Omer_eker_the_complete_journey_v3.ipynb

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  "metadata": {
    "colab": {
      "name": "Omer_eker_the_complete_journey_v3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPIe9BBmNhLaZomfLS9VbJt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/omerfarukeker/The-Complete-Journey/blob/master/Omer_eker_the_complete_journey_v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hT_a7h7pvQts",
        "colab_type": "text"
      },
      "source": [
        "# The Complete Journey Dataset Analysis: Churn Prediction\n",
        "## by Omer Eker\n",
        "\n",
        "In this notebook we will make a churn prediction model using a retail dataset. Let's start importing necessary packages for the analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2j_33pBjx8L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpMA5cNht6yP",
        "colab_type": "text"
      },
      "source": [
        "We will use ***The Complete Journey Dataset from Dunnhumby*** which is available in our Google Drive.\n",
        "\n",
        "Let's first mount our drive from Google Drive:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aveJyZqikijw",
        "colab_type": "code",
        "outputId": "cd66437f-9d44-4f50-a3cd-5b18bc87f853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyMjS6e1uR8x",
        "colab_type": "text"
      },
      "source": [
        "Load the dataframe from the .sav file (1.2 GB file size)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-oTNvborWhr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "dfs = pickle.load(open(\"/content/drive/My Drive/Databases/complete_journey.sav\",\"rb\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeMyeWfStyT3",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFreIdIrIx1x",
        "colab_type": "text"
      },
      "source": [
        "The dataset consist of **eight** tables in separate .csv files, we will go through six of them which we will use in this notebook. The dataset covers a two year span purchase transactions of **2500** **households**. Also demographics information of households, campaign and coupon redemption informations are available. In the modeling phase we will join these tables to make our final dataset.\n",
        "\n",
        "1. **Campaign Descriptions** (_campaign_desc.csv_)\n",
        "2. **Campaigns** (_campaign_table.csv_)\n",
        "3. **Coupons** (_coupon.csv_)\n",
        "4. **Coupon Redemptions** (_coupon_redempt.csv_)\n",
        "5. **Transactions** (_transaction_data.csv_)\n",
        "6. **Demographics** (_hh_demographic.csv_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMe56XeTBHI-",
        "colab_type": "text"
      },
      "source": [
        "### Campaign Descriptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7tMc8akDO4z",
        "colab_type": "text"
      },
      "source": [
        "Campaign description data is a look up table, containing the start and the end days of each campaign (**30 campaigns in total**). Also it gives which campaign belongs to what category (Type A, B and C)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGzX6s1wBHhS",
        "colab_type": "code",
        "outputId": "fe196b36-deb2-4673-a770-1bbf1be9feed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"campaign_desc\"].head()"
      ],
      "execution_count": 4,
      "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>DESCRIPTION</th>\n",
              "      <th>CAMPAIGN</th>\n",
              "      <th>START_DAY</th>\n",
              "      <th>END_DAY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TypeB</td>\n",
              "      <td>24</td>\n",
              "      <td>659</td>\n",
              "      <td>719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TypeC</td>\n",
              "      <td>15</td>\n",
              "      <td>547</td>\n",
              "      <td>708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TypeB</td>\n",
              "      <td>25</td>\n",
              "      <td>659</td>\n",
              "      <td>691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TypeC</td>\n",
              "      <td>20</td>\n",
              "      <td>615</td>\n",
              "      <td>685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TypeB</td>\n",
              "      <td>23</td>\n",
              "      <td>646</td>\n",
              "      <td>684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  DESCRIPTION  CAMPAIGN  START_DAY  END_DAY\n",
              "0       TypeB        24        659      719\n",
              "1       TypeC        15        547      708\n",
              "2       TypeB        25        659      691\n",
              "3       TypeC        20        615      685\n",
              "4       TypeB        23        646      684"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9605zzLD5cb",
        "colab_type": "text"
      },
      "source": [
        "Let's calculate and visualise how long each campaign lasted within two years period:\n",
        "\n",
        "* Campaign **No:15** lasts the longest with a staggering 160 days figure, where other campaigns are fairly close to each other ranging from 30 to 70 days\n",
        "* Average campaign duration is **37** days (median)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RduCSsrmBja0",
        "colab_type": "code",
        "outputId": "7d09bd06-817c-405d-cbed-adec9c7f73ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "dfs[\"campaign_desc\"][\"DUR\"] = dfs[\"campaign_desc\"].END_DAY-dfs[\"campaign_desc\"].START_DAY\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "sns.barplot(x=\"CAMPAIGN\",y=\"DUR\",data=dfs[\"campaign_desc\"],orient=\"v\",order=dfs[\"campaign_desc\"].sort_values(by=\"DUR\",ascending=False).CAMPAIGN.values)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb941baeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
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dMd2yLFVXV19x83F/q6nxeA9Znlj+mq0aEY9N1IkTFa3cGQAAQF2NHbJsdA/ZgQMHWr0Z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\nQAYAAGAYgQwAAMCw/w/gh2YuMhbOMgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5T5l05j7HbfN",
        "colab_type": "text"
      },
      "source": [
        "### Campaign "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fb6xNVgmMV9R",
        "colab_type": "text"
      },
      "source": [
        "Campaign dataset contains identifying information for the marketing campaigns each household participated in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPecDn4VMlT-",
        "colab_type": "code",
        "outputId": "6ef9bbfc-9ab6-4106-bbd0-eaec6f7969ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"campaign_table\"].head()"
      ],
      "execution_count": 6,
      "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>DESCRIPTION</th>\n",
              "      <th>household_key</th>\n",
              "      <th>CAMPAIGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TypeA</td>\n",
              "      <td>17</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TypeA</td>\n",
              "      <td>27</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TypeA</td>\n",
              "      <td>212</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TypeA</td>\n",
              "      <td>208</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TypeA</td>\n",
              "      <td>192</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  DESCRIPTION  household_key  CAMPAIGN\n",
              "0       TypeA             17        26\n",
              "1       TypeA             27        26\n",
              "2       TypeA            212        26\n",
              "3       TypeA            208        26\n",
              "4       TypeA            192        26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ry97O6bjZzoe",
        "colab_type": "text"
      },
      "source": [
        "Out of 2500 households, **1584** of them received a campaign once, the rest never received a campaign."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFtg4xXFYYKF",
        "colab_type": "code",
        "outputId": "0548d49c-edf5-4d1c-c458-ccc525b91ddb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "total_households = len(dfs[\"transaction_data\"].household_key.unique())\n",
        "total_households"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tk2hb-m3Zps2",
        "colab_type": "code",
        "outputId": "d1605172-b0a7-449b-a4eb-b5603b395424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dfs[\"campaign_table\"].household_key.unique())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1584"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTNTIrP1W7ZC",
        "colab_type": "text"
      },
      "source": [
        "On the other side, a very small portion of the households received double-digit number of campaigns. We will investigate how this will effect churn rates in the following sections. The barchart below groups the households and shows counts of the group populations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kS3EhIhONOLL",
        "colab_type": "code",
        "outputId": "3d5d26f4-8896-4275-b661-744de52a0441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "no_of_received_campaigns_per_house = dfs[\"campaign_table\"].household_key.value_counts()\n",
        "no_of_received_campaigns = no_of_received_campaigns_per_house.value_counts()\n",
        "no_of_received_campaigns[0] = total_households-len(no_of_received_campaigns_per_house)\n",
        "no_of_received_campaigns = pd.DataFrame(list(zip(no_of_received_campaigns.index,no_of_received_campaigns)),columns=[\"Number of Campaigns Received\",\"Number of Households Reached To\"]).sort_values(by=\"Number of Campaigns Received\",ascending=True)\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.barplot(y=\"Number of Campaigns Received\",x=\"Number of Households Reached To\",data=no_of_received_campaigns,orient=\"h\",order=no_of_received_campaigns[\"Number of Campaigns Received\"])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb92e091d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qaMmSJerbt68SExNLbSthCcNUaGiwtTG9QH5RoXKzmUYEAMBbOC287Ha7Tpw4obVr13ps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\nrQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lt3USH1hz9Dh",
        "colab_type": "text"
      },
      "source": [
        "Graph below gives the most frequent campaigns. Campaign number **18, 13 and 8** being the most frequent ones reaching 1000 and more households each (no campaigns applied to same customer more than once therefore Frequency axis gives unique number of households)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SARc_ruFzIs_",
        "colab_type": "code",
        "outputId": "ed2a8d65-cfc8-426c-fb27-b6a66ec7d4bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "freq_campaigns = pd.DataFrame(list(zip(dfs[\"campaign_table\"].CAMPAIGN.value_counts().index, dfs[\"campaign_table\"].CAMPAIGN.value_counts())),columns=[\"Campaign\",\"Frequency\"])\n",
        "fig = plt.figure(figsize=(10,7))\n",
        "sns.barplot(y=\"Campaign\",x=\"Frequency\",data = freq_campaigns,orient=\"h\",order=freq_campaigns.Campaign)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb9295a518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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envbt26eqqiolJydr6tSpOnnypF5++WVJ0ogRI5Sdne1UeAAAAL9j2bZtu53E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\nAGAIjRcAAIAhNF4AAACG/D9vUAVcSHSsGAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gw5fOtQ9xgcy",
        "colab_type": "text"
      },
      "source": [
        "### Coupons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqJ36JOjyAfY",
        "colab_type": "text"
      },
      "source": [
        "Is a lookup table which lists all the coupons sent to customers as part of a campaign, as well as the products for which each coupon is redeemable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAvdjPA8ECPJ",
        "colab_type": "code",
        "outputId": "be3a3233-6630-426f-b2ed-c8c769ffd7a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"coupon\"].head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>COUPON_UPC</th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>CAMPAIGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000089061</td>\n",
              "      <td>27160</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000089064</td>\n",
              "      <td>27754</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10000089073</td>\n",
              "      <td>28897</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51800009050</td>\n",
              "      <td>28919</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52100000076</td>\n",
              "      <td>28929</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    COUPON_UPC  PRODUCT_ID  CAMPAIGN\n",
              "0  10000089061       27160         4\n",
              "1  10000089064       27754         9\n",
              "2  10000089073       28897        12\n",
              "3  51800009050       28919        28\n",
              "4  52100000076       28929        25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5DYbMsX_Iz6j"
      },
      "source": [
        "### Coupon Redemptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmNGq2jqHO0n",
        "colab_type": "text"
      },
      "source": [
        "Is a data table, ordered by ***household_key***, gives which household redeemed what coupon number. Also the day of the redeem and the campaign number is given for each household."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PTi_vR8HPE9",
        "colab_type": "code",
        "outputId": "9bcbef16-3cba-48ec-9058-ee57b62b9b3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"coupon_redempt\"].head()"
      ],
      "execution_count": 12,
      "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>household_key</th>\n",
              "      <th>DAY</th>\n",
              "      <th>COUPON_UPC</th>\n",
              "      <th>CAMPAIGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>421</td>\n",
              "      <td>10000085364</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>421</td>\n",
              "      <td>51700010076</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>427</td>\n",
              "      <td>54200000033</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>597</td>\n",
              "      <td>10000085476</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>597</td>\n",
              "      <td>54200029176</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   household_key  DAY   COUPON_UPC  CAMPAIGN\n",
              "0              1  421  10000085364         8\n",
              "1              1  421  51700010076         8\n",
              "2              1  427  54200000033         8\n",
              "3              1  597  10000085476        18\n",
              "4              1  597  54200029176        18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2w9_jBuJhjo",
        "colab_type": "text"
      },
      "source": [
        "Out of 2500 households, only 434 of them (**17%**) redeemed coupons within this period."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGXKdZnDJhYl",
        "colab_type": "code",
        "outputId": "73d662ae-41ab-4ba0-c1d1-f9aee66d9c2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dfs[\"coupon_redempt\"].household_key.unique())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "434"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCKs7Xk5J-In",
        "colab_type": "text"
      },
      "source": [
        "Most frequent campaigns (18, 13 and 8, **all belongs to TypeA**) attracts the most number of redeems as expected. The barchart below gives the number of redeems per campaign."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuPIVbC7J9-5",
        "colab_type": "code",
        "outputId": "3c16080a-da3a-4be7-ecac-d34656686879",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "redeem_frequency = pd.DataFrame(list(zip(dfs[\"coupon_redempt\"].CAMPAIGN.value_counts().index, dfs[\"coupon_redempt\"].CAMPAIGN.value_counts())),columns=[\"Campaign\",\"No of Redeems\"])\n",
        "fig = plt.figure(figsize=(10,7))\n",
        "sns.barplot(y=\"Campaign\",x=\"No of Redeems\",data = redeem_frequency,orient=\"h\",order=redeem_frequency.Campaign)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb9272aac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gjfr888/tLgMAAMB2tgevv/3tb7r66qvVsWNHdejQQbGxsVq3bp3dZQAAANjO9uAVFRWl\nXbt2qbKyUqdOndKmTZt0+PBhu8sAAACwne031/fr10/333+/xo0bp44dO2rgwIEKCuJUCwAA0PY5\nknhGjhypZcuWacGCBeratav69u3rRBkAAAC2ciR4VVRUSJK++uorrVu3TmlpaU6UAQAAYCtHzvGa\nMGGCqqqq1KFDBz3xxBMKCwtzogwAAABbORK83n777RY9vtFs/L+DS51XW3fa6RIAAECACoiT62Ua\n+poh2QAAIMAFRvDyo1mNZ+rO6HhVrdNlAACAABQQwSvICFLW8mSny5Ak/W74+5IIXgAAoOUs+1Rj\nbm6u4uLilJqa2uT6m2++qeTkZKWkpOiZZ56xankAAAC/Y9mOV0ZGhsaMGaMpU6Z4r23fvl1FRUV6\n9913FRIS4j1WAgAAoD2wbMcrNjZWXbt2bXJt4cKFGj9+vEJCQiRJERERVi0PAADgd2w9QPXgwYP6\n6KOPNHLkSI0ZM0Z79uyxc3kAAABH2XpzfUNDg44dO6bFixdr7969mjhxooqKimQYhp1lAAAAOMLW\nHa+ePXtqyJAhMgxDV199tYKCglRZWWlnCQAAAI6xNXjdfvvt2rFjhySppKREdXV16t69u50lAAAA\nOMaytxpzcnK0c+dOVVZWKiEhQRMmTFBmZqby8vKUmpqqSy65RDNnzuRtRgAA0G5YFryef/75815/\n7rnnrFoSAADArwXEyfWNZuP/nRjvvDN1Z5wuAQAABKiACF4MyQYAAG1BYAQvm4dkn66rVXUVO1sA\nAKB1BUTwCjKCdIfnIdvWe8/9sqpF8AIAAK3L9iHZkjRv3jwNGDBAR48etWp5AAAAv2NZ8MrIyFBB\nQcG3rpeVlWnLli26/PLLrVoaAADAL9k6JFuSZsyYoccee4zzuwAAQLtj68n169evl8vl0sCBA+1c\nFgAAwC/YdnP9qVOnNGfOHM2bN8+uJQEAAPyKbTteX3zxhUpLS+V2u5WYmKjDhw8rIyNDX3/9tV0l\nAAAAOMq2Ha8BAwZo27Zt3u8TExNVWFio8PBwu0oAAABwlGU7Xjk5ORo9erRKSkqUkJCgJUuWWLUU\nAABAQDBM0zSdLuL7NJqNCjLs+xxAWzy5vlu3TqqqOul0GQGPPvqOHvqOHrYO+ug7enh+F5q2ExAn\n1zOrEQAAtAW2HicBAADQngXGjpeNQ7Lb4tuMAADAPwRE8AoygjRs+TRb1loz/FcMyAYAAJawLHiV\nlZVp8uTJqqiokGEYGjVqlMaOHauqqipNmjRJX375pXr37q3Zs2efd7QQAABAW2PZPV7BwcGaOnWq\n1qxZo0WLFuntt9/WX//6V82dO1dxcXFat26d4uLiNHfuXKtKAAAA8CuWBS+Xy6Xo6GhJUmhoqCIj\nI1VeXq6ioiKlp6dLktLT07V+/XqrSgAAAPArtnyqsbS0VPv371dMTIwqKirkcrkkST169FBFRYUd\nJQAAADjO8uBVU1Oj7Oxs5eXlKTQ0tMnPDMOQYRhWlwAAAOAXLA1edXV1ys7OVlpampKSkiRJERER\nOnLkiCTpyJEjzGoEAADthmXByzRN5efnKzIyUllZWd7riYmJWrFihSRpxYoVuu2226wqAQAAwK9Y\ndpzErl275PF4FBUVJbfbLens4Ozx48dr4sSJKiws1OWXX67Zs2dbVQIAAIBfYUj2P2irJ9czyLR1\n0Eff0UPf0cPWQR99Rw/PjyHZAAAAfiAwgpdx4fToq9N1daquOm3Z6wMAAEgBEryCDEMpy1607PVX\nZ2SrWgQvAABgLctunMrNzVVcXJxSU1O91w4cOKC77rpLaWlp+vd//3edOHHCquUBAAD8TrN2vEpK\nSvTaa6/pq6++Un19vff6G2+88Z3PycjI0JgxYzRlyhTvtfz8fE2ZMkU33HCDCgsLVVBQoIkTJ/pQ\nPgAAQOBoVvD65S9/qdGjR2vUqFEKCmreJllsbKxKS0ubXDt48KBiY2MlSfHx8Ro3bhzBCwAAtBvN\nCl4dOnTQz372M58X69+/v4qKinT77bfr/fffV1lZmc+vCQAAECiatX116623asGCBTpy5Iiqqqq8\n/7TU008/rbffflsZGRmqqalRSEhIi18DAAAgUDVrx2v58uWSpNdee817zTAMFRUVtWixfv36ad68\neZLO3je2cePGFj0fAAAgkDUreG3YsKFVFquoqFBERIQaGxv16quvavTo0a3yugAAAIGgWcFr3bp1\n37rWpUsXRUVFKSIi4rzPycnJ0c6dO1VZWamEhARNmDBBJ0+e1Ntvvy1JGjJkiDIzM30oHQAAILA0\nK3gVFhbq448/1qBBgyRJO3fuVHR0tEpLS/WLX/xC6enp33rO888/f97XGjt2rA/lAgAABK5mBa+G\nhgatWbNGl112mSTpm2++0ZQpU7R48WKNGTPmvMGrNTWaplZnZFv2+qfr6ix7bQAAgHOaFbzKysq8\noUuSIiIiVFZWpm7duqlDBxumDpliSDYAAAh4zUpNN9xwgx544AElJydLktauXasbbrhBJ0+eVJcu\n1g2v9rJgSDaDsQEAgN0M0zTN73uQaZpau3at/vjHP0qSrrvuOg0dOlSGYVhe4DkpSwta9fVWZ96v\nr79uP7to3bp1UlXVSafLCHj00Xf00Hf0sHXQR9/Rw/O70GZRs3a8DMNQcnKyd8erOXJzc7Vx40ZF\nRERo1apVkqRZs2bpww8/1CWXXKIrrrhCM2bMUFhYWLNfEwAAIJBd8OT6u+++W5J07bXX6rrrrvP+\nc+77C8nIyFBBQdNdqvj4eK1atUorV65U3759NWfOHB/LBwAACBwX3PFauHChJGn37t0tfuHzDcke\nPHiw9+trrrlG77//fotfFwAAIFC16COJFRUVOnPmjPf7yy+//KIXXrp0qe64446Lfj4AAECgaVbw\nKioq0qxZs3TkyBGFh4frq6++Ur9+/bR69eqLWvTVV19VcHCw7rzzzot6PgAAQCC64D1e5/zmN7/R\nokWL1LdvX23YsEGvv/66YmJiLmrBZcuWaePGjXruueds/VQkAACA05oVvDp06KDu3bursbFRjY2N\nuvHGG7Vv374WL7Zp0yYVFBTo1VdfVceOHVv8fAAAgEDWrLcaw8LCVFNTo+uvv16PPvqowsPD1alT\npws+53xDsufOnava2lplZWVJkmJiYvTkk0/6/lsAAAAEgGYdoHry5EldeumlMk1TK1euVHV1tdLS\n0tS9e3c7alSjaSqold+WbG8n13PIXeugj76jh76jh62DPvqOHp6fzweodurUSV9//bX27Nmjrl27\navDgwbaFLknMagQAAG1Cs+7xWrJkiUaOHKkPPvhAa9eu1V133aXCwkKrawMAAGhTmrXjVVBQoOXL\nl3t3uSorKzV69GiNGDHC0uK8WnFIdnt7ixEAAPiPZgWv7t27q3Pnzt7vO3fubOtbjUGGodTCBa3y\nWqtG3KNqEbwAAID9mhW8rrjiCo0aNUq33XabDMNQUVGRBgwYoN/97neS5P2U4t8rKyvT5MmTVVFR\nIcMwNGrUKI0dO1YHDhzQE088oZMnT6p379567rnnFBoa2rq/FQAAgB9qdvC64oorvN/fdtttkqSa\nmprvfE5wcLCmTp2q6OhonThxQpmZmYqPj1d+fr6mTJmiG264QYWFhSooKNDEiRN9/DUAAAD8X7OC\n18MPP9ziF3a5XHK5XJKk0NBQRUZGqry8XAcPHlRsbKwkKT4+XuPGjSN4AQCAdqFZwWvv3r367W9/\nq6+++kr19fXe6ytXrmzWIqWlpdq/f79iYmLUv39/FRUV6fbbb9f777+vsrKyi6scAAAgwDQreD36\n6KOaPHmyoqKiFBTUrBMovGpqapSdna28vDyFhobq6aef1tNPP61XXnlFiYmJCgkJuajCAQAAAk2z\ngld4eLj3vq6WqKurU3Z2ttLS0pSUlCRJ6tevn+bNmydJKikp0caNG1v8ugAAAIGoWcErOztb+fn5\niouLa7JDdS5MnY9pmsrPz1dkZGSTTz1WVFQoIiJCjY2NevXVVzV69GgfygcAAAgczQpeS5cu1Wef\nfab6+vombzVeKHjt2rVLHo9HUVFRcrvdks4Ozj548KDefvttSdKQIUOUmZnpS/0AAAABo1lDsocO\nHaq1a9faUc95teaQ7PZ6cj2DTFsHffQdPfQdPWwd9NF39PD8fB6Sfd111+mvf/2rrrzyylYrqkUY\nkg0AANqAZgWvjz/+WOnp6erdu3eTe7yae5yEz1o4q/F0Xb2qq05ZWBAAAEDLNXtItpOCDENphcua\n/fiVIzLE/hgAAPA3zTqUq3fv3urdu7d+8IMfyDAM7z8Xkpubq7i4OKWmpnqvVVVVKSsrS0lJScrK\nytKxY8d8qx4AACCANCt4FRUVKSkpSbfddpvGjBmjxMRE/du//dsFn5ORkfGtnbK5c+cqLi5O69at\nU1xcnObOnXvxlQMAAASYZgWv3/zmN1q0aJH69u2rDRs26PXXX1dMTMwFnxMbG6uuXbs2uVZUVKT0\n9HRJUnp6utavX3+RZQMAAASeZgWvDh06qHv37mpsbFRjY6NuvPFG7du3r8WLVVRUeAdn9+jRQxUV\nFS1+DQAAgEDVrJvrw8LCVFNTo9jYWD366KMKDw9Xp06dfFq4OfeJAQAAtCUX3PH6/PPPtWvXLr3y\nyivq2LGjcnNzdfPNN6tbt2761a9+1eLFIiIidOTIEUnSkSNHFB4efnFVAwAABKALBq/p06crNDRU\nnTp1UlBQkDp06KDhw4dryJAh+u///u8WL5aYmKgVK1ZIklasWHFRg7cBAAAC1QWD1zfffKMBAwZ8\n6/qAAQP05ZdfXvCFc3JyNHr0aJWUlCghIUFLlizR+PHjtWXLFiUlJWnr1q0aP368b9UDAAAEkAve\n41Vd/d3HkJ4+feF5h88///x5r8+fP78ZZQEAALQ9FwxeP/7xj7V48WKNGjWqyfUlS5YoOjra0sL+\nXqNpauWIjGY//nRdvYXVAAAAXJwLBq+8vDw9/PDDWrlypTdo7du3T3V1dRd1j9dFY0g2AABoAy4Y\nvC677DK988472r59uz799FNJ0i233KK4uDhbivNqwZBsBmQDAAB/1axzvG688UbdeOONVtfynYIM\nQ+7C95v1WM+IZAZkAwAAv9Ss4NXaEhMT1blzZwUFBSk4OFjLli1zogwAAABbORK8pLOfbuQAVQAA\n0J40a1YjAAAAfOdY8Bo3bpwyMjK0aNEip0oAAACwlSNvNS5cuFA9e/ZURUWFsrKyFBkZqdjYWCdK\nAQAAsI0jO149e/aUdHZo9pAhQ7Rnzx4nygAAALCV7cHr5MmTOnHihPfrLVu2qH///naXAQAAYDvb\n32qsqKjQQw89JElqaGhQamqqEhIS7C4DAADAdrYHrz59+ujdd99t0XMaTVOeEcnNeixzGgEAgL9y\n7ByvFmFWIwAAaAM4xwsAAMAmgbHj1Ywh2QzHBgAA/i4ggleQYWj40t9f8DHLMwczHBsAAPg1x95q\nbGhoUHp6uh544AGnSgAAALCVY8HrjTfeUL9+/ZxaHgAAwHaOBK/Dhw9r48aNGjFihBPLAwAAOMKR\n4DV9+nQ99thjCgriQ5UAAKD9sD35fPjhhwoPD9ePf/xju5cGAABwlO2favzjH/+oDRs2aNOmTTpz\n5oxOnDihRx99VM8995zdpQAAANjK9uD1yCOP6JFHHpEk7dixQ/PmzSN0AQCAdoGbrAAAAGzi6AGq\ngwYN0qBBg773cY2mqeWZgy/4GIZjAwAAfxcQJ9czJBsAALQFvNUIAABgk8DY8fqeIdmn6xpUXXXS\nxoIAAABaLiCCV5BhaOTSPd/58yWZVzMgGwAA+D3L3mosKyvTvffeq2HDhiklJUXz589v8vN58+Zp\nwIABOnr0qFUlAAAA+BXLdryCg4M1depURUdH68SJE8rMzFR8fLyuvPJKlZWVacuWLbr88sutWh4A\nAMDvWLbj5XK5FB0dLUkKDQ1VZGSkysvLJUkzZszQY489JsMwrFoeAADA79jyqcbS0lLt379fMTEx\nWr9+vVwulwYOHGjH0gAAAH7D8pvra2pqlJ2drby8PAUHB2vOnDmaN2+e1csCAAD4HUt3vOrq6pSd\nna20tDQlJSXpiy++UGlpqdxutxITE3X48GFlZGTo66+/trIMAAAAv2DZjpdpmsrPz1dkZKSysrIk\nSQMGDNC2bdu8j0lMTFRhYQj9glIAABctSURBVKHCw8OtKgMAAMBvWLbjtWvXLnk8Hm3fvl1ut1tu\nt1vFxcVWLQcAAOD3DNM0TaeL+D6NpqmgC3wCkpPrv1+3bp1URY98Rh99Rw99Rw9bB330HT08vwtN\n2wmIk+sZkg0AANqCwAhe3zGr8Uxdg46TtAEAQIAIiOAVZBjKXn7oW9dfHN7HgWoAAAAujmU31+fm\n5iouLk6pqanea++9955SUlI0cOBA7d2716qlAQAA/JJlwSsjI0MFBQVNrkVFRemll15SbGysVcsC\nAAD4LcveaoyNjVVpaWmTa/369bNqOQAAAL9ny6xGAAAAELwAAABsQ/ACAACwCcELAADAJpbdXJ+T\nk6OdO3eqsrJSCQkJmjBhgrp166annnpKR48e1QMPPKCrrrpKr732mlUlAAAA+JWAntXIyfXNxzyt\n1kEffUcPfUcPWwd99B09PD9mNQIAAPgB7vECAACwSUDseBnfMSS7tq5Rx6pqHKgIAACg5QIkeBl6\neXn5t64/NLynA9UAAABcHNuD12effaZJkyZ5vz906JCys7N133332V0KAACArWwPXpGRkfJ4PJKk\nhoYGJSQkaMiQIXaXAQAAYDtHb67ftm2b+vTpo969eztZBgAAgC0cDV6rV69WamqqkyUAAADYxrHg\nVVtbqw0bNig5OdmpEgAAAGzlWPDatGmToqOjddlllzlVAgAAgK0cC16rV69WSkqKU8sDAADYzpHg\ndfLkSW3dulVJSUlOLA8AAOAIRw5Q7dSpk3bs2NHsx5umed7DUmvrGluzLAAAAEsFxMn1pil9w5Bs\nAAAQ4BiSDQAAYJOA2PH6xyHZdXWNqmI4NgAACDABErwMLSv8xvt9xgiOoAAAAIHHsrcay8rKdO+9\n92rYsGFKSUnR/PnzJUnvvfeeUlJSNHDgQO3du9eq5QEAAPyOZTtewcHBmjp1qqKjo3XixAllZmYq\nPj5eUVFReumll/TEE09YtTQAAIBfsix4uVwuuVwuSVJoaKgiIyNVXl6u+Ph4q5YEAADwa7Z8qrG0\ntFT79+9XTEyMHcsBAAD4JcuDV01NjbKzs5WXl6fQ0FCrlwMAAPBblgavuro6ZWdnKy0tjfFAAACg\n3bMseJmmqfz8fEVGRiorK8uqZQAAAAKGZTfX79q1Sx6PR1FRUXK73ZKknJwc1dbW6qmnntLRo0f1\nwAMP6KqrrtJrr71mVRkAAAB+wzBN03S6iO9jmqYMw/B+z8n1LdetWydVVZ10uoyARx99Rw99Rw9b\nB330HT08v7+ftvOPAuLkeoZkAwCAtiAgghezGgEAQFsQIMHL0IcLvvZ+f+s9PRysBgAA4OLYHrzK\nyso0efJkVVRUyDAMjRo1SmPHjrW7DAAAANvZHry+a4bjlVdeaXcpAAAAtrJlZNDfc7lcio6OltR0\nhiMAAEBbZ3vw+nvMcAQAAO2JY8GLGY4AAKC9cSR4McMRAAC0R7YHL2Y4AgCA9sr24HVuhuP27dvl\ndrvldrtVXFxsdxkAAAC2s/04ieuvv15//vOfW/Qc0zSbHJpaV9fY2mUBAABYLiBOrmdWIwAAaAsc\nPU4CAACgPQmI4HVuSHb3rp2dLgUAAOCiBUjwMrS74Ig6hAREuQAAAOdl+z1eZ86c0T333KPa2lo1\nNDRo6NChys7OtrsMAAAA29kevEJCQjR//nx17txZdXV1+tnPfqaEhARdc801dpcCAABgK9vfuzMM\nQ507n71Xq76+XvX19TIMw+4yAAAAbOfITVMNDQ1yu9266aabdNNNNzEkGwAAtAuOBK/g4GB5PB4V\nFxdrz549+stf/uJEGQAAALZy9GOCYWFhGjRokDZv3uxkGQAAALawPXgdPXpUx48flySdPn1aW7du\nVWRkpN1lAAAA2M72TzUeOXJEU6dOVUNDg0zTVHJysm699Va7ywAAALCd7cFr4MCBWrFiRYueY5qm\nrr3fpfpahmMDAIDAxZBsAAAAmzCDBwAAwCYBEbzODckOZ0g2AAAIYAESvAwdnH1YwQzJBgAAAcyy\nJJObm6u4uDilpqZ6r7300ku6+eab5Xa75Xa7VVxcbNXyAAAAfseym+szMjI0ZswYTZkypcn1++67\nT+PGjbNqWQAAAL9l2Y5XbGysunbtatXLAwAABBzbb5pasGCB0tLSlJubq2PHjtm9PAAAgGNsDV53\n3323PvjgA3k8HrlcLs2cOdPO5QEAABxla/C67LLLFBwcrKCgII0cOVJ79+61c3kAAABH2Rq8jhw5\n4v16/fr16t+/v53LAwAAOMqyTzXm5ORo586dqqysVEJCgiZMmKCdO3fqwIEDkqTevXvrySeftGp5\nAAAAv2OYpmk6XcT3MU1ThmGoobZRR4/VOF1OQOrWrZOqqk46XUbAo4++o4e+o4etgz76jh6eX48e\nXb7zZwzJBgAAsElAzOAxJIV37eR0GQAAAD4JjOAVZCg4JNjpMgAAAHxiWfAqKyvTvffeq2HDhikl\nJUXz58+XJO3fv1+jRo2S2+1WRkaG9uzZY1UJAAAAfsWye7yCg4M1depURUdH68SJE8rMzFR8fLye\nffZZPfTQQ7rllltUXFysZ599Vm+++aZVZQAAAPgNy4KXy+WSy+WSJIWGhioyMlLl5eUyDEM1NWc/\nmVhdXe19DAAAQFtny6caS0tLtX//fsXExCgvL0/jxo3TrFmz1NjYqHfeeceOEgAAABxn+c31NTU1\nys7OVl5enkJDQ7Vw4ULl5uaquLhYubm5ys/Pt7oEAAAAv2Bp8Kqrq1N2drbS0tKUlJQkSVq+fLn3\n6zvuuIOb6wEAQLthWfAyTVP5+fmKjIxUVlaW97rL5dLOnTslSdu3b1ffvn2tKgEAAMCvWHaP165d\nu+TxeBQVFSW32y3p7PzGp556StOnT1d9fb0uvfRS5jUCAIB2w7Lgdf311+vPf/7zeX+2bNmyFr2W\n2Wiqsb6xNcoCAABwTECcXG9KOnqMIZwAACCwBUTwAgAAaAsCIngZBkOyAQBA4AuQ4MWQbAAAEPgs\nu7m+rKxMkydPVkVFhQzD0KhRozR27FhNnDhRJSUlks6ODOrSpYs8Ho9VZQAAAPgN24dkz5492/uY\nmTNnKjQ01KoSAAAA/IplbzW6XC5FR0dLajok+xzTNPXee+8pNTXVqhIAAAD8ii33eP39kOxzPvro\nI0VERHByPQAAaDdsH5J9zqpVq9jtAgAA7YrtQ7Ilqb6+Xh988IGGDRtm5fIAAAB+xfYh2ZK0detW\nRUZGqlevXlYtDwAA4HcsC17nhmRv375dbrdbbrdbxcXFkqQ1a9YoJSXFqqUBAAD8kmGapul0Ed/H\nNE011jUyr9EH3bp1UlUV/fMVffQdPfQdPWwd9NF39PD8evTo8p0/C4iT602TIdkAACDwBUTwAgAA\naAsCInidHZLd0ekyAAAAfBIgwctQcIhl040AAABs4Ujwys3NVVxcHAeoAgCAdsWR4JWRkaGCggIn\nlgYAAHCMI8ErNjZWXbt2dWJpAAAAxwTEPV4AAABtAcELAADAJgQvAAAAmxC8AAAAbOJI8MrJydHo\n0aNVUlKihIQELVmyxIkyAAAAbOXIqaTPP/98ix5/dkh2g0XVAAAA2CMg3mo8OyT7lNNlAAAA+CQg\nghezGgEAQFsQIMGLWY0AACDwORK8jh8/ruzsbCUnJ+uOO+7Q7t27nSgDAADAVo5sIz399NO6+eab\n9eKLL6q2tlanT592ogwAAABb2b7jVV1drT/84Q8aMWKEJCkkJERhYWF2lwEAAGA724NXaWmpwsPD\nlZubq/T0dOXn5+vkyZN2lwEAAGA724NXfX29PvnkE919991asWKFOnbsqLlz59pdBgAAgO1sD169\nevVSr169FBMTI0lKTk7WJ598YncZAAAAtrM9ePXo0UO9evXSZ599Jknatm2b+vXrZ3cZAAAAtnPk\nU42PP/64Hn30UdXV1alPnz6aMWOGE2UAAADYypHgddVVV2nZsmVOLA0AAOCYgDgOniHZAACgLTBM\n0zSdLgIAAKA9CIhZjQAAAG0BwQsAAMAmBC8AAACbELwAAABsQvACAACwCcELAADAJgQvAAAAm/h1\n8Nq0aZOGDh2qIUOGaO7cuU6X49dyc3MVFxen1NRU77WqqiplZWUpKSlJWVlZOnbsmKSzB9JOmzZN\nQ4YMUVpamv70pz85VbZfKSsr07333qthw4YpJSVF8+fPl0QfW+LMmTMaMWKE7rzzTqWkpOjFF1+U\nJB06dEgjR47UkCFDNHHiRNXW1kqSamtrNXHiRA0ZMkQjR45UaWmpk+X7nYaGBqWnp+uBBx6QRB9b\nKjExUWlpaXK73crIyJDE3/PFOH78uLKzs5WcnKw77rhDu3fvpo++MP1UfX29edttt5lffPGFeebM\nGTMtLc389NNPnS7Lb+3cudPct2+fmZKS4r02a9Ysc86cOaZpmuacOXPMZ555xjRN09y4caM5btw4\ns7Gx0dy9e7c5YsQIR2r2N+Xl5ea+fftM0zTN6upqMykpyfz000/pYws0NjaaJ06cME3TNGtra80R\nI0aYu3fvNrOzs81Vq1aZpmmajz/+uLlgwQLTNE3zrbfeMh9//HHTNE1z1apV5i9/+UtnCvdT8+bN\nM3Nycszx48ebpmnSxxa69dZbzYqKiibX+HtuucmTJ5uLFy82TdM0z5w5Yx47dow++sBvd7z27Nmj\nf/qnf1KfPn0UEhKilJQUFRUVOV2W34qNjVXXrl2bXCsqKlJ6erokKT09XevXr29y3TAMXXPNNTp+\n/LiOHDlie83+xuVyKTo6WpIUGhqqyMhIlZeX08cWMAxDnTt3liTV19ervr5ehmFo+/btGjp0qCRp\n+PDh3r/lDRs2aPjw4ZKkoUOHatu2bTIZpiFJOnz4sDZu3KgRI0ZIOruTQB99x99zy1RXV+sPf/iD\n99/DkJAQhYWF0Ucf+G3wKi8vV69evbzf9+zZU+Xl5Q5WFHgqKirkcrkkST169FBFRYWkb/e2V69e\n9PYflJaWav/+/YqJiaGPLdTQ0CC3262bbrpJN910k/r06aOwsDB16HB2NOzf96m8vFw//OEPJUkd\nOnRQly5dVFlZ6Vjt/mT69Ol67LHHFBR09j/TlZWV9PEijBs3ThkZGVq0aJEk/rvYUqWlpQoPD1du\nbq7S09OVn5+vkydP0kcf+G3wQusyDEOGYThdRkCoqalRdna28vLyFBoa2uRn9PH7BQcHy+PxqLi4\nWHv27NFnn33mdEkB58MPP1R4eLh+/OMfO11KQFu4cKGWL1+u//mf/9GCBQv0hz/8ocnP+Xv+fvX1\n9frkk0909913a8WKFerYseO37rmmjy3jt8GrZ8+eOnz4sPf78vJy9ezZ08GKAk9ERIR3i/fIkSMK\nDw+X9O3eHj58mN7+n7q6OmVnZystLU1JSUmS6OPFCgsL06BBg/Txxx/r+PHjqq+vl9S0Tz179lRZ\nWZmks/+Br66uVvfu3R2r2V/88Y9/1IYNG5SYmKicnBxt375dTz/9NH1soXP9iYiI0JAhQ7Rnzx7+\nnluoV69e6tWrl2JiYiRJycnJ+uSTT+ijD/w2eP3kJz/RwYMHdejQIdXW1mr16tVKTEx0uqyAkpiY\nqBUrVkiSVqxYodtuu63JddM09fHHH6tLly7eLeP2zDRN5efnKzIyUllZWd7r9LH5jh49quPHj0uS\nTp8+ra1bt6pfv34aNGiQ1q5dK0lavny59285MTFRy5cvlyStXbtWN954I//nLOmRRx7Rpk2btGHD\nBj3//PO68cYb9V//9V/0sQVOnjypEydOeL/esmWL+vfvz99zC/Xo0UO9evXy7lxv27ZN/fr1o48+\nMEw/vgOzuLhY06dPV0NDgzIzM/Xggw86XZLfysnJ0c6dO1VZWamIiAhNmDBBt99+uyZOnKiysjJd\nfvnlmj17trp16ybTNPXkk09q8+bN6tixo6ZPn66f/OQnTv8Kjvvoo490zz33KCoqyntfTU5Ojq6+\n+mr62EwHDhzQ1KlT1dDQINM0lZycrIcffliHDh3SpEmTdOzYMV111VV67rnnFBISojNnzuixxx7T\n/v371bVrV73wwgvq06eP07+GX9mxY4fmzZunOXPm0McWOHTokB566CFJZ+87TE1N1YMPPqjKykr+\nnlto//79ys/PV11dnfr06aMZM2aosbGRPl4kvw5eAAAAbYnfvtUIAADQ1hC8AAAAbELwAgAAsAnB\nCwAAwCYELwAAAJsQvADYYsCAAZo5c6b3+9dee00vvfSSz69bW1ur++67T263W2vWrGnys6lTpyox\nMVFut1t33nmntm3b1qLXLi0tVWpqqs81AsA5BC8AtggJCdG6det09OjRVn3dTz75RJLk8Xg0bNiw\nb/188uTJ8ng8ysvL0xNPPNGqawNASxG8ANiiQ4cOuuuuuzR//vxv/ay0tFT/+q//qrS0NI0dO1Zf\nffXVtx5TVVWlX/ziF0pLS9OoUaN04MABVVRU6LHHHtPevXvldrv1xRdffOf61157bZNhvfv27dOY\nMWOUkZGhcePGecef7Nu3T3feeafuvPNOLViwwPv4hoYGzZo1S5mZmUpLS9M777zj/VlBQYH3+osv\nvuj9nZKTkzV16lQNHTpUjzzyiLZu3arRo0crKSlJe/bskSTt3LlTbrdbbrdb6enp3tPWAbRNBC8A\ntrnnnnu0cuVKVVdXN7k+bdo0DR8+XCtXrlRaWpqmTZv2ree+9NJL+pd/+RetXLlSkyZN0pQpUxQR\nEaFp06bp+uuvl8fj0RVXXPGda2/evFm33367pLMzOadNm6YXX3xRy5YtU2Zmpl544QVJUm5urh5/\n/HG9++67TZ5fWFioLl26aOnSpVq6dKkWL16sQ4cO6fe//70+//xzFRYWyuPx6E9/+pN3GPMXX3yh\nrKwsvffeeyopKdHKlSu1cOFCTZ48Wb/97W8lSfPmzdN//Md/yOPxaMGCBfrBD35w8Q0G4Pc6OF0A\ngPYjNDRUbrdbb7zxRpOAsXv3bu/9Xm63W88+++y3nrtr1y7vY+Li4lRVVdWs3aFnnnlGL7zwgg4f\nPqxFixZJkkpKSvSXv/zFO5OzsbFRPXr00PHjx1VdXa3Y2FhvLZs3b5YkbdmyRX/+85+9sxKrq6v1\n+eefa8uWLdqyZYvS09MlnZ0LePDgQf3whz/Uj370Iw0YMECSdOWVVyouLk6GYWjAgAH68ssvJUnX\nXXedZs6c6R3M3rlz5xZ2FUAgIXgBsNXYsWOVkZGhjIwMW9abPHmykpOT9eabbyovL0/Lli2TaZrq\n37+/N4idc27A9/mYpqlf/epXuvnmm5tc//3vf6/x48dr9OjRTa6XlpYqJCTE+31QUJD3e8Mw1NDQ\nIEkaP368brnlFhUXF+vuu+9WQUGB+vXr59PvDMB/8VYjAFt169ZNycnJKiws9F679tprtXr1aknS\nypUrdf3113/reddff7337b8dO3aoe/fuCg0Nbfa6Y8aMUWNjozZv3qx//ud/1tGjR7V7925JZ996\n/PTTTxUWFqYuXbroo48+8tZyzuDBg7Vw4ULV1dVJOrtrdvLkSQ0ePFhLly5VTU2NJKm8vFwVFRXN\nruuLL77QgAEDNH78eP3kJz9RSUlJs58LIPCw4wXAdj//+c+b3Lj++OOPKzc3V6+99prCw8M1Y8aM\nbz3n4YcfVl5entLS0tSxY8cmR1M0h2EYevDBB1VQUKCbb75ZL774oqZNm6bq6mo1NDRo7Nix6t+/\nv2bMmKG8vDwZhqH4+Hjv80eOHKkvv/xSGRkZMk1T3bt31yuvvKLBgwfrb3/7m3fHq1OnTnr22WcV\nFNS8/6+dP3++duzYIcMw1L9/fyUkJLTo9wIQWAzTNE2niwAAAGgPeKsRAADAJgQvAAAAmxC8AAAA\nbELwAgAAsAnBCwAAwCYELwAAAJsQvAAAAGzy/wBeBliF+ltSvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQLZHFVpOl9n",
        "colab_type": "text"
      },
      "source": [
        "The barchart below gives the redeem rates of each campaign in a descending order. Campaign 13 and 18 are clearly have higher redeem rates (~**60%**) as compared to the rest of campaigns.\n",
        "\n",
        "Based on these results, campaign organisers did well by promoting the right campaigns (13 and 18) more frequently."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YldI0YUeOlyc",
        "colab_type": "code",
        "outputId": "9f7dcdbb-45e1-4708-f741-e55ae9d7e675",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "source": [
        "redems_per_camp = dfs[\"coupon_redempt\"].CAMPAIGN.value_counts().sort_values()/dfs[\"campaign_table\"].CAMPAIGN.value_counts().sort_values()\n",
        "redems_per_camp = pd.DataFrame(list(zip(redems_per_camp.index,redems_per_camp)),columns=[\"Campaign\",\"Redeem_Rate\"]).sort_values(by=\"Redeem_Rate\",ascending=False)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.barplot(y=\"Campaign\",x=\"Redeem_Rate\",data=redems_per_camp,orient=\"h\",order=redems_per_camp.Campaign)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb9262c588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VmZmpnJwchYSEyOPx6MSJE1q2bJkOHDigCRMmqLCwUJZlOR0FAADAVY7ueNXV1SkzM1Op\nqalKSkqSJEVGRmrAgAGyLEs33nijAgICVFVV5WQMAAAAn+BY42XbtnJzcxUVFaXRo0c3Xu/fv7/e\neecdSVJxcbHq6urUsWNHp2IAAAD4DMduNe7du1f5+fmKiYlRWlqapC8HZw8bNkw5OTlKSUlRq1at\nNG3aNG4zAgCAFsEvhmQ32A0KYEg2cF6cXP/tmItpDrU2h1qb0+KGZMu29DlDso3gF9kcag0ALY9/\nNF7MajSKWpvzVa3P1p3VF8drXU4DAHCaXzReAVaARq8e5HYMwDGvDH1DEo0XADR3xmc1fvDBB3ri\niSdUU1Ojzp07a+bMmQoJCXEqBgAAgM9w7In1r2Y1rl+/XkuXLtWSJUt08OBB5ebm6pFHHtHatWvV\nv39/5eXlORUBAADApzjWeEVERCg2NlbSubMaS0pKFB8fL0lKSEjQpk2bnIoAAADgU4zParz++utV\nWFgoSXrjjTdUVlZmIgIAAIDrHG+8/nVW4+9+9zstWbJE6enpqq6uVnBwsNMRAAAAfIKjn2o836zG\n6OhozZs3T9KXI4O2bdvmZAQAAACfYXxWY2VlpSSpoaFBc+bM0YgRI5yKAAAA4FOMz2osKSnRkiVL\nJEkDBgzQsGHDnIoAAADgUxxrvG6++WZ9+OGH37h+2223adSoUU4tCwAA4LP84uT6Brvh/072Bpqn\ns3Vn3Y4AADDALxovhmSbw+Bmc6g1ALQ8/tF4MSTbKGr9TWfqanXyOLtSAIBL4xeNV4AVoDvzH3Q7\nBlqwDWkv6KRovAAAl8axxuvs2bO67777VFtbK4/Ho4EDByozM1OHDh1SVlaWjh8/rtjYWD3zzDMc\nogoAAFoEx87xCg4O1oIFC/T6669rzZo1euutt7Rv3z7NnDlTDzzwgDZv3qzQ0FCtWLHCqQgAAAA+\nxbHGy7IstWvXTpJUX1+v+vp6WZalt99+WwMHDpQkDR06tHFuIwAAQHPn6KxGj8ejtLQ0/ehHP9KP\nfvQjdenSRaGhoQoK+vIOZ6dOnVReXu5kBAAAAJ/haOMVGBio/Px8bd++Xfv379fHH3/s5HIAAAA+\nzdHG6yuhoaG65ZZbtG/fPn3xxReqr6+XJB05ckSRkZEmIgAAALjOscbr2LFj+uKLLyRJZ86c0a5d\nuxQdHa1bbrlFGzdulCStXr1a/fr1cyoCAACAT3HsOImKigpNnjxZHo9Htm1r0KBB6tu3r6677jo9\n/PDDmjVrlnr06KHhw4c7FQEAAMCnWLZt226H+DYNdoMCLCN3RYHzcuLkekYGmUOtzaHW5lBrc5pa\n6wtNgPGLk+uZ1WgOv8gAADiHbSQAAABD/GPHiyHZRrXkWjMMGwDgJL9ovAKsAA1e/Vu3Y6AFWD/0\ncYZhAwAc41jjVVZWpkmTJqmyslKWZSkjI0OjRo3S9OnT9eabb6pVq1b67ne/q6lTpyo0NNSpGAAA\nAD7DsWe8AgMDNXnyZK1fv15Lly7VkiVLdPDgQSUkJKigoEBr165V165d9dJLLzkVAQAAwKc41nhF\nREQoNjZWkhQSEqKoqCiVl5erT58+jbMae/bsqSNHjjgVAQAAwKcY+VRjaWmpioqKFBcXd871lStX\nKjEx0UQEAAAA1zneeFVXVyszM1M5OTkKCQlpvD5nzhwFBgbqxz/+sdMRAAAAfIKjn2qsq6tTZmam\nUlNTlZSU1Hh91apV2rZtm+bPny/LspyMAAAA4DMca7xs21Zubq6ioqI0evToxus7duxQXl6eXn31\nVbVp08ap5QEAAHyOY43X3r17lZ+fr5iYGKWlpUmSsrKy9Nvf/la1tbWNzVhcXJymTJniVAwAAACf\nwZBs4GtMnlzPXExzqLU51Nocam0OQ7LhGH6RAQBwDttIAAAAhvjHjpfVsgc3m9bSan2mrk4nj59x\nOwYAoAUw3nidPXtW9913n2pra+XxeDRw4EBlZmZe8GcCLEvJq543lBAtzbr0TJ0UjRcAwHleNV7F\nxcWaO3euDh8+rPr6+sbrCxcubPKCwcHBWrBggdq1a6e6ujrde++9SkxMVM+ePZv8XgAAAP7Eq8br\noYce0ogRI5SRkaGAgEt7LMyyLLVr106SVF9fr/r6eg5RBQAALYJXjVdQUJDuvffey7aox+NRenq6\nPv30U917773fmOEIAADQHHm1fdW3b18tXrxYFRUVOn78eOP/LlZgYKDy8/O1fft27d+/X//4xz8u\n+r0AAAD8hVc7XqtXr5YkzZ07t/GaZVkqLCy8pMVDQ0N1yy236K233lJMTMwlvRcAAICv86rx2rp1\n62Vb8NixYwoKClJoaKjOnDmjXbt26Wc/+9lle38AAABf5VXjtWnTpm9ca9++vWJiYhQeHt6kBSsq\nKjR58mR5PB7Ztq1Bgwapb9++TXoPAAAAf+RV47VixQrt27dPt9xyiyRpz549io2NVWlpqX7xi19o\nyJAhXi/YvXt3rVmz5uLSAgAA+DGvGi+Px6P169frqquukiQdPXpUjz32mJYtW6aRI0c2qfG6GA22\nrXXpFz5kFbhYZ+rq3I4AAGghvGq8ysrKGpsuSQoPD1dZWZmuvPJKBQUZOPzeFkOyDWFINgAAzvGq\na/rhD3+on//85xo0aJAkaePGjfrhD3+ompoatW9vYK4fsxqN8udaM3cRAODLLNu27W97kW3b2rhx\no/7yl79Ikm666SYNHDjQ6InzySvzjK0F/7Vu2Fh9/rl/7I6yu2gOtTaHWptDrc1paq0vtIHh1Y6X\nZVkaNGhQ446XN7Kzs7Vt2zaFh4eroKBAkjR9+nS9+eabatWqlb773e9q6tSpCg0N9fo9AQAA/NkF\nT66/5557JEn/+Z//qZtuuqnxf1/9+ULS09OVl3fuLlVCQoIKCgq0du1ade3aVS+99NIlxgcAAPAf\nF9zxeu211yRJ7733XpPfOD4+XqWlpedc69OnT+PXPXv21BtvvNHk9wUAAPBXTfpIYmVlpc6ePdv4\n52uuueaiF165cqXuvPPOi/55AAAAf+NV41VYWKjp06eroqJCYWFhOnz4sKKjo7Vu3bqLWnTOnDkK\nDAzUj3/844v6eQAAAH90wWe8vvLcc89p6dKl6tq1q7Zu3ar58+crLi7uohZctWqVtm3bppkzZxr9\nVCQAAIDbvGq8goKC1LFjRzU0NKihoUG9evXS+++/3+TFduzYoby8PM2ZM0dt2rRp8s8DAAD4M69u\nNYaGhqq6ulo333yzJk6cqLCwMLVt2/aCP5OVlaU9e/aoqqpKiYmJGj9+vF5++WXV1tZq9OjRkqS4\nuDhNmTLl0v8WAAAAfsCrA1RramrUunVr2battWvX6uTJk0pNTVXHjh1NZFSDbSuA25Lwgj+dXM/h\nh+ZQa3OotTnU2hzjB6i2bdtWn3/+ufbv368OHTqoT58+xpouScxqNIhfZAAAnOPVM17Lly/X8OHD\ntXnzZm3cuFF33323VqxY4XQ2AACAZsWrHa+8vDytXr26cZerqqpKI0aM0F133eVouEYMyTbKX2vt\nT7cZAQAtk1eNV8eOHdWuXbvGP7dr187orcYAy1LKisXG1oN/KrjrPp0UjRcAwHd5davxu9/9rjIy\nMjR79mz98Y9/1N13362uXbvqlVde0SuvvHLen8nOzlbv3r2VkpLyje/NmzdP3bp107Fjxy4tPQAA\ngB/xuvHq379/44Gnd9xxh6699lpVV1erurr6vD9zviHZklRWVqadO3de0rghAAAAf+TVrcZf/vKX\nTX7j8w3JlqSpU6fq0Ucf1S9+8YsmvycAAIA/86rxOnDggF588UUdPnxY9fX1jdfXrl3bpMW2bNmi\niIgIde/evWkpAQAAmgGvGq+JEydq0qRJiomJUUCAV3cnv+H06dN66aWXNG/evIv6eQAAAH/nVeMV\nFhamO+6445IW+vTTT1VaWqq0tDRJ0pEjR5Senq7ly5fr6quvvqT3BgAA8AdeNV6ZmZnKzc1V7969\nFRwc3Hg9KSnJ64W6deum3bt3N/65X79+WrFihcLCwpoQFwAAwH951XitXLlSH3/8serr68+51Xih\nxut8Q7KHDx9+6YkBAAD8lFdDsgcOHKiNGzeayHNeDMmGN/zt5HrmYppDrc2h1uZQa3OMD8m+6aab\ndPDgQV133XVeL3pZMSTbGH6RAQBwjleN1759+zRkyBB17tz5nGe8mnqcBAAAQEvm9ZBsVzEk2yi3\na32mrl4nj592NQMAAE7wqvHq3LmzJKmyslJnz569LAt7PB4NGzZMkZGReumlly742gDLUuqKVZdl\nXfi+tXelixvLAIDmyKvGq7CwUNOnT1dFRYXCwsJ0+PBhRUdHa926dRe98MKFCxUdHa1Tp05d9HsA\nAAD4E6+OoX/uuee0dOlSde3aVVu3btX8+fMVFxd30YseOXJE27Zt01133XXR7wEAAOBvvGq8goKC\n1LFjRzU0NKihoUG9evXS+++/f9GLPv3003r00UcvevwQAACAP/LqVmNoaKiqq6sVHx+viRMnKiws\nTG3btr2oBd98802FhYXphhtu0DvvvHNR7wEAAOCPLth4ffLJJzp69Kj+3//7f7riiiuUnZ2ttWvX\n6rPPPtPjjz9+UQv+5S9/0datW7Vjxw6dPXtWp06d0sSJEzVz5syLej8AAAB/ccF7fU8//bRCQkLU\ntm1bBQQEKCgoSEOHDtWAAQP0xz/+8aIWfOSRR7Rjxw5t3bpVzz77rHr16kXTBQAAWoQLNl5Hjx5V\nt27dvnG9W7du+uyzzxwLBQAA0Bxd8FbjyZP//jSlM2cufSbeLbfcoltuueWS3wcAAMAfXLDxuuGG\nG7Rs2TJlZGScc3358uWKjY11NNjXNdi21t6Vbmw9uOtMXb3bEQAAcMQFG6+cnBz98pe/1Nq1axsb\nrffff191dXUX/YzXRWFItjEMyQYAwDmWbdv2t73o7bff1j//+U9J0nXXXafevXs7HuzrGmxbAZZl\ndE24oyXNaaTJNYdam0OtzaHW5jS11heaeezVOV69evVSr169vF7wcguwLKWteMO19WFO/l2DmNMI\nAGi2vGq8LkZZWZkmTZqkyspKWZaljIwMjRo1SsePH9fDDz+szz77TJ07d9asWbPUoUMHp2IAAAD4\nDMdm9gQGBmry5Mlav369li5dqiVLlujgwYN6+eWX1bt3b23atEm9e/fWyy+/7FQEAAAAn+JY4xUR\nEdH4QH5ISIiioqJUXl6uwsJCDRkyRJI0ZMgQbdmyxakIAAAAPsXIlOrS0lIVFRUpLi5OlZWVioiI\nkCRdffXVqqysNBEBAADAdY43XtXV1crMzFROTo5CQkLO+Z5lWbL4tCIAAGghHG286urqlJmZqdTU\nVCUlJUmSwsPDVVFRIUmqqKhQWFiYkxEAAAB8hmONl23bys3NVVRUlEaPHt14vV+/flqzZo0kac2a\nNbrjjjucigAAAOBTHDtOYu/evcrPz1dMTIzS0tIkSVlZWRo3bpwmTJigFStW6JprrtGsWbOcigAA\nAOBTvDq53m2cXN9ycHI9nECtzaHW5lBrc4yfXO86ZjUawy8yAADOMXKcBAAAAPxlx8u68LYdLq/L\nWeuWdOsQAIBv4xeNV4BlaejK/3U7Bi7C6mF9GHoNAMD/cexWY3Z2tnr37q2UlJTGa8ePH9fo0aOV\nlJSk0aNH68SJE04tDwAA4HMca7zS09OVl5d3zjUGZAMAgJbMscYrPj5eHTp0OOcaA7IBAEBLZvRT\njQzIBgAALZlrx0kwIBsAALQ0RhsvBmQDAICWzGjjxYBsAADQkjnWeGVlZWnEiBEqLi5WYmKili9f\nrnHjxmnnzp1KSkrSrl27NG7cOKeWBwAA8DkMyYajOLn+32MupjnU2hxqbQ61Noch2XAMv8gAADiH\nIdkAAACG+MeOF0OyjbqUWp+p8+gkO2YAAJyXXzReAZal4Sv3ux0DXlg+7EaGYgMA8G8YHZI9e/Zs\n3XrrrUpLS1NaWpq2b9/u1PIAAAA+x7Edr/T0dI0cOVKPPfbYOdcfeOABjRkzxqllAQAAfJbRIdkA\nAAAtmfFPNS5evFipqanKzs7WiRMnTC8PAADgGqON1z333KPNmzcrPz9fERERmjZtmsnlAQAAXGW0\n8brqqqsUGBiogIAADR8+XAcOHDC5PAAAgKuMNl4VFRWNX2/ZskXXX3+9yeUBAABc5dinGrOysrRn\nzx5VVVUpMTFR48eP1549e1BvK7IAABaHSURBVPTBBx9Ikjp37qwpU6Y4tTwAAIDPYUg2LitOrvce\nczHNodbmUGtzqLU5DMmGY/hFBgDAOf7ReDGr0aiv1/psnUdf0IgBAHBZ+EXjFWBZylx9yO0YLdLz\nQ7u4HQEAgGbDeONVVlamSZMmqbKyUpZlKSMjQ6NGjTIdAwAAwDjjjVdgYKAmT56s2NhYnTp1SsOG\nDVNCQoKuu+4601EAAACMMj4yKCIiQrGxsZKkkJAQRUVFqby83HQMAAAA44w3Xl9XWlqqoqIixcXF\nuRkDAADACNcar+rqamVmZionJ0chISFuxQAAADDGlcarrq5OmZmZSk1NVVJSkhsRAAAAjDPeeNm2\nrdzcXEVFRWn06NGmlwcAAHCN8cZr7969ys/P19tvv620tDSlpaVp+/btpmMAAAAYZ/w4iZtvvlkf\nfvhhk36mwbY5yNMlZ+s8bkcAAKDZ8IuT65nVaA6zGgEAcI6rx0kAAAC0JH6x42UxJNuY+voGtyMA\nANBs+UnjZemF1Zxub8KDQyPdjgAAQLPl2K3GsrIy3X///Ro8eLCSk5O1YMGCc74/b948devWTceO\nHXMqAgAAgE9xbMfrQsOwy8rKtHPnTl1zzTVOLQ8AAOBzHNvxutAw7KlTp+rRRx+VZVlOLQ8AAOBz\njHyq8evDsLds2aKIiAh1797dxNIAAAA+w/GH678+DDswMFAvvfSS5s2b5/SyAAAAPsfRHa9/HYb9\n6aefqrS0VGlpaerXr5+OHDmi9PR0ff75507GAAAA8AmO7Xidbxh2t27dtHv37sbX9OvXTytWrFBY\nWJhTMQAAAHyGYzteDMMGAAA4l2Xbtu12iG9j2zafgDSkvr5BVVXVbsdoEZiLaQ61Nodam0OtzWlq\nrS80bccvTq63bekoQ7KNuPLKtm5HAACg2WJINgAAgCF+sePFkOymqatr0PHj3C4EAMDX+EnjZWnV\niqNux/Ab6Xdd5XYEAABwHo41XmVlZZo0aZIqKytlWZYyMjI0atQoTZgwQcXFxZKkkydPqn379srP\nz3cqBgAAgM8wPiR71qxZja+ZNm2aQkJCnIoAAADgU1wZki19eUTEhg0blJKS4lQEAAAAn2J8SPZX\n3n33XYWHh6tr164mIgAAALjO8cbr60Oyv35bsaCggN0uAADQohgdkv2V+vp6bd68WYMHD3ZyeQAA\nAJ/iWON1viHZX9m1a5eioqLUqVMnp5YHAADwOa4MyV6/fr2Sk5OdWhoAAMAnMSS7GbqUk+sZumoO\ntTaHWptDrc2h1uYwJBsAAMAP+UXjxazGf4+5jAAA+A8/abwsvbn4c7dj+KS+913tdgQAAOAlVxqv\nfv36qV27dgoICFBgYKBWrVrlRgwAAACjXNvxWrBggcLCwtxaHgAAwDgjI4MAAADgYuM1ZswYpaen\na+nSpW5FAAAAMMqVW42vvfaaIiMjVVlZqdGjRysqKkrx8fFuRAEAADDGlR2vyMhISVJ4eLgGDBig\n/fv3uxEDAADAKOONV01NjU6dOtX49c6dO3X99debjgEAAGCc8VuNlZWVevDBByVJHo9HKSkpSkxM\nNB0DAADAOOONV5cuXfT666+bXhYAAMB1fnFyvW3bnND+b9TVNbgdAQAAeMlPGi+GZAMAAP/nF40X\nQ7LPr762QVUnGJANAIC/8JPGy9J7eRVux/A5/zk2wu0IAACgCRw7TqKsrEz333+/Bg8erOTkZC1Y\nsECStGHDBiUnJ6t79+46cOCAU8sDAAD4HMd2vAIDAzV58mTFxsbq1KlTGjZsmBISEhQTE6PZs2fr\niSeecGppAAAAn+RY4xUREaGIiC9vhYWEhCgqKkrl5eVKSEhwakkAAACfZuTk+tLSUhUVFSkuLs7E\ncgAAAD7J8carurpamZmZysnJUUhIiNPLAQAA+CxHG6+6ujplZmYqNTVVSUlJTi4FAADg8xxrvGzb\nVm5urqKiojR69GinlgEAAPAbjj1cv3fvXuXn5ysmJkZpaWmSpKysLNXW1uqpp57SsWPH9POf/1w9\nevTQ3LlznYoBAADgMyzbtm23Q3wb27ZlWZbbMXyOEyfXX3llWx0/XnNZ3xPnR63NodbmUGtzqLU5\nTa31habt+MXJ9cxqBAAAzYGR4yQAAADgJzteDMk+P09tg44xJBsAAL/hJ42XpZJZR9yO4XO6Tujk\ndgQAANAErtxqzM7OVu/evZWSkuLG8gAAAK5wpfFKT09XXl6eG0sDAAC4xpXGKz4+Xh06dHBjaQAA\nANfwqUYAAABDaLwAAAAMofECAAAwhMYLAADAEFcar6ysLI0YMULFxcVKTEzU8uXL3YgBAABglCsH\nqD777LNNer1t2xwWeh6e2ga3IwAAgCbwi5PrGZINAACaA79ovCw1v1mNnlqPjp2ocTsGAAAwyD8a\nrwBLR2YedDvGZdVp4nVuRwAAAIY59nB9WVmZ7r//fg0ePFjJyclasGCBJKmoqEgZGRlKS0tTenq6\n9u/f71QEAAAAn+LYjldgYKAmT56s2NhYnTp1SsOGDVNCQoJmzJihBx98ULfddpu2b9+uGTNmaNGi\nRU7FAAAA8BmONV4RERGKiIiQJIWEhCgqKkrl5eWyLEvV1dWSpJMnTza+BgAAoLkz8oxXaWmpioqK\nFBcXp5ycHI0ZM0bTp09XQ0OD/vSnP5mIAAAA4DrHD1Ctrq5WZmamcnJyFBISotdee03Z2dnavn27\nsrOzlZub63QEAAAAn+Bo41VXV6fMzEylpqYqKSlJkrR69erGr++8804ergcAAC2GY42XbdvKzc1V\nVFSURo8e3Xg9IiJCe/bskSS9/fbb6tq1q1MRAAAAfIpjz3jt3btX+fn5iomJUVpamqQvZzQ+9dRT\nevrpp1VfX6/WrVtrypQpTkUAAADwKY41XjfffLM+/PDD835v1apVTi0LAADgs/zi5Hq7wW52J717\naj1uRwAAAIb5R+Ml6ejnDMkGAAD+zS8aL8vyjSHZDLYGAACXwk8aL0vls/a6HUORE/7L7QgAAMCP\nOXacRHZ2tnr37q2UlJTGaxs2bFBycrK6d++uAwcOOLU0AACAT3Ks8UpPT1deXt4512JiYjR79mzF\nx8c7tSwAAIDPcuxWY3x8vEpLS8+5Fh0d7dRyAAAAPs/xWY0AAAD4Eo0XAACAITReAAAAhtB4AQAA\nGOLYw/VZWVnas2ePqqqqlJiYqPHjx+vKK6/UU089pWPHjunnP/+5evTooblz5zoVAQAAwKdYtm3b\nbof4NrZty7Ist2O0iJPrr7yyrY4fb95/R19Brc2h1uZQa3OotTlNrfWFpu34xcn1ti0dPcqsRgAA\n4N94xgsAAMAQv9jxcntItqe2XsdOnHZtfQAA0Dz4SeNlqfz5ba6tH5l5u2trAwCA5sN44/Xxxx/r\n4YcfbvzzoUOHlJmZqQceeMB0FAAAAKOMN15RUVHKz8+XJHk8HiUmJmrAgAGmYwAAABjn6sP1u3fv\nVpcuXdS5c2c3YwAAABjhauO1bt06paSkuBkBAADAGNcar9raWm3dulWDBg1yKwIAAIBRrjVeO3bs\nUGxsrK666iq3IgAAABjlWuO1bt06JScnu7U8AACAca40XjU1Ndq1a5eSkpLcWB4AAMAVrhyg2rZt\nW73zzjtev962bVcPMfXU1ru2NgAAaD784uR6hmQDAIDmwC8ar4ud1ciMRQAA4Ev8pPGyVPHC2ib/\nXMSDqQ6kAQAAuDiuPFz/xRdfKDMzU4MGDdKdd96p9957z40YAAAARrmy4/W73/1Ot956q55//nnV\n1tbqzJkzbsQAAAAwyviO18mTJ/XnP/9Zd911lyQpODhYoaGhpmMAAAAYZ7zxKi0tVVhYmLKzszVk\nyBDl5uaqpqbGdAwAAADjjDde9fX1+vvf/6577rlHa9asUZs2bfTyyy+bjgEAAGCc8carU6dO6tSp\nk+Li4iRJgwYN0t///nfTMQAAAIwz3nhdffXV6tSpkz7++GNJ0u7duxUdHW06BgAAgHGufKrxV7/6\nlSZOnKi6ujp16dJFU6dOdSMGAACAUa40Xj169NCqVavcWBoAAMA1fnFyvW3bF3UKPcOtAQCAL7Fs\n27bdDgEAANASuDIyCAAAoCWi8QIAADCExgsAAMAQGi8AAABDaLwAAAAMofECAAAwhMYLAADAEJ9q\nvHbs2KGBAwdqwIABevnll7/x/draWk2YMEEDBgzQ8OHDVVpa6kLK5uHbav3nP/9ZQ4cO1fe//329\n8cYbLiRsPr6t1q+88ooGDx6s1NRUjRo1Sp999pkLKZuHb6v1a6+9ptTUVKWlpemee+7RwYMHXUjZ\nPHxbrb+yceNGdevWTQcOHDCYrnn5tlqvWrVKvXr1UlpamtLS0rR8+XIXUjYP3vy7Xr9+vQYPHqzk\n5GQ98sgjTV/E9hH19fX2HXfcYX/66af22bNn7dTUVPuf//znOa959dVX7V/96le2bdt2QUGB/dBD\nD7kR1e95U+tDhw7ZRUVF9qOPPmpv2LDBpaT+z5ta7969266pqbFt27YXL17Mv+uL5E2tT5482fj1\nli1b7J/+9KemYzYL3tTatr+s97333msPHz7c3r9/vwtJ/Z83tV65cqX95JNPupSw+fCm1sXFxXZa\nWpp9/Phx27Zt++jRo01ex2d2vPbv36/vfe976tKli4KDg5WcnKzCwsJzXrN161YNHTpUkjRw4EDt\n3r1bNgfvN5k3tb722mvVvXt3BQT4zD8Rv+RNrXv16qU2bdpIknr27KkjR464EdXveVPrkJCQxq9P\nnz4ty7JMx2wWvKm1JD333HP62c9+ptatW7uQsnnwtta4dN7UetmyZbrvvvvUoUMHSVJ4eHiT1/GZ\n/1ctLy9Xp06dGv8cGRmp8vLyb7zmO9/5jiQpKChI7du3V1VVldGczYE3tcbl0dRar1ixQomJiSai\nNTve1nrx4sXq37+/ZsyYoccff9xkxGbDm1r/7W9/05EjR3T77bcbTte8ePvvetOmTUpNTVVmZqbK\nyspMRmw2vKl1SUmJiouLNWLECGVkZGjHjh1NXsdnGi+gpcvPz9f777+vsWPHuh2lWbvvvvu0ZcsW\nTZw4UXPmzHE7TrPU0NCgadOm6bHHHnM7SovQt29fbd26VWvXrtWPfvQj6u4gj8ejTz75RIsWLdLv\nf/97/epXv9IXX3zRpPfwmcYrMjLynFss5eXlioyM/MZrvurk6+vrdfLkSXXs2NFozubAm1rj8vC2\n1rt27dKLL76oOXPmKDg42GTEZqOp/66Tk5O1ZcsWE9GanW+rdXV1tf7xj3/oJz/5ifr166d9+/bp\nf/7nf3jA/iJ48++6Y8eOjf/dGD58uP72t78ZzdhceNuH9OvXT61atVKXLl3UtWtXlZSUNGkdn2m8\nfvCDH6ikpESHDh1SbW2t1q1bp379+p3zmn79+mn16tWSvvykTK9evXhG4yJ4U2tcHt7U+u9//7t+\n/etfa86cORf1vAC+5E2tv/4fyG3btul73/ue4ZTNw7fVun379nrnnXe0detWbd26VT179tScOXP0\ngx/8wMXU/smbf9cVFRWNX2/dulXR0dGmYzYL3tS6f//+2rNnjyTp2LFjKikpUZcuXZq0TtBlS3yJ\ngoKC9Otf/1pjx46Vx+PRsGHDdP311+u5557TDTfcoDvuuEN33XWXHn30UQ0YMEAdOnTQH/7wB7dj\n+yVvar1//3798pe/1BdffKE333xTs2fP1rp169yO7ne8qfUzzzyjmpoaPfTQQ5Kk73znO3rxxRdd\nTu5/vKn1q6++qt27dysoKEihoaGaPn2627H9kje1xuXhTa0XLVqkrVu3KjAwUB06dNDUqVPdju2X\nvKn1rbfeqp07d2rw4MEKDAzUpEmTmnznzbL5WCAAAIARPnOrEQAAoLmj8QIAADCExgsAAMAQGi8A\nAABDaLwAAAAMofECAAAwxGfO8QLQcvTo0UMxMTHyeDy69tpr9cwzzyg0NNTrn589e7batm2rMWPG\nOJjym+6//35VVFSodevWatWqlX7729+qR48eF/yZ+fPn6+67724chA6gZWPHC4BxV1xxhfLz81VQ\nUKAOHTpo8eLFbkfy2syZM/X666/r3nvv1TPPPPOtr1+4cKFOnz5tIBkAf8COFwBX9ezZUx9++GHj\nn/Py8rRhwwbV1tZqwIAByszMlCTNmTNHa9asUVhYmL7zne8oNjZWkvTpp5/qySefVFVVla644go9\n9dRTio6O1rFjx/TEE0/o8OHDkqScnBz913/9l2bPnq3S0lIdOnRIZWVlys7O1r59+/TWW28pIiJC\nL774olq1auVV7rlz5zb++YknntCBAwd09uxZDRw4UJmZmVq4cKEqKio0atQoXXnllVq0aJH+93//\nV7Nnz1Ztba26dOmiqVOnql27dpezpAB8mQ0AhvXs2dO2bduur6+3x48fb2/fvt22bdt+66237Mcf\nf9xuaGiwPR6PPW7cOHvPnj32gQMH7JSUFLumpsY+efKk3b9/fzsvL8+2bdv+yU9+YhcXF9u2bdv7\n9u2z77//ftu2bTsrK8v+85//bNu2bX/22Wf2oEGDbNu27eeff94eMWKEXVtbaxcVFdk33nijvW3b\nNtu2bfsXv/iFvXnz5n+be+TIkfb+/ftt27btV155xf7973/f+L2qqqrGv9PIkSPtoqIi27Ztu2/f\nvnZlZaVt27ZdWVlp33vvvXZ1dbVt27b90ksv2bNnz76UUgLwM+x4ATDuzJkzSktLU3l5uaKjo5WQ\nkCBJ2rlzp3bu3KkhQ4ZIkmpqalRSUqLq6mr179+/8TmprwbXVldX67333muccylJtbW1kqRdu3bp\n4MGDjddPnTql6upqSVJiYqJatWrV+JxZYmKiJCkmJkalpaUXzD5x4kTV1dWppqZG+fn5jdc3bNig\nZcuWqb6+Xp9//rk++ugjde/e/Zyf/etf/6qDBw/qnnvukSTV1dWpZ8+eTaweAH9G4wXAuK+e8Tp9\n+rTGjBmjxYsX6yc/+Yls29a4ceM0YsSIc14/f/78876PbdsKDQ09pwH6SkNDg5YtW6bWrVt/43vB\nwcGSpICAALVq1UqWZTX+2ePxXDD7zJkzdcMNN+iZZ57RU089pT/+8Y86dOiQ5s2bpxUrVqhDhw6a\nPHmyzp49e968CQkJevbZZy+4BoDmi4frAbimTZs2evzxx/XKK6+ovr5effr00cqVKxt3psrLy1VZ\nWan4+Hht2bJFZ86c0alTp/Tmm29KkkJCQnTttddqw4YNkr5sbD744ANJUp8+fbRo0aLGtYqKii5b\nbsuy9NBDD2nfvn366KOPVF1drTZt2qh9+/Y6evSoduzY0fjadu3aNf59evbsqb/85S/65JNPJH25\no1dcXHzZcgHwfex4AXDV97//fXXr1k0FBQUaMmSIPvroo8Ydr7Zt22rGjBmKjY3V4MGDlZaWprCw\nMP3gBz9o/PkZM2boN7/5jebMmaP6+noNHjxY3bt3V25urqZMmaLU1FR5PB7dfPPNmjJlymXLfcUV\nV+inP/2p5s6dq6efflrf//73deedd6pTp0666aabGl+XkZGhsWPHKiIiQosWLdLUqVOVlZXVeEt0\nwoQJ+o//+I/LlguAb7Ns27bdDgEAANAScKsRAADAEG41AsC/ePDBB7/x6caJEyfq1ltvdSkRgOaC\nW40AAACGcKsRAADAEBovAAAAQ2i8AAAADKHxAgAAMOT/A3XqD4cAZ2cOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbxHR3IUsOSS",
