McCarthy_Fair_V6_French.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "YqAzy49D0Qrt",
        "3Qzt5H2PTHNR",
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        "LNLh9cAiRx29",
        "IZLxrL8LR5SZ",
        "i1RyYbiZSdQ4",
        "X7tekLGXSjQn",
        "Oj775-hISpuw",
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        "0sf2VqkOZcQe"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Michael-AI-ML/fairness/blob/main/McCarthy_Fair_V6_French.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqAzy49D0Qrt"
      },
      "source": [
        "# MetaData"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtM7Qas00WX7"
      },
      "source": [
        "*Coding and model development by* Michael B. McCarthy mbmccart@utica.edu\n",
        "\n",
        "Article Title : Fairness–accuracy tradeoff: activation function choice in a neural network\n",
        "\n",
        "DOI : 10.1007/s43681-022-00250-9\n",
        "\n",
        "AIET-D-22-00158R1\n",
        "\n",
        "in \n",
        "\n",
        "*AI and Ethics*\n",
        "\n",
        "By \n",
        "\n",
        "Michael B. McCarthy\n",
        "\n",
        "and\n",
        "\n",
        "Sundaraparipurnan Narayanan  \n",
        "\n",
        "sundar.narayanan@aitechethics.com\n",
        "\n",
        "This is built for Google's cloud-based Colab.  A libraries are !pip installed for each run.\n",
        "\n",
        "Data stored in Google Drive.\n",
        "\n",
        "V2 on 02Nov2021\n",
        "\n",
        "V3 on 16NOV2021\n",
        "\n",
        "V4 on 29NOV2021\n",
        "\n",
        "V5, Added random seed control.  \n",
        "\n",
        "V6 Stable and used for experiments. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Qzt5H2PTHNR"
      },
      "source": [
        "## Google Drive File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvOh3OwyWelW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b87a33cc-2e8a-4c94-9847-fd7b3f6d1550"
      },
      "source": [
        "#https://pypi.org/project/PyDrive/\n",
        "import time\n",
        "start = time.time()\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "end = time.time()\n",
        "print(\"The time of execution of above program is :\", end-start)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The time of execution of above program is : 9.661772012710571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siFOuBXWWh7v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19d9b00d-2a72-4e8b-ae7d-0ff1f640ddeb"
      },
      "source": [
        "start = time.time() #to meausure how long it takes\n",
        "\n",
        "#authenticate users to have acces to google Drive.  Click the link to get the code.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "end = time.time()\n",
        "print(\"The time of execution of above program is :\", end-start)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The time of execution of above program is : 15.447955131530762\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5y6FdYsS-xa"
      },
      "source": [
        "## Library Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JMF6jYsVwsR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "217651d3-2e75-4bc2-d654-87ae3e2ec5bf"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "#It is a function that renders the figure in a notebook (instead of displaying a dump of the figure object).\n",
        "%matplotlib inline\n",
        "# To remove the scientific notation from numpy arrays\n",
        "#np.set_printoptions(suppress=True)\n",
        "\n",
        "#Setting Seed for reproducable results (important to have tensorflow random seed set as well)\n",
        "#https://datascience.stackexchange.com/questions/13314/causes-of-inconsistent-results-with-neural-network\n",
        "np.random.seed(1)\n",
        "\n",
        "# package for descriptive statistics\n",
        "!pip install researchpy\n",
        "import researchpy as rp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting researchpy\n",
            "  Downloading researchpy-0.3.2-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: patsy in /usr/local/lib/python3.7/dist-packages (from researchpy) (0.5.2)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from researchpy) (0.10.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from researchpy) (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from researchpy) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from researchpy) (1.4.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->researchpy) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->researchpy) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->researchpy) (1.15.0)\n",
            "Installing collected packages: researchpy\n",
            "Successfully installed researchpy-0.3.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TEqf6-GWtAe"
      },
      "source": [
        "#Get the file\n",
        "##data wrangled outside this Python notebook via Alteryx Designer. \n",
        "\n",
        "# Source: Mabilama, J. M. (2020). E-commerce - Users of a C2C fashion store. [online] Data.world. \n",
        "# Available at: https://data.world/jfreex/e-commerce-users-of-a-french-c2c-fashion-store \n",
        "# Target Variable made for transactions (items bought or sold)\n",
