MLE_LM_Assigment.ipynb

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  "metadata": {
    "colab": {
      "name": "MLE LM Assigment.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "v4ZX_R6USm9W",
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        "KCUsAm9hWvgE",
        "Vrru3pn9donW",
        "K05HXr3Od1n0"
      ],
      "include_colab_link": true
    },
    "interpreter": {
      "hash": "046dee07d2d0cc172bf251368be63687cceb49b43349e831388869b86d5850e6"
    },
    "kernelspec": {
      "display_name": "Python 3.8.5 64-bit ('base': conda)",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sandeepyadav10011995/Machine-Learning-Templates/blob/master/MLE_LM_Assigment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OcOMNinVMFa",
        "outputId": "4382bd28-ed95-478f-c4d0-45fa9ac6252b"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "print(\"pandas version - \", pd.__version__)\n",
        "print(\"numpy version - \", pd.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pandas version -  1.1.5\n",
            "numpy version -  1.1.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RytBuvyl9e7d"
      },
      "source": [
        "## do not change this\n",
        "np.random.seed(101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujvHenL7aVtj"
      },
      "source": [
        "# Load The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4ZX_R6USm9W"
      },
      "source": [
        "### Read Data from CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uoshwo5eSuc1",
        "outputId": "25a9e3a2-4706-47d3-f5f8-09dd819a7200"
      },
      "source": [
        "## your code here\n",
        "df = pd.read_csv(\"Test Data.csv\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>Prefecture</th>\n",
              "      <th>TV_Area_code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28-03-2016</td>\n",
              "      <td>National</td>\n",
              "      <td>110011</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4/4/2016</td>\n",
              "      <td>National</td>\n",
              "      <td>110011</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11/4/2016</td>\n",
              "      <td>National</td>\n",
              "      <td>110011</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18-04-2016</td>\n",
              "      <td>National</td>\n",
              "      <td>110011</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25-04-2016</td>\n",
              "      <td>National</td>\n",
              "      <td>110011</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  ... Catgry_PP_SalesForPromotedItems\n",
              "0           28-03-2016  ...                               0\n",
              "1             4/4/2016  ...                               0\n",
              "2            11/4/2016  ...                               0\n",
              "3           18-04-2016  ...                               0\n",
              "4           25-04-2016  ...                               0\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5gEtHgMTA9K"
      },
      "source": [
        "### Summarise the data\n",
        " 1. Stats about all the columns\n",
        " 2. DataTypes of various columns\n",
        " 3. Number of Data Points, Features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BJ9f_bKc1d-"
      },
      "source": [
        "### *1. Stats about all the columns*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rAtA4f5WlBA",
        "outputId": "180e4ced-c4ac-4f66-fad1-14907bfd48f1"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>Prefecture</th>\n",
              "      <th>TV_Area_code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>4.100000e+02</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>410.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>110019.500000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3316.392683</td>\n",
              "      <td>1.773312e+06</td>\n",
              "      <td>530.095366</td>\n",
              "      <td>19.618217</td>\n",
              "      <td>2.284506</td>\n",
              "      <td>105.037524</td>\n",
              "      <td>118.946262</td>\n",
              "      <td>66792.294845</td>\n",
              "      <td>9755.504827</td>\n",
              "      <td>2068.897561</td>\n",
              "      <td>719.512195</td>\n",
              "      <td>78.780488</td>\n",
              "      <td>2724.390244</td>\n",
              "      <td>56.829268</td>\n",
              "      <td>328.786829</td>\n",
              "      <td>328.786829</td>\n",
              "      <td>4.071004e+05</td>\n",
              "      <td>7.595366e+05</td>\n",
              "      <td>4.926696e+05</td>\n",
              "      <td>5.772737e+07</td>\n",
              "      <td>5.897958e+07</td>\n",
              "      <td>35132.843902</td>\n",
              "      <td>1.286018e+06</td>\n",
              "      <td>72002.117073</td>\n",
              "      <td>460.763415</td>\n",
              "      <td>15803.636585</td>\n",
              "      <td>11796.239024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.510385</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>584.944609</td>\n",
              "      <td>4.186248e+05</td>\n",
              "      <td>48.577134</td>\n",
              "      <td>7.684544</td>\n",
              "      <td>3.551912</td>\n",
              "      <td>1.598141</td>\n",
              "      <td>13.415880</td>\n",
              "      <td>75283.146816</td>\n",
              "      <td>4733.144921</td>\n",
              "      <td>3154.328079</td>\n",
              "      <td>4300.094928</td>\n",
              "      <td>533.854806</td>\n",
              "      <td>6333.316280</td>\n",
              "      <td>199.791322</td>\n",
              "      <td>219.142627</td>\n",
              "      <td>219.142627</td>\n",
              "      <td>2.744129e+05</td>\n",
              "      <td>1.868751e+06</td>\n",
              "      <td>1.493622e+06</td>\n",
              "      <td>1.410942e+08</td>\n",
              "      <td>1.410258e+08</td>\n",
              "      <td>94461.379590</td>\n",
              "      <td>4.151076e+05</td>\n",
              "      <td>26549.366381</td>\n",
              "      <td>251.610665</td>\n",
              "      <td>50304.076450</td>\n",
              "      <td>39745.240046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>110011.000000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2075.000000</td>\n",
              "      <td>9.760400e+05</td>\n",
              "      <td>405.600000</td>\n",
              "      <td>4.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>101.157381</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1386.000000</td>\n",
              "      <td>4.182180e+05</td>\n",
              "      <td>17110.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>110011.000000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2816.250000</td>\n",
              "      <td>1.378423e+06</td>\n",
              "      <td>493.950000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>104.086118</td>\n",
              "      <td>110.600000</td>\n",
              "      <td>19113.956970</td>\n",
              "      <td>6149.621602</td>\n",
              "      <td>1222.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>166.700000</td>\n",
              "      <td>166.700000</td>\n",
              "      <td>2.034137e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.500000e+05</td>\n",
              "      <td>4999.000000</td>\n",
              "      <td>1.013149e+06</td>\n",
              "      <td>53495.000000</td>\n",
              "      <td>266.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>110019.500000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3356.500000</td>\n",
              "      <td>1.825165e+06</td>\n",
              "      <td>522.150000</td>\n",
              "      <td>19.900000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>104.638092</td>\n",
              "      <td>118.766667</td>\n",
              "      <td>57262.416060</td>\n",
              "      <td>9759.896120</td>\n",
              "      <td>1534.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>331.200000</td>\n",
              "      <td>331.200000</td>\n",
              "      <td>4.059732e+05</td>\n",
              "      <td>1.000000e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.600000e+04</td>\n",
              "      <td>3.477697e+06</td>\n",
              "      <td>8052.000000</td>\n",
              "      <td>1.216229e+06</td>\n",
              "      <td>69282.000000</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>110028.000000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3719.500000</td>\n",
              "      <td>2.103831e+06</td>\n",
              "      <td>571.050000</td>\n",
              "      <td>26.257143</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>105.524297</td>\n",
              "      <td>124.600000</td>\n",
              "      <td>83572.520700</td>\n",
              "      <td>11764.450840</td>\n",
              "      <td>1945.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>469.600000</td>\n",
              "      <td>469.600000</td>\n",
              "      <td>5.835178e+05</td>\n",
              "      <td>4.000000e+05</td>\n",
              "      <td>7.769700e+04</td>\n",
              "      <td>3.408533e+07</td>\n",
              "      <td>3.628200e+07</td>\n",
              "      <td>15039.000000</td>\n",
              "      <td>1.482700e+06</td>\n",
              "      <td>86673.000000</td>\n",
              "      <td>642.250000</td>\n",
              "      <td>235.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110028.000000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5433.000000</td>\n",
              "      <td>3.162632e+06</td>\n",
              "      <td>763.200000</td>\n",
              "      <td>33.600000</td>\n",
              "      <td>29.900000</td>\n",
              "      <td>111.765995</td>\n",
              "      <td>161.677419</td>\n",
              "      <td>653893.114100</td>\n",
              "      <td>23691.027020</td>\n",
              "      <td>41121.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>25000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1052.500000</td>\n",
              "      <td>1052.500000</td>\n",
              "      <td>1.284300e+06</td>\n",
              "      <td>1.333000e+07</td>\n",
              "      <td>1.129049e+07</td>\n",
              "      <td>9.777600e+08</td>\n",
              "      <td>9.807000e+08</td>\n",
              "      <td>552314.000000</td>\n",
              "      <td>3.075072e+06</td>\n",
              "      <td>169580.000000</td>\n",
              "      <td>1131.000000</td>\n",
              "      <td>325221.000000</td>\n",
              "      <td>378801.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Shop_Code  ...  Catgry_PP_SalesForPromotedItems\n",
              "count     410.000000  ...                       410.000000\n",
              "mean   110019.500000  ...                     11796.239024\n",
              "std         8.510385  ...                     39745.240046\n",
              "min    110011.000000  ...                         0.000000\n",
              "25%    110011.000000  ...                         0.000000\n",
              "50%    110019.500000  ...                         0.000000\n",
              "75%    110028.000000  ...                         0.000000\n",
              "max    110028.000000  ...                    378801.000000\n",
              "\n",
              "[8 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkJwE1RTc7YH"
      },
      "source": [
        "### *2. DataTypes of various columns*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTdT6Hm40uIX",
        "outputId": "aef20016-9c1c-4209-ff2b-a16e92a8985a"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business_Mon_WkBegin                object\n",
              "Geography                           object\n",
              "Shop_Code                            int64\n",
              "Prefecture                           int64\n",
              "TV_Area_code                         int64\n",
              "NoOfCustomerVisits                   int64\n",
              "SalesMeter3MoneyTaxIncluded          int64\n",
              "Price_PerCustomer                  float64\n",
              "Temperature                        float64\n",
              "Precipitation                      float64\n",
              "RPC_INDEX                          float64\n",
              "Japanese_Noodle_Expen              float64\n",
              "AppCoup01_Regular                  float64\n",
              "App_Daily_Users                    float64\n",
              "App_Downloads                        int64\n",
              "Postal_02                            int64\n",
              "Handout_03                           int64\n",
              "Instore_04                           int64\n",
              "InstoreFlier_05                      int64\n",
              "TV_ST                              float64\n",
              "TV_LT                              float64\n",
              "TV_GA                              float64\n",
              "PR_Web                               int64\n",
              "PR_Print                             int64\n",
              "PR_TV                                int64\n",
              "Total_PR                             int64\n",
              "Tweets                               int64\n",
              "Page_Views                           int64\n",
              "Unique_Users                         int64\n",
              "Total_MealCount                      int64\n",
              "Catgry_Disc_Sales                    int64\n",
              "Catgry_PP_SalesForPromotedItems      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVkeTa80DsOz",
        "outputId": "c9cc1f62-fa83-4d30-9bcf-68d57203c3a7"
      },
      "source": [
        "df.apply(lambda x: len(x.unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business_Mon_WkBegin               205\n",
              "Geography                            1\n",
              "Shop_Code                            2\n",
              "Prefecture                           1\n",
              "TV_Area_code                         1\n",
              "NoOfCustomerVisits                 363\n",
              "SalesMeter3MoneyTaxIncluded        410\n",
              "Price_PerCustomer                  369\n",
              "Temperature                        278\n",
              "Precipitation                      119\n",
              "RPC_INDEX                           46\n",
              "Japanese_Noodle_Expen               46\n",
              "AppCoup01_Regular                  145\n",
              "App_Daily_Users                    197\n",
              "App_Downloads                      186\n",
              "Postal_02                            4\n",
              "Handout_03                          12\n",
              "Instore_04                          17\n",
              "InstoreFlier_05                      5\n",
              "TV_ST                              180\n",
              "TV_LT                              180\n",
              "TV_GA                              181\n",
              "PR_Web                              45\n",
              "PR_Print                            61\n",
              "PR_TV                              101\n",
              "Total_PR                           141\n",
              "Tweets                             203\n",
              "Page_Views                         205\n",
              "Unique_Users                       191\n",
              "Total_MealCount                    331\n",
              "Catgry_Disc_Sales                  103\n",
              "Catgry_PP_SalesForPromotedItems     84\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYCgJ8JgECHS"
      },
      "source": [
        "##### From the above we can conclude that \"Geography\",\"Prefecture\" and \"TV_Area_code\"  are same in all the rows/records. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtK-ETf5DTsL",
        "outputId": "0052ec38-4897-4630-89f2-e98bbda9c11c"
      },
      "source": [
        "categorical = df.dtypes[df.dtypes==\"object\"].index\n",
        "print(categorical)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Business_Mon_WkBegin', 'Geography'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYIvm_b8DkCt",
        "outputId": "99d0cc6b-9e4e-42cf-85fb-e09293fbfd17"
      },
      "source": [
        "df[categorical].apply(lambda x: len(x.unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business_Mon_WkBegin    205\n",
              "Geography                 1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XZhYYWJEyLe"
      },
      "source": [
        "#### So they(\"Geography\",\"Prefecture\" and \"TV_Area_code\") will not play any role in the model building. Therefore we can drop these columns from the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uhUKc8iE8EG"
      },
      "source": [
        "df.drop([\"Geography\",\"Prefecture\",\"TV_Area_code\"], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuuJO62OdE-s"
      },
      "source": [
        "### *3. Number of Data Points, Features*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fs_WQnN-56yg",
        "outputId": "2f135bfe-387c-4a47-8d4b-b9aa0803169f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 410 entries, 0 to 409\n",
            "Data columns (total 29 columns):\n",
            " #   Column                           Non-Null Count  Dtype  \n",
            "---  ------                           --------------  -----  \n",
            " 0   Business_Mon_WkBegin             410 non-null    object \n",
            " 1   Shop_Code                        410 non-null    int64  \n",
            " 2   NoOfCustomerVisits               410 non-null    int64  \n",
            " 3   SalesMeter3MoneyTaxIncluded      410 non-null    int64  \n",
            " 4   Price_PerCustomer                410 non-null    float64\n",
            " 5   Temperature                      410 non-null    float64\n",
            " 6   Precipitation                    410 non-null    float64\n",
            " 7   RPC_INDEX                        410 non-null    float64\n",
            " 8   Japanese_Noodle_Expen            410 non-null    float64\n",
            " 9   AppCoup01_Regular                410 non-null    float64\n",
            " 10  App_Daily_Users                  410 non-null    float64\n",
            " 11  App_Downloads                    410 non-null    int64  \n",
            " 12  Postal_02                        410 non-null    int64  \n",
            " 13  Handout_03                       410 non-null    int64  \n",
            " 14  Instore_04                       410 non-null    int64  \n",
            " 15  InstoreFlier_05                  410 non-null    int64  \n",
            " 16  TV_ST                            410 non-null    float64\n",
            " 17  TV_LT                            410 non-null    float64\n",
            " 18  TV_GA                            410 non-null    float64\n",
            " 19  PR_Web                           410 non-null    int64  \n",
            " 20  PR_Print                         410 non-null    int64  \n",
            " 21  PR_TV                            410 non-null    int64  \n",
            " 22  Total_PR                         410 non-null    int64  \n",
            " 23  Tweets                           410 non-null    int64  \n",
            " 24  Page_Views                       410 non-null    int64  \n",
            " 25  Unique_Users                     410 non-null    int64  \n",
            " 26  Total_MealCount                  410 non-null    int64  \n",
            " 27  Catgry_Disc_Sales                410 non-null    int64  \n",
            " 28  Catgry_PP_SalesForPromotedItems  410 non-null    int64  \n",
            "dtypes: float64(10), int64(18), object(1)\n",
            "memory usage: 93.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XG9L_vcEC1kK"
      },
      "source": [
        "##### **Number Of Data Points = 410**\n",
        "##### **Number Of Features = 29** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryv-hJ4pXOw4"
      },
      "source": [
        "### Missing Data Processing\n",
        " 1. Check for Missing Data\n",
        " 2. Remove rows with Missing dates.\n",
        " 3. Fill zeros for other numerical missing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4Q7xpBpclzq"
      },
      "source": [
        "### *1. Check for Missing Data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ1zTTlfXC_8",
        "outputId": "1f84cb3b-254c-4f26-845d-6984a3aa18bc"
      },
      "source": [
        "df.apply(lambda x: sum(x.isnull()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business_Mon_WkBegin               0\n",
              "Shop_Code                          0\n",
              "NoOfCustomerVisits                 0\n",
              "SalesMeter3MoneyTaxIncluded        0\n",
              "Price_PerCustomer                  0\n",
              "Temperature                        0\n",
              "Precipitation                      0\n",
              "RPC_INDEX                          0\n",
              "Japanese_Noodle_Expen              0\n",
              "AppCoup01_Regular                  0\n",
              "App_Daily_Users                    0\n",
              "App_Downloads                      0\n",
              "Postal_02                          0\n",
              "Handout_03                         0\n",
              "Instore_04                         0\n",
              "InstoreFlier_05                    0\n",
              "TV_ST                              0\n",
              "TV_LT                              0\n",
              "TV_GA                              0\n",
              "PR_Web                             0\n",
              "PR_Print                           0\n",
              "PR_TV                              0\n",
              "Total_PR                           0\n",
              "Tweets                             0\n",
              "Page_Views                         0\n",
              "Unique_Users                       0\n",
              "Total_MealCount                    0\n",
              "Catgry_Disc_Sales                  0\n",
              "Catgry_PP_SalesForPromotedItems    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z15qaqetb5K-"
      },
      "source": [
        "### *2. Remove rows with Missing dates --> No row has missing date, therefore no need of removing rows.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqZc1zm-cPf7"
      },
      "source": [
        "### *3. Fill zeros for other numerical missing data --> No quatitave variable has missing data, therefore no need to fill zeros.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCUsAm9hWvgE"
      },
      "source": [
        "### Convert Date columns to Python DateTime \n",
        " * Convert\n",
        " * Extract new column with Month of the Dates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vrru3pn9donW"
      },
      "source": [
        "### *Convert*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcjRktGnWczm",
        "outputId": "bab05c3b-1622-43d5-922e-492eae509cfa"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28-03-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4/4/2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11/4/2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2/5/2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4408</td>\n",
              "      <td>2454310</td>\n",
              "      <td>556.8</td>\n",
              "      <td>20.914286</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22303333</td>\n",
              "      <td>22303333</td>\n",
              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>761</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9/5/2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3254</td>\n",
              "      <td>1791317</td>\n",
              "      <td>550.5</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>3.857143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>16-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3432</td>\n",
              "      <td>1902657</td>\n",
              "      <td>554.4</td>\n",
              "      <td>23.014286</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>23-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3477</td>\n",
              "      <td>1914337</td>\n",
              "      <td>550.6</td>\n",
              "      <td>23.428571</td>\n",
              "      <td>1.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>636000000</td>\n",
              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>30-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4016</td>\n",
              "      <td>2107220</td>\n",
              "      <td>524.7</td>\n",
              "      <td>23.185714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>45394.23871</td>\n",
              "      <td>185.005236</td>\n",
              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
              "      <td>0</td>\n",
              "      <td>1541816</td>\n",
              "      <td>3127</td>\n",
              "      <td>1067006</td>\n",
              "      <td>49808</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  ...  Catgry_PP_SalesForPromotedItems\n",
              "0           28-03-2016  ...                                0\n",
              "1             4/4/2016  ...                                0\n",
              "2            11/4/2016  ...                                0\n",
              "3           18-04-2016  ...                                0\n",
              "4           25-04-2016  ...                                0\n",
              "5             2/5/2016  ...                                0\n",
              "6             9/5/2016  ...                                0\n",
              "7           16-05-2016  ...                                0\n",
              "8           23-05-2016  ...                                0\n",
              "9           30-05-2016  ...                                0\n",
              "\n",
              "[10 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApX0dWzvV4H1",
        "outputId": "9f546a14-62d4-4d49-b3c7-72d4c6b97ab1"
      },
      "source": [
        "import datetime as dt\n",
        "def format_map(x):\n",
        "    if \"/\" in x:\n",
        "        return \"%d/%m/%Y\"\n",
        "    return \"%d-%m-%Y\"\n",
        "format_mapping = [format_map(date) for date in df['Business_Mon_WkBegin']]\n",
        "for idx, format in enumerate(format_mapping):\n",
        "    df['Business_Mon_WkBegin'][idx] = dt.datetime.strptime(df['Business_Mon_WkBegin'][idx], format).strftime(\"%d-%m-%Y\")\n",
        "\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "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>Business_Mon_WkBegin</th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28-03-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>04-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25-04-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>02-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4408</td>\n",
              "      <td>2454310</td>\n",
              "      <td>556.8</td>\n",
              "      <td>20.914286</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22303333</td>\n",
              "      <td>22303333</td>\n",
              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>761</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>09-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3254</td>\n",
              "      <td>1791317</td>\n",
              "      <td>550.5</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>3.857143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>16-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3432</td>\n",
              "      <td>1902657</td>\n",
              "      <td>554.4</td>\n",
              "      <td>23.014286</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>23-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>3477</td>\n",
              "      <td>1914337</td>\n",
              "      <td>550.6</td>\n",
              "      <td>23.428571</td>\n",
              "      <td>1.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>636000000</td>\n",
              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>30-05-2016</td>\n",
              "      <td>110011</td>\n",
              "      <td>4016</td>\n",
              "      <td>2107220</td>\n",
              "      <td>524.7</td>\n",
              "      <td>23.185714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>45394.23871</td>\n",
              "      <td>185.005236</td>\n",
              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
              "      <td>0</td>\n",
              "      <td>1541816</td>\n",
              "      <td>3127</td>\n",
              "      <td>1067006</td>\n",
              "      <td>49808</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  ...  Catgry_PP_SalesForPromotedItems\n",
              "0           28-03-2016  ...                                0\n",
              "1           04-04-2016  ...                                0\n",
              "2           11-04-2016  ...                                0\n",
              "3           18-04-2016  ...                                0\n",
              "4           25-04-2016  ...                                0\n",
              "5           02-05-2016  ...                                0\n",
              "6           09-05-2016  ...                                0\n",
              "7           16-05-2016  ...                                0\n",
              "8           23-05-2016  ...                                0\n",
              "9           30-05-2016  ...                                0\n",
              "\n",
              "[10 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHPoRC-3_oxz",
        "outputId": "953d4ab9-fabd-43b2-e2fe-c4efdca55171"
      },
      "source": [
        "df.Business_Mon_WkBegin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      28-03-2016\n",
              "1      04-04-2016\n",
              "2      11-04-2016\n",
              "3      18-04-2016\n",
              "4      25-04-2016\n",
              "          ...    \n",
              "405    27-01-2020\n",
              "406    03-02-2020\n",
              "407    10-02-2020\n",
              "408    17-02-2020\n",
              "409    24-02-2020\n",
              "Name: Business_Mon_WkBegin, Length: 410, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJocrC9RdMl6",
        "outputId": "8b11672d-11c8-480e-f404-2a437da82a39"
      },
      "source": [
        "df['Business_Mon_WkBegin'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    28-03-2016\n",
              "1    04-04-2016\n",
              "2    11-04-2016\n",
              "3    18-04-2016\n",
              "4    25-04-2016\n",
              "5    02-05-2016\n",
              "6    09-05-2016\n",
              "7    16-05-2016\n",
              "8    23-05-2016\n",
              "9    30-05-2016\n",
              "Name: Business_Mon_WkBegin, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zUAk6S4YrzN",
        "outputId": "a7f651a1-ab67-4d6d-9030-3a8bb49e0aeb"
      },
      "source": [
        "df['Business_Mon_WkBegin'] = pd.to_datetime(df['Business_Mon_WkBegin'], format=\"%d-%m-%Y\")\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>110011</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>110011</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>110011</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>110011</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>110011</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>110011</td>\n",
              "      <td>4408</td>\n",
              "      <td>2454310</td>\n",
              "      <td>556.8</td>\n",
              "      <td>20.914286</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22303333</td>\n",
              "      <td>22303333</td>\n",
              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>761</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-05-09</td>\n",
              "      <td>110011</td>\n",
              "      <td>3254</td>\n",
              "      <td>1791317</td>\n",
              "      <td>550.5</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>3.857143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-05-16</td>\n",
              "      <td>110011</td>\n",
              "      <td>3432</td>\n",
              "      <td>1902657</td>\n",
              "      <td>554.4</td>\n",
              "      <td>23.014286</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-05-23</td>\n",
              "      <td>110011</td>\n",
              "      <td>3477</td>\n",
              "      <td>1914337</td>\n",
              "      <td>550.6</td>\n",
              "      <td>23.428571</td>\n",
              "      <td>1.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>636000000</td>\n",
              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-05-30</td>\n",
              "      <td>110011</td>\n",
              "      <td>4016</td>\n",
              "      <td>2107220</td>\n",
              "      <td>524.7</td>\n",
              "      <td>23.185714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>45394.23871</td>\n",
              "      <td>185.005236</td>\n",
              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
              "      <td>0</td>\n",
              "      <td>1541816</td>\n",
              "      <td>3127</td>\n",
              "      <td>1067006</td>\n",
              "      <td>49808</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  ...  Catgry_PP_SalesForPromotedItems\n",
              "0           2016-03-28  ...                                0\n",
              "1           2016-04-04  ...                                0\n",
              "2           2016-04-11  ...                                0\n",
              "3           2016-04-18  ...                                0\n",
              "4           2016-04-25  ...                                0\n",
              "5           2016-05-02  ...                                0\n",
              "6           2016-05-09  ...                                0\n",
              "7           2016-05-16  ...                                0\n",
              "8           2016-05-23  ...                                0\n",
              "9           2016-05-30  ...                                0\n",
              "\n",
              "[10 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RTT0IoNebh9",
        "outputId": "ab41b0c4-4cec-49bd-dce4-b3de012fadb6"
      },
      "source": [
        "df.Business_Mon_WkBegin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2016-03-28\n",
              "1     2016-04-04\n",
              "2     2016-04-11\n",
              "3     2016-04-18\n",
              "4     2016-04-25\n",
              "         ...    \n",
              "405   2020-01-27\n",
              "406   2020-02-03\n",
              "407   2020-02-10\n",
              "408   2020-02-17\n",
              "409   2020-02-24\n",
              "Name: Business_Mon_WkBegin, Length: 410, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K05HXr3Od1n0"
      },
      "source": [
        "### *Extract new column with Month of the Dates*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTjxofKmAQRE"
      },
      "source": [
        "# data['Month_No'] = data.Business_Mon_WkBegin.dt.month\n",
        "df['Month_No'] = df.Business_Mon_WkBegin.dt.month"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PcFHPYP50gw"
      },
      "source": [
        "# # data['Month_Name'] = data.Business_Mon_WkBegin.dt.strftime(\"%B\")\n",
        "# df['Month_Name'] = df.Business_Mon_WkBegin.dt.strftime(\"%B\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Npx5OIw8VC_X",
        "outputId": "6c4729d4-c0f5-461b-f16f-b482dc9a0582"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>Shop_Code</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>110011</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>110011</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>110011</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>110011</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>110011</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>110011</td>\n",
              "      <td>4408</td>\n",
              "      <td>2454310</td>\n",
              "      <td>556.8</td>\n",
              "      <td>20.914286</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22303333</td>\n",
              "      <td>22303333</td>\n",
              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>761</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-05-09</td>\n",
              "      <td>110011</td>\n",
              "      <td>3254</td>\n",
              "      <td>1791317</td>\n",
              "      <td>550.5</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>3.857143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-05-16</td>\n",
              "      <td>110011</td>\n",
              "      <td>3432</td>\n",
              "      <td>1902657</td>\n",
              "      <td>554.4</td>\n",
              "      <td>23.014286</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-05-23</td>\n",
              "      <td>110011</td>\n",
              "      <td>3477</td>\n",
              "      <td>1914337</td>\n",
              "      <td>550.6</td>\n",
              "      <td>23.428571</td>\n",
              "      <td>1.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>636000000</td>\n",
              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-05-30</td>\n",
              "      <td>110011</td>\n",
              "      <td>4016</td>\n",
              "      <td>2107220</td>\n",
              "      <td>524.7</td>\n",
              "      <td>23.185714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>45394.23871</td>\n",
              "      <td>185.005236</td>\n",
              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
              "      <td>0</td>\n",
              "      <td>1541816</td>\n",
              "      <td>3127</td>\n",
              "      <td>1067006</td>\n",
              "      <td>49808</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  Shop_Code  ...  Catgry_PP_SalesForPromotedItems  Month_No\n",
              "0           2016-03-28     110011  ...                                0         3\n",
              "1           2016-04-04     110011  ...                                0         4\n",
              "2           2016-04-11     110011  ...                                0         4\n",
              "3           2016-04-18     110011  ...                                0         4\n",
              "4           2016-04-25     110011  ...                                0         4\n",
              "5           2016-05-02     110011  ...                                0         5\n",
              "6           2016-05-09     110011  ...                                0         5\n",
              "7           2016-05-16     110011  ...                                0         5\n",
              "8           2016-05-23     110011  ...                                0         5\n",
              "9           2016-05-30     110011  ...                                0         5\n",
              "\n",
              "[10 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jf5OrzvzY8Zw"
      },
      "source": [
        "### Convert Categorical Variables to Numeric Type which Model can Handle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coah9apugakJ"
      },
      "source": [
        "#### By analysing the data, we infer that the data is provided for two SHOP's for the same time frame. So Shop Code is a categorical variable just like ZipCode or Pin Code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpAwFxg2Zsno",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "801edaa8-0307-4a39-a3e2-bf98df4f1585"
      },
      "source": [
        "# Geography, Prefecture, TV_Area_code contains only single value --> Will not play any role in valuation.\n",
        "# Shop Code is only the Categorical Variable, although it has numerical values just like ZipCode or Pin Code.\n",
        "cleanup_nums = {\"Shop_Code\": {110011: \"110011\", 110028: \"110028\"}}\n",
        "df2 = df.copy()\n",
        "df2.replace(cleanup_nums, inplace=True)\n",
        "df2.Shop_Code"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      110011\n",
              "1      110011\n",
              "2      110011\n",
              "3      110011\n",
              "4      110011\n",
              "        ...  \n",
              "405    110028\n",
              "406    110028\n",
              "407    110028\n",
              "408    110028\n",
              "409    110028\n",
              "Name: Shop_Code, Length: 410, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9SApiT2e7Zf",
        "outputId": "8baac428-0070-49ff-cd32-59abbce903f9"
      },
      "source": [
        "# Converting the dtype of ShopCode to \"category\"\n",
        "df2[\"Shop_Code\"] = df2[\"Shop_Code\"].astype('category')\n",
        "df2.Shop_Code"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      110011\n",
              "1      110011\n",
              "2      110011\n",
              "3      110011\n",
              "4      110011\n",
              "        ...  \n",
              "405    110028\n",
              "406    110028\n",
              "407    110028\n",
              "408    110028\n",
              "409    110028\n",
              "Name: Shop_Code, Length: 410, dtype: category\n",
              "Categories (2, object): ['110011', '110028']"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Iux-6NrffYX"
      },
      "source": [
        "# Splitting the data for both the ShopCode : \"df2_110011\" and \"df2_110028\"\n",
        "df2_110011 = df2[df2['Shop_Code'] == '110011'].drop('Shop_Code', axis=1)\n",
        "df2_110028 = df2[df2['Shop_Code'] == '110028'].drop('Shop_Code', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "bPjlKjUxfhhW",
        "outputId": "671e0810-75c0-4cef-a5cf-9486f40adfa4"
      },
      "source": [
        "df2_110011.head(10)"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>503.0</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>547.5</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>4408</td>\n",
              "      <td>2454310</td>\n",
              "      <td>556.8</td>\n",
              "      <td>20.914286</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22303333</td>\n",
              "      <td>22303333</td>\n",
              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>761</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-05-09</td>\n",
              "      <td>3254</td>\n",
              "      <td>1791317</td>\n",
              "      <td>550.5</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>3.857143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-05-16</td>\n",
              "      <td>3432</td>\n",
              "      <td>1902657</td>\n",
              "      <td>554.4</td>\n",
              "      <td>23.014286</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-05-23</td>\n",
              "      <td>3477</td>\n",
              "      <td>1914337</td>\n",
              "      <td>550.6</td>\n",
              "      <td>23.428571</td>\n",
              "      <td>1.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>636000000</td>\n",
              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-05-30</td>\n",
              "      <td>4016</td>\n",
              "      <td>2107220</td>\n",
              "      <td>524.7</td>\n",
              "      <td>23.185714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>45394.23871</td>\n",
              "      <td>185.005236</td>\n",
              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
              "      <td>0</td>\n",
              "      <td>1541816</td>\n",
              "      <td>3127</td>\n",
              "      <td>1067006</td>\n",
              "      <td>49808</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  ...  Month_No\n",
              "0           2016-03-28  ...         3\n",
              "1           2016-04-04  ...         4\n",
              "2           2016-04-11  ...         4\n",
              "3           2016-04-18  ...         4\n",
              "4           2016-04-25  ...         4\n",
              "5           2016-05-02  ...         5\n",
              "6           2016-05-09  ...         5\n",
              "7           2016-05-16  ...         5\n",
              "8           2016-05-23  ...         5\n",
              "9           2016-05-30  ...         5\n",
              "\n",
              "[10 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
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        "df2_110028.head(10)"
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              "      <th></th>\n",
              "      <th>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
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              "      <th>205</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>3568</td>\n",
              "      <td>1655673</td>\n",
              "      <td>464.0</td>\n",
              "      <td>18.233333</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0</td>\n",
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              "      <td>102</td>\n",
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              "      <td>1481051</td>\n",
              "      <td>502.7</td>\n",
              "      <td>17.500000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>17791</td>\n",
              "      <td>28</td>\n",
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              "      <td>0</td>\n",
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              "      <th>207</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3091</td>\n",
              "      <td>1534455</td>\n",
              "      <td>496.4</td>\n",
              "      <td>18.828571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>208</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>2980</td>\n",
              "      <td>1474976</td>\n",
              "      <td>495.0</td>\n",
              "      <td>18.271429</td>\n",
              "      <td>2.285714</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3589</td>\n",
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              "      <td>468.1</td>\n",
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              "      <td>0</td>\n",
              "      <td>15000</td>\n",
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              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>3951</td>\n",
              "      <td>1970607</td>\n",
              "      <td>498.8</td>\n",
              "      <td>20.957143</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1398052</td>\n",
              "      <td>494.7</td>\n",
              "      <td>21.157143</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>1210667</td>\n",
              "      <td>1260667</td>\n",
              "      <td>3175</td>\n",
              "      <td>418218</td>\n",
              "      <td>17110</td>\n",
              "      <td>305</td>\n",
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              "      <td>2016-05-16</td>\n",
              "      <td>3043</td>\n",
              "      <td>1505707</td>\n",
              "      <td>494.8</td>\n",
              "      <td>23.642857</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>212.7</td>\n",
              "      <td>212.7</td>\n",
              "      <td>259544.628</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32910667</td>\n",
              "      <td>32910667</td>\n",
              "      <td>4334</td>\n",
              "      <td>548089</td>\n",
              "      <td>21719</td>\n",
              "      <td>352</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>2016-05-23</td>\n",
              "      <td>3113</td>\n",
              "      <td>1566896</td>\n",
              "      <td>503.3</td>\n",
              "      <td>24.171429</td>\n",
              "      <td>3.357143</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>123244.044</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>636000000</td>\n",
              "      <td>3360</td>\n",
              "      <td>587639</td>\n",
              "      <td>23121</td>\n",
              "      <td>440</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>214</th>\n",
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              "      <td>3359</td>\n",
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              "      <td>7681</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>337.1</td>\n",
              "      <td>337.1</td>\n",
              "      <td>411342.234</td>\n",
              "      <td>400000</td>\n",
              "      <td>1141816</td>\n",
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            "text/plain": [
              "    Business_Mon_WkBegin  ...  Month_No\n",
              "205           2016-03-28  ...         3\n",
              "206           2016-04-04  ...         4\n",
              "207           2016-04-11  ...         4\n",
              "208           2016-04-18  ...         4\n",
              "209           2016-04-25  ...         4\n",
              "210           2016-05-02  ...         5\n",
              "211           2016-05-09  ...         5\n",
              "212           2016-05-16  ...         5\n",
              "213           2016-05-23  ...         5\n",
              "214           2016-05-30  ...         5\n",
              "\n",
              "[10 rows x 29 columns]"
            ]
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDNjK7GHZzAx"
      },
      "source": [
        "# Data Transformation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8W_PlvuZ4XC"
      },
      "source": [
        "### Lag transformation\n",
        " * Convert 'NoOfCustomerVisits' column to lag difference of 2 weeks."