        "colab_type": "text"
      },
      "source": [
        "### Transactional Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeUsOLXtuWCu",
        "colab_type": "text"
      },
      "source": [
        "Transactional data contains purchase history of each household. It contains the product ID and sales value, store ID and all other transactional features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2U2742ztKfM",
        "colab_type": "code",
        "outputId": "207b27ee-5b30-487f-cd30-c948f1e7d9e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"transaction_data\"].head()"
      ],
      "execution_count": 16,
      "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>household_key</th>\n",
              "      <th>BASKET_ID</th>\n",
              "      <th>DAY</th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>QUANTITY</th>\n",
              "      <th>SALES_VALUE</th>\n",
              "      <th>STORE_ID</th>\n",
              "      <th>RETAIL_DISC</th>\n",
              "      <th>TRANS_TIME</th>\n",
              "      <th>WEEK_NO</th>\n",
              "      <th>COUPON_DISC</th>\n",
              "      <th>COUPON_MATCH_DISC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2375</td>\n",
              "      <td>26984851472</td>\n",
              "      <td>1</td>\n",
              "      <td>1004906</td>\n",
              "      <td>1</td>\n",
              "      <td>1.39</td>\n",
              "      <td>364</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>1631</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2375</td>\n",
              "      <td>26984851472</td>\n",
              "      <td>1</td>\n",
              "      <td>1033142</td>\n",
              "      <td>1</td>\n",
              "      <td>0.82</td>\n",
              "      <td>364</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1631</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2375</td>\n",
              "      <td>26984851472</td>\n",
              "      <td>1</td>\n",
              "      <td>1036325</td>\n",
              "      <td>1</td>\n",
              "      <td>0.99</td>\n",
              "      <td>364</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>1631</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2375</td>\n",
              "      <td>26984851472</td>\n",
              "      <td>1</td>\n",
              "      <td>1082185</td>\n",
              "      <td>1</td>\n",
              "      <td>1.21</td>\n",
              "      <td>364</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1631</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2375</td>\n",
              "      <td>26984851472</td>\n",
              "      <td>1</td>\n",
              "      <td>8160430</td>\n",
              "      <td>1</td>\n",
              "      <td>1.50</td>\n",
              "      <td>364</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>1631</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   household_key    BASKET_ID  DAY  ...  WEEK_NO  COUPON_DISC  COUPON_MATCH_DISC\n",
              "0           2375  26984851472    1  ...        1          0.0                0.0\n",
              "1           2375  26984851472    1  ...        1          0.0                0.0\n",
              "2           2375  26984851472    1  ...        1          0.0                0.0\n",
              "3           2375  26984851472    1  ...        1          0.0                0.0\n",
              "4           2375  26984851472    1  ...        1          0.0                0.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR-wuyVNZloC",
        "colab_type": "text"
      },
      "source": [
        "Now lets do some descriptive analysis on the transactional data. First, let's group the data by household numbers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0C49Ie7ZxWM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grouped_sum = dfs[\"transaction_data\"].groupby(\"household_key\").sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_m1GeeoZ0uL",
        "colab_type": "text"
      },
      "source": [
        "Average amount of purchase by a household within these two years is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrA5wYfGZ0l9",
        "colab_type": "code",
        "outputId": "06733cf1-433d-4d61-b331-37c63de8e236",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "av_purc = round(dfs['transaction_data'].SALES_VALUE.sum()/len(grouped_sum),1)\n",
        "av_purc"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3223.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VagcYZeVZ0S9",
        "colab_type": "text"
      },
      "source": [
        "Average total number of products purchased by a household within two years is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S49aB4QJZ0GT",
        "colab_type": "code",
        "outputId": "2b42ad23-6764-4896-c3bb-8a31ee097158",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "av_tot_prod = round(grouped_sum[\"QUANTITY\"].mean(),1)\n",
        "av_tot_prod"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104274.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weM-xGEJaqFH",
        "colab_type": "text"
      },
      "source": [
        "Average total number of unique products purchased by a household within two years is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZvOLR_Lqap9Z",
        "colab_type": "code",
        "outputId": "ecea0ca0-e6aa-4636-e5d3-0569eded3fb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "av_uniq_prod = round(len(dfs[\"transaction_data\"].groupby([\"household_key\",\"PRODUCT_ID\"]).sum()[\"QUANTITY\"])/len(grouped_sum),1)\n",
        "av_uniq_prod"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "560.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HPu4GwIapy9",
        "colab_type": "text"
      },
      "source": [
        "Average number of store visits per household within two years is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23hwbKlPapq3",
        "colab_type": "code",
        "outputId": "ec3ee48d-6409-47d9-8161-28a7e4dbd17d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "av_days_visited = round(len(dfs[\"transaction_data\"].groupby([\"household_key\",\"DAY\"]).count())/len(grouped_sum),1)\n",
        "av_days_visited"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "90.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7hZK4Zw2lAq",
        "colab_type": "text"
      },
      "source": [
        "**Top 20** stores based on total sales amount (USD) is calculated and plotted below. Stores with IDs of **367** and **406** made the most sales amongst 582 stores with over $200,000 each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38T22Nv5yfSk",
        "colab_type": "code",
        "outputId": "812ec77b-03af-419e-d8d2-f7b72ee90d11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "sales_per_store = dfs[\"transaction_data\"].groupby(\"STORE_ID\").sum()[\"SALES_VALUE\"].sort_values(ascending=False)\n",
        "sales_per_store = pd.DataFrame(list(zip(sales_per_store.index,sales_per_store)),columns=[\"Store ID\",\"Total Sales (USD)\"])\n",
        "fig_store = plt.figure(figsize=(10,5))\n",
        "sns.barplot(y=\"Store ID\",x=\"Total Sales (USD)\",data = sales_per_store[:20],order=sales_per_store[:20][\"Store ID\"],orient=\"h\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb924ebd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UVETnzp0xDIOQkBDS09PZsWMHABdffDH9+/enXbt2dOzYkZiYGL766iuLIxcRERFpfrYq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\nOCIiIiIHYogjIiIicqAP4ynenYUNHOsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hgegdcay3CVx",
        "colab_type": "text"
      },
      "source": [
        "**Top 20** customers based on total purchase amount (USD) is calculated and displayed below. Top customer is with the ID of **1023** who made the most purchases amongst 2500 house holds with almost $40,000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsvKg7AQ2jZq",
        "colab_type": "code",
        "outputId": "b5113809-b5ba-485a-830d-da3526ec8b40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "purc_per_cust = dfs[\"transaction_data\"].groupby(\"household_key\").sum()[\"SALES_VALUE\"].sort_values(ascending=False)\n",
        "purc_per_cust = pd.DataFrame(list(zip(purc_per_cust.index,purc_per_cust)),columns=[\"household_key\",\"Total Purchase (USD)\"])\n",
        "fig_store = plt.figure(figsize=(10,5))\n",
        "sns.barplot(y=\"household_key\",x=\"Total Purchase (USD)\",data = purc_per_cust[:20],order=purc_per_cust[:20][\"household_key\"],orient=\"h\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb9254af60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NG/jwww8dGbqIiIiI23Jogbd7925SU1Np164dcXFxACQlJdGrV69q91+/fj3vv/8+3t7e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\nn5EATwghhBDCz/wDMMbyoW0pZvoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUw4hCTYsDrD",
        "colab_type": "text"
      },
      "source": [
        "### Demographics Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IekTgqWw2hg",
        "colab_type": "text"
      },
      "source": [
        "Demographics data contains household demographical information such as age group, marital status and househols size. We will be using this table as a base and add few columns after feature engineering. Target variable for the churn prediction will be added in the final dataset before moving on to the predictive modelling. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZkyphpc3NT9",
        "colab_type": "code",
        "outputId": "13288963-f05d-4bec-b201-5a3072b02ee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dfs[\"hh_demographic\"].head()"
      ],
      "execution_count": 24,
      "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>AGE_DESC</th>\n",
              "      <th>MARITAL_STATUS_CODE</th>\n",
              "      <th>INCOME_DESC</th>\n",
              "      <th>HOMEOWNER_DESC</th>\n",
              "      <th>HH_COMP_DESC</th>\n",
              "      <th>HOUSEHOLD_SIZE_DESC</th>\n",
              "      <th>KID_CATEGORY_DESC</th>\n",
              "      <th>household_key</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65+</td>\n",
              "      <td>A</td>\n",
              "      <td>35-49K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults No Kids</td>\n",
              "      <td>2</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45-54</td>\n",
              "      <td>A</td>\n",
              "      <td>50-74K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults No Kids</td>\n",
              "      <td>2</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25-34</td>\n",
              "      <td>U</td>\n",
              "      <td>25-34K</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2 Adults Kids</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25-34</td>\n",
              "      <td>U</td>\n",
              "      <td>75-99K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults Kids</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45-54</td>\n",
              "      <td>B</td>\n",
              "      <td>50-74K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>Single Female</td>\n",
              "      <td>1</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  AGE_DESC MARITAL_STATUS_CODE  ... KID_CATEGORY_DESC household_key\n",
              "0      65+                   A  ...      None/Unknown             1\n",
              "1    45-54                   A  ...      None/Unknown             7\n",
              "2    25-34                   U  ...                 1             8\n",
              "3    25-34                   U  ...                 2            13\n",
              "4    45-54                   B  ...      None/Unknown            16\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7PxdjpFTp-b",
        "colab_type": "text"
      },
      "source": [
        "Lets first start defining the functions for exploratory analysis of demographic variables:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3vkn9Pcxd03",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pie_categorical(data):\n",
        "    #function to plot the histogram of categorical variables in pie graph\n",
        "    features = data.columns\n",
        "    #plot pie charts of categorical variables\n",
        "    fig_pie_cat = plt.figure(figsize=(15,15))\n",
        "    count = 1\n",
        "    #calculate dynamic numbers of subplot rows and columns\n",
        "    cols = int(np.ceil(np.sqrt(len(features))))\n",
        "    rows = int(np.ceil(len(features)/cols))\n",
        "    for i in features:\n",
        "        ax = fig_pie_cat.add_subplot(rows,cols,count)\n",
        "        data[i].value_counts().plot(kind=\"pie\",autopct=\"%.1f%%\",ax=ax)\n",
        "        plt.ylabel(\"\")\n",
        "        plt.title(i,fontweight=\"bold\",fontsize=8)\n",
        "        count += 1\n",
        "\n",
        "def hist_numeric(data):\n",
        "    #function to plot the histogram of numeric variables\n",
        "    features = data.columns\n",
        "    fig_hists = plt.figure(figsize=(15,15))\n",
        "    fig_hists.subplots_adjust(hspace=0.5,wspace=0.5)\n",
        "    count = 1\n",
        "    #calculate dynamic numbers of subplot rows and columns\n",
        "    cols = int(np.ceil(np.sqrt(len(features))))\n",
        "    rows = int(np.ceil(len(features)/cols))\n",
        "    for i in features:\n",
        "        ax = fig_hists.add_subplot(rows,cols,count)\n",
        "        data[i].plot(kind=\"hist\",alpha=.5,bins=25,edgecolor=\"navy\",legend=False,ax=ax)\n",
        "        ax.set_xlabel(\"\")\n",
        "        ax.set_title(i,fontweight=\"bold\",fontsize=10)\n",
        "        count += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_zle35Px4lh",
        "colab_type": "text"
      },
      "source": [
        "Since all variables are categorical in the demographics dataset, we will use our _pie_categorical_ function. The pie charts below gives the distribution of each categorical variable in the demographics dataset.\n",
        "\n",
        "Some interesting findings:\n",
        "* Majority of the customers age in between **35-54** (~60%)\n",
        "* Married couples are almost **3** times the singles\n",
        "* Almost **half** of the population have a yearly salary between **$35-74K**\n",
        "* Majority of the customers own a house (**63%**)\n",
        "* Majority of the customers does not have a child (**~70%**)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpcOApykyVDu",
        "colab_type": "code",
        "outputId": "cb54531c-d68f-448e-81be-efb3e18e3837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        }
      },
      "source": [
        "pie_categorical(dfs[\"hh_demographic\"].drop(\"household_key\",axis=1))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CiIuT5E8IIYS4AsHBwdSsWZONGzcCkJ2dzbp16/jHP/7h4ciEEEKI81MMwzA8HYQQQghR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\nfoPa9j7MB1eg5CSj12yCfl2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QOXMvwXsvJXS9sPHKTfJ0JGXLWYAysz/drw9hRJdGno5G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\nQRAEFAoFffr0yRZsGTp0KD179mTy5MlcuHCB69e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nYdBL0F7zwGSFmMWvkBCnpIJ2Ma3e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\nNbgX1f21dHo6wGLLunnb0XNsA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lO6avF5qz4OgWUl8SsBJPkrBI5qRwLdAlnZdSU1PUr/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3z66ac5j3333XdMmTKFDz/8kJIlSwJw7NgxZsyYwZ49e+jfvz/t27dn6dKl7NixgwULFnDq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\nZBnfTnY6StQFJv9ON506Iuqz/9yGW8/1f05N0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            "text/plain": [
              "<Figure size 1080x1080 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2Kbd-BjzMrr",
        "colab_type": "text"
      },
      "source": [
        "## Churn Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tb0mE6vTAlb5",
        "colab_type": "text"
      },
      "source": [
        "Let's start working on the _\"Churn Prediction\"_ problem from here.\n",
        "\n",
        "Datasets have been examined and found out that it does not contain a column which indicates whether customer(household) is churned or not. Therefore we will have to **define our own churn** definition and move on to the modelling.\n",
        "\n",
        "Churn rate works well for subscription-based products or ones that have regular \n",
        "nature of interactions like Netflix subscription. It’s clear that \n",
        "customer has churned at the moment when he cancels or misses the next planned payment. \n",
        "\n",
        "When we know which customers have churned we can ask them for reasons and \n",
        "prioritize fixes for them. But for not regular, transactional products churn \n",
        "rate is hard to measure since we don’t know which customers are churned and \n",
        "which are dormant.\n",
        "\n",
        "A generally accepted retail churn rate is between five to sevent percent per year. Less than five percent is a great goal, but a churn rate over ten percent is cause for concern. Even as you acquire more customers, your business can't grow unless you have a greater volume of incoming customers than outgoing ones.\n",
        "\n",
        "Satisfying existing customers is actually more profitable than obtaining new ones. It costs five times more to obtain a new customer than it does to retain an existing customer. Decreasing your churn rate by five percent increases profits up to 125%.\n",
        "\n",
        "Let's define transactional churn:\n",
        "\n",
        "*A customer will be considered as churned if not purchased from a store **2 weeks** or more.* \n",
        "\n",
        "2 weeks out threshold is chosen as it splits households around 85%/15% No Churn/Churn.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivCqyurwJPmW",
        "colab_type": "code",
        "outputId": "ea2a01eb-e686-4704-d959-d97fb99a7e68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "out_weeks_threshold = 2\n",
        "\n",
        "#weekly customer purchase amount\n",
        "weekly_purchase = dfs[\"transaction_data\"].groupby([\"household_key\",\"WEEK_NO\"]).sum()[\"SALES_VALUE\"]\n",
        "weekly_purchase = weekly_purchase.unstack()\n",
        "\n",
        "#customer churned by their last 2 week's purchasing behaviour\n",
        "#go through all households and calculate out weeks\n",
        "target = []\n",
        "common_houses = set(dfs[\"hh_demographic\"].household_key) & set(dfs[\"transaction_data\"].household_key)\n",
        "for house in common_houses:\n",
        "  target.append(102-weekly_purchase.loc[house].dropna().index[-1])\n",
        "\n",
        "target = pd.DataFrame(list(zip(common_houses,target)),columns=[\"household_key\",\"No. of Churned Weeks\"])\n",
        "ax = sns.distplot(target[\"No. of Churned Weeks\"],kde=False)\n",
        "ax.axvline(x=out_weeks_threshold,c=\"red\",label=\"Out weeks threshold\")\n",
        "ax.legend()\n",
        "ax.set_yscale(\"log\")\n",
        "ax.set_ylabel(\"Frequency (Log)\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency (Log)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u7zqIiKgCKFMo/HnUVCIiqhoMnj7SN2Q2UHiU8OzdSGSYTga0eRLsCwqPrFJzJQCAnbUK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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVKwz6DTJZem",
        "colab_type": "text"
      },
      "source": [
        "Now lets generate our target variable for modelling: \n",
        "* Churned -> True\n",
        "* Not Churned -> False"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1bxy120MpbW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target[\"isChurned\"] = target[\"No. of Churned Weeks\"]>=out_weeks_threshold\n",
        "target.drop(\"No. of Churned Weeks\",axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCwS5VtIQbQ5",
        "colab_type": "text"
      },
      "source": [
        "Let's see how households split with the defined churn:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RhHV6o2Qnbj",
        "colab_type": "code",
        "outputId": "957689aa-025b-4477-bd01-d6ccf49819f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "target.isChurned.value_counts().plot(kind=\"pie\",autopct=\"%.1f%%\",labels=[\"Non-Churned\",\"Churned\"])\n",
        "plt.ylabel(\"\")\n",
        "plt.title(\"Churn Variable Distribution\",fontweight=\"bold\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Churn Variable Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cyg35DpWW9WrpXUaN8+OhM7z140m0cYsg+Bq9y/EetRuByTd+UCWg3GA0KLQM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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nw5TGCe1MrFS",
        "colab_type": "text"
      },
      "source": [
        "### Feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhAKN2Z8N3D8",
        "colab_type": "text"
      },
      "source": [
        "Let's generate some features from the _\"Campaign Table\"_, _\"*Transaction Data*\"_ and _\"Coupon Redempt\"_ tables to be used in the churn prediction model:\n",
        "\n",
        "\n",
        "**Feature 1**: List of campaigns received by each household:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QE4mteFNMt6i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "household_per_campaign = dfs[\"campaign_table\"].groupby(\"CAMPAIGN\")[\"household_key\"].apply(list)\n",
        "total_campaigns = len(dfs[\"campaign_table\"].CAMPAIGN.unique())\n",
        "df_camp = pd.DataFrame(np.full((total_households,total_campaigns),0),columns=[\"Camp_\"+str(i) for i in range(1,total_campaigns+1)],index=range(1,total_households+1))\n",
        "for camp in household_per_campaign.index:\n",
        "    df_camp.loc[household_per_campaign[camp],\"Camp_\"+str(camp)] = 1\n",
        "\n",
        "df_camp[\"household_key\"] = df_camp.index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDaqILAouTpl",
        "colab_type": "text"