        "# outliers removed at 3 standard deviations above the mean\n",
        "# Target Variable made for transactions (items bought or sold)\n",
        "# outliers removed at 3 standard deviations above the mean\n",
        "#   \n",
        "#French Data Stratified Random Sample Jan 05, 2022 Random SEED == 47\n",
        "\"\"\"\n",
        "downloaded = drive.CreateFile({'id':'1BRlzi6jETpw66sDGB5Dhf53CkLp1zPtu'}) # replace the id with id of file you want to access\n",
        "downloaded.GetContentFile('FrenchC2Train1.csv') \n",
        "downloaded = drive.CreateFile({'id':'1P-Z4qCWjuOyEOmoXRhZI6TpTnzNaPukL'}) # replace the id with id of file you want to access\n",
        "downloaded.GetContentFile('FrenchC2CTEST1.csv')\n",
        "\"\"\"\n",
        "#French Data Stratified Random Sample Jan 10, 2022 Random SEED == 67\n",
        "downloaded = drive.CreateFile({'id':'1mOjfbmr7Vk3YqwpdbKlvfnXfMp1A8gJb'}) # replace the id with id of file you want to access\n",
        "downloaded.GetContentFile('FrenchC2Train2.csv') \n",
        "downloaded = drive.CreateFile({'id':'1jVMKvdJVswMJj9S9wqSdWdoYw9IF_-XH'}) # replace the id with id of file you want to access\n",
        "downloaded.GetContentFile('FrenchC2CTEST2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5mbIzCpXRg0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2cb1f42-71c2-40a1-e0e3-920b9450672a"
      },
      "source": [
        "## read the training file\n",
        "\n",
        "dfTrain = pd.read_csv('FrenchC2Train2.csv') \n",
        "print(\"Training Data: \",dfTrain.shape)\n",
        "\n",
        "## read the TEST file\n",
        "dfTEST = pd.read_csv(\"FrenchC2CTEST2.csv\")\n",
        "print(\"Testing Data: \",dfTEST.shape)\n",
        "\n",
        "## show a sample of n records\n",
        "#dfTrain.sample(10)\n",
        "#dfTEST.sample(10)\n",
        "del dfTrain['RecordID']  #\"RecordID\" included in dataset but removed because not variable in the analysis\n",
        "del dfTEST['RecordID']   #\"RecordID\" included in dataset but removed because not variable in the analysis\n",
        "## show the top 5 for Train\n",
        "print(\"\\n Training Data \\n\")\n",
        "print(dfTrain.head())\n",
        "#Displace the varaible types for Train\n",
        "print(dfTrain.dtypes)\n",
        "print(dfTrain.shape)\n",
        "## show the top 5 for Testing\n",
        "print(\"\\n Testing Data \\n\")\n",
        "print(dfTEST.head())\n",
        "#Displace the varaible types for Testing\n",
        "print(dfTEST.dtypes)\n",
        "print(dfTEST.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data:  (68976, 20)\n",
            "Testing Data:  (29562, 20)\n",
            "\n",
            " Training Data \n",
            "\n",
            "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
            "0            0.000947             0.0               0.0             0.0   \n",
            "1            0.000947             0.0               0.0             0.0   \n",
            "2            0.000936             0.0               0.0             0.0   \n",
            "3            0.000947             0.0               0.0             0.0   \n",
            "4            0.000947             0.0               0.0             0.0   \n",
            "\n",
            "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
            "0                1.0                0.0          0.06015                  0.0   \n",
            "1                1.0                0.0          0.06015                  0.0   \n",
            "2                1.0                0.0          0.06015                  0.0   \n",
            "3                1.0                0.0          0.06015                  0.0   \n",
            "4                1.0                0.0          0.06015                  0.0   \n",
            "\n",
            "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
            "0            0            1            0            0            0          0   \n",
            "1            0            0            0            1            0          1   \n",
            "2            0            0            0            1            0          1   \n",
            "3            0            1            0            0            0          0   \n",
            "4            0            1            0            0            0          0   \n",
            "\n",
            "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  transact  \n",
            "0              0          0                  1            1         0  \n",
            "1              1          0                  1            1         0  \n",
            "2              1          0                  1            1         0  \n",
            "3              0          0                  1            1         0  \n",
            "4              0          0                  1            1         0  \n",
            "daysSinceLastLogin     float64\n",
            "productsListed         float64\n",
            "productsPassRate       float64\n",
            "productsWished         float64\n",
            "seniorityAsMonths      float64\n",
            "socialNbFollowers      float64\n",