      ]
    },
    {
      "cell_type": "code",
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        "id": "Sc0myTmBaDnU",
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          "height": 225
        },
        "outputId": "59121e7a-dafd-48ec-bf5f-4415fdac02fc"
      },
      "source": [
        "# data['NoOfCustomerVisits_Lag_2'] = abs(data.NoOfCustomerVisits.diff(2))\n",
        "df2_110011['NoOfCustomerVisits_Lag_2'] = abs(df2_110011.NoOfCustomerVisits.diff(2))\n",
        "df2_110011.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>561.9</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>650.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>556.6</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>529.0</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>538.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  NoOfCustomerVisits  ...  Month_No  NoOfCustomerVisits_Lag_2\n",
              "0           2016-03-28                4084  ...         3                       NaN\n",
              "1           2016-04-04                3555  ...         4                       NaN\n",
              "2           2016-04-11                3434  ...         4                     650.0\n",
              "3           2016-04-18                3384  ...         4                     171.0\n",
              "4           2016-04-25                3972  ...         4                     538.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "y1v-U2gJhhh1",
        "outputId": "4d7562f2-e678-48e7-b879-c88f8dd72530"
      },
      "source": [
        "df2_110028['NoOfCustomerVisits_Lag_2'] = abs(df2_110028.NoOfCustomerVisits.diff(2))\n",
        "df2_110028.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
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              "      <th>Month_No</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>3568</td>\n",
              "      <td>1655673</td>\n",
              "      <td>464.0</td>\n",
              "      <td>18.233333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>102</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>2946</td>\n",
              "      <td>1481051</td>\n",
              "      <td>502.7</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>3.357143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3091</td>\n",
              "      <td>1534455</td>\n",
              "      <td>496.4</td>\n",
              "      <td>18.828571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>477.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>2980</td>\n",
              "      <td>1474976</td>\n",
              "      <td>495.0</td>\n",
              "      <td>18.271429</td>\n",
              "      <td>2.285714</td>\n",
              "      <td>102.808975</td>\n",
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              "      <td>325.4</td>\n",
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              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3589</td>\n",
              "      <td>1679880</td>\n",
              "      <td>468.1</td>\n",
              "      <td>19.428571</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>498.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Business_Mon_WkBegin  ...  NoOfCustomerVisits_Lag_2\n",
              "205           2016-03-28  ...                       NaN\n",
              "206           2016-04-04  ...                       NaN\n",
              "207           2016-04-11  ...                     477.0\n",
              "208           2016-04-18  ...                      34.0\n",
              "209           2016-04-25  ...                     498.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD3kGWRyaEmh"
      },
      "source": [
        "### Log Transformation\n",
        "* Tranform 'Price_PerCustomer' to log of it.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91SyL_UyaHEy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "0946d95e-c9ec-41b0-d9db-c64f2f524364"
      },
      "source": [
        "# data.Price_PerCustomer = np.log10(data.Price_PerCustomer)\n",
        "df2_110011.Price_PerCustomer = np.log10(df2_110011.Price_PerCustomer)\n",
        "df2_110011.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>2.701568</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>2.738384</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>2.749659</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>650.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>2.745543</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>2.723456</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>538.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  NoOfCustomerVisits  ...  Month_No  NoOfCustomerVisits_Lag_2\n",
              "0           2016-03-28                4084  ...         3                       NaN\n",
              "1           2016-04-04                3555  ...         4                       NaN\n",
              "2           2016-04-11                3434  ...         4                     650.0\n",
              "3           2016-04-18                3384  ...         4                     171.0\n",
              "4           2016-04-25                3972  ...         4                     538.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "xKdYaYemhzwf",
        "outputId": "034ed62c-288b-4dc1-ace7-cc0ad4475589"
      },
      "source": [
        "df2_110028.Price_PerCustomer = np.log10(df2_110028.Price_PerCustomer)\n",
        "df2_110028.head(6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
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              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
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              "      <th>Month_No</th>\n",
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              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>3568</td>\n",
              "      <td>1655673</td>\n",
              "      <td>2.666518</td>\n",
              "      <td>18.233333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>206</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>2946</td>\n",
              "      <td>1481051</td>\n",
              "      <td>2.701309</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>3.357143</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3091</td>\n",
              "      <td>1534455</td>\n",
              "      <td>2.695832</td>\n",
              "      <td>18.828571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>477.0</td>\n",
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              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>2980</td>\n",
              "      <td>1474976</td>\n",
              "      <td>2.694605</td>\n",
              "      <td>18.271429</td>\n",
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              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3589</td>\n",
              "      <td>1679880</td>\n",
              "      <td>2.670339</td>\n",
              "      <td>19.428571</td>\n",
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              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>498.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>3951</td>\n",
              "      <td>1970607</td>\n",
              "      <td>2.697926</td>\n",
              "      <td>20.957143</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>103.413376</td>\n",
              "      <td>123.064516</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3964</td>\n",
              "      <td>583431</td>\n",
              "      <td>21198</td>\n",
              "      <td>426</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>971.0</td>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "    Business_Mon_WkBegin  ...  NoOfCustomerVisits_Lag_2\n",
              "205           2016-03-28  ...                       NaN\n",
              "206           2016-04-04  ...                       NaN\n",
              "207           2016-04-11  ...                     477.0\n",
              "208           2016-04-18  ...                      34.0\n",
              "209           2016-04-25  ...                     498.0\n",
              "210           2016-05-02  ...                     971.0\n",
              "\n",
              "[6 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_1tvekAaHb-"
      },
      "source": [
        "### AdStock Transformation\n",
        "* Transform 'AppCoup01_Regular' using AdStock\n",
        "    - Learn or fade value - 0.8\n",
        "    - Decay - 0.6\n",
        "\n",
        "#### References - https://www.ashokcharan.com/Marketing-Analytics/~mx-mmm-dynamic-effects.php"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cogKNa_KaOfR"
      },
      "source": [
        "## your code here\n",
        "# Helper Method\n",
        "def apply_adstock(x, L, f, r):\n",
        "    '''\n",
        "    params:\n",
        "    x: original media variable, array\n",
        "    f: length\n",
        "    f: fade in effect\n",
        "    r: decay, retain rate\n",
        "    returns:\n",
        "    array, adstocked media variable\n",
        "    '''\n",
        "    weights = np.zeros(L)\n",
        "    for l in range(L):\n",
        "        if l == 0:\n",
        "            weights[l] = f\n",
        "        else:\n",
        "            weights[l] = r ** l\n",
        "\n",
        "    adstocked = []\n",
        "    const = (1 - r) / (f * (1 - r) + r)\n",
        "    for i in range(L):\n",
        "        x_array = x[:i + 1]\n",
        "        # print(x_array)\n",
        "        # print(\"======================\")\n",
        "        w_array = weights[:i + 1]\n",
        "        # print(w_array)\n",
        "        # print(\"***************************\")\n",
        "        xi = sum(x_array * w_array)\n",
        "        adstocked.append(const * xi)\n",
        "    adstocked = np.array(adstocked)\n",
        "    return adstocked"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "jlKbjQQNif2R",
        "outputId": "982e9c13-6db1-48d0-d70f-951fc342f292"
      },
      "source": [
        "x1 = df2_110011.AppCoup01_Regular\n",
        "adstocked1 = apply_adstock(x1, 205, f=0.8, r=0.6)\n",
        "df2_110011['adstocked'] = adstocked1\n",
        "df2_110011.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
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              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "      <th>adstocked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>4084</td>\n",
              "      <td>2054260</td>\n",
              "      <td>2.701568</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>3555</td>\n",
              "      <td>1946250</td>\n",
              "      <td>2.738384</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3434</td>\n",
              "      <td>1929610</td>\n",
              "      <td>2.749659</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>643</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>650.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>3384</td>\n",
              "      <td>1883552</td>\n",
              "      <td>2.745543</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>171.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3972</td>\n",
              "      <td>2101362</td>\n",
              "      <td>2.723456</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>538.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Business_Mon_WkBegin  NoOfCustomerVisits  ...  NoOfCustomerVisits_Lag_2  adstocked\n",
              "0           2016-03-28                4084  ...                       NaN        0.0\n",
              "1           2016-04-04                3555  ...                       NaN        0.0\n",
              "2           2016-04-11                3434  ...                     650.0        0.0\n",
              "3           2016-04-18                3384  ...                     171.0        0.0\n",
              "4           2016-04-25                3972  ...                     538.0        0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "CMycM2ZOiji6",
        "outputId": "14032447-9fc0-40a0-a233-83d84bbfb449"
      },
      "source": [
        "x2 = df2_110028.AppCoup01_Regular\n",
        "adstocked2 = apply_adstock(x2, 205, f=0.8, r=0.6)\n",
        "df2_110028['adstocked'] = adstocked2\n",
        "df2_110028.tail()"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "      <th>adstocked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>3568</td>\n",
              "      <td>1655673</td>\n",
              "      <td>2.666518</td>\n",
              "      <td>18.233333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>2946</td>\n",
              "      <td>1481051</td>\n",
              "      <td>2.701309</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>3.357143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3091</td>\n",
              "      <td>1534455</td>\n",
              "      <td>2.695832</td>\n",
              "      <td>18.828571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>477.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>2980</td>\n",
              "      <td>1474976</td>\n",
              "      <td>2.694605</td>\n",
              "      <td>18.271429</td>\n",
              "      <td>2.285714</td>\n",
              "      <td>102.808975</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3589</td>\n",
              "      <td>1679880</td>\n",
              "      <td>2.670339</td>\n",
              "      <td>19.428571</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>498.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Business_Mon_WkBegin  ...  adstocked\n",
              "205           2016-03-28  ...        0.0\n",
              "206           2016-04-04  ...        0.0\n",
              "207           2016-04-11  ...        0.0\n",
              "208           2016-04-18  ...        0.0\n",
              "209           2016-04-25  ...        0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2_QaHsKaOVf"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2KmwBzqaQkF"
      },
      "source": [
        "### Split Data into test and train (70-30)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "zxtjMTf5xibI",
        "outputId": "bb870bc9-33ce-4e63-d62e-c40c9f58b1d1"
      },
      "source": [
        "drop_list = ['SalesMeter3MoneyTaxIncluded','Business_Mon_WkBegin', 'Page_Views', 'Total_MealCount', 'Total_PR', 'Tweets', 'Month_No', 'NoOfCustomerVisits_Lag_2', 'adstocked']\n",
        "X = df2_110011.drop(drop_list, axis=1)\n",
        "y = df2_110011['SalesMeter3MoneyTaxIncluded']\n",
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4084</td>\n",
              "      <td>2.701568</td>\n",
              "      <td>18.066667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18593</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3555</td>\n",
              "      <td>2.738384</td>\n",
              "      <td>16.585714</td>\n",
              "      <td>2.642857</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
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              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>17791</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3434</td>\n",
              "      <td>2.749659</td>\n",
              "      <td>18.585714</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18793</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3384</td>\n",
              "      <td>2.745543</td>\n",
              "      <td>18.371429</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17110</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3972</td>\n",
              "      <td>2.723456</td>\n",
              "      <td>19.542857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21999</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NoOfCustomerVisits  ...  Catgry_PP_SalesForPromotedItems\n",
              "0                4084  ...                                0\n",
              "1                3555  ...                                0\n",
              "2                3434  ...                                0\n",
              "3                3384  ...                                0\n",
              "4                3972  ...                                0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FncZ1EdaYLc"
      },
      "source": [
        "## your code here\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "jugs6nRlp5mP",
        "outputId": "90c85624-8d33-4a7f-98d6-09a08bb0c2e5"
      },
      "source": [
        "x = df2_110028.drop(drop_list, axis=1)\n",
        "x.head(2)\n",
        "df2_110028.head()"
      ],
      "execution_count": null,
      "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>Business_Mon_WkBegin</th>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "      <th>adstocked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2016-03-28</td>\n",
              "      <td>3568</td>\n",
              "      <td>1655673</td>\n",
              "      <td>2.666518</td>\n",
              "      <td>18.233333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1386</td>\n",
              "      <td>459451</td>\n",
              "      <td>18593</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>2016-04-04</td>\n",
              "      <td>2946</td>\n",
              "      <td>1481051</td>\n",
              "      <td>2.701309</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>3.357143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469.8</td>\n",
              "      <td>469.8</td>\n",
              "      <td>573267.816</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6355733</td>\n",
              "      <td>6355733</td>\n",
              "      <td>2787</td>\n",
              "      <td>557626</td>\n",
              "      <td>17791</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>3091</td>\n",
              "      <td>1534455</td>\n",
              "      <td>2.695832</td>\n",
              "      <td>18.828571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>378.2</td>\n",
              "      <td>378.2</td>\n",
              "      <td>461494.026</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>3709</td>\n",
              "      <td>506013</td>\n",
              "      <td>18793</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>477.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2016-04-18</td>\n",
              "      <td>2980</td>\n",
              "      <td>1474976</td>\n",
              "      <td>2.694605</td>\n",
              "      <td>18.271429</td>\n",
              "      <td>2.285714</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>325.4</td>\n",
              "      <td>325.4</td>\n",
              "      <td>397065.450</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50000</td>\n",
              "      <td>4111</td>\n",
              "      <td>484135</td>\n",
              "      <td>17110</td>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>3589</td>\n",
              "      <td>1679880</td>\n",
              "      <td>2.670339</td>\n",
              "      <td>19.428571</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>102.808975</td>\n",
              "      <td>101.733333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>357.9</td>\n",
              "      <td>357.9</td>\n",
              "      <td>436723.170</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>633921</td>\n",
              "      <td>21999</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>498.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Business_Mon_WkBegin  ...  adstocked\n",
              "205           2016-03-28  ...        0.0\n",
              "206           2016-04-04  ...        0.0\n",
              "207           2016-04-11  ...        0.0\n",
              "208           2016-04-18  ...        0.0\n",
              "209           2016-04-25  ...        0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpWm8vDOaeLB"