      },
      "source": [
        "**Feature 2**: Total number of received campaigns per household:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cnobewOuUe1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = dfs[\"campaign_table\"].household_key.value_counts().sort_index()\n",
        "no_of_received_campaigns = pd.DataFrame(list(zip(temp.index,temp.values.astype(int))),columns=[\"household_key\",\"no_of_received_campaigns\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfTA5VZF1MSO",
        "colab_type": "text"
      },
      "source": [
        "Lets see if the \"number\" of campaigns being received affects a household's purchase behaviour.\n",
        "\n",
        "The correlation between the number of campaigns received by a household and their total purchase amount is visualised in a scatter plot below. Linear line in the graph shows the regression model fitted to the scattered data. One can say the aforementioned variables are **linearly related**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KvIg2bLO3KEA",
        "outputId": "cf27ea19-f6d6-4fcf-cf71-1f3adac1f635",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "no_of_received_campaigns_vs_sumsales = pd.merge(no_of_received_campaigns,purc_per_cust,on=\"household_key\")\n",
        "sns.lmplot(x=\"no_of_received_campaigns\",y=\"Total Purchase (USD)\",data=no_of_received_campaigns_vs_sumsales)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fdb90671d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F3HjBOE4sshvqQ5LAYbVQ5LCws6GD327Zz0GTN99jEuOJGtgdO3Zw0kknUVAQ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\n92C/03+XVaa00I6m6bR0BQfU4s0HZCm59KZRF24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B3KkoAfcCPGdPnIyh2ZIuMXyVMsga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\nhHgIJWBCCPEQSsCEEOIhlIAJIcRD/h8vg8QJxvfX+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptNwLhTs38Sc",
        "colab_type": "code",
        "outputId": "5bcbf0a7-b70a-416f-e173-26ecc23ae045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "no_of_received_campaigns_vs_sumsales.drop(\"household_key\",axis=1).corr()"
      ],
      "execution_count": 39,
      "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>no_of_received_campaigns</th>\n",
              "      <th>Total Purchase (USD)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>no_of_received_campaigns</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.704409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Purchase (USD)</th>\n",
              "      <td>0.704409</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          no_of_received_campaigns  Total Purchase (USD)\n",
              "no_of_received_campaigns                  1.000000              0.704409\n",
              "Total Purchase (USD)                      0.704409              1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1ULs_JD6Dgh",
        "colab_type": "text"
      },
      "source": [
        "The scatter plot and the correlation value above (71%) indicates that the total purchase amount of a household is positively correlated to the number of campaigns received by them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryWCjBWyOC9c",
        "colab_type": "text"
      },
      "source": [
        "**Feature 3**: List of campaigns resulted in coupon redemption:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xn8OBOTIOIUH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = dfs[\"coupon_redempt\"].groupby(\"household_key\")[\"CAMPAIGN\"].apply(list)\n",
        "rede_camp_history_per_household = pd.DataFrame(list(zip(temp.index,temp.values)),columns=[\"household_key\",\"redeemed_CAMPAIGN_list\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4e_hKdZOIDB",
        "colab_type": "text"
      },
      "source": [
        "**Feature 4**: The number of redemptions made by each household:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aPYnfu8OM_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = dfs[\"coupon_redempt\"].household_key.value_counts().sort_index()\n",
        "no_of_rede_per_household = pd.DataFrame(list(zip(temp.index,temp.values.astype(int))),columns=[\"household_key\",\"no_of_redeems\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcxAKxOB8jl_",
        "colab_type": "text"
      },
      "source": [
        "**Feature 5**: Most Frequent Campaign Type (A,B,C) received by each household:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTaVat1K8jx6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "camp_list = dfs[\"campaign_table\"].groupby(\"household_key\")[\"DESCRIPTION\"].apply(list)\n",
        "most_freq_type = pd.DataFrame(list(zip(camp_list.index,[pd.Series(i).value_counts().idxmax() for i in camp_list])),columns=[\"household_key\",\"most_freq_camp_type\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sWGs-yEv3XSc"
      },
      "source": [
        "**Feature 6**: Top 20 stores with high number of households which have more high out weeks:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxXc_AIE3jmD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "store_per_house = dfs[\"transaction_data\"].groupby(\"household_key\")[\"STORE_ID\"].apply(list).apply(np.unique)\n",
        "\n",
        "churn_houses = set(target.household_key[target.isChurned == True].sort_values().values) & set(dfs[\"hh_demographic\"].household_key.unique())\n",
        "churn_stores = []\n",
        "for i in churn_houses:\n",
        "  churn_stores.extend(store_per_house[i])\n",
        "\n",
        "churn_stores = pd.Series(churn_stores).value_counts()/len(churn_houses)\n",
        "\n",
        "df_store = dfs[\"transaction_data\"].groupby([\"STORE_ID\",\"household_key\"]).count()[\"BASKET_ID\"].unstack(\"STORE_ID\")\n",
        "df_store.replace(np.nan,0,inplace=True)\n",
        "df_store[df_store>0] = 1\n",
        "\n",
        "#select the top stores with high number of churners\n",
        "df_store = df_store.loc[:,churn_stores.index[:20]]\n",
        "df_store.columns=[\"Store_\"+str(i) for i in df_store.columns]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "B1ac18dsmuYB"
      },
      "source": [
        "**Feature 7**: Amount of purchase of a household within two years:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9MoAvkbmy7n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "purc_per_cust = dfs[\"transaction_data\"].groupby(\"household_key\").sum()[\"SALES_VALUE\"].sort_values(ascending=False)\n",
        "purc_per_cust = pd.DataFrame(list(zip(purc_per_cust.index,purc_per_cust)),columns=[\"household_key\",\"Total Purchase (USD)\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "56w_aDRZVHFs"
      },
      "source": [
        "### Last touches on the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McVtCmZ_ONHp",
        "colab_type": "text"
      },
      "source": [
        "Generate the final dataframe to be used for predictive modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lZB2k6LONTP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.merge(dfs[\"hh_demographic\"],df_camp,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,no_of_received_campaigns,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,df_store,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,no_of_rede_per_household,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,most_freq_type,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,purc_per_cust,on=\"household_key\",how=\"left\")\n",
        "data = pd.merge(data,target,on=\"household_key\",how=\"left\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZqOBH0GUo6c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3eacf6b5-ca97-4b58-ebb3-f1a5009f57e8"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(801, 92)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqoxV9bqUlBx",
        "colab_type": "text"
      },
      "source": [
        "Now we have 91 features and a target variable in the final dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7t68MpyeOcr8",
        "colab_type": "code",
        "outputId": "19e2b3f5-b337-4bd6-98ff-76322c148f53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 46,
      "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>AGE_DESC</th>\n",
              "      <th>MARITAL_STATUS_CODE</th>\n",
              "      <th>INCOME_DESC</th>\n",
              "      <th>HOMEOWNER_DESC</th>\n",
              "      <th>HH_COMP_DESC</th>\n",
              "      <th>HOUSEHOLD_SIZE_DESC</th>\n",
              "      <th>KID_CATEGORY_DESC</th>\n",
              "      <th>household_key</th>\n",
              "      <th>Camp_1</th>\n",
              "      <th>Camp_2</th>\n",
              "      <th>Camp_3</th>\n",
              "      <th>Camp_4</th>\n",
              "      <th>Camp_5</th>\n",
              "      <th>Camp_6</th>\n",
              "      <th>Camp_7</th>\n",
              "      <th>Camp_8</th>\n",
              "      <th>Camp_9</th>\n",
              "      <th>Camp_10</th>\n",
              "      <th>Camp_11</th>\n",
              "      <th>Camp_12</th>\n",
              "      <th>Camp_13</th>\n",
              "      <th>Camp_14</th>\n",
              "      <th>Camp_15</th>\n",
              "      <th>Camp_16</th>\n",
              "      <th>Camp_17</th>\n",
              "      <th>Camp_18</th>\n",
              "      <th>Camp_19</th>\n",
              "      <th>Camp_20</th>\n",
              "      <th>Camp_21</th>\n",
              "      <th>Camp_22</th>\n",
              "      <th>Camp_23</th>\n",
              "      <th>Camp_24</th>\n",
              "      <th>Camp_25</th>\n",
              "      <th>Camp_26</th>\n",
              "      <th>Camp_27</th>\n",
              "      <th>Camp_28</th>\n",
              "      <th>Camp_29</th>\n",
              "      <th>Camp_30</th>\n",
              "      <th>no_of_received_campaigns</th>\n",
              "      <th>Store_450</th>\n",
              "      <th>Store_318</th>\n",
              "      <th>Store_404</th>\n",
              "      <th>Store_289</th>\n",
              "      <th>Store_31582</th>\n",
              "      <th>Store_298</th>\n",
              "      <th>Store_372</th>\n",
              "      <th>Store_412</th>\n",
              "      <th>Store_381</th>\n",
              "      <th>Store_335</th>\n",
              "      <th>Store_380</th>\n",
              "      <th>Store_343</th>\n",
              "      <th>Store_321</th>\n",
              "      <th>Store_421</th>\n",
              "      <th>Store_424</th>\n",
              "      <th>Store_402</th>\n",
              "      <th>Store_375</th>\n",
              "      <th>Store_292</th>\n",
              "      <th>Store_433</th>\n",
              "      <th>Store_367</th>\n",
              "      <th>no_of_redeems</th>\n",
              "      <th>most_freq_camp_type</th>\n",
              "      <th>Total Purchase (USD)</th>\n",
              "      <th>isChurned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65+</td>\n",
              "      <td>A</td>\n",
              "      <td>35-49K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults No Kids</td>\n",
              "      <td>2</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>TypeB</td>\n",
              "      <td>4330.16</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45-54</td>\n",
              "      <td>A</td>\n",
              "      <td>50-74K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults No Kids</td>\n",
              "      <td>2</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TypeB</td>\n",
              "      <td>3400.05</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25-34</td>\n",
              "      <td>U</td>\n",
              "      <td>25-34K</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2 Adults Kids</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TypeB</td>\n",
              "      <td>5534.97</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25-34</td>\n",
              "      <td>U</td>\n",
              "      <td>75-99K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>2 Adults Kids</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>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</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>TypeB</td>\n",
              "      <td>13190.92</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45-54</td>\n",
              "      <td>B</td>\n",
              "      <td>50-74K</td>\n",
              "      <td>Homeowner</td>\n",
              "      <td>Single Female</td>\n",
              "      <td>1</td>\n",
              "      <td>None/Unknown</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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</td>\n",
              "      <td>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</td>\n",
              "      <td>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</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TypeA</td>\n",
              "      <td>1512.02</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  AGE_DESC MARITAL_STATUS_CODE  ... Total Purchase (USD) isChurned\n",
              "0      65+                   A  ...              4330.16     False\n",
              "1    45-54                   A  ...              3400.05     False\n",
              "2    25-34                   U  ...              5534.97     False\n",
              "3    25-34                   U  ...             13190.92     False\n",
              "4    45-54                   B  ...              1512.02      True\n",
              "\n",
              "[5 rows x 63 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKoyZWk3nQ0j",
        "colab_type": "text"
      },
      "source": [
        "Change NaN or None/Unknown values to zero for the columns of \"number of received campaigns\", \"kid category\" and the \"number of redeems\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRvjEppSnQmt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.no_of_received_campaigns.replace(np.nan,0,inplace=True)\n",
        "data.no_of_received_campaigns = data.no_of_received_campaigns.astype(int)\n",
        "data.no_of_redeems.replace(np.nan,0,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pt7U_jsajq7M",
        "colab_type": "text"
      },
      "source": [
        "Change other object type elements in the columns to integer for modeling purpose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrotyUkkjqxn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.KID_CATEGORY_DESC.replace([\"None/Unknown\",\"3+\"],[0,3],inplace=True)\n",
        "data.KID_CATEGORY_DESC = data.KID_CATEGORY_DESC.astype(int)\n",
        "data.HOUSEHOLD_SIZE_DESC.replace(\"5+\",5,inplace=True)\n",
        "data.HOUSEHOLD_SIZE_DESC = data.HOUSEHOLD_SIZE_DESC.astype(int)\n",
        "data[\"Total Purchase (USD)\"] = data[\"Total Purchase (USD)\"].astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fa8Uy2ZWPWBi",
        "colab_type": "code",
        "outputId": "cab96d94-a0b0-4c82-83a8-a4cb9be05715",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 801 entries, 0 to 800\n",
            "Data columns (total 63 columns):\n",
            "AGE_DESC                    801 non-null object\n",
            "MARITAL_STATUS_CODE         801 non-null object\n",
            "INCOME_DESC                 801 non-null object\n",
            "HOMEOWNER_DESC              801 non-null object\n",
            "HH_COMP_DESC                801 non-null object\n",
            "HOUSEHOLD_SIZE_DESC         801 non-null int64\n",
            "KID_CATEGORY_DESC           801 non-null int64\n",
            "household_key               801 non-null int64\n",
            "Camp_1                      801 non-null int64\n",
            "Camp_2                      801 non-null int64\n",
            "Camp_3                      801 non-null int64\n",
            "Camp_4                      801 non-null int64\n",
            "Camp_5                      801 non-null int64\n",
            "Camp_6                      801 non-null int64\n",
            "Camp_7                      801 non-null int64\n",
            "Camp_8                      801 non-null int64\n",
            "Camp_9                      801 non-null int64\n",
            "Camp_10                     801 non-null int64\n",
            "Camp_11                     801 non-null int64\n",
            "Camp_12                     801 non-null int64\n",
            "Camp_13                     801 non-null int64\n",
            "Camp_14                     801 non-null int64\n",
            "Camp_15                     801 non-null int64\n",
            "Camp_16                     801 non-null int64\n",
            "Camp_17                     801 non-null int64\n",
            "Camp_18                     801 non-null int64\n",
            "Camp_19                     801 non-null int64\n",
            "Camp_20                     801 non-null int64\n",
            "Camp_21                     801 non-null int64\n",
            "Camp_22                     801 non-null int64\n",
            "Camp_23                     801 non-null int64\n",
            "Camp_24                     801 non-null int64\n",
            "Camp_25                     801 non-null int64\n",
            "Camp_26                     801 non-null int64\n",
            "Camp_27                     801 non-null int64\n",
            "Camp_28                     801 non-null int64\n",
            "Camp_29                     801 non-null int64\n",
            "Camp_30                     801 non-null int64\n",
            "no_of_received_campaigns    801 non-null int64\n",
            "Store_450                   801 non-null float64\n",
            "Store_318                   801 non-null float64\n",
            "Store_404                   801 non-null float64\n",
            "Store_289                   801 non-null float64\n",
            "Store_31582                 801 non-null float64\n",
            "Store_298                   801 non-null float64\n",
            "Store_372                   801 non-null float64\n",
            "Store_412                   801 non-null float64\n",
            "Store_381                   801 non-null float64\n",
            "Store_335                   801 non-null float64\n",
            "Store_380                   801 non-null float64\n",
            "Store_343                   801 non-null float64\n",
            "Store_321                   801 non-null float64\n",
            "Store_421                   801 non-null float64\n",
            "Store_424                   801 non-null float64\n",
            "Store_402                   801 non-null float64\n",
            "Store_375                   801 non-null float64\n",
            "Store_292                   801 non-null float64\n",
            "Store_433                   801 non-null float64\n",
            "Store_367                   801 non-null float64\n",
            "no_of_redeems               801 non-null float64\n",
            "most_freq_camp_type         760 non-null object\n",
            "Total Purchase (USD)        801 non-null int64\n",
            "isChurned                   801 non-null bool\n",
            "dtypes: bool(1), float64(21), int64(35), object(6)\n",
            "memory usage: 395.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLC-gB2VPftQ",
        "colab_type": "text"
      },
      "source": [
        "Household_key is the ID of a household, therefore will not be used in the modelling. The rest of the data will be used.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5ZHefnyPgN5",
        "colab_type": "code",
        "outputId": "3270a4af-d98d-4c06-b089-1611f3af6ec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "data.dtypes.value_counts()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int64      35\n",
              "float64    21\n",
              "object      6\n",
              "bool        1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBkGGt1EROAT",
        "colab_type": "text"
      },
      "source": [
        "Let's see how our features interact with the target variable. Start with the _\"**Age**\"_ variable:\n",
        "\n",
        "* Households with the **age of 55-64** tend to churn less then the rest of other age groups\n",
        "* No monotonic increase or decrease observed with age\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xv3X4eYIR8iy",
        "colab_type": "code",
        "outputId": "a6fffd00-2d89-4f26-f301-d0dfff37b990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "ax = sns.barplot(x=\"AGE_DESC\",y=\"isChurned\",data=data,order=[\"19-24\",\"25-34\",\"35-44\",\"45-54\",\"55-64\",\"65+\"])\n",
        "ax.axhline(y=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb922cf588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gGmLjxo3Iz8/H6tWrr3oZgiDi3LkKF1ZV/7oae53kOtx+1FS547UZENCqznmKHWLS6/XS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\niGT9PzJGO83edrPCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNkwsxGl0BWQ",
        "colab_type": "text"
      },
      "source": [
        "**Marital Status** vs Churn rate:\n",
        "* Married couples (Group A) tend to churn more compared to singles (Group B)\n",
        "* Unknown (Group U) households increase the chun rate of the population"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nMFaBP1wxq-",
        "colab_type": "code",
        "outputId": "005afd11-530d-4fb9-df1f-d21f252dcbba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "ax = sns.barplot(x=\"MARITAL_STATUS_CODE\",y=\"isChurned\",data=data,order=[\"A\",\"B\",\"U\"])\n",