            "socialNbFollows        float64\n",
            "socialProductsLiked    float64\n",
            "language_de              int64\n",
            "language_en              int64\n",
            "language_es              int64\n",
            "language_fr              int64\n",
            "language_it              int64\n",
            "hasAnyApp                int64\n",
            "hasAndroidApp            int64\n",
            "hasIosApp                int64\n",
            "hasProfilePicture        int64\n",
            "Gender_Byte              int64\n",
            "transact                 int64\n",
            "dtype: object\n",
            "(68976, 19)\n",
            "\n",
            " Testing Data \n",
            "\n",
            "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
            "0            0.000947             0.0               0.0             0.0   \n",
            "1            0.000947             0.0               0.0             0.0   \n",
            "2            0.000947             0.0               0.0             0.0   \n",
            "3            0.000787             0.0               0.0             0.0   \n",
            "4            0.000947             0.0               0.0             0.0   \n",
            "\n",
            "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
            "0                1.0                0.0          0.06015             0.000000   \n",
            "1                1.0                0.0          0.06015             0.000000   \n",
            "2                1.0                0.0          0.06015             0.000000   \n",
            "3                1.0                0.0          0.06015             0.000783   \n",
            "4                1.0                0.0          0.06015             0.000000   \n",
            "\n",
            "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
            "0            0            1            0            0            0          0   \n",
            "1            1            0            0            0            0          0   \n",
            "2            0            1            0            0            0          0   \n",
            "3            0            1            0            0            0          0   \n",
            "4            0            1            0            0            0          0   \n",
            "\n",
            "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  transact  \n",
            "0              0          0                  1            1         0  \n",
            "1              0          0                  1            1         0  \n",
            "2              0          0                  1            1         0  \n",
            "3              0          0                  1            1         0  \n",
            "4              0          0                  1            1         0  \n",
            "daysSinceLastLogin     float64\n",
            "productsListed         float64\n",
            "productsPassRate       float64\n",
            "productsWished         float64\n",
            "seniorityAsMonths      float64\n",
            "socialNbFollowers      float64\n",
            "socialNbFollows        float64\n",
            "socialProductsLiked    float64\n",
            "language_de              int64\n",
            "language_en              int64\n",
            "language_es              int64\n",
            "language_fr              int64\n",
            "language_it              int64\n",
            "hasAnyApp                int64\n",
            "hasAndroidApp            int64\n",
            "hasIosApp                int64\n",
            "hasProfilePicture        int64\n",
            "Gender_Byte              int64\n",
            "transact                 int64\n",
            "dtype: object\n",
            "(29562, 19)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AO96LUMjbHNc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58e65bc1-8cf9-40b9-81af-1529a89172c5"
      },
      "source": [
        "#install fairlearn\n",
        "#if installing on a local IDE (e.g., Jupyter), do not use the exclamation point \n",
        "!pip install fairlearn\n",
        "#install modeling packages\n",
        "!pip install tensorflow\n",
        "!pip install -U scikit-learn\n",
        "!pip install keras\n",
        "import sklearn as sk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fairlearn\n",
            "  Downloading fairlearn-0.7.0-py3-none-any.whl (177 kB)\n",
            "\u001b[K     |████████████████████████████████| 177 kB 7.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from fairlearn) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.7/dist-packages (from fairlearn) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from fairlearn) (1.21.6)\n",
            "Requirement already satisfied: pandas>=0.25.1 in /usr/local/lib/python3.7/dist-packages (from fairlearn) (1.3.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.1->fairlearn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.1->fairlearn) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.1->fairlearn) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.1->fairlearn) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.1->fairlearn) (1.1.0)\n",