      },
      "source": [
        "### Create a Linear Model using the training Data\n",
        "\n",
        "- Dependent variable - 'SalesMeter3MoneyTaxIncluded'\n",
        "- Independent variables - "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "_9HX-MQrq_AW",
        "outputId": "bde3d442-c1de-435b-d3d6-590cce03b2f4"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "\n",
        "model = RandomForestRegressor(random_state=1)\n",
        "model.fit(X_train, y_train)\n",
        "pred = model.predict(X_test)\n",
        "# Visualizing Feature Importance\n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(25).plot(kind='barh',figsize=(10,10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f584761ed50>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fCkSsZntAob",
        "outputId": "611da4ab-3fc3-4612-a3cd-9c3eb512c01e"
      },
      "source": [
        "X.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['NoOfCustomerVisits', 'Price_PerCustomer', 'Temperature',\n",
              "       'Precipitation', 'RPC_INDEX', 'Japanese_Noodle_Expen',\n",
              "       'AppCoup01_Regular', 'App_Daily_Users', 'App_Downloads', 'Postal_02',\n",
              "       'Handout_03', 'Instore_04', 'InstoreFlier_05', 'TV_ST', 'TV_LT',\n",
              "       'TV_GA', 'PR_Web', 'PR_Print', 'PR_TV', 'Unique_Users',\n",
              "       'Catgry_Disc_Sales', 'Catgry_PP_SalesForPromotedItems'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yzs0JYdttwF"
      },
      "source": [
        "features = \"+\".join(['Price_PerCustomer', 'Temperature', 'Precipitation', 'RPC_INDEX',\n",
        "       'Japanese_Noodle_Expen', 'AppCoup01_Regular', 'App_Daily_Users',\n",
        "       'App_Downloads', 'Postal_02', 'Handout_03', 'Instore_04',\n",
        "       'InstoreFlier_05', 'TV_ST', 'TV_LT', 'TV_GA', 'PR_Web', 'PR_Print',\n",
        "       'PR_TV', 'Unique_Users', 'Catgry_Disc_Sales',\n",
        "       'Catgry_PP_SalesForPromotedItems', 'NoOfCustomerVisits_Lag_2'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "ZJOkpsfWt-bW",
        "outputId": "eb520209-f1fa-47ae-d7f8-6922f0c759e0"
      },
      "source": [
        "features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Price_PerCustomer+Temperature+Precipitation+RPC_INDEX+Japanese_Noodle_Expen+AppCoup01_Regular+App_Daily_Users+App_Downloads+Postal_02+Handout_03+Instore_04+InstoreFlier_05+TV_ST+TV_LT+TV_GA+PR_Web+PR_Print+PR_TV+Unique_Users+Catgry_Disc_Sales+Catgry_PP_SalesForPromotedItems+NoOfCustomerVisits_Lag_2'"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxEkuzYCsfW8"
      },
      "source": [
        "import statsmodels.formula.api as sm\n",
        "# model = sm.ols(formula=\"SalesMeter3MoneyTaxIncluded~Price_PerCustomer+Temperature+Precipitation+RPC_INDEX+Japanese_Noodle_Expen+AppCoup01_Regular+App_Daily_Users+App_Downloads+Postal_02+Handout_03+Instore_04+InstoreFlier_05+TV_ST+TV_LT+TV_GA+PR_Web+PR_Print+PR_TV+Unique_Users+Catgry_Disc_Sales+Catgry_PP_SalesForPromotedItems+NoOfCustomerVisits_Lag_2\", data=df2_110011).fit()\n",
        "model = sm.ols(formula=\"SalesMeter3MoneyTaxIncluded~NoOfCustomerVisits+Price_PerCustomer+Temperature+RPC_INDEX+Japanese_Noodle_Expen+TV_ST+TV_LT+TV_GA\", data=df2_110011).fit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpYeoi_4dMi7"
      },
      "source": [
        "### Print Model Summary\n",
        " 1. Adjusted R Square\n",
        " 2. Coefficients of the features\n",
        " 3. Std Err for the features.\n",
        " 4. P-value for the features.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWcjBrjBd-0O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f21ec21-76f1-490e-9988-c4afb2a99a48"
      },
      "source": [
        "## your code here\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:     SalesMeter3MoneyTaxIncluded   R-squared:                       0.996\n",
            "Model:                                     OLS   Adj. R-squared:                  0.996\n",
            "Method:                          Least Squares   F-statistic:                     6548.\n",
            "Date:                         Tue, 02 Nov 2021   Prob (F-statistic):          1.47e-229\n",
            "Time:                                 08:33:36   Log-Likelihood:                -2251.6\n",
            "No. Observations:                          205   AIC:                             4519.\n",
            "Df Residuals:                              197   BIC:                             4546.\n",
            "Df Model:                                    7                                         \n",
            "Covariance Type:                     nonrobust                                         \n",
            "=========================================================================================\n",
            "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------\n",
            "Intercept              -1.46e+07   1.37e+05   -106.795      0.000   -1.49e+07   -1.43e+07\n",
            "NoOfCustomerVisits      579.2728      3.394    170.654      0.000     572.579     585.967\n",
            "Price_PerCustomer      5.364e+06   4.49e+04    119.350      0.000    5.27e+06    5.45e+06\n",
            "Temperature             213.3233    146.973      1.451      0.148     -76.520     503.166\n",
            "RPC_INDEX             -1899.4753    680.735     -2.790      0.006   -3241.938    -557.013\n",
            "Japanese_Noodle_Expen  -102.1947     93.611     -1.092      0.276    -286.804      82.414\n",
            "TV_ST                   -17.1307      8.552     -2.003      0.047     -33.995      -0.266\n",
            "TV_LT                   -17.1307      8.552     -2.003      0.047     -33.995      -0.266\n",
            "TV_GA                     0.0151      0.013      1.135      0.258      -0.011       0.041\n",
            "==============================================================================\n",
            "Omnibus:                      162.486   Durbin-Watson:                   1.560\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3384.519\n",
            "Skew:                           2.761   Prob(JB):                         0.00\n",
            "Kurtosis:                      22.124   Cond. No.                     2.37e+19\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 8.79e-26. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eV3ULBL2axO6"
      },
      "source": [
        "### Evaluate the Model on the testing Data\n",
        " \n",
        " 1. MSE - Mean Squared Error\n",
        " 2. RMSE - Root Mean Squared Error\n",
        " 3. MAE - Mean Absolute Error\n",
        " 4. MAPE - Mean Absolute Percentage Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-qOoH9Aa8KY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "cf8ab8ad-5454-4537-c008-1763159903cc"
      },
      "source": [
        "## your code here\n",
        "y_pred = model.predict()\n",
        "labels = df2_110011['SalesMeter3MoneyTaxIncluded']\n",
        "df_temp = pd.DataFrame({'Actual': labels, 'Predicted':y_pred})\n",
        "df_temp.head()\n",
        "# Creating Line Graph\n",
        "from matplotlib.pyplot import figure\n",
        "figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n",
        "y1 = df_temp['Actual']\n",
        "y2 = df_temp['Predicted']\n",
        "plt.plot(y1, label = 'Actual')\n",
        "plt.plot(y2, label = 'Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nfipLMbZ7KOF",
        "outputId": "c8ef72cb-32b4-4a63-aabd-42362ce3c498"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "# define data values\n",
        "x = np.array([i for i in range(205)])  # X-axis points\n",
        "y = df2_110011.adstocked  # Y-axis points\n",
        "  \n",
        "plt.plot(x, y)  # Plot the chart\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "FeYh-W9eJeNb",
        "outputId": "58878fd0-8575-4163-e568-0787e6b6ff50"
      },
      "source": [
        "df2_110011.drop('adstocked', axis=1).corr()"
      ],
      "execution_count": null,
      "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>NoOfCustomerVisits</th>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <th>App_Downloads</th>\n",
              "      <th>Postal_02</th>\n",
              "      <th>Handout_03</th>\n",
              "      <th>Instore_04</th>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <th>TV_ST</th>\n",
              "      <th>TV_LT</th>\n",
              "      <th>TV_GA</th>\n",
              "      <th>PR_Web</th>\n",
              "      <th>PR_Print</th>\n",
              "      <th>PR_TV</th>\n",
              "      <th>Total_PR</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Page_Views</th>\n",
              "      <th>Unique_Users</th>\n",
              "      <th>Total_MealCount</th>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <th>Month_No</th>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NoOfCustomerVisits</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.791259</td>\n",
              "      <td>-0.293747</td>\n",
              "      <td>0.195658</td>\n",
              "      <td>-0.112848</td>\n",
              "      <td>0.124939</td>\n",
              "      <td>0.395319</td>\n",
              "      <td>0.014432</td>\n",
              "      <td>0.020632</td>\n",
              "      <td>0.050183</td>\n",
              "      <td>-0.089953</td>\n",
              "      <td>0.156094</td>\n",
              "      <td>0.395628</td>\n",
              "      <td>0.261367</td>\n",
              "      <td>0.242896</td>\n",
              "      <td>0.242896</td>\n",
              "      <td>0.152612</td>\n",
              "      <td>0.102545</td>\n",
              "      <td>0.074719</td>\n",
              "      <td>-0.031996</td>\n",
              "      <td>-0.029861</td>\n",
              "      <td>0.158974</td>\n",
              "      <td>0.433752</td>\n",
              "      <td>0.434233</td>\n",
              "      <td>0.060332</td>\n",
              "      <td>0.348937</td>\n",
              "      <td>0.312709</td>\n",
              "      <td>0.021652</td>\n",
              "      <td>0.233826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SalesMeter3MoneyTaxIncluded</th>\n",
              "      <td>0.791259</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.348337</td>\n",
              "      <td>0.016303</td>\n",
              "      <td>-0.131728</td>\n",
              "      <td>0.186990</td>\n",
              "      <td>0.428023</td>\n",
              "      <td>-0.073158</td>\n",
              "      <td>0.187093</td>\n",
              "      <td>-0.043924</td>\n",
              "      <td>-0.075435</td>\n",
              "      <td>0.093550</td>\n",
              "      <td>0.247632</td>\n",
              "      <td>0.279708</td>\n",
              "      <td>0.271939</td>\n",
              "      <td>0.271939</td>\n",
              "      <td>0.199834</td>\n",
              "      <td>0.128682</td>\n",
              "      <td>0.080648</td>\n",
              "      <td>-0.023976</td>\n",
              "      <td>-0.021428</td>\n",
              "      <td>0.154146</td>\n",
              "      <td>0.380798</td>\n",
              "      <td>0.362327</td>\n",
              "      <td>0.308648</td>\n",
              "      <td>0.078883</td>\n",
              "      <td>0.355870</td>\n",
              "      <td>0.108305</td>\n",
              "      <td>0.143402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price_PerCustomer</th>\n",
              "      <td>-0.293747</td>\n",
              "      <td>0.348337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.277189</td>\n",
              "      <td>-0.036726</td>\n",
              "      <td>0.123893</td>\n",
              "      <td>0.075362</td>\n",
              "      <td>-0.134372</td>\n",
              "      <td>0.290478</td>\n",
              "      <td>-0.156484</td>\n",
              "      <td>0.022031</td>\n",
              "      <td>-0.105133</td>\n",
              "      <td>-0.240119</td>\n",
              "      <td>0.017993</td>\n",
              "      <td>0.076176</td>\n",
              "      <td>0.076176</td>\n",
              "      <td>0.099344</td>\n",
              "      <td>0.048614</td>\n",
              "      <td>0.009579</td>\n",
              "      <td>0.019207</td>\n",
              "      <td>0.019962</td>\n",
              "      <td>0.005428</td>\n",
              "      <td>-0.073498</td>\n",
              "      <td>-0.093182</td>\n",
              "      <td>0.404729</td>\n",
              "      <td>-0.400895</td>\n",
              "      <td>0.079130</td>\n",
              "      <td>0.120917</td>\n",
              "      <td>-0.128071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperature</th>\n",
              "      <td>0.195658</td>\n",
              "      <td>0.016303</td>\n",
              "      <td>-0.277189</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.275873</td>\n",
              "      <td>0.115178</td>\n",
              "      <td>0.334222</td>\n",
              "      <td>0.076945</td>\n",
              "      <td>-0.012544</td>\n",
              "      <td>0.111077</td>\n",
              "      <td>-0.000418</td>\n",
              "      <td>0.063341</td>\n",
              "      <td>0.016349</td>\n",
              "      <td>-0.017103</td>\n",
              "      <td>0.107585</td>\n",
              "      <td>0.107585</td>\n",
              "      <td>0.054527</td>\n",
              "      <td>0.089444</td>\n",
              "      <td>0.014625</td>\n",
              "      <td>0.071553</td>\n",
              "      <td>0.072928</td>\n",
              "      <td>0.079238</td>\n",
              "      <td>0.010320</td>\n",
              "      <td>0.003815</td>\n",
              "      <td>-0.279964</td>\n",
              "      <td>0.052969</td>\n",
              "      <td>0.289479</td>\n",
              "      <td>0.298687</td>\n",
              "      <td>0.063703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precipitation</th>\n",
              "      <td>-0.112848</td>\n",
              "      <td>-0.131728</td>\n",
              "      <td>-0.036726</td>\n",
              "      <td>0.275873</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.053825</td>\n",
              "      <td>0.066646</td>\n",
              "      <td>-0.061617</td>\n",
              "      <td>0.149060</td>\n",
              "      <td>0.049019</td>\n",
              "      <td>0.028992</td>\n",
              "      <td>0.097038</td>\n",
              "      <td>-0.071685</td>\n",
              "      <td>-0.107981</td>\n",
              "      <td>-0.087845</td>\n",
              "      <td>-0.087845</td>\n",
              "      <td>-0.092600</td>\n",
              "      <td>-0.016432</td>\n",
              "      <td>-0.001876</td>\n",
              "      <td>-0.015711</td>\n",
              "      <td>-0.015957</td>\n",
              "      <td>0.053737</td>\n",
              "      <td>0.037985</td>\n",
              "      <td>0.053926</td>\n",
              "      <td>-0.183187</td>\n",
              "      <td>0.140275</td>\n",
              "      <td>0.193251</td>\n",
              "      <td>0.106269</td>\n",
              "      <td>-0.044624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RPC_INDEX</th>\n",
              "      <td>0.124939</td>\n",
              "      <td>0.186990</td>\n",
              "      <td>0.123893</td>\n",
              "      <td>0.115178</td>\n",
              "      <td>0.053825</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.335190</td>\n",
              "      <td>-0.107214</td>\n",
              "      <td>0.237015</td>\n",
              "      <td>-0.159902</td>\n",
              "      <td>-0.010580</td>\n",
              "      <td>0.082322</td>\n",
              "      <td>0.085091</td>\n",
              "      <td>0.200952</td>\n",
              "      <td>0.070881</td>\n",
              "      <td>0.070881</td>\n",
              "      <td>0.068158</td>\n",
              "      <td>0.240930</td>\n",
              "      <td>0.057257</td>\n",
              "      <td>0.153709</td>\n",
              "      <td>0.157583</td>\n",
              "      <td>0.376689</td>\n",
              "      <td>0.335080</td>\n",
              "      <td>0.347787</td>\n",
              "      <td>-0.049780</td>\n",
              "      <td>0.246921</td>\n",
              "      <td>0.234846</td>\n",
              "      <td>-0.022567</td>\n",
              "      <td>-0.048153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japanese_Noodle_Expen</th>\n",
              "      <td>0.395319</td>\n",
              "      <td>0.428023</td>\n",
              "      <td>0.075362</td>\n",
              "      <td>0.334222</td>\n",
              "      <td>0.066646</td>\n",
              "      <td>0.335190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002404</td>\n",
              "      <td>0.211564</td>\n",
              "      <td>-0.161231</td>\n",
              "      <td>-0.010412</td>\n",
              "      <td>0.070103</td>\n",
              "      <td>0.042976</td>\n",
              "      <td>0.091130</td>\n",
              "      <td>0.136196</td>\n",
              "      <td>0.136196</td>\n",
              "      <td>0.093545</td>\n",
              "      <td>0.249367</td>\n",
              "      <td>0.035875</td>\n",
              "      <td>0.075519</td>\n",
              "      <td>0.079240</td>\n",
              "      <td>0.214229</td>\n",
              "      <td>0.214154</td>\n",
              "      <td>0.233162</td>\n",
              "      <td>0.138862</td>\n",
              "      <td>0.224493</td>\n",
              "      <td>0.383225</td>\n",
              "      <td>0.355233</td>\n",
              "      <td>-0.001523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AppCoup01_Regular</th>\n",
              "      <td>0.014432</td>\n",
              "      <td>-0.073158</td>\n",
              "      <td>-0.134372</td>\n",
              "      <td>0.076945</td>\n",
              "      <td>-0.061617</td>\n",
              "      <td>-0.107214</td>\n",
              "      <td>-0.002404</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054819</td>\n",
              "      <td>-0.001095</td>\n",
              "      <td>0.028673</td>\n",
              "      <td>-0.075584</td>\n",
              "      <td>-0.077444</td>\n",
              "      <td>-0.133775</td>\n",
              "      <td>0.046141</td>\n",
              "      <td>0.046141</td>\n",
              "      <td>0.028829</td>\n",
              "      <td>-0.048930</td>\n",
              "      <td>-0.083896</td>\n",
              "      <td>-0.031452</td>\n",
              "      <td>-0.033004</td>\n",
              "      <td>-0.058925</td>\n",
              "      <td>-0.092623</td>\n",
              "      <td>-0.111783</td>\n",
              "      <td>0.049729</td>\n",
              "      <td>-0.070187</td>\n",
              "      <td>-0.043474</td>\n",
              "      <td>0.082918</td>\n",
              "      <td>0.053699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>App_Daily_Users</th>\n",
              "      <td>0.020632</td>\n",
              "      <td>0.187093</td>\n",
              "      <td>0.290478</td>\n",
              "      <td>-0.012544</td>\n",
              "      <td>0.149060</td>\n",
              "      <td>0.237015</td>\n",
              "      <td>0.211564</td>\n",
              "      <td>-0.054819</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.147688</td>\n",
              "      <td>0.021280</td>\n",
              "      <td>0.042771</td>\n",
              "      <td>0.094159</td>\n",
              "      <td>0.042635</td>\n",
              "      <td>0.177122</td>\n",
              "      <td>0.177122</td>\n",
              "      <td>0.167166</td>\n",
              "      <td>0.055654</td>\n",
              "      <td>-0.016848</td>\n",
              "      <td>0.104973</td>\n",
              "      <td>0.105583</td>\n",
              "      <td>0.037438</td>\n",