        "ax.axhline(y=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb922cf208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lYvjw4VRWVlJdXU3Pnj0ZPHhwoA5BRERqEdB7HH379qVv374+y8aMGeP9vmnT\npixYsKDWbW+99Vbeeecdn2UhISGsWrXq4hcqIiJ+C9ilKhERuTwpOERExBAFh4iIGKLgEBERQxQc\nIiJiiIJDREQMUXCIiIghCg4RETFEwSEiIoYoOERExBAFh0g9aRbs8fkq0lgoOETqSfo1FXRu5SL9\nmor6LkXEkAb9ICeRy1nXNi66tnHVdxkihumMQ0REDFFwiIiIIQoOERExRMEhIiKGBDQ4cnNzSUlJ\nITk5mSVLltRY73K5GDt2LMnJyQwaNIjDhw8DcPjwYRITExkwYAADBgwgKyvLu82ePXtIS0sjOTmZ\n5557Do9Hb2UUEbmUAhYcbreb6dOns3TpUux2O+vXr+fAgQM+bVasWEHLli3ZvHkzw4YNY+7cud51\nHTp0YO3ataxdu5bp06d7l0+dOpUZM2bw/vvvk5eXR25ubqAOQUREahGw4HA4HMTGxhITE4PFYiE1\nNZWcnByfNlu3biU9PR2AlJQUdu7cWecZRFFREeXl5XTr1g2TycTAgQNr9CkiIoEVsOBwOp1ERUV5\nX1utVpxOZ4020dHRAJjNZsLCwigpKQHOXq4aOHAg//Ef/8Enn3xSa59RUVE1+hQRkcBqkB8AbNu2\nLdu2bSMiIoI9e/YwatQo7Hb7BfcXHGwiPDzkIlYoP5XGo3HT+DVeF2PsAhYcVquVwsJC72un04nV\naq3RpqCggKioKKqqqjhx4gQRERGYTCYsFgsA8fHxdOjQgYMHD9bos7CwsEaftXG7PZSWnvSr7sjI\nML/ayU/j73gYpfG7NAIxfhq7S8PI2J1rTAJ2qSohIYG8vDzy8/NxuVzY7XZsNptPG5vNxurVqwHY\ntGkTPXr0wGQyUVxcjNvtBiA/P5+8vDxiYmJo27YtoaGhfP7553g8HtasWUNSUlKgDkFERGoRsDMO\ns9lMVlYWw4cPx+12k5GRQVxcHPPnzyc+Pp6kpCQyMzMZP348ycnJtGrViuzsbAB27drFggULMJvN\nBAUFMW3aNMLDwwGYMmUKkyZN4vTp09x+++3cfvvtgToEERGphclzBXwQorLSbehSVffxbwa4oivb\npy8+wNGjJwLSd2RkGIemJwSkbzmrQ9YXARm/yMgwei3sddH7lX/58NEPDY3dJb9UJSIilycFh4iI\nGKLgEBERQxQcIiJiiIJDREQMUXCIiIghCg4RETFEwSEiIoYoOERExBAFh4iIGKLgEBERQxQcIiJi\niIJDREQMUXCIiIghCg4RETFEwSEiIoYoOERExBAFh4iIGBKwZ44D5ObmMnPmTKqrqxk0aBAjRozw\nWe9yuZgwYQJ79+4lPDyc7JqchvcAAA5FSURBVOxs2rdvz4cffshLL71EZWUlTZo0Yfz48fTs2ROA\nIUOGUFRURLNmzQBYtmwZbdq0CeRhiIjIjwQsONxuN9OnT2f58uVYrVYyMzOx2Wx07NjR22bFihW0\nbNmSzZs3Y7fbmTt3LvPmzSMiIoJXX30Vq9XK/v37efDBB9m+fbt3u7lz55KQoOdKi4jUh4BdqnI4\nHMTGxhITE4PFYiE1NZWcnByfNlu3biU9PR2AlJQUdu7cicfj4frrr8dqtQIQFxfHmTNncLlcgSpV\nREQMCNgZh9PpJCoqyvvaarXicDhqtImOjj5biNlMWFgYJSUltG7d2ttm06ZNXH/99VgsFu+yyZMn\nExQURL9+/Rg5ciQmk6nOWoKDTYSHh1yMw5KLROPRuGn8Gq+LMXYBvcfxU33zzTfMnTuXZcuWeZfN\nnTsXq9VKeXk5jz32GGvXrmXgwIF19uN2eygtPenXPiMjw35SzeIff8fDKI3fpRGI8dPYXRpGxu5c\nYxKwS1VWq5XCwkLva6fT6b389OM2BQUFAFRVVXHixAkiIiIAKCwsZPTo0cyePZsOHTr4bAMQGhrK\n3XffXeMsRkREAitgwZGQkEBeXh75+fm4XC7sdjs2m82njc1mY/Xq1cDZS1I9evTAZDJRVlbGiBEj\nePLJJ+nevbu3fVVVFcXFxQBUVlbywQcfEBcXF6hDEBGRWgTsUpXZbCYrK4vhw4fjdrvJyMggLi6O\n+fPnEx8fT1JSEpmZmYwfP57k5GRatWpFdnY2AG+99RaHDh1i0aJFLFq0CDj7ttvmzZszfPhwKisr\nqa6upmfPngwePDhQhyAiIrUweTweT30XEWiVlW5D9zi6j38zwBVd2T598QGOHj0RkL4jI8M4NF1v\n1Q6kDllfBGT8IiPD6LWw10XvV/7lw0c/NDR2l/weh4iIXJ4UHCIiYoiCQ0REDFFwiIiIIQoOEREx\nRMEhIiKGKDhERMQQBYeIiBii4BAREUMUHCIiYoiCQ0REDFFwiIiIIQoOERExRMEhIiKGKDhERMQQ\nBYeIiBii4BAREUMUHCIiYkhAgyM3N5eUlBSSk5NZsmRJjfUul4uxY8eSnJzMoEGDOHz4sHfda6+9\nRnJyMikpKWzfvt3vPkVEJLACFhxut5vp06ezdOlS7HY769ev58CBAz5tVqxYQcuWLdm8eTPDhg1j\n7ty5ABw4cAC73Y7dbmfp0qVMmzYNt9vtV58iIhJYAQsOh8NBbGwsMTExWCwWUlNTycnJ8WmzdetW\n0tPTAUhJSWHnzp14PB5ycnJITU3FYrEQExNDbGwsDofDrz5FRCSwzIHq2Ol0EhUV5X1ttVpxOBw1\n2kRHR58txGwmLCyMkpISnE4nXbt29dnW6XQCnLfP2jRpEkxkZJjftX/64gN+t5ULY2Q8jOqQ9UXA\n+pazAjV+Hz76YUD6lX+5GGOnm+MiImJIwILDarVSWFjofe10OrFarTXaFBQUAFBVVcWJEyeIiIg4\n57b+9CkiIoEVsOBISEggLy+P/Px8XC4Xdrsdm83m08Zms7F69WoANm3aRI8ePTCZTNhsNux2Oy6X\ni/z8fPLy8khMTPSrTxERCayA3eMwm81kZWUxfPhw3G43GRkZxMXFMX/+fOLj40lKSiIzM5Px48eT\nnJxMq1atyM7OBiAuLo677rqL/v37ExwcTFZWFsHBwQC19ikiIpeOyePxeOq7CBERaTx0c1xERAxR\ncIiIiCEKjkZsy5YtdOrUiX/84x/1XYoY1KVLFwYMGMA999xDeno6u3fvru+SxE+HDx/m7rvv9lm2\ncOFCXn/99Xqq6NJTcDRi69evp3v37tjt9vouRQxq1qwZa9eu5S9/+QtPPPEEL7/8cn2XJOI3BUcj\nVVFRwaeffsrMmTMVHI1ceXk5LVu2rO8yRPwWsLfjSmDl5OTQp08frr32WiIiItizZw/x8fH1XZb4\n6fTp0wwYMIAzZ85w9OhR/uu//qu+SxLxm844Gim73U5qaioA/fv311lHI/PDpaqNGzeydOlSnnrq\nKfTO+MbBZDIZWn450hlHI1RaWsrHH3/M/v37MZlMuN1uTCYTEyZMuKJ+eS8XN954IyUlJRQXF9Om\nTZv6LkfOIzw8nOPHj/ssO378OO3bt6+nii49nXE0Qps2bWLAgAFs27aNrVu38te//pX27dvzySef\n1HdpcgH+8Y9/4Ha7CQ8Pr+9SxA8tWrQgMjKSnTt3Amf/R2779u107969niu7dHTG0QitX7+ehx56\nyGdZv379WL9+Pb/4xS/qqSox4od7HAAej4fZs2d7p9WRhm/OnDlMmzaNF154AYBRo0bRoUOHeq7q\n0tGUIyIiYoguVYmIiCEKDhERMUTBISIihig4RETEEAWHiIgYouAQERFD9DkOaXA6depEWloac+fO\nBaCqqorevXvTtWtXXnvtNW+7kSNHcuzYMd555x3vsoULF/LOO+/QunVrKisrGTlypHcK7IkTJ/LL\nX/6SdevWcfjwYU6ePElxcbH3E79Tpkzhpptuori4mD59+vDMM89w3333efu22WysXLmS1q1b11n/\nsWPHePrppykoKKCqqop27doxbtw4JkyYAEBBQQGhoaGEhYURERHBG2+8AcAbb7zBSy+9xEcffURY\nWBjbt2/3/gwOHTpE27ZtadasGZ06daJHjx7s2bOHrKws736HDBnChAkTSEhIYOXKld75rzweD2PH\njuWOO+44Z82vv/46K1asoGnTppjNZoYMGcLAgQNxuVy8+OKLfPDBB5hMJn7+858zZcoUoqKigLPT\nw1933XVUVVURHBzMwIEDGTZsGEFBQfztb39j5MiRPp+ofuqpp7jtttvq/PlJw6fgkAYnJCSEb775\nhtOnT9OsWTM+/PBDrFarT5uysjL27t1LSEgI+fn5xMTEeNcNGzaMBx98kLy8PO69915SUlJo0qSJ\nd/2iRYsA+Nvf/sayZct8wghg48aNdO3aFbvd7hMc/lqwYAG33XYbQ4cOBeDrr7+mU6dOrF27FvhX\ngN15550+29ntdhISEnj//ffJyMigT58+9OnTB/ANBYBVq1adc/+FhYUsXryY1atXExYWRkVFBcXF\nxeds//bbb/PRRx+xcuVKQkNDKS8vZ/PmzQBkZ2dTUVHBxo0bCQ4O5t1332X06NGsWLECk8nknXML\n4Pvvv+fJJ5+kvLycxx57DICbb765xs9XGj9dqpIGqW/fvnzwwQeA74SOP3j//ff51a9+RWpq6jkn\neLzmmmto3rw5ZWVlhvZtt9uZOHEiTqeTwsJCw7UXFRV5/48coHPnzufd5tChQ5w8eZKxY8f+5Akr\nv//+e1q0aEFISAhwdoqMHwfrv3vttdeYOnUqoaGhAISGhpKens6pU6dYtWoVkydP9n6qPSMjA4vF\nwscff1yjnzZt2jBjxgz+9Kc/acLGy5yCQxqk/v37s2HDBs6cOcO+ffvo2rWrz3q73c7dd99dZ3Ds\n3buX2NhYQxMHFhQUcPToURITE7nrrrvYsGGD4drvv/9+nn76aYYMGcKrr76K0+k87zZ2u53+/ftz\n8803c/DgQY4dO2Z4vz/o3LkzV111FUlJSUyaNImtW7ees215eTkVFRW1Bsu3335LdHS0N1B+EB8f\nzzfffFNrfzExMbjdbr7//nsAPvnkEwYMGOD9d+jQoQs+Lmk4FBzSIHXu3JnDhw+zfv16+vbt67Pu\n2LFjfPvtt3Tv3p1rr70Ws9nM/v37vevfeOMNUlNTGTx4MP/5n/9paL8bNmzgrrvuAs6G1/r16w3X\n3qdPH7Zs2cLgwYP55z//SXp6ep2XiuBfZ1VBQUH069ePjRs31tm+rqm9g4ODWbp0KQsWLOCaa65h\n1qxZLFy40PBxXAw333wza9eu9f67kuZzupwpOKTBstlszJkzp8Zlqvfee4/jx4+TlJSEzWbjyJEj\nPmcdw4YNw263s2DBAp5++mnOnDnj9z7tdjurVq3CZrMxcuRI9u/fT15enuHaw8PDSUtL48UXXyQh\nIYFdu3ads+2+ffvIy8vj97//PTabDbvdft7Aqm1q79LSUiIiIoCzAZKYmMjDDz/Myy+/zPvvv19r\nP6Ghod77RP+uQ4cOFBQUUF5e7rN87969xMXF1dpffn4+wcHBmh7+MqfgkAYrMzOTUaNG0alTJ5/l\ndrudpUuXsnXrVrZu3cq7775b6+WqpKQk4uPjWb16tV/7O3jwIBUVFWzfvt3b94gRIwyfdezcuZNT\np04BZy8FHTp0iOjo6HO2t9vtPProo9597tixg6KiIo4cOXLObRISEvjss884evQoAF988QUul4vo\n6GicTid79+71tv3666+5+uqrz9nXiBEjmDZtmjcgKioqWLNmDSEhIQwcOJAXXngBt9sNwJo1azh1\n6hQ9evSo0U9xcTFTpkzh/vvv13NhLnN6V5U0WFFRUTzwwAM+yw4fPsyRI0fo1q2bd1lMTAxhYWH8\n/e9/r9HHqFGjePLJJxk8ePB592e320lOTvZZ1q9fPx5//HFGjx4NwD333ENQ0Nn/37rrrruYNGlS\njX727t3LjBkzCA4OxuPxMGjQIBITE+vc75IlS3yWJScnY7fbGTFiRK3bXHXVVUyePJkRI0ZQXV1N\nSEgIL7/8MkFBQVRVVTF79myKiopo2rQprVu3Ztq0aefc/29/+1tOnjxJRkYGTZo0wWw287vf/Q6A\nJ598ktmzZ5OSkkJQUBA/+9nPWLRokTcYfpge/oe34w4YMMC7LfzrHscPHnnkkRrvJpPGR9Oqi4iI\nIbpUJSIihuhSlcgFevfdd3nzzTd9lt10001MmTKlniqq27Rp09i9e7fPsgceeICMjIx6qkgaK12q\nEhERQ3SpSkREDFFwiIiIIQoOERExRMEhIiKG/H81LZRyiReI1gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "leCN37KFqM7q",
        "colab_type": "text"
      },
      "source": [
        "**Income** vs Churn Rate:\n",
        "* No monotonic increase or decrease observed over the income groups\n",
        "* However as seen in the barchart below, no churn was observed when a household's income reaches to 175K and more (27 samples out of 801 households)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGkHHCxGzdfv",
        "colab_type": "code",
        "outputId": "061de87a-e686-4e8e-8e97-89920fa8c736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "fig = plt.figure(figsize=(8,5))\n",
        "ax = sns.barplot(x=\"INCOME_DESC\",y=\"isChurned\",data=data,\n",
        "                 order=[\"Under 15K\",\"15-24K\",\"25-34K\",\"35-49K\",\"50-74K\",\"75-99K\",\"100-124K\",\"125-149K\",\"150-174K\",\"175-199K\",\"200-249K\",\"250K+\"])\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=45)\n",
        "ax.axhline(y=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb9222a128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eeeYZuLi4AADatGmDzMxMucohIiL6S5FtDd5oNMLb21u6r9PpYDAY7jn9+vXr0blzZ+l+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\nAerMv1ihXzBPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PLYnteNqjin",
        "colab_type": "text"
      },
      "source": [
        "**Home Ownership** vs Churn Rate:\n",
        "* No significant differences between owners and renters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SctrUDB53Tlg",
        "colab_type": "code",
        "outputId": "f4f82725-e4c7-4a9f-d2d4-b46aac640d06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=\"HOMEOWNER_DESC\",x=\"isChurned\",data=data,orient=\"h\")\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb9228de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/MWDAAOLj41m5cqW9axMREXEIiz1DKJrTcN26dezdu5f27dvz7rvvEhwcbO/a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\nRJyewlBERJze/wePGNBu+PTjpwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EyCegBpHrsl5"
      },
      "source": [
        "**Number of Redeems** vs Churn Rate:\n",
        "\n",
        "Lets group our data based on household's number of redeems. We will use pandas.cut which groups the number of redeems into the bins. Graph below separates 0 redeemers, redeemed once and the final group redeemed more than once:\n",
        "* Churn rate decreases as the number of redeems increase"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luww_lDFZ4Ua",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "29759bc8-829e-40fa-c340-a0e4d57f830d"
      },
      "source": [
        "ax = sns.barplot(y=pd.cut(data.no_of_redeems,bins=[-0.1,0,1,35],duplicates=\"drop\"),x=\"isChurned\",data=data,orient=\"h\")\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb7e67bd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8eERHR6Nv376ws+PXo+6msrND6twAW8cgsrrm8acB6G0dg2oh2eVUr149jBgxwpJZiIiI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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCrdW4tvwYD2",
        "colab_type": "text"
      },
      "source": [
        "Churn rate vs **Number of campaigns received by a household**\n",
        "\n",
        "* Churn rate decreases as the number of campaigns received by a household increases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6lAZvhOwX1L",
        "colab_type": "code",
        "outputId": "af4f4cd4-9236-4c31-ad47-7dce2c20981b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=pd.qcut(data.no_of_received_campaigns,5,duplicates=\"drop\"),x=\"isChurned\",data=data,orient=\"h\")\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb906627f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5tzIREZErMB1gb7/9tivrEBERqRDTAZadnc3WrVs5deoUdrvd8f4TTzzhksJERESuxHSA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\nPJICTEREPJICTEREPJICTEREPNL/AyVqlcnRjz5qAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxlq_m5E6wlq",
        "colab_type": "text"
      },
      "source": [
        "Not very clear, however churn rate tends to decrease when **children starts to appear** in households"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYxj5G-t6AFl",
        "colab_type": "code",
        "outputId": "38ce7e4b-a052-40ff-a416-15b49cc3dfdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=\"HH_COMP_DESC\",x=\"isChurned\",data=data,orient=\"h\",order=['Unknown','Single Female','Single Male','2 Adults No Kids','2 Adults Kids','1 Adult Kids'])\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb921c91d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LYmIiiYmJ+Pr6Eh0dTfPmzQus5+rqypQpU9i8eTN/+9vf+Pzzz/n555/zLXvy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\nIoASooiICKCEKCIiAighioiIAPD/oEyr8gW2qi4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJjfuF6_7YFO",
        "colab_type": "text"
      },
      "source": [
        "Churn rate tends to decrease as the number of **family members** increases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vz0yfyv37Jlf",
        "colab_type": "code",
        "outputId": "d0b442a7-e947-4f43-aefa-7399531e5384",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=\"HOUSEHOLD_SIZE_DESC\",x=\"isChurned\",data=data,orient=\"h\")\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb905a2b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiEgJBiHTBU9ISMDmzZtrva7rva2ExQKDE0eQiGzyl79U/3ffPkemkJgry3GppMrmdrQ4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\nItIxFnkiIh1jkSci0jEWeSIiHft/viR58ugwsxwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57pCXWmT8IQV",
        "colab_type": "text"
      },
      "source": [
        "**Campaign Type** vs Churn rate:\n",
        "\n",
        "* TypeC campaign fails as all households which was approached with campaign C are churned. However note that it has extremely lower sample size (1% of all population)\n",
        "* TypeB is the most successful one with the lowest churn rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKwWgFlD7ivs",
        "colab_type": "code",
        "outputId": "ce332194-ba16-4b30-a7f0-67bbe50163af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=\"most_freq_camp_type\",x=\"isChurned\",data=data,orient=\"h\",order=['TypeA','TypeB','TypeC'])\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb90598f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fffaZbdzX15eXX37ZqeFEROTGV2mRREZGEhkZyZ49e+jQocNvbvfOO+/wzDPPODSciIjc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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqF4_cxz8Q5O",
        "colab_type": "text"
      },
      "source": [
        "**Total purchase of a household** vs Churn rate:\n",
        "* Total purchase amount of a customer is a clear separator, expected to perform well as a feature in the churn prediction model\n",
        "* Churn rate decreases as total amount of purchase of a household (within 2 years) increases\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K9X435D8Quu",
        "colab_type": "code",
        "outputId": "76502451-270a-43cf-e501-4a5a94aa4585",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ax = sns.barplot(y=pd.qcut(data[\"Total Purchase (USD)\"],5,duplicates=\"drop\"),x=\"isChurned\",data=data,orient=\"h\")\n",
        "ax.axvline(x=data.isChurned.sum()/len(data),c=\"red\",label=\"population mean\")\n",
        "ax.legend()\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdb8da3dda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhRf0Q+RTp04lKysLRVFo164db775JgC7du1iy5YtqNVqbGxsiImJQaVSsWPH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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYyZzjHSEwcm",
        "colab_type": "text"
      },
      "source": [
        "Correlation between **Campaign Number** and the Target (isChurned) are calculated and displayed in the barchart below:\n",
        "* Campaign 25 as a feature expected to be performing well in the model as it has the highest correlation with the target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNhSdLc_AHQE",
        "colab_type": "code",
        "outputId": "8903ae4b-b122-4b19-adfb-9b7be56b6450",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "corrs = abs(data[[\"Camp_\"+str(i) if i<31 else \"isChurned\" for i in range(1,32)]].corr()[\"isChurned\"])\n",
        "corrs.drop(\"isChurned\",inplace=True)\n",
        "corrs = pd.DataFrame(list(zip(corrs.index,corrs)),columns=[\"Campaign No.\",\"Correlation with Target\"]).sort_values(by=\"Correlation with Target\",ascending=False)\n",
        "fig_store = plt.figure(figsize=(10,8))\n",
        "sns.barplot(y=\"Campaign No.\",x=\"Correlation with Target\",data = corrs,orient=\"h\")"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb8da75ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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REYWqgN8FLEkSysvL0dbWBoVCAZfLhb6+PgBAdHQ0RFEEAMTFxUGn00EQBIiiiPPnz/vH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\ngg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "crzg_R5xQxN0"
      },
      "source": [
        "Correlation between the **stores** with high number of churner customers and the Target (isChurned) are calculated and displayed in the barchart below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2344759a-b0b4-40ec-e3ae-d27eef2ffa02",
        "id": "Ju_OeTh6Q0lf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "cols =list(df_store.columns)\n",
        "cols.extend([\"isChurned\"])\n",
        "corrs = abs(data[cols].corr()[\"isChurned\"])\n",
        "corrs.drop(\"isChurned\",inplace=True)\n",
        "corrs = pd.DataFrame(list(zip(corrs.index,corrs)),columns=[\"Store ID\",\"Correlation with Target\"]).sort_values(by=\"Correlation with Target\",ascending=False)\n",
        "fig_store = plt.figure(figsize=(10,6))\n",
        "sns.barplot(y=\"Store ID\",x=\"Correlation with Target\",data = corrs,orient=\"h\")"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb8d7c65c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yPH3Bc2OdgohI2IQqIIuLizl06NAl23Jzc8nJyRmV+IZh4cMPR7YHQYZmov+q\nNBO1hYjI+DOhCsjVq1ePdQoiIiIi496EKiDHnMUYdD6BjC61hXlMhLboC57j49PBsU5DROSaUAE5\niqwWK0X/rIdoRCaior+oBVRAisj1wTTvgRQRERGR8UEFpIiIiIgMi+kKyLKyMpYsWYLL5SIrK4vD\nhw+zZcsWent7RzTu888/H4754IMP0t7eDsCJEydYtmwZf/qnf8r//b//95JztmzZwpIlS8jIyOBv\n/uZvOH/+/IjmKCIiImIGpiog/X4/DQ0N7Nixg127drF582ZmzpxJRUXFsAvIUCg0rOPz8/PZtWsX\nr776Kl/84hfZuHEjANOnT+fpp58mLy/vkuPb29upqKjglVdeobq6Ory0ooiIiMj1zlQFZGdnJ1FR\nUdjtdgAcDge1tbV0dHTgdrtZsWIFANXV1bhcLjIyMli3bl34/AULFrB27VoyMzPx+/0cOXKE5cuX\nk52dTV5eHh0dHQPGnjp1avjv3t5eLBYLADNmzCAxMZGIiMufNwqFQpw7d44LFy5w7tw5nE7nNbkP\nIiIiImZmqgIyJSWFtrY20tPTKSoq4sCBA+Tm5uJ0OikvL6eyspL29nbWr19PeXk5VVVVNDU1UVdX\nB1xcTzsxMZGdO3cyf/58vF4vpaWl+Hw+cnJyKCkpGTR+SUkJqamp7Nq1i8cee2zQY2NiYnjwwQf5\n0pe+xN13383UqVO5++67r9m9EBERETErUxWQkZGR+Hw+PB4PDoeDgoICfD7fJcc0NTWRlJSEw+Eg\nIiICl8tFY2MjADabjfT0i6/JaWlp4dixY6xcuZKsrCzKysrC8xoHUlBQwJ49e3C5XGzdunXQYz/+\n+GPq6+upr6/nV7/6Fb29vbz66quf4tuLiIiIjA+mew+kzWYjOTmZ5ORkEhISqKqqGvK5kyZNwmaz\nAWAYBvHx8Wzbtm3YObhcLlatWsWjjz464DG//vWvueWWW3A4HAB8+ctfxu/3k5WVNex4IiIiIuOJ\nqXogm5ubaW1tDX8+evQosbGxREZGcvbsWQASExNpbGwkEAiEH1xZuHDhZdeKi4sjEAjg9/sBCAaD\nHD9+fMDYvx+3vr6eOXPmDJprbGwshw8fpre3F8Mw2LdvH3Pnzh3GtxUREREZn0zVA9nT04PX66Wr\nqwubzcbs2bPxeDzU1NSQn5+P0+mksrKSwsJC3G43hmGQmppKWlraZdey2+2Ulpbi9Xrp7u4mFArh\ndruJj4+/YuwNGzbQ0tKCxWJh1qxZFBcXAxcf7MnJyeHMmTNYrVbKy8t57bXXmD9/Punp6TzwwANE\nRETwuc99jmXLlo3o/REREWtE5vMAACAASURBVBExA4thGMZYJzGRaClDkYmp6C9q6ezsHus0BjV9\n+hROn+4Z6zQEtYVZTPR2iI6eNuA+U/VAXu/6jf7/Wg9XRCaavuC5sU5BROSamXAFZHFxMYcOHbpk\nW25uLjk5OSMf3LDQ+aG5eyAmion+q9JM1BYiIuPPhCsgV69ePdYpiIiIiIxrE66AHFMWY9D5BDK6\n1BYj61zwPN2n+8Y6DRERGQEqIEeR1WLlq6+OwlC5iAn8v6xX6EYFpIjI9chU74EUEREREfNTASki\nIiIiw2K6IeyysjKqq6uxWq1YrVY8Hg9+v59ly5YxefLkEYv7/PPPU19fj9VqZcaMGaxZs4aYmBi6\nu7t58skn+d3vfkcoFOLBBx8MP7Gdl5fH4cOH+bM/+zM2bdo0YrmJiIiImImpCki/309DQwM7duzA\nbrcTCAQIBoNUVFSQmZk5rAIyFAqF18Ueivz8fB5//HEAKioq2LhxIx6Ph3/8x39k7ty5/P3f/z2B\nQICvfOUruFwu7HY7+fn59Pb2/kHrbYuIiIiMV6Yawu7s7CQqKgq73Q6Aw+GgtraWjo4O3G43K1as\nAKC6uhqXy0VGRgbr1q0Ln79gwQLWrl1LZmYmfr+fI0eOsHz5crKzs8nLy6Ojo2PA2FOnTg3/3dvb\ni8ViAcBisXD27FkMw+Ds2bPcfPPNRERcrLvvuusuIiMjr/l9EBERETEzUxWQKSkptLW1kZ6eTlFR\nEQcOHCA3Nxen00l5eTmVlZW0t7ezfv16ysvLqaqqoqmpibq6OuDiWtqJiYns3LmT+fPn4/V6KS0t\nxefzkZOTQ0lJyaDxS0pKSE1NZdeuXTz22GMAfOtb3+LEiRPcc889ZGZm8vTTT2O1muq2iYiIiIwq\nU1VCkZGR+Hw+PB4PDoeDgoICfD7fJcc0NTWRlJSEw+EgIiICl8tFY2MjADabjfT0i2tNt7S0cOzY\nMVauXElWVhZlZWW0t7cPGr+goIA9e/bgcrnYunUrAP/6r//K5z73OX71q19RVVWFx+PhzJkzI/Dt\nRURERMYHU82BhItFYHJyMsnJySQkJFBVVTXkcydNmhSe92gYBvHx8X/Q/ESXy8WqVat49NFH8fl8\nrFq1CovFwuzZs7nllltobm4mMTFx2NcVERERuR6YqgeyubmZ1tbW8OejR48SGxtLZGQkZ8+eBSAx\nMZHGxkYCgQChUIiamhoWLlx42bXi4uIIBAL4/X4AgsEgx48fHzD278etr69nzpw5AHzmM59h3759\nAHz44Ye0tLRwyy23fNqvKiIiIjJumaoHsqenB6/XS1dXFzabjdmzZ+PxeKipqSE/Px+n00llZSWF\nhYW43W4MwyA1NZW0tLTLrmW32yktLcXr9dLd3U0oFMLtdhMfH3/F2Bs2bKClpQWLxcKsWbMoLi4G\n4K/+6q/4/ve/j8vlwjAMnnjiCRwOBwB/+Zd/SXNzMz09PSxatIgf/vCH3HPPPSN3g0RERERMwGIY\nhjHWSUwkWspQJor/l/UKnZ3dVz1u+vQpnD7dMwoZydWoLcxDbWEOE70doqOnDbjPVD2Q17t+o5//\nl/XKWKchMirOBc+PdQoiIjJCJlwBWVxczKFDhy7ZlpubG15dZkQZFjo/vHqPjIy8if6rUkRE5NOY\ncAXk6tWrxzoFERERkXHNVE9hi4iIiIj5TbgeyDFlMQadkCqjS20xNOeCfXSf1nxGERH5byogR5HV\nYuVrO7xjnYbIsLz2wDN0owJSRET+m4awRURERGRYVECKiIiIyLCYroAsKytjyZIluFwusrKyOHz4\nMFu2bKG3t3dE4z7//PPhmA8++CDt7e2X7H/77bf54z/+Y375y18C8J//+Z888MADZGVlsWTJEl56\n6aURzU9ERETELEw1B9Lv99PQ0MCOHTuw2+0EAgGCwSAVFRVkZmYyefLkIV8rFAphs9mGfHx+fj6P\nP/44ABUVFWzcuBGPxxO+1vr160lJSQkfHx0dzbZt27Db7Zw9exaXy8W9995LTEzMkGOKiIiIjEem\n6oHs7OwkKioKu90OgMPhoLa2lo6ODtxuNytWrACguroal8tFRkYG69atC5+/YMEC1q5dS2ZmJn6/\nnyNHjrB8+XKys7PJy8ujo6NjwNhTp04N/93b24vFYgl/rqysJD09nRkzZoS32e32cJ59fX309/df\nm5sgIiIiYnKmKiBTUlJoa2sjPT2doqIiDhw4QG5uLk6nk/LyciorK2lvb2f9+vWUl5dTVVVFU1MT\ndXV1APT09JCYmMjOnTuZP38+Xq+X0tJSfD4fOTk5lJSUDBq/pKSE1NRUdu3axWOPPQZAe3s7dXV1\nfPOb37zs+La2NlwuF1/84hf5zne+o95HERERmRBMVUBGRkbi8/nweDw4HA4KCgrw+XyXHNPU1ERS\nUhIOh4OIiAhcLheNjY0A2Gw20tPTAWhpaeHYsWOsXLmSrKwsysrKLpvX+D8VFBSwZ88eXC4XW7du\nBeCHP/whTzzxBFbr5bfqM5/5DLt27eL1119nx44dfPjhh9fiNoiIiIiYmqnmQMLFIjA5OZnk5GQS\nEhKoqqoa8rmTJk0Kz3s0DIP4+Hi2bds27BxcLherVq3i0Ucf5ciRI/zN3/wNAB999BF79uwhIiKC\ntLS08PExMTHEx8dz8OBBvvKVrww7noiIiMh4YqoeyObmZlpbW8Ofjx49SmxsLJGRkZw9exaAxMRE\nGhsbCQQChEIhampqWLhw4WXXiouLIxAI4Pf7AQgGgxw/fnzA2L8ft76+njlz5gCwe/fu8L/09HRW\nr15NWloaJ0+e5Ny5cwB8/PHHHDp0iLi4uE97C0RERERMz1Q9kD09PXi9Xrq6urDZbMyePRuPx0NN\nTQ35+fk4nU4qKyspLCzE7XZjGAapqamX9AZ+wm63U1paitfrpbu7m1AohNvtJj4+/oqxN2zYQEtL\nCxaLhVmzZlFcXDxoridOnGDt2rVYLBYMw+DBBx9k3rx51+Q+iIiIiJiZxTAMY6CdFy5cYO/evTQ3\nNwMwd+5c7rnnHiIiTFV3jitaylDGm9ceeIbOzu4Ru/706VM4fbpnxK4vQ6e2MA+1hTlM9HaIjp42\n4L4BC8j29vbwE9Cf+9znMAyDo0eP0tnZSUVFhZ44/gP0G/1YLaaaNSByVeeCfXSfHrm1sCf6f9Bm\norYwD7WFOUz0dhisgBywK7GkpIRvfvObfPvb375ke0VFBT/5yU947rnnrlmCo6m4uJhDhw5dsi03\nN5ecnJyRD25Y6Pxw5HpyZOgm+n8KIiIin8aABeRbb73F2rVrL9uem5sbflXOeLR69eqxTkFERERk\nXBuwgLzxxhsHPGk4SwrK77EM3h0so0ttMTTngkG6T58b6zRERMREBiwgu7u7ef311y/bbhgGZ86c\nGdGkrldWi4UlvrKxTkNkWGqyH6YbFZAiIvLfBiwgk5KSeOONN66470rvXRQRERGRiWHAAnLNmjWj\nmYeIiIiIjBMDFpCbN28e9MSVK1de82QAysrKqK6uxmq1YrVa8Xg8+P1+li1bNqJzL59//nnq6+ux\nWq3MmDGDNWvWEBMTQ11dHT/96U+xWq3YbDaeeuopvvCFLwDwuc99joSEBODiuth///d/P2L5iYiI\niJjFgAXkJ0sHjia/309DQwM7duzAbrcTCAQIBoNUVFSQmZk5rAIyFAqF18Ueivz8fB5//HHg4quK\nNm7ciMfj4a677uK+++7DYrHw7rvv8vjjj/PLX/4SuPig0auvvjq8LykiIiIyzg1YQP71X//1aOYB\nQGdnJ1FRUdjtdgAcDgcVFRV0dHTgdruZPn06lZWVVFdXs2nTpvBShk8++SQACxYsYNmyZfz617/m\n7/7u77jxxhtZu3YtPT09REVFsWbNGpxO5xVjT506Nfx3b28vFosFgMjIyCtuFxEREZmoTLUmYUpK\nChs3biQ9PZ277rqLr33ta+Tm5rJlyxbKy8txOBy0t7ezfv16fD4fN910Ew8++CB1dXWkpaXR09ND\nYmIi3/ve9wgGg6xYsYKf/exnOBwOXnvtNUpKSgad21lSUkJVVRXTpk2joqIivP1f/uVf2LBhA4FA\ngE2bNoW3nz9/nuzsbCIiIli1atUV1+QWERERud6YqoCMjIzE5/Nx8OBB9u/fT0FBAYWFhZcc09TU\nRFJSEg6HAwCXy0VjYyNpaWnYbLbwS85bWlo4duxYeK5mf38/0dHRg8YvKCigoKCATZs2sXXrVh59\n9FEAFi9ezOLFi2lsbOSnP/0pW7ZsAeCNN94gJiaG999/H7fbTUJCArfddtu1vCUiIiIipmOqAhLA\nZrORnJxMcnIyCQkJVFVVDfncSZMmhec9GoZBfHw827ZtG3YOLpeLVatWhQvITyxcuJD333+fQCCA\nw+EIrwd+6623kpSUxL//+7+rgBQREZHrnvVqB3z44Yc89dRT5OfnA/Af//EfbN++fUSSaW5uprW1\nNfz56NGjxMbGEhkZGX6oJzExkcbGRgKBAKFQiJqamiu+lzIuLo5AIIDf7wcgGAxy/PjxAWP/ftz6\n+nrmzJkDwG9/+1sMwwDgnXfeoa+vj6ioKD7++GP6+voACAQCHDp0iM9+9rOf6vuLiIiIjAdX7YH8\n3ve+R3Z2dvgVNbfffjsFBQV8/etfv+bJ9PT04PV66erqwmazMXv2bDweDzU1NeTn5+N0OqmsrKSw\nsBC32x1+iOZKcw/tdjulpaV4vV66u7sJhUK43W7i4+OvGHvDhg20tLRgsViYNWsWxcXFANTW1vLq\nq68SERHBjTfeSElJCRaLhRMnTrB69WosFguGYfCd73xHBaSIiIhMCBbjk+61AeTk5PDKK69w//33\nh4eTs7Ky9PqaP5CWMpTxpib7YTo7u0fs+tOnT+H06Z4Ru74MndrCPNQW5jDR2yE6etqA+67aAzll\nyhQ++uij8Otr3nrrLaZNG/iCMrB+w6Am++GxTkNkWM4Fg2OdgoiImMyQhrAffvhh3nvvPb7xjW/w\n0Ucf8dOf/nQ0chsRxcXFHDp06JJtubm55OTkjHxwAzo/HLmeHBm6if6rUkRE5NMYtIDs7+/n/Pnz\nbN26lZaWFgzDIC4ujhtuuGG08rvmVq9ePdYpiIiIiIxrgxaQn6xFXVVVNeDDJzIMlsHnE8joUlsM\nzblgkO7T58Y6DRERMZGrDmHfdddd1NbW8uUvf1nL+H1KVouFjJf/cazTEBmW6qXfohsVkCIi8t+u\nWkD+0z/9E5s3b8ZmszFp0iQMw8BisVw2j1BEREREJoarFpCfvIhbRERERASGuJRhfX09Bw8eBCAp\nKYkvfelLI5JMWVkZ1dXVWK3W8PxLv9/PsmXLmDx58ojEBHjuued44403uOGGG7jttttYs2YNN910\nE319faxevZojR45gsVh4+umnSU5OBqC6uppNmzYB4HQ6WbduXXh9bhEREZHr2VWXMly/fj0VFRXM\nnTuXuXPnUlFRwYYNG655In6/n4aGBnbs2MGuXbvYvHkzM2fOpKKigt7e3mFdKxQKDev4lJQUqqur\n2bVrF7fffnu4MPxkycZP8nnuuefo7+/nwoUL/PCHP6S8vJxdu3Yxb948/vEfNbdRREREJoarFpB7\n9uxh8+bNLF26lKVLl/LCCy/Q0NBwzRPp7OwkKioKu90OgMPhoLa2lo6ODtxuNytWrAAu9vy5XC4y\nMjJYt25d+PwFCxawdu1aMjMz8fv9HDlyhOXLl5OdnU1eXh4dHR0Dxr777ruJiLjYGXvnnXdy8uRJ\n4OK635/0OM6YMYNp06Zx5MgRDMPAMAx6e3sxDIMzZ87gdDqv+T0RERERMaOrFpAAXV1d4b+7u0fm\nRdgpKSm0tbWRnp5OUVERBw4cIDc3F6fTSXl5OZWVlbS3t7N+/XrKy8upqqqiqamJuro64OI62omJ\niezcuZP58+fj9XopLS3F5/ORk5NDSUnJkPJ45ZVXWLRoEQB33HEHu3fv5sKFC7z//vu88847tLW1\nccMNN1BUVITL5eKee+7hxIkTLF26dETui4iIiIjZXHUO5EMPPcQDDzxAcnIyhmHQ2NhIYWHhNU8k\nMjISn8/HwYMH2b9/PwUFBZfFaWpqIikpKTzX0OVy0djYSFpaGjabjfT0dABaWlo4duwYK1euBC6+\nED06OvqqOZSVlWGz2cjMzAQurgN+4sQJcnJyiI2NZcGCBdhsNoLBIC+99BJVVVXceuutPPvss2za\ntIm/+qu/upa3RERERMSUrlpAZmRkkJSURFNTEwBPPPHEkIqxP4TNZiM5OZnk5GQSEhKoqqoa8rmT\nJk3CZrMBYBgG8fHxbNu2bcjn+3w+Ghoa2LJlS/h9lxERETz11FPhY77xjW9w++23c/ToUQBuu+02\nAL761a/yD//wD0OOJSIiIjKeXXUI2+1243Q6ue+++7jvvvuIjo7G7XZf80Sam5tpbW0Nfz569Cix\nsbFERkZy9uxZABITE2lsbCQQCBAKhaipqWHhwoWXXSsuLo5AIBB+BVEwGOT48eMDxt67dy8vvPAC\nZWVllzzt3dvbS0/PxfWS33zzTWw2G5/97GeJiYnhxIkTBAKB8L65c+d+6nsgIiIiMh4M2AN5/vx5\nent7+eijj/j4448xDAOAM2fO0N7efs0T6enpwev10tXVhc1mY/bs2Xg8HmpqasjPz8fpdFJZWUlh\nYSFutxvDMEhNTSUtLe2ya9ntdkpLS/F6vXR3dxMKhXC73QMux/jss8/S19cXHvKeP38+Ho+HU6dO\nkZeXh9VqJSYmhh//+McAxMTE8Mgjj/Ctb32LiIgIZs2axZo1a675PRERERExI4vxSWX4P5SXl1Ne\nXk5HRwcxMTHhAnLq1Kn8xV/8BcuXLx/VRK8XWspQxpvqpd+is3NkHp4DmD59CqdP94zY9WXo1Bbm\nobYwh4neDtHR0wbcN2AB+YnKysrwK3Tk0+k3DKxaT1zGmXPBIN2nR24t7In+H7SZqC3MQ21hDhO9\nHQYrIAccwn777bf5zGc+Ey4eq6qqqK2tZdasWfz1X/8106dPv/aZjrDi4uLL1vDOzc0lJydndBIw\noPPDkevJkaGb6P8piIiIfBoD9kA+8MADbN68menTp9PY2EhBQQE/+MEPOHr0KM3NzZSWlo52ruNe\nf7/BqVNnxjoNQQWkmagtzENtYR5qC3OY6O0wWA/kgE9hh0KhcC/ja6+9xrJly0hPT+fxxx/nt7/9\n7bXPUkRERETGhQGHsD9Z8zkiIoJ9+/bx7LPPhvcNd61p+S+Wwat5GV1qi8GdC16g+/Tw1qEXEZGJ\nYcACcsmSJSxfvpyoqChuvPFGvvCFLwDw29/+lqlTp45agtcTq8VC5su7xjoNkSHZudSFZuyKiMiV\nDFhAPvzww9x11110dnaSkpISXp2lv7+fH/zgB6OWoIiIiIiYy6BLGd55552XbYuLixuxZERERETE\n/K66lOFoKysrY8mSJbhcLrKysjh8+DBbtmyht3d05mK9+OKLzJs3L7xM4c6dO3G5XLhcLr7xjW/w\n7rvvXnJ8KBTi/vvv56GHHhqV/ERERETG2qA9kKPN7/fT0NDAjh07sNvtBAIBgsEgFRUVZGZmXrJO\n9dWEQiFsNtuw4re1tfHmm28SGxsb3nbLLbewdetWbr75Zvbs2cMPfvADtm/fHt5fUVHB3LlzOXNG\nr+cRERGRicFUPZCdnZ1ERUVht9sBcDgc1NbW0tHRgdvtDr/UvLq6GpfLRUZGBuvWrQufv2DBAtau\nXUtmZiZ+v58jR46wfPlysrOzycvLo6OjY9D4a9as4cknnwzP9wT4/Oc/z8033wxcHNI/efJkeN/J\nkydpaGhg6dKl1+weiIiIiJidqQrIlJQU2traSE9Pp6ioiAMHDpCbm4vT6aS8vJzKykra29tZv349\n5eXlVFVV0dTURF1dHQA9PT0kJiayc+dO5s+fj9frpbS0FJ/PR05ODiUlJQPGrqurw+l0cscddwx4\nzMsvv8yiRYvCn3/0ox/x5JNPYrWa6jaKiIiIjChTDWFHRkbi8/k4ePAg+/fvp6CggMLCwkuOaWpq\nIikpCYfDAYDL5aKxsZG0tDRsNhvp6ekAtLS0cOzYMVauXAlcfHo8Ojr6inF7e3vZtGkTL7744oC5\n/du//Rsvv/wyv/jFLwB44403cDgc/Omf/in79+//1N9dREREZLwwVQEJYLPZSE5OJjk5mYSEBKqq\nqoZ87qRJk8LzHg3DID4+nm3btl31vPfee48PPviArKws4OLQdHZ2Ntu3byc6Opp3332XZ555hp//\n/OdERUUBcOjQIXbv3s3evXs5f/48Z86c4YknnmD9+vV/wLcWERERGT9MNfba3NxMa2tr+PPRo0eJ\njY0lMjKSs2fPApCYmEhjYyOBQIBQKERNTQ0LFy687FpxcXEEAgH8fj8AwWCQ48ePXzHuvHnz2Ldv\nH7t372b37t3MnDkTn89HdHQ0v/vd7/jud7/Lj3/840teYVRYWMjevXvZvXs3P/nJT/jzP/9zFY8i\nIiIyIZiqB7Knpwev10tXVxc2m43Zs2fj8XioqakhPz8fp9NJZWUlhYWFuN1uDMMgNTWVtLS0y65l\nt9spLS3F6/XS3d1NKBTC7XYTHx8/rJw2btzI6dOnKS4uBi72kPp8vmvyfUVERETGI4thGMZYJzGR\naClDGS92LnXR2TnyixlOnz6F06d7RjyOXJ3awjzUFuYw0dshOnragPtM1QN5ves3DHYudY11GiJD\nci54YaxTEBERk5pwBWRxcTGHDh26ZFtubi45OTkjH9yAzg9HvkdHrm6i/6oUERH5NCZcAbl69eqx\nTkFERERkXJtwBeSYsgw+n0BGlxnb4lzwAt2nR2fddxERkT+UCshRZLVYeOCVfx3rNMTEduTcjSY5\niIiI2ZnqPZAiIiIiYn4qIEVERERkWExXQJaVlbFkyRJcLhdZWVkcPnyYLVu20Ns7OvPCXnzxRebN\nm0cgEAAuLono9XpZvHgxLpeLd955B7i4Ss6yZcvCub722mujkp+IiIjIWDPVHEi/309DQwM7duzA\nbrcTCAQIBoNUVFSQmZnJ5MmTh3ytUCgUXhd7qNra2njzzTeJjY0Nb9u7dy+tra28/vrrHD58mKKi\nIrZv386NN97Ic889x+233057ezs5OTncfffd3HTTTcOKKSIiIjLemKoHsrOzk6ioKOx2OwAOh4Pa\n2lo6Ojpwu92sWLECgOrqalwuFxkZGaxbty58/oIFC1i7di2ZmZn4/X6OHDnC8uXLyc7OJi8vj46O\njkHjr1mzhieffBKLxRLeVl9fz/3334/FYuHOO++kq6uLjo4O4uLiuP322wGIiYnB4XCEey1FRERE\nrmemKiBTUlJoa2sjPT2doqIiDhw4QG5uLk6nk/LyciorK2lvb2f9+vWUl5dTVVVFU1MTdXV1wMW1\ntBMTE9m5cyfz58/H6/VSWlqKz+cjJyeHkpKSAWPX1dXhdDq54447Ltne3t7OzJkzw59nzpxJe3v7\nJce8/fbbBINBbrvttmt4N0RERETMyVRD2JGRkfh8Pg4ePMj+/fspKCigsLDwkmOamppISkrC4XAA\n4HK5aGxsJC0tDZvNRnp6OgAtLS0cO3aMlStXAtDf3090dPQV4/b29rJp0yZefPHFYefc0dHBk08+\nyXPPPYfVaqp6XERERGREmKqABLDZbCQnJ5OcnExCQgJVVVVDPnfSpEnheY+GYRAfH8+2bduuet57\n773HBx98QFZWFgAnT54kOzub7du3ExMTw8mTJ8PHnjx5kpiYGADOnDnDQw89REFBAXfeeedwvqaI\niIjIuGWqLrPm5mZaW1vDn48ePUpsbCyRkZGcPXsWgMTERBobGwkEAoRCIWpqali4cOFl14qLiyMQ\nCOD3+wEIBoMcP378inHnzZvHvn372L17N7t372bmzJn4fD6io6O59957qaqqwjAM3nrrLaZNm4bT\n6aSvr49HHnmErKwsvvKVr1z7myEiIiJiUqbqgezp6cHr9dLV1YXNZmP27Nl4PB5qamrIz8/H6XRS\nWVn5/9u7+6gq63z//8/Ntu0hdBQMNNIxPKGmE2gnpA4mhRglbEiwnBplZ1pNM6kpOafbpXI4o6ZF\nw8oYuzERT3MsZ4MCU84gWekxxdhj2NHRFFMmBGyrIJDg9vr94W/2d1jegTewgddjLdfiuvlcn/d1\nvZfy9nPdfEhJScFms2EYBpGRkURHR59zLIvFQkZGBmlpadTW1uJyubDZbAQHB7cqpsjISD777DPG\njRuHt7c3v/3tbwH4+OOP2bFjB8ePHycnJweARYsWceutt175hRARERHxYCbDMIz2DqIr0VSGcjE5\nSaOpru5akxn27n09x4/Xt3cYgnLhSZQLz9DV8+Dv3/OC2zxqBLKzO2MY5CSNbu8wxIP92HS6vUMQ\nERG5pC5XQC5YsICSkpJm65KTk0lKSrr2nRtQfbRrjS55qq7+v0oREZEr0eUKyHnz5rV3CCIiIiId\nWpcrINuV6eLPE0jbuha5+LHJRa1GNkVEpJNTAdmGvEwmJtm/be8w5Bpak3gLekhBREQ6O4/6DqSI\niIiIeD4VkCIiIiLSKh51CzszM5P8/Hy8vLzw8vIiNTUVh8PBpEmT8Pb2vmb9Ll68mE8//ZTrrruO\nn/70pyxcuJCf/OQnNDY2Mm/ePHbt2oXJZOKll14iPDychoYGZs2axaFDhzCbzdx7770899xz1yw+\nEREREU/iMSOQDoeDTZs2kZOTQ15eHu+//z79+vVj1apVNDQ0tOpYLperVftHRESQn59PXl4eN998\nM8uXLwfgo48+AnDHs3jxYs6cOQPA448/zieffEJOTg4lJSV89tlnrepTREREpKPymAKyuroaX19f\nLBYLAH5+fmzYsIGqqipsNhtTpkwBID8/H6vVSlxcHEuWLHG3HzlyJIsWLSI+Ph6Hw8GuXbuYPHky\niYmJTJs2jaqqqgv2jb5VIgAAIABJREFUPXr0aLp1OzsYO2LECI4cOQLAt99+S3h4OAB9+vShZ8+e\n7Nq1C29vb+68807g7JSJw4YNo7Ky8upfFBEREREP5DEFZEREBBUVFcTExDB//ny2b99OcnIyAQEB\nZGVlkZ2dTWVlJUuXLiUrK4vc3FxKS0spLCwEzs6jHRISwvr16wkNDSUtLY2MjAzsdjtJSUmkp6e3\nKI4//vGPjBkzBoChQ4dSVFTE6dOnOXz4MN988w0VFRXN9q+pqeHTTz/lrrvuuroXRERERMRDecwz\nkD4+Ptjtdnbs2MG2bduYPXs2KSkpzfYpLS1l1KhR+Pn5AWC1WikuLiY6Ohqz2UxMTAwAZWVl7N27\nl6lTpwJw5swZ/P39LxlDZmYmZrOZ+Ph4AJKSkti/fz9JSUkEBgYycuRIzGaze//Tp08zZ84cpkyZ\nwoABA67KdRARERHxdB5TQAKYzWbCw8MJDw9n8ODB5Obmtrht9+7d3cWdYRgEBwezZs2aFre32+1s\n2rSJlStXYjKZAOjWrRsvvviie5+f//zn3Hzzze7lV155hZtvvpnHHnusxf2IiIiIdHQecwv7wIED\nHDx40L28e/duAgMD8fHxoa6uDoCQkBCKi4txOp24XC4KCgoICws751hBQUE4nU4cDgcATU1N7Nu3\n74J9f/7557z77rtkZmY2e9u7oaGB+vqzs4ps2bIFs9nMLbfcAkB6ejonT55sVmCKiIiIdAUeMwJZ\nX19PWloaNTU1mM1mBg4cSGpqKgUFBUyfPp2AgACys7NJSUnBZrNhGAaRkZFER0efcyyLxUJGRgZp\naWnU1tbicrmw2WwEBweft+///M//pLGx0X3LOzQ0lNTUVH744QemTZuGl5cXffv25dVXXwXgyJEj\n/P73v2fQoEFMmDABgMmTJ/PQQw9do6sjIiIi4jlMhmEY7R1EV6KpDDu3NYm3UF2tyQxbo3fv6zmu\n+cM9gnLhOZQLz9DV8+Dv3/OC2zxmBLIrOGMYrEm8pb3DkGvox6bWfYNURESkI+pSBeSCBQsoKSlp\nti45OZmkpKS2CcCA6qManfIEXf1/lSIiIleiSxWQ8+bNa+8QRERERDo8j3kLW0REREQ6hi41Atne\nTKaLP5AqLdfYdIYTx+vaOwwREZEuSQVkGzKZTCzL0ZzZV8OvJ/Rt7xBERES6LN3CFhEREZFWUQEp\nIiIiIq3icbewMzMzyc/Px8vLCy8vL1JTU3E4HEyaNKnZNINX2xtvvMHGjRvx8vKiT58+LFy4kL59\n+1JbW8vcuXP5/vvvcblcPP744+7P/uTk5JCZmQnA008/7Z6VRkRERKQz86gC0uFwsGnTJnJycrBY\nLDidTpqamli1ahXx8fGtKiBdLhdms7nF+0+fPp1nn30WgFWrVrFs2TJSU1P57//+b/71X/+V3//+\n9zidTu6//36sViv19fW8+eab/PGPf8RkMpGYmEhUVBS9evVq9XmLiIiIdCQedQu7uroaX19fLBYL\nAH5+fmzYsIGqqipsNhtTpkwBID8/H6vVSlxcHEuWLHG3HzlyJIsWLSI+Ph6Hw8GuXbuYPHkyiYmJ\nTJs2jaqqqgv23aNHD/fPDQ0NmEwm4OyLL3V1dRiGQV1dHb169aJbt25s3ryZiIgIevfuTa9evYiI\niOCLL764FpdFRERExKN4VAEZERFBRUUFMTExzJ8/n+3bt5OcnExAQABZWVlkZ2dTWVnJ0qVLycrK\nIjc3l9LSUgoLCwGor68nJCSE9evXExoaSlpaGhkZGdjtdpKSkkhPT79o/+np6URGRpKXl8esWbMA\n+MUvfsH+/fu5++67iY+P56WXXsLLy4vKykr69evnbtu3b18qK/WGtYiIiHR+HlVA+vj4YLfbSU1N\nxc/Pj9mzZ2O325vtU1payqhRo/Dz86Nbt25YrVaKi4sBMJvNxMTEAFBWVsbevXuZOnUqCQkJZGZm\nXrLAmz17Np999hlWq5XVq1cDsHnzZm699Va++OILcnNzSU1N5eTJk9fg7EVEREQ6Bo96BhLOFoHh\n4eGEh4czePBgcnNzW9y2e/fu7uceDcMgODiYNWvWtDoGq9XKk08+ycyZM7Hb7Tz55JOYTCYGDhxI\n//79OXDgAH379mX79u3uNpWVlYwaNarVfYmIiIh0NB41AnngwAEOHjzoXt69ezeBgYH4+PhQV3d2\n1pGQkBCKi4txOp24XC4KCgoICws751hBQUE4nU4cDgcATU1N7Nu374J9/3O/GzduZNCgQQDceOON\nbN26FYCjR49SVlZG//79GT16NJs3b+bEiROcOHGCzZs3M3r06Cu9BCIiIiIez6NGIOvr60lLS6Om\npgaz2czAgQNJTU2loKCA6dOnExAQQHZ2NikpKdhsNgzDIDIykujo6HOOZbFYyMjIIC0tjdraWlwu\nFzabjeDg4PP2/dprr1FWVobJZOKmm25iwYIFAPzqV7/ihRdewGq1YhgGzz33HH5+fu5tEydOBODX\nv/41vXv3vkZXRkRERMRzmAzDMNo7iK5EUxleHb+e0Jfq6trLbt+79/UcP15/FSOSy6VceA7lwnMo\nF56hq+fB37/nBbd51AhkZ2cYhuZwvkoam860dwgiIiJdVpcrIBcsWEBJSUmzdcnJye7ZZa4lw4Cj\nRy9/1ExERETEE3S5AnLevHntHYKIiIhIh9blCsj2ZDJd/HkC+X+ams5w/Hhde4chIiIi56ECsg2Z\nTCY+XnO0vcPoEB6YdEN7hyAiIiIX4FHfgRQRERERz6cCUkRERERaxaMKyMzMTGJjY7FarSQkJLBz\n505WrlxJQ0NDm/S/YsUKhgwZgtPpBGD9+vVYrVasVis///nP2bNnj3vfF154gbvuuou4uLg2iU1E\nRETEU3hMAelwONi0aRM5OTnk5eXx/vvv069fP1atWtXqAtLlcrW6/4qKCrZs2UJgYKB7Xf/+/Vm9\nejV5eXk8/fTTvPLKK+5tiYmJvPvuu63uR0RERKSj85gCsrq6Gl9fXywWCwB+fn5s2LCBqqoqbDYb\nU6ZMASA/Px+r1UpcXBxLlixxtx85ciSLFi0iPj4eh8PBrl27mDx5MomJiUybNo2qqqqL9r9w4ULm\nzp2LyWRyr7v99tvp1asXACNGjODIkSPubWFhYe5tIiIiIl2JxxSQERERVFRUEBMTw/z589m+fTvJ\nyckEBASQlZVFdnY2lZWVLF26lKysLHJzcyktLaWwsBA4O492SEgI69evJzQ0lLS0NDIyMrDb7SQl\nJZGenn7BvgsLCwkICGDo0KEX3Gft2rWMGTPmqp+3iIiISEfjMZ/x8fHxwW63s2PHDrZt28bs2bNJ\nSUlptk9paSmjRo3Cz88PAKvVSnFxMdHR0ZjNZmJiYgAoKytj7969TJ06FYAzZ87g7+9/3n4bGhpY\nvnw5K1asuGBsX375JWvXruWDDz64GqcqIiIi0qF5TAEJYDabCQ8PJzw8nMGDB5Obm9vitt27d8ds\nNgNn55wODg5mzZo1l2x36NAhysvLSUhIAODIkSMkJiby0Ucf4e/vz549e3j55Zd555138PX1vbwT\nExEREelEPOYW9oEDBzh48KB7effu3QQGBuLj40Nd3dkZSUJCQiguLsbpdOJyuSgoKCAsLOycYwUF\nBeF0OnE4HAA0NTWxb9++8/Y7ZMgQtm7dSlFREUVFRfTr1w+73Y6/vz/ff/89M2bM4NVXXyUoKOjq\nn7SIiIhIB+QxI5D19fWkpaVRU1OD2Wxm4MCBpKamUlBQwPTp0wkICCA7O5uUlBRsNhuGYRAZGUl0\ndPQ5x7JYLGRkZJCWlkZtbS0ulwubzUZwcHCrYlq2bBnHjx9nwYIFwNkRUrvdDsCcOXPYvn07x44d\nY8yYMcyYMYOHHnroyi+EiIiIiIczGYZhtHcQXYmmMmyZBybdQHV17TU7fu/e13P8eP01O760nHLh\nOZQLz6FceIaungd//54X3OYxI5BdgWEYmuO5hZqazrR3CCIiInIBXaqAXLBgASUlJc3WJScnk5SU\n1Cb9GwYcPXrtRtVERERE2kKXKiDnzZvX3iGIiIiIdHhdqoBsbybTxZ8n6MhON57h2Im69g5DRERE\n2oAKyDZkMplwvHvxKRU7qpHTA9o7BBEREWkjHvMdSBERERHpGFRAioiIiEireNQt7MzMTPLz8/Hy\n8sLLy4vU1FQcDgeTJk3C29v7mvX7xhtvsHHjRry8vOjTpw8LFy6kb9++vPvuu+Tl5QHgcrnYv38/\nW7dupaGhgd/85jf88MMPmEwmHn74YWw22zWLT0RERMSTeMyHxB0OB4sWLSI7OxuLxYLT6aSpqYlH\nHnmEtWvX4ufn1+JjuVwu97zYLXHy5El69OgBwKpVq/j2229JTU1ttk9RURErV65k1apVVFVVUV1d\nzfDhwzl58iRJSUksW7aMW2655dLn2YmfgbyWH/6+2rr6x2E9iXLhOZQLz6FceIaunoeLvfjrMbew\nq6ur8fX1xWKxAODn58eGDRuoqqrCZrMxZcoUAPLz87FarcTFxbFkyRJ3+5EjR7Jo0SLi4+NxOBzs\n2rWLyZMnk5iYyLRp06iqunDh9o/iEaChoQGTyXTOPgUFBcTFxQEQEBDA8OHD3W0HDRpEZWXllV8E\nERERkQ7AYwrIiIgIKioqiImJYf78+Wzfvp3k5GQCAgLIysoiOzubyspKli5dSlZWFrm5uZSWllJY\nWAicnUs7JCSE9evXExoaSlpaGhkZGdjtdpKSkkhPT79o/+np6URGRpKXl8esWbOabWtoaOCLL77g\nvvvuO6ddeXk5u3fvJjQ09OpdDBEREREP5jEFpI+PD3a7ndTUVPz8/Jg9ezZ2u73ZPqWlpYwaNQo/\nPz+6deuG1WqluLgYALPZTExMDABlZWXs3buXqVOnkpCQQGZm5iVHCGfPns1nn32G1Wpl9erVzbZ9\n+umn3H777fTu3bvZ+rq6OmbOnMmLL77YbBRTREREpDPzqJdozGYz4eHhhIeHM3jwYHJzc1vctnv3\n7u7nHg3DIDg4mDVr1rQ6BqvVypNPPsnMmTPd6woKCoiNjW22X1NTEzNnzsRqtZ53ZFJERESks/KY\nEcgDBw5w8OBB9/Lu3bsJDAzEx8eHurqzM5yEhIRQXFyM0+nE5XJRUFBAWFjYOccKCgrC6XTicDiA\ns8Xevn37Ltj3P/e7ceNGBg0a5F6ura2luLiYsWPHutcZhsFLL73EoEGDmDp16uWesoiIiEiH5DEj\nkPX19aSlpVFTU4PZbGbgwIGkpqZSUFDA9OnTCQgIIDs7m5SUFGw2G4ZhEBkZSXR09DnHslgsZGRk\nkJaWRm1tLS6XC5vNRnBw8Hn7fu211ygrK8NkMnHTTTexYMEC97a//OUvREREcP3117vXffXVV6xb\nt47BgweTkJAAwJw5c4iMjLzKV0VERETE83jMZ3y6Cn3GxzN09U8zeBLlwnMoF55DufAMXT0PF/uM\nj8eMQHYFhmF02jmjTzeeae8QREREpI10qQJywYIFlJSUNFuXnJxMUlJSm/RvGHD0aMcZpRMRERE5\nny5VQM6bN6+9QxARERHp8DzmLWwRERER6Ri61AhkezNx8QdSPYGr0YXzRNd9YFhEREQuTQVkGzJ5\nmah49e/tHcZF3fibm9o7BBEREfFwuoUtIiIiIq3iUSOQmZmZ5Ofn4+XlhZeXF6mpqTgcDiZNmoS3\nt/c16/eNN95g48aNeHl50adPHxYuXEjfvn1Zv34977zzDnB2ru758+czdOhQAF544QU2bdpEnz59\nyM/Pv2axiYiIiHgajxmBdDgcbNq0iZycHPLy8nj//ffp168fq1atoqGhoVXHcrlcrdp/+vTp5OXl\nsW7dOu655x6WLVsGQP/+/Vm9ejV5eXk8/fTTvPLKK+42iYmJvPvuu63qR0RERKQz8JgCsrq6Gl9f\nXywWCwB+fn5s2LCBqqoqbDYbU6ZMASA/Px+r1UpcXBxLlixxtx85ciSLFi0iPj4eh8PBrl27mDx5\nMomJiUybNo2qqgvPANOjRw/3zw0NDZhMJgBuv/12evXqBcCIESM4cuSIe7+wsDD3NhEREZGuxGMK\nyIiICCoqKoiJiWH+/Pls376d5ORkAgICyMrKIjs7m8rKSpYuXUpWVha5ubmUlpZSWFgInJ1LOyQk\nhPXr1xMaGkpaWhoZGRnY7XaSkpJIT0+/aP/p6elERkaSl5fHrFmzztm+du1axowZc03OXURERKQj\n8ZgC0sfHB7vdTmpqKn5+fsyePRu73d5sn9LSUkaNGoWfnx/dunXDarVSXFwMgNlsJiYmBoCysjL2\n7t3L1KlTSUhIIDMzk8rKyov2P3v2bD777DOsViurV69utu3LL79k7dq1PPfcc1fxjEVEREQ6Jo96\nicZsNhMeHk54eDiDBw8mNze3xW27d++O2WwGzs45HRwczJo1a1odg9Vq5cknn2TmzJkA7Nmzh5df\nfpl33nkHX1/fVh9PREREpLPxmBHIAwcOcPDgQffy7t27CQwMxMfHh7q6OgBCQkIoLi7G6XTicrko\nKCggLCzsnGMFBQXhdDpxOBwANDU1sW/fvgv2/c/9bty4kUGDBgHw/fffM2PGDF599VWCgoKuwlmK\niIiIdHweMwJZX19PWloaNTU1mM1mBg4cSGpqKgUFBUyfPp2AgACys7NJSUnBZrNhGAaRkZFER0ef\ncyyLxUJGRgZpaWnU1tbicrmw2WwEBweft+/XXnuNsrIyTCYTN910EwsWLABg2bJlHD9+3L1sNpvd\nt9XnzJnD9u3bOXbsGGPGjGHGjBk89NBD1+jqiIiIiHgOk2EYRnsH0ZV0hJloqqtr2zuMa6537+s5\nflxTNnoC5cJzKBeeQ7nwDF09DxebftljbmGLiIiISMfgMbew28KCBQsoKSlpti45OZmkpKQ26d84\nY3j8XNOuxtZ9hF1ERES6ni5VQM6bN69d+zeAo13g9rCIiIh0brqFLSIiIiKt0qVGINubyXTxB1Kv\nJlejC+eJrvvgr4iIiFw7KiDbkMlkovKNr9qkr77P/lub9CMiIiJdj25hi4iIiEirqIAUERERkVbx\nuAIyMzOT2NhYrFYrCQkJ7Ny5k5UrV9LQ0NAm/a9YsYIhQ4bgdDoBKCwsdMeSmJjIjh07APj73//O\nhAkTSEhIIDY2lj/84Q9tEp+IiIhIe/OoZyAdDgebNm0iJycHi8WC0+mkqamJVatWER8fj7e3d4uP\n5XK5MJvNreq/oqKCLVu2EBgY6F531113MXbsWEwmE3v27OHZZ5/lk08+wd/fnzVr1mCxWKirq8Nq\ntRIVFUXfvn1b1aeIiIhIR+NRI5DV1dX4+vpisVgA8PPzY8OGDVRVVWGz2ZgyZQoA+fn5WK1W4uLi\nWLJkibv9yJEjWbRoEfHx8TgcDnbt2sXkyZNJTExk2rRpVFVVXbT/hQsXMnfuXEwmk3udj4+Pe7mh\nocH9s8ViccfZ2NjImTNnrt6FEBEREfFgHlVARkREUFFRQUxMDPPnz2f79u0kJycTEBBAVlYW2dnZ\nVFZWsnTpUrKyssjNzaW0tJTCwkIA6uvrCQkJYf369YSGhpKWlkZGRgZ2u52kpCTS09Mv2HdhYSEB\nAQEMHTr0nG1/+ctfuP/++3nqqaf47W9/615fUVGB1Wrlnnvu4YknntDoo4iIiHQJHnUL28fHB7vd\nzo4dO9i2bRuzZ88mJSWl2T6lpaWMGjUKPz8/AKxWK8XFxURHR2M2m4mJiQGgrKyMvXv3MnXqVADO\nnDmDv7//efttaGhg+fLlrFix4rzbx40bx7hx4yguLuZ3v/sdK1euBODGG28kLy+PyspKfv3rXxMT\nE8MNN9xwNS6FiIiIiMfyqAISwGw2Ex4eTnh4OIMHDyY3N7fFbbt37+5+7tEwDIKDg1mzZs0l2x06\ndIjy8nISEhIAOHLkCImJiXz00UfNis6wsDAOHz6M0+l0F7AAffv2JTg4mB07dnD//fe3OF4RERGR\njsijbmEfOHCAgwcPupd3795NYGAgPj4+1NXVARASEkJxcTFOpxOXy0VBQQFhYWHnHCsoKAin04nD\n4QCgqamJffv2nbffIUOGsHXrVoqKiigqKqJfv37Y7Xb8/f357rvvMAwDgG+++YbGxkZ8fX05cuQI\nP/74IwAnTpygpKSEoKCgq3k5RERERDySR41A1tfXk5aWRk1NDWazmYEDB5KamkpBQQHTp08nICCA\n7OxsUlJSsNlsGIZBZGQk0dHR5xzLYrGQkZFBWloatbW1uFwubDYbwcHBrYppw4YNrFu3jm7duvEv\n//IvpKenYzKZ2L9/P4sWLcJkMmEYBo8//jhDhgy5WpdCRERExGOZjH8Mr0mbaMupDKura9ukr46o\nd+/rOX5cc4V7AuXCcygXnkO58AxdPQ/+/j0vuM2jRiA7O8Mw2myOalejq036ERERka6nyxWQCxYs\noKSkpNm65ORkkpKSrnnfhgFHj2pUUERERDq2LldAzps3r71DEBEREenQulwB2Z5Mpos/T3AhrsbT\nOE+0zVzgIiIiIpeiArINmUwmqt78c6vbBTxz3zWIRkREROTyeNR3IEVERETE86mAFBEREZFW8agC\nMjMzk9jYWKxWKwkJCezcuZOVK1fS0NA2z/+tWLGCIUOG4HQ6Adi/fz+TJk3iZz/7Ge+99557v4qK\nCqZMmcL48eOJjY0lKyurTeITERER8QQe8wykw+Fg06ZN5OTkYLFYcDqdNDU1sWrVKuLj4/H29m7x\nsVwul3tO7JaqqKhgy5YtBAYGutf17t2bl156iY0bNzbb12w28/zzzzN8+HBOnjxJUlISERER3HLL\nLa3qU0RERKQj8pgRyOrqanx9fbFYLAD4+fmxYcMGqqqqsNlsTJkyBYD8/HysVitxcXEsWbLE3X7k\nyJEsWrSI+Ph4HA4Hu3btYvLkySQmJjJt2jSqqqou2v/ChQuZO3cuJpPJva5Pnz6EhITQrVvzOjsg\nIIDhw4cD0KNHDwYNGkRlZeVVuQ4iIiIins5jCsiIiAgqKiqIiYlh/vz5bN++neTkZAICAsjKyiI7\nO5vKykqWLl1KVlYWubm5lJaWUlhYCJydRzskJIT169cTGhpKWloaGRkZ2O12kpKSSE9Pv2DfhYWF\nBAQEMHTo0FbHXV5ezu7duwkNDb3scxcRERHpSDzmFraPjw92u50dO3awbds2Zs+eTUpKSrN9SktL\nGTVqFH5+fgBYrVaKi4uJjo7GbDYTExMDQFlZGXv37mXq1KkAnDlzBn9///P229DQwPLly1mxYkWr\nY66rq2PmzJm8+OKL9OjRo9XtRURERDoijykg4eyzheHh4YSHhzN48GByc3Nb3LZ79+7u5x4NwyA4\nOJg1a9Zcst2hQ4coLy8nISEBgCNHjpCYmMhHH310waIToKmpiZkzZ2K1WrnvPn2nUURERLoOj7mF\nfeDAAQ4ePOhe3r17N4GBgfj4+FBXVwdASEgIxcXFOJ1OXC4XBQUFhIWFnXOsoKAgnE4nDocDOFvs\n7du377z9DhkyhK1bt1JUVERRURH9+vXDbrdftHg0DIOXXnqJQYMGuUc5RURERLoKjxmBrK+vJy0t\njZqaGsxmMwMHDiQ1NZWCggKmT59OQEAA2dnZpKSkYLPZMAyDyMhIoqOjzzmWxWIhIyODtLQ0amtr\ncblc2Gw2goODWxVTdXU1SUlJnDx5Ei8vL7KysvjTn/7Enj17WLduHYMHD3aPXM6ZM4fIyMirci1E\nREREPJnJMAyjvYPoSi53KsPq6tprEE3X1bv39Rw/Xt/eYQjKhSdRLjyHcuEZunoe/P17XnCbx4xA\ndgWGYVzWvNauxtPXIBoRERGRy9OlCsgFCxZQUlLSbF1ycjJJSUlt0r9hwNGjGkkUERGRjk23sEVE\nRESkVTzmLWwRERER6RhUQIqIiIhIq6iAFBEREZFWUQEpIiIiIq2iAlJEREREWkUFpIiIiIi0igrI\nq+Tzzz8nJiaGcePG8fbbb5+zvbGxkWeffZZx48bx0EMPUV5e7t62fPlyxo0bR0xMDF988UVbht0p\nXW4utmzZQmJiIlarlcTERLZu3drWoXc6V/L3AuD7779n5MiRvPfee20Vcqd0JXnYs2cPkyZNIjY2\nFqvVyqlTp9oy9E7ncnPR1NTEf/zHf2C1WnnggQdYvnx5W4fe6VwqF8XFxUyYMIFhw4bxySefNNuW\nk5PDfffdx3333UdOTk5bhexZDLlip0+fNsaOHWscOnTIOHXqlGG1Wo19+/Y122f16tXGK6+8YhiG\nYeTn5xuzZs0yDMMw9u3bZ1itVuPUqVPGoUOHjLFjxxqnT59u83PoLK4kF998841x5MgRwzAM429/\n+5sxevTotg2+k7mSXPzDjBkzjBkzZhjvvvtum8Xd2VxJHpqamoy4uDhj9+7dhmEYhtPp1L9PV+BK\ncrF+/Xrj2WefNQzDMOrr6417773XOHz4cNueQCfSklwcPnzY2L17tzF37lzj448/dq8/duyYERUV\nZRw7dsw4fvy4ERUVZRw/frytT6HdaQTyKvj6668ZOHAgAwYMwGKxEBsby8aNG5vtU1RUxIQJEwCI\niYlh69atGIbBxo0biY2NxWKxMGDAAAYOHMjXX3/dHqfRKVxJLoYNG0bfvn0BCA4O5tSpUzQ2Nrb5\nOXQWV5ILgMLCQm666SaCg4PbPPbO5ErysGXLFoYMGcLQoUMB8PX1xWw2t/k5dBZXkguTyURDQwOn\nT5/mxx9/5LrrrqNHjx7tcRqdQkty0b9/f4YOHYqXV/NSafPmzURERNC7d2969epFREREl7x7qALy\nKqisrKRfv37u5b59+1JZWXnOPjfeeCMA3bp1o2fPnhw7dqxFbaXlriQX/2zDhg0MGzYMi8Vy7YPu\npK4kF3V1dbzzzjs888wzbRpzZ3QleSgrK8NkMjFt2jQmTJjAO++806axdzZXkouYmBi8vb0ZPXo0\n9957L48//jh4tHAbAAAKHklEQVS9e/du0/g7kyv53avf22d1qbmwRVpi3759LF26lBUrVrR3KF3W\nm2++ic1mw8fHp71D6dJcLhdfffUVa9euxdvbm8cee4yf/exn3HXXXe0dWpfz9ddf4+XlxRdffEFN\nTQ2PPvoo//7v/86AAQPaOzTpojQCeRX07duXI0eOuJcrKyvdt0L/eZ+KigoATp8+TW1tLb6+vi1q\nKy13JbkAOHLkCM888wyLFy/mpz/9adsF3gldSS527tzJ0qVLiYqKIisri+XLl7N69eo2jb+zuJI8\n9OvXj7CwMPz8/PD29mbMmDF88803bRp/Z3IlucjPz+fuu+/muuuuo0+fPtx+++2Ulpa2afydyZX8\n7tXv7bNUQF4Ft912GwcPHuTw4cM0NjZSUFBAVFRUs32ioqLcb2pt2LCBO++8E5PJRFRUFAUFBTQ2\nNnL48GEOHjxISEhIe5xGp3AluaipqeHJJ58kJSWFf/u3f2uP8DuVK8nFBx98QFFREUVFRdhsNp56\n6ikmT57cHqfR4V1JHkaPHs3evXvdz94VFxdzyy23tMdpdApXkosbb7yRbdu2AVBfX8/OnTsZNGhQ\nm59DZ9GSXFzI6NGj2bx5MydOnODEiRNs3ryZ0aNHX+OIPVC7vsLTiWzatMm47777jLFjxxpvvfWW\nYRiG8cYbbxiFhYWGYRjGjz/+aMyYMcOIjo42kpKSjEOHDrnbvvXWW8bYsWON++67z9i0aVO7xN+Z\nXG4uli1bZoSGhhrx8fHuP0ePHm238+gMruTvxT9kZGToLewrdCV5yM3NNcaPH2/ExsYaixcvbpf4\nO5PLzcXJkyeNGTNmGOPHjzceeOAB45133mm3c+gsLpWLnTt3GnfffbcRGhpqjBo1yhg/fry77Ucf\nfWRER0cb0dHRxtq1a9sl/vZmMoz//5VHEREREZEW0C1sEREREWkVFZAiIiIi0ioqIEVERESkVVRA\nioiIiEirqIAUERERkVZRASkinVp1dTWzZ88mOjqaxMREnnjiCcrKyq5JX1OmTLnkx51XrlxJQ0OD\ne/mJJ56gpqbmmsTzzzZu3Mjbb78NnJ1n/Ntvv3Vvu1Tcf/vb30hISCAhIYFRo0YRFRVFQkICjz32\n2DWNeevWrfz1r3+9pn2IyOXRVIYi0mkZhsEzzzzDgw8+SHp6OgB79uzhhx9+ICgo6JLtT58+Tbdu\n/++fScMwMAwDL6/L/7/3qlWriI+Px9vbG6DN5pceO3YsY8eOBc4WkPfcc0+LPwo+ZMgQ1q1bB8Dz\nzz/PPffcw/3339/ivi/3un355Zf4+voyYsSIVrUTkWtPI5Ai0ml9+eWXdOvWjUceecS9bujQodxx\nxx0YhsHixYuJi4vDarXypz/9CYBt27bx6KOP8stf/pLY2FjKy8uJiYnhN7/5DXFxcVRUVLB582Ym\nTZrEhAkTmDlzJnV1def0PW/ePBITE4mNjSUjIwM4WzxWVVVhs9mYMmUKcHbmEafTCcD7779PXFwc\ncXFxrFy5EoDy8nIeeOABXn75ZWJjY3n88cf58ccfm/XlcrmIiorCMAxqamq49dZbKS4uBuAXv/gF\nBw8exG63k5qaSklJCUVFRbz66qskJCRw6NAhAD755BMmTpxITEwMO3bsaPE1PnnyJMnJyUyYMAGr\n1cqnn34KwHfffcf48eNJSUkhNjaW6upq/ud//oeYmBgeeughXnrpJf7rv/4LgKNHj/LMM8+QmJjI\nxIkT+etf/8qhQ4dYu3Yt7733HgkJCZSUlLQ4JhG59jQCKSKd1r59+xg+fPh5t/35z39mz549rFu3\njmPHjjFx4kTuuOMOAP7v//6PvLw8BgwYQHl5Od999x2LFy9mxIgROJ1OMjMzef/997n++ut5++23\nef/993nmmWeaHX/27Nn07t0bl8vFY489xp49e0hOTmblypVkZWXh5+fXbP9du3Zht9v58MMPMQyD\nhx9+mFGjRvGTn/yE7777jtdff520tDRmzZrFhg0bSEhIcLc1m80EBQXx7bffUl5ezrBhw9ixYweh\noaFUVFRw8803uwuw22+/naioqHNGEV0uF2vXruWzzz7jzTffdBewl9K9e3feeustevTowQ8//MAj\njzzCvffeC8CBAwdYvHgxt912GxUVFbz99tvk5OTg7e1NcnIyt912GwBpaWlMnz6dESNGUF5ezi9/\n+Uvy8/OZOHEivr6+1/xWuYi0ngpIEemSvvrqK2JjYzGbzdxwww2EhYVRWlpKjx49uO222xgwYIB7\n38DAQPdt1J07d/Ltt9+6RzWbmprOe4v1448/5sMPP+T06dNUV1ezf/9+hg4detF4oqOjuf766wEY\nN24cO3bsICoqiv79+3PrrbcCMHz4cP7+97+f0/6OO+6guLiY8vJynnrqKT788EPCwsLcRdqljBs3\n7qLHvxDDMFi6dClfffUVXl5eVFRUuEdUf/rTn7r737lzJ3feeSe9evUCICYmhu+//x44+6zjPz+X\neuLEiXNGWUXEs6iAFJFOKzg4mA0bNrS63T+KuPMtG4ZBREQEr7/++gXbHz58mBUrVrB27Vp69erF\n888/z6lTp1odxz9YLBb3z2az+bzHCgsL4w9/+ANVVVXMmjWL9957j+3bt7tHVVvah5eXFy6Xq8Wx\nrVu3jtraWnJycujWrRtjxoyhsbERwP2c56UYhsFHH33U7DxFxLPpGUgR6bTuvPNOGhsbWbNmjXvd\nnj172LFjB3fccQcff/wxLpcLp9PJjh07CAkJueQxR4wYQUlJCd999x0A9fX157zVXVdXh7e3Nz17\n9uTo0aN8/vnn7m0+Pj7nfWbyjjvuoLCwkIaGBurr6yksLGxx8QcQEhKCw+HAZDLRvXt3hg4dypo1\nawgLCztn3wvFcDlqa2vp06cP3bp1Y8uWLVRWVl4wvm3btlFTU0NTUxN//vOf3dvuuusuPvjgA/fy\n7t27r3qcInJ1qYAUkU7LZDLx5ptv8r//+79ER0cTGxvL66+/zg033MC4ceMYPHgwCQkJ2Gw25s6d\ni7+//yWP6efnx8KFC5kzZw5Wq5VJkyZx4MCBZvsMHTqUYcOG8cADD5CSksLtt9/u3vbwww8zffp0\n90s0/zB8+HASExN56KGHePjhh5k4cSLDhg1r8blaLBb69evnvp1+xx13UFdXx+DBg8/Zd/z48bz3\n3ns8+OCD7pdoLldCQgIOhwOr1UpBQQE333zzefcLDAxk2rRpTJw4kUcffZQBAwbQs2dP4OwLRyUl\nJVitVsaPH8+HH34InH1z/JNPPuHBBx/USzQiHsZkGIbR3kGIiEjnV1dXh4+PD01NTfzqV7/ikUce\nISoqqr3DEpHLoGcgRUSkTfzud79j+/btnDp1irvvvtv9traIdDwagRQRERGRVtEzkCIiIiLSKiog\nRURERKRVVECKiIiISKuogBQRERGRVlEBKSIiIiKtogJSRERERFrl/wM4shsBpb1L2QAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PrPiCDFMYR-O"
      },
      "source": [
        "### ML Model Training & Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8gq0V3pYgqM",
        "colab_type": "text"
      },
      "source": [
        "In this section we will train a Machine Learning model with the training data. The machine learning model is chosen as **XGBoost** (Extreme Gradient Boosting) as they are known to be performing well with imbalanced datasets like ours."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdPKBlj9YtK8",
        "colab_type": "code",
        "outputId": "0bbba646-a102-4325-c908-077d8f7e489a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "#one hot encoding categorical data for modelling\n",
        "encoded = pd.get_dummies(data[data.columns[data.dtypes==object]])\n",
        "data_encoded = pd.concat([encoded, data[data.columns[data.dtypes != object]]],axis=1)\n",
        "\n",
        "#Features (X) and the target (y)\n",
        "X = data_encoded.drop(\"isChurned\",axis=1)\n",
        "y = data_encoded.isChurned\n",
        "\n",
        "#lets start with the default hyperparameters and hold-out mechanism for train/test split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)\n",
        "\n",
        "#XGBoost\n",
        "xgb_mdl = XGBClassifier().fit(X_train.values,y_train.values)\n",
        "xgb_mdl"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0cRkaRgkNnK",
        "colab_type": "text"
      },
      "source": [
        "We first transformed our categorical variables using one-hot encoding algorithm (get_dummies) to be able to use in our classifier. Then separated the data into train and test 75%/25%. Then generated an XGBoost classifier with it's default parameters and trained it with the training set. \n",
        "\n",
        "Now let's test our trained classifier with test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGe2evoDkNdg",
        "colab_type": "code",
        "outputId": "482ffb0f-ba9f-4455-b590-7ba1254091d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        }
      },
      "source": [
        "from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix, average_precision_score\n",
        "\n",
        "#XGBoost\n",
        "y_pred_train = xgb_mdl.predict(X_train.values)\n",
        "y_pred = xgb_mdl.predict(X_test.values)\n",
        "\n",
        "print(\"Train Data Classification Report:\\n\")\n",
        "print(classification_report(y_train,y_pred_train))\n",
        "\n",
        "print(\"Test Data Classification Report:\\n\")\n",
        "print(classification_report(y_test,y_pred))\n",
        "\n",
        "#generate a confusion matrix to visualise precision, recall, misclassification and false alarms\n",
        "cm = pd.DataFrame(confusion_matrix(y_test, y_pred), index = list(set(y)), columns = list(set(y)))\n",
        "\n",
        "#visualise the confusion matrix in the heatmap form\n",
        "plt.figure()\n",
        "sns.heatmap(cm, annot = True, fmt=\"d\",\n",
        "            cmap=sns.color_palette(\"GnBu\")).set(xlabel='predicted values', \n",
        "                                                ylabel='real values', \n",
