1            1   \n",
              "13                    8                     0            2            1   \n",
              "14                    1                     2            0            1   \n",
              "15                    8                     0            2            1   \n",
              "16                    3                     2            1            2   \n",
              "17                    9                     0            1            1   \n",
              "18                    5                     2            1            3   \n",
              "19                    6                     1            2            1   \n",
              "20                    1                     2            0            1   \n",
              "21                    5                     2            1            3   \n",
              "22                    6                     1            2            1   \n",
              "23                    4                     0            2            0   \n",
              "24                    8                     0            2            1   \n",
              "25                    2                     1            1            1   \n",
              "26                    6                     1            2            1   \n",
              "27                    1                     2            0            1   \n",
              "28                    0                     2            1            0   \n",
              "29                    4                     0            2            0   \n",
              "...                 ...                   ...          ...          ...   \n",
              "5651                  1                     2            0            1   \n",
              "5652                  7                     1            1            1   \n",
              "5653                  6                     1            2            1   \n",
              "5654                  1                     2            0            1   \n",
              "5655                  5                     2            1            3   \n",
              "5656                  5                     2            1            3   \n",
              "5657                  8                     0            2            1   \n",
              "5658                  1                     2            0            1   \n",
              "5659                  9                     0            1            1   \n",
              "5660                  1                     2            0            1   \n",
              "5661                  5                     2            1            3   \n",
              "5662                  2                     1            1            1   \n",
              "5663                  3                     2            1            2   \n",
              "5664                  3                     2            1            2   \n",
              "5665                  5                     2            1            3   \n",
              "5666                  8                     0            2            1   \n",
              "5667                  8                     0            2            1   \n",
              "5668                  0                     2            1            0   \n",
              "5669                  5                     2            1            3   \n",
              "5670                  2                     1            1            1   \n",
              "5671                  9                     0            1            1   \n",
              "5672                  9                     0            1            1   \n",
              "5673                  3                     2            1            2   \n",
              "5674                  8                     0            2            1   \n",
              "5675                  9                     0            1            1   \n",
              "5676                  8                     0            2            1   \n",
              "5677                  3                     2            1            2   \n",
              "5678                  7                     1            1            1   \n",
              "5679                  2                     1            1            1   \n",
              "5680                  7                     1            1            1   \n",
              "\n",
              "      Outlet_Years  \n",
              "0                4  \n",
              "1                0  \n",
              "2                4  \n",
              "3                5  \n",
              "4                7  \n",
              "5                0  \n",
              "6                7  \n",
              "7                8  \n",
              "8                3  \n",
              "9                1  \n",
              "10               4  \n",
              "11               6  \n",
              "12               4  \n",
              "13               6  \n",
              "14               7  \n",
              "15               6  \n",
              "16               0  \n",
              "17               4  \n",
              "18               8  \n",
              "19               2  \n",
              "20               7  \n",
              "21               8  \n",
              "22               2  \n",
              "23               8  \n",
              "24               6  \n",
              "25               1  \n",
              "26               2  \n",
              "27               7  \n",
              "28               5  \n",
              "29               8  \n",
              "...            ...  \n",
              "5651             7  \n",
              "5652             3  \n",
              "5653             2  \n",
              "5654             7  \n",
              "5655             8  \n",
              "5656             8  \n",
              "5657             6  \n",
              "5658             7  \n",
              "5659             4  \n",
              "5660             7  \n",
              "5661             8  \n",
              "5662             1  \n",
              "5663             0  \n",
              "5664             0  \n",
              "5665             8  \n",
              "5666             6  \n",
              "5667             6  \n",
              "5668             5  \n",
              "5669             8  \n",
              "5670             1  \n",
              "5671             4  \n",
              "5672             4  \n",
              "5673             0  \n",
              "5674             6  \n",
              "5675             4  \n",
              "5676             6  \n",
              "5677             0  \n",
              "5678             3  \n",
              "5679             1  \n",
              "5680             3  \n",
              "\n",
              "[14204 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "metadata": {