              "      <td>0.321750</td>\n",
              "      <td>0.358561</td>\n",
              "      <td>0.214172</td>\n",
              "      <td>0.121749</td>\n",
              "      <td>0.025445</td>\n",
              "      <td>0.240928</td>\n",
              "      <td>-0.108677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>App_Downloads</th>\n",
              "      <td>0.050183</td>\n",
              "      <td>-0.043924</td>\n",
              "      <td>-0.156484</td>\n",
              "      <td>0.111077</td>\n",
              "      <td>0.049019</td>\n",
              "      <td>-0.159902</td>\n",
              "      <td>-0.161231</td>\n",
              "      <td>-0.001095</td>\n",
              "      <td>-0.147688</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.023979</td>\n",
              "      <td>-0.022794</td>\n",
              "      <td>-0.035976</td>\n",
              "      <td>-0.063162</td>\n",
              "      <td>0.150968</td>\n",
              "      <td>0.150968</td>\n",
              "      <td>0.134199</td>\n",
              "      <td>-0.090258</td>\n",
              "      <td>-0.041276</td>\n",
              "      <td>-0.078310</td>\n",
              "      <td>-0.079981</td>\n",
              "      <td>-0.050161</td>\n",
              "      <td>0.174282</td>\n",
              "      <td>0.167219</td>\n",
              "      <td>-0.063958</td>\n",
              "      <td>-0.038716</td>\n",
              "      <td>-0.093895</td>\n",
              "      <td>0.042429</td>\n",
              "      <td>0.026831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Postal_02</th>\n",
              "      <td>-0.089953</td>\n",
              "      <td>-0.075435</td>\n",
              "      <td>0.022031</td>\n",
              "      <td>-0.000418</td>\n",
              "      <td>0.028992</td>\n",
              "      <td>-0.010580</td>\n",
              "      <td>-0.010412</td>\n",
              "      <td>0.028673</td>\n",
              "      <td>0.021280</td>\n",
              "      <td>-0.023979</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.074566</td>\n",
              "      <td>-0.033554</td>\n",
              "      <td>-0.028131</td>\n",
              "      <td>0.011791</td>\n",
              "      <td>0.011791</td>\n",
              "      <td>0.006314</td>\n",
              "      <td>-0.034093</td>\n",
              "      <td>0.011372</td>\n",
              "      <td>0.018060</td>\n",
              "      <td>0.017737</td>\n",
              "      <td>0.008979</td>\n",
              "      <td>-0.030573</td>\n",
              "      <td>-0.039281</td>\n",
              "      <td>-0.035798</td>\n",
              "      <td>-0.013248</td>\n",
              "      <td>-0.053088</td>\n",
              "      <td>0.058135</td>\n",
              "      <td>0.055440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Handout_03</th>\n",
              "      <td>0.156094</td>\n",
              "      <td>0.093550</td>\n",
              "      <td>-0.105133</td>\n",
              "      <td>0.063341</td>\n",
              "      <td>0.097038</td>\n",
              "      <td>0.082322</td>\n",
              "      <td>0.070103</td>\n",
              "      <td>-0.075584</td>\n",
              "      <td>0.042771</td>\n",
              "      <td>-0.022794</td>\n",
              "      <td>0.074566</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.291641</td>\n",
              "      <td>0.335728</td>\n",
              "      <td>0.011972</td>\n",
              "      <td>0.011972</td>\n",
              "      <td>0.007133</td>\n",
              "      <td>0.104642</td>\n",
              "      <td>0.015238</td>\n",
              "      <td>-0.039773</td>\n",
              "      <td>-0.038244</td>\n",
              "      <td>0.222022</td>\n",
              "      <td>0.213660</td>\n",
              "      <td>0.228263</td>\n",
              "      <td>-0.158988</td>\n",
              "      <td>0.102723</td>\n",
              "      <td>-0.013588</td>\n",
              "      <td>0.115432</td>\n",
              "      <td>-0.062678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Instore_04</th>\n",
              "      <td>0.395628</td>\n",
              "      <td>0.247632</td>\n",
              "      <td>-0.240119</td>\n",
              "      <td>0.016349</td>\n",
              "      <td>-0.071685</td>\n",
              "      <td>0.085091</td>\n",
              "      <td>0.042976</td>\n",
              "      <td>-0.077444</td>\n",
              "      <td>0.094159</td>\n",
              "      <td>-0.035976</td>\n",
              "      <td>-0.033554</td>\n",
              "      <td>0.291641</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.599157</td>\n",
              "      <td>-0.031968</td>\n",
              "      <td>-0.031968</td>\n",
              "      <td>-0.029072</td>\n",
              "      <td>0.093267</td>\n",
              "      <td>0.097269</td>\n",
              "      <td>-0.023776</td>\n",
              "      <td>-0.021522</td>\n",
              "      <td>0.119119</td>\n",
              "      <td>0.265786</td>\n",
              "      <td>0.239617</td>\n",
              "      <td>-0.095907</td>\n",
              "      <td>0.048697</td>\n",
              "      <td>0.042638</td>\n",
              "      <td>-0.009290</td>\n",
              "      <td>0.059874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InstoreFlier_05</th>\n",
              "      <td>0.261367</td>\n",
              "      <td>0.279708</td>\n",
              "      <td>0.017993</td>\n",
              "      <td>-0.017103</td>\n",
              "      <td>-0.107981</td>\n",
              "      <td>0.200952</td>\n",
              "      <td>0.091130</td>\n",
              "      <td>-0.133775</td>\n",
              "      <td>0.042635</td>\n",
              "      <td>-0.063162</td>\n",
              "      <td>-0.028131</td>\n",
              "      <td>0.335728</td>\n",
              "      <td>0.599157</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>-0.004721</td>\n",
              "      <td>0.144547</td>\n",
              "      <td>0.081704</td>\n",
              "      <td>-0.046028</td>\n",
              "      <td>-0.043269</td>\n",
              "      <td>0.087972</td>\n",
              "      <td>0.274565</td>\n",
              "      <td>0.279660</td>\n",
              "      <td>-0.034795</td>\n",
              "      <td>0.048409</td>\n",
              "      <td>0.102868</td>\n",
              "      <td>0.129287</td>\n",
              "      <td>0.025533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV_ST</th>\n",
              "      <td>0.242896</td>\n",
              "      <td>0.271939</td>\n",
              "      <td>0.076176</td>\n",
              "      <td>0.107585</td>\n",
              "      <td>-0.087845</td>\n",
              "      <td>0.070881</td>\n",
              "      <td>0.136196</td>\n",
              "      <td>0.046141</td>\n",
              "      <td>0.177122</td>\n",
              "      <td>0.150968</td>\n",
              "      <td>0.011791</td>\n",
              "      <td>0.011972</td>\n",
              "      <td>-0.031968</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.956386</td>\n",
              "      <td>0.054009</td>\n",
              "      <td>0.083189</td>\n",
              "      <td>0.038028</td>\n",
              "      <td>0.039644</td>\n",
              "      <td>0.165877</td>\n",
              "      <td>0.160487</td>\n",
              "      <td>0.191665</td>\n",
              "      <td>0.253531</td>\n",
              "      <td>-0.054144</td>\n",
              "      <td>0.086936</td>\n",
              "      <td>0.141047</td>\n",
              "      <td>0.011900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV_LT</th>\n",
              "      <td>0.242896</td>\n",
              "      <td>0.271939</td>\n",
              "      <td>0.076176</td>\n",
              "      <td>0.107585</td>\n",
              "      <td>-0.087845</td>\n",
              "      <td>0.070881</td>\n",
              "      <td>0.136196</td>\n",
              "      <td>0.046141</td>\n",
              "      <td>0.177122</td>\n",
              "      <td>0.150968</td>\n",
              "      <td>0.011791</td>\n",
              "      <td>0.011972</td>\n",
              "      <td>-0.031968</td>\n",
              "      <td>0.001598</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.956386</td>\n",
              "      <td>0.054009</td>\n",
              "      <td>0.083189</td>\n",
              "      <td>0.038028</td>\n",
              "      <td>0.039644</td>\n",
              "      <td>0.165877</td>\n",
              "      <td>0.160487</td>\n",
              "      <td>0.191665</td>\n",
              "      <td>0.253531</td>\n",
              "      <td>-0.054144</td>\n",
              "      <td>0.086936</td>\n",
              "      <td>0.141047</td>\n",
              "      <td>0.011900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV_GA</th>\n",
              "      <td>0.152612</td>\n",
              "      <td>0.199834</td>\n",
              "      <td>0.099344</td>\n",
              "      <td>0.054527</td>\n",
              "      <td>-0.092600</td>\n",
              "      <td>0.068158</td>\n",
              "      <td>0.093545</td>\n",
              "      <td>0.028829</td>\n",
              "      <td>0.167166</td>\n",
              "      <td>0.134199</td>\n",
              "      <td>0.006314</td>\n",
              "      <td>0.007133</td>\n",
              "      <td>-0.029072</td>\n",
              "      <td>-0.004721</td>\n",
              "      <td>0.956386</td>\n",
              "      <td>0.956386</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.064485</td>\n",
              "      <td>0.120128</td>\n",
              "      <td>0.054257</td>\n",
              "      <td>0.056410</td>\n",
              "      <td>0.150877</td>\n",
              "      <td>0.119908</td>\n",
              "      <td>0.165091</td>\n",
              "      <td>0.238757</td>\n",
              "      <td>-0.127634</td>\n",
              "      <td>0.041783</td>\n",
              "      <td>0.097366</td>\n",
              "      <td>-0.073121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PR_Web</th>\n",
              "      <td>0.102545</td>\n",
              "      <td>0.128682</td>\n",
              "      <td>0.048614</td>\n",
              "      <td>0.089444</td>\n",
              "      <td>-0.016432</td>\n",
              "      <td>0.240930</td>\n",
              "      <td>0.249367</td>\n",
              "      <td>-0.048930</td>\n",
              "      <td>0.055654</td>\n",
              "      <td>-0.090258</td>\n",
              "      <td>-0.034093</td>\n",
              "      <td>0.104642</td>\n",
              "      <td>0.093267</td>\n",
              "      <td>0.144547</td>\n",
              "      <td>0.054009</td>\n",
              "      <td>0.054009</td>\n",
              "      <td>0.064485</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.382793</td>\n",
              "      <td>0.012705</td>\n",
              "      <td>0.030017</td>\n",
              "      <td>0.261880</td>\n",
              "      <td>0.333991</td>\n",
              "      <td>0.344697</td>\n",
              "      <td>0.003209</td>\n",
              "      <td>0.095009</td>\n",
              "      <td>0.428797</td>\n",
              "      <td>0.113987</td>\n",
              "      <td>-0.045209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PR_Print</th>\n",
              "      <td>0.074719</td>\n",
              "      <td>0.080648</td>\n",
              "      <td>0.009579</td>\n",
              "      <td>0.014625</td>\n",
              "      <td>-0.001876</td>\n",
              "      <td>0.057257</td>\n",
              "      <td>0.035875</td>\n",
              "      <td>-0.083896</td>\n",
              "      <td>-0.016848</td>\n",
              "      <td>-0.041276</td>\n",
              "      <td>0.011372</td>\n",
              "      <td>0.015238</td>\n",
              "      <td>0.097269</td>\n",
              "      <td>0.081704</td>\n",
              "      <td>0.083189</td>\n",
              "      <td>0.083189</td>\n",
              "      <td>0.120128</td>\n",
              "      <td>0.382793</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080386</td>\n",
              "      <td>-0.064761</td>\n",
              "      <td>0.260262</td>\n",
              "      <td>0.184887</td>\n",
              "      <td>0.207069</td>\n",
              "      <td>0.038605</td>\n",
              "      <td>-0.030496</td>\n",
              "      <td>0.290750</td>\n",
              "      <td>-0.045623</td>\n",
              "      <td>-0.012403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PR_TV</th>\n",
              "      <td>-0.031996</td>\n",
              "      <td>-0.023976</td>\n",
              "      <td>0.019207</td>\n",
              "      <td>0.071553</td>\n",
              "      <td>-0.015711</td>\n",
              "      <td>0.153709</td>\n",
              "      <td>0.075519</td>\n",
              "      <td>-0.031452</td>\n",
              "      <td>0.104973</td>\n",
              "      <td>-0.078310</td>\n",
              "      <td>0.018060</td>\n",
              "      <td>-0.039773</td>\n",
              "      <td>-0.023776</td>\n",
              "      <td>-0.046028</td>\n",
              "      <td>0.038028</td>\n",
              "      <td>0.038028</td>\n",
              "      <td>0.054257</td>\n",
              "      <td>0.012705</td>\n",
              "      <td>-0.080386</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999803</td>\n",
              "      <td>0.049787</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.114566</td>\n",
              "      <td>-0.042483</td>\n",
              "      <td>0.091498</td>\n",
              "      <td>0.046542</td>\n",
              "      <td>-0.018125</td>\n",
              "      <td>-0.048058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_PR</th>\n",
              "      <td>-0.029861</td>\n",
              "      <td>-0.021428</td>\n",
              "      <td>0.019962</td>\n",
              "      <td>0.072928</td>\n",
              "      <td>-0.015957</td>\n",
              "      <td>0.157583</td>\n",
              "      <td>0.079240</td>\n",
              "      <td>-0.033004</td>\n",
              "      <td>0.105583</td>\n",
              "      <td>-0.079981</td>\n",
              "      <td>0.017737</td>\n",
              "      <td>-0.038244</td>\n",
              "      <td>-0.021522</td>\n",
              "      <td>-0.043269</td>\n",
              "      <td>0.039644</td>\n",
              "      <td>0.039644</td>\n",
              "      <td>0.056410</td>\n",
              "      <td>0.030017</td>\n",
              "      <td>-0.064761</td>\n",
              "      <td>0.999803</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056038</td>\n",
              "      <td>0.122179</td>\n",
              "      <td>0.121383</td>\n",
              "      <td>-0.042052</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.055327</td>\n",
              "      <td>-0.017107</td>\n",
              "      <td>-0.048813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tweets</th>\n",
              "      <td>0.158974</td>\n",
              "      <td>0.154146</td>\n",
              "      <td>0.005428</td>\n",
              "      <td>0.079238</td>\n",
              "      <td>0.053737</td>\n",
              "      <td>0.376689</td>\n",
              "      <td>0.214229</td>\n",
              "      <td>-0.058925</td>\n",
              "      <td>0.037438</td>\n",
              "      <td>-0.050161</td>\n",
              "      <td>0.008979</td>\n",
              "      <td>0.222022</td>\n",
              "      <td>0.119119</td>\n",
              "      <td>0.087972</td>\n",
              "      <td>0.165877</td>\n",
              "      <td>0.165877</td>\n",
              "      <td>0.150877</td>\n",
              "      <td>0.261880</td>\n",
              "      <td>0.260262</td>\n",
              "      <td>0.049787</td>\n",
              "      <td>0.056038</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.173241</td>\n",
              "      <td>0.237002</td>\n",
              "      <td>-0.097388</td>\n",
              "      <td>-0.007770</td>\n",
              "      <td>0.148569</td>\n",
              "      <td>0.055562</td>\n",
              "      <td>0.052942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Page_Views</th>\n",
              "      <td>0.433752</td>\n",
              "      <td>0.380798</td>\n",
              "      <td>-0.073498</td>\n",
              "      <td>0.010320</td>\n",
              "      <td>0.037985</td>\n",
              "      <td>0.335080</td>\n",
              "      <td>0.214154</td>\n",
              "      <td>-0.092623</td>\n",
              "      <td>0.321750</td>\n",
              "      <td>0.174282</td>\n",
              "      <td>-0.030573</td>\n",
              "      <td>0.213660</td>\n",
              "      <td>0.265786</td>\n",
              "      <td>0.274565</td>\n",
              "      <td>0.160487</td>\n",
              "      <td>0.160487</td>\n",
              "      <td>0.119908</td>\n",
              "      <td>0.333991</td>\n",
              "      <td>0.184887</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.122179</td>\n",
              "      <td>0.173241</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.952922</td>\n",
              "      <td>0.049017</td>\n",
              "      <td>0.568647</td>\n",
              "      <td>0.277748</td>\n",
              "      <td>0.139672</td>\n",
              "      <td>0.015182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unique_Users</th>\n",
              "      <td>0.434233</td>\n",
              "      <td>0.362327</td>\n",
              "      <td>-0.093182</td>\n",
              "      <td>0.003815</td>\n",
              "      <td>0.053926</td>\n",
              "      <td>0.347787</td>\n",
              "      <td>0.233162</td>\n",
              "      <td>-0.111783</td>\n",
              "      <td>0.358561</td>\n",
              "      <td>0.167219</td>\n",
              "      <td>-0.039281</td>\n",
              "      <td>0.228263</td>\n",
              "      <td>0.239617</td>\n",
              "      <td>0.279660</td>\n",
              "      <td>0.191665</td>\n",
              "      <td>0.191665</td>\n",
              "      <td>0.165091</td>\n",
              "      <td>0.344697</td>\n",
              "      <td>0.207069</td>\n",
              "      <td>0.114566</td>\n",
              "      <td>0.121383</td>\n",
              "      <td>0.237002</td>\n",
              "      <td>0.952922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005806</td>\n",
              "      <td>0.575827</td>\n",
              "      <td>0.304810</td>\n",
              "      <td>0.096364</td>\n",
              "      <td>0.005468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_MealCount</th>\n",
              "      <td>0.060332</td>\n",
              "      <td>0.308648</td>\n",
              "      <td>0.404729</td>\n",
              "      <td>-0.279964</td>\n",
              "      <td>-0.183187</td>\n",
              "      <td>-0.049780</td>\n",
              "      <td>0.138862</td>\n",
              "      <td>0.049729</td>\n",
              "      <td>0.214172</td>\n",
              "      <td>-0.063958</td>\n",
              "      <td>-0.035798</td>\n",
              "      <td>-0.158988</td>\n",
              "      <td>-0.095907</td>\n",
              "      <td>-0.034795</td>\n",
              "      <td>0.253531</td>\n",
              "      <td>0.253531</td>\n",
              "      <td>0.238757</td>\n",
              "      <td>0.003209</td>\n",
              "      <td>0.038605</td>\n",
              "      <td>-0.042483</td>\n",