            "Installing collected packages: fairlearn\n",
            "Successfully installed fairlearn-0.7.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.8.0+zzzcolab20220506162203)\n",
            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
            "\u001b[K     |████████████████████████████████| 462 kB 8.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.17.3)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.46.1)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.5.3)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.1.0)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (14.0.1)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.15.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.21.6)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (4.2.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.26.0)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.0.0)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.0)\n",
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            "Installing collected packages: tf-estimator-nightly\n",
            "Successfully installed tf-estimator-nightly-2.8.0.dev2021122109\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xQVEQVbTTSV"
      },
      "source": [
        "## Data File Wrangle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Na3Wv079C3h"
      },
      "source": [
        "# Separate Target Variable and Predictor Variables\n",
        "# define variable to class: Churn\n",
        "#\"transact\" is a dictomous variable used to identify customers who stop using the company's services\n",
        "TargetVariable=[\"transact\"]\n",
        "#it is VERY important that the list of variables matches the left to right order in the CSVs!\n",
        "PredictorVariables=[\"daysSinceLastLogin\",\"productsListed\",\"productsPassRate\", \"productsWished\", \"seniorityAsMonths\",\n",
        "                    \"socialNbFollowers\",\"socialNbFollows\",\"socialProductsLiked\",\"language_de\", \"language_en\",\n",
        "                    \"language_es\", \"language_fr\",\"language_it\", \"hasAnyApp\",\"hasAndroidApp\",\"hasIosApp\", \"hasProfilePicture\",\"Gender_Byte\"]\n",
        "#NumericalPredictorVariables=[\"tenure\",\"MonthlyCharges\",\"TotalCharges\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfV9759On6A5"
      },
      "source": [
        "## Descriptive Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KspXUV998uWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "28af0509-be92-45ef-a2a9-e29bb0d99fe6"
      },
      "source": [
        "print(\"dfTrain Predictor Variables\")\n",
        "rp.summary_cont(dfTrain[PredictorVariables])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dfTrain Predictor Variables\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Variable        N    Mean      SD      SE  95% Conf.  Interval\n",
              "0    daysSinceLastLogin  68976.0  0.0009  0.0108  0.0000     0.0008    0.0010\n",
              "1        productsListed  68976.0  0.0028  0.0322  0.0001     0.0026    0.0031\n",
              "2      productsPassRate  68976.0  0.0057  0.0717  0.0003     0.0052    0.0063\n",
              "3        productsWished  68976.0  0.0007  0.0097  0.0000     0.0006    0.0008\n",
              "4     seniorityAsMonths  68976.0  0.6007  0.4769  0.0018     0.5972    0.6043\n",
              "5     socialNbFollowers  68976.0  0.0073  0.0346  0.0001     0.0070    0.0075\n",
              "6       socialNbFollows  68976.0  0.0609  0.0102  0.0000     0.0608    0.0609\n",
              "7   socialProductsLiked  68976.0  0.0006  0.0071  0.0000     0.0005    0.0006\n",
              "8           language_de  68976.0  0.0726  0.2595  0.0010     0.0707    0.0745\n",
              "9           language_en  68976.0  0.5217  0.4995  0.0019     0.5180    0.5255\n",
              "10          language_es  68976.0  0.0609  0.2391  0.0009     0.0591    0.0626\n",
              "11          language_fr  68976.0  0.2677  0.4427  0.0017     0.2644    0.2710\n",
              "12          language_it  68976.0  0.0771  0.2668  0.0010     0.0751    0.0791\n",
              "13            hasAnyApp  68976.0  0.2625  0.4400  0.0017     0.2592    0.2658\n",
              "14        hasAndroidApp  68976.0  0.0490  0.2158  0.0008     0.0474    0.0506\n",
              "15            hasIosApp  68976.0  0.2152  0.4110  0.0016     0.2121    0.2183\n",
              "16    hasProfilePicture  68976.0  0.9826  0.1306  0.0005     0.9817    0.9836\n",
              "17          Gender_Byte  68976.0  0.7696  0.4211  0.0016     0.7664    0.7727"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95f459c6-9104-4437-8bcf-01821951c3f3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>N</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>SE</th>\n",
              "      <th>95% Conf.</th>\n",
              "      <th>Interval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>daysSinceLastLogin</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0009</td>\n",
              "      <td>0.0108</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>productsListed</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0028</td>\n",