        "id": "FWT0tOXqB2rn",
        "colab_type": "code",
        "outputId": "66ba6fa7-aacd-4cdb-ca96-0be61db20b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# one hot encoding\n",
        "\n",
        "data = pd.get_dummies(data)\n",
        "\n",
        "print(data.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(14204, 47)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aFLPI6s6F9wd",
        "colab_type": "code",
        "outputId": "de37937b-cf24-4c93-c658-fad86a40db6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# splitting the data into dependent and independent variables\n",
        "\n",
        "x = data.drop('Item_Outlet_Sales', axis = 1)\n",
        "y = data.Item_Outlet_Sales\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(14204, 46)\n",
            "(14204,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WiIC8tLGG5tD",
        "colab_type": "code",
        "outputId": "62c8fde2-b3ca-4358-98a0-c6b5d324fc64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# splitting the dataset into train and test\n",
        "\n",
        "train = data.iloc[:8523,:]\n",
        "test = data.iloc[8523:,:]\n",
        "\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8523, 47)\n",
            "(5681, 47)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iZVLdlz1I8Gz",
        "colab_type": "code",
        "outputId": "5d18aad4-0821-4b8b-815e-108c49fe30fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "# making x_train, x_test, y_train, y_test\n",
        "\n",
        "from sklearn.cross_validation import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9942, 46)\n",
            "(9942,)\n",
            "(4262, 46)\n",
            "(4262,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hYcMSR4tLHJT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Modelling**"
      ]
    },
    {
      "metadata": {
        "id": "YKGQZU2cLLSH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Linear Regression**"
      ]
    },
    {
      "metadata": {
        "id": "DkJcqm9GfggD",
        "colab_type": "code",
        "outputId": "addb78ec-fe67-4e98-debf-34cc92ef2d0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the  test set results\n",
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)\n",
        "\n",
        "# finding the mean squared error and variance\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print('RMSE :', np.sqrt(mse))\n",
        "print('Variance score: %.2f' % r2_score(y_test, y_pred))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 972. 1415. 2688. ... 1691. 1899. 2924.]\n",
            "RMSE : 1260.7862215968942\n",
            "Variance score: 0.28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9Dw0XhOUjLwT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**AdaBoost Regressor**"
      ]
    },
    {
      "metadata": {
        "id": "slCa1K8gRJW4",
        "colab_type": "code",
        "outputId": "25849ad3-ce87-476e-9080-cf0d9c7ea0b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "\n",
        "model= AdaBoostRegressor(n_estimators = 100)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# RMSE\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"RMSE :\", np.sqrt(mse))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE : 1325.4592300385511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jEtvLeXtSRdl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**XgBoost Regressor**"
      ]
    },
    {
      "metadata": {
        "id": "08tGyTmxSWLq",
        "colab_type": "code",
        "outputId": "444103a2-b6ec-4fef-c0a3-ec55c53ba121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "model = GradientBoostingRegressor()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Calculating the root mean squared error\n",
        "print(\"RMSE :\", np.sqrt(((y_test - y_pred)**2).sum()/len(y_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 923.97218386 1336.34388704 2925.63206089 ... 1678.57819436 1933.02150588\n",
            " 2725.07424099]\n",
            "RMSE : 1250.1758915019273\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tpmO4CCljW5p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Random Forest Regression**"
      ]
    },
    {
      "metadata": {
        "id": "PwGPKIu9DCCa",
        "colab_type": "code",
        "outputId": "c1731cb4-8e39-4138-fd80-42a6436f9f52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "model = RandomForestRegressor(n_estimators = 100 , n_jobs = -1)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the  test set results\n",
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)\n",
        "\n",
        "# finding the mean squared error and variance\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"RMSE :\",np.sqrt(mse))\n",
        "print('Variance score: %.2f' % r2_score(y_test, y_pred))\n",
        "\n",
        "print(\"Result :\",model.score(x_train, y_train))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 720.235808 1298.769402 2161.106904 ... 1434.872238 1873.39475\n",
            " 2182.219422]\n",
            "RMSE : 1305.9134750687463\n",
            "Variance score: 0.23\n",
            "Result : 0.8878705426075347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YHAsYzHpHhNT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Decision Tree Regressor**"
      ]
    },
    {
      "metadata": {
        "id": "dv6bPTeoDCI8",
        "colab_type": "code",
        "outputId": "7d7c1caf-cfeb-4bf2-973f-ccbb784ee9af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "model = DecisionTreeRegressor()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)\n",
        "\n",
        "print(\" RMSE : \" , np.sqrt(((y_test - y_pred)**2).sum()/len(y_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 171.7764  378.1744  958.752  ...  778.986  3547.3824 2603.278 ]\n",
            " RMSE :  1748.8944066233912\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HPzhV_tGZdjK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Support vector machine**"
      ]
    },
    {
      "metadata": {
        "id": "jSVLYKUdZdDN",
        "colab_type": "code",
        "outputId": "37dd874a-31df-4301-b322-55508d0fc1a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "model = SVR()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the x test results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Calculating the RMSE Score\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"RMSE :\", np.sqrt(mse))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE : 1662.4657939382228\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MymOe0RbR9mL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Neural Networks**"
      ]
    },
    {
      "metadata": {
        "id": "JmxCX-QXDB_J",
        "colab_type": "code",
        "outputId": "2daeec8e-99d8-4a43-961c-1a5eeaf4109b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x_train = np.asmatrix(x_train)\n",
        "x_test = np.asmatrix(x_test)\n",
        "y_train = np.asmatrix(y_train.T)\n",
        "y_test = np.asmatrix(y_test.T)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9942, 46)\n",
            "(4262, 46)\n",