              "      <td>-0.042052</td>\n",
              "      <td>-0.097388</td>\n",
              "      <td>0.049017</td>\n",
              "      <td>-0.005806</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.140372</td>\n",
              "      <td>-0.003817</td>\n",
              "      <td>0.248581</td>\n",
              "      <td>0.038420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Catgry_Disc_Sales</th>\n",
              "      <td>0.348937</td>\n",
              "      <td>0.078883</td>\n",
              "      <td>-0.400895</td>\n",
              "      <td>0.052969</td>\n",
              "      <td>0.140275</td>\n",
              "      <td>0.246921</td>\n",
              "      <td>0.224493</td>\n",
              "      <td>-0.070187</td>\n",
              "      <td>0.121749</td>\n",
              "      <td>-0.038716</td>\n",
              "      <td>-0.013248</td>\n",
              "      <td>0.102723</td>\n",
              "      <td>0.048697</td>\n",
              "      <td>0.048409</td>\n",
              "      <td>-0.054144</td>\n",
              "      <td>-0.054144</td>\n",
              "      <td>-0.127634</td>\n",
              "      <td>0.095009</td>\n",
              "      <td>-0.030496</td>\n",
              "      <td>0.091498</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>-0.007770</td>\n",
              "      <td>0.568647</td>\n",
              "      <td>0.575827</td>\n",
              "      <td>-0.140372</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.137350</td>\n",
              "      <td>-0.003074</td>\n",
              "      <td>0.027805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Catgry_PP_SalesForPromotedItems</th>\n",
              "      <td>0.312709</td>\n",
              "      <td>0.355870</td>\n",
              "      <td>0.079130</td>\n",
              "      <td>0.289479</td>\n",
              "      <td>0.193251</td>\n",
              "      <td>0.234846</td>\n",
              "      <td>0.383225</td>\n",
              "      <td>-0.043474</td>\n",
              "      <td>0.025445</td>\n",
              "      <td>-0.093895</td>\n",
              "      <td>-0.053088</td>\n",
              "      <td>-0.013588</td>\n",
              "      <td>0.042638</td>\n",
              "      <td>0.102868</td>\n",
              "      <td>0.086936</td>\n",
              "      <td>0.086936</td>\n",
              "      <td>0.041783</td>\n",
              "      <td>0.428797</td>\n",
              "      <td>0.290750</td>\n",
              "      <td>0.046542</td>\n",
              "      <td>0.055327</td>\n",
              "      <td>0.148569</td>\n",
              "      <td>0.277748</td>\n",
              "      <td>0.304810</td>\n",
              "      <td>-0.003817</td>\n",
              "      <td>0.137350</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.103118</td>\n",
              "      <td>-0.012804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month_No</th>\n",
              "      <td>0.021652</td>\n",
              "      <td>0.108305</td>\n",
              "      <td>0.120917</td>\n",
              "      <td>0.298687</td>\n",
              "      <td>0.106269</td>\n",
              "      <td>-0.022567</td>\n",
              "      <td>0.355233</td>\n",
              "      <td>0.082918</td>\n",
              "      <td>0.240928</td>\n",
              "      <td>0.042429</td>\n",
              "      <td>0.058135</td>\n",
              "      <td>0.115432</td>\n",
              "      <td>-0.009290</td>\n",
              "      <td>0.129287</td>\n",
              "      <td>0.141047</td>\n",
              "      <td>0.141047</td>\n",
              "      <td>0.097366</td>\n",
              "      <td>0.113987</td>\n",
              "      <td>-0.045623</td>\n",
              "      <td>-0.018125</td>\n",
              "      <td>-0.017107</td>\n",
              "      <td>0.055562</td>\n",
              "      <td>0.139672</td>\n",
              "      <td>0.096364</td>\n",
              "      <td>0.248581</td>\n",
              "      <td>-0.003074</td>\n",
              "      <td>0.103118</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NoOfCustomerVisits_Lag_2</th>\n",
              "      <td>0.233826</td>\n",
              "      <td>0.143402</td>\n",
              "      <td>-0.128071</td>\n",
              "      <td>0.063703</td>\n",
              "      <td>-0.044624</td>\n",
              "      <td>-0.048153</td>\n",
              "      <td>-0.001523</td>\n",
              "      <td>0.053699</td>\n",
              "      <td>-0.108677</td>\n",
              "      <td>0.026831</td>\n",
              "      <td>0.055440</td>\n",
              "      <td>-0.062678</td>\n",
              "      <td>0.059874</td>\n",
              "      <td>0.025533</td>\n",
              "      <td>0.011900</td>\n",
              "      <td>0.011900</td>\n",
              "      <td>-0.073121</td>\n",
              "      <td>-0.045209</td>\n",
              "      <td>-0.012403</td>\n",
              "      <td>-0.048058</td>\n",
              "      <td>-0.048813</td>\n",
              "      <td>0.052942</td>\n",
              "      <td>0.015182</td>\n",
              "      <td>0.005468</td>\n",
              "      <td>0.038420</td>\n",
              "      <td>0.027805</td>\n",
              "      <td>-0.012804</td>\n",
              "      <td>-0.084313</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 NoOfCustomerVisits  ...  NoOfCustomerVisits_Lag_2\n",
              "NoOfCustomerVisits                         1.000000  ...                  0.233826\n",
              "SalesMeter3MoneyTaxIncluded                0.791259  ...                  0.143402\n",
              "Price_PerCustomer                         -0.293747  ...                 -0.128071\n",
              "Temperature                                0.195658  ...                  0.063703\n",
              "Precipitation                             -0.112848  ...                 -0.044624\n",
              "RPC_INDEX                                  0.124939  ...                 -0.048153\n",
              "Japanese_Noodle_Expen                      0.395319  ...                 -0.001523\n",
              "AppCoup01_Regular                          0.014432  ...                  0.053699\n",
              "App_Daily_Users                            0.020632  ...                 -0.108677\n",
              "App_Downloads                              0.050183  ...                  0.026831\n",
              "Postal_02                                 -0.089953  ...                  0.055440\n",
              "Handout_03                                 0.156094  ...                 -0.062678\n",
              "Instore_04                                 0.395628  ...                  0.059874\n",
              "InstoreFlier_05                            0.261367  ...                  0.025533\n",
              "TV_ST                                      0.242896  ...                  0.011900\n",
              "TV_LT                                      0.242896  ...                  0.011900\n",
              "TV_GA                                      0.152612  ...                 -0.073121\n",
              "PR_Web                                     0.102545  ...                 -0.045209\n",
              "PR_Print                                   0.074719  ...                 -0.012403\n",
              "PR_TV                                     -0.031996  ...                 -0.048058\n",
              "Total_PR                                  -0.029861  ...                 -0.048813\n",
              "Tweets                                     0.158974  ...                  0.052942\n",
              "Page_Views                                 0.433752  ...                  0.015182\n",
              "Unique_Users                               0.434233  ...                  0.005468\n",
              "Total_MealCount                            0.060332  ...                  0.038420\n",
              "Catgry_Disc_Sales                          0.348937  ...                  0.027805\n",
              "Catgry_PP_SalesForPromotedItems            0.312709  ...                 -0.012804\n",
              "Month_No                                   0.021652  ...                 -0.084313\n",
              "NoOfCustomerVisits_Lag_2                   0.233826  ...                  1.000000\n",
              "\n",
              "[29 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XLVDbruAUe5s",
        "outputId": "731c08ef-f4d7-449c-8af1-5cb3f8b5e0bf"
      },
      "source": [
        "labels = df2_110011['SalesMeter3MoneyTaxIncluded']\n",
        "features = df2_110011.drop(['SalesMeter3MoneyTaxIncluded'], axis=1)\n",
        "# Scatter graph b/w response and features\n",
        "for x in features:\n",
        "    plt.plot(labels, features[x], 'ro')  # arguments are passed to np.histogram\n",
        "    plt.title(\"SalesMeter3MoneyTaxIncluded vs \" + x)\n",
        "    plt.xlabel(x)\n",
        "    plt.ylabel(\"SalesMeter3MoneyTaxIncluded\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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62xynwZDj7eMtf2cz3bSm80xFRX7m7T2NLFot8H77rfuAqTOKZNUKTUre8u8Dea3psv7oWedV9TMvamkPwm+9Xz75jlMjuJ+/U0iWL/jUFNx33/SW9uQkbLcdrFsHExOwcWO+b35VP/OiiJfut+44pXA/f6eYrEHNdeu2NLHcf3/YD0HxQ75vflWzSdGAs5thHKdrXPmPI2m2/Tp81LPs7p14D+W5Jo6i37pH3XT6jJt9xo0sk8rRR8OKFVvunz17cyu/DE2P+99EfGEXp0e42cfZTJanzNlnp7emTzqpeDJWkqbOcm0yHnXTGQCu/IeFuswCeZOx0kwtSRNLEW5374xhW4jGGQlc+Q8DdS7G0UncnlalkFcBTEyMppmiqNKto1Lu18IuPq7gJMnyAW1SGns//zpnmHYzO3blSrPJyS3lmDVrNP3eO5mZ3Glc/17PWO73rGinEZDj598zhQ1sDfwMuAy4CvjXuP8hwE+B3wJfAmYVlTX2yr/TSU1ZE5O6mbC0cqXZ1NRmGaamRleBFFW6dVfKvZxE5iEqxpJBKX8Bc+P3yajwDwTOBF4U958MLCsqa+yVfycvbi9beuMy27Wo0h2mFbKGSVanNvKUf89s/vHa98TNyZgMeDJwVty/Anhur2QYGToNsdwLD5JxWgy8yBY/TIvGD5OsTl/o6YCvpAlJa4DbgO8CvwPuNLMH4ik3Abtn5F0qabWk1bfffnsvxWw+vQix3Cnj5JZYVOkO00zjYZLV6Q9ZXYLQY2CnvJSXt62cHYDzgIOB3yb27wlcWZR/7M0+nVAUYrlTs824mQ+K7tUwmcCGSVanFug0sJuk6wimGgELgD/G7zsAN5jZQ8pWMpLeBdwHHAfsYmYPSDoIONHMDsvL6zN8O+B1r4OTTw6quUXeTN6ybpoeVM1xhoaOZ/ia2UPM7K+B7wFHmtk8M5sCngV8p+Ci8yXtEL/PBp4G/JLQA3h+PO1o4OtVfsxY0alf9qpVQcEnFb8UFP/ZZ3dntnHzgeOMBlldgmQCriizr+34vsAvgMuBK4F3xf1/TXAB/S3wZWCrouuPpdmnG2+dPJNPHWYbNx84zlBAt/H8JZ0L/AhYGXctAQ6xAnNNXYyl2acb88qMGdNb/S2k4N3hZhvHGQvqCOz2YmA+8DXgq/H7i+sRr0d0YjJp0vT3Mt46WfLmufW52cZxHKg2yQvYpsr5daXKZp9OTCZNm/5eNLGraNnForAEbrZxnJGHbmf4Ao8HriZ4+ADsB3yqTN46UmXl38mM2KZNf09T4C17/cKF00MsZFUOruAdZ6zJU/5lzT4fAQ4D1sXewmXAIbV2QeqkkwlOTQur2x5KWdpsx1+7NnuBlaaEAW6SCa2J8pRlWOV2mk9WrZBMwE/j5y8S+y4rk7eO1LiWf79b1Vmy5U3iGqQJa9DXb7o8ZRlWuZ3GQA1mn7MIpp9LCTF6/hn4Ypm8daRG2fwH8UJmuWe2p5YcgzZhDfr6TZenLMMqt9MY6lD+84BVwK2EOD0rgakyeetIHfn5d9I6L5NnEC9k1jWnptLlHXQIhn5fv+h/G/T96JRhldtpDF0r/0Gnnkzy6tR0M4gXsmpvI6uymJioN7Z/FlUrq24oc2+GtQU9rHI7jaFj5Q98HPhYVsrLW2eqXfn3avZsL6mipNN+X5qJaNmy3piw0q4/ORlW/Kr7WmX+j2G1nQ+r3E5j6Eb5Hx3TKcCPgTfEdAFwcl7eOlPtyr8bBT4sL2SyspiYSP+9WfvrqMjaK6si19ROKdsTG1bX12GV22kEecq/bHiHi4GDLcbhlzQJ/MjMDuzAwagytYd3yAt/sGlTcf5Vq0IgtBtu2DxrtskLl2f93jwWLqz3d3V7z7PwKKOOk0kd4R12BLZLbM+N+4aTblc1WrIkKJZNm8JnUkHW7ZddR3lZv2tiIjtP1gpdncrTq5WkPFyF43RGVpcgmYBXAGuB0wlLL14HHF0mbx2pUTb/fpZbV3lZ5aTZ/HtlO/c1hR2n71CHtw+wC/CcmHYpm6+O1ChvnzzyvFzqLK8TO3nW782bF9BuO+9WHlfSjtNX8pR/WZt/aigHM7ug+75HMUMT0jnPtr5yZXX7ea/s5GmUsZ33Ux7HcbqmDpv/2xLpncA3gRNrkW6U2Gmn7GOdLHDeKzt5GmVs572Up8pYQta5HgfHccqT1SXIS4SF17/SSd5O0kBX8iprqli5cks/9jzXw7LX7qdbaftvXbZsy+1Bj5VUGb9ooguu4/QR6p7hS1jE/epO8naSBqb8qyilouBrnfqz12En7zTURZaS7ddYSftgc9497uWcBccZUrpW/kyf6fsJwoSvlWXy1pEGpvyrDHDmBV8r0wLt1WBop72Hfs5kLpqoVWbGcp09LscZEfKUf9kB36MTmw8A15vZhbXZngoY2IBvlQHOrAHTiQlYsSJ/sHfVquBTf++9m/fNmRPi+Xc7yarTSVBNGmzOOp5kYgI2bswuw3HGkK4HfM1sRSKt6qfiHyhVBjizBkyLFD+EweCk4oew3ckgcTudLlLTpMHmIlnnzAmVp0/2cpzyZHUJYo/gCuDylHQFcHle3jrTUNj8W+c3LVJop+abfv32MvnzbP29jlDqOEMMXQR2W5iX8vLWmXqq/IsURj8USq+Cnpl1Pyu3rKdTLz1thiWYnuM0jI6V/4MnwUOArRPbs4FFZfLWkXqm/OtQKnW0eNNcRCcn61WevazA+jE47K16x6lMHcp/NTArsT0L+HmZvHWknin/OsIVdFt51B0SolO6Ua6+4pTjNJI85V92hu9MM9uQGCfYECuATCTtKek8SVdLukrSsXH/iZJulrQmpiNKylA/nQ6GtqhjoDbrWuvXly+jW1reRmvXBrWdFdGzdW77LNp+Dg47jlMLZZX/7ZKe3dqQ9BzgjoI8DwBvNbO9gQOB10vaOx77iJk9KqazK0tdF90qrW4rjzpkqIOylVhWJXHEETCrrS0wa5Z72jhOgymr/F8LHC/pBkk3AscBr8nLYGa3mNml8fvdwC+B3bsRtna6jQVfh+LuVIY649iUrcSyKokzz9xyTkD7tuM4zSLLHpSWCIu4zK2SJ+ZbBNxAWBDmROB6gsvoqcCOGXmWEsYaVi9YsKBHFjHrztZdZPOv4i1TRYasNXKnpqaXUbbcsmMfebOY+zUb2HGc0lDDgO9WwEuA44F3tVLJvHOBS4Dnxe2dgQlCr2M5cGpRGQMN7FZEXpz8XnkSFcURalUGZRdMLytrmev6gK/jNIY6lP+3gS8Bbwfe2kol8k0C5wJvyTi+CLiyqJxGK/8seulJVLUFXub6ZXoJaTJJZnPndvdbHcfpCXnKv2xsnyvNbJ8q5iRJIiz5uN7M3pTYv6uZ3RK/vxl4nJm9KK+soVnMJUm3sXHy4t1AcaybLLqNzfO618HJJ0//bZOTodwNGzbvqys2keM4HVPHYi4XSXpkxes+AXgp8OQ2t84PSLpC0uXAk4A3Vyx3OOilJ9Hy5Vt613QrV1nOPnvLSu3++2HbbUPFJIXPThV/XQPZvrCL4+Qys+R5BwMvl3Qd8BdCPH8zs32zMpjZj+N57QzOtbOfLF+eHqmziidRWuu+pbyLemxZrfFu3S/z5iXcUeT9W0B7dNOWKylUq0jqKsdxRpiyLf/DgYcBTweOBJ4VP508Zs/e/H1qqlprOM8F9IQTQmu7nYmJzS3v006DU08N102Tp1N6OS+hruimvYyS6jgjQq7yl7STpJ2AuzOSk0ar5blu3eZ9990HF15Y3hSxZEmoLNJMKVmt702bQrr++s2VzH33bT6+bl32zN2ydDs3Io86Js3VWY7jjDJZI8FxIPg64Nr42Z6uzctbZxo6b58sT592L51OI1OW9SSqI+BamhdQr4Ks1RUgrp+rkDlOg6HuNXz7nYZO+VdxxexEIZX1y+8k4FpSsU9NlZ8rUAd1hW72ENCOY2Y1KH/gmLbtCeDdZfLWkYZO+VeZDNXpRKgyre+q6wSUXSu3lb8XPYC6yvQQ0I5Ti/L/AsFLZ1dgH+DnwIfK5K0jDZ3yr7LgeK9MEStXhlm+7debNat6mIe0CqufrWtX5I7TEbWYfYAXEiJ5rgWeUDZfHWnolL9ZUFATE/lKtNNwD7C57Cxl2Mk6AWXNVQsX9s+u7iYcx+mYOlr+DwMuAv4LuAA4GZhTJm8daSiVv1m+Mq0jiFzd9v4yLf+iEBNlzFhVWvI+eOs4HVOH8r8GeEr8LkJsn6vK5K0jDa3yr1NxFSnmiYnpyrSTa5eNFtrNb6vakvdVwhynY+pQ/tul7NurTN460tAq/zpNFlU8iObMMVu2rLNr57XK6/AEqlppeMvfcTqmLpv/PsALgJe1Utm83aahVf5m3a8X0MpbNH6QZVZKXnvZsnrXLsjqFeRRtSU/bDZ/H5x2GkQdLf93A+cBtwKnAX8AziqTt4401Mq/U6p4DJVRpt0q0UFOwBoWhTpsFZUz8uQp/7Ihna8A9gN+YWb7SdoZWGlmTyvMXANDGdK5W7JCOk9MwMaNW362s3BhCPNQprwVK4pjDnUborpFe9A1GJ3wz3lhuJP/heP0iTpCOt9nZpuAByRtB9wG7FmXgE4KefF7zOCBB8LnihXlYu1klbdxY7l4P3UFdMuLWTTseEwhZ4goCux2evy6WtIOwKcJSzJeCvykt6KNMatWhZZ2Gu3KtqwyzVPSZSJe1hnQbcmS0BJuD0I37PQy4umw4espNJ8se1A0B12asm8RsG9evrrTWNn8q/rz11FuWdfJYbG9Dwq3+QfG5T4MwftApwO+BP/+RwP7p6W8vHWmsVL+WQOiExPdP1x5s46npgb/IA/By1TIKPyGbhkH99whqeDylH/ugK+kuwlxfNJW5DIze3JNHZBcxmrAt66B1SzSBlxnzQrXTC4QU3UQdtWqYDq64YZg5li+vLvVtzqRwWkGvX6Gm8CQDO7nDfgWtfx/kXe8X6mnLf+mtdT60Wpq/81Vo3+mlddtK2gYW4tNe3aawjD+l1UZkpnndGH2GW3l38Su2yBk6vZBruNlH5KX6UGa+Ow0hXG4N0NSweUp/yJXz+Ni1+FISWXdQoeHJq712vLeyVp7t4oXRdlzu/VSqcPFcdg8ZZr47FSlVx45o+zO26KXy5n2i6xaIZmAlcDvgA8ADy+Tp87Us5Z/U1ubWS2nKvF6qrS+mjD7d9hai019dsoybPe7iQyB2Y+aYvtsB7wGuJjg478U2LZs/m5Sz5R/U7tueR4/ZeWt+tu6jUFU1/KLDX+ZHqSpz05Zhl1+pxS1KP9QDlPAm4DrgXOA3wBvqFJGJ6mvNn8IA6CDVDxVInhmtTb73TIdJsVdB8Pech72notTijzlX8qOL+nZkr4GnA9MAo81s8MJ8X7empFnT0nnSbpa0lWSjo37d5L0XUm/iZ87VrRU1UeafR1g3bpyIQ+qUMW+mmXnnpgof36/beijOms3i2G3aw/bGItTP1m1QjIBK4BDMo49JWP/rsSJYMC2wK+BvQnjBu+I+98BvL/o+j2f5NXrLnDVVmK/bf7O+OHPx1hATTb/hcBT4/fZVLT3A18Hngb8CtjVNlcQvyrK23Pl3+sucJ1hjKuYV8bNFONUw5+Pkadr5Q+8mjDT93dx+2HA98vkjecvAm4gDBrfmdiv5HZbnqXAamD1ggULenuHet3y75d9tduX2ZWB44wUecq/rO/+64EnAHdFU9FvgL8qk1HSXOArwJvM7K7ksSicpeUzs1PMbLGZLZ4/f35JMTukE5/dOmz4ddpXW+ER1q4NVcvatdXGLbrN7zjOcJFVK9j0VvhP4+cv4udM4PIS+SaBc4G3JPY1z+xjVt2cUocNv86Wdbe9F3f9c5yRgxpW8voAcCdh7d43AK8DrjazzOmMkkQYKF5vZm9K7P8gsM7M3ifpHcBOZvb2vOs3LrBbJ0Gdug18VkS3wbTGIRiX44wZeYHdyir/GcAxwNMJdvpzgc9YTmZJBwM/Aq4AWtrjeOCnwJnAAmAt8AIzW593/cYp/yYqym6jDA5JlELHccrT9TKOZrbJzD5tZv9oZs+P33NrDTP7sZnJzPY1s0fFdLaZrTOzp5jZw8zsqUWKv5E00Ue621gjoxCrJImvJNVXvhsAABp2SURBVOU4uZSd5PWEOCHr15KulXSdpGt7LVxjaaKi7HbSUVFAuaaRp9x98NpxiskaDEgmwopehxM8fKZaqUzeOlIjV/IaRbfKYZn4UySnD147jpnlD/hW8vYZVGqk8s+ijFJvqpLNCyjXpEqqSLl73BrHMbN85V/Wz/88SR+UdJCk/Vup/n7IkFPW3FAlFnwntutO7d1Z8fc3bux87kBLjnnzQqrDBl+0fkATx2Qcp2lk1QrJBJyXkn5QJm8daWha/mXNDWVbpp30ENLyzJoVIpUWtd6z5O/EfJIVMbWOnk7RfW5qz8px+gx1hXQeVBoa5V9WqZetJDqxXZdR4FWCwXVqPikjR6c2+DLKvYljKo7TZ7pW/sD2wIeJsXaA/wC2L5O3jjQ0yj9rIfSpqennlW2ZdmK7LtNyz1O8SaVZZfGYdsqsSdCNDd6Vu+MUkqf8y9r8TwXuBl4Q013AaTVZnsaL1kzfe+/dHJ8/yy0zy0a9007pNv1Vq4KbZxmy7ObJuPwrVnTu0lrGvt6NDX7c1g9wnLrJqhWSCVhTZl+v0tC0/Ita6nXEBJo1y2xyMr2Msjb7KiaX9hb2smXlWty9tPk7jlMKajD7/AQ4OLH9BOAnZfLWkYZG+RfZ6OuI659lWqqi+DtVvJ1UXi3Zp6bKDTo7jlMbecq/bGyfRxGCtG1PiO2zHni5mV1Wf19kSxoX2yeLlqtn0o1zzpzNJp06YgLllTFjRnDLTMuz557dB5Xz+D+OM1TUEdtnjZntB+wLPNLMHt0vxT9UFIVYqMP/PK+MNMUPm+3i7fbxqvMBivzrHccZGmbmHZT0loz9AJjZh3sg03CzZEl2q3r58vSeQZWYQHllnHBCdsu8nfZeSmsCV+s3pLFgQXr5PnnKcYaOopb/h4CjCLF85hIWYk8mpwpVgq9ltcrzysgKOHfEEVuWVWWWcYsmBrRzHKczsgYD4ljAfsD7gDXAZ4GnEtcA6GcamgHfbmgfHJ01q7NB2jTvnLRB2k59792/3nGGBrod8AWQ9HjgxbECOM7MvtGLyiiNoRnw7ZS0geI0OhlYzRqknZhIHyOYmAj+/e437zhDT9cDvpLmA48GHgncBNxWn3hOqgkmjU4GVvOCtbWbcFr7X/lKj33vOCNOrvKX9EpJ3wa+THDxfIGZPc3MLu6LdONCWaXeycBqVp7WWMGMlEdgwwY49tjq13IcZ2goavl/BtiNENrhMOAzkr7RSj2Xblwoo9Q7HVjNG6RdsiR7fsG6ddWv5TjO0JDr6gk8qS9SjDtp7puTk7DddrB+fXcTs1p5Tjih+0lejuOMDLnK38x+2C9BxppeK+i8uQdTU+mt/ORavo7jjBxFNv+HSzpH0v9I+htJp0u6U9LPJP1dv4QcCwYVpfKkk0IvI8nkZNjvOM7IUmTzPwX4FLAS+AHwbWBH4N+AT/RWNKcvLFkCp502fdLYaae5WchxRpwi5b+tmX3TzM4A7jezL8a5A98kVALOKNCvXkenaws7jlM7Rcp/IvG9PY7PrJplcUaZsovbJ8/3isJxekaR8v+kpLkAZvap1k5JDwW+l5dR0qmSbpN0ZWLfiZJulrQmpiO6Ed4ZIFWVc5VYQlUrCsdxKlMY3kHSBPBGM/tIpYKlQ4B7gM+Z2T5x34nAPWb2oSpljXx4h2GjaN2CNKqsZeDrBjhOLXQV3sHMNhJi+lTCzC4gLPriDIJemk06iQhaZS0DXzfAcXpO2QXcL5T0CUn/R9L+rdThNf9J0uXRLJQ5aCxpqaTVklbffvvtHV5qTEkzm7z0paGVXUdF0IlyrhIOukpF4WMDjtMZWeE+kwk4LyX9oES+RcCVie2dCYPIM4DlwKllrj8WIZ3rpGg9324XT+9kLWKz8uGgy64VXHVNYccZM+h2AfdOU7vyL3usPTVe+fcixn03ZUr5yr+Moi6SrddKt8zv77QScpwxoWvlH1vsnwXOidt7A8eUyNfe8t818f3NwBfLXL/Ryr8XinDlSrPJyellTk7mt5STinJqqlj5Fy3aUkbGQS/qklXJdfvbHGdEqEP5nwO8ALgsbs8ErijIcwZwC3A/YQ2AY4DPA1cAlwPfSFYGeanRyr8Xrc8s5T01teW5aZXP5OSWK4GNYuu47L1vQkXlOAOgDuX/8/j5i8S+NWXy1pEarfx70frMU9rtZCnAqanNx9plHEa7eJoCL9Pr8nEBZ4zJU/5lvX3+LGkKMABJBwJ/Kpl3tKnimdILsjxs1q8PPvFm8PnPl1s0vhfU4Y2TNekLshezb9GJW6rjjANZtUIyAfsDFxIU/oXAr4H9yuStIzW65d+LlmUVs0+TBz3rujfd/EYfF3DGGGpo+V8FPBF4PPAa4BHANXVXREPJkiXFrc+qnHQSzGoLnTRrVnqY5Sr+893QSQu+rlZ3N5O+Bt0zc5ymklUrJBNwaZl9vUqNbvn3iiqDlL0e0Oy0BV9Xq7ublr/b/J0xhk5b/pJ2kXQAMFvSoxOzew8F5uTldfpIr0MyZ7XgixZ5r6vV3U3vJqtnBj4z2Blrisw+hwEfAvYghHT+j5jeDBzfW9HGmLQBzle+EubNG4yyyjKvrFuXL0ddJqm6TWsXXuhRQx0nq0uQTMA/lDmvV2nszD5F4Rl6ZbrIMh/lyZM0vWS5Yw7Sxz7N7JNljmrCILnj1Ag5Zp/CkM4QzD+EWDy7mdnhkvYGDjKzz/a0ZoqMXUjnrPDH7dQZ4jgvTDPAUUel52uFZO4kzHM/yAoPnUZaeGnHGWLyQjqXVf7nAKcBJ5jZfpJmEiZ8PbJeUdMZO+VfVmHVqayKYujPmxfMPFnHmxqDv2xFCoOX1XFqpqt4/pF5ZnYmsAnAzB4ANtYkn9NOmq08jTrdFbPs+mvXBgUKW7qfJu33TY3BX/Ye9cI91nEajM/wbSLtA5xTUzA5Of2cTpVVlr9+npI0C63+DRs275uamm7Saao/fZmKdGJi8OYpx+k3WYMByUT6DN99y+StI43dgG8adQyc5vm8px2rMuDcZH/61r3L+i0+29cZUagjnj8hkucjgH2AybL56khjr/zr8pgpmiyVvE6ZCmDYomc2ORSG4/SAPOVfNMnrea0EPBv4W2Av4Mi4z+k1WUHNOvFJL7LLJyeLLVxYvbwqk83qWn6xSjn9CoXhOMNAVq0QKg02AZcCp8Z0WiKVWoKxjjSSLf+yreS8kM1VqdLyLWMG6rTFXHWxmrxy0nz4ly3Lz9Pk3onj1Aidmn2A5wJfBFYD7wQemnd+r9LIKf8q9vE8E0wdyjLPLp+0lde5JkCVqKV5ZFVm0nApda+QnB7RsfJ/8CTYBngJ8HXgx8ATy+SrK42c8q/SAi87u7YsZRRNr2fq5vUmqpBXMaaNYzRRsTZ5oNwZeupQ/hPAMwnLMK4BDiuTr640csq/SrTLlSuzFVwvvFT6oYzqUv5FHjzDoFh9ENrJooaGSzdmnycDp0SF/yFgcd75vUojp/yrvvBZZpJeKIh+KKOs3zNjRrUHfOXK/Dg9w6BYfbEZJ42aGi55yr9oktf3gMdGU89WwMskfayVuhhnHm+qep2cdFK9Xip5HjL9mKmbtlgNBC+hKp5MS5bAa18bJsIlad2bps46TtLUyXHOYOnH8qNZtUKoNDg6L+XlrTONXMvfrHqXrq5JXmmt7mSLol+t5ZUrQ0u/jmtVjUbapJb/MJimnP5TU4+QOiZ5PZghhITYrmq+btJIKv9+U+S6mRwg7Ycy6sdYxrJl6eXnuYIOgqYPSjv9p6aGS9fKH/gCsB3B6+dq4CbgbWXy1pFc+ddA0RoBSYXbD2VUtxdTlWs0qeXvOGk0wObfYm8zu4vg938O8BDgpTVYnZx+UWTnTtqYkzN1ly8Pdsb28YFuZ+jmyXPEEdXKqnqNJtn8HSeNulevSyOrVkgm4CpgEvgy0ccfuKwgz6nAbcCViX07Ad8FfhM/dyxzfW/5V6CT1biyWhRZrY9ly7pvlZSVp5teiLf8nTGHGsw+bwRuBs4GBCwEflSQ5xBCNNCk8v8A8I74/R3A+8tcf2yUf5aiK6sAO4naOTVVPbTExET6/qmp8oq6aAxiaqp4cLrM/fTBVGeM6Vr5p2aEmSXOWdSm/H8F7Bq/7wr8qsy1xkL519HKrhK1s0wrumx0z6o9iuRv7qTcKi13H0x1xpg85V92GcedgX+n4hq+khYB3zKzfeL2nWa2Q/wu4I+t7ZS8S4GlAAsWLDhgbdl1WIeVrGUQJyZgY8qiaWlLDmYtWdjpco9VZUqjaGnEKmvstvC1dh2nFHUs43g6cC6wW9z+NfCmboSKtVJmzWNmp5jZYjNbPH/+/G4u1Rw6mVyVpWTTzs+aGGTW2aBs1mS0pUvLLTMJxYOrZZesTOIToByna/q9hu+tknYFiJ+3dVDGcFIUlz9LoU1MpO9fsGDLyuSII7IVaSfrAGR5HHzqU5v3F1GkqNOuMTWVfb7H3x9t6lrnwSkmyx6UTMD5wBRwadw+EPhhiXyLmG7z/yDTB3w/UOb6Q23zL1pCsGhyVZbNP29/P3zoW3TiRVREJ4PTzvDjA/S1Qw3ePpXX8AXOAG4B7idMCjsmViDfJ7h6fg/Yqcz1h1b5l1kQJW9yVVKRtzxsWoOWRYO7/QoY1itF7QO144e75tZO18o/lOFr+FamaFZt3oOdtUpVK09RZdLPF2lQitoriNHCI5zWTp7yn5lnEspZp3cvSZjZV0tal8aTosHOPPt1WlQ/i+Pja9cG+3hrO0nLxr58ebDxJ8volb18yZJ6Zx6WoTWG0vp9rTGNljzO8LFgQbrnlw/w94SiAd8jc9KzeivaCJD30BZN1y6qOMyyQxlDf6aHD5J+hLx1+kvVUOdOd2R1CZqUhtbs080AVhmTUcuMI22eETsuJhA3EdRLU0xoTZFjRKAmm/8zgbcD72qlsnm7TUOr/M06f5jLDBb3Owxzk/DBwfoYx+dnTOha+QMnA58DbgTeDVwBfLZM3jpSY5V/r1spSa+e9pbuIBZgaRKusOpjHJ+fMaEO5X952+dcCgK71Zkaqfz7rXzyKppRNYEUVa7DbiJoivyj+vw4tSj/n8bPiwkhHrYGflsmbx2pkcq/Sa2lOmVpikIa9ZZ9k35fk55lp1bqUP7vBHYAnkeYuHUL8G9l8taRBqb8h6W1XZciGZRCSrvPo66QmvT7mlQRObXSsfIHHgPskth+GfAd4GOUnJ1bRxqI8i96IZr08rbk7bbFPojflHWfswa5R8UU0aTGg1lzenxOrXSj/C9tKXnC4iy/B/4B+DfgrLy8daaBKP8ysfG7aS018WUbhEKqumCMt/wdpzR5yr9okteEma2P318InGJmXzGzdwIPLTGNYHgpWv+1m0lURRE+B0XWpLRezrDMC2U9yhN+fEKTM2iyaoVQaXAlccUu4BrgkOSxvLx1pka2/JtadjcMwvabdy+a2Duqk1H/fc7AoQuzzwmEKJ5fB34BD6789VDgwry8daZG2vy7oWn23iT9Vkg+2Og4PSNP+Rcu4yjpQMJ6u98xsz/HfXsBc83s0po7IqksXrzYVq9e3Y9LTWfVqhAr5oYbgulj+fJ6YuNkLV1YtOThqNKr++w4Y07eMo6l1vAdNANT/r2iPSIlBHvvKAVecxxn4NSxhq9TJ/2MuOnL4jmOk4Ir/0GxZEkw8WzaFD57pfjr9iryysRxRgJX/qNM3THvm+qi6jhOZVz5jzJFcxWq4guoOM7I4Mp/lKl70lbdlYnjOAPDlf8oU/cs0kHMAHYcpye48h9l6vYq8pAEjjMyzBy0AE6PWbKkPk+iVjk+Ictxhh5v+TvpZLl09sNF1XGcnuMtf2dL2mcgt1w6wZW944wIA2n5S7pe0hWS1kgaobgNI4K7dDrOyDPIlv+TzOyOAV7fycJdOh1n5HGbv7Ml7tLpOCPPoJS/Ad+RdImkpWknSFoqabWk1bfffnufxRtz3KXTcUaeQSn/g81sf+Bw4PWSDmk/wcxOMbPFZrZ4/vz5/ZdwnOln1FHHcQbCQGz+ZnZz/LxN0teAxwIXDEIWJ4M65wc4jtM4+t7yl7SNpG1b34GnE9YKdnqBh2B2HCeFQbT8dwa+Jql1/S+Y2bcHIMfo4/76juNk4Ms4jjK+VrDjjDW+jOO44v76juNk4Mp/lHF/fcdxMnDlP8q4v77jOBm48h9l3F/fcZwMPKrnqOP++o7jpOAtf8dxnDHElb/jOM4Y4srfcRxnDHHl7ziOM4a48nccxxlDhiK8g6TbgZQ4BT1hHjAsK4y5rL3BZe0NLmtvyJN1oZmlxsQfCuXfTyStzoqF0TRc1t7gsvYGl7U3dCqrm30cx3HGEFf+juM4Y4gr/y05ZdACVMBl7Q0ua29wWXtDR7K6zd9xHGcM8Za/4zjOGOLK33EcZwwZS+Uv6VRJt0lKXThe0vaSvinpMklXSXpFv2VMyLKnpPMkXR1lOTblHEn6mKTfSrpc0v4NlnVJlPEKSRdJ2q+psibOfYykByQ9v58yJq5fSlZJh0paE8/5Yb/ljDKUeQYa8X5J2lrSzxJy/GvKOVtJ+lJ8t34qaVFD5XxLvOeXS/q+pIWFBZvZ2CXgEGB/4MqM48cD74/f5wPrgVkDknVXYP/4fVvg18DebeccAZwDCDgQ+GmDZX08sGP8fniTZY3HJoAfAGcDz2+qrMAOwNXAgrj9Vw2WtRHvV3xf5sbvk8BPgQPbznkdcHL8/iLgSw2V80nAnPh9WRk5x7Llb2YXEB64zFOAbSUJmBvPfaAfsm0hiNktZnZp/H438Etg97bTngN8zgIXAztI2rXPopaS1cwuMrM/xs2LgT36K+WDcpS5rwBvAL4C3NZH8aZRUtaXAF81sxvieQORt6SsjXi/4vtyT9ycjKndA+Y5wIr4/SzgKVHuvlFGTjM7z8zujZul3quxVP4l+ATwd8DvgSuAY81s02BFgtjlfDSh5k+yO3BjYvsm0hVZ38iRNckxhB7LQMmSVdLuwN8D/9l/qdLJua97ATtKOl/SJZJe1m/Z2smRtTHvl6QJSWsIlft3zSzz3TKzB4A/AVP9lbKUnElKvVeu/NM5DFgD7AY8CviEpO0GKZCkuYQW6JvM7K5BylJEGVklPYnwkB7XT9lS5MiT9aPAcU2o+KFQ1pnAAcAzCc/vOyXt1WcRH6RA1sa8X2a20cweRWgpP1bSPoOQo4iycko6ClgMfLCoTFf+6byC0IU2M/stcB3w8EEJI2mS8CKtMrOvppxyM7BnYnuPuK/vlJAVSfsCnwGeY2br+ilfmxxFsi4GvijpeuD5wKckPbePIj5ICVlvAs41sz+b2R3ABcCgBtOLZG3U+wVgZncC5wHPaDv04LslaSawPTCwZzZHTiQ9FTgBeLaZ/aWoLFf+6dwAPAVA0s7A3wLXDkKQaF/8LPBLM/twxmnfAF4WvX4OBP5kZrf0TchIGVklLQC+CrzUzH7dT/na5CiU1cweYmaLzGwRwd77OjP77z6KCZR+Br4OHCxppqQ5wOMI9va+UlLWRrxfkuZL2iF+nw08Dbim7bRvAEfH788HfmBxVLVflJFT0qOB/yIo/lLjPWM5w1fSGcChhFCotwLvJgyiYGYnS9oNOJ3guSDgfWa2ckCyHgz8iGAbbZkfjgcWwIPyimBHfQZwL/AKM1vdUFk/A/wDm0N0P2ADiJ5YRta2808HvmVmZ/VRzNa1S8kq6W2EVvUm4DNm9tEmytqU9yv2QFcQPLpmAGea2XskvQdYbWbfkLQ18HnC2MV64EVm1teKqqSc3wMeCbQafTeY2bNzyx1H5e84jjPuuNnHcRxnDHHl7ziOM4a48nccxxlDXPk7juOMIa78HcdxGoYKgk+mnP+CRDC9L5TK494+zrAjaQr4ftzcBdgI3B63H2tmGwYiWAqSDgU2mNlFg5bFaS6SDgHuIcTsyp11LOlhwJnAk83sj5L+qoyv/8x6RHWcwRFnCT8KQNKJwD1m9qFBySNpZowDk8ahhJe6tPIvKM8ZQczsgvbw0ZL+BvgkIRLqvcCrzewa4NXAJ1sBE8tO8nKzjzOSSDpA0g9jkLNzW1FOY+Czj0haLemXCrH6vyrpN5LeG89ZJOkaSaviOWfFWbNF5X5U0mrgWElHKsR//4Wk70naOb7MrwXerBB3//9IOl2JdQIk3RM/D5X0I0nfAK5WCOz1QUk/V4jZ/pp+3k+nEZwCvMHMDgD+GfhU3L8XsJekCyVdLGmL0A9peMvfGUUEfJwQO+h2SS8ElgOvjMc3mNlihYVGvk4IiLYe+J2kj8Rz/hY4xswulHQq8DpJJxWUO6s1W1nSjoSY6ybpVcDbzeytkk4m0TORdEzO79gf2MfMrpO0lBC24zGStgIulPQdM7uu+9vlNB2FQHmPB76szRGlt4qfM4GHEXqVewAXSHpkjAOUiSt/ZxTZCtgH+G58USbYPO0dQrwWCCEIrmrFQZJ0LSGI153AjWZ2YTxvJfBG4NsF5X4p8X0P4EuxZzCLELysKj9LKPenA/smegnbE154V/7jwQzgzhjZs52bCIsi3Q9cJ+nXhGfj53kFuvJ3RhERlPpBGcdbEQ83Jb63tlvvRLsnhJUo98+J7x8HPhzjrhwKnJiR5wGi+VXSDEJFkVaeCF3+czPKcUYYM7tL0nWS/tHMvhzjee1rZpcB/w28GDhN0jyCGagw/pDb/J1R5C/AfEkHQQgxLOkRFctY0MpPWCXrx8CvKpS7PZvDah+d2H83YXnDFtcTzE4AzyYGGEzhXGCZQrhkJO0laZvyP8cZJmLwyZ8AfyvppmgeXAIcI+ky4CrCKmMQno11kq4mhHt+W5lQ6d7yd0aRTYTwux+TtD3hOf8o4YUpy6+A10d7/9XAf5rZhmh2KVPuiQT77B8JawA/JO7/JnCWpOcQloj8NPD1+EJ/m+mt/SSfARYBl8ZW3+3AQNYWcHqPmb0449AWg7kxxPRbYiqN+/k7ThvRK+dbRf7VjjPMuNnHcRxnDPGWv+M4zhjiLX/HcZwxxJW/4zjOGOLK33EcZwxx5e84jjOGuPJ3HMcZQ/4/aAupEgyEuE0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wWz0iOY7jOP2kbI/hMGAmcDpwWjw+LC+BpCWS7pS0MnHtjZKulrRe0pyW+B+VdKOk6yS9or3HcBzHcaqiVI8hzj46WtLmZvZwybxPBk4Avpe4thJ4PfCtZERJuwCHAs8Gtgf+W9LOZrauZFmO4zhORZTqMUh6gaRrgGvj+R6SFuWlMbMLgHtbrl1rZtelRD8I+IGZ/a+Z3QzcCDy3jGyO4zhOtZQ1JX0FeAVwD4CZ/RHYr0I5dgBuTZyvidc2QtJ8SSskrbjrrrsqFMFxHMeBNlY+m9mtLZf6YuYxs8VmNsfM5sycObMfIjiO44xrys5KulXSCwCTNBU4mmhWqog/A09OnO8YrzmO4zg9pmyP4d3AewnmnT8De8bzqjgDOFTSJpKeAuwE/L7C/B3HcZySlJ2VdDdh7UJpJJ0C7A/MkLQGOJYwGP11wnTXn0u6wsxeYWZXSzoVuAZ4DHivz0hyHMfpD0VO9L5O8JWUipl9IOde1jqH0zPiLwQW5snjOI7j1E+RKWkFwZHepsDewA0x7AlMq1c0x3Ecpx/k9hjMbCmApAXAi8zssXh+IvCb+sVzHMdxek3ZwecnAFsmzqfHa47jOM44o+x01c8Bl0s6l7Afw37Ap+oSynEcx+kfZWclnSTpbOB58dKHzeyO+sRyHMdx+kUpxSCp4f7ivvi5c3Ryd0E9YjmO4zj9oqwp6UOJ400JDu4uBV5auUSO4zhOXylrSjoweS7pycBXa5HIcRzH6Sulnei1sAZ4VpWCOI7jOINB2TGG5AroSYQFbpfVJZTjOI7TP8qOMaxIHD8GnGJmF9Ygj+M4jtNnyo4xLK1bEMdxHGcwKHKidxXpTvQEmJntXotUjuM4Tt8o6jG8pidSOI7jOANDkRO91QBx85zbzexv8XwzYNv6xXMcx3F6Tdnpqj8E1ifO18VrjuM4zjijrGKYYmZ/b5zEY9+PwXEcZxxSVjHcJem1jRNJBwF31yOS4ziO00/KrmN4N7Bc0gmEGUm3Am+tTSrHcRynb5Rdx3ATsK+k6fH8oVqlchzHcfpGWZcYmwD/BMwGpkgCwMw+U5tkjuM4Tl8oa0r6KfAAwdX2/9YnjuM4jtNvyiqGHc3sgFolcRzHcQaCsrOSLpK0W62SOI7jOANB2R7Di4C3SbqZYEpyX0mO4zjjlLKK4ZW1SuE4juMMDEXeVbeJh3/tgSyO4zjOAFDUY7iU4HZbKfcMeGrlEjmO4zh9pci76lO6yVzSEoLr7jvNbNd4bRvgvwhrIlYBh5jZfZK2ApYBY1GuL5nZSd2U7ziO47RPqVlJko5qOZ8s6dgSSU8GWqe5fgQ4x8x2As6J5wDvBa4xsz2A/YHjJLmjPsdxnB5TdrrqXElnSdpO0q7AJcAWRYnM7ALg3pbLBwGNrUKXAgc3ogNbKCyrnh7TPVZSPsdxHKciyvpKerOkNwFXAQ8DbzazCzssc1szuz0e38GGDX9OAM4AbiMonTeZ2fqU9I7jOE6NlDUl7QQcDfwYWA28RdJIt4WbmbFhT+lXAFcA2wN7AidI2jJFlvmSVkhacdddd3UrguM4jtNCWVPSmcAnzexdwIuBG4A/dFjmXyRtBxA/74zXjwROs8CNwM3AM1sTm9liM5tjZnNmzpzZoQiO4zhOFmUVw3PN7BwIrXwzOw54XYdlngEcEY+PIDjoA7gFmAsgaVvgGcD/dFiG4ziO0yFlxxgejIPOuwCbJm5dn5dO0imEGUYzJK0BjgU+B5waZzqtBg6J0f8DOFnSVYR1Ex82M98lznEcp8eU3Y/hWMILfhfgLIKLjN8C38tLZ2aHZdyamxL3NuAfy8jjOI7j1EdZU9IbCC/zO8zsSGAPYKvapHIcx3H6RlnF8EicOvpYnCl0J/Dk+sRyxg3Ll8Ps2TBpUvhcvrz6fKoqo06GQUbHiRQ50TvZzN4GrJC0NfBtgv+kh4CL6xfPGWqWL4f582Ht2nC+enU4B5g3r5p8oJoy6qSqenCcHqGwlCDjpnSZme3dcm02sKWZXVmvaMXMmTPHVqxY0W8xnCxmzw4vwVZmzYJVq6rJB6opo06qqgfHqQhJl5rZnKz7RYPPI5L2IsW7qqS9zeyybgV0xjG33NLe9SrzabeMOqmqHhynRxQphh2A48h2u/3SyiVyxg9jY+kt5bGxavOpoow6qaoeHKdHFA0+32hmLzWzl6QEVwpOPgsXwkiL55SREXjVq9obiM3KZ+HC/Ht1U3ZAuRMZezlY7QPjTitmlhmAy/Pu9zvss88+5gw4y5aZzZplJoXPBQvMRkbMYEMYGQnx2sknGT/vXl0sW9bec7QjY7t59/I5nHEBsMJy3q1Fg8//aGa/lHQg8HMbMG+nPvg8hIyXgdg6n6OXdTRevg+nLYoGn3NNSWb2y3j4JuAGSV+QtJFjO8cpzXgZiK3zOXpZR+Pl+3AqpdQCNzM7HNgLuIngz+ji6P66cLMeJ4e6bLtF+fbTppw14Jq8Pgw27zLPMYh597MsZ3jIszO1BmAU+CBhr+azCe63399OHlWGoR5jqMu2W5Rvv23Kgy5fWeqU08cYnJqhYIyhrEJ4LXA6YQe3DwFPjNdHgFVl8qgjDLVimDWr+c/YCLNm1ZtvXeW2Q95A7CDIV5Y6B717OaDej8F7p69UpRiWAvtl3JtbJo86wlArBin9BSjVm2875Vbxwmg3j27rpUx5yTijoyH4S9GZQFSiGEI+zAJeFo83A7Yom7auMNSKYdB7DFWYGDrJo5t6KVNeWhw3ozgTjKp6DO8kbOV5UzzfCTinTNo6w1Arhjpsu8uWhdZv68tu6tQNreLp08u9DKtQXJ3k0U29lCkvK86gm62c4WLAzXNVKYYrgGkkFrwBV5VJW2cYasVgVu2PJ6slvPnmZtOmZb8EpbDorJUqTF2d5tFpvZQpLytOleY8Z2IzBAP6RYohd4FbA0m/M7PnSbrczPaSNAW4zMx272AiVGX4ArcEWQuVJk+Gdevy06YtZqpi4VOvF0+VKS8rTlZ8x2mXIVg02NUCtwTnS/oYsJmklwM/BM6sQkCnIrIWJBUphay0Vfgg6rUfozLlpcXplXzOxGA8LBrM6040AkGBvJOgEH4Uj1UmbZ1h6E1JVZJlO588udh0kmVT78espG7xWUlOvxmCKddUYUoaVNyUlKB1lzAIrd8jjoClS5uvJxkZgcWLfScxx6mKrP/iAP3PKjElSXqhpF9Jul7S/0i6WdL/VCem0zXz5oUf3qxZIIXPxYth0aLm66OjIUAYf1i7Fo4+GmbMCPenTAmfRa4ohsFtheP0g6z/4oAohTKUHXz+E/DPhP2eHzdam9k99YlWjPcYOiStRZNGVitnCFpEjuNkU9RjaGtWUqWSVYArhg4pMzOnQV0zlhzH6RtVzUo6V9IXJT1f0t6NUJGMTt0sX77BVCSVVwoQZlK0mo2y0q9e7SYlxxkHlFUMzwPmAP9J2AP6OOBLdQnlVERDIRx+ONzTodVvm22C2Wj16jC3YvXqoFyymD9/g3JoKJR2xi4cH79Jw+ukt+RNWRr04NNVcyjyCVQmjIyku9horA7Om/6aV/6ArQIdKIZg1WzP8TqpHAqmq5adlbSVpC9LWhHDcZK2qllnOWmUbTl9/OPFg8tJJsWfwuTJ4XPWrDDVNaunkTc2dcst+eWvXRvu18mwtjDT6q0X9TXIeJ30nLKmpCXAX4FDYngQOKkuoZwMGrOBkmadpOkmSZlVlkmT0Pr1YWbR0qUh74ULw3EWs2aFkMbYWHH5da4CbaeeBo3xsGq2arxOek9ed6IRgCvKXGu5vwS4E1iZuLYN8CvCzm+/Ap6QuLc/wVnf1cD5ZeSacKakdlZUlvEimpdXXvpGNz6vi19Ufp2rQIdg5Wkmwyx7XXidVA5VmJKARyS9qHEi6YXAIwVpTgYOaLn2EYK77p2Ac+I5krYGFgGvNbNnA28sKdfEop2WU55PoMYCt7y88lpjjfUKeQt58sqv2x/RMLcwe+1fahjwOuk9eVqjEYA9gT8S9npeDVwO7FEi3WyaewzXAdvF4+2A6+Lxe4DPlpElGbzHUNBy6mYLzSpaacmeQ8NnUy/8EQ17C3PAffn3Ba+TSqGqHdxCXmwJbNlG/FbFcH/iWI1z4KvAN4DzCKur35qT53xgBbBibGysxqobQKqcnVGU1zDPBBlm2R2nB3SlGIBj8kJeWitQDPH8vvh5AnAJsDkwgzAGsXNR/hOux2BW/eY+ybwWLMg/77SsbmTuNK23MMvjdTXh6FYxrAcuAxYC/w4cmwx5aS1dMWSZkj4CfDoR77vAG4vyn5CKoS7qamV3k6+3/OvH63hCUqQYcn0lSdoDOIwwiHwpcAph8Dg7UXP62cDPzGzXeP5F4B4z+5ykjwDbmNm/SXpW7DW8grCF6O+BQ81sZV7+7iupQuryf9RNvu6TqX68jicklTjRixm9gKAkXgZ82MzOKIh/CmEK6gzgL4Rexk+AU4ExwiD2IWZ2b4z/IeBIQi/lO2b21SKZXDFUyKRJ6YvWpLDGoR/51iWTswGv4wlJkWKYUjKTmcBewG7AGsL6hFzM7LCMW3Mz4n8R+GIZeZwaGBtLbzmOjfUv37pkcjbgdeykkLuOQdLbJf0/wpaeIrTwX25ml/REOiebTl0+tHpanTEjXMuaK/70p29wgNcI7ZT3qldt7HRvZCRcL5Lf56/XT6/reFhdlUw08gYgCGadK4EzYzgjGfLS9iJM2MHnTgcMly0zmzatOR2YTZ26YSVzcnbK3Lkbx223vFY5pZBvWfl9xkz99KqOfaB7YKDLWUkvzgt5aXsReqYYqvjj5LmJaORZtpwsj6d5C7iWLduwyKxs2rz4rbKnkfW8WZ5Z61qA5sqlfeqos2FfeDiO6EoxDHroiWKoopVTxgX2tGmh5V5UzrJl2XlInZeflrZIKSRlHx3d+CWS55q7Hfm7wVup7VNXnWX9Hur43p1cuu0xPBM4G/g58DSC/6P7CdNJn5WXthehJ4qhilZOpw7tILTay7iyyJOpbPkLFjSnK9NjyPqjNxbHtZOujpajt1Lbp6468+9iYOhWMVwAHEiYproaOJQwCH0gYT3D+FcMVbRy2m05t4Zkay0vr6wWXTvlJ5XDggWdy9xQDp08Y5V4K7V96qoz770NDN0qhssTxze23LssL20vwoToMbSWl5XX6Gh62UVjC61h8uTm9NOndydz0f267f7eSm2fOuvMx3sGgiLFUOR2e3Li+Mst96YVpB0fVDGdL88FdVkaLqM33zz9/p57bnytsWHNunXly1m3rnka4Ykndi776tXZ8o6OhnppzKM/4oj0qbCt0xtf9rKwy1xj6uz06flTHn3Ka/uk1ZkUvqdup5jOmxdWVK9fHz7nzetCUKc28rQG8C5gesr1pwNfzUvbizBuZiWVbX3nDTy3tvTNuiuv1dNq1kyoojB16sY9lqlTg5mpaE/osvtWT5qU/514K7V9kr/XVtOSm3+GHrqdlUToNfxzUbx+hKFfx1DW9l92V7SGgmiME3Q7ttFqOuhUQYyObvxiLnqW0dH2TGBuGqoHN8WNS4oUQylfSZJ+b2bPravX0ilD7yspy4HZ6GgwkdxySzC1LFwYutxZfm3SWLAAzjorPf+yZPnLyZK7nXzaeZZOy3C6x30pjUuKfCWV3drzQkknSPoHSXs3QkUyTlyy7N/HH59uh23Hf83ixd2PbWSVt3AhTJ3aXT5V++Jx3z71kFWvXt/jmrKKYU/g2cBngONi+FJdQk0Y8vZMTqOdF/26dc35Qxi0hXC+YMGGckdHYVrLXIK8Adp58+Ckk/L3jm6Vu5tnKWLSJB9MrgsfvJ+Y5NmZBj0M/RhDJ7QOpGaNI6QNRreTb7uDi1njAXlz37P2hM4ax5g8OfhZmjRpw7XNN/eB0LrxwftxB1W4xAC2JeyqdnY83wU4qkzaOsOEVAytZC0ia13FPExy+EIox6mVIsVQ1pR0MvALYPt4fj3wwQo7Lk6nLFoUzEINM9HkyeF80aLhlaNdE5vjOJVSVjHMMLNTCW64MbPHgDZWTTld+aFvpJU27I2QXOR16qmwdGloWy9dGmYjTZoU9lqYMaM3vu+XLw/lrl8fXuRLl3amFBrPevjhcC3IH5cAABVVSURBVOut4ZlWr4ajj05f+Jask8Yz5t1rTe/7AjjOxuR1JxoBOA8YJbrBAPYFzi+Tts4wNKakbkwjZRd5FS0aq9McU5Xpp+hZk/tGZMVL81KbHO9orJFo3ZeiyrrJGjupsu7d7u90ARWNMewNXAg8ED+vB/Yok7bOMDSKoZtFQu2sXi67f8IgPV+7zzprVjW+p+qqmzylVZXy8TEYp0uqUgybEPaHfjawKzAV2KRM2jrD0CiGbrxVdrt6OavMdlu1eS3UqrxxlnlWqfo66VTeNMo4DqyrDF+N7JSkSDGUHWO42MweM7OrzWylmT0KXFyBJWt80mq/3mab9HhlFgnVsRBs+XI48sgNq5cbTvZWrw5O91rt7Q1nfKtXh1fQ6tXwlrdssN1nPd+kScU2/GRdTSrxcxwbyy6vW6qo64azw07vp9H6e8padd5J3o6TRp7WAJ4E7ANcC+xFMCntDewP/CkvbS/CQPYY0rr5U6akt/DKTOUsO8ZQJjTMDUX+jlpbnkWt4KlT0/eSLjJ1tPtsjXGUrDEEyB9jaFe+Tqi6x5C1b7b3GJwuoMv9GI4AzgX+Gj8b4afA6/PS9iIMpGJox/5d9o+cZvZJLvJKHueV1XjxFcVtNamUMd0kHeVljXWUVThpzzg6Wux8r3Xv7GRerc8wdWr6dqTdUvUYQ9l9s32MwWmDrhTD45Hgn8rE63UYSMXQjv27ql3EispsfSG3q7DKKLvks5Qdc+hkbKLT8YxezuKpclZS3nfrs5KcDqlKMTwJX/ncTNaLppMeQzcvraLtM9NaknmmpE5NPkllkpV/WYWT15Mqk6Zsfbaa1Rq9kkHCB5qdGqhKMZwNHAL8MZ5PAa4qk7bO0DfFkDddcNmyYnt7a/xOpx4WKYWsF12WjHkvxrIbt2Tl3ViDULYeO6n7dvJctix9LGLatMFSDj411amBqhTDH+Jncg/oK8qkrTP0TTEUteLynMC108PIau2W2eim6MXR2qpesKDY/FHGRNLJntRpcuS19vN6BGXrs6j+6lqY1g69WCjnDB8VmEWrUgzn4SufN1Bk527HDl40PtDaOiw7k6cdygyYlm25drOmoYrWcdn6bGcsqB8tdO8pOGlU9LuoSjGkrXzevUS6JcCdwMrEtW2AXwE3xM8ntKR5DvAY8Iai/HuiGNK0c1GPoR27cNnVvu3ET7bSy/xgykyxLHqmMjOGOpWjHXt6Xaune23T97EFJ42KfheVKIaQT/PK55Jp9otKJakYvgB8JB5/BPh84t5k4NfAWQOhGLK089y56V9OY11CVgs87UVdpgdQZsZPVkiz7SfL7ta9hFT8DGVbNFWsoC5bn1ljDGW+g15Q1WpyZ3xR0e+i23UMr88LeWkTecxuUQzXAdvF4+2A6xL3Pgi8l+Dmu/+KoWiefZ7WzlpIljUQWra13cmLPK01UdXCuaLWdzs20KpayWXrs8xiv05l6BbvMThpDEKPgeBm+7JoEloCnJQIS/LSJvJoVQz3J47VOAd2AM4nuALPVAzAfGAFsGJsbKyTqi1Pu63zVq3d7pdYNNspa1ZQJ63dbnsKZeRot3VbtV29nfx64fyuLtmdicMgjDEABwM/iC/iTwJPz4ufkUemYojn98XPHwL7xuN6ewxFo/pFLc6sHkNy9W9e+jLbXSZl67Z1nzYjqFtHdGXST57c/ous6oVo7eQ3aLOAerkozxkeBmhW0ubAmwmuMH4LvLhMOktXDKmmJOBmYFUMDxEGrQ/Oy7sjxdDJPPjWuGn7HqT56KnKp023rfu0cYZO82x34NZbuY4zcBQphrLeVf9GmJH0IDAd2LRkujTOIPhgIn7+FMDMnmJms81sNvAj4D1m9pMuyknn4x+HtWubr61dG65n3W/Q2GJy0aKNt57cYgt49NHm+GbhfpKREVi4sD2Zu/Wa+eijG54PgrfOhx5qP5+G7O3Ik6zbNKreSa0ov9b773lP9+X7bnDOeCNPawAvBRYDVwBfAubkxU9JfwpwO/AosAY4irAe4hzCdNX/BrZJSXcydZmSqlyDUCbfRiu7G3NAVeMBZp2bpaQNs67alSer7no9plDm2dst38cCnCGECgafrwBOAL4OfC0Z8tL2InSkGKpcg9BOvt2Q9vJpd4yg6Pna2f2t3QViWXVQdZ11+t12U77PHnKGkCLFUGRKOhL4CvAHwgD0pS1h+Fi4MJhEkiTNO0X3O823G+bN29h09e53b1yeBHPn5suRZQZat27jdK000s6bF8pvNZNlkVUHWbJ0ajoryq9svu2UX/UzOM4gkKc10gJhOumW7aarI9Q6K6kTr5vJfEdH6/H3X+Y5OvElNHlys7+kMq3gZDlZe0Jk+UjKk8V7DI5TK1Q0K+n7wJaE2UnXEMYLPlQmbZ2htgVuWaabMjuuZaUvWtjWq+mR3fhFynNwl+XpNa/OxusYw7Rp9TcKHKcLqlIMV8TPecBxwFTgyjJp6wy1KYasVmDDlUKn6VtXRvdrQdWyZcWrt9O8nua9dDttOfd63ULac3VbfmtPsXXasg9GOwNGkWJQiJOPpKuBPWPP4QQzO1/SH81sjyrNWu0yZ84cW7FiRfUZT5oU/tJpzJoFq1Z1ll6C9evDcd6m7mXL6YYyMibJkrchZ7v5jVeK6slxBgBJl5rZnKz7ZdcxfIuw8Gxz4AJJswhrGsYnY2PZ98oMKmalT14vyqfuwcsyMiYpGmRtN7/xig9GO+OAUorBzL5mZjuY2atiT2Q18JKaZesfCxdmz7gp86IrM0OpKJ+6X6jtzqIqevHXOStrmHAF6YwH8uxMjQBsy0Tb83nBgvwtLIsoY+vut9O2dv0IFQ0Uu28fX/DmDAX4ns9dUPeLbtCcthXhL/5yeD05A06RYig7+PwHM3uOpMvNbK947Qoz27PyLkwb1Db47DiOM46pavD5YUmjgMVM9yU41XMcx3HGGVNKxjuG4BX1aZIuBGYCb6hNKsdxHKdvlFIMZnaZpBcDzyDsunadmT1akMxxHMcZQnJNSZJe3wjAawmKYWfgwHjNaYcyfvvr2C+gG3naiec4zrigqMdwYM49A06rUJbxzfLlMH/+hk2AVq8O5xC8lWbF+eY3N+SRlqZOedqJ5zjOuKHUrKRBZahmJZVxlVDkJiMtTZ3ytBPPcZyhoWhWUmnFIOnVwLNJbOtpZp/pWsIuGCrFUMaXUJ6Ppqw0dcrTTjzHcYaGSqarSjoReBPwfsLg8xuBWZVIOFEo4yqhrNuEKtwrlHXd4C4eHGfCUXYdwwvM7K3AfWb2aeD5hEFopyxlfAmlxWmlKv9DZX0buQ8kx5lwlFUMj8TPtZK2Bx4DtqtHpHFK2vacixc3D+CmxVmwID9NnfK0E89xnHFDWZcYnwS+DrwU+Ea8/B0z+2SNshUyVGMMjuM4A0LRGEPudFVJzwFuNbP/iOfTgauAPwFfqVJQx3EcZzAoMiV9C/g7gKT9gM/Faw8Ai+sVzXEcx+kHRQvcJpvZvfH4TcBiM/sx8GNJV9QrmuM4jtMPinoMkyU1lMdc4NeJe2Ud8DmO4zhDRNHL/RTgfEl3E2Ym/QZA0tNxt9uO4zjjklzFYGYLJZ1DmJr6S9swhWkSYbGb4ziOM84oNAeZ2SUp166vRxzHcRyn35Rd4NY2kpZIulPSysS1bST9StIN8fMJ8fo8SVdKukrSRZL2qEsux3EcJ5/aFANwMnBAy7WPAOeY2U7AOfEc4GbgxWa2G/Af+FRYx3GcvlGbYjCzC4B7Wy4fBCyNx0uBg2Pci8zsvnj9EmDHuuRyHMdx8qmzx5DGtmZ2ezy+A9g2Jc5RwNlZGUiaL2mFpBV33XVXHTI6juNMaHqtGB4nznBqctQk6SUExfDhnHSLzWyOmc2ZOXNmzVI6juNMPHqtGP4iaTuA+Hln44ak3YHvAAeZ2T09lstxHMeJ9FoxnAEcEY+PAH4KIGmMsH/0W3wqrOM4Tn+pza2FpFOA/YEZktYAxxKc8J0q6ShgNXBIjP7vwCiwSBLAY3kuYR3HcZz6qE0xmNlhGbfmpsR9B/COumRxHMdxytO3wWfHcRxnMHHF4DiO4zThisFxHMdpwhWD4ziO04QrBsdxHKcJVwyO4zhOE64YHMdxnCZcMQwyy5fD7NkwaVL4XL683xLVS97zpt2baPXjOD2itgVuTpcsXw7z58PateF89epwDjBvXv/kqou854WN77397WAGjz66cfzxWD+O00O0YRvn4WPOnDm2YsWKfotRD7Nnh5ddK7NmwapVvZamfvKeF9LvpTFe68dxKkTSpXluh7zHMKjcckt714edqp53vNaP4/QQH2MYVMbG2rs+7OQ9bzvPPF7rx3F6iCuGQWXhQhgZab42MhKuj0fynjft3rRpMHVqenzHcbrCFcOgMm8eLF4cbOZS+Fy8ePwOrOY9b9q9JUvgpJMmTv04Tg/xwWfHcZwJRtHgs/cYHMdxnCZcMTiO4zhNuGJwHMdxmnDF4DiO4zThisFxHMdpYqhnJUm6CyjpK6ESZgB397C8bhgWWYdFTnBZ68JlrYc8WWeZ2cyshEOtGHqNpBV5U7wGiWGRdVjkBJe1LlzWeuhGVjclOY7jOE24YnAcx3GacMXQHov7LUAbDIuswyInuKx14bLWQ8ey+hiD4ziO04T3GBzHcZwmXDE4juM4TbhiaEHSEkl3SlqZcX8rSWdK+qOkqyUd2WsZoxxPlnSupGuiHEenxJGkr0m6UdKVkvYeYFnnRRmvknSRpD0GVdZE3OdIekzSG3opY6L8UrJK2l/SFTHO+b2WM8pQ5jcwKP+tTSX9PiHHp1PibCLpv+J/63eSZvde0tKyHhPr/UpJ50iaVZixmXlIBGA/YG9gZcb9jwGfj8czgXuBaX2Qcztg73i8BXA9sEtLnFcBZwMC9gV+16c6LSPrC4AnxONXDrKs8d5k4NfAWcAbBlVWYGvgGmAsnj9xgGUdlP+WgOnxeCrwO2DfljjvAU6Mx4cC/9Wnei0j60uAkXi8oIys3mNowcwuIPwgM6MAW0gSMD3GfawXsjUJYXa7mV0Wj/8KXAvs0BLtIOB7FrgE2FrSdj0WtZSsZnaRmd0XTy8BduytlI/LUaZeAd4P/Bi4s4fiNVFS1jcDp5nZLTFeX+QtKeug/LfMzB6Kp1NjaJ2lcxCwNB7/CJgb5e4pZWQ1s3PNbG08LfXfcsXQPicAzwJuA64Cjjaz9f0UKHZj9yK0FpLsANyaOF9D+kuuZ+TImuQoQk+nr2TJKmkH4HXAN3svVTo59boz8ARJ50m6VNJbey1bKzmyDsx/S9JkSVcQFP+vzCzzv2VmjwEPAKO9lTJQQtYkpf5brhja5xXAFcD2wJ7ACZK27JcwkqYTWq4fNLMH+yVHGcrIKuklhB/vh3spW4ocebJ+FfhwvxsEDQpknQLsA7ya8Nv9pKSdeyzi4xTIOjD/LTNbZ2Z7ElrXz5W0az/kKENZWSUdDswBvliUpyuG9jmS0DU3M7sRuBl4Zj8EkTSV8CdbbmanpUT5M/DkxPmO8VrPKSErknYHvgMcZGb39FK+FjmKZJ0D/EDSKuANwCJJB/dQxMcpIesa4Bdm9rCZ3Q1cAPRrYL9I1oH5bzUws/uBc4EDWm49/t+SNAXYCujbbxZyZUXSy4CPA681s/8tyssVQ/vcAswFkLQt8Azgf3otRLRnfhe41sy+nBHtDOCtcXbSvsADZnZ7z4SMlJFV0hhwGvAWM7u+l/K1yFEoq5k9xcxmm9lsgn35PWb2kx6KCZT+DfwUeJGkKZJGgOcR7Ps9paSsg/Lfmilp63i8GfBy4E8t0c4AjojHbwB+bXF0t5eUkVXSXsC3CEqh1BiTr3xuQdIpwP4El7V/AY4lDOhgZidK2h44mTDLQsDnzGxZH+R8EfAbgi22YdL4GDCWkFUEu+0BwFrgSDNbMaCyfgf4Jza4UX/M+uDFsoysLfFPBn5mZj/qoZiNskvJKulDhNb4euA7ZvbVQZR1gP5buxMGlicTGs+nmtlnJH0GWGFmZ0jaFPi/hLGSe4FDzawfSqyMrP8N7AY0GoW3mNlrc/N1xeA4juMkcVOS4ziO04QrBsdxHKcJVwyO4zhOE64YHMdxnCZcMTiO4wwJKnDymRL/kITjwu+XLsdnJTmO4wwHkvYDHiL4QMtdjS1pJ+BU4KVmdp+kJ5Zdx+A9BmdCIWmdggvqldHFc2Nx0GxJj8R710g6UdKkeG9nSWdJukHSZZJOjQuw0vLfX9LP4vHbJK2Pc80b91dGX0FIWqXgZvyqWOZn4/z4Vnka4a2StpB0U/zTI2lqTP+8OuvNGQzSnHxKepqk/6fgC+s3khqrxd8JfKPhnLIdB4quGJyJxiNmtmdsbd0LvDdx76boc2Z3YBfg4Pii/jnwTTPbycz2BhYR3EKXYQ3BFUEWLzGz3YDnAk8lrFBtkicRvhc9k36UsHAR4F+Biwocpznjm8XA+81sH8LvYVG8vjOws6QLJV0iaSNXGVlMqUFIxxkWLiYogSbM7DFJFwFPJ7itvtjMzkzcP6+NMn4G7CfpGWZ2XVYkM3tI0ruBWyVtk5ehmZ0q6ShJ/wa8m7D61pmARKeELwB+qA1evzeJn1OAnQieHHYELpC0W/SplIv3GJwJiaTJBL88Z6TcG4n3rgJ2BS7toqj1wBcI7h9yid5Gbyb8mQGe1mJK+odE9KOBzwOfNbO8/UOc8c0k4P6WnuWz4r01wBlm9qiZ3UzYHGmnzJxaMnWcicRmCr7r7wC2BX6VuPe0eO9C4OdmVtWeEN8H9pX0lBJxk5u9tJqSfpO4dwDB983AuoN26qfRmJD0Rnh8O9+G99yfEHoLSJpBMC2V8ufkisGZaDwSxxFmEV7CG40xmNleZvapeO1qwn4GHRM3cjmOgj0mJG0BzCa07PLibQ98gDAu8ark4LYzvolOPi8GniFpjaSjgHnAUZL+SPi9HhSj/wK4R9I1BHfcHyrrzt7HGJwJiZmtlfQB4CeSFuVE/T7wUUmvNrOfw+NTBu81s1JzySMnA/9G2O94I6KteBHwkzi1cKucvL4C/KeZrZF0DPANSfv1w+2z01vM7LCMWxsNLMffwzExtIX3GJwJi5ldDlwJZP3ZMLNHgNcA74/TVa8hbAR/V5tl/R34GvDEllvnxsVKvyfsR/CuxL3WMYYPSHo5wVX1d2O+ZwL3AX3fstMZP/gCN8dxHKcJ7zE4juM4TfgYg+N0gKRXEKaLJrnZzF7XD3kcp0rclOQ4juM04aYkx3EcpwlXDI7jOE4Trhgcx3GcJlwxOI7jOE38fx/XPOEvOBFPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nAssTfi8mbAO5QTzfIv7uCFwDbABsT9j6sC8vDzeO9yjDw2aDg2ZS+O1VQ2yr7qPdz2ei/B9OYehm43jclvJnZrZTPD8bWGJVWxxKOiEqsf+N5+cBJ5nZ77PSdxuH4zhOeXrNxrED8EJJl0n6jaTnRf9tgNsT4e6IfushaYGkUUmja9asabG4juM4TjXtVhxTgZnA7sBxwNmSVCYBM1tiZvPMbN7s2XUvJ+84juPUSbsVxx3AD2M32+XAOmAWcCewXSLcttHPcRzH6TLarTh+TDCQI2kHYDpwD3AO8AZJG0jaHngacHmbZXMcx3EK0LIdACWdCewDzJJ0B/ARYCmwNA7R/SdwRLTwXxcN59cDjwNvN7MnWiWb4ziOUz8+c9xxHGeS0WujqhzHcZwexxWH4ziOUwpXHI7jOE4pXHE4juM4pXDF4TiO45TCFYfjOI5TClccjuM4TilccTiO4zilcMXhOI7jlMIVh+M4jlMKVxyO4zhOKVxxOI7jOKVwxeE4juOUwhWH4ziOU4rM/Tgkzcy6bmb3NVccx3Ecp9vJ28jpCsAAAXOA++Px5sAqYPuWSuc4juN0HZldVWa2vZn9G/Br4CAzm2VmA8CBwC/bIaDjOI7TXRS1cexuZj+vnJjZucCerRHJcRzH6WaK7jn+F0kfBIbj+XzgL60RyXEcx+lmirY43gjMBn4E/DAevzErgqSlklZLWp7wO0nSnZKuju6AxLUTJN0i6SZJ+5W/FcdxHKcdFFIcZnafmR0L7GVmc83s3QVGVJ0OvKKG/8lm9pzofg4gaUfgDcCzYpyvSOorfBdOcxgZgaEhmDIl/I6MdD7dVsnkjOHP2CmLmeU6gj3jemBVPN8F+EqBeEPA8sT5ScD7a4Q7ATghcX4esEde+rvuuqs5TWJ42Ky/3wzGXH9/8O9Uuq2SyRnDn/GkBBi1AmV/mivaVXUysB9wb1Q21wB716mr3iFpWezKelL02wa4PRHmjui3HpIWSBqVNLpmzZo6RXDWq2UeeyysXTs+zNq1sGhRdry82umiRcXSLRP32GMnVw25lS2CRv4fZ/JSRLsAl8XfqxJ+1xSIN8T4FseWQB+hi2wxsDT6nwIcmgh3GnBIXvre4qiTWrXMNCdlx8urnUr56ZaNW+0mcg251S2CRv4fp2ehTS2O2yXtCZikaZLeD9xQh5L6q5k9YWbrgK8Du8VLdwLbJYJuG/2cVlCrlpnGnDnZ8fJqp8n4RfzLhikiQy/T6hZBI/+PM2kpqjjeBryd0H10J/CceF4KSVslTl8DVEZcnQO8QdIGkrYHngZcXjZ9pyCrVhUL198Pixfnx8tKb/HikE5WumXiplH0nnqNep55GRr5f5zJSyPNlSwHnAncBTxGsFkcDXwbuBZYRlAWWyXCLwJuBW4C9i+Sh3dV1cngYO3uiYGBcE0Kv9XdIWnxBgez8xsezk63TNyBgfpk6FXqfeZlaOT/cXoSGuyqUkijNpK+RFirKk3pvKsJuqtu5s2bZ6Ojo50UoTcZGYEFC8Z3gfT3w5IlMH9+8+M1k26QoZ1Mtvt12oKkK8xsXr3x87qqRgkLHW4IzAVuju45wPR6M3U6zPz5oeAZHAQp/BYpiOqN10y6QYZ2Mtnu1+kJMlsc/wok/YEw+e/xeD4N+K2Z7d5i+TLxFofjOE55Wt3iqPAkYEbifJPo5ziO40wyii5y+AngKkkXEvbj2JswC9xxHMeZZBRSHGb2TUnnAs+PXseb2d2tE8txHMfpVgopDkmV5UXuj787SNrBzC5ujViO4zhOt1K0q+q4xPGGhBnfVwAvabpEjuM4TldTtKvqoOS5pO2Az7dEIsdxHKerKTqqqpo7gGc2UxDHcRynNyhq40jOIJ9CmAB4ZauEchzHcbqXojaO5Cy7x4EzzeySFsjjOI7jdDlFbRxntFoQx3EcpzfIVBySrqX2IocCzMx2bolUjuM4TteS1+I4sC1SOI7jOD1DpuIws5UAcXOlu8zsH/F8I8I2sI7jOM4ko+hw3O8B6xLnT0Q/x3EcZ5JRVHFMNbN/Vk7ise/H4TiOMwkpqjjWSHpV5UTSwcA9rRHJcRzH6WaKzuN4GzAi6RTCiKrbgcNbJpXjOI7TtRRqcZjZrXG3vx2BZ5rZnmZ2S1YcSUslrZa0vMa190kySbPiuSR9UdItkpZJmlvPzTiO4zitp+iSIxsA/w4MAVMlAWBmH8uIdjpwCvCtqrS2A14OrEp47w88LbrnA19lbO8Px3Ecp4soauP4CXAwYbmRRxIulbhXx301Lp0M/BfjJxYeDHzLAn8ANpe0VUHZHMdxnDZS1MaxrZm9otHMolH9TjO7ptJqiWxDsJtUuCP63VUjjQXAAoA5c+Y0KpLjOI5TkqItjkslPbuRjCT1AycCH24kHTNbYmbzzGze7NmzG0nKcRzHqYOiLY69gCMl3QY8Sn1rVT0F2B6otDa2Ba6UtBtwJ7BdIuy20c9xHMfpMoq2OCrG65cDBxHWsDooM0YVZnatmW1hZkNmNkTojpprZncD5wCHx9FVuwMPmtl63VRODzIyAkNDMGVK+B0ZaX46zcqjlfSCjI5TkLzVcWfGw7+VTVjSmcA+wCxJdwAfMbPTUoL/