        "                                                title = 'Confusion Matrix')"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Data Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.89      1.00      0.94       511\n",
            "        True       0.96      0.29      0.45        89\n",
            "\n",
            "    accuracy                           0.89       600\n",
            "   macro avg       0.93      0.65      0.69       600\n",
            "weighted avg       0.90      0.89      0.87       600\n",
            "\n",
            "Test Data Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.89      0.99      0.94       178\n",
            "        True       0.00      0.00      0.00        23\n",
            "\n",
            "    accuracy                           0.88       201\n",
            "   macro avg       0.44      0.50      0.47       201\n",
            "weighted avg       0.78      0.88      0.83       201\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(33.0, 0.5, 'real values'),\n",
              " Text(0.5, 15.0, 'predicted values'),\n",
              " Text(0.5, 1, 'Confusion Matrix')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+NmzYgB9/rLkzfOmll7Bq1apGr7P2rD5V8kvGb1gW8B569ekp/oby+tu+SEn4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\niEjHMLiJiHTM/wHI781CQfYonQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zguHRA6c4ull",
        "colab_type": "code",
        "outputId": "42a40579-a614-4f98-b897-3f0983af802d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "roc_auc_score(y_test,y_pred)\n",
        "# average_precision_score(y_test,y_pred)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.49719101123595505"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwvfdWtHo4OS",
        "colab_type": "text"
      },
      "source": [
        "Even though accuracy for the test set is 88% it is misleading as our target variable is **skewed** towards not churned (86% are not churned). Even a very basic model which selects majority class all times would score 86% accuracy.\n",
        "\n",
        "Therefore we will have to focus on how well our model performs on the minority class (churned households). On the test set we have 201 samples only 23 of them being churned. Our model could not manage to detect any of them, therefore test set recall has come out as 0%. This is the part we need to aim to increase.\n",
        "\n",
        "Now lets try to enhance our model by optimising hyperparameters by using \"**Randomised Search**\" with \"**Cross-Validation**\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgybbtTanm1F",
        "colab_type": "text"
      },
      "source": [
        "We need to determine the evaluation criteria (scorer) to be optimised for the Randomised Search. Lets see the available scorers:\n",
        "* **average precision**, **balanced accuracy**, **roc_auc**, **f1** are the scorers which are commonly used for imbalanced dataset classifications as they handle situation well as compared to other metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz_VXXwenID5",
        "colab_type": "code",
        "outputId": "de2adf79-648e-402c-e920-750b58bfb882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "source": [
        "import sklearn\n",
        "sorted(sklearn.metrics.SCORERS.keys())"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['accuracy',\n",
              " 'adjusted_mutual_info_score',\n",
              " 'adjusted_rand_score',\n",
              " 'average_precision',\n",
              " 'balanced_accuracy',\n",
              " 'completeness_score',\n",
              " 'explained_variance',\n",
              " 'f1',\n",
              " 'f1_macro',\n",
              " 'f1_micro',\n",
              " 'f1_samples',\n",
              " 'f1_weighted',\n",
              " 'fowlkes_mallows_score',\n",
              " 'homogeneity_score',\n",
              " 'jaccard',\n",
              " 'jaccard_macro',\n",
              " 'jaccard_micro',\n",
              " 'jaccard_samples',\n",
              " 'jaccard_weighted',\n",
              " 'max_error',\n",
              " 'mutual_info_score',\n",
              " 'neg_brier_score',\n",
              " 'neg_log_loss',\n",
              " 'neg_mean_absolute_error',\n",
              " 'neg_mean_gamma_deviance',\n",
              " 'neg_mean_poisson_deviance',\n",
              " 'neg_mean_squared_error',\n",
              " 'neg_mean_squared_log_error',\n",
              " 'neg_median_absolute_error',\n",
              " 'neg_root_mean_squared_error',\n",
              " 'normalized_mutual_info_score',\n",
              " 'precision',\n",
              " 'precision_macro',\n",
              " 'precision_micro',\n",
              " 'precision_samples',\n",
              " 'precision_weighted',\n",
              " 'r2',\n",
              " 'recall',\n",
              " 'recall_macro',\n",
              " 'recall_micro',\n",
              " 'recall_samples',\n",
              " 'recall_weighted',\n",
              " 'roc_auc',\n",
              " 'roc_auc_ovo',\n",
              " 'roc_auc_ovo_weighted',\n",
              " 'roc_auc_ovr',\n",
              " 'roc_auc_ovr_weighted',\n",
              " 'v_measure_score']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EG_6ZWVxqCqt",
        "colab_type": "code",
        "outputId": "9cf39f78-eb74-489c-c53e-aadc546a4abc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold, RandomizedSearchCV\n",
        "\n",
        "#handle class imbalance problem by undersampling (not used)\n",
        "# no_churn_down_sampled = data_encoded[data_encoded.isChurned == False].sample(sum(data_encoded.isChurned))\n",
        "# data_encoded_balanced = pd.concat([no_churn_down_sampled,data_encoded[data_encoded.isChurned]])\n",
        "#shuffle the dataset to avoiding sampling biases\n",
        "# data = data_encoded_balanced.sample(frac=1)\n",
        "\n",
        "#shuffle the dataset to avoiding sampling biases\n",
        "data = data_encoded.sample(frac=1)\n",
        "X = data.drop(\"isChurned\",axis=1)\n",
        "y = data.isChurned\n",
        "\n",
        "#generate an XGB classifier\n",
        "mdl = XGBClassifier()\n",
        "\n",
        "#parameter ranges\n",
        "param_list = {\n",
        "    'silent': [False],\n",
        "    'max_depth': range(2,51),\n",
        "    'learning_rate': [0.001, 0.01, 0.1, 0.15],\n",
        "    'subsample': np.arange(0,1.1,.1),\n",
        "    'colsample_bytree': np.arange(0,1.1,.1),\n",
        "    'colsample_bylevel': np.arange(0,1.1,.1),\n",
        "    'min_child_weight': [0.5, 0.7, 1.0, 2.0, 3.0],\n",
        "    'gamma': [0, 0.25, 0.5, 0.75, 0.9, 1.0],\n",
        "    'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0, 500.0, 1000.0],\n",
        "    'n_estimators': [2, 5, 10, 20, 50, 100],\n",
        "    'scale_pos_weight': [1, 1.5, 2, 6, 6.1, 6.3, 6.5, 8],\n",
        "    'max_delta_step': [1, 2, 3, 5, 10]\n",
        "}\n",
        "\n",
        "kfold = 5\n",
        "cv_strat = RepeatedStratifiedKFold(n_splits=kfold,n_repeats=10)\n",
        "\n",
        "#Randomized Search\n",
        "# cv = RandomizedSearchCV(mdl,param_list,cv=cv_strat,n_iter=100,verbose=1,scoring=\"roc_auc\",n_jobs=-1).fit(X.values,y.values)\n",
        "cv = RandomizedSearchCV(mdl,param_list,cv=cv_strat,n_iter=100,verbose=1,scoring=\"balanced_accuracy\",n_jobs=-1).fit(X.values,y.values)\n",
        "# cv = RandomizedSearchCV(mdl,param_list,cv=cv_strat,n_iter=100,verbose=1,scoring=\"average_precision\",n_jobs=-1).fit(X.values,y.values)\n",
        "\n",
        "#use the best estimator after the hyperparameter optimisation\n",
        "mdl_best = cv.best_estimator_"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 50 folds for each of 100 candidates, totalling 5000 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 188 tasks      | elapsed:    4.8s\n",
            "[Parallel(n_jobs=-1)]: Done 1256 tasks      | elapsed:   32.8s\n",
            "[Parallel(n_jobs=-1)]: Done 2540 tasks      | elapsed:  1.3min\n",
            "[Parallel(n_jobs=-1)]: Done 3834 tasks      | elapsed:  2.1min\n",
            "[Parallel(n_jobs=-1)]: Done 5000 out of 5000 | elapsed:  3.1min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOj2EJO7k_jl",
        "colab_type": "text"
      },
      "source": [
        "Hyperparameter optimisation is completed. Now lets train and test the XGBoost Classifier with the optimised hyperparameters "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwXa8TGKsytl",
        "colab_type": "code",
        "outputId": "f43a341c-f76b-422b-c3e1-6ceaa83d1ad2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "cv.best_params_"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bylevel': 0.5,\n",
              " 'colsample_bytree': 0.5,\n",
              " 'gamma': 0.25,\n",
              " 'learning_rate': 0.01,\n",
              " 'max_delta_step': 5,\n",
              " 'max_depth': 2,\n",
              " 'min_child_weight': 2.0,\n",
              " 'n_estimators': 50,\n",
              " 'reg_lambda': 100.0,\n",
              " 'scale_pos_weight': 6,\n",
              " 'silent': False,\n",
              " 'subsample': 1.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW0uo7x_lUnb",
        "colab_type": "text"
      },
      "source": [
        "_**scale_pos_weight**_ is the parameter used for biasing (weighing more) of the minority class samples. The higher the value the more bias towards minority class. In our dataset, the ratio between majority class samples and the minority class is **6.15** which coheres with this parameter (optimised value came out as 6).\n",
        "\n",
        "Lets test our optimised model with the data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-kcT_CpthCb",
        "colab_type": "code",
        "outputId": "aa7d75b3-62eb-44f9-d260-4171df09abff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_predict, cross_val_score, cross_validate\n",
        "\n",
        "cv_strat = RepeatedStratifiedKFold(n_splits=kfold,n_repeats=20)\n",
        "scores = cross_validate(mdl_best,X.values,y.values,cv=cv_strat,verbose=3,n_jobs=-1,return_train_score=True,\n",
        "                        scoring={\"roc_auc\":\"roc_auc\",\n",
        "                                 \"recall\":\"recall\",\n",
        "                                 \"precision\":\"precision\",\n",
        "                                 \"accuracy\":\"accuracy\",\n",
        "                                 \"balanced_accuracy\":\"balanced_accuracy\",\n",
        "                                 \"average_precision\":\"average_precision\"}) \n",
        "\n",
        "pd.DataFrame(pd.DataFrame(scores).mean(),columns=[\"Score\"]).drop([\"fit_time\",\"score_time\"])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  52 tasks      | elapsed:    2.2s\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    4.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "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>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>test_roc_auc</th>\n",
              "      <td>0.624930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_roc_auc</th>\n",
              "      <td>0.741549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_recall</th>\n",
              "      <td>0.699941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_recall</th>\n",
              "      <td>0.804433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_precision</th>\n",
              "      <td>0.184466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_precision</th>\n",
              "      <td>0.212835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_accuracy</th>\n",
              "      <td>0.525773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_accuracy</th>\n",
              "      <td>0.555137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_balanced_accuracy</th>\n",
              "      <td>0.598740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_balanced_accuracy</th>\n",
              "      <td>0.659518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_average_precision</th>\n",
              "      <td>0.213034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train_average_precision</th>\n",
              "      <td>0.334697</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            Score\n",
              "test_roc_auc             0.624930\n",
              "train_roc_auc            0.741549\n",
              "test_recall              0.699941\n",
              "train_recall             0.804433\n",
              "test_precision           0.184466\n",
              "train_precision          0.212835\n",
              "test_accuracy            0.525773\n",
              "train_accuracy           0.555137\n",
              "test_balanced_accuracy   0.598740\n",
              "train_balanced_accuracy  0.659518\n",
              "test_average_precision   0.213034\n",
              "train_average_precision  0.334697"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clb0iwWnnfzQ",
        "colab_type": "text"
      },
      "source": [
        "Even though getting lower accuracy in total, now we are getting much better results for the minority class as recall and ROC AUC (receiver operating characteristics, area under curve) increased significantly.\n",
        "\n",
        "The score table above gives all metric results for both test and train sets. Train and test results are close which is an indication that our model **did not overfit**.\n",
        "\n",
        "Let's check which parameters were more important to separate churners/non churners. The barchart below list the features ordered by their importance values for the XGB Classifier. \n",
        "\n",
        "As expected, \"**Total Purchase**\", \"**Number of Received Campaigns**\", \"**Campaign no. 25**\", and \"**Numer of redeems**\" features are the most important features for the classifier. As we recall from feature engineering section, these features were either a good separator or highly correlated to the target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEjGcd01aJle",
        "colab_type": "code",
        "outputId": "c2aa941a-6ddd-4394-aafc-c92aba21288a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "feat_imp = pd.DataFrame(list(zip(data.columns,mdl_best.feature_importances_)),columns=[\"Feature\",\"Importance\"]).sort_values(by=\"Importance\",ascending=False)\n",
        "fig_store = plt.figure(figsize=(10,20))\n",
        "sns.barplot(y=\"Feature\",x=\"Importance\",data = feat_imp,orient=\"h\")"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb7f320cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yZB3JU93sKUe1FQvy1adoNCdPnrTsep2dnV1v4+7bt4+ZM2fW23hCCCGEEOIK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QghhBCiKpDOgqh0CQkJWFhYKKcxAzg4OJCbm8uoUaO4ffs29+/fZ8qUKXh4eJCWloa/vz/t\n27fn9OnTODk54efnx4oVK8jOziYsLIx27doRERGBTqdDp9Oh1+vx9/dn8ODBpbbD1dXV5JyJB159\n9dVn8t5CCCGEEFWdTEMSlS4lJQVHR8di6VZWVkRGRrJt2zaioqJYvHixcn6BTqdjzJgx7Ny5k9TU\nVOLi4ti0aRPBwcGsXr1aKSM5OZmoqCg2b95MZGQk6enpFfZeQgghhBDVnYwsiCrLaDQSHh7O8ePH\nUavVpKenk5mZCUCzZs3QarVA0aFprq6uqFQqtFqtyenK7u7uaDQaNBoNnTp1IjExkcaNG1fK+wgh\nhBBCVDcysiAqXevWrTl79myx9Li4OLKzs4mOjiY2NpaGDRuSl5cHgKWlpXKfWq1WrlUqFQUFBUqe\nSvYwFUIIIYR4YtJZEJWuc+fOGAwGk4XFSUlJXLt2DVtbW2rXrk1CQoLJiIG59u3bR15eHnq9nmPH\njuHs7Pw0my6EEEIIUaNJZ0FUOpVKxcqVKzl8+DAeHh7069eP8PBwevTowZkzZ/D29iY2NpZWrVqV\nu2ytVsvIkSN59913CQwMfOwUpGHDhjFlyhSOHDlCjx49+P777wHYsGEDPXr04Pr16wwYMIC//e1v\nZdYrx5cIIYQQoiaQQ9lEjRUREYGNjQ1jx46t8LoLC41kZeVUeL3VTXU6sKaySIzMI3Eqm8TIPBIn\n80icyladYiSHsgkhhBBCCCHKTXZDEjXWpEmTiqUlJycTHBxskmZpacnWrVufat2yrloIIYQQNYF0\nFsRzRavVEhsb+8zrkV2YhBBCCFET1KhpSK+//rrJdXR0NPPnzweK5q+vW7fOJN/NzY3s7OxSy8vI\nyGDatGl4eHgwaNAgxo0bR2pqKlB0kNjIkSPp27cvffr0ITIyUlnUGh0djVar5fDhw0pZe/fuRavV\nsmvXLgBGjBhB3759GTBgAEOGDOHSpUultuPBvd7e3nh6ejJ//nxu376t5Ldt2xYfHx/lZ82aNQDs\n378fX19fBgwYgJeXF5s3b1aeiYmJoX///nh7e+Pr61ssNg/773//yzvvvIOPjw9vvfUWERERQNFO\nQw/qehIjRowgMTHR7PtnzZqFm5ub8p4bNmx44rrN8ejfkxBCCCHE80ZGFkphNBoJCgrC19eX5cuX\nA0XbeWZlZdG0aVMCAgL46KOP6NatG3fv3mXSpEls3LiRP//5zwC0adOG+Ph4unTpAsCOHTtwcHAw\nqSMsLAxnZ2e2bNnCkiVLTE4eftSDew0GA+Hh4QQGBvLll18CoNFoin1bnp+fz5w5c/jqq69o0qQJ\nBoOBtLQ0AA4ePEhUVBTr1q2jcePGGAwGYmJiSq175syZ/POf/8TBwYGCggKlw+Tu7o67u3t5wvq7\nBQcH4+npWaF1CiGEEEI8r2rUyMLTlJCQgIWFBUOHDlXSHBwccHFxIS4ujg4dOtCtWzcArK2tmTt3\nrsm37C4uLvz000/k5+eTm5uLTqejbdu2Jdbl4uKCTqczq12WlpZ88MEHXLt2jaSkpFLvy83NpaCg\ngPr16yvPPdh6dM2aNQQHByvbiFpaWjJ48OBSy8rOzqZRo0YA1KpVC3t7e8B05GbWrFksWLCAIUOG\n4O7uroygFBYW8tFHH+Hp6cmYMWMYN26ckvewQ4cO8e677zJw4EAmT55Mbm6uWfF43LNubm4sW7YM\nHx8fBg0axNmzZxk7diweHh5s2rRJidOoUaMYOHAg3t7e7N27t8Q6Pv30U/z8/PD29mbFihVmt00I\nIYQQojqrUSML9+7dw8fHR7m+desWbm5uyvX69evZvn27cn3jxo1Sy0pJScHR0bHEvAsXLhTLa9Gi\nBXfu3CEnp2i7TJVKRZcuXTh06BC//fYbbm5uyjf7j9q/fz9t2rQp+wX/V61atXBwcODSpUs4ODgU\ne+/x48fj5eWFm5sbb775Jq6urvTq1Yv+/fujVqtJSUnBycnJ7PpGjRqFp6cnHTt2pHv37gwcOBAr\nK6ti9924cYONGzdy6dIlAgIC8PT0ZM+ePVy9epWvv/6arKwsvLy88PPzM3kuOzubjz/+mM8//xwb\nGxvWrFnD559/TlBQULE6lixZwscff6z8u1GjRo99tmnTpsTGxhIaGsqsWbPYtGkTBoOB/v37M3To\nUKysrIiMjKRu3bpkZ2fz7rvv4u7ubrLm4NChQ1y+fJmvvvoKo9FIQEAAx48f54033jA7hkIIIYQQ\n1VGN6iw8Oh0nOjqaM2fOKNejR4822XP/4Y7Es9CvXz82bNhATk4OM2fO5JNPPjHJnzFjBhqNBjs7\nO+bMmVOush8+HqOkaUgAISEhJCcnc+TIET777DMOHz7MokWLyv0eQUFBDBgwgEOHDrFjxw7i4+P5\n4osvit3n4eGBWq3G3t6ezMxMAE6ePImnpydqtZpGjRrRqVOnYs/9+OOPXLhwQRnFyc/Pp3379iW2\n5dFpSPv373/ssw+mSbVp04Y7d+5Qt25doGg05fbt21hbWxMeHs7x48dRq9Wkp6eTmZmpjKQA/PDD\nD/zwww/4+voCcOfOHX755RfpLAghhBCixqtRnYWnqXXr1uzevbvEPHt7e44fP26SduXKFWxsbJQP\nowDt2rXj/PnzWFtb07Jly2LlPFiHUF4FBQWcP3/erBONtVotWq2WAQMG4O7uzqJFi7C3t+fMmTO4\nurqaXWeLFi0YNmwYgwcPxtXVFb1eX+weS0vLcr3HA0ajka5duxIeHv7Un61duzYAarXapH1qtZr7\n9+8TFxdHdnY20dHR1K5dGzc3N/Ly8orV8d577zFkyJByt08IIYQQojqTNQul6Ny5MwaDgS1btihp\nSUlJnDhxAm9vb06ePKnsdnTv3j0WLFiAv79/sXKmT5/OtGnTnlq78vPzWbZsGU2bNi22YPphubm5\nHD161KTtdnZ2QNE0paVLl5KRkQGAwWB47DkDBw4cUEYyLl++jFqtpl69ema1t0OHDuzZs4fCwkIy\nMzM5duxYsXvat2/PqVOnuHz5MlD0zf2DRdRl+T3PAvz222/Y2tpSu3ZtEhISuHr1arF7unXrxn/+\n8x9lLUR6ejpZWVlm1yGEEEIIUV3JyEIpVCoVK1euJDQ0lLVr12JlZYWdnR2zZ89Go9GwatUqFixY\nwLx58ygsLMTHx4fhw4cXK6dnz55PpT0zZszA0tISg8FAly5dWLVqlZL36JqF7t27M2HCBD799FPm\nzp2LRqPB2tqahQsXKm3KzMxkzJgxGI1GVCpVsXUED4uNjWXhwoVoNBpq1apFWFgYtWrVMqvdffv2\n5ciRI3h5edG0aVNeffVVXnjB9Ejxl156iYULF/L+++9jMBgAmDp1aomjMY/6Pc8CeHt7ExAQgLe3\nN05OTiWO1nTr1o2LFy8qIws2NjYsXboUW1vbUst9eJqYEEIIIUR1pTLKpxrxjOXm5lKnTh30ej3v\nvPMOmzZtMlkTUBMVFhrJysqp7GZUefXr23Dz5p3KbkaVJjEyj8SpbBIj80iczCNxKlt1ilGjRi+U\nmicjC+KZmzBhArdv3yY/P5/AwMAa31EQQgghhKgpnvvOgl6vZ/To0cXS169fT4MGDSq0LRMnTiy2\nveqMGTPo3r17hdQ/b948Tp06ZZI2cuTIx05RMkdJOyfVdA/tvCqEEEIIUW3JNCQhnpGMjN8quwlV\nXnUaoq0sEiPzSJzKJjEyj8TJPBKnslWnGD1uGpLshvQUvf766wCkpaWh1WpNvlGfP38+0dHRyvW6\ndevw9PTEx8cHPz8/YmJigKKdiUJCQujduzd9+vQhICCA69evK89ptVpmzJihXN+/f5/OnTszfvx4\noOhsic6dO+Pj46P8XLhwocT2pqWl0a5dO3x9fXnrrbd4++23TdpYWlmFhYUsWLCA/v374+3tjZ+f\nH1euXAGK1ifMnTsXDw8PBg0axIgRI/jxxx9Ljdlf//pXXF1d6d+/v0n64sWL8fT0xNvbm4kTJ3L7\n9m2TNj9oz9y5c0ssV6/XM2LECF5//XXllOlHTZgwwaTepKQk3n33Xby9vZkwYYJywN7Ro0eV+AIs\nX76csWPHKguqhRBCCCFqqud+GtKzYmtry4YNG3j33XeLnT+wadMmDh8+zFdffUXdunXJycnhm2++\nAYo+iObm5rJr1y5q1arFf/7zH4KCgti6dSsqlQobGxtSUlK4d+8eGo2GH374gcaNG5uU7+XlVeqH\n6Ee1aNFC6ahcuXKFoKAgjEajMvWopLJ27NjBjRs32L59O2q1muvXr2NtbQ3A3//+d5o1a8aePXtQ\nq9VcuXKFixcvllr/oEGDGD58ODNnzjRJ79q1K9OnT8fCwoKlS5fyySef8MEHHyhtLukQuodZWVkx\nZcoUUlJSSElJKZa/Z88e6tSpY5L2t7/9jZkzZ9KxY0e++uorPv30U6ZOnWpyz6pVqzh16hRr1659\n4nMlhBBCCCGqCxlZeEZeeuklXF1dlQ/iD/vkk0/46KOPlAPc6taty8CBA7l79y7R0dHMnj1b2ZrU\nz88PS0tLEhISlOd79uzJgQMHAIiPj6dfv35Ppc3Nmzdn1qxZZa4xyMjIoFGjRqjVRX8+TZo04cUX\nX0Sn0/Hjjz8ydepUJa958+b06tWr1LLeeOMNXnzxxWLp3bp1w8KiqC/bvn17k9EVc9jY2ODi4oKV\nlVWxvNzcXD7//HMCAgJM0h8+lblr167s2bPHJP+zzz7ju+++Y/Xq1Wg0mnK1RwghhBCiOpLOwjM0\nbtw41q1bR0FBgZKWk5NDbm4uzZs3L3b/5cuXadq0qckp0ABOTk4m3457eXnx9ddfk5eXR3JyMq+9\n9prJ/V9//bXJ1KF79+6Z3WZHR0cuXbr02LLeeust9u/fj4+PD4sWLeLcuXMApKSk0LZtW7PPYDDX\nf/7zH3r06KFcp6Wl4evry/Dhwzlx4kS5y/vnP//JX/7yl2If+Fu3bs2+ffsA2LVrF7/++quSd+rU\nKTZv3synn35abERCCCGEEKKmks7CM9S8eXNee+014uLinmq5Dg4OpKWlsWPHjhIPffPy8iI2Nlb5\nKc+34I+udy+prCZNmrBr1y7ef/99VCoVo0eP5siRI7/7vUry8ccfU6tWLQYMGADAyy+/zP79+4mJ\niWHWrFlMnz5dWVtgjp9//hmdTkfv3r2L5YWEhLBx40YGDRpEbm6uyTSjFi1aYDQa+eGHH37/Swkh\nhBBCVBOyZuEZGz9+PFOmTFGmt9StWxcbGxuuXLlSbHShRYsW/Prrr+Tk5JiMLpw9e5Y333zT5F43\nNzeWLFnChg0buHnz5lNr77lz53jllVfKvM/S0pKePXvSs2dPGjZsyN69exk1ahRJSUkUFBQ8ldGF\n6OhoDhw4wPr161H9716klpaWyod4JycnWrRoQWpqKtevX2flypUALFiwAGdn5xLLPH36NGfOnMHN\nzY379++TnZ3NiBEj+OKLL3jllVf47LPPAEhNTVWmegE0bNiQsLAwRo8ezYsvvkjnzp1/9/sJIYQQ\nQlR1MrLwjL3yyiu88sor7N+/X0l77733mDdvnvKNeG5uLjExMdjY2ODr68uiRYuUqUsxMTHcvXu3\n2IfTt99+m4kTJ6LVap9aW9PS0liyZAnDhw9/7H1nz54lPT0dgMLCQpKTk/nDH/5AixYtcHJyYsWK\nFcoIRVpamsmHbnN99913fPrpp3z88cfK4mmA7OxsJTZXrlzhl19+oXnz5vTu3VsZ/SitowAwbNgw\nDh06xLfffsvGjRv54x//qKzRyMrKUt7p448/ZsiQISbPtmzZkoiICD744AN+/vnncr+TEEIIIUR1\nIyMLFSAgIABfX1/letiwYdy5cwc/Pz9q166NhYUFY8aMAWD69OksXryYvn37olaradWqFZGRkco3\n6w80adKEkSNHlljf119/zcmTJ5XrDz/8kA4dOpR4r06nw9fXl7y8POrUqcOIESMYNGjQY8vKyclh\nzpw5ytahzs7OSgcjJCSERYsW0bt3bzQaDQ0aNFB2MSrJ+++/z7Fjx9Dr9fTo0YNJkybxzjvv8P/+\n3//DYDAocXnttdeYP38+x48fZ8WKFVhYWKBWq5k3bx7169cvsWw3NzdycnLIz89n7969fPbZZ9jb\n25falh07drBx40YAevfuXeJhdO3atWPhwoUEBASwYcMGWrRoUWJZcnyJEEIIIWoCOZRNiGegsNBI\nVpb5aymeV9XpwJrKIjEyj8SpbBIj80iczCNxKlt1ipEcyiaEEEIIIYQoN5mG9BxITk4mODjYJM3S\n0pKtW7dWSP16vZ7Ro0cXS1+/fj0NGjSokDZUtEdmjQkhhBBCVEvSWXgOaLXaMk88fpYaNGhQqfVX\nhkfXmAghhBBCVEcyDUlUCRkZGUybNg0PDw8GDRrEuHHjSE1NrdA2jB07FhcXF8aPH19i/oIFC3j9\n9dcrtE1CCCGEEJVJRhZEpTMajQQFBeHr68vy5csBSEpKIisri5YtW1ZYO/z9/bl79y5btmwplpeY\nmMitW7cqrC1CCCGEEFWBdBZEpUtISMDCwoKhQ4cqaQ4ODuTm5jJq1Chu377N/fv3mTJlCh4eHqSl\npeHv70/79u05ffo0Tk5O+Pn5sWLFCrKzswkLC6Ndu3ZERESg0+nQ6XTo9Xr8/f0ZPHhwqe1wdXXl\n6NGjxdILCgpYsmQJy5YtY+/evc8kBkIIIYQQVZFMQxKVLiUlBUdHx2LpVlZWREZGsm3bNqKioli8\neLFyfoFOp2PMmDHs3LmT1NRU4uLi2LRpE8HBwaxevVopIzk5maioKDZv3kxkZKRymFx5fPnll7i7\nu/Pyyy8/+UsKIYQQQlRDMrIgqiyj0Uh4eDjHjx9HrVaTnp5OZmYmAM2aNVNOr7a3t8fV1RWVSoVW\nq+Xq1atKGe7u7mg0GjQaDZ06dSIxMZHGjRub3Yb09HR27dqlnPIshBBCCPE8kZEFUelat27N2bNn\ni6XHxcWRnZ1NdHQ0sbGxNGzYkLy8PKBo69cH1Gq1cq1SqSgoKFDyfu+uRD///DM6nY4+ffrg5ubG\n3bt36d279+8qUwghhBCiupDOgqh0nTt3xmAwmCwsTkpK4tq1a9ja2lK7dm0SEhJMRgzMtW/fPvLy\n8tDr9Rw7dgxnZ+dyPd+rVy9++OEHg9GrrAAAIABJREFUvv32W7799lusra355ptvyt0OIYQQQojq\nSKYhiUqnUqlYuXIloaGhrF27FisrK+zs7AgKCiIkJARvb2+cnJxo1apVucvWarWMHDkSvV5PYGDg\nY6cgDRs2jEuXLnHnzh169OhBSEgI3bt3f6J3erC2QgghhBCiOlMZ5VONqKEiIiKwsbFh7NixFV53\nYaGRrKycCq+3uqlf34abN+9UdjOqNImReSROZZMYmUfiZB6JU9mqU4waNXqh1DyZhiTEMyAHOAsh\nhBCiJpBpSKLGmjRpUrG05ORkgoODTdIsLS3ZunXrU6379y6sFkIIIYSoCqSzIJ4rWq2W2NjYym6G\nEEIIIUS1UOWnIb3++usm19HR0cyfPx8ompO+bt06k3w3Nzeys7NLLS8jI4Np06bh4eHBoEGDGDdu\nHKmpqUDR4WAjR46kb9++9OnTh8jISGWhanR0NFqtlsOHDytl7d27F61Wy65duwAYMWIEffv2ZcCA\nAQwZMoRLly6V2o4H93p7e+Pp6cn8+fO5ffu2kt+2bVt8fHyUnzVr1gCwf/9+fH19GTBgAF5eXmze\nvFl5JiYmhv79++Pt7Y2vr2+x2Dxs8eLFeHp64u3tzcSJE03qftT69etxdnbmt99+e+z7JCYmlpoP\npr+v6Ojoch2QFh0djYODA0lJSUpa//79SUtLM7uMh9t45coV+vTpw/fff09iYiILFiwo8Zmy/p6E\nEEIIIWqyKt9ZeJqMRiNBQUF07NiRvXv3Eh0dzfTp08nKyuLevXsEBATw3nvvsXv3bmJjYzl9+jQb\nN25Unm/Tpg3x8fHK9Y