            "(9942, 1)\n",
            "(4262, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1iY2JvplWvAD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "D = x_train.shape[1]\n",
        "\n",
        "# Creating the placeholders for storing the X and Y variables \n",
        "tf_X = tf.placeholder(tf.float32 , [None,D])\n",
        "tf_Y = tf.placeholder(tf.float32 , [None,1])\n",
        "\n",
        "# Layer 1 \n",
        "W1 = tf.Variable(tf.random_normal([D, 20], stddev = 0.01))\n",
        "b1 = tf.Variable(tf.zeros([20]))\n",
        "Layer_1 = tf.nn.relu(tf.matmul(tf_X, W1) + b1)\n",
        "\n",
        "# Layer 2 \n",
        "W2 = tf.Variable(tf.random_normal([20, 15], stddev = 0.01))\n",
        "b2 = tf.Variable(tf.zeros([15]))\n",
        "Layer_2 = tf.nn.relu(tf.matmul(Layer_1, W2) + b2)\n",
        "\n",
        "# Layer 3\n",
        "W3 = tf.Variable(tf.random_normal([15, 10], stddev = 0.01))\n",
        "b3 = tf.Variable(tf.zeros([10]))\n",
        "Layer_3 = tf.nn.relu(tf.matmul(Layer_2, W3) + b3)\n",
        "\n",
        "# Output layer\n",
        "W4 = tf.Variable(tf.random_normal([10, 1] , stddev = 0.01))\n",
        "b4 = tf.Variable(tf.zeros([1]))\n",
        "output = tf.add(tf.matmul(Layer_3, W4) , b4)\n",
        "\n",
        "# Defining our cost function which we have to reduce \n",
        "cost = tf.reduce_mean(tf.square(output - tf_Y))\n",
        "\n",
        "# Defining the function for Gradient Descent \n",
        "train = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pXvPYshYXOk_",
        "colab_type": "code",
        "outputId": "1ab01c1c-a302-488f-db4d-82f4c74718f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185902
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "with tf.Session() as sess:\n",
        "    \n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    \n",
        "    ctrain = []\n",
        "    ctest = []\n",
        "    for i in range(10000):\n",
        "        sess.run(train,feed_dict ={tf_X :x_train ,tf_Y :y_train})\n",
        "\n",
        "        ctrain.append(sess.run(cost, feed_dict={tf_X :x_train, tf_Y :y_train}))\n",
        "        ctest.append(sess.run(cost, feed_dict={tf_X :x_test, tf_Y :y_test}))\n",
        "        print('Epoch :',i,'Cost :',ctrain[i])\n",
        "        \n",
        "\n",
        "# visualization of training data\n",
        "plt.plot(ctrain)\n",
        "plt.title(\" Training Costs vs Epochs \")\n",
        "plt.xlabel(\" Epochs \")\n",
        "plt.ylabel(\" Costs \")\n",
        "plt.show()\n",
        "\n",
        "# visualization of testing data\n",
        "plt.plot(ctest)\n",
        "plt.title(\" Testing Costs vs Epochs \")\n",
        "plt.xlabel(\" Epochs \")\n",
        "plt.ylabel(\" Costs \")\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch : 0 Cost : 4895451.5\n",
            "Epoch : 1 Cost : 4894295.0\n",
            "Epoch : 2 Cost : 4892480.5\n",
            "Epoch : 3 Cost : 2832973.5\n",
            "Epoch : 4 Cost : 4.1737935e+24\n",
            "Epoch : 5 Cost : 1.6694897e+17\n",
            "Epoch : 6 Cost : 1.6688217e+17\n",
            "Epoch : 7 Cost : 1.6681545e+17\n",
            "Epoch : 8 Cost : 1.667487e+17\n",
            "Epoch : 9 Cost : 1.6668203e+17\n",
            "Epoch : 10 Cost : 1.6661535e+17\n",
            "Epoch : 11 Cost : 1.665487e+17\n",
            "Epoch : 12 Cost : 1.6648209e+17\n",
            "Epoch : 13 Cost : 1.664155e+17\n",
            "Epoch : 14 Cost : 1.6634895e+17\n",
            "Epoch : 15 Cost : 1.662824e+17\n",
            "Epoch : 16 Cost : 1.662159e+17\n",
            "Epoch : 17 Cost : 1.6614942e+17\n",
            "Epoch : 18 Cost : 1.6608295e+17\n",
            "Epoch : 19 Cost : 1.6601653e+17\n",
            "Epoch : 20 Cost : 1.6595011e+17\n",
            "Epoch : 21 Cost : 1.6588375e+17\n",
            "Epoch : 22 Cost : 1.6581738e+17\n",
            "Epoch : 23 Cost : 1.6575105e+17\n",
            "Epoch : 24 Cost : 1.6568474e+17\n",
            "Epoch : 25 Cost : 1.656185e+17\n",
            "Epoch : 26 Cost : 1.6555225e+17\n",
            "Epoch : 27 Cost : 1.6548602e+17\n",
            "Epoch : 28 Cost : 1.6541986e+17\n",
            "Epoch : 29 Cost : 1.6535368e+17\n",
            "Epoch : 30 Cost : 1.6528756e+17\n",
            "Epoch : 31 Cost : 1.6522145e+17\n",
            "Epoch : 32 Cost : 1.6515537e+17\n",
            "Epoch : 33 Cost : 1.6508932e+17\n",
            "Epoch : 34 Cost : 1.6502331e+17\n",
            "Epoch : 35 Cost : 1.649573e+17\n",
            "Epoch : 36 Cost : 1.6489134e+17\n",
            "Epoch : 37 Cost : 1.648254e+17\n",
            "Epoch : 38 Cost : 1.6475948e+17\n",
            "Epoch : 39 Cost : 1.6469358e+17\n",
            "Epoch : 40 Cost : 1.6462771e+17\n",
            "Epoch : 41 Cost : 1.6456186e+17\n",
            "Epoch : 42 Cost : 1.6449606e+17\n",
            "Epoch : 43 Cost : 1.6443026e+17\n",
            "Epoch : 44 Cost : 1.6436452e+17\n",
            "Epoch : 45 Cost : 1.6429877e+17\n",
            "Epoch : 46 Cost : 1.6423306e+17\n",
            "Epoch : 47 Cost : 1.6416738e+17\n",
            "Epoch : 48 Cost : 1.6410172e+17\n",
            "Epoch : 49 Cost : 1.6403609e+17\n",
            "Epoch : 50 Cost : 1.6397048e+17\n",
            "Epoch : 51 Cost : 1.639049e+17\n",
            "Epoch : 52 Cost : 1.6383934e+17\n",
            "Epoch : 53 Cost : 1.6377382e+17\n",
            "Epoch : 54 Cost : 1.6370833e+17\n",
            "Epoch : 55 Cost : 1.6364284e+17\n",
            "Epoch : 56 Cost : 1.635774e+17\n",
            "Epoch : 57 Cost : 1.6351196e+17\n",
            "Epoch : 58 Cost : 1.6344658e+17\n",
            "Epoch : 59 Cost : 1.6338121e+17\n",
            "Epoch : 60 Cost : 1.6331586e+17\n",
            "Epoch : 61 Cost : 1.6325054e+17\n",
            "Epoch : 62 Cost : 1.6318524e+17\n",
            "Epoch : 63 Cost : 1.6311997e+17\n",
            "Epoch : 64 Cost : 1.6305474e+17\n",
            "Epoch : 65 Cost : 1.6298952e+17\n",
            "Epoch : 66 Cost : 1.6292434e+17\n",
            "Epoch : 67 Cost : 1.6285916e+17\n",
            "Epoch : 68 Cost : 1.6279404e+17\n",
            "Epoch : 69 Cost : 1.6272892e+17\n",
            "Epoch : 70 Cost : 1.6266386e+17\n",
            "Epoch : 71 Cost : 1.6259877e+17\n",
            "Epoch : 72 Cost : 1.6253374e+17\n",
            "Epoch : 73 Cost : 1.6246873e+17\n",
            "Epoch : 74 Cost : 1.6240376e+17\n",
            "Epoch : 75 Cost : 1.6233879e+17\n",
            "Epoch : 76 Cost : 1.6227386e+17\n",
            "Epoch : 77 Cost : 1.6220897e+17\n",
            "Epoch : 78 Cost : 1.621441e+17\n",
            "Epoch : 79 Cost : 1.6207922e+17\n",
            "Epoch : 80 Cost : 1.6201441e+17\n",
            "Epoch : 81 Cost : 1.619496e+17\n",
            "Epoch : 82 Cost : 1.6188482e+17\n",
            "Epoch : 83 Cost : 1.6182009e+17\n",
            "Epoch : 84 Cost : 1.6175536e+17\n",
            "Epoch : 85 Cost : 1.6169066e+17\n",
            "Epoch : 86 Cost : 1.61626e+17\n",
            "Epoch : 87 Cost : 1.6156135e+17\n",
            "Epoch : 88 Cost : 1.6149671e+17\n",