              "      <td>0.0322</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.0026</td>\n",
              "      <td>0.0031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>productsPassRate</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0057</td>\n",
              "      <td>0.0717</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.0052</td>\n",
              "      <td>0.0063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>productsWished</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.0097</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>seniorityAsMonths</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.6007</td>\n",
              "      <td>0.4769</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>0.5972</td>\n",
              "      <td>0.6043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>socialNbFollowers</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0073</td>\n",
              "      <td>0.0346</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.0070</td>\n",
              "      <td>0.0075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>socialNbFollows</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0609</td>\n",
              "      <td>0.0102</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0608</td>\n",
              "      <td>0.0609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>socialProductsLiked</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>language_de</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0726</td>\n",
              "      <td>0.2595</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.0707</td>\n",
              "      <td>0.0745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>language_en</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.5217</td>\n",
              "      <td>0.4995</td>\n",
              "      <td>0.0019</td>\n",
              "      <td>0.5180</td>\n",
              "      <td>0.5255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>language_es</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0609</td>\n",
              "      <td>0.2391</td>\n",
              "      <td>0.0009</td>\n",
              "      <td>0.0591</td>\n",
              "      <td>0.0626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>language_fr</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.2677</td>\n",
              "      <td>0.4427</td>\n",
              "      <td>0.0017</td>\n",
              "      <td>0.2644</td>\n",
              "      <td>0.2710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>language_it</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0771</td>\n",
              "      <td>0.2668</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.0751</td>\n",
              "      <td>0.0791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>hasAnyApp</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.2625</td>\n",
              "      <td>0.4400</td>\n",
              "      <td>0.0017</td>\n",
              "      <td>0.2592</td>\n",
              "      <td>0.2658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>hasAndroidApp</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0490</td>\n",
              "      <td>0.2158</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>0.0506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>hasIosApp</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.2152</td>\n",
              "      <td>0.4110</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>0.2121</td>\n",
              "      <td>0.2183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>hasProfilePicture</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.9826</td>\n",
              "      <td>0.1306</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.9817</td>\n",
              "      <td>0.9836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Gender_Byte</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.7696</td>\n",
              "      <td>0.4211</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>0.7664</td>\n",
              "      <td>0.7727</td>\n",
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              "</table>\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A93rGcaxqGnI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "9b0952ca-8a30-4084-b94a-d3ef196303da"
      },
      "source": [
        "print(\"dfTrain Target Variable\")\n",
        "rp.summary_cont(dfTrain[TargetVariable])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dfTrain Target Variable\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Variable        N    Mean      SD      SE  95% Conf.  Interval\n",
              "0  transact  68976.0  0.0654  0.2472  0.0009     0.0636    0.0672"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a098b183-9d5a-456c-911b-4bd0a369c58a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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>Variable</th>\n",
              "      <th>N</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>SE</th>\n",
              "      <th>95% Conf.</th>\n",
              "      <th>Interval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>transact</td>\n",
              "      <td>68976.0</td>\n",
              "      <td>0.0654</td>\n",
              "      <td>0.2472</td>\n",