HDgAuAVYCxxVNj+nCxkZgQULYO3acL5yZTgHmD+/OelAc/JoJc16Do7TLZhZqgNuA/4cf6vdn7PitsPtuuuu5tTB8LDZ4KCZFH6Hh1uTz+CgGazvBgfLyZKVTtq1yvVW3VuSvHso8hwcp40Ao9ZA2dvRgr9R54qjDoaHzfr7xxdg/f2tKWCl2gWmVE6WrHTSrrX63ioUuYe85+A4baZRxVHIxiHp6KrzPkkfaWLDx2kXixaNdZlUWLs2+DebtOHSFf+ismSlkzcku1X3VqHIPeQ9B8fpMYoax/eV9HNJW0naCfgDsGkL5XJaxapV5fwbYfFi6O8f79ffH/zLyJKVTq1reek1kyL3kPccHKfXKNo0AV5PWBF3JfCCRpo5zXLeVVUH7e5vz+r/LyNLVjqVa1m2jlZR9B7aZVdynALQDhsHYSjupcDXgIuBU4H+RjJuhnPFUQfttHG0W5ZO3Fs3PU/HKUi7FMeNwL7xWMD7gOsaybgZzhVHnXRT7bfZsnTi3rrpeTpOARpVHAppZCNphpk9VOW3g5n9qQm9ZXUzb948Gx0d7aQIjuM4PYekK8xsXr3xCy05YmYPRaP4jsCGiUsdVRyO4zhO+ym6H8dHCLPAdyTM8t4f+B1Ve204juM4E5+iw3EPAfYF7jazo4BdgM1aJpXjOI7TtRRVHH83s3XA45JmAKsZv5qt4ziOM0nIVBySTo+Ho5I2B74OXAFcCfy+taI5juM43UiejWNnADM7Jp6fKukXwAwzW9ZSyRzHcZyuJE9x9Et6LmHuxjgkzTWzK1sjluM4jtOt5Nk4tgE+m+I+01rRnJ6jslmRBFOnht+imxalbXTUyAZIvnmS47SGrNmBwFWNzC5stfOZ411EraU3ii7BkbZsx8KF9S/n4UuBOE4qtHLmuKSrzOy57VJiZfGZ413E0FDY2S6NwUFYsaJc3L4+eOKJcmnlpVkkruNMcBqdOZ7XVXV8zOQgSUWH7jqTkbyly7Oup12rpTSK5JUVppVLrDvOJCFTGZjZL+Ph64GbJX1K0jNaL5bTc+RtSpR1Pe1aX199eWWFmTkzPU4jNppW4DYapxbd8F4U7dMCZgBvJWzi9HtgAbBpRvilhImCyxN+/w0sA64GfglsHf0FfBG4JV6fW0Qmt3F0Ec22cUybZrbJJvlp1VqZdnjYbGCgtizTp4+FScarZU/ppG3EbTROLZr0XtDOPceBAeDdwArgXOBm4J0pYfcG5lYpjhmJ43cBp8bjA2J6AnYHLisiz6RUHNWF4sBAcWNxq5cvT26o1Ndn/9rQqKx8AwOhgK8uwKvvtdZHNH16UDpZ+5APDKwfL2/v8lZuBlWLdm+45fQGTXov2qI4gFcBPwKuBY4Dtoj+/cCKjHhDScVRde0E4Kvx+GvAGxPXbgK2ypNr0imO4eHaBeq0afWNWurWDZOKfhxZu/4120nNubeipCmydsvhdBdNei/apTjOAPZOubZvRrz1FAewGLgdWA7Mjn4/A/ZKhDkfmJeS5gJgFBidM2dOqYfV89S7PWqRgrhMi6QZtZ7qFsbAwFjeRQvvvFZCM523OJxuoJdaHCEfBoGXxuONsuwbiTh5LY6PWknFkXSTrsVRtkactxd3JU7ZFkTRWk9a11WWPaGSTpGPo54WR39/uv0jLd922xbSbDST2cbhuywGesnGAbwF+CNwazx/GnB+gXhZimNO5Zp3VRWkUvgWKVSzjNXVccrWYoq2YNLyL9JSqA5T6+MoauOopJW0xaRNOKzXRtMs0p5bUVvWRMQHCoynCUq0XYrjamA6iZnkwLUF4o1THMDTEsfvBL4fj19ZZRy/vIhck05xZBW01S9PXm08+eGV7Tct8iE3w/5Q5OPIMtJnxe3WGqx3Ua2PP5Om0y7FcVn8vSr+TgWW5cQ5E7gLeAy4Azga+EG0bSwDfgpsE8MK+DJwK8EAn9tNZZNRcaR9QAMD64fNqtVXF5T1fJh5BW+j9ofJWii4UXx9/Jk0nXYpjk8BJwI3Ai8jjLBa3EjGzXCTTnGUabKXUQa10q3u3ikqX0WZZHWrJdPPaw01m1rzN7qp5eG16/XxZ9J02qU4pkQ7x/eA78djNZJxM9ykUxxmxbtYyvYLJw3ZRewLRfLLUgzJArt6VFUrlUaejJ3uO/f+/PXxZ9J02jaqqhvdpFQcZainH7/e2l1avErLo1PG5iIydltNtlvtL53En0lTaVRxZK6OW0HSC4CTCENyp0abhJnZv+VGbiG+Om4LmDIlFJ/VSLBuXfPjtZM0GavpJpkdpwW0enXcCqcBnwP2Ap4HzIu/zkQjbXHAehcxLLIgYbsoKks3yew4XUhRxfGgmZ1rZqvN7N6Ka6lkTmdYvBj6+8f79fcH/1bEaye1ZKym22R2nC6kqOK4UNKnJe0haW7FtVQypzPMnw9LloQNj6Twu2RJ8G9FvHZSS8aFC7tbZsfpRooYQoALa7gLGjGuNMO5cbyKNANi1rpQvWZkdCOp0+30wDuKj6qaZGQph6L7dhedr9FtH0CZYZndJrszOeiRocNtURzAZgTj+Gh0nwU2ayTjZrhJpzgWLlx/joU0fo2lRlzyBe/GD6DoUOFulN2ZHPTIZMVGFUfR4biVpULOiF6HAbuY2Wub12lWnkk1HHdkBA47LH3Ia4H/sRCDg7BiRdiScuXK9OudoOiQ326U3Zkc9MKwdNo3HPcpZvYRM/tzdB8FOjqHoy20em/fMntcL1qUrhzM0vfnLsuqVeN/q1m5snP7HBcd8psme5p/s+mGPaGdztALw9KbQZFmCWGP8eR+GS8Aft9IU6cZrqVdVa3u7ii7R3e7Ni3KW2q9k10/Rf+TTnYXeDfZ5KZH/n/aZON4DnANYa/xlcBVhK6qias4Wl345BXMZTctGhxM36Ao6aZMGQuXtSZVmf082knRJdM79fH2SB+300J6YGBGWxTHvwLDDGBGIxk207VUcbR6Kee8FkSt3fTyWih5hX114Zn3ghfdQbAb6dTH60uAOz1Ao4oj08Yh6b1JB7wZeHPifOLS6r7Kskt4JCevwZhNIzlprXqC28BAcGmT2+bPD8bidevCb/XEt8r1Sp5l76EekvaBWbOCq2Ur6FY7wmTp43YmN1laBVgHXAksBj4MfCTpGtFYzXCTysbRSdrV9VO0xZQnTye7qnqkj9uZ3NDKripgF+AThK1jTwNeShfsw1FxLZ/H0erujmRXUDcsO55FO7p+isxFGRzMtyN02s7QA33czuSmUcVRaB4HgKQ9gTdG5XG8mZ3TvHZPfUyqeRzNZGQkDO9dtSp0oRxwAPz852GobV8fPPFE6J5avDh93abqNLLCFqXIsudS+K0VrjJWvkfG0jtOp2jLPA5Js4HnAs8m7B++ut4MnQ4zMgILFgQlYRZ+v/rVsQlzTzwRfleuhKOOgk03DQWuFApkKdgd3vSm8WksWFDbznDMMWNzVKZODedp9okidoA5c7LtCCMjId2s691oG5ks+POfGGQ1R4A3AWXCs2UAAByISURBVL8ALgLeAWxRtCkDLCUomOUJv08T9i1fRti3fPPEtROAW4CbgP2K5DHplhxpBs1YmiSrGynZTbPxxrXDTZky/rzoqDDJbN99a6ebty5X2nW3P+TTrK43t/90DbTYxrEuFvI/je6cpMuJuzcwt0pxvByYGo8/CXwyHu9ImCeyAbA9cCvQlye8K446aPVEwqL7jqfZH4oonloua72uvr5wvWJHqnW9mfaIdtiu2mVHaWZh32nbk/MvWq04XpTlchOHoaTiqLr2GmAkHp8AnJC4dh6wR176rjjqoJUtjrSCuYirNc+hjKyVQrRRhdZoDbgdo+XaWXNvZmHvc1y6hpYqjkZdjuL4KXBoPD6lchzPTwMOSYm3gLhK75w5c5r8OJtMM5v4aUupJ2eLDwzk51FkRngnXK2CqEzrqPJsmqHQGqkBl10RoJl5tKLm3szC3lscXUOrWxzPAM4F/g94CnA68ABwOfDM3MRTFAewKNo4KqO6CiuOpOvqFkezaoVZ+2xMm7b+Rzh9ejHlkbY8ScX+UKabqKxLs3FUU7bFkfasysrXSA247IoAzcyjFTX3Zhb2buPoGlqtOC4GDiIMw10JvAFQ9Ds/N/EaigM4krBoYn/Cb+J1VWUVemVaH/V0LRX5qOudCwHlavBTpowpioqtoWhLrGjraNq02rsdJo31zX5+9f5fvdbiaHZh73NcuoJWK46rEse3VF27MjfxKsUBvAK4HphdFe5ZVcbxP/e8cTyv5ln04yurNIrWPPNqrVnXi3YhFek6y6O6oFm4sDndc60aZTXRbByV/Lywn1C0WnEsSxwfU3Wtpu0icf1M4C7gMcLcj6MJw21vJ8xEvxo4NRF+EWE01U3A/kWE72rFUXQWdB71GJxb2eLImrndjhpwvWTZiZpdKE6kUVXOhKTViuOtwCY1/J8KfL6RjJvh2qI46i1winSzFGkZ1NPiKFIbb2S9pzIG9k4UamW6wrzwdSYhLR9VBfQB72kkk1a5tqxV1UgXR17/eiMtg6Iu2f9f6/6KLKte63oZ20E7DaBFu3HcUOtMYtoyHBe4vJFMWuVarjjKDu9MUwR5tfdGWy55rtYEt2bUtoeHi3eltavbqqjh2IeGOpOYdimOk+OQ2RcSZoPPBeY2knEzXMsVR9lZ1lldT7UK6jK142oDcb0tkWYZhcsqtDJDRRtRakWHqvpkNGcS0y7FcWENd0EjGTfD9UyLw6zcMNGitd56lUc9LaZqY29Zo33RZT0a7ULK+s+a+ewdp4dpi+LoVtf1No68dBqtnQ8P16c4yuTbipnmWc+q0QK9iLy+4KEzyWlXi2PLOJv73Hi+I3B0Ixk3w9WlOMoahCvdQo3YCJrZcqmWM6uArJ6hXU++rVrbKu0em9GFlPxvsu7VR1U5k5R2KY5zgdcB18TzqcC1jWTcDFdacTQyBLURyiy+VyS/IrXqvr7mtJhatZpumiJodheS2zIcZz3apTj+GH+TM8mvbiTjZrjSiqORSW+NkJVuPbXeIq2AhQtD2EYnvuXllWfr2GSTcs+02crbbRmOsx7tUhwXAQOVZUaA3YHfNJJxM1xpxdHIMhuN0OzCMKsVUFkPqlkUWUIjS5567r2ZXUg+X8Nx1qNdimMucAnwYPz9E7BLIxk3w/VMi8OsuYVhu2vReUto5MnTaVtCp/N3nC6jXYpjg2jXeBawEzAN2KCRjJvhesbG0Wy6Tc5uk8dxnEzapTjWWwm3yOq4rXZtGVXVrYVft8nZbfI4jpNKo4qjspFSTSQ9GdgGGAb+k7AXB8CMuLLtM1Ijt4F58+bZ6OhoJ0VwHMfpOSRdYWbz6o0/Jef6fsBngG2BzwGfje49wIn1ZjphGRmBoSGYMiX8joz0lhxl49UK3y3PwHGc1lGkWQL8eyPNmla5rtqPo1v6+euVo2y8WuGnT19/O1u3dThO10GbbBxPZqLMHG8VzVgqoxl7SKTJMTDQ2MioovfrcyYmBm6zmtC0S3FMjJnjraSROSDN3EOi7EzvvLkYafKXycdnafcW3dJ6dlpGo4ojz8ZRYZaZnQ2si91bjwNPNKezrAco0m8/Z07tuNX+xxwDU6eCNOaOOALWrh0fbu1aWLRovN+iRbXDHXHEmGwzZ5a4sUQ+afIn06s8BykUJ0VJS7sWbiPpPGnvWfX76ExeimgXJsrM8XpoZmtg4cJyrYFKN0+lu6BI+Fp2hiItguHh2vGmT0/fO6RI3mVqqgsXrt+SydrwyrtTWoOv7zXhoYMzx3fOibMUWA0sT/j9B3AdoeUyryr8CcAtwE3AfkXkaoviKNP3n1eQld3DovoDLto9VLFnVOQYGMhXTmbp4QYHi61Z1UhhntVdNjDghvh24ut7TXjaojhCPuNnjhcIv3dUOEnF8Uzg6bEFMy/hvyNwDWGG+vbArUBfXh5tURzNrH01ojTKKI9q2YqsN5V3r3n5Nlobbdby7V64NY7bOCY8jSqOTBuHpNdWHPCqWOjvABwU/bK6wC4G7qvyu8HMbqoR/GDgLDN71Mxuiy2P3bLSbxtFbRdF6Osrdr2vL3yutTCDwcFgZ0hLr1q2+fNhyZIQL5nPxhvDP/4Bhx4a7C79/enp5d3vzJmN2SZWrSoXvtXpTGaS74sUfpcsCf6OQ/4EwO8DHwQOjO6ghDuwiXJsA9yeOL8j+q2HpAWSRiWNrlmzpokipLB48foFan9/8C/LggXp1/r7w/X+fngiY9zB4CCsWAHr1sEZZxSXbf78EM8MHn8cFi6ERx4J6UDI85FHQsFfK71az6HC9Onw0EOwcmVIf+XKcC9llEeaYpJgYKDxdJxyVN6XdevCrysNJ0lWcwR4NXAWMAp8CHhqmeYMMESiqyrhfxHju6pOAQ5NnJ8GHJKXftuG4zbTCLtwYW1bx8BAvi0izShf1micteXslCnpcdNWyc2yjRQlrTtt441rbzzlNg7HqRvaZBzfmLBW1U+A3wEvKhivqOI4ATghcX4esEde+l01j6MsZffyLqqw6lkBuNqVpVl2oOHh2kqosmuhj6pynKbQLsXRB7wS+DZwNQVHPZVQHM9ivHH8z3SLcbweirQC8loXZWvueXuQ5+05UnF9feXvN2tP9bIz4ZvRenEcJ5OWKg7gJcCSqCw+Q9UQ2py4ZwJ3AY8RbBZHA6+Jx48CfwXOS4RfRBhNdROwf5E8ulJxFF3Dqagr0j1Vqysnrfafl1/17oFFavVFWjFF577kye84TsO0WnGsi0rjFOBLwBeTrpGMm+G6UnE0Oqy0eg5GkcK2yBDdSlpllUbRYZlJBZM2X6W61eDrXTlOR2hUceTtx3FE6kXAzM7Iut5qunI/jilTQlFXD/39+cMeh4bCqKV60l20KD3uwADcc0+xvCoju9JIewbS2CiurHDVFHkujuMUptH9OKZmXaylGCRNATYxs4fqzXRCM2dO8YJ9YAA22STMPZgzJwx5zSscy85TGBwcS/eww9LD3Xff+n5peeXJkPYMqofKpoWr57k4jtM2Ci1yKOk7kmZI2hhYDlwv6bjWitaj1JrvMH06TJs23q+/H77whfJj5bPmO1SnPzw8Pt2sOQ61rtU7+bHo3Je0cPU8F8dx2keR/izg6vg7n7AD4DRgWSN9ZM1wXWnjMGvtgnxpdodaw1WTskCYo1HLfjBtWnGjtzTeFpJ2X83aX8RxnKZDm4bjXheVxfeIcziIe3N00nWt4mg1ZQrlvFFLAwPZhXXeirW+ppHj9ByNKo5M43gFSe8CjifMtXglMAcYNrMXNrf9U46uNI53E3mG9Dwjd1YalXWv6jGeO47TURo1jhdSHCkZT7WwoVPHcMWRQ96opepRTmXSqNhU0q7lpes4TsdoVHEUNY5vKek0SefG8x2BzKG6TheQZ8QusiBgloG8mSsHO47TMxTdOvZ0wvpRW8fzPwHvboVAThPJWtG26Aq/WSOkmrlysOM4PYPvOT6RSduHo8z+Cll7M/i+DY4zKSlqHL8I+HfgV2Y2V9LuwCfN7EUtli8Tt3E4juOUpy02DuC9wDnAUyRdAnwLeGe9mTotZGSk2E58lXBS2P1PSg9fNE3HcSYFmUuOVDCzKyW9iLB1rICbzOyxlkrmlGdkJOy8t3ZtOK/sxAfju4+qw1V2HKwVvmiajuNMGlq25/iEJq8GXraG3kiNPhn3iCPGCvgKa9eGxQ2TLFq0fri08LXC1kqzF/GWlOPUR9bsQOCbGW5pIzMPm+E6MnO8nh328pYESVsmvbLXRtZWrkX2s6jeyyJvGfZk+Gbt7tdt+Kx3ZxJDO2aOdysdMY7nLTWedl0aP1muyFLntUguMV50ifXqmdxlZpTPmgX33rt+mFrLsPcS9S4Z7zgTgLbNHJf0SsIWrxtW/MzsY/Vm3Aw6ojjy9poosx/H4GBYOrys8q4UbkXyqrWXxchIWGI97T6+/e2x8BNVcRTdM8RxJiDtmjl+KvB6wkgqAf8BDNabaU+TN1u6zKzpyn4TZansh5EWt68ve17F/PnpCsdsfPha+3Rk+fcKPuvdceqm6HDcPc3scOB+M/sosAfBSD75yJstXet69V4ZFSqbFKXN7k6jUrilyXLGGfl7WQym6P1q/4lawPqsd8epnyKGEOCy+PsHwrIjGwK35MRZCqwGlif8ZgK/Am6Ov0+K/iLsY34LsAyYW0Suji2rnresefX1hQvzDeqVPTPyDNfVBtx697MoahyeyEZk3wvEmaTQpv04PgRsDrwWuCu6/86Jszcwt0pxfAr4QDz+AGH2OcABwLlRgexeUVR5rqf246h3Y6OsUVWdkskLWMfpaRpVHJnGcUnPA243s7vj+eHAocCNwElmltnRLWkI+JmZ7RTPbwL2MbO7JG0FXGRmT5f0tXh8ZnW4rPR9yRHHcZzytNo4/jXgnzGjvYFPRL8HgSV15LdlQhncDWwZj7cBbk+EuyP6rYekBZJGJY2uWbOmDhEcx3GcRshTHH2JVsXrgSVm9gMz+xDw1EYyjs2l0pNIzGyJmc0zs3mzZ89uRATHcRynDnIVh6TKelb7AhckrhVa56qKv8YuKuLv6uh/J7BdIty20c9xHMfpMvIUx5nAbyT9BPg78FsASU8ldFeV5RzGdg48AvhJwv9wBXYHHsyzb7ScRtcx6oV1kJotYy/cs+M4jZNnPSeMcnoNsHHCbwdyhswSlM5dwGMEm8XRwABwPmE47q+BmTY2HPfLwK3AtcC8Ipb9lo2qanQIai8MYW22jL1wz47jmFmLR1V1Oy0bVdXoOka9sA5Ss2XshXt2HAdo41pV3UjLFEej6xj1wjpIzZaxF+7ZcRygfTsATi4aXWajF5bpKCtj1o6BIyNBcaSl57YPx5lQuOKoRaPrGPXCOkhlZKzsAljpikruGHjUUfCmN435Vad3wAFjcc3GdhB05eE4vUsjBpJOu5YuOdLoMhu9sExHURkr62iVcX1949fgqnaDg227TcdxxoMbx33JkZZTZo+RCnn7k7jtw3E6hts4nNZTj20mb3+SbrL3OI5TClccTj5Ze4ZMmwbTp4/3y9ufpNvsPY7jlMIVh5PP/PlhJ8HKJk99feF3cBC++U1YujQc19p1MBk3a1dCx3F6BrdxOI7jTDLcxuE4juO0FVccjuM4TilccTiO4zilcMXhOI7jlMIVh+M4jlMKVxyO4zhOKVxxOI7jOKVwxdFtFFmCPGuJ81bm6ziOA0zttABOgsry5WvXhvPKEuQwNtO6OkxyifPqsM3M13EcJ+Izx7uJItuvpoWpFbaZ+TqOM2HoyZnjko6VtFzSdZLeHf1mSvqVpJvj75M6IVtHWbUq3z8tTNHr9ebrOI4TabvikLQT8BZgN2AX4EBJTwU+AJxvZk8Dzo/nk4siS5DnLUfeyBLozUjLcZwJTydaHM8ELjOztWb2OPAb4LXAwcAZMcwZwKs7IFtnKbIEedYS5/UuV+5LnzuOU4JOKI7lwAslDUjqBw4AtgO2NLO7Ypi7gS1rRZa0QNKopNE1a9a0R+J2UWQJ8qwlzutdrtyXPnccpwQdMY5LOho4BngEuA54FDjSzDZPhLnfzDLtHBPOOO44jtMGetI4bmanmdmuZrY3cD/wJ+CvkrYCiL+rOyGb4ziOk02nRlVtEX/nEOwb3wHOAY6IQY4AftIJ2RzHcZxsOjUB8AeSBoDHgLeb2QOSPgGcHbuxVgKv65BsjuM4TgYdURxm9sIafvcC+3ZAHMdxHKcEvlaV4ziOU4qeXnJE0hpCt1a7mAXc08b86qVX5ASXtVW4rK1hosg6aGaz6024pxVHu5E02sgQtnbRK3KCy9oqXNbW4LIGvKvKcRzHKYUrDsdxHKcUrjjKsaTTAhSkV+QEl7VVuKytwWXFbRyO4zhOSbzF4TiO45TCFYfjOI5TClccVUhaKmm1pOUp1zeT9FNJ18QdDI9qt4xRju0kXSjp+ijHsTXCSNIXJd0iaZmkuV0s6/wo47WSLpW0S7fKmgj7PEmPSzqknTIm8i8kq6R9JF0dw/ym3XJGGYq8A93ybW0o6fKEHB+tEWYDSd+N39ZlkobaL2lhWd8bn/sySedLGmw4YzNzl3DA3sBcYHnK9ROBT8bj2cB9wPQOyLkVMDceb0pYYXjHqjAHAOcCAnYnbKDViWdaRNY9gSfF4/27WdZ4rQ+4APg5cEi3ygpsDlwPzInnW3SxrN3ybQnYJB5PAy4Ddq8Kcwxwajx+A/DdDj3XIrK+GOiPxwubIau3OKows4sJL2xqEGBTSQI2iWEfb4ds44Qwu8vMrozHfwNuALapCnYw8C0L/AHYvLJ0fTspIquZXWpm98fTPwDbtlfKf8lR5LkCvBP4AR1c/r+grP8J/NDMVsVwHZG3oKzd8m2ZmT0cT6dFVz2KKLlj6feBfaPcbaWIrGZ2oZmtjadN+bZccZTnFML2t38BrgWONbN1nRQoNpOfS6htJNkGuD1xfge1C8G2kSFrkqMJLaWOkiarpG2A1wBfbb9Utcl4rjsAT5J0kaQrJB3ebtmqyZC1a74tSX2SriZUDH5lZqnfloUtsB8EBtorZaCArEma8m254ijPfsDVwNbAc4BTJM3olDCSNiHUfN9tZg91So4iFJFV0osJL/fx7ZSthhxZsn4eOL7TFYYKObJOBXYFXkl4dz8kaYc2i/gvcmTtmm/LzJ4ws+cQaue7SdqpE3IUoaiskg4F5gGfbjRPVxzlOYrQ9DczuwW4DXhGJwSRNI3wEY6Y2Q9rBLmTsJ97hW2jX9spICuSdga+ARxsYZn9jlBA1nnAWZJWAIcAX5H06jaK+C8KyHoHcJ6ZPWJm9wAXA50aeJAna9d8WxXM7AHgQuAVVZf+9W1JmgpsBnTsnYVMWZH0UmAR8Coze7TRvFxxlGcVcd8QSVsCTwf+3G4hYn/qacANZva5lGDnAIfH0VW7Aw+a2V1tEzJSRFaF3SB/CBxmZn9qp3xVcuTKambbm9mQmQ0R+rePMbMft1FMoPA78BNgL0lTJfUDzyfYF9pKQVm75duaLWnzeLwR8DLgxqpgyR1LDwEusGh9bidFZJX0XOBrBKXRFBuXzxyvQtKZwD6EJYn/CnyEYHDCzE6VtDVwOmGUiIBPmNlwB+TcC/gtoS+40mVyIjAnIasI/cavANYCR5nZaJfK+g3g3xlbJv9x68AqpEVkrQp/OvAzM/t+G8Ws5F1IVknHEWrz64BvmNnnu1HWLvq2diYYvvsIleuzzexjkj4GjJrZOZI2BL5NsNXcB7zBzDqh5IrI+mvg2UCl0rjKzF7VUL6uOBzHcZwyeFeV4ziOUwpXHI7jOE4pXHE4juM4pXDF4TiO45TCFYfjOE6PoJxFWGuEf11iYcnvNE0OH1XlOI7TG0jaG3iYsAZd5mx2SU8DzgZeYmb3S9qiWfM4vMXhtBVJD+eH6m7iuk+jifN5ki5qYtrz4vEKSbPqSGOFwvL0V0f3xWbI5nSeWouwSnqKpF/Etch+K6ky2/4twJcri4c2c4HLqc1KyHEmGVtI2t/MOr4YYwovjkuMOBOfJcDbzOxmSc8HvgK8hLDAJZIuIUwQPMnMftGMDL3F4bQdSZvEDWWujDXjg6P/kKQbJY1IukHS9+MyGUj6sKQ/SlouaUmcFV+poX9SYTObP0l6YfTvk/TpGGeZpLdG/60kXRxr4ssT4V8u6fdRpu8pLMaXxacJa/9U39uGkr4Z7+sqhUUbs/w3knRWvN8fARulPLND4z1eLelrkvpKPvOp8VnsE8//V9LieLxC0qeibJdLemr0ny3pBzHeHyW9IPqfFPvaL5L0Z0nvKiOL0zzie7on8D2FFXK/Rph5D6Fh8DTCShhvBL6uuDxJwxTZtMOdu2Y5Qv/sVGBGPJ8F3EJYYmKIsJfAC+K1pcD74/HMRBrfBg6KxxcBn43HBwC/jscLgA/G4w2AUWB74H3AoujfR9hUaBZh8b+No//xwIcz7uEiwkKHFxA2yZkHXBSvvQ9YGo+fQVh/acMM//cm/Hcm7D8xL56viLI9E/gpMC36fwU4PEO+FYSlPa6O7j3R/1mEdapeClxF3CQphq88k8MJS6gAfAfYKx7PIawzBXAScGl8rrMIi/tN6/S7NVlc/E6Wx+MZwF0p4U4lLDNUOT8feF4zZPCuKqcTCPifaOhbR9jbYMt47XYzuyQeDwPvAj4DvFjSfwH9wEzgOkJhCmFxRIArCB8VwMuBnTW2retmhNrXH4GlCiu1/tjMrpb0ImBH4JLYkJkO/L7AfXwc+CDjl4DfC/gSgJndKGklocsgzX9v4IvRf5mkZTXy2ZewNPofo3wbkb+B1HpdVWZ2naRvAz8D9jCzfyYun5n4PTkevxTYUWP7E81ItMT+z8Iqq49KWk34/+7IkclpMmb2kKTbJP2HmX0vtsR3NrNrgB8TWhrfjLayHWjSopGuOJxOMJ+wNeiuZvaYwvLkG8Zr1cP8TGFBua8QauK3SzopER6gskz0E4y90wLeaWbnVWceFdYrgdMlfQ64n7ABzhvL3ISZXSDp44RteVuJgDPM7IQmpPVs4AFgiyp/q3E8hbAN6T/GCRMUSXJp7uRzd1qIEouwSrqDsAjrfOCrkj5IWJD1LOAa4Dzg5ZKuJ/xHx1mTtitwG4fTCTYDVkel8WJgMHFtjqQ94vF/Ar9jTEncE2u8h5DPecDC2LJA0g6SNpY0CPzVzL5O2PtjLmE7zRck+vY3VvHNjj4O/Ffi/LeED5mYxhzgpgz/i+N9orABz8418jgfOETSFjHczHgfpZD0WkJrbW/gS1X93a9P/FZaW78kbJFbif+csnk6zcXM3mhmW5nZNDPb1sxOM7PbzOwVZraLme1oZh+LYc3M3hv9nm1mZzVLDq8lOG1DYcObR4ER4KeSriXYHpL7B9wEvF3SUuB64KtmtlbS14HlwN2E7qY8vkHotroyNt/XAK8m1NaOk/QYwd5yuJmtkXQkcKakDWL8DwK5+4KY2c8lrUl4fYVQ+7uWYK840swelZTm/1VCV8INBPvDFTXyuD7WJn8paQrwGPB2xpagr8WFkp6Ix8sItpRPAPvGVtspwBcY21PiSbGb7FFC9waEbsIvR/+pBCX3trxn4kx8fAKg0zYk7QJ83cx2S7k+RDDMdu02nROR2FU4r9om4jhpeFeV0xYkvY1geP1gp2VxHKcxvMXhOCnEeRXbV3kfX8vg3gkkXUYYEpvkMDO7thPyOJMHVxyO4zhOKbyrynEcxymFKw7HcRynFK44HMdxnFK44nAcx3FK8f+4cHbgU04mwgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZmXVO0VpD35V0AWkAGYCPRsTfygvLzMw6pVBCIKnSncSD+e82kraJiEvKCcvMzDqlaNbQUVXv1wZ2IdUmemXbIzIzs44qmjX0xuppSVsAXy4lIjMz66jCnc7VWAI8v52BmJlZdxQtI6huYTyO1KDs6rKCMjOzzilaRrCg6v0y4LSIuLSEeMzMrMOKlhGcWnYgZmbWHa06nfsj9TudExARsX0pUZmZWce0eiJ4Q0eiMDOzrmnV6dxiAElbAn+NiMfz9DrAxuWHZ2ZmZStaffRMYEXV9PI8z8zM+lzRhGBCRDxZmcjvW45HIGlPSbdIWijp6DrLp0q6SNI1kq6X9LrioZuZWTsUTQjulbR3ZULSPsB9zTaQNB44AdgLmAHsJ2lGzWofB86IiB2BfYETiwZuZmbtUbQdwXuA+ZK+TqoxdCdwYIttdgEWRsTtAJJOB/YBbqpaJ3h6nIMNgbsLxmNmZm1StB3BbcCuktbL0/8ssNlmpASjYglPd2NdcRzwC0nvB9YF9qi3I0mzgdkAU6dOLRKymZkVVLSLibWAtwLTgQmSAIiI48d4/P2AUyLiC5JeDHxf0rYRUV0wTUTMBeYCzJw5s167BjMzG6WiWUM/Bh4mdT39RMFt7gK2qJrePM+rdiiwJ0BEXC5pbWAycE/BY5iZ2RgVTQg2j4g9R7jvPwBb5zYId5EKg/evWecO4FXAKZKeTxrr4N4RHsfMzMagaK2hyyRtN5IdR8Qy4AjgQuBmUu2gGyUdX1UD6UPAYZKuA04DDo4IZ/2YmXWQilx3Jd0EPBf4CylrqGt9Dc2cOTMWLFjQekUzM1tJ0lURMbPesqJZQ3u1MR4zM+shrXoffWZ++48OxGJmZl3Q6ongKlKjL9VZFsBz2h6RmZl1VKveR7fsVCBmZtYdhWoNSTq0Znq8pE+VE5KZmXVS0eqjr5J0vqRNJG0LXAGsX2JcZmbWIUX7Gtpf0juBPwKPAvt78Hozs8FQNGtoa+BI4EfAYuBdkiaWGZiZmXVG0ayhnwCfiIh3Ay8H/kzqQsLMzPpc0QZlu0TEI5CaEwNfkPST8sIyM7NOKVpG8EguJJ5B6hiu4tZSojIzs44pOh7Bp4DdSQnB+aQuJ34HfK+0yMzMrCOKlhG8jdRd9N8i4hDghaShJc3MrM8VTQgey6OGLZO0AWngmC1abGNmZn2gaUIg6ZT8doGkjYBvkfofuhq4vNzQzMysE1qVEWwPEBHvzdMnSfo5sEFEXF9qZGZm1hGtEoKJknakTu+jknaKiKvLCcvMzDqlVUKwGfAFGndD/cq2R2RmZh3VKiFYGBG+2JuZDbCitYbMzGxAtUoIPgog6Y2SnGiYmQ2gphf3iPhFfvtO4M+SPi/peeWHZWZmnVLoLj8iDgB2BG4DTpF0uaTZkjw4zaCZPx+mT4dx49Lf+fO7HZGZlaxwdk/uffQs4HRgE+DNwNWS3l9SbNZp8+fD7NmweDFEpL+zZzsxMBtwRQem2VvSOcDFwBqkbqn3IvU59KHywrOOOvZYWLp01XlLl6b5Zjawio5H8FbgSxFxSfXMiFhaO7C99bE77hjZfDMbCEXLCA4CFkvaA0DSOpXygYj4VaPtJO0p6RZJCyUd3WCdd0i6SdKNkn4wis9g7TJ16sjmm9lAKJo1dBipfOCbedbmwLktthkPnEAau2AGsJ+kGTXrbA18DHhJRLwA+MCIorf2mjMHJtYMRT1xYppvZgOraGHx+4CXAJXhKv8MPKvFNruQWibfHhFPkgqZ96lZ5zDghIh4MO/3nqKBWwlmzYK5c2HaNJDS37lz03wzG1hFywieiIgnpdTlkKQJpL6GmtkMuLNqegnwopp1tsn7uxQYDxwXET+v3ZGk2cBsgKnOpijXrFm+8JsNmaJPBL+RdAywjqRXA2cC7Ri8fgKwNWkYzP2Ab+VxD1YREXMjYmZEzJwyZUobDmtmZhVFE4KjgXuBPwLvJo1b/PEW29zFqqOYbZ7nVVsCnBcRT0XEX4BbSQmDmZl1SKGsoTxM5bfyq6g/AFtL2pKUAOwL7F+zzrmkJ4HvSppMyiq6fQTHMDOzMSqUEEh6CXAcMC1vIyAi4jmNtomIZZKOAC4k5f+fHBE3SjoeWBAR5+Vlr5F0E7AcOCoi7h/LBzIzs5FRRKsyX5D0J+A/SeMVL6/M78ZFe+bMmbFgwYJOH9bMrK9JuioiZtZbVrTW0MMRcUEbYzIzsx5RNCG4SNJ/A2cDT1RmesxiM7P+VzQhqNT/r36s8JjFZmYDoGitoVeUHYiZmXVH0b6GNpT0RUkL8usLkjYsOzgzMytf0QZlJwP/AN6RX48A3y0rKDOrw6PHWUmKJgRbRcSncgdyt0fEp4GGbQjMRqQdF7hBv0h69DgrUdGE4DFJL61M5AZmj5UTkvWdsVyE23GBG4aLpEePsxIVbVC2A3AqsCGpVfEDwMERcV254a3ODcp6TOUiXH2RmjixePfV06enC3etadNg0aJiMbRjH71u3LiUyNWSYMWKzsdjfadZg7JCCUHVjjaAlQPZd4UTgh4z1otwOy5ww3CRHIbEzko16pbFkj7YYD4AEfHFMUdn/W2s4xxPnVr/AjeScSfasY9eN2dO/Scvjx5nbdCqjOB/gAOAScB6wPo1Lxt2Yx3nuB3DYw7DEJsePc7KFBENX8ALgc8C1wLfAfYgZyd167XzzjtHX5o3L2LatAgp/Z03r9sRtce8eRETJ0akzJn0mjhxZJ+vHedmUM+vWZuQen2ue10tXEYgaTfS2AF7AB+N1I10x/VlGcFYC1R73fz5qfbKHXekJ4E5cwbjc5kNkDEXFkuaQmpI9nbgKeATEXFFW6MsqC8TAhf0mVmXjaWw+N9ICcDawFnAOyLinvaHOODGWqBqZlaiVp3OfRu4AVgMvJY0mtjKhRGxd3mhDZBhqNViZn2rVULgXkfbwVX/zKyHNU0IIuI3nQpkoFUKTl2gamY9qGk7AknPk3SBpJ9J2krSKZIekvR7Sc/vVJADYdasVDC8YkX660TAzHpEqwZlc4ETgXnAr4GfA88APgN8vdzQzMysE1olBOtHxE8i4jTgqYg4PbdN+AkpQTAzsz7XKiEYX/W+tl+hNdsci5mZdUGrhOAESesBRMSJlZmSngv8sszAzMysM5omBBHxTdKgNP9ZM39hRHyg1MjMzKwjWo5QFhHLSX0MmZnZACo6VOWlkr4u6f9I2qnyarWRpD0l3SJpoaSjm6z3VkkhqW4/GGZmVp6iCcEOwAuA44Ev5Nf/NNtA0njgBGAvYAawn6QZddZbHzgSuLJ42GY2JmMZZ9oGTqsuJgCIiNF0NbELsDAibgeQdDqwD3BTzXqfAT4HHDWKY5jZSNV2i754cZoGN3QcUoWeCCRtLOk7ki7I0zMkHdpis82AO6uml+R51fvdCdgiIn7W4vizJS2QtODee+8tErJZf+rEnfqxx67a7xWk6WOPbf+xrC8UzRo6BbgQ2DRP3wqMqdaQpHGktgkfarVuRMyNiJkRMXPKlCljOaxZ76rcqS9enMZ6q9yptzsxcLfoVqNoQjA5Is4AVgBExDJgeYtt7gK2qJrePM+rWB/YFrhY0iJgV+A8Fxjb0OrUnfpYx5m2gVM0IXhU0iQgACTtCjzcYps/AFtL2lLSmsC+wMrhLSPi4YiYHBHTI2I6cAWwd0T02fBjZm3SqTv1OXNSN+jV3C36UCuaEHyQdBHfStKlwPeA/2i2QX5qOIKUpXQzcEZE3CjpeEke0MasVqfu1GfNSuNlT5sGUvo7KONn26gUHbN4LVJW0L8AAm4BxkXEE+WGt7q+HLO4GzygfP+prc0D6U7dF2lrg2ZjFhd9Irg8IpZFxI0RcUNEPAVc3r4Qra06Veho7eU7deuSpk8Ekp5NqvI5D9if9DQAsAFwUkQ8r/QIa/iJoIDp0+uPkTxtWhoUx8yGTrMnglYNyl4LHEyq8VPdDfUjwDFtic7az9UDzWwEWvU+empuVXxwRLyi6rVPRJzdoRiHSzsaFLl6oJmNwEg6nRtpy2IbqXbl7bt6oJmNQNGE4Lu0uWWx1dGuBkWNCh3BHY2Z2WrKbFlsI9XOvP1Zs1LB8IoVTxcQuyaRmdVRZstiG6ky8/bd0ZiZNTCWlsXvLy2qYVVm3r5rEpmVp8/Hdyg6HsHVkl5OVcvi3KjM2qnScKiMFsFTp9ZvW+CaRGZjMwDjO7RqUPaWZht3owqpG5SNkrsvMCtHnzTgHEuDsrOAa/MLnm5ZDKm8wG0J+kWZTxtmw2wAsl1bJQRvIXUfvT3wY+C0iFhYelRWjlmzfOE3a7cByHZt1bL43IjYF3g5cBvwBUm/y+UFZmY2AA04i9YaepxUXfQRYD1g7dIiMjPrJwPQa2zTrCFJryRlDe0C/BL4ikcQMzOr0efZrq3KCH4JXA/8DlgLOFDSgZWFEdF0lDIzM+t9rRKCQzoShZmZdU3ThCAiTq2dJ2kcsF5EPFJaVNbfPEymWV8pVFgs6QeSNpC0LnADcJOko8oNzfqSh8k06ztFaw3NyE8AbwIuALYE3lVaVNa/3LmdWd8pmhCsIWkNUkJwXu5nqHHfFDa8BqCVpdmwKZoQfBNYBKwLXCJpGqlNgdmqPEymWd8plBBExFcjYrOIeF0ki4FXlByb9aMBaGVpNmyKFhZvXDtmMXBQqZFZfyrSyrLP+243GzRNu6FeuVJKAL4LHBsRL5Q0AbgmIrYrO8Ba7oa6z7k7bLOuaNYNdaljFkvaU9ItkhZKOrrO8g9KuknS9ZJ+lcsebJC5VpFZzyltzGJJ44ETgL2AGcB+OUup2jXAzIjYnjT2wedHELv1o7HWKnK2klnblTlm8S7Awoi4PSKeBE4H9qleISIuiojK7eEVwOaFI7f+NJZaRW6sZlaKorWGriaNSbAb8G7gBRFxfYvNNgPurJpekuc1ciipsdpqJM2WtEDSgnvvvbdIyNarxlKryNlKZqVo1Q11ozGLt5HUtjGLJR0AzCQlNquJiLnAXEiFxe04pnXJWIbMdGM1s1K06n30jU2WtRqz+C5gi6rpzfO8VUjaAzgWeHlEPNEiHhsERfpur9dx3QAMCWjWi1r1PjqWbqj/AGwtaUtSArAvsH/1CpJ2JLVa3jMi7hnDsWyQ1FYxrZQFHHQQnHrq6lVP3VjNbEyKFhYj6fWSPiLpk5VXs/VzFdMjgAuBm4EzIuJGScdL2juv9t+koS/PlHStpPNG+TlskDQqCzj//PYMCeiaR2arKNqg7CRgIqlbiW8DbwN+HxGHlhve6tygbAiMG5dqBdWSYMWKse3bDdpsSLWjQdluEXEg8GBEfBp4MbBNuwLsa767bL8yO65zzSOz1RRNCB7Lf5dK2hRYBmxSTkh9xPXay1Fmx3WueWS2mqIJwU8lbURq+XsV8BfgtNKi6he+uyxHkY7rRsvdZJutpmlCIOlfJT07Ij4TEQ+RCnb/CJwJfKkTAfY0312WZ9YsWLQolQksWtS+/Ht3k222mlZPBN8EngSQ9DLgs3new+QGXkPNd5f9p8ynDbM+1SohGB8RD+T37wTmRsSPIuITwHPLDa0P+O6yP5X1tGHWp1omBHnsAYBXAb+uWtaqVfLgqa0hBIN9d1m0RtSw1Zwats9rgy8iGr5IXT9cCvyY1GV0pd3Bc4FLm21b1mvnnXeOrpg3L2LixIhUPyi9Jk5M8wdR0c/by+dl3ryIadMipPS3HTH18uc1awJYEA2uqy0blOWxBzYBfhERj+Z52wDrReqVtKO61qBs+vT6/dxMm5ayFwZN0c/bq+elrIZjvfp5zVpo1qCsUMviXtK1hKDM1q69SGo8v/rz9up5KeuC3auf16yFdrQstlY1hAYp33j+/MYJQe156NWaU2VV7e3Vz2s2Bk4IimpWQ2jQWhgfe2zju97aGlG9WnOqrAt2r35es7FoVHjQq6+uFRZHNC58nDZt1cLDymvatO7FOhZS/c8D9dcvo1B2rMos1O3Fz2vWAmMpLO41Pdn76KDlGw9KgWi9wW0GpWqv2Qi5jKBsncg37mQZxKBkf1Qajn3/+2n6Xe/q//IbsxI4IWiHsi+cnS6DGKRuGAat/MasDI3yjHr11dUygmZq840PP7x9+ciDVgYxUmPJk4HuDzEAAAuESURBVB/2c2eW0aSMwE8E7VLdf82cOWls3XbdhY6kKuQgVWOF+nf0hxwCkycX+4zuIdasJScEZWjnOAXz56cLXj21ZRCDmA1S71w+9RTcf3+xz+h6/2YtOSGoZ6x31e26C61c2JcvX31ZvTKITg6U0+4nj0b7K3LOmn3GQSn4NitTozyjXn2VXkbQjvrn7cqXbrSf8ePrx9Oo/r80suO2cvjhqx9rLHX0m53zRudgJJ/R9f7NmpYRdP3CPtJX6QlBOy7i7WrM1OzCXu/iNpLYR3txnDevcVyjLYBtFne9czmIhb9OrKxkTghGol131e34YTe6QE6aVD+hOfzw1edLaX5tbKNNqFrdoY/mc7Y659XnctKkiDXXHHsi20vctbV1gBOCkeil6ob1LhDNun+oVFttlW0zls/Y7PijvYCNNJ6xJLK9eOfdS985G1hOCEby42908a29qx7N8SHl71dne4xk21YX4crna3VRGctTT5E8+5FewNp1R9zq/9yrd97NzqVZmwx3QjCaH387C0Ob5XGPZJ9FL8BFLvJjuQMtkmc/msLp2uyfSZNWv6A3S1DLfhIqU+Wz1L7Gj+9uXN3Wi09v3dKGc9G1hADYE7gFWAgcXWf5WsAP8/Irgemt9jnihKDIj7/6AjNuXOML3Pjxze82J01aff1WF+96rxkzItZdd3Tb9uprtOeiyKvZ/wyeTliKxFfkibH2B1n7v68kSI0StFrN4irrItiqJXw7W8aP5iLWq09v3dCmc9GVhAAYD9wGPAdYE7gOmFGzznuBk/L7fYEfttrviBOCIgWRRWql1L6q/xHz5q1egOlX/74a/cjqfVfWXLN4Itdov0We9tp5ERzNd76dT8RF9tWrT2/d0KZz0