            "Epoch : 89 Cost : 1.6143214e+17\n",
            "Epoch : 90 Cost : 1.6136757e+17\n",
            "Epoch : 91 Cost : 1.61303e+17\n",
            "Epoch : 92 Cost : 1.612385e+17\n",
            "Epoch : 93 Cost : 1.61174e+17\n",
            "Epoch : 94 Cost : 1.6110955e+17\n",
            "Epoch : 95 Cost : 1.6104509e+17\n",
            "Epoch : 96 Cost : 1.6098068e+17\n",
            "Epoch : 97 Cost : 1.609163e+17\n",
            "Epoch : 98 Cost : 1.6085194e+17\n",
            "Epoch : 99 Cost : 1.607876e+17\n",
            "Epoch : 100 Cost : 1.607233e+17\n",
            "Epoch : 101 Cost : 1.6065899e+17\n",
            "Epoch : 102 Cost : 1.6059475e+17\n",
            "Epoch : 103 Cost : 1.6053048e+17\n",
            "Epoch : 104 Cost : 1.604663e+17\n",
            "Epoch : 105 Cost : 1.6040212e+17\n",
            "Epoch : 106 Cost : 1.6033797e+17\n",
            "Epoch : 107 Cost : 1.6027383e+17\n",
            "Epoch : 108 Cost : 1.6020972e+17\n",
            "Epoch : 109 Cost : 1.6014564e+17\n",
            "Epoch : 110 Cost : 1.600816e+17\n",
            "Epoch : 111 Cost : 1.6001756e+17\n",
            "Epoch : 112 Cost : 1.5995355e+17\n",
            "Epoch : 113 Cost : 1.5988957e+17\n",
            "Epoch : 114 Cost : 1.5982563e+17\n",
            "Epoch : 115 Cost : 1.5976169e+17\n",
            "Epoch : 116 Cost : 1.596978e+17\n",
            "Epoch : 117 Cost : 1.596339e+17\n",
            "Epoch : 118 Cost : 1.5957006e+17\n",
            "Epoch : 119 Cost : 1.5950624e+17\n",
            "Epoch : 120 Cost : 1.5944245e+17\n",
            "Epoch : 121 Cost : 1.5937868e+17\n",
            "Epoch : 122 Cost : 1.5931492e+17\n",
            "Epoch : 123 Cost : 1.5925119e+17\n",
            "Epoch : 124 Cost : 1.5918752e+17\n",
            "Epoch : 125 Cost : 1.5912383e+17\n",
            "Epoch : 126 Cost : 1.5906018e+17\n",
            "Epoch : 127 Cost : 1.5899656e+17\n",
            "Epoch : 128 Cost : 1.5893296e+17\n",
            "Epoch : 129 Cost : 1.5886941e+17\n",
            "Epoch : 130 Cost : 1.5880585e+17\n",
            "Epoch : 131 Cost : 1.5874235e+17\n",
            "Epoch : 132 Cost : 1.5867884e+17\n",
            "Epoch : 133 Cost : 1.5861538e+17\n",
            "Epoch : 134 Cost : 1.5855193e+17\n",
            "Epoch : 135 Cost : 1.584885e+17\n",
            "Epoch : 136 Cost : 1.5842514e+17\n",
            "Epoch : 137 Cost : 1.5836175e+17\n",
            "Epoch : 138 Cost : 1.5829842e+17\n",
            "Epoch : 139 Cost : 1.5823512e+17\n",
            "Epoch : 140 Cost : 1.5817183e+17\n",
            "Epoch : 141 Cost : 1.5810855e+17\n",
            "Epoch : 142 Cost : 1.5804533e+17\n",
            "Epoch : 143 Cost : 1.5798209e+17\n",
            "Epoch : 144 Cost : 1.5791892e+17\n",
            "Epoch : 145 Cost : 1.5785573e+17\n",
            "Epoch : 146 Cost : 1.5779261e+17\n",
            "Epoch : 147 Cost : 1.577295e+17\n",
            "Epoch : 148 Cost : 1.5766641e+17\n",
            "Epoch : 149 Cost : 1.5760334e+17\n",
            "Epoch : 150 Cost : 1.5754031e+17\n",
            "Epoch : 151 Cost : 1.574773e+17\n",
            "Epoch : 152 Cost : 1.5741433e+17\n",
            "Epoch : 153 Cost : 1.5735137e+17\n",
            "Epoch : 154 Cost : 1.5728842e+17\n",
            "Epoch : 155 Cost : 1.5722552e+17\n",
            "Epoch : 156 Cost : 1.5716265e+17\n",
            "Epoch : 157 Cost : 1.5709977e+17\n",
            "Epoch : 158 Cost : 1.5703694e+17\n",
            "Epoch : 159 Cost : 1.5697413e+17\n",
            "Epoch : 160 Cost : 1.5691134e+17\n",
            "Epoch : 161 Cost : 1.568486e+17\n",
            "Epoch : 162 Cost : 1.5678586e+17\n",
            "Epoch : 163 Cost : 1.5672315e+17\n",
            "Epoch : 164 Cost : 1.5666048e+17\n",
            "Epoch : 165 Cost : 1.565978e+17\n",
            "Epoch : 166 Cost : 1.5653517e+17\n",
            "Epoch : 167 Cost : 1.5647256e+17\n",
            "Epoch : 168 Cost : 1.5640998e+17\n",
            "Epoch : 169 Cost : 1.5634743e+17\n",
            "Epoch : 170 Cost : 1.562849e+17\n",
            "Epoch : 171 Cost : 1.562224e+17\n",
            "Epoch : 172 Cost : 1.5615993e+17\n",
            "Epoch : 173 Cost : 1.5609744e+17\n",
            "Epoch : 174 Cost : 1.5603505e+17\n",
            "Epoch : 175 Cost : 1.5597261e+17\n",
            "Epoch : 176 Cost : 1.5591023e+17\n",
            "Epoch : 177 Cost : 1.5584787e+17\n",
            "Epoch : 178 Cost : 1.5578556e+17\n",
            "Epoch : 179 Cost : 1.5572325e+17\n",
            "Epoch : 180 Cost : 1.5566095e+17\n",
            "Epoch : 181 Cost : 1.555987e+17\n",
            "Epoch : 182 Cost : 1.5553649e+17\n",
            "Epoch : 183 Cost : 1.5547428e+17\n",
            "Epoch : 184 Cost : 1.554121e+17\n",
            "Epoch : 185 Cost : 1.5534995e+17\n",
            "Epoch : 186 Cost : 1.552878e+17\n",
            "Epoch : 187 Cost : 1.5522568e+17\n",
            "Epoch : 188 Cost : 1.5516363e+17\n",
            "Epoch : 189 Cost : 1.5510156e+17\n",
            "Epoch : 190 Cost : 1.5503954e+17\n",
            "Epoch : 191 Cost : 1.5497754e+17\n",
            "Epoch : 192 Cost : 1.5491557e+17\n",
            "Epoch : 193 Cost : 1.5485359e+17\n",
            "Epoch : 194 Cost : 1.5479167e+17\n",
            "Epoch : 195 Cost : 1.5472975e+17\n",
            "Epoch : 196 Cost : 1.5466789e+17\n",
            "Epoch : 197 Cost : 1.5460604e+17\n",
            "Epoch : 198 Cost : 1.5454421e+17\n",
            "Epoch : 199 Cost : 1.544824e+17\n",
            "Epoch : 200 Cost : 1.5442058e+17\n",
            "Epoch : 201 Cost : 1.5435884e+17\n",
            "Epoch : 202 Cost : 1.542971e+17\n",
            "Epoch : 203 Cost : 1.5423539e+17\n",
            "Epoch : 204 Cost : 1.5417368e+17\n",
            "Epoch : 205 Cost : 1.5411202e+17\n",
            "Epoch : 206 Cost : 1.5405036e+17\n",
            "Epoch : 207 Cost : 1.5398875e+17\n",
            "Epoch : 208 Cost : 1.5392714e+17\n",
            "Epoch : 209 Cost : 1.538656e+17\n",
            "Epoch : 210 Cost : 1.5380403e+17\n",
            "Epoch : 211 Cost : 1.5374253e+17\n",
            "Epoch : 212 Cost : 1.5368103e+17\n",
            "Epoch : 213 Cost : 1.5361956e+17\n",
            "Epoch : 214 Cost : 1.5355812e+17\n",
            "Epoch : 215 Cost : 1.534967e+17\n",
            "Epoch : 216 Cost : 1.5343528e+17\n",
            "Epoch : 217 Cost : 1.5337393e+17\n",
            "Epoch : 218 Cost : 1.5331259e+17\n",
            "Epoch : 219 Cost : 1.5325127e+17\n",
            "Epoch : 220 Cost : 1.5318997e+17\n",
            "Epoch : 221 Cost : 1.531287e+17\n",
            "Epoch : 222 Cost : 1.5306746e+17\n",
            "Epoch : 223 Cost : 1.5300623e+17\n",
            "Epoch : 224 Cost : 1.5294504e+17\n",
            "Epoch : 225 Cost : 1.5288388e+17\n",
            "Epoch : 226 Cost : 1.528227e+17\n",
            "Epoch : 227 Cost : 1.527616e+17\n",
            "Epoch : 228 Cost : 1.527005e+17\n",
            "Epoch : 229 Cost : 1.5263941e+17\n",
            "Epoch : 230 Cost : 1.5257837e+17\n",
            "Epoch : 231 Cost : 1.5251736e+17\n",
            "Epoch : 232 Cost : 1.5245636e+17\n",