4dO3BwcDCpIywsjO3btzNw4ECWLFny2PaEhYURFxfH9u3bsbS0JDAwUMnT\naDTExsYqP++99x75+fnMmTOH1atXs337dmJiYujYsSMABw8eJCoqinXr1hEXF8e///1vXnih9MUq\nXbt2ZceOHcTFxfHHP/6RTz75pNR74+PjcXZ2Zs+ePY99n/LYtm0bN27cKNczTZo0MTmd+Uldv34d\nf39/Zs6cSffu3XF2dubvf//77y5XCCGEEKKmea46CwkJCVhYWDB06FAlzcHBARcXF+Li4ujQoQPd\nunUDwNramrlz5yrf6AO4uLjw008/kZ+fT25uLjqdjrb/v707D6uqXBs//t0bhC2pOZWaQw7pxgQ0\nU3MeEAdABKEsTckBpVROEohDJ4dSEyPtJ2I55FFPZR4VRdyKpmhlpqn55pAgJR7AAUFwAIQtsH5/\n8Lpet4CAIAjen+vyus5+1lrPc69b8vDsZ2rbtsC2OnXqRFxcXLHisrCwYNq0aVy+fNnkm/MHpaen\nk5OTQ+3atdXn7m0numrVKgICAtStQS0sLBg+fHihdfXs2RNz87xZaB06dODq1asF3hcXF0dGRgZT\np0416ShlZmbi6+uLo6MjkydPJjMzU712/2hQREQEM2bMMKkzIiKCM2fO4O/vj6urK5mZmQQFBeHk\n5ISLiwuBgYEFxtK3b1/++uuvAkdsdu7ciYuLC0OGDOGzzz4r9L2TkpIYN24cvr6+9O/fH4CjR4/i\n7e0NQGpqKuPGjcPZ2ZkPP/xQHVnKyMhg4sSJDB06lCFDhrBr165C2xBCCCGEqCqe+DULmZmZuLq6\nqp9v3ryJvb29+nndunXs2LFD/fywb6tjYmJo165dgdf++uuvfNeaNWtGRkYGaWl5W2BqNBq6d+/O\noUOHuH37Nvb29oVOgzlw4ABt2rQp+gX/l5mZGdbW1ly4cAFra+t87+3t7Y2TkxP29vb069ePbt26\n0bdvX4YMGYJWqyUmJgYbG5tit3e/rVu34ujoWOA1g8GAk5MTnTp1IjY2luTkZOrXr8/GjRvR6XTs\n3r2bqKgo3N3di93e4MGD+fbbbwkICMDW1pbU1FR++OEHIiIi0Gg0hU6J0mq1eHl5sXLlSpMORWJi\nIkFBQYSGhlKrVi3GjRvHvn37cHBwyFfHjBkzeP/99xk8eHCBbYSEhNCxY0emTJnCwYMH2bJlCwA/\n//wzzz//vNp5fNiULCGEEEKIquKJH1l4cDrOP/7xD5PrY8aMMbn+/PPPP9Z4nJ2dMRgM7Nq1C2dn\n53zX731b/vvvvzN9+vQS1X3/kRcPvreTkxMACxYsYN26ddjZ2bF27VpmzZpVqvf58ssvMTMzY+jQ\noQVeNxgMODs7o9VqGThwoLo+49ixY+oz1tbW6PX6R46hZs2aWFpaMmvWLPbu3YtOpyv03iFDhvA/\n//M/xMfHq2WnT5+mS5cu1K1bF3Nzc1xcXDh27FiBz3fr1o3w8HDu3LlT4PVjx46pnbS+ffvy7LPP\nAnlT0A4fPsxnn33G8ePHHzrFSwghhBCiqnjiOwtlqXXr1pw9e7bAay+99FK+a/Hx8VhZWVGjRg21\nzM7OjvPnz5OamkqLFi3y1RMUFERYWBgrVqygUaNGxY4tJyeH8+fPF+uUYr1ez5gxY1i7di179uxR\n4z9z5kyx24O8RcMHDx4kKCiowK0+o6OjuXjxIuPGjcPe3h6DwcDOnTtL1EZWVlaR95ibm7NlyxYG\nDx7MgQMH8PLyeui948aNY/Xq1SWK4x4vLy9sbGx4//33yc7OLvZzLVq0IDQ0lDZt2vDFF1+wfPny\nR2pfCCGEEKIyeao6C127dsVoNLJp0ya1LCoqiuPHj+Pi4sKJEyfU3Y4yMzOZP39+gb+4+vn54evr\nW2Zx3b17l88//5xGjRrlWzB9v/T0dI4ePWoSe+PGjYG8aUqfffYZSUlJABiNxoeeHfDTTz+xZs0a\nvvzyS6pXr17gPQaDAR8fHyIjI4mMjOTQoUNcu3aNS5cu0blzZ7XjcP78eaKjo9Xn6tevz99//01u\nbi779u0rsO5nnnmG9PR09b1u375Nnz59mDVrlkldBRk2bBi//vqrukuRnZ0dx44dIyUlhZycHAwG\nA507dy70+Q8//JAaNWqYrEm4p3PnzoSHhwN5i8Zv3rwJ5E11ql69Oq6urowfP54///zzoTEKIYQQ\nQlQFT/yahbKk0WhYvnw5CxcuZPXq1VhaWtK4cWNmzZqFTqdjxYoVzJ8/n3nz5pGbm4urqyujRo3K\nV0+fPn3KJB5/f38sLCwwGo10796dFStWqNceXLPQq1cv3n33XdasWcPs2bPR6XRUr16dTz/9VI0p\nOTmZsWPHoigKGo0GDw+PQtv+5JNPMBqNjB07FoD27durW9LeYzAYTBZ4AwwYMACDwYCnpyczZ87E\n0dGRVq1amaz38PPzw9vbm7p162JjY0NGRv6jzocNG8acOXPQ6XSsXr2aSZMmqaMQDy6IfpCFhQWj\nR49mwYIFADz//PP4+fnxzjvvoCgKffr0KXC9wj0ajYZFixbx7rvvsnjxYvr27atemzx5Mn5+fjg7\nO/PKK6/wwgsvAHkdosWLF6PVajE3N2fu3LkPjfHBTogQQgghRGWkUeS3GiHKXG6uwvXraRUdxhOv\ndm0rbtzI35kU/0dyVDySp6JJjopH8lQ8kqeiVaYcPfdc4Wsxn6ppSEKUlwKWgAghhBBCVDpVchpS\namoqY8aMyVe+bt066tSpU66xTJ48Od/2qv7+/vTq1atc2p83bx6///67SZmnp+dDpyiJ0itowbgQ\nQgghRGUj05CEeEySkuQshqJUpiHaiiI5Kh7JU9EkR8UjeSoeyVPRKlOOZBqSeOIlJSXh6+uLg4MD\n7u7uTJgwgdjY2HKNYfz48XTq1Ek9zfkeRVFYunQpgwYNwtHRkQ0bNpRrXEIIIYQQFaVKTkMSlYui\nKEyZMgU3NzeWLl0K5G0Le/369QLPsnhcvLy8uHPnjsnWupB3HsWVK1fYvXs3Wq2W69evl1tMQggh\nhBAVSToLosIdOXIEc3NzRowYoZZZW1uTnp7OO++8w61bt8jOzub999/HwcGBhIQEvLy86NChAydP\nnsTGxgYPDw+WLVtGSkoKQUFB2NnZERwcTFxcHHFxcaSmpuLl5cXw4cMLjaNbt24m51jcs3HjRj7/\n/HO02ryBuHr16pV9EoQQQgghnkAyDUlUuJiYGJNzGu6xtLQkJCSEbdu2sX79egIDA9XzC+Li4hg7\ndiy7d+8mNjaW8PBwNm7cSEBAAF999ZVaR3R0NOvXr+f7778nJCSExMTEEscXHx/Prl27cHd3x8vL\ni4sXLz7yuwohhBBCVCYysiCeWIqisGTJEo4dO4ZWqyUxMZHk5GQAmjRpgl6vB+Cll16iW7duaDQa\n9Ho9ly5dUuvo378/Op0OnU7Ha6+9xunTp2nQoEGJ4jAajVhaWhIaGsrevXuZNWsW3333Xdm9qBBC\nCCHEE0pGFkSFa926NWfPns1XHh4eTkpKCqGhoYSFhVG/fn31lGcLCwv1Pq1Wq37WaDTk5OSo18pi\nC9MGDRowYMAAIO8E6+jo6FLXKYQQQghRGUhnQVS4rl27YjQaTRYWR0VFcfnyZerVq0e1atU4cuSI\nyYhBce3fv5+srCxSU1P57bffsLW1LXEdDg4O6lqG3377jebNm5e4DiGEEEKIykimIYkKp9FoWL58\nOQsXLmT16tVYWlrSuHFjpkyZwoIFC3BxccHGxoaWLVuWuG69Xo+npyepqalMmjTpoVOQRo4cyYUL\nF8jIyKB3794sWLCAXr16MXHiRPz9/Vm/fj1WVlYsWLCgyHbl+BIhhBBCVAVyKJuosoKDg7GysmL8\n+PHl3nZursL162nl3m5lU5kOrKkokqPikTwVTXJUPJKn4pE8Fa0y5UgOZROinJXBUgkhhBBCiAon\n05BEleXj45OvLDo6moCAAJMyCwsLNm/eXKZtl8XCaiGEEEKIiiadBfFU0ev1hIWFVXQYQgghhBCV\nQpWfhvTKK6+YfA4NDeXjjz8G8ua0f/311ybX7e3tSUlJKbS+pKQkfH19cXBwwN3dnQkTJhAbGwvk\nHS7m6enJoEGDGDhwICEhIepC19DQUPR6PYcPH1br2rdvH3q9noiICABGjx7NoEGDGDp0KG+99RYX\nLlwoNI5797q4uDB48GA+/vhjbt26pV5v27Ytrq6u6p9Vq1YBcODAAdzc3Bg6dChOTk58//336jPb\nt29nyJAhuLi44Obmli839wsMDGTw4MG4uLgwefJkk7YftG7dOmxtbbl9+/ZD3+f06dOFXgfTv6/Q\n0NASHbB2/997bm4u06dPZ+bMmSiKwoQJEwqMv6CfDyGEEEKIp0mV7yyUJUVRmDJlCl26dGHfvn2E\nhobi5+fH9evXyczM5L333mPixIns2bOHsLAwTp48aXJ4V5s2bTAYDOrnnTt3Ym1tbdJGUFAQO3bs\nYNiwYSxevPih8QQFBREeHs6OHTuwsLBg0qRJ6jWdTkdYWJj6Z+LEidy9e5ePPvqIr776ih07drB9\n+3a6dOkCwI8//sj69ev5+uuvCQ8P5z//+Q81axa+2KVHjx7s3LmT8PBwmjdvzsqVKwu912AwYGtr\ny969ex/6PiWxbds2rl27VuLnFEVhzpw5ZGdns2DBAjQaDatXr6ZWrVplFpsQQgghRFUhnYUSOHLk\nCObm5owYMUIts7a2plOnToSHh9OxY0d69uwJQPXq1Zk9e7b6jT5Ap06dOHXqFHfv3iU9PZ24uDja\ntm1bYFudOnUiLi6uWHFZWFgwbdo0Ll++TFRUVKH3paenk5OTQ+3atdXn7m1HumrVKgICAtStRS0s\nLBg+fHihdfXs2RNz87xZbB06dODq1asF3hcXF0dGRgZTp0416ShlZmbi6+uLo6MjkydPJjMzU712\n/2hQREQEM2bMMKkzIiKCM2fO4O/vj6urK5mZmQQFBeHk5ISLiwuBgYGFxj1//nxu3LhBYGAgWm3e\nj//9o0lffvklgwYNYsSIEeqIEcCGDRvU+n19fQutXwghhBCiKqnyaxYyMzNxdXVVP9+8eRN7e3v1\n87p169ixY4f6+WHfVsfExNCuXbsCr/3111/5rjVr1oyMjAzS0vK20NRoNHTv3p1Dhw5x+/Zt7O3t\nSUhIKLC+AwcO0KZNm6Jf8H+ZmZlhbW3NhQsXsLa2zvfe3t7eODk5YW9vT79+/ejWrRt9+/ZlyJAh\naLVaYmJisLGxKXZ799u6dSuOjo4FXjMYDDg5OdGpUydiY2NJTk6mfv36bNy4EZ1Ox+7du4mKisLd\n3b3Y7Q0ePJhvv/2WgIAAbG1tSU1N5YcffiAiIgKNRlPolKidO3fSsmVL/v3vf6sdnfudOXOGXbt2\nsX37dnJychg2bJj6d7pq1SoiIyOxsLB46JQrIYQQQoiqpMp3Fu5Nx7knNDSUM2fOqJ/HjBljsg//\n/R2Jx8HZ2ZkNGzaQlpbG9OnT803f8ff3R6fT0bhxYz766KMS1X3/kRkPvvc9CxYsIDo6ml9//ZW1\na9dy+PBhFi1a9GgvQ9438WZmZgwdOrTA6waDgeXLl6PVahk4cCARERGMGjWKY8eOMXr0aCBvdEav\n1z9yDDVr1sTS0pJZs2bRr18/+vbtW+B9L7/8MhcuXODUqVO8+uqr+a4fP34cBwcHqlevDpj+LOj1\nevz9/enfvz8ODg6PHKsQQgghRGUi05BKoHXr1pw9e7bAay+99FK+a/Hx8VhZWVGjRg21zM7OjvPn\nz5OamkqLFi3y1RMUFERYWBgrVqygUaNGxY4tJyeH8+fPF+uUY71ez5gxY1i7di179uxR47+/E1Uc\noaGhHDx4kKCgoAK3Co2OjubixYuMGzcOe3t7DAYDO3fuLFEbWVlZRd5jbm7Oli1bGDx4MAcOHMDL\ny6vA+1q2bMkXX3yBr68vMTExJYpj1apVjBw5kj///JPXX3+d7OzsEj0vhBBCCFEZSWehBLp27YrR\naGTTpk1qWVRUFMePH8fFxYUTJ06oux1lZmYyf/78An9x9fPzK9N573fv3uXzzz+nUaNG+RZM3y89\nPZ2jR4+axN64cWMgb5rSZ599RlJSEgBGo/GhZw/89NNPrFmzhi+//FL9Jv5BBoMBHx8fIiMjiYyM\n5NChQ1y7do1Lly7RuXNnteNw/vx5oqOj1efq16/P33//TW5uLvv27Suw7meeeYb09HT1vW7fvk2f\nPn2YNWuWSV0P6tixI3PnzsXb25vLly+bXOvcuTP79u0jMzOTtLQ0Dhw4AOTtnnTlyhW6du2Kv78/\nt2/fJiOjcpzIKIQQQghRGlV+GlJZ0mg0LF++nIULF7J69WosLS1p3Lgxs2bNQqfTsWLFCubPn8+8\nefPIzc3F1dWVUaNG5aunT58+ZRKPv78/FhYWGI1GunfvzooVK9RrD65Z6NWrF++++y5r1qxh9uzZ\n6HQ6qlevzqeffqrGlJyczNixY1EUBY1Gg4eHR6Ftf/LJJxiNRsaOHQtA+/bt1a1J7zEYDCYLvAEG\nDBiAwWDA09OTmTNn4ujoSKtWrUzWe/j5+eHt7U3dunWxsbEp8BfzYcOGMWfOHHQ6HatXr2bSpEnq\nKMSDC6IfZG9vT2pqKl5eXnz77bdqebt27XBycsLV1ZW6detia2sL5I3aTJs2jbS0NBRFwdPTs8jd\nk+6fEiaEEEIIUVlpFPmtRogyl5urcP16WkWH8cSrXduKGzdklOZhJEfFI3kqmuSoeCRPxSN5Klpl\nytFzzxW+Xb5MQxLiMShgCYcQQgghRKUj05AKkJqaypgxY/KVr1u3jjp16pRrLJMnT863vaq/vz+9\nevUql/bnzZvH77//blLm6en50ClKggIXfAshhBBCVDYyDUmIxyQp6XZFh/DEq0xDtBVFclQ8kqei\nSY6KR/JUPJKnolWmHD1sGpKMLJSAXq/HxcWFoKAgALKzs+nZsyft27c3OS9h0qRJJCcn85///Ect\nCw4O5j//+Q9169bl7t27TJo0iSFDhgB5C3L79u1LeHg4CQkJZGRkkJKSQpMmTQCYM2cOHTt2JCUl\nhV69evHPf/7T5BRpe3t7tmzZQt26dR8af3JyMh9++CFXrlwhOzubxo0b4+/vT0BAAABXrlyhRo0a\n1KxZkzp16rBu3Togb0Tl888/5/Dhw9SsWZOff/5ZzUFcXBzPP/88Op0OvV5P165dOXPmDLNnz1bb\nHT16tHqA2pYtW1i/fj2Qtwh46tSpDz234Ouvv2bz5s1YWlpibm7O6NGjcXNzw2g08tlnn3Hw4EE0\nGg2tWrVizpw5NGzYEIC2bdvSpk0bsrOzMTMzw83NjTFjxqDVajl69CiTJk1S8wswffp0unfvXmgc\n+/btY/LkyezatYtWrVo9NM9CCCGEEFWFdBZKwMrKipiYGDIzM9HpdPzyyy80aNDA5J5bt25x9uxZ\nrKysiI+Pp2nTpuq1ewfAXbx4EXd3dwYNGkS1atXU6yEhIQAcPXqUtWvX5juwLSIigvbt22MwGEw6\nC8W1bNkyunfvzjvvvAPkbZ2q1+vVw9vudVoGDx5s8pzBYMDW1pa9e/fi4eFBr1691GlQ93cEIO/s\nhcJcvXqVr776im3btlGzZk3S09NJSUkp9P6NGzdy+PBhtmzZQo0aNUhLS+OHH34AYOnSpaSnpxMR\nEYGZmRlbt25lypQpbN68GY1GY3Io3fXr1/Hz8yMtLY1//OMfAHTq1Clffh9m586dvPrqqxgMBrUO\nIYQQQoiqThY4l1CfPn04ePAgkPdLtLOzs8n1vXv30q9fP5ydnTEYDAXW0bx5c6pXr86tW7dK1LbB\nYGDGjBkkJiZy9erVEsd+7do19Zt34KFnMtwTFxdHRkYGU6dOLfR9iuv69es888wzWFlZAXlnJdzf\nmXrQypUrmTt3rnqoXY0aNRg2bBh37twhNDSUWbNmYWZmBoCHhwcWFhYcOXIkXz316tXjk08+4dtv\nv32kLU3T09M5ceIECxYsKHUOhBBCCCEqE+kslJCTkxO7du0iKyuL6Oho2rdvb3LdYDAwZMiQh3YW\nzp49y4svvki9evWK3e6VK1dISkrCzs4OR0dHdu3aVeLY3377bT788ENGjx7Nl19+SWJiYpHPGAwG\nnJyc6NSpE7GxsSQnJ5e43Xusra2pX78+/fv3Z+bMmURGRhZ6b1paGunp6QV2Jv773//SqFEjk5Ox\nAWxsbAo9mblp06bk5ORw/fp1AI4fP46rq6v6Jy4urtBY9u/fT69evWjRogV16tQp8UnXQgghhBCV\nlXQWSsja2pqEhAR27tyZ73C15ORk/vvf//Lqq6/SokULzM3NOX/+vHp93bp1ODs7M3z4cN59990S\ntbtr1y4cHR2BvA7LvdOPS6JXr17s27eP4cOHc+HCBYYNG/bQaUDwf6MnWq2WgQMHEhER8dD7C9sF\nSKPRYGZmxpo1a1i2bBnNmzfn008/JTg4uMTvURY6depEWFiY+qdZs2aF3nv/CJKTk5OMLgghhBDi\nqSFrFh6Bvb09ixcvZsOGDdy4cUMt3717Nzdv3qR///5A3rfjBoOBNm3aAP+3ZmH//v18+OGH7Nu3\nD0tLy2K1aTAYSEpKIjw8HMibUnTx4kWaN29eothr166Ni4sLLi4ueHt7c+zYMQYNGlTgvdHR0Vy8\neJFx48YBYDQaadKkSYGnUt9f/82bN03Kbty4oW45q9FosLOzw87Oju7duzNr1ix8fHzy1VOjRo0C\n130ANGvWjCtXrpCWlmYyunD27Fn69etXYFzx8fGYmZlRr149/v7770Ljf9CNGzc4cuQI58+fR6PR\nkJOTg0ajISAgQLZHFUIIIUSVJyMLj+D1119n8uTJ6PV6k3KDwcCaNWuIjIwkMjKSrVu3FvgtdP/+\n/bGxsWHbtm3Fai82Npb09HR+/vlnte6JEyeWeHTh119/5c6dO0BeRyYuLo5GjRoVer/BYMDHx0dt\n89ChQ1y7do1Lly4V+oytrS0nT54kKSkJgNOnT2M0GmnUqBGJiYmcPXtWvTcqKooXXnih0LomTpzI\nvHnzSEvLOwk5PT2d7du3Y2VlhZubG4sWLSInJweA7du3c+fOHbp27ZqvnpSUFObMmcPbb79d4l/w\n9+zZg6urKwcOHCAyMpIff/yRJk2acPz48RLVI4QQQghRGcnIwiNo2LAhnp6eJmUJCQlcunSJDh06\nqGVNmzalZs2a/PHHH/nqmDx5Mn5+fgwfPrzI9gwGAwMGDDApGzhwIL6+vkyZMgWAoUOHotXm9f0c\nHR2ZOXNmvnrOnj3LJ598gpmZGYqi8MYbb2BnZ/fQdletWmVSNmDAAAwGAxMnTizwmfr16zNr1iwm\nTpxIbm4uVlZWLFmyBK1WS3Z2NoGBgVy7dg1LS0vq1q3LvHnzCm1/5MiRZGRk4OHhQbVq1TA3N2fs\n2LEA+Pn5ERgYyKBBg9BqtbRs2ZKQkBC1M5CZmYmrq6u6daqrq6v6LPzfmoV73nvvvXy7QEHeLkgT\nJkwwKRs4cCA7d+6kc+fOhcYux5cIIYQQoiqQQ9mEeAxycxWuX0+r6DCeeJXpwJqKIjkqHslT0SRH\nxSN5Kh7JU9EqU44ediibTEMS4jGQ5QxCCCGEqApkGlIVtHXrVjZs2GBS1rFjR+bMmVNBET3cvHnz\n+P33303KPD098fDwKLcYUlNTGTNmTL7ydevWqYuzS0IWPwshhBCiKpBpSEI8JklJtys6hCdeZRqi\nrSiSo+KRPBVNclQ8kqfikTwVrTLlSKYhiSdeUlISvr6+ODg44O7uzoQJE4iNjS3XGMaPH0+nTp3w\n9vY2Kf/1118ZNmwYQ4YMYfr06WRnZ5drXEIIIYQQFUU6C6LCKYrClClT6NKlC/v27SM0NBQ/Pz/1\ntOXy4uXlxeLFi03KcnNzmTFjBkuWLGHnzp288MILxd7yVgghhBCispM1C6LCHTlyBHNzc0aMGKGW\nWVtbk56ezjvvvMOtW7fIzs7m/fffx8HBgYSEBLy8vOjQoQMnT57ExsYGDw8Pli1bRkpKCkFBQdjZ\n2REcHExcXBxxcXGkpqbi5eX10K1qu3XrxtGjR03Kbty4QbVq1WjRogUAPXr0YOXKlbzxxhuPJxlC\nCCGEEE8QGVkQFS4mJoZ27dpYsILTAAAgAElEQVTlK7e0tCQkJIRt27axfv16AgMD1fML4uLiGDt2\nLLt37yY2Npbw8HA2btxIQEAAX331lVpHdHQ069ev5/vvvyckJITExMQSxVanTh1ycnI4ffo0ABER\nEVy9erUUbyuEEEIIUXnIyIJ4YimKwpIlSzh27BharZbExESSk5MBaNKkiXqC9ksvvUS3bt3QaDTo\n9XqTE6b79++PTqdDp9Px2muvcfr0aRo0aFDsGDQaDUuWLOHTTz/FaDTSo0cP9fA7IYQQQoiqTjoL\nosK1bt2aPXv25CsPDw8nJSWF0NBQqlWrhr29PVlZWQBYWFio92m1WvWzRqMhJydHvVYWW5i+8sor\nfPfddwAcOnSIixcvlrpOIYQQQojKQL4iFRWua9euGI1GNm3apJZFRUVx+fJl6tWrR7Vq1Thy5IjJ\niEFx7d+/n6ysLFJTU/ntt9+wtbUtcR33FlobjUZWr17NW2+9VeI6hBBCCCEqIxlZEBVOo9GwfPly\nFi5cyOrVq7G0tKRx48ZMmTKFBQsW4OLigo2NDS1btixx3Xq9Hk9PT1JTU5k0adJDpyCNHDmSCxcu\nkJGRQe/evVmwYAG9evVizZo1HDx4kNzcXEaMGEG3bt2KbFeOLxFCCCFEVSCHsokqKzg4GCsrK8aP\nH1/ubefmKly/nlbu7VY2lenAmooiOSoeyVPRJEfFI3kqHslT0SpTjuRQNiHKWRkslRBCCCGEqHAy\nDUlUWT4+PvnKoqOjCQgIMCmzsLBg8+bNZdp2WSysFkIIIYSoaNJZEE+EpKQkFi5cyOnTp6lVqxb1\n6tVj1qxZ6mFoZUWv1xMWFpav/Ny5c8ydO5e0tDS0Wi3vvfceTk5OAMTHx/PBBx9w48YN2rVrx+LF\ni012YxJCCCGEqKpkGpKocIqiMGXKFLp06cK+ffsIDQ3Fz89P3YWoPOh0OgIDAzEYDKxZs4aFCxdy\n69YtAIKCghgzZgw//PADtWrVYsuWLeUWlxBCCCFERZKRBVHhjhw5grm5OSNGjFDLrK2tSU9P5513\n3uHWrVtkZ2fz/vvv4+DgQEJCAl5eXnTo0IGTJ09iY2ODh4cHy5YtIyUlhaCgIOzs7AgODiYuLo64\nuDhSU1Px8vJi+PDhBcZw/whGgwYNqFu3LikpKdSsWZMjR47w+eefAzBs2DCWL1/OyJEjH29ShBBC\nCCGeADKyICpcTEwM7dq1y1duaWlJSEgI27ZtY/369QQGBqpbksbFxTF27Fh2795NbGws4eHhbNy4\nkYCAAL766iu1jujoaNavX8/3339PSEgIiYmJRcZz6tQp7t69S7NmzUhNTaVWrVqYm+f1qxs2bFis\nOoQQQgghqgIZWRBPLEVRWLJkCceOHUOr1ZKYmEhycjIATZo0Qa/XA/DSSy/RrVs3NBoNer3e5PC2\n/v37o9Pp0Ol0vPbaa5w+ffqhZy1cu3aNadOmERgYiFYrfWkhhBBCPN3ktyFR4Vq3bs3Zs2fzlYeH\nh5OSkkJoaChhYWHUr1+frKwsAJMFxlqtVv2s0WjIyclRr5VkV6K0tDS8vb3x9fWlQ4cOANSpU0ed\nBgVw9erVh3Y2hBBCCCGqEuksiArXtWtXjEYjmzZtUsuioqK4fPky9erVo1q1ahw5csRkxKC49u/f\nT1ZWFqmpqfz222/Y2toWeJ/RaGTy5Mm4uroyePBgtVyj0fDaa6+xZ88eALZt24a9vX2J4xBCCCGE\nqIyksyAqnEajYfny5Rw+fBgHBwecnZ1ZsmQJvXv35syZM7i4uBAWFkbLli1LXLder8fT05M333yT\nSZMmFToqsHv3bo4fP862bdtwdXXF1dWVc+fOATBt2jT+9a9/MWDAAG7cuMEbb7xRqvcVQgghhKgs\nNMq9FaNCVDHBwcFYWVkxfvz4cm9bURSSk9PKvd3KpnZtK27cyKjoMJ5okqPikTwVTXJUPJKn4pE8\nFa0y5ei552oWek1GFoR4DKQLLoQQQoiqQHZDElWWj49PvrLo6GgCAgJMyiwsLNi8eXOZtl2CddVC\nCCGEEE8s6SyIp4perycsLOyxt1OSXZiEEEIIIZ5UMg1JPBGSkpLw9fXFwcEBd3d3JkyYQGxsbLm1\nf+7cOd58802cnZ1xcXFh165d6rVvvvmGAQMGoNfrSUlJKbeYhBBCCCEqmowsiAqnKApTpkzBzc2N\npUuXAnlbp16/fp0WLVqUSww6nY7AwECaN29OYmIiHh4e9OzZk1q1atGxY0f69u2Lp6dnucQihBBC\nCPGkkM6CqHBHjhzB3NycESNGqGXW1takp6fzzjvvqIeivf/++zg4OJCQkICXlxcdOnTg5MmT2NjY\n4OHhwbJly0hJSSEoKAg7OzuCg4OJi4sjLi6O1NRUvLy8GD58eIEx3N8padCgAXXr1iUlJYVatWrx\n8ssvP/YcCCGEEEI8iWQakqhwMTExtGvXLl+5paUlISEhbNu2jfXr1xMYGMi9nX7j4uIYO3Ysu3fv\nJjY2lvDwcDZu3EhAQABfffWVWkd0dDTr16/n+++/JyQkhMTExCLjOXXqFHfv3qVZs2Zl95JCCCGE\nEJWQjCyIJ5aiKCxZsoRjx46h1WpJTEwkOTkZgCZNmqDX6wF46aWX6NatGxqNBr1eb3LSc//+/dHp\ndOh0Ol577TVOnz5d6MFsANeuXWPatGkEBgai1UpfWgghhBBPN/ltSFS41q1bc/bs2Xzl4eHhpKSk\nEBoaSlhYGPXr1ycrKwvI2+70Hq1Wq37WaDTk5OSo10qyK1FaWhre3t74+vrSoUOHR30dIYQQQogq\nQzoLosJ17doVo9HIpk2b1LKoqCguX75MvXr1qFatGkeOHDEZMSiu/fv3k5WVRWpqKr/99hu2trYF\n3mc0Gpk8eTKurq4MHjz4kd9FCCGEEKIqkc6CqHAajYbly5dz+PBhHBwccHZ2ZsmSJfTu3ZszZ87g\n4uJCWFgYLVu2LHHder0eT09P3nzzTSZNmlToFKTdu3dz/Phxtm3bhqurK66urpw7dw6ADRs20Lt3\nb65evcrQoUP58MMPS/W+QgghhBCVhUa5t2JUiComODgYKysrxo8fX+5tK4pCcnJaubdb2dSubcWN\nGxkVHcYTTXJUPJKnokmOikfyVDySp6JVphw991zNQq/JyIIQj4F0wYUQQghRFchuSKLK8vHxyVcW\nHR1NQECASZmFhQWbN28u07ZLsK5aCCGEEOKJJZ0F8VTR6/WEhYU99nZKsguTEEIIIcST6qmdhvTK\nK6+YfA4NDeXjjz8G8ua6f/311ybX7e3tSUlJKbS+pKQkfH19cXBwwN3dnQkTJhAbGwvkHTrm6enJ\noEGDGDhwICEhIerhYqGhoej1eg4fPqzWtW/fPvR6PREREQCMHj2aQYMGMXToUN566y0uXLhQaBz3\n7nVxcWHw4MF8/PHH3Lp1S73etm1bdQGvq6srq1atAuDAgQO4ubkxdOhQnJyc+P7779Vntm/fzpAh\nQ3BxccHNzS1fbu63e/dunJ2dsba25vTp04XeB7Bu3TpsbW25ffv2Q9+nqHru//sKDQ0t9OC1GTNm\nqDm9ceMGbm5ubN26lcTERP7xj388cvtCCCGEEFWVjCyUAUVRmDJlCm5ubixduhTI2/rz+vXrNGrU\niPfee4+5c+fSs2dP7ty5g4+PD9999x1vv/02AG3atMFgMNC9e3cAdu7cibW1tUkbQUFB2NrasmnT\nJhYvXmxySvGD7t1rNBpZsmQJkyZN4ptvvgFAp9Pl+2b97t27fPTRR2zZsoWGDRtiNBpJSEgA4Mcf\nf2T9+vV8/fXXNGjQAKPRyPbt2wttu02bNgQHBzNnzpwi82YwGLC1tWXv3r14eHgUeX9xbNu2jdat\nWz/04LXbt28zfvx4hg8frra7bNmyMmlfCCGEEKIqeWpHFsrSkSNHMDc3Z8SIEWqZtbU1nTp1Ijw8\nnI4dO9KzZ08AqlevzuzZs9Vv9AE6derEqVOnuHv3Lunp6cTFxdG2bdsC2+rUqRNxcXHFisvCwoJp\n06Zx+fJloqKiCr0vPT2dnJwcateurT53b5vSVatWERAQoP7ybWFhwfDhwwutq1WrVsXa4jQuLo6M\njAymTp2KwWBQyzMzM/H19cXR0ZHJkyeTmZmpXrt/NCgiIoIZM2aY1BkREcGZM2fw9/fH1dXV5Nl7\nMjIymDBhAi4uLowcORKAhIQEhgwZ8tD2c3JymDFjhjrCsm7duiLfUQghhBCisntqRxYyMzNxdXVV\nP9+8eRN7e3v187p169ixY4f6+dq1a4XWFRMTQ7t27Qq89tdff+W71qxZMzIyMkhLy9taU6PR0L17\ndw4dOsTt27ext7dXv9l/0IEDB2jTpk3RL/i/zMzMsLa25sKFC1hbW+d7b29vb5ycnLC3t6dfv350\n69aNvn37MmTIELRaLTExMdjY2BS7veIyGAw4OTnRqVMnYmNjSU5Opn79+mzcuBGdTsfu3buJiorC\n3d292HUOHjyYb7/9loCAgEIPX1u0aBGvv/46Y8aMKfB6Ye2fO3eOxMREdu7cCWAytUsIIYQQoqp6\najsLD07HCQ0N5cyZM+rnMWPGmOzPf39H4nFwdnZmw4YNpKWlMX36dFauXGly3d/fH51OR+PGjfno\no49KVPf9R2kUNA0JYMGCBURHR/Prr7+ydu1aDh8+zKJFix7tZYrBYDCwfPlytFotAwcOJCIiglGj\nRnHs2DFGjx4N5I3O6PX6Mm23a9eu7N+/n/Hjx1OvXr181wtrv2nTpsTHx/PJJ5/Qp08fdaRICCGE\nEKIqk2lIZaB169acPXu2wGsvvfRSvmvx8fFYWVlRo0YNtczOzo7z58+TmppKixYt8tUTFBREWFgY\nK1asoFGjRsWOLScnh/PnzxdrapBer2fMmDGsXbuWPXv2qPHf34kqC9HR0Vy8eJFx48Zhb2+PwWBQ\nv7EvrqysrEdq28nJiREjRjBhwgR1ZKc4nn32WcLCwujSpQvff/+9nOIshBBCiKeCdBbKQNeuXTEa\njWzatEkti4qK4vjx47i4uHDixAl1t6PMzEzmz5+Pl5dXvnr8/Pzw9fUts7ju3r3L559/TqNGjfIt\nmL5feno6R48eNYm9cePGQN40pc8++4ykpCQAjEZjqc8kMBgM+Pj4EBkZSWRkJIcOHeLatWtcunSJ\nzp07qx2H8+fPEx0drT5Xv359/v77b3Jzc9m3b1+BdT/zzDOkp6c/tP0xY8bQrVs3fHx8MBqNJtcK\naz8lJQVFURg0aBBTp07lzz//fOT3F0IIIYSoLJ7aaUhlSaPRsHz5chYuXMjq1auxtLSkcePGzJo1\nC51Ox4oVK5g/fz7z5s0jNzcXV1dXRo0ala+ePn36lEk8/v7+WFhYYDQa6d69OytWrFCvPbhmoVev\nXrz77rusWbOG2bNno9PpqF69Op9++qkaU3JyMmPHjkVRFDQazUN3Lvrhhx/45JNPSElJwdvbm7Zt\n2+bbatVgMJgs8AYYMGAABoMBT09PZs6ciaOjI61atTJZ7+Hn54e3tzd169bFxsaGjIz8R6gPGzaM\nOXPmoNPp2LRpEzqdrsA4p02bxsyZMwkICMDPz08tHzFiRIHtX7t2jZkzZ5KbmwvABx98UGgOhBBC\nCCGqCo1y/4R2IUSZUBSF5OTiT3N6WtWubcWNG/k7feL/SI6KR/JUNMlR8UieikfyVLTKlKPnnqtZ\n6DWZhiTEYyBdcCGEEEJUBTINqQRSU1ML3HJz3bp11KlTp1xjmTx5cr7tVf39/enVq1e5tD9v3jx+\n//13kzJPT88yO1ytstNoKjoCIYQQQojSk2lIQjwmSUm3KzqEJ15lGqKtKJKj4pE8FU1yVDySp+KR\nPBWtMuVIpiGVk3snDCckJKDX6/n3v/+tXvv4448JDQ1VP3/99dcMHjwYV1dXPDw82L59O5C329CC\nBQsYMGAAAwcO5L333uPq1avqc3q9Hn9/f/VzdnY2Xbt2xdvbG8g7L6Jr1664urqqf/76668C401I\nSMDOzg43NzccHR15/fXXTWIsrK7c3Fzmz5+vnmbs4eFBfHw8kLez0uzZs3FwcMDd3Z3Ro0fzxx9/\nFJqzmTNn0q1bN/UE5Xtu3LjB2LFjGThwIGPHjuXmzZtA3lqA+fPnM2DAAFxcXArdsvbYsWMMGzaM\nl19+mYiICLX83LlzvPnmmzg7O+Pi4sKuXbvyPTt//nyT06KDg4PVRdpZWVmMHTuW4ODgQt9JCCGE\nEKKqkGlIj0m9evXYsGEDb775JhYWFibXNm7cyOHDh9myZQs1atQgLS2NH374AYClS5eSnp5OREQE\nZmZmbN26lSlTprB582Y0Gg1WVlbExMSQmZmJTqfjl19+oUGDBib1Ozk5MXv27GLF2axZM7WjEh8f\nz5QpU1AURZ1OVFBdO3fu5Nq1a+zYsQOtVsvVq1epXr06AP/85z9p0qQJe/fuRavVEh8fz99//11o\n++7u7owaNYrp06eblK9atYpu3boxceJEVq1axapVq5g2bRo//fQTFy9eZO/evfzxxx/MnTu3wK1c\nGzVqxKeffsratWtNynU6HYGBgTRv3pzExEQ8PDzo2bMntWrVAuD06dNqx+RBRqMRHx8f2rVrh4+P\nz8PSKoQQQghRJcjIwmNSt25dunXrpv4ifr+VK1cyd+5c9VC2GjVqMGzYMO7cuUNoaCizZs3CzMwM\nAA8PDywsLDhy5Ij6fJ8+fTh48CCQtw2ps7NzmcTctGlTZsyYYTIiUpCkpCSee+45tNq8H5+GDRvy\n7LPPEhcXxx9//MHUqVPVa02bNqVv376F1tW5c2eeffbZfOX79+/Hzc0NADc3N/VchXvlGo2GDh06\ncOvWLa5du5bv+SZNmmBtba3GcU+LFi1o3rw5AA0aNKBu3bqkpKQAeQfYLV68mGnTpuWrLycnB19f\nX1588UWTkR0hhBBCiKpMOguP0YQJE/j666/JyclRy9LS0khPT6dp06b57v/vf/9Lo0aNTE52BrCx\nsSEmJkb97OTkxK5du8jKyiI6Opr27dub3L9r1y6TqUOZmZnFjrldu3ZcuHDhoXU5Ojpy4MABXF1d\nWbRokXpAWUxMDG3btlU7OqVx/fp1nn/+eQCee+45rl+/DkBiYiINGzZU72vYsCGJiYmP1MapU6e4\ne/cuzZo1A+Cbb76hf//+arv3W7NmDdWqVZOTm4UQQgjxVJFpSI9R06ZNad++PeHh4WVar7W1NQkJ\nCezcubPAg9xKMg3pQQ+udy+oroYNGxIREcGvv/7KkSNHGDNmDP/v//2/R2qvODQaDZoy3l7o2rVr\nTJs2jcDAQLRaLYmJiURERBQ6qtKxY0dOnjxJbGwsLVq0KNNYhBBCCCGeVDKy8Jh5e3uzZs0a9Zfw\nGjVqYGVlpS4Ivl+zZs24cuUKaWmmh3mdPXuW1q1bm5TZ29uzePHiMpuCdM+ff/5Jq1atirzPwsKC\nPn36MH36dLy9vdm3bx+tW7cmKirKZCTlUdWrV0+dXnTt2jXq1q0L5E0dun/B99WrV2nQoAFLly5V\nRz+KkpaWhre3N76+vnTo0AHIW/gcFxfHwIEDsbe3586dOwwYMEB9pnPnzsyaNYsJEyYUOO1JCCGE\nEKIqks7CY9aqVStatWrFgQMH1LKJEycyb948tVOQnp7O9u3bsbKyws3NjUWLFqm/cG/fvp07d+7Q\ntWtXk3pff/11Jk+ejF6vL7NYExISWLx4MaNGjXrofWfPnlWn/uTm5hIdHc0LL7xAs2bNsLGxYdmy\nZWrnKCEhQV1fURL29vbqeo/t27fTv39/k3JFUfif//kfatasyfPPP4+vry9hYWGEhYU9tF6j0cjk\nyZNxdXVl8ODBannfvn355ZdfiIyMJDIykurVq6uLzu8ZNGgQ48aNw8vLi1u3bpX4nYQQQgghKhuZ\nhlQO3nvvPXWxLsDIkSPJyMjAw8ODatWqYW5uztixYwHw8/MjMDCQQYMGodVqadmyJSEhIfmm4TRs\n2BBPT88C29u1axcnTpxQP8+ZM4eOHTsWeG9cXBxubm5kZWXxzDPPMHr0aNzd3R9aV1paGh999BFG\noxEAW1tbtYOxYMECFi1axIABA9DpdNSpU6fABcP3fPDBB/z222+kpqbSu3dvfHx8eOONN5g4cSJT\np05ly5YtvPDCC3zxxRdA3uLuH3/8kQEDBlC9enUWLlxYYL2nTp1iypQp3Lp1iwMHDhAcHIzBYGD3\n7t0cP36cGzdusG3bNgAWLVpE27ZtC43xfiNHjuT69eu89957rF27FktLy2I9J4QQQghRGcmhbEI8\nBoqikJycVvSNT7nKdGBNRZEcFY/kqWiSo+KRPBWP5KlolSlHDzuUTToLQgghhBBCiALJNKSnQHR0\nNAEBASZlFhYWBR5m9jikpqYyZsyYfOXr1q2jTp065RKDEEIIIYQoORlZEEIIIYQQQhRIdkMSQggh\nhBBCFEg6C0IIIYQQQogCSWdBCCGEEEIIUSDpLAghhBBCCCEKJJ0FIUrop59+YtCgQQwYMIBVq1bl\nu240Gpk6dSoDBgzgjTfeICEhQb22cuVKBgwYwKBBg/j555/LM+xy9ag5+uWXX3B3d8fFxQV3d3d+\n/fXX8g69XJXmZwng8uXLvPLKK3z99dflFXK5K02OoqKiePPNN3F2dsbFxYWsrKzyDL1cPWqe7t69\ny/Tp03FxccHR0ZGVK1eWd+jlqqg8HTt2jGHDhvHyyy8TERFhcm3btm0MHDiQgQMHqod6VkWPmqNz\n586Z/Pe2a9eu8gy73JXmZwkgLS2N3r178/HHH5dHuKWjCCGKLTs7W+nfv78SFxenZGVlKS4uLkpM\nTIzJPd98843y0UcfKYqiKDt37lTef/99RVEUJSYmRnFxcVGysrKUuLg4pX///kp2dna5v8PjVpoc\nnT17Vrl69aqiKIoSHR2t9OzZs3yDL0elydM9Pj4+io+Pj7JmzZpyi7s8lSZHd+/eVYYMGaKcO3dO\nURRFSUlJqZL/vSlK6fK0Y8cOZerUqYqiKEpGRobSr18/JT4+vnxfoJwUJ0/x8fHKuXPnlGnTpim7\nd+9Wy1NTUxV7e3slNTVVuXHjhmJvb6/cuHGjvF/hsStNji5cuKDExsYqiqIoV69eVXr06KHcvHmz\nPMMvN6XJ0z2ffPKJ8sEHHyjz5s0rr7AfmYwsCFECp06d4sUXX6Rp06ZYWFjg7OzM/v37Te6JjIxk\n2LBhAAwaNIhff/0VRVHYv38/zs7OWFhY0LRpU1588UVOnTpVEa/xWJUmRy+//DINGjQAoHXr1mRl\nZWE0Gsv9HcpDafIEsG/fPho3bkzr1q3LPfbyUpoc/fLLL+j1eqytrQGoU6cOZmZm5f4O5aE0edJo\nNNy5c4fs7GwyMzOpVq0aNWrUqIjXeOyKk6cmTZpgbW2NVmv669GhQ4fo0aMHtWvX5tlnn6VHjx5V\ncnS4NDlq0aIFzZs3B6BBgwbUrVuXlJSU8gq9XJUmTwBnzpzh+vXr9OjRo7xCLhXpLAhRAomJiTRs\n2FD93KBBAxITE/Pd06hRIwDMzc2pWbMmqampxXq2KihNju63Z88eXn75ZSwsLB5/0BWgNHlKT09n\n9erVTJkypVxjLm+lyVFsbCwajYbx48czbNgwVq9eXa6xl6fS5GnQoEFUr16dnj170q9fP8aNG0ft\n2rXLNf7yUpp/g+Xf75I5deoUd+/epVmzZmUZ3hOjNHnKzc0lMDCQ6dOnP67wypyc4CyEeOLExMQQ\nFBTE2rVrKzqUJ9Ly5ct55513eOaZZyo6lCdWTk4OJ06cYMuWLVSvXp0xY8ZgY2NDt27dKjq0J8qp\nU6fQarX8/PPP3Lp1i5EjR9K9e3eaNm1a0aGJSuratWtMmzaNwMDAAr9Vf9p999139O7d26Sz8aST\nzoIQJdCgQQOuXr2qfk5MTFSnzdx/z5UrV2jYsCHZ2dncvn2bOnXqFOvZqqA0OQK4evUqU6ZMITAw\nsMp+KwWly9Mff/zBnj17CAoK4tatW2i1WiwtLRk1alR5v8ZjVZocNWzYkM6dO1O3bl0Aevfuzdmz\nZ6tkZ6E0eQoODqZXr15Uq1aNevXq0bFjR06fPl0lOwul+Te4QYMG/PbbbybPdunSpcxjrGil/f+p\ntLQ0vL298fX1pUOHDo8jxCdCafJ08uRJTpw4wcaNG0lPT+fu3btYWVnh7+//uMItNenyCVECtra2\nXLx4kfj4eIxGIwaDAXt7e5N77O3t1Z0y9uzZQ9euXdFoNNjb22MwGDAajcTHx3Px4kXs7Owq4jUe\nq9Lk6NatW0ycOBE/Pz9effXVigi/3JQmT9999x2RkZFERkbyzjvv4O3tXeU6ClC6HPXs2ZPz58+r\n8/GPHTvGSy+9VBGv8diVJk+NGjXi6NGjAGRkZPDHH3/QsmXLcn+H8lCcPBWmZ8+eHDp0iJs3b3Lz\n5k0OHTpEz549H3PE5a80OTIajUyePBlXV1cGDx78mCOtWKXJ0+eff87BgweJjIxk+vTpuLm5PdEd\nBUB2QxKipA4ePKgMHDhQ6d+/v7JixQpFURTliy++UPbt26coiqJkZmYqPj4+ioODg+Lh4aHExcWp\nz65YsULp37+/MnDgQOXgwYMVEn95eNQchYSEKO3bt1eGDh2q/klOTq6w93jcSvOzdM+yZcuq7G5I\nilK6HG3fvl1xcnJSnJ2dlcDAwAqJv7w8ap7S0tIUHx8fxcnJSXF0dFRWr15dYe9QHorK0x9//KH0\n6tVLad++vdKlSxfFyclJfXbz5s2Kg4OD4uDgoGzZsqVC4i8Pj5qj7du3Ky+//LLJv99//vlnhb3H\n41aan6V7tm7dWil2Q9Ioyv9urSGEEEIIIYQQ95FpSEIIIYQQQogCSWdBCCGEEEIIUSDpLAghhBBC\nCCEKJJ0FIYQQQgghRIGksyCEEEIIIYQokHQWhBBCiFJ45ZVXyrW9hIQEwsPDy7VNIcTTSzoLQggh\nRCWRnZ3NpUuX2LlzZ/+ocjMAAAPiSURBVEWHIoR4SphXdABCCCFEVXD06FGCg4OpWbMm58+fx9HR\nkTZt2rBhwwaysrIICQmhWbNmzJgxAwsLC86cOUN6ejozZsygX79+ZGVlMXfuXM6cOYOZmRkzZsyg\na9euhIaGsnfvXjIyMsjNzcVoNPL333/j6urKsGHDcHBwICAggDt37gDw0Ucf0bFjR44ePcry5cup\nU6cO58+fp127dgQFBaHRaDh16hQLFy4kIyMDCwsL1q1bR/Xq1QkKCuK3337DaDTy9ttv89Zbb1Vw\nVoUQFU06C0IIIUQZiYqKYteuXdSuXZv+/fvzxhtvsGXLFtavX8+///1vPvzwQwAuXbrEli1biIuL\nw9PTk+7du/Ptt98CEB4ezt9//8348ePZs2cPAH/++Sc7duygdu3aHD16lLVr17Jy5UoA7ty5w7/+\n9S8sLS25ePEiH3zwAaGhoepzBoOB559/nhEjRnDixAns7Ozw9fVl6dKl2NnZkZaWhk6nY8uWLdSs\nWZOtW7diNBp566236NGjB02bNq2ATAohnhTSWRBCCCHKiK2tLc8//zwAzZo1o0ePHgC0adOGo0eP\nqvc5Ojqi1Wpp3rw5TZs25cKFC5w4cYJRo0YB0KpVK1544QViY2MB6NGjB7Vr1y6wzezsbD7++GOi\noqLQarVcvHhRvWZnZ0fDhg0BsLa25tKlS9SsWZPnnnsOOzs7AGrUqAHAL7/8QnR0tNpBuX37Nv/9\n73+lsyDEU046C0IIIUQZsbCwUP+3VqtVP2u1WnJyctRrGo3G5LkHPz+oevXqhV5bt24d9evXJyws\njNzcXLUT8GA8ZmZmJjE8SFEU/vnPf9KrV6+HxiKEeLrIAmchhBCinEVERJCbm0tcXBzx8fG0aNGC\nTp06qbscxcbGcuXKFVq2bJnv2WeeeYb09HT18+3bt3nuuefQarWEhYU9tEMA0KJFC5KSkjh16hQA\naWlpZGdn07NnTzZu3Mjdu3fVGDIyMsrqlYUQlZSMLAghhBDlrFGjRrz++uukp6czb948LC0tGTly\nJHPnzsXFxQUzMzM+/fRTk5GBe/R6PVqtlqFDh+Lu7s7IkSPx8fFh+/bt9OrVCysrq4e2bWFhwdKl\nS5k/fz6ZmZnodDr+9a9/8cYbb3Dp0iXc3d1RFIU6deqwYsWKx5UCIUQloVEURanoIIQQQoinxYwZ\nM+jbty+DBw+u6FCEEKJIMg1JCCGEEEIIUSAZWRBCCCGEEEIUSEYWhBBCCCGEEAWSzoIQQgghhBCi\nQNJZEEIIIYQQQhRIOgtCCCGEEEKIAklnQQghhBBCCFEg6SwIIYQQQgghCvT/AXm4EPt5dQRcAAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}