a2E4MXAhVXTHwM+VrPOhcCL8/sJwH3k4TMbvdr+RFC0nnpZ+/CrN1/1fmTt+D83qsrbySqyo/0cozn+aC9inWoT0w/adC6aJQRltizeDLizanpJnld3nYhYBjwMTKrdkaTZkhZIWnDvvfeOLIpWLUvH0udMZVv3WzN46v1P2/F/rreP2t5eR7Jtu2Ioa7vRtrJ31yBP68C56IsuJiJibkTMjIiZU6ZMGdnGrbpUHsvJrGw7jF/OQVfvf9qO/3OjfVR3Wjht2si2bVcMZWw32ouYuwZ5WifORaNHhbG+6JWsoVZaPZavsUb9hlouI+iN13rrtX+fnS4jKHKcYSsjqGzrWkNJv9Yayhf224Etebqw+AU167yPVQuLz2i131IalDWqNTRp0upVF8daa6gyr1mbANcaav5ad91Vz//hhzfff6NaQ2uu+fT8btYaKnqcdur1WkPWds0SglLHLJb0OuDLpBpEJ0fEHEnH54DOk7Q28H1gR+ABYN+IuL3ZPntyzGIzsx7XbMziCWUeOCLOB86vmffJqvePA28vMwYzM2uuLwqLzcysPE4IzMyGnBMCM7Mh54TAzGzIlVprqAyS7gUWd+hwk0ltG/qBYy2HYy2HYy1Hs1inRUTdFrl9lxB0kqQFjapb9RrHWg7HWg7HWo7RxuqsITOzIeeEwMxsyDkhaG5utwMYAcdaDsdaDsdajlHF6jICM7Mh5ycCM7Mh54TAzGzIOSEAJJ0s6R5JNzRYvqGkn0i6TtKNkg7pdIw5ji0kXSTpphzHkXXWkaSvSloo6XpJO/VwrLNyjH+UdJmkF/ZqrFXr/qukZZLe1skYq45fKFZJu0u6Nq/zm07HmWMo8h3old/W2pJ+XxXHp+uss5akH+bf1pWSpvdonB/M5/x6Sb+SNK3ljhv1Tz1ML+BlwE7ADQ2WHwN8Lr+fQuoye80uxLkJsFN+vz5wKzCjZp3XARcAAnYFruzSOS0S627AM/L7vXo51rxsPPBrUo+6b+vVWIGNgJuAqXn6WT0ca6/8tgSsl9+vAVwJ7FqzzntZdfyUH/ZonK8AJub3hxeJ008EQERcQvoCNlwFWF+SgPXyuss6EdsqQUT8NSKuzu//AdzM6uNA7wN8L5IrgI0kbdLhUAvFGhGXRcSDefIKYPPORrkyjiLnFeD9wI+AezoY3ioKxro/cHZE3JHX60q8BWPtld9WRMQ/8+Qa+VVbk2Yf4NT8/izgVTnujikSZ0RcFBFL82Sh35UTgmK+DjwfuBv4I3BkRKzoZkD5sXRH0h1Btc2AO6uml1D/otYxTWKtdijpSaarGsUqaTPgzcA3Oh9VfU3O6zbAMyRdLOkqSQd2OrZaTWLtmd+WpPGSriUl9P8bEQ1/WxGxDHgYmNTZKAvFWa3Q78oJQTGvBa4FNgV2AL4uaYNuBSNpPdKd6Qci4pFuxVFEkVglvYL0hf1oJ2OrE0ezWL8MfLTbNwAVLWKdAOwMvJ703f2EpG06HOJKLWLtmd9WRCyPiB1Id9C7SNq2G3G0UjROSQcAM4H/brVPJwTFHEJ61I6IWAj8BXheNwKRtAbpRzU/Is6us8pdwBZV05vneR1XIFYkbQ98G9gnIu7vZHw1cbSKdSZwuqRFwNuAEyW9qYMhrlQg1iXAhRHxaETcB1wCdKsgvlWsPfPbqoiIh4CLgD1rFq38bUmaAGwIdO072yROJO0BHAvsHRFPtNqXE4Ji7gBeBSBpY+BfgKZjK5ch50d+B7g5Ir7YYLXzgANz7aFdgYcj4q8dCzIrEqukqcDZwLsi4tZOxlcTR8tYI2LLiJgeEdNJ+cPvjYhzOxgmUPg78GPgpZImSJoIvIiUP99RBWPtld/WFEkb5ffrAK8G/lSz2nnAQfn924BfRy6R7ZQicUraEfgmKREoVD7klsWApNOA3UlduP4d+BSpEIaIOEnSpsAppFoQAj4bEfO6EOdLgd+S8lIrWRTHAFOrYhUp33VPYClwSEQs6NFYvw28lae7FV8WXejlsUisNeufAvw0Is7qYJiVYxeKVdJRpLvtFcC3I+LLvRhrD/22ticVBI8n3SCfERHHSzoeWBAR50laG/g+qazjAWDfiOhoolUwzl8C2wGVG8A7ImLvpvt1QmBmNtycNWRmNuScEJiZDTknBGZmQ84JgZnZkHNCYGbWw9SiU8w667+jqqO/HxTaxrWGzMx6l6SXAf8k9SHWtLWzpK2BM4BXRsSDkp5VpC2BnwhsqElartRd8w2SzswNsGrn/6TSiKfBPqZLeiyvf5OkkySt9tuStKmklm0PJB0ztk9lg6Rep5iStpL089yX1G8lVVpjHwacUOnMsWiDMicENuwei4gd8p3Wk8B76sx/AHhfi/3clvt/2R6YAazS/YSkCRFxd0QUGcfACYG1Mhd4f0TsDHwYODHP3wbYRtKlkq6QtFr3E/VMKClIs370W9KFvNblDeavJiKWSboMeK6kg4G3kLpXHi/pIFKL5G3zsr2BicBWwDkR8RFJnwXWyb1L3hgRs8b6oWyw5E78dgPOrOoFe638dwKwNamnhM2BSyRtl/slasgJgRkrOxHbC/h5zfzxpL5wvlNwPxPz+p8ENiYNeLR9RDyg1Ue02oHUXcETwC2SvhYRR0s6Ij9dmNUzDniowXdkCWmAp6eAv0i6lZQw/KHVDs2GWeXuewGpA7Tv1Mz/G+mC/r8t9rNVXv9S4GcRUekD/n8jotGgR7+KiIcj4nHSiGKthxS0oZe78v6LpLfDyuFpK73Lnkt6GkDSZFJWUcv+kPxEYMPusQZ3Vo9FxA75Dv9CUhnBV5vs57YG+3m0yTbV3QMvx79Hq6O6U0xJS0idYs4CviHp46QOMk8HriN9V18j6SbSd+qoIt27+4tn1kRELJX0H8C5kk7MI1OV7SlJa+THextyEbFfg0WrFQTnbrE/mF+FOWvIrIWIuAa4Hmj0g2y3ucD1kuZ36Hg25NygzMxsyPmJwMxsyLmMwKwgSduRRqiq9kREvKgb8Zi1i7OGzMyGnLOGzMyGnBMCM7Mh54TAzGzIOSEwMxty/x9U27aLEMHxngAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hmS2Nu/7NipDikxgaFntxTMtKdxzHcXqI0hVOjOZamfBoSNgTC6NOWzbyVNJ0SYOSBpctW9aqYh3HcVrDvHkwZQqMGBF+59WayaO/KFXhxBDWFwDzzOzCmHxfNJURf++P6UsYOg/DBjEtK30lzGyWmU01s6kTJpTmSu44jlM/8+bB9OmwcCGYhd/p04eV0inTS02ECbRuNbMTErsuJoRsJ/7+PJF+cPRW2xF4NJreLiPMcLlOdBZ4U0xzHMfpHWbMgKeeGpr21FMhfZhQ5sDPVxPnmZA0P6Z9Bvg68FNJhxMCPu4X911CmIp1AWGOhfcCmNlDkr5MmKcB4Etm9lCJcjuO47SeRRnzEmal9yGlKRwz+wvZ0z7vlpLfgA9mlHUmYfZGx3Gc3mTy5GBGS0sfJnikAcdxnHYwcyaMHTs0bezYkD5McIXjOI7TDqZNg1mzYGAApPA7a1ZIHyb0bfBOx3GcrmPatGGlYKrxFo7jOI7TFlzhOI7jOG3BFY7jOI7TFlzhOI7jOG3BFY7jOI7TFlzhOI7jOG3BFY7jOI7TFlzhOI7jOG3BFY7jOI7TFlzhOI7jOG3BFY7jOI7TFlzhOI7jOG3BFY7jOI7TFsqcYvpMSfdLuimR9hNJ8+NyT2UmUElTJD2d2HdG4pjtJN0oaYGkk+PU1Y7jOE6PUeb0BLOBU4GzKglm9u7KuqTjgUcT+e80s61TyjkdeD/wN8I01LsDl5Ygr+M4jlMipbVwzOxPwENp+2IrZT/gnLwyJE0E1jSzq+MU1GcBb2+1rI7jOE75dKoP5zXAfWZ2RyJtI0n/kHSFpNfEtEnA4kSexTHNcRzH6TE6NePnAQxt3SwFJpvZg5K2Ay6StEW9hUqaDkwHmDx5cksEdRzHcVpD21s4kkYB+wA/qaSZ2TNm9mBcvxa4E9gUWAJskDh8g5iWipnNMrOpZjZ1woQJZYjvOI7jNEgnTGpvAP5pZs+byiRNkDQyrm8MbALcZWZLgcck7Rj7fQ4Gft4BmR3HcZwmyTWpSVo3b7+ZpToFxGPPAXYF1pO0GPiCmf0Q2J+VnQV2Ab4k6X/AcuADibKPIni8rUbwTnMPNcdxnB5EwfkrY6d0N2CAgMnAw3F9bWCRmW3UDiEbYerUqTY4ONhpMRzHcXoGSdea2dSyys81qZnZRma2MfA74G1mtp6ZjQfeCvymLKEcx3Gc/qNoH86OZnZJZcPMLgV2Kkckx3Ecpx8p6hb9L0mfBebG7WnAv8oRyXEcx+lHirZwDgAmAD8DLozrB5QllOM4jtN/FGrhRI+xoyWtbmZPliyT4ziO04cUauFI2knSLcCtcXsrSaeVKpnjOI7TVxQ1qX0HeDNQiQZwPWHsjOM4juMUonCkATO7tyrpuRbL4jiO4/QxRb3U7pW0E2CSRgNHE81rjuM4jlOEoi2cDwAfJEwNsATYOm47juM4TiGKeqk9QBh74ziO4zgNUSt45ymEWGqpmNn/tVwix3Ecpy+pZVIbBK4FVgW2Be6Iy9bAmHJFcxzHcfqJWsE755jZHOAVwK5mdoqZnQLsRlA6Tjcwbx5MmQIjRoTfefM6LZHjOM5KFPVSWwdYE6jMUTMupjmdZt48mD4dnnoqbC9cGLYBpnm3m+M43UNRL7WvA/+QNFvSHOA64KvlieUUZsaMFcqmwlNPhXTHcZwuoqiX2o8kXQrsEJM+bWb/Lk8spzCLFtWX7jiO0yGKxlLbBdiUMOPnw8CmMS3vmDMl3S/ppkTacZKWSJoflz0T+46VtEDSbZLenEjfPaYtkHRMvRfY90yeXF+64zhOhyjah/PJxPqqwPYE77XX5xwzGzgVOKsq/Ttm9u1kgqTNgf2BLYD1gd9J2jTu/i7wRmAxcI2ki83sloJy9z8zZw7twwEYOzakO47jdBFFTWpvS25L2hA4scYxf5I0paAcewPnmtkzwN2SFhCUGsACM7srnvfcmNcVToWKY8CMGcGMNnlyUDbuMOA4TpdROHhnFYuBlzV47Ick3RBNbhVPt0lAMjjo4piWlZ6KpOmSBiUNLlu2rEHxepBp0+Cee2D58vDrysYpC3fBd5qgUAunKuLACMIYnOsaON/pwJdjWV8GjgcOa6CcVMxsFjALYOrUqZkREhzHaQB3wXeapGgfzmBi/VngHDO7st6Tmdl9lXVJ3wd+GTeXABsmsm4Q08hJdxynneS54LvCcQpQtA9nTitOJmmimS2Nm+8AKh5sFwM/lnQCwWlgE+DvgIBNJG1EUDT7A+9phSyO49SJu+A7TVIreOeNpAfvFGBm9oqcY88BdgXWk7QY+AKwq6StY5n3AEcQCrpZ0k8JzgDPAh80s+diOR8CLgNGAmea2c31XKDjOC1i8uRgRktLd5wCyCy7q0PSQN7BZpby9HUHU6dOtcHBwdoZHccpRnUfDgQX/Fmz3KTWJ0i61symllV+reCdC6NSGQHcl9i+n9DKcRxnuDBtWlAuAwMghV9XNk4dFHUaOA/YKbH9XEx7Zcslchyne5k2zRWM0zBFx+GMMrP/Vjbius+H4ziO4xSmqMJZJmmvyoakvYEHyhHJcRzH6UeKmtQ+AMyTdCqh7+Ze4ODSpHIcx3H6jqLjcO4EdpQ0Lm4/UapUjuM4Tt9RNLTNKsA7gSnAKCk4qJnZl0qTzHEcx+krivbh/JwQpflZ4MnE4jiO0zweFHRYULQPZwMz271USRzHGZ54UNBhQ9EWzlWSXl6qJI7jDE/ygoI6fUXRFs7OwKGS7gaeoUAsNcdxnEJ4UNBhQ9EWzh6ECM5vAt4GvDX+Oo7TDN53kR3804OC9h25CkfSupLWBR7PWBzHaZRK38XChWC2ou9iuCmdmTNDENAkY8eGdKevqBUt+m7CVAJpgTrNzDYuS7Bm8WjRTtczZUp6uP+BgTBV+HBi3rzQZ7NoUWjZzJzpDgMdoOxo0bkKp5dxheN0PSNGhJZNNRIsX95+eZxhT0enJ0gIcXjV9khJXyhHJMcZJnjfhTPMKOo0sJukSyRNlLQlcDWwRt4Bks6UdL+kmxJp35L0T0k3SPqZpLVj+hRJT0uaH5czEsdsJ+lGSQsknaxKmAPH6XW878IZZhRSOGb2HmAOcCPwK+AjZvaJGofNBqoHi/4W2DK6U98OHJvYd6eZbR2XDyTSTwfeT/CS2ySlTMfpTXxCM2eYUdSktglwNHABsBA4SNLYvGPM7E/AQ1VpvzGzZ+Pm1cAGNc47EVjTzK620Nl0FvD2IjI7Tk8wbVpwEFi+PPy6snH6mKImtV8AnzOzI4DXAncA1zR57sOASxPbG0n6h6QrJL0mpk0CFifyLI5pqUiaLmlQ0uCyZcuaFM9xHMdpJUUjDWxvZo9B8IUGjpf0i0ZPKmkGIRBoZcDBUmCymT0oaTvgIklb1Fuumc0CZkHwUmtUPsdxHKf1FJ0P57HoLLA5sGpi1+31nlDSoYRIBbtF5YWZPUMImYOZXSvpTmBTYAlDzW4bxDTHcRynxyjah/MF4JS4vA74JrBX7kHp5ewOfArYy8yeSqRPkDQyrm9McA64y8yWAo9J2jF6px1MmCrBcRzH6TGK9uHsC+wG/NvM3gtsBayVd4Ckc4C/AptJWhzH8pxKcKf+bZX78y7ADZLmA+cDHzCzisPBUcAPgAXAnQzt93G6EY8P5jhOCkX7cJ42s+WSnpW0JnA/sGHeAWZ2QEryDzPyXkDwgEvbNwhsWVBOp9P43CaO42RQK3jn7Lg6GAdpfh+4FriO0HpxnKH43CaO42RQq4XzCgAzOypunyHp14SxMTeUKpnTm/jcJo7jZFBL4YyVtA0p0aIlbWtm15UjltOzTJ6cHgHZ44M5zrCnlsKZBBxPxvQEwOtbLpHT28ycObQPBzw+mOM4QG2Fs8DMXKk4xak4BvjcJo7jVFHUS81xijNtmisYx3FWotY4nE8DSHqbpKJjdhzHcRxnJXKViJn9Jq6+G7hD0jclvbR8sYYhPljScZw+p+h8OAcC2xBG+s+W9NcYmTl3EjanIJXBkgsXhimHK4MlXek4jtNHFDaTxWjR5wPnAhOBdwDXSfpwSbINH3ywpOM4w4CiwTv3kvQz4HJgNGG6gj0IMdU+Xp54wwQfLOk4zjCgqJfaO4HvxFk8n8fMnopBOZ1m8MGSjuMMA4r24RwCLJT0BgBJq1X6b8zs9yXKNzyYOTMMjkzigyUdx+kziprU3k/ov/leTNoAuKgsoYYd06bBrFkwMABS+J01y8eyOI7TVxR1Gvgg8GqgMs30HcALyhKqp2nUvXnaNLjnHli+PPy6suk/3PXdGeYU7cN5xsz+GybdBEmjCLHUnCQ+F4yThT8bjlO4hXOFpM8Aq0l6I3Ae8ItaB0k6U9L9km5KpK0r6beS7oi/68R0STpZ0gJJN0jaNnHMITH/HZIOqe8S24i7NztZ+LPhOIUVzjHAMuBG4AjgEuCzBY6bDeyeUtbvzWwT4PdxG2APYJO4TAdOh6CggC8AOwDbA1+oKKmuw92bnSz82XCcwl5qy83s+2b2LjPbN67XNKlFN+qHqpL3BubE9TnA2xPpZ1ngamBtSROBNwO/NbOHzOxh4LesrMS6gyw3ZndvLpde6BvxZ8NxCnupvTqav26XdJekuyXd1eA5X2hmS+P6v4EXxvVJwL2JfItjWlZ6mpzTJQ1KGly2bFmD4jVBq9ybe6EC7RZ6JSyQu747TmGT2g+BE4CdgVcCU+NvU8RWUsucD8xslplNNbOpEyZMaFWxxWmFe3OvVKDdQq/0jbjru+MU9lJ71MwubdE575M00cyWRpPZ/TF9CbBhIt8GMW0JsGtV+uUtkqX1NDsXTF4F6pXTyvRS34jPE+QMc4q2cP4o6VuSXiVp28rS4DkvBiqeZocAP0+kHxy91XYkKLmlwGXAmyStE50F3hTT+pNeqkC7Ae8bcZyeoajC2YFgRvsqcHxcvl3rIEnnAH8FNpO0OMZd+zrwRkl3AG+I2xA83+4CFgDfB44CMLOHgC8D18TlSzGt9XRD34lXoPXhfSOO0zOogLNZTzJ16lQbHBwsfkD1wDwIFVe77ezdIkcvMW9eMDkuWhQU88yZfq8cpwEkXWtmU0srv4jCkbQWYSzMLjHpCkJL49GyBGuWuhXOlCnpEZsHBkKomXbiFajjOB2gWxTOBcBNrBg/cxCwlZntU5ZgzVK3whkxIniFVSOF+GaO4zh9TtkKp6iX2ovN7J2J7S9Kml+GQB3D56RxHMcplaJOA09L2rmyIenVwNPliNQhvPPZcRynVIq2cI4E5sS+HBHC1RxallAdodJH4n0njuM4pVA0ltp8M9sKeAXwcjPbxsyuL1e0DuBz0pRPN7ieO47TEXJbOJI+lpEOgJmdUIJMTr/ic8I4zrCmVgvn28CBwHhgHLBG1eI4xemVuGeO45RCLYWzDfAb4C3AAHAlYfzNF83si2UL5/QZ9YTtcdOb4/QduQrHzK43s2PMbGtCxOi9gVsk7dUW6Zz+omjYHo+Y7Th9SdH5cCYQWjsvJ8xHc3/+Ec6woZ6WSFHXcze9OU5fUstp4DBgP2BV4HxgPzNzZeME6nUCKOp67hGzHacvyQ1tI2k5IaRNZQj+kMxm1rWmtbpD2zj1U1b8uW6Ka+c4w4hOh7Z5XVkndvqAsloiM2emR8z2qA+O09PkKhwzu6Jdgjg9SFnx5zzqg+P0JblOA5JeKulSSb+S9GJJsyU9Iunvkl7WLiGdLqXM+HMe9cFx+o5aXmqzgNOAucAfgF8D6xBm4Dy1kRNK2kzS/MTymKSPSDpO0pJE+p6JY46VtEDSbZLe3Mh5nRKYNi1MDDcwEKZxGBjwieIcx8mkltPAP8xsm7i+wMxekth3nZlt29TJpZHAEsIU1u8FnjCzb1fl2Rw4B9geWB/4HbCpmT2XV7Y7DTiO49RH2U4DtVo4IxPr1XHTxrTg/LsBd5pZSkfA8+wNnGtmz5jZ3cACgvJxeoVejBrQizI7TpdTS+F8V9I4ADM7rZIo6SWElkaz7E9ovVT4kKQbJJ0paZ2YNgm4N5FncUxbCUnTJQ1KGly2bFkLxHOaphejBvSizI7TA9QKbfM9wuRrH61KX2BmH2nmxJLGAHsB58Wk04EXA1sDS4Hj6y3TzGaZ2VQzm/ff71kAACAASURBVDphwoRmxMvHv36L04tRA3pRZsfpAWqGtol9JQeUcO49gOvM7L54nvvM7DkzWw58nxVmsyXAhonjNohpnWG4f/3Wq2y7MWpArWvoRpkdpw8oOsX0lZJOlfQaSdtWlibPfQAJc5qkiYl97yBEOAC4GNhf0iqSNgI2Af7e5LkbZzh//TaibIsG7GwXRa6h22R2nD4h10vt+UzSH1OSzcxe39BJpdWBRcDGZvZoTDubYE4z4B7gCDNbGvfNAA4DngU+YmaX1jpHaV5qI0aEiqoaKYwZ6WcaCTlTHW8NwlidTrlPF7mGbpPZcdpE2V5qmFlfLtttt52VwsCAWVA5Q5eBgXLOVw9z5wY5pPA7d25rj5HSr11qvVxlUfQauklmx2kTwKCVWC8XywQvJMyHc2nc3hw4vEzBml1KUzhz55qNHbtyZVVROp2qmNLkGjs2X556j+lmZVuUfrgGxymJshVO0T6c2cBlhIGXALcDTXmp9SyV0fXjx69Is2hia8SBoFUeb430LWUdc+CB6bK0MpRNpzz98q7BvQ8dp1yKaCXgmvj7j0Ta/DI1YbNLKS2cipkl2app5mu53hZGnpmnEXNX3jVkydIKU1MjrbFWnTurnEZlcpw+gi4xqV0OjCe4MQPsCFxRpmDNLi1XOGkVUtZSq0+jQj3mnVoVYiOmoqxjyjY1NSJr2Qoh714M5z4c78saVnSLwtkWuBJ4NP7eDmxVpmDNLi1XOEUq58oyfnyxl7SeVkmtSrpVfTiNKs96aKQ1VnbfSyOtvX7HW33Djm5ROKsQ5s7ZAtgSGA2sUqZgzS4tVzi1KqTKMmaM2ejRxV7SeirRIpV0M15q3d7CadRDrlmZyr4X3Yw7WAw7ukXhXFckrZuWjrRwxo8PS9FWTz1fkGW//K34mi2q8Bo5Vyeuvx2tvW6mbCXvdB0dVTjAi4DtgFuBbaJpbVtgV+CfZQrW7NKWPpw0d+iiLaFKBdtMJd1qd+xm7PWtdIBotvxGr6MTrb1uxls4w45OK5xDgD8Cj8ffyvJzYJ8yBWt2KdVLLa8iq6evp94XN89LrpO29blzzUaOLL9yKnL/W9VS874Lvw/DkG4xqb2zTCHKWEob+FmLMrzZqun0l2ey4h8/fuU+q06aX/LuTT0tnyOPXKFER44M28MR91IbVnSLwnkRHmmgONUvaVa/TqMKopO29XoUavIa21Vx5Zk06zHJ+Ze9MwzpFoVzKbAfcH3cHgXcWKZgzS4dVTjVtLoC62QLpx6TYbKfql0VeJZ89Zj8Ot2CzMJbG07JdIvC8UgDzdLKyqKTX+BFnSJGjmxuUGqjZN2bekx+3eid5a0upw2UrXCKxlJ7UtJ4wAAk7RgHgTpFmTYthL9fvjz8NhLmvhLr66CDYLXVQjw3KYTWb1fo/CJzwowdC3PmrJCnnROaVWLdDQwMvTcDA+n5066nG+fDGc7zMDn9QxGtRHqkgVeUqQmbXbquhdMs7fjCTXrBVUxQ1a2xWn040sod7N1goqrXrbrbWhPd2Opy+g66waQW5BgaaaBMoVqx9J3C6eTAx+rKtt7xKt1Sgc+dO9SBY/z41o0TKptuUNpO31O2wsmd8VPSPjVaRxc22rKSdA9hfM9zwLNmNlXSusBPgCmEWT/3M7OHJQk4CdgTeAo41Myuyyu/tBk/O0XZM41mzYRZIW1Wz3pkmjcvmH8WLQqmqZkz2z97Zi/P5NnLsjs9Q9kzftZSOMuB+XEBUGK3mdlhDZ84KJypZvZAIu2bwENm9nVJxwDrmNmnJe0JfJigcHYATjKzHfLK7zuF08j0zvWQpTwqpCmRsmVqNb0mbzXdoLSdvqZshVPLaWAfYn8NcDcw08zeG5eGlU0OewNz4voc4O2J9LNiq+9qYG1JE0s4f/dSZPKzZiYQq9Uhnra/bJlaTbucF8q65lY4njhOJylidwNWB95DCGnzF+C1zdryCArsOuBaYHpMeySxX5Vt4JfAzol9vye0jnqzD6fZWF9px2X1weT1U1SXXbQPpxmZOtn53o5+kG67ZsepA7rBaQAYCbwFOJtgXntz0yeGSfH3BcD1wC5JhRP3PWx1KBxgOjAIDE6ePLlFf0GLKatCyuvEL1r+kUeu7A2V5nXWrEyd6uhuhzLotmt2nDroqMIBXg/Mikrm27VaFQ0LAccBnwBuAybGtInAbXH9e8ABifzP58taSm3hNNNCaWWQy6QcWcqmnvJbXVl2oytv2d5n3XjNjlOQshVOrT6c3wHbRzPaKsDBkk6uLDWOzUTS6pLWqKwDbwJuAi4mRKgm/v48rl8cz63KoFMzW9ro+Zui4i20cGGoShYuDNu17PSV4557Ln1/vf0I1XLUIll+Vh9DlgwLFzbWD9GNAyjL7gfpxmt2nG4hTxsRKv3MpVEtB2xMMKNdD9wMzIjp4wnmsjsIym7dmC7gu8CdwI0UaGmV1sJptBVQKwZZva2IemKaJcvPMyvVa5ar1VrImsOnGyMvt6rl4304Tg9DN/ThDDkgeLatWaZQrVhKUziNmkxqRTGut0IqGtOsslScB2qF78+LIpBUikUr1rR+oW6rgFutJLpt0GhZDJfrHEZ0hcIBfgysSfBWuwVYDHyyTMGaXXqmhZMMctkqOapH1Ccr0SxlUlGYc+fWzlPPfeiFTvRaURMq/49XsCvwllxf0i0KZ378nQYcD4wGbihTsGaX0hROoy9aGV/ReeXVG6Z//Pj6zH5FW3q90Ileq7U4enRoqXkFu4Je+JBw6qZbFM7NUcmcRxyDQ5wbp1uXrvVSK3pc0emUs/LUMxFZkUUaWn7RCidr8rnx44vds3ZQpD8s637mVbD93CLqhQ8Jp266ReH8H7AEuCR24A8Afy5TsGaXrh74WYtWtIYaMbnVWhqRcdy47lc49c5iWqSC7XeTk7dw+pKuUDipB8KoMgVrdulphdOKl7lRk1vekhZJIG86g6L9QWmy19syaLY1UctLL++epNHvFXK/K9RhSlcoHOCFwA+BS+P25sDhZQrW7NLTCifPHFbPC92oyS1vSZuqoBE366yKt5GKrJWVX1bLb9y4+s4xHExO/WwyHKZ0i8K5FNiv0m9DmBvnxjIFa3bpaYXTijA1tSqDWl/zeQqpUl4thdKI4mykZdDK1sTcuWZjxgwtZ8yYkF5PBdvvLRynL+kWhXNN/P1HIm1+mYI1u/S0wqlnPEzR49NaJtUVa/JrPs8cVimvVmsoyyMuT+HkKcAsWt2aaMWXu5ucnB6kWxTO5TEKwHVxe0fgijIFa3bpaYVj1nj/h1m+Z1iyIl199ezy8+K+tWJJU5pz59bvDdbq+HStxE1OTo9RtsLJnYCtgqRtgVMI00vfBEwA9jWzG2oe3CH6YgK2RiYMmzcPDjyw+XMPDOTPANos9UzoJsHZZ68c9yxtFswKPhum49RNpydgA8DCdM6vBXYCjgC26GZl0zcUmeCsmhkzWnPuRYuC0klDSk+vh7RgllnBQ83SFceMGenKZuTI4aFsumlyO8cpQK7CkbRPZQH2AjYDNgXeFtOcMpk2LVScAwOhkh8YqF2Rtmr2yrFj4YknVk4fPTpU6M2WnaY08yIqT5kCRx01tILNaoEtX15M2fRyhd1o1HLH6SC5JjVJP8o51qycaaZbQl+Y1BohryJulvHjw++DDzZexsiRMGdOukLIM5GlIYXKtpo8k2PeuXrJDNeIudVxalC2Sa1QH04vMmwVTr2Vdj0MDIQWVN4zM348jBuXrfTS+m6SzJsXTGWNKs2K0oBQzqJFoeU0c+ZQRdLrFfaIEen/Q6376zg5dEUfThTkLZI+JenzlaUsoZwmqJjhKq2RVlKpvLMYOxZOOilU2Fn9P7UmIqtMkNZIP5EEhxwS1muZm7JMj60ySZbNuuump/tEb04XU0jhSDoDeDfwYUIstXcR4qk53ci0aaGV0WoqLYVqRwZYUdlXWhFZ+Z54olg/QyMVpxlcckm6M8FTTwX5Kv01WRV2Vno3MW8ePP74yumjR+c7lDhOhynawtnJzA4GHjazLwKvIjgPON1CdQd4PSap8eNhzJj8PJWO/qwWlFnom0kqk9VWW7mcBx9c0drI67TPUli1WLQou5Xy3HMrWjyPPZbu/PD4493f8T5jBvz3vyunr7lm+O1VRwin/ykyWAf4W/y9GlgfWBVY0MjAH2BD4I+EidxuBo6O6ccRIlLPj8ueiWOOBRYAtwFvLnKenh/4WQ+tiHZcPUjxyCMbC40zcmT63DFpg1CLREOoyFA0uvXAQPEgnCNGdOeA0VrUM/WERzdw6oAuiTTwOWBtYB9gaVy+3NAJYSKwbVxfA7idEAz0OOATKfk3B64HVgE2Au4ERtY6z7BSOI1EOc6KPlBL0VTIq/QaDQyaF4GgiEKtVK7NKOCkEu5W6p1cr9sVqNM1lK1wao3DeaWkF5nZl83sEWAccCNhIrbv1N2cAsxsqYWBpJjZ48CtwKScQ/YGzjWzZ8zsbkJLZ/tGzt23NNrRPXp0MCElO9dPP73Y2I68PpbwodAYadeSNcATVjgXVMYoJfNXTGZZ44ay0ru94z1rQPBzz6Xn7xVHiEbo5bFUw5BafTjfA/4LIGkX4Osx7VFgVrMnlzQF2Ab4W0z6kKQbJJ0paZ2YNgm4N3HYYjIUlKTpkgYlDS5btqxZ8cqhnhekaN56KsiRI1cMIl1zzfS+gCSVzvbqczfax1KLeiIQQFBuAwMrOssr3mkQKuCxY0NaWgWdlV52x3uzlWTWgOBGPQN7FR/82nvkNX9ITCMNfBc4LrHdVLRoQmvpWmCfuP1CYCRBCc4EzozppwIHJo77ISGOW++Z1OqJIJxlFho/fuX8tSI7Z52rHtNXmpx5gTMbMauNHh2ur9qcV8RkOHZsdjDS5HQKlf6gynmS6+0IsFlmFOnhFqHap4BoOXSyD4cQqHNUXP8nsEtyX8MnhdHAZcDHMvZPqZRPcBg4NrHvMuBVtc7RlQqnnhek3jlxsirbESManxOniJxHHpmed7fd6isbzEaNSr/OVvbJNDrBW6uiPpddSQ6nCNXDYZK7NtNphTMDuBL4OfAPVkQmeAlwZUMnDON4zgJOrEqfmFj/KKHfBmALhjoN3EWvOg3U84LUaiFUV1B5UxKYpVdEjVTkRVsf9XiLFbnORqeArr5X9Vb4rW41eCXZOryF03I6qnDC+dkReAeweiJtU6KnWd0nhJ0BA24g4QINnE1wSLgBuLhKAc0geKfdBuxR5DxdqXCyXpBqT7EilWt1BZVXkeVVmo1W5JXj8+RrtmVSfZ2NllfEjJhV4be6UvNKsnUMNxNiG+i4wunVpSsVTtoLMnr0yqakZCVY5KvdrLGWRpEv/7yl0veRV3YzLZNWyVj0PqVRxmyiXkm2juFkQmwDrnAaXLpS4Zit/IKMG1d/JZrVgV9dkY0ZU3vAZN7xjS6V1k2SRpwIxo4dOi6o1jmr00aPLnaf8ir8MlokXkk6XYornAaXrlU41RT9Si9SQVV7Yo0enV/uyJErH190RH+tpVrOoq2TkSOHDkJtRglWt27S7lOtCt9bJM4wwhVOg0tfKZxKiyGtkswKSVO0Uq6mFR39aRVz0RZU0lTVrCyt6oj3FokzTHCF0+DSMwony525+ks97Su72RZAmlmoHtNXmlx5rYwi/TlJmZoJkZN1fY7Tr7Tgw6hshVN4PhynBObNg//9Lz+PBP/5T3q4/VmzGp9oLWtEfdHw/JW5b/JGuEOIDl0Z+V2Z6yYrvzRUpqwR8kWmuJZgzz1r53OcfqBXoi6Uqc06ufREC6eV5qt6TU1HHpkuU1YfzrhxjUWPTmtppJnXqmXK6k/Katmlefp5X4szXGiRcwtuUutjhdOsySgrrEwz5qZG3YCzIg5kHZvX/C8S1qf6+CxFmTeg0/tlnH6hRe77ZSscN6l1kmaCKmYFnyzKwoXpASSzZKol6yWXZO+bPHnlgJUQzGvLl4ffykyhkB0dety4Ffkq5rnK8Q89lH7utMCfRx0FBx3U/eYHxylKo+9tuylTm3Vy6YkWTpZpKe1LJcs1utGBldXnyYtbVsQ0lddaSzOB5ZXZyNdaUZPC3Lm1B6s6AW8F9g4tct/HTWp9rHDM0t2aG3lw6lEueRVudd9JWnTqNPmzzj1uXLbprxKpufpcjdiji75webJ6PLMV+Pij3qMHvNQ6rhjKWnpG4aTRyINTJPZapdLOy5flfp3Vuspzix4zpvbg07RjiijdrGCkaWOSknnqCRc0nPGYb8MSVzjDUeE0QpGBlZXKol4TXJb5rZZnWqNRC5IBTIs6FaQppLQ8WTKlheMZznhU62GJK5wGl2GncMxqK4FKZdGKuGl58c0qlXejZdeq1JoJRpo2WDXPTXy44i2cYUnZCse91LqJVkw9nDewsuKxUj1FcZGBlNUsXJjvAXPggfWXWaGWZ03WlNPJ9Kw8Dz208vTMZ58Np53WmKz9StoU4u2Yftvpb8rUZp1ceq6F08pO2qwxMVlf8Y2MBxo5srURppNLrWtudrqFXva4aqfnmHupDTtwk1qfKpxmBy7mUa85JCt/rYGl1dfRzEDUWkqx+t410ofTCmXeSdxzzCkZVzgVQWF3woyfC4BjauXvaoVTT8ugkU7aWp5YRbzNajkGNBL4M6v/pJ5WR1KmioLLOraIY0M7abbF4P0qTsm4wgnKZiRhiumNgTHA9cDmecd0tcKpx0uslS2cLG8zs/zpD4p+VeddV/WU1o1Uuq0elNpOj6tWtE664TqcvsYVTlA4rwIuS2wfCxybd0xXK5yifSaNmkvqiWBQRKEVVRJFYqA1Q6Nf+N3QMmiFDN1wHU5f4wonKJh9gR8ktg8CTk3JNx0YBAYnT57czH0vlzyX3VZ10lYriSyl1uqv4zI7mhv9wu+Gvo9WtE664TqcvsYVTh0KJ7l0dQunExVHP3wdN3MNnfa4atX97/R1OH1N2QqnV8bhLAE2TGxvENN6k+pxMAMDYTsZMbnV9MO4imauoTq6dJn3Oo1W3f9OX4fjNEOZ2qxVCzAKuAvYiBVOA1vkHdPVLZxO0Q9fx718Db0suzMsoOQWjsI5uh9JewInEjzWzjSz3E/DqVOn2uDgYFtkcxzH6QckXWtmU8sqf1RZBbcaM7sEyJnly3Ecx+lmeqUPx3Ecx+lxXOE4juM4bcEVjuM4jtMWXOE4juM4baFnvNTqRdIyYGGbTrce8ECbztUsLms59IqsvSInuKxlkSfrgJlNKOvEfatw2omkwTJdCVuJy1oOvSJrr8gJLmtZdFJWN6k5juM4bcEVjuM4jtMWXOG0hlmdFqAOXNZy6BVZe0VOcFnLomOyeh+O4ziO0xa8heM4juO0BVc4juM4TltwhVMQSWdKul/STRn715L0C0nXS7pZ0nvbLWNClg0l/VHSLVGWo1PySNLJkhZIukHStl0q57Qo342SrpK0VbvlLCprIu8rJT0rad92ypg4fyFZJe0qaX7Mc0W75YwyFHkGuuLdkrSqpL8n5PhiSp5VJP0kvld/kzSl/ZIWlvVj8b7fIOn3kgZKF6zMuQ/6aQF2AbYFbsrY/xngG3F9AvAQMKZDsk4Eto3rawC3A5tX5dkTuBQQsCPwty6Vcydgnbi+RyfkLCpr3DcS+AMhsvm+3SorsDZwCzA5br+gi2Xtincrvivj4vpo4G/AjlV5jgLOiOv7Az/p0H0tIuvrgLFx/ch2yOotnIKY2Z8ID3pmFmANSQLGxbzPtkO2lQQxW2pm18X1x4FbgUlV2fYGzrLA1cDakiZ2m5xmdpWZPRw3rybM9tp2Ct5TgA8DFwD3t1G8IRSU9T3AhWa2KObriLwFZe2Kdyu+K0/EzdFxqfa62huYE9fPB3aLcreVIrKa2R/N7Km42ZZ3yxVO6zgVeBnwL+BG4GgzW95ZkSA26bchfOEkmQTcm9heTHoF2hZy5ExyOKFV1lGyZJU0CXgHcHr7pUon575uCqwj6XJJ10o6uN2yVZMja9e8W5JGSppP+KD4rZllvldm9izwKDC+vVIGCsiapC3vliuc1vFmYD6wPrA1cKqkNTspkKRxhK/tj5jZY52UJY8ickp6HeGl+HQ7ZUuRI0/WE4FPd8OHBtSUdRSwHfAWwrP7OUmbtlnE56kha9e8W2b2nJltTWgNbC9py07IUYSisko6EJgKfKtsmVzhtI73EkwUZmYLgLuBl3ZKGEmjCS/wPDO7MCXLEmDDxPYGMa2tFJATSa8AfgDsbWYPtlO+KjlqyToVOFfSPcC+wGmS3t5GEZ+ngKyLgcvM7EkzewD4E9Aph4xasnbVuwVgZo8AfwR2r9r1/HslaRSwFtCxZxZyZUXSG4AZwF5m9kzZsrjCaR2LgN0AJL0Q2Ay4qxOCRJvxD4FbzeyEjGwXAwdHb7UdgUfNbGnbhKSYnJImAxcCB5nZ7e2Ur0qOmrKa2UZmNsXMphDs90eZ2UVtFBMo/P//HNhZ0ihJY4EdCP0nbaWgrF3xbkmaIGntuL4a8Ebgn1XZLgYOiev7An+w2CvfTorIKmkb4HsEZdOWPjyPNFAQSecAuxJCe98HfIHQEYeZnSFpfWA2wetGwNfNbG6HZN0Z+DPB3l0x73wGmAzPyyuCbXx34CngvWY22IVy/gB4JyummnjWOhDptoisVflnA780s/PbKGbl3IVklfRJQuthOfADMzuxG2XtlncrtrTnEDwRRwA/NbMvSfoSMGhmF0taFTib0Bf1ELC/mXVCORaR9XfAy4HKh+YiM9urVLlc4TiO4zjtwE1qjuM4TltwheM4juO0BVc4juM4TltwheM4juO0BVc4juM4fYBqBBhOyb9fImjqj8uWD9xLzXEcpy+QtAvwBCFGYm4EBEmbAD8FXm9mD0t6QTvG4ngLx2kbkkzS8YntT0g6rsBx0yX9My5/j2M3KvteE7/Q5ktaTdKmki6RdIek6yT9NA4WrFfWQ+P4j7Yh6UeSjqhKe7ukSyVNlXRyzrHrSzo/rm8tac8Gzj+l6NdxA2VvLemv8b+6QdK7yzjPcCYtwLCkF0v6dYyX92dJlQgN7we+WwmM266Bn65wnHbyDLCPpPWKHiDprcARwM5m9lLgA8CPJb0oZpkGfC3GjDLgV8DpZraJmW0LnEYIaV8vhxJid7WFOBD3HEJI+yT7A+eY2aCZ/V/W8Wb2LzOrzL+zNWH6iW7iKeBgM9uCMNj4xMpIeKdUZgEfNrPtgE8Q3gcIwVs3lXSlpKslrRT2pgxc4Tjt5FnCC/DR6h3x6/oPWjEZ1OS469PAJ2O8L2Io+znAByW9D9gP+LKkeYSQ+381s19UyjWzy83spthiOTVxvl8qTEA2UtJsSTcpTPL2UYWJ06YC8xItp90k/SPmOVPSKrGceyR9LeYblLStpMsk3SnpA4nzfVLSNfH6vpi45tsknQXcBNwGvFRxmghJqwNvAC6Ksv4ypr82nm9+lGmNSutE0hjgS8C74/53p+Wv50+T9P4o+/WSLlAIhVP5er463pOvSHoiqwwzu93M7ojr/yJEMG7kQ8ApiEJA1J2A8xSiRn+PEK0BQvDWTQjRUw4Avt+ODwBXOE67+S4wTdJaVemnAHPM7BXAPKBiPtoCuLYq7yCwhZn9gBC76pNmNg3YMiVvLbYGJpnZlmb2cuBHMRzNIDAt0XKaDbw75hlFmLCqwqKY788x376ESe0qiuVNhJd7+3i+7aK9nZh+mpltYWYLCUEs94v73gZcnhI9+RPAB+M5XwM8XdlhZv8FPk+YTGtrM/tJXv6CXGhmrzSzrQjx1g6P6ScBJ8V7srhoYZK2B8YAd9Yph1MfI4BH4nNQWV4W9y0GLjaz/5nZ3YSJ7zZph0CO0zZi5XkWUG0eehVQ8ZQ5G9iZ9nAXsLGkU6JZIW16hM2AuxPBQ+cQZoCtcHH8vZEwI+njZrYMeCZ+Nb4pLv8AriNEOq683AstTIBXIWlW2z9uV3MlcIKk/wPWjvOu5FFv/mq2jPb/GwkmzC1i+quA8+J6IS+n2Ho7mxC7ryumcehX4rt2t6R3wfPTylcigl9EaN0QTdyb0oaAqK5wnE5wIuErefUCeW8hzNuSZDvg5pS8N6fkrfAsQ5/3VQFip+lWwOWE/qEfFJCpmkpY9+WJ9cr2KELAya8lvjJfYmY/jHmerCrrKmBirBh2IvRJDcHMvg68D1gNuDLREZxKvflTmA18KLZkvki8d/WiMIfNr4AZVUrWaQEKAYb/CmwmabGkwwkfCIdLup7wfuwds18GPCjpFsLUBZ9sx9QfrnCctmNmDxFcMg9PJF/Fii/7aQTzFMA3gW9IGg/B24nQoX8aK/NjYCdJb6kkSNpFYeKpe4CtJY2QtCHBvFX5uhthZhcAnwW2jYc+DlT6Om4Dpkh6Sdw+CLiijku+DDgs2tSRNEnSC9IyxlD2PyG0oi41s/9U55H0YjO70cy+AVzDynPDJGUvkr8WawBLFeatmZZIv5oQyRtWdnaolnkM8DOCy27bI2gPB8zsADObaGajzWwDM/uhmd1tZrub2VZmtrmZfSnmNTP7WEx7uZmd2w4ZR7XjJI6TwvHAhxLbHwZ+pBAyfxkhbD4xjPok4CpJRqhMD7SUuXvM7Ono1XaipBOB/wE3AEcTvu7uJrSYbiWYtiBMCfwjSZWPr2Pj72zgDElPE0xH7yV0vo4iVNpDpiPIw8x+I+llwF8Vprd/AjgQeC7jkHOATwHHZOz/iMIMqMvjdV3Kis5gCF+sx8SO4q8R5r2pzp/FZpKS/TEfBT5HmPZ5WfytKLOPAHMlzQB+TZhOOYv9CGbI8ZIOjWmHmtn8nGOcPsMHfjqO0xDRW+1pMzNJ+wMHmNnetY5zhi/ewnEcp1G2A05VaLY9AhzWYXmcLsdbOI4zzJD0coKnWJJnzGyHbi7b6X1c4TiO4zhtwb3UHMdxnLbgCsdxHMdpC65wHMdxnLbgCsdxsKLfXAAAAApJREFUHMdpC/8PRjaU+FIQqBEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moZb8vk4UfEq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}