            "Epoch : 233 Cost : 1.5239539e+17\n",
            "Epoch : 234 Cost : 1.5233443e+17\n",
            "Epoch : 235 Cost : 1.5227351e+17\n",
            "Epoch : 236 Cost : 1.5221261e+17\n",
            "Epoch : 237 Cost : 1.5215174e+17\n",
            "Epoch : 238 Cost : 1.5209087e+17\n",
            "Epoch : 239 Cost : 1.5203006e+17\n",
            "Epoch : 240 Cost : 1.5196926e+17\n",
            "Epoch : 241 Cost : 1.5190847e+17\n",
            "Epoch : 242 Cost : 1.5184773e+17\n",
            "Epoch : 243 Cost : 1.5178701e+17\n",
            "Epoch : 244 Cost : 1.517263e+17\n",
            "Epoch : 245 Cost : 1.5166559e+17\n",
            "Epoch : 246 Cost : 1.5160494e+17\n",
            "Epoch : 247 Cost : 1.515443e+17\n",
            "Epoch : 248 Cost : 1.5148369e+17\n",
            "Epoch : 249 Cost : 1.514231e+17\n",
            "Epoch : 250 Cost : 1.5136252e+17\n",
            "Epoch : 251 Cost : 1.5130197e+17\n",
            "Epoch : 252 Cost : 1.5124145e+17\n",
            "Epoch : 253 Cost : 1.5118098e+17\n",
            "Epoch : 254 Cost : 1.511205e+17\n",
            "Epoch : 255 Cost : 1.5106005e+17\n",
            "Epoch : 256 Cost : 1.5099963e+17\n",
            "Epoch : 257 Cost : 1.5093922e+17\n",
            "Epoch : 258 Cost : 1.5087885e+17\n",
            "Epoch : 259 Cost : 1.5081852e+17\n",
            "Epoch : 260 Cost : 1.507582e+17\n",
            "Epoch : 261 Cost : 1.5069788e+17\n",
            "Epoch : 262 Cost : 1.5063761e+17\n",
            "Epoch : 263 Cost : 1.5057736e+17\n",
            "Epoch : 264 Cost : 1.5051715e+17\n",
            "Epoch : 265 Cost : 1.5045695e+17\n",
            "Epoch : 266 Cost : 1.5039678e+17\n",
            "Epoch : 267 Cost : 1.5033662e+17\n",
            "Epoch : 268 Cost : 1.5027649e+17\n",
            "Epoch : 269 Cost : 1.502164e+17\n",
            "Epoch : 270 Cost : 1.5015632e+17\n",
            "Epoch : 271 Cost : 1.5009626e+17\n",
            "Epoch : 272 Cost : 1.5003625e+17\n",
            "Epoch : 273 Cost : 1.4997622e+17\n",
            "Epoch : 274 Cost : 1.4991625e+17\n",
            "Epoch : 275 Cost : 1.4985629e+17\n",
            "Epoch : 276 Cost : 1.4979636e+17\n",
            "Epoch : 277 Cost : 1.4973644e+17\n",
            "Epoch : 278 Cost : 1.4967657e+17\n",
            "Epoch : 279 Cost : 1.4961671e+17\n",
            "Epoch : 280 Cost : 1.4955686e+17\n",
            "Epoch : 281 Cost : 1.4949704e+17\n",
            "Epoch : 282 Cost : 1.4943722e+17\n",
            "Epoch : 283 Cost : 1.4937747e+17\n",
            "Epoch : 284 Cost : 1.4931772e+17\n",
            "Epoch : 285 Cost : 1.49258e+17\n",
            "Epoch : 286 Cost : 1.4919826e+17\n",
            "Epoch : 287 Cost : 1.491386e+17\n",
            "Epoch : 288 Cost : 1.4907897e+17\n",
            "Epoch : 289 Cost : 1.4901932e+17\n",
            "Epoch : 290 Cost : 1.4895972e+17\n",
            "Epoch : 291 Cost : 1.4890014e+17\n",
            "Epoch : 292 Cost : 1.488406e+17\n",
            "Epoch : 293 Cost : 1.4878107e+17\n",
            "Epoch : 294 Cost : 1.4872154e+17\n",
            "Epoch : 295 Cost : 1.486621e+17\n",
            "Epoch : 296 Cost : 1.4860262e+17\n",
            "Epoch : 297 Cost : 1.4854318e+17\n",
            "Epoch : 298 Cost : 1.4848377e+17\n",
            "Epoch : 299 Cost : 1.484244e+17\n",
            "Epoch : 300 Cost : 1.4836502e+17\n",
            "Epoch : 301 Cost : 1.4830568e+17\n",
            "Epoch : 302 Cost : 1.4824638e+17\n",
            "Epoch : 303 Cost : 1.4818711e+17\n",
            "Epoch : 304 Cost : 1.4812784e+17\n",
            "Epoch : 305 Cost : 1.4806859e+17\n",
            "Epoch : 306 Cost : 1.4800937e+17\n",
            "Epoch : 307 Cost : 1.479502e+17\n",
            "Epoch : 308 Cost : 1.4789101e+17\n",
            "Epoch : 309 Cost : 1.4783185e+17\n",
            "Epoch : 310 Cost : 1.4777271e+17\n",
            "Epoch : 311 Cost : 1.477136e+17\n",
            "Epoch : 312 Cost : 1.4765453e+17\n",
            "Epoch : 313 Cost : 1.4759547e+17\n",
            "Epoch : 314 Cost : 1.4753644e+17\n",
            "Epoch : 315 Cost : 1.4747741e+17\n",
            "Epoch : 316 Cost : 1.4741845e+17\n",
            "Epoch : 317 Cost : 1.4735949e+17\n",
            "Epoch : 318 Cost : 1.4730054e+17\n",
            "Epoch : 319 Cost : 1.4724162e+17\n",
            "Epoch : 320 Cost : 1.4718274e+17\n",
            "Epoch : 321 Cost : 1.4712388e+17\n",
            "Epoch : 322 Cost : 1.47065e+17\n",
            "Epoch : 323 Cost : 1.470062e+17\n",
            "Epoch : 324 Cost : 1.4694741e+17\n",
            "Epoch : 325 Cost : 1.4688864e+17\n",
            "Epoch : 326 Cost : 1.468299e+17\n",
            "Epoch : 327 Cost : 1.4677116e+17\n",
            "Epoch : 328 Cost : 1.4671248e+17\n",
            "Epoch : 329 Cost : 1.4665379e+17\n",
            "Epoch : 330 Cost : 1.4659515e+17\n",
            "Epoch : 331 Cost : 1.4653652e+17\n",
            "Epoch : 332 Cost : 1.4647788e+17\n",
            "Epoch : 333 Cost : 1.464193e+17\n",
            "Epoch : 334 Cost : 1.4636075e+17\n",
            "Epoch : 335 Cost : 1.463022e+17\n",
            "Epoch : 336 Cost : 1.4624367e+17\n",
            "Epoch : 337 Cost : 1.4618519e+17\n",
            "Epoch : 338 Cost : 1.4612671e+17\n",
            "Epoch : 339 Cost : 1.4606826e+17\n",
            "Epoch : 340 Cost : 1.4600984e+17\n",
            "Epoch : 341 Cost : 1.4595146e+17\n",
            "Epoch : 342 Cost : 1.4589308e+17\n",
            "Epoch : 343 Cost : 1.4583472e+17\n",
            "Epoch : 344 Cost : 1.4577638e+17\n",
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            "Epoch : 5310 Cost : 1.9994832e+16\n",
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            "Epoch : 5575 Cost : 1.7983659e+16\n",
            "Epoch : 5576 Cost : 1.7976466e+16\n",
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            "Epoch : 5699 Cost : 1.7113327e+16\n",
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            "Epoch : 5709 Cost : 1.7045004e+16\n",
            "Epoch : 5710 Cost : 1.7038186e+16\n",
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            "Epoch : 5727 Cost : 1.692271e+16\n",
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            "Epoch : 6686 Cost : 1.1530751e+16\n",
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            "Epoch : 6688 Cost : 1.152153e+16\n",
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            "Epoch : 6699 Cost : 1.1470943e+16\n",
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            "Epoch : 6702 Cost : 1.1457185e+16\n",
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            "Epoch : 6708 Cost : 1.1429719e+16\n",
            "Epoch : 6709 Cost : 1.1425148e+16\n",
            "Epoch : 6710 Cost : 1.1420578e+16\n",
            "Epoch : 6711 Cost : 1.1416009e+16\n",
            "Epoch : 6712 Cost : 1.1411444e+16\n",
            "Epoch : 6713 Cost : 1.1406878e+16\n",
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            "Epoch : 6715 Cost : 1.1397754e+16\n",
            "Epoch : 6716 Cost : 1.1393196e+16\n",
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            "Epoch : 6720 Cost : 1.1374981e+16\n",
            "Epoch : 6721 Cost : 1.1370432e+16\n",
            "Epoch : 6722 Cost : 1.1365885e+16\n",
            "Epoch : 6723 Cost : 1.1361338e+16\n",
            "Epoch : 6724 Cost : 1.1356794e+16\n",
            "Epoch : 6725 Cost : 1.1352252e+16\n",
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            "Epoch : 6727 Cost : 1.1343173e+16\n",
            "Epoch : 6728 Cost : 1.1338635e+16\n",
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            "Epoch : 6731 Cost : 1.1325036e+16\n",
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            "Epoch : 6733 Cost : 1.1315978e+16\n",
            "Epoch : 6734 Cost : 1.1311454e+16\n",
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            "Epoch : 6751 Cost : 1.1234788e+16\n",
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            "Epoch : 6761 Cost : 1.1189934e+16\n",
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            "Epoch : 6767 Cost : 1.1163107e+16\n",
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            "Epoch : 6813 Cost : 1.0959566e+16\n",
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            "Epoch : 6819 Cost : 1.093329e+16\n",
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            "Epoch : 6822 Cost : 1.0920176e+16\n",
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            "Epoch : 6868 Cost : 1.0721064e+16\n",
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            "Epoch : 9000 Cost : 4569124000000000.0\n",
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            "Epoch : 9781 Cost : 3343075000000000.0\n",
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            "Epoch : 9794 Cost : 3325734400000000.0\n",
            "Epoch : 9795 Cost : 3324404300000000.0\n",
            "Epoch : 9796 Cost : 3323074700000000.0\n",
            "Epoch : 9797 Cost : 3321746000000000.0\n",
            "Epoch : 9798 Cost : 3320417200000000.0\n",
            "Epoch : 9799 Cost : 3319089300000000.0\n",
            "Epoch : 9800 Cost : 3317761300000000.0\n",
            "Epoch : 9801 Cost : 3316434400000000.0\n",
            "Epoch : 9802 Cost : 3315108400000000.0\n",
            "Epoch : 9803 Cost : 3313782000000000.0\n",
            "Epoch : 9804 Cost : 3312457300000000.0\n",
            "Epoch : 9805 Cost : 3311132000000000.0\n",
            "Epoch : 9806 Cost : 3309807600000000.0\n",
            "Epoch : 9807 Cost : 3308484000000000.0\n",
            "Epoch : 9808 Cost : 3307160500000000.0\n",
            "Epoch : 9809 Cost : 3305838000000000.0\n",
            "Epoch : 9810 Cost : 3304515600000000.0\n",
            "Epoch : 9811 Cost : 3303193800000000.0\n",
            "Epoch : 9812 Cost : 3301873000000000.0\n",
            "Epoch : 9813 Cost : 3300552400000000.0\n",
            "Epoch : 9814 Cost : 3299231700000000.0\n",
            "Epoch : 9815 Cost : 3297912400000000.0\n",
            "Epoch : 9816 Cost : 3296593600000000.0\n",
            "Epoch : 9817 Cost : 3295275000000000.0\n",
            "Epoch : 9818 Cost : 3293957000000000.0\n",
            "Epoch : 9819 Cost : 3292639500000000.0\n",
            "Epoch : 9820 Cost : 3291322600000000.0\n",
            "Epoch : 9821 Cost : 3290006000000000.0\n",
            "Epoch : 9822 Cost : 3288690000000000.0\n",
            "Epoch : 9823 Cost : 3287374700000000.0\n",
            "Epoch : 9824 Cost : 3286060200000000.0\n",
            "Epoch : 9825 Cost : 3284745600000000.0\n",
            "Epoch : 9826 Cost : 3283431600000000.0\n",
            "Epoch : 9827 Cost : 3282118400000000.0\n",
            "Epoch : 9828 Cost : 3280806000000000.0\n",
            "Epoch : 9829 Cost : 3279493700000000.0\n",
            "Epoch : 9830 Cost : 3278182400000000.0\n",
            "Epoch : 9831 Cost : 3276871000000000.0\n",
            "Epoch : 9832 Cost : 3275560600000000.0\n",
            "Epoch : 9833 Cost : 3274250000000000.0\n",
            "Epoch : 9834 Cost : 3272940600000000.0\n",
            "Epoch : 9835 Cost : 3271632000000000.0\n",
            "Epoch : 9836 Cost : 3270323000000000.0\n",
            "Epoch : 9837 Cost : 3269015600000000.0\n",
            "Epoch : 9838 Cost : 3267707800000000.0\n",
            "Epoch : 9839 Cost : 3266401000000000.0\n",
            "Epoch : 9840 Cost : 3265094300000000.0\n",
            "Epoch : 9841 Cost : 3263788300000000.0\n",
            "Epoch : 9842 Cost : 3262483500000000.0\n",
            "Epoch : 9843 Cost : 3261178600000000.0\n",
            "Epoch : 9844 Cost : 3259874300000000.0\n",
            "Epoch : 9845 Cost : 3258570200000000.0\n",
            "Epoch : 9846 Cost : 3257267200000000.0\n",
            "Epoch : 9847 Cost : 3255964200000000.0\n",
            "Epoch : 9848 Cost : 3254661800000000.0\n",
            "Epoch : 9849 Cost : 3253360400000000.0\n",
            "Epoch : 9850 Cost : 3252059600000000.0\n",
            "Epoch : 9851 Cost : 3250758700000000.0\n",
            "Epoch : 9852 Cost : 3249458400000000.0\n",
            "Epoch : 9853 Cost : 3248158700000000.0\n",
            "Epoch : 9854 Cost : 3246859700000000.0\n",
            "Epoch : 9855 Cost : 3245561000000000.0\n",
            "Epoch : 9856 Cost : 3244263100000000.0\n",
            "Epoch : 9857 Cost : 3242965500000000.0\n",
            "Epoch : 9858 Cost : 3241668700000000.0\n",
            "Epoch : 9859 Cost : 3240372000000000.0\n",
            "Epoch : 9860 Cost : 3239076000000000.0\n",
            "Epoch : 9861 Cost : 3237780200000000.0\n",
            "Epoch : 9862 Cost : 3236485500000000.0\n",
            "Epoch : 9863 Cost : 3235190800000000.0\n",
            "Epoch : 9864 Cost : 3233896700000000.0\n",
            "Epoch : 9865 Cost : 3232603400000000.0\n",
            "Epoch : 9866 Cost : 3231310600000000.0\n",
            "Epoch : 9867 Cost : 3230018300000000.0\n",
            "Epoch : 9868 Cost : 3228726000000000.0\n",
            "Epoch : 9869 Cost : 3227435000000000.0\n",
            "Epoch : 9870 Cost : 3226143700000000.0\n",
            "Epoch : 9871 Cost : 3224853400000000.0\n",
            "Epoch : 9872 Cost : 3223563800000000.0\n",
            "Epoch : 9873 Cost : 3222274500000000.0\n",
            "Epoch : 9874 Cost : 3220985500000000.0\n",
            "Epoch : 9875 Cost : 3219697300000000.0\n",
            "Epoch : 9876 Cost : 3218409600000000.0\n",
            "Epoch : 9877 Cost : 3217122200000000.0\n",
            "Epoch : 9878 Cost : 3215835600000000.0\n",
            "Epoch : 9879 Cost : 3214549200000000.0\n",
            "Epoch : 9880 Cost : 3213263700000000.0\n",
            "Epoch : 9881 Cost : 3211978400000000.0\n",
            "Epoch : 9882 Cost : 3210694000000000.0\n",
            "Epoch : 9883 Cost : 3209409700000000.0\n",
            "Epoch : 9884 Cost : 3208125800000000.0\n",
            "Epoch : 9885 Cost : 3206843000000000.0\n",
            "Epoch : 9886 Cost : 3205560700000000.0\n",
            "Epoch : 9887 Cost : 3204278000000000.0\n",
            "Epoch : 9888 Cost : 3202996600000000.0\n",
            "Epoch : 9889 Cost : 3201715600000000.0\n",
            "Epoch : 9890 Cost : 3200435100000000.0\n",
            "Epoch : 9891 Cost : 3199155000000000.0\n",
            "Epoch : 9892 Cost : 3197875600000000.0\n",
            "Epoch : 9893 Cost : 3196596500000000.0\n",
            "Epoch : 9894 Cost : 3195318200000000.0\n",
            "Epoch : 9895 Cost : 3194040500000000.0\n",
            "Epoch : 9896 Cost : 3192762700000000.0\n",
            "Epoch : 9897 Cost : 3191485800000000.0\n",
            "Epoch : 9898 Cost : 3190209000000000.0\n",
            "Epoch : 9899 Cost : 3188933500000000.0\n",
            "Epoch : 9900 Cost : 3187657600000000.0\n",
            "Epoch : 9901 Cost : 3186382800000000.0\n",
            "Epoch : 9902 Cost : 3185108600000000.0\n",
            "Epoch : 9903 Cost : 3183834600000000.0\n",
            "Epoch : 9904 Cost : 3182561000000000.0\n",
            "Epoch : 9905 Cost : 3181288200000000.0\n",
            "Epoch : 9906 Cost : 3180015800000000.0\n",
            "Epoch : 9907 Cost : 3178744000000000.0\n",
            "Epoch : 9908 Cost : 3177472400000000.0\n",
            "Epoch : 9909 Cost : 3176202000000000.0\n",
            "Epoch : 9910 Cost : 3174931400000000.0\n",
            "Epoch : 9911 Cost : 3173661700000000.0\n",
            "Epoch : 9912 Cost : 3172392000000000.0\n",
            "Epoch : 9913 Cost : 3171123300000000.0\n",
            "Epoch : 9914 Cost : 3169855000000000.0\n",
            "Epoch : 9915 Cost : 3168587200000000.0\n",
            "Epoch : 9916 Cost : 3167319300000000.0\n",
            "Epoch : 9917 Cost : 3166052600000000.0\n",
            "Epoch : 9918 Cost : 3164787000000000.0\n",
            "Epoch : 9919 Cost : 3163521000000000.0\n",
            "Epoch : 9920 Cost : 3162255600000000.0\n",
            "Epoch : 9921 Cost : 3160991000000000.0\n",
            "Epoch : 9922 Cost : 3159726400000000.0\n",
            "Epoch : 9923 Cost : 3158462600000000.0\n",
            "Epoch : 9924 Cost : 3157199300000000.0\n",
            "Epoch : 9925 Cost : 3155937000000000.0\n",
            "Epoch : 9926 Cost : 3154674400000000.0\n",
            "Epoch : 9927 Cost : 3153412500000000.0\n",
            "Epoch : 9928 Cost : 3152151400000000.0\n",
            "Epoch : 9929 Cost : 3150891000000000.0\n",
            "Epoch : 9930 Cost : 3149630500000000.0\n",
            "Epoch : 9931 Cost : 3148371000000000.0\n",
            "Epoch : 9932 Cost : 3147111200000000.0\n",
            "Epoch : 9933 Cost : 3145852600000000.0\n",
            "Epoch : 9934 Cost : 3144594400000000.0\n",
            "Epoch : 9935 Cost : 3143336800000000.0\n",
            "Epoch : 9936 Cost : 3142079700000000.0\n",
            "Epoch : 9937 Cost : 3140823000000000.0\n",
            "Epoch : 9938 Cost : 3139567000000000.0\n",
            "Epoch : 9939 Cost : 3138310900000000.0\n",
            "Epoch : 9940 Cost : 3137055700000000.0\n",
            "Epoch : 9941 Cost : 3135801300000000.0\n",
            "Epoch : 9942 Cost : 3134547000000000.0\n",
            "Epoch : 9943 Cost : 3133293500000000.0\n",
            "Epoch : 9944 Cost : 3132040000000000.0\n",
            "Epoch : 9945 Cost : 3130787700000000.0\n",
            "Epoch : 9946 Cost : 3129535200000000.0\n",
            "Epoch : 9947 Cost : 3128283500000000.0\n",
            "Epoch : 9948 Cost : 3127032300000000.0\n",
            "Epoch : 9949 Cost : 3125781600000000.0\n",
            "Epoch : 9950 Cost : 3124531300000000.0\n",
            "Epoch : 9951 Cost : 3123282000000000.0\n",
            "Epoch : 9952 Cost : 3122032700000000.0\n",
            "Epoch : 9953 Cost : 3120784000000000.0\n",
            "Epoch : 9954 Cost : 3119536000000000.0\n",
            "Epoch : 9955 Cost : 3118288000000000.0\n",
            "Epoch : 9956 Cost : 3117040600000000.0\n",
            "Epoch : 9957 Cost : 3115794000000000.0\n",
            "Epoch : 9958 Cost : 3114547600000000.0\n",
            "Epoch : 9959 Cost : 3113302000000000.0\n",
            "Epoch : 9960 Cost : 3112057000000000.0\n",
            "Epoch : 9961 Cost : 3110812300000000.0\n",
            "Epoch : 9962 Cost : 3109567900000000.0\n",
            "Epoch : 9963 Cost : 3108324500000000.0\n",
            "Epoch : 9964 Cost : 3107081000000000.0\n",
            "Epoch : 9965 Cost : 3105838200000000.0\n",
            "Epoch : 9966 Cost : 3104595600000000.0\n",
            "Epoch : 9967 Cost : 3103354000000000.0\n",
            "Epoch : 9968 Cost : 3102113100000000.0\n",
            "Epoch : 9969 Cost : 3100872200000000.0\n",
            "Epoch : 9970 Cost : 3099632000000000.0\n",
            "Epoch : 9971 Cost : 3098392400000000.0\n",
            "Epoch : 9972 Cost : 3097153000000000.0\n",
            "Epoch : 9973 Cost : 3095914200000000.0\n",
            "Epoch : 9974 Cost : 3094676000000000.0\n",
            "Epoch : 9975 Cost : 3093438400000000.0\n",
            "Epoch : 9976 Cost : 3092201000000000.0\n",
            "Epoch : 9977 Cost : 3090964500000000.0\n",
            "Epoch : 9978 Cost : 3089728600000000.0\n",
            "Epoch : 9979 Cost : 3088492500000000.0\n",
            "Epoch : 9980 Cost : 3087257000000000.0\n",
            "Epoch : 9981 Cost : 3086022600000000.0\n",
            "Epoch : 9982 Cost : 3084788000000000.0\n",
            "Epoch : 9983 Cost : 3083554300000000.0\n",
            "Epoch : 9984 Cost : 3082321000000000.0\n",
            "Epoch : 9985 Cost : 3081088200000000.0\n",
            "Epoch : 9986 Cost : 3079856400000000.0\n",
            "Epoch : 9987 Cost : 3078624500000000.0\n",
            "Epoch : 9988 Cost : 3077393200000000.0\n",
            "Epoch : 9989 Cost : 3076162400000000.0\n",
            "Epoch : 9990 Cost : 3074932000000000.0\n",
            "Epoch : 9991 Cost : 3073702000000000.0\n",
            "Epoch : 9992 Cost : 3072472500000000.0\n",
            "Epoch : 9993 Cost : 3071244000000000.0\n",
            "Epoch : 9994 Cost : 3070015500000000.0\n",
            "Epoch : 9995 Cost : 3068788000000000.0\n",
            "Epoch : 9996 Cost : 3067560000000000.0\n",
            "Epoch : 9997 Cost : 3066333700000000.0\n",
            "Epoch : 9998 Cost : 3065107200000000.0\n",
            "Epoch : 9999 Cost : 3063881200000000.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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1nAEAaImIMwAAhvHqONtk8/QSAABoNK+OMwAALRFxBgDAMMQZAADDEGcAAAxDnAEAMAxx\nBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxD\nnAEAMAxxBgDAMMQZAADDEGcAAAzj1XG2ZHl6CQAANJpXxxkAgJaIOAMAYBjiDACAYYgzAACGIc4A\nABjGq+Nsk83TSwAAoNG8Os4AALRExBkAAMMQZwAADEOcAQAwDHEGAMAwxBkAAMMQZwAADEOcAQAw\nDHEGAMAwxBkAAMMQZwAADEOcAQAwDHEGAMAwxBkAAMMQZwAADEOcAQAwDHEGAMAwDndtuKKiQrGx\nsTp+/LjOnDmjSZMm6Y477nDX7gAA8Bpui/OmTZvUr18//ed//qfy8/P1hz/8odnjbMlq1v0BAOAK\nbovzr3/9a+efDx8+rE6dOrlrVwAAeBW3xfmccePG6ciRI1qwYIG7dwUAgFdwe5zT0tL03Xff6YUX\nXtCaNWtks9ku+LigoHZyOOwu3XeHwCsUGhrg0m22Nsyv6ZihazDHpmOGTddcM3RbnHfv3q2QkBBd\nddVV+uUvf6mamhoVFRUpJCTkgo8vLi53+RpKSitUUFDm8u22FqGhAcyviZihazDHpmOGTefqGV4s\n9G57K1VOTo6WLl0qSSosLFR5ebmCgoLctTsAALyG2+I8btw4FRUV6eGHH9bEiRM1Y8YM+fjwtmoA\nABrittPabdu2VXJysrs2DwCA1/LqQ1mbLvzNZwAAmMyr4wwAQEtEnAEAMAxxBgDAMMQZAADDEGcA\nAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMF4d\nZ0uWp5cAAECjeXWcAQBoiYgzAACGIc4AABiGOAMAYBjiDACAYYgzAACGIc4AABiGOAMAYBjiDACA\nYYgzAACGIc4AABjGq+Nsk83TSwAAoNG8Os4AALRExBkAAMMQZwAADEOcAQAwDHEGAMAwxBkAAMMQ\nZwAADEOcAQAwDHEGAMAwxBkAAMMQZwAADEOcAQAwjFfH2ZLl6SUAANBoDcY5Ly9PO3bskCR9+OGH\nmj59uvbv3+/2hQEA0Fo1GOdp06bJ19dX3377rT766CNFRUVp9uzZzbE2AABapQbjbLPZNGDAAG3Y\nsEG/+93vNGLECFkWp4sBAHCXBuNcXl6uXbt2KTMzU7fddpsqKytVWlraHGsDAKBVajDOf/jDHxQf\nH6+xY8cqODhYKSkp+s1vftMcawMAoFVyNPQAy7L0ySefOD/+4x//qPT0dLcuCgCA1qzeOH/77bfa\ns2ePli5dqoqKCuft1dXVmj9/vsaPH98sCwQAoLWpN85t2rTR8ePHVVZW5nwrlXT2G8RiYmKaZXEA\nALRG9ca5Z8+e6tmzp2655RbdeOONzttra2vl4+PVP7sEAACParCy//u//6vly5erpqZG48eP1113\n3aUVK1Y0x9oAAGiVGoxzenq6xowZow0bNqhXr17auHGj1q1b1xxrAwCgVWowzm3atJGfn5+++OIL\n3XPPPZzSBgDAzS6ptDNnztTOnTs1dOhQff3116qsrHT3ugAAaLUajPPcuXPVvXt3LViwQHa7Xfn5\n+Zo5c2ZzrK3JbLJ5egkAADRagz+EJCwsTP369dPmzZv1xRdfaODAgerbt+8lbTwpKUk7duxQdXW1\nnnzySY0aNarJCwYAwNs1GOe33npL27Zt00033SRJmj17tkaNGqUnn3zyos/78ssv9cMPPyg9PV3F\nxcV64IEHiDMAAJegwThnZ2crLS3N+Y1g1dXVio6ObjDOQ4YM0YABAyRJgYGBqqioUE1Njex2uwuW\nDQCA92rwmvO//9ARh8Mhm63ha7l2u13t2rWTJK1cuVK33XYbYQYA4BI0eOTcr18/PfXUU4qIiJAk\nbd++Xf3797/kHXz++edauXKlli5detHHBQW1k8Ph2nh36HCFQkMDXLrN1ob5NR0zdA3m2HTMsOma\na4YNxnn69Olat26dcnNzZbPZdN999+mee+65pI1v2bJFCxYs0OLFixUQcPEXVFxcfmkrboSSkgoV\nFJS5fLutRWhoAPNrImboGsyx6Zhh07l6hhcL/UXjfPDgQXXt2lWjR4/W6NGjVVFRoaNHj17Sae2y\nsjIlJSVp2bJl6tixY+NXDQBAK1XvNeesrCyNHz9eZWX/+irh4MGDeuKJJ7R79+4GN7x27VoVFxdr\nypQpeuSRR/TII4/o0KFDrlk1AABerN4j53nz5mnp0qV1Tkf37t1b77zzjhITE7V48eKLbnjs2LEa\nO3as61YKAEArUe+Rs2VZ6t2793m39+rVS2fOnHHrogAAaM3qjXN5ef3foHXixAm3LMbVLFmeXgIA\nAI1Wb5x79eqlDz744LzbFy1apIEDB7p1UQAAtGb1XnOOiYnRM888o08++UT9+vVTbW2tdu7cKX9/\nfy1cuLA51wgAQKtSb5xDQ0P14YcfKisrSz/88IPsdrvuueceDRkypDnXBwBAq9PgDyEJDw9XeHh4\nc6wFAADoEn62NgAAaF7EGQAAwxBnAAAMQ5wBADAMcQYAwDDEGQAAwxBnAAAMQ5wBADAMcQYAwDDE\nGQAAwxBnAAAM49Vxtsnm6SUAANBoXh1nAABaIuIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMA\nAIbx6jhbsjy9BAAAGs2r4wwAQEtEnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMMQZAADD\nEGcAAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMAxxBgDAMMQZAADDEGcAAAxDnAEAMIxXx9km\nm6eXAABAo3l1nAEAaImIMwAAhiHOAAAYhjgDAGAY4gwAgGG8Os6WLE8vAQCARvPqOAMA0BIRZwAA\nDEOcAQAwDHEGAMAwxBkAAMO4Nc579+7VyJEj9f7777tzNwAAeBW3xbm8vFyzZs1SeHi4u3YBAIBX\ncluc/fz8tGjRIoWFhblrFwAAeCWH2zbscMjhcNvmAQDwWsbUMyionRwOu0u32SHwCoWGBrh0m60N\n82s6ZugazLHpmGHTNdcMjYlzcXG5y7dZUlqhgoIyl2+3tQgNDWB+TcQMXYM5Nh0zbDpXz/Bioeet\nVAAAGMZtR867d+9WYmKi8vPz5XA4lJmZqZSUFHXs2NFduwQAwCu4Lc79+vVTamqquzYPAIDX4rQ2\nAACGIc4AABiGOAMAYBjiDACAYYgzAACGIc4AABjGq+Nsk83TSwAAoNG8Os4AALRExBkAAMMQZwAA\nDOPVcbZkeXoJAAA0mlfHGQCAlog4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYh\nzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBh\niDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBg\nGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGOIMAIBhiDMAAIYhzgAA\nGIY4AwBgGOIMAIBhiDMAAIYhzgAAGIY4AwBgGIc7N/7KK68oNzdXNptN06dP14ABA9y5OwAAvILb\n4vzVV1/p//7v/5Senq79+/dr+vTpSk9Pd9fuAADwGm6Lc1ZWlkaOHClJ6tmzp0pKSnTy5En5+/u7\na5fn+WjTfmXtOdps+/M2bdo4dOZMtaeX0aIxQ9dgjk3HDJtu5NDu6t05oFn25bY4FxYW6oYbbnB+\nHBwcrIKCgnrjHBTUTg6H3aVrOFJUriNF5S7dJgCgdWp/hZ8iB3Zuln259Zrzz1mWddH7i4tdF9El\nL94h37Z+Kio65bJttkYhIf46fvykp5fRojFD12COTccMm65H92AVFJS5bHuhofUfhbstzmFhYSos\nLHR+fOzYMYWGhrprd3XYbDYFBbZV9ZmqZtmft+oY0EZVpys9vYwWjRm6BnNsOmbYdDabrdn25ba3\nUkVGRiozM1OStGfPHoWFhTXr9WYAAFoqtx05Dx48WDfccIPGjRsnm82mhIQEd+0KAACv4tZrzs8/\n/7w7Nw8AgFfiJ4QBAGAY4gwAgGGIMwAAhiHOAAAYhjgDAGAY4gwAgGGIMwAAhiHOAAAYxmY19Bsp\nAABAs+LIGQAAwxBnAAAMQ5wBADAMcQYAwDDEGQAAwxBnAAAM49bf5+wpr7zyinJzc2Wz2TR9+nQN\nGDDA00syTlJSknbs2KHq6mo9+eST6t+/v2JiYlRTU6PQ0FC99tpr8vPz05o1a/Tuu+/Kx8dHDz30\nkMaMGaOqqirFxsbq0KFDstvtevXVV9W1a1dPv6Rmd/r0ad17772aNGmSwsPDmd9lWLNmjRYvXiyH\nw6HnnntOffr0YY6NcOrUKb344osqKSlRVVWVnnnmGYWGhuqll16SJPXp00czZ86UJC1evFgZGRmy\n2Wz6r//6L40YMUJlZWWaOnWqysrK1K5dOyUnJ6tjx44efEXNa+/evZo0aZImTJig6OhoHT58uMmf\nf99///0F599olpfJzs62Jk6caFmWZe3bt8966KGHPLwi82RlZVlPPPGEZVmWVVRUZI0YMcKKjY21\n1q5da1mWZSUnJ1vLly+3Tp06ZY0aNcoqLS21KioqrNGjR1vFxcXW6tWrrZdeesmyLMvasmWLNXny\nZI+9Fk96/fXXrd/+9rfWqlWrmN9lKCoqskaNGmWVlZVZR48eteLi4phjI6Wmplpz5861LMuyjhw5\nYkVFRVnR0dFWbm6uZVmW9ac//cnavHmz9dNPP1kPPPCAdebMGev48eNWVFSUVV1dbaWkpFiLFi2y\nLMuy0tLSrKSkJI+9luZ26tQpKzo62oqLi7NSU1Mty7Jc8vl3oflfDq87rZ2VlaWRI0dKknr27KmS\nkhKdPHnSw6syy5AhQ/TWW29JkgIDA1VRUaHs7GzdddddkqQ77rhDWVlZys3NVf/+/RUQEKC2bdtq\n8ODB2rlzp7KysvSrX/1KkhQREaGdO3d67LV4yv79+7Vv3z7dfvvtksT8LkNWVpbCw8Pl7++vsLAw\nzZo1izk2UlBQkE6cOCFJKi0tVceOHZWfn+88W3huhtnZ2br11lvl5+en4OBgdenSRfv27aszw3OP\nbS38/Py0aNEihYWFOW9r6udfZWXlBed/ObwuzoWFhQoKCnJ+HBwcrIKCAg+uyDx2u13t2rWTJK1c\nuVK33XabKioq5OfnJ0kKCQlRQUGBCgsLFRwc7HzeuVn+/HYfHx/ZbDZVVlY2/wvxoMTERMXGxjo/\nZn6Nl5eXp9OnT+upp57Sww8/rKysLObYSKNHj9ahQ4f0q1/9StHR0YqJiVFgYKDz/sbMMCQkRMeO\nHWv21+ApDodDbdu2rXNbUz//CgsLLzj/y1rfZT2rBbH46aT1+vzzz7Vy5UotXbpUo0aNct5e38wa\ne7u3+vjjj3XjjTfWe32T+V26EydOaN68eTp06JAeffTROrNgjg375JNP1LlzZy1ZskTff/+9nnnm\nGQUEBDjvb8ysWuP8LsYVn39NmanXHTmHhYWpsLDQ+fGxY8cUGhrqwRWZacuWLVqwYIEWLVqkgIAA\ntWvXTqdPn5YkHT16VGFhYRec5bnbz301WFVVJcuynF9ttgabN2/Wxo0b9dBDD+mjjz7SX//6V+Z3\nGUJCQjRo0CA5HA5169ZN7du3V/v27ZljI+zcuVPDhw+XJPXt21dnzpxRcXGx8/76Zvjz28/N8Nxt\nrVlT/x6HhoY6LzP8fBuXw+viHBkZqczMTEnSnj17FBYWJn9/fw+vyixlZWVKSkrSwoULnd+ZGRER\n4Zzb+vXrdeutt2rgwIH65ptvVFpaqlOnTmnnzp26+eabFRkZqYyMDEnSpk2bNGzYMI+9Fk948803\ntWrVKn344YcaM2aMJk2axPwuw/Dhw/Xll1+qtrZWxcXFKi8vZ46N1L17d+Xm5kqS8vPz1b59e/Xs\n2VM5OTmS/jXDW265RZs3b1ZlZaWOHj2qY8eO6brrrqszw3OPbc2a+vnn6+urHj16nDf/y+GVv5Vq\n7ty5ysnJkc1mU0JCgvr27evpJRklPT1dKSkpuvbaa523zZkzR3FxcTpz5ow6d+6sV199Vb6+vsrI\nyNCSJUtks9kUHR2t++67TzV4bNX3AAADlUlEQVQ1NYqLi9OBAwfk5+enOXPm6KqrrvLgK/KclJQU\ndenSRcOHD9eLL77I/BopLS1NK1eulCQ9/fTT6t+/P3NshFOnTmn69Ok6fvy4qqurNXnyZIWGhmrG\njBmqra3VwIEDNW3aNElSamqqPv30U9lsNk2ZMkXh4eE6deqUXnjhBZ04cUKBgYF67bXX6pwW92a7\nd+9WYmKi8vPz5XA41KlTJ82dO1exsbFN+vzbt2/fBeffWF4ZZwAAWjKvO60NAEBLR5wBADAMcQYA\nwDDEGQAAwxBnAAAM4/U/IQzwVnl5ebr77rs1aNCgOrePGDFCTzzxhEv2kZ2drTfffFMffPCBS7YH\n4NIQZ6AFCw4OVmpqqqeXAcDFiDPgpa6//npNmjRJ2dnZOnXqlObMmaPevXsrNzdXc+bMkcPhkM1m\n04wZM3TdddfpwIEDio+PV21trdq0aaNXX31VklRbW6uEhAR999138vPz08KFCyVJU6dOVWlpqaqr\nq3XHHXfo6aef9uTLBbwK15wBL1VTU6NevXopNTVV48eP19tvvy1JiomJ0bRp05SamqrHHnvM+cvg\nExIS9Pjjj2v58uV68MEHtW7dOklnfz3ms88+qw8//FAOh0Nbt27V9u3bVV1drRUrVigtLU3t2rVT\nbW2tx14r4G2IM9CCFRUV6ZFHHqnz365du5z3n/ulCIMHD9a+fftUWlqq48ePO3/f7NChQ7V7925J\n0q5duzR06FBJZ38V4YQJEyRJPXr00JVXXilJ+sUvfqHS0lINHjxYR48e1eTJk/Xxxx9rzJgx8vHh\nnxPAVTitDbRgDV1z/vlP57XZbLLZbPXeL+mCR792u/2820JCQvTJJ5/o66+/1saNG/Xggw/qb3/7\n23m/HxfA5eFLXcCLffnll5KkHTt2qE+fPgoICFBoaKjzNxllZWXpxhtvlHT26HrLli2SpLVr1+r1\n11+vd7tbt27V5s2bddNNNykmJkbt2rXT8ePH3fxqgNaDI2egBTt3Wvvnrr76auc3c3377bf64IMP\nVFJSosTERElSYmKi5syZI7vdLh8fH7300kuSpPj4eMXHx2vFihVyOBx65ZVX9NNPP11wv9dee61i\nY2O1ePFi2e12DR8+XF26dHHfCwVaGX4rFeCl+vTpoz179sjh4GtwoKXhtDYAAIbhyBkAAMNw5AwA\ngGGIMwAAhiHOAAAYhjgDAGAY4gwAgGGIMwAAhvl/nDbaXOsU9koAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b7ac37b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B5tSpUyY3N9f069fPlJeXm/T0dDN37lxjjDGLFy8206dPD9h9qW/FxcUmKSnJ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\nIc4AAFiGOAMAYBniDACAZf4/SvWITwv42aIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f99b77e9b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}