              "      <td>0.0009</td>\n",
              "      <td>0.0636</td>\n",
              "      <td>0.0672</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a098b183-9d5a-456c-911b-4bd0a369c58a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a098b183-9d5a-456c-911b-4bd0a369c58a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a098b183-9d5a-456c-911b-4bd0a369c58a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIs_80o78gSX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "eb31fa51-2a41-4f46-a265-476794419d64"
      },
      "source": [
        "print(\"dfTEST Predictor Variables\")\n",
        "rp.summary_cont(dfTEST[PredictorVariables])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dfTEST Predictor Variables\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Variable        N    Mean      SD      SE  95% Conf.  Interval\n",
              "0    daysSinceLastLogin  29562.0  0.0008  0.0082  0.0000     0.0008    0.0009\n",
              "1        productsListed  29562.0  0.0032  0.0359  0.0002     0.0028    0.0037\n",
              "2      productsPassRate  29562.0  0.0061  0.0739  0.0004     0.0053    0.0070\n",
              "3        productsWished  29562.0  0.0008  0.0100  0.0001     0.0006    0.0009\n",
              "4     seniorityAsMonths  29562.0  0.5982  0.4774  0.0028     0.5927    0.6036\n",
              "5     socialNbFollowers  29562.0  0.0081  0.0388  0.0002     0.0076    0.0085\n",
              "6       socialNbFollows  29562.0  0.0608  0.0080  0.0000     0.0607    0.0609\n",
              "7   socialProductsLiked  29562.0  0.0006  0.0065  0.0000     0.0005    0.0007\n",
              "8           language_de  29562.0  0.0727  0.2596  0.0015     0.0697    0.0757\n",
              "9           language_en  29562.0  0.5214  0.4995  0.0029     0.5157    0.5271\n",
              "10          language_es  29562.0  0.0612  0.2398  0.0014     0.0585    0.0640\n",
              "11          language_fr  29562.0  0.2647  0.4412  0.0026     0.2597    0.2697\n",
              "12          language_it  29562.0  0.0800  0.2712  0.0016     0.0769    0.0831\n",
              "13            hasAnyApp  29562.0  0.2642  0.4409  0.0026     0.2592    0.2693\n",
              "14        hasAndroidApp  29562.0  0.0471  0.2118  0.0012     0.0447    0.0495\n",
              "15            hasIosApp  29562.0  0.2185  0.4133  0.0024     0.2138    0.2232\n",
              "16    hasProfilePicture  29562.0  0.9818  0.1337  0.0008     0.9803    0.9833\n",
              "17          Gender_Byte  29562.0  0.7696  0.4211  0.0024     0.7648    0.7744"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b43dd7a-458c-4af8-b1b9-91f213f6488c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Variable</th>\n",
              "      <th>N</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>SE</th>\n",
              "      <th>95% Conf.</th>\n",
              "      <th>Interval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>daysSinceLastLogin</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0082</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>productsListed</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0032</td>\n",
              "      <td>0.0359</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>0.0028</td>\n",
              "      <td>0.0037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>productsPassRate</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.0739</td>\n",
              "      <td>0.0004</td>\n",
              "      <td>0.0053</td>\n",
              "      <td>0.0070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>productsWished</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>seniorityAsMonths</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.5982</td>\n",
              "      <td>0.4774</td>\n",
              "      <td>0.0028</td>\n",
              "      <td>0.5927</td>\n",
              "      <td>0.6036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>socialNbFollowers</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0081</td>\n",
              "      <td>0.0388</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>0.0076</td>\n",
              "      <td>0.0085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>socialNbFollows</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0608</td>\n",
              "      <td>0.0080</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0607</td>\n",
              "      <td>0.0609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>socialProductsLiked</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>language_de</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0727</td>\n",
              "      <td>0.2596</td>\n",
              "      <td>0.0015</td>\n",
              "      <td>0.0697</td>\n",
              "      <td>0.0757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>language_en</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.5214</td>\n",
              "      <td>0.4995</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>0.5157</td>\n",
              "      <td>0.5271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>language_es</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0612</td>\n",
              "      <td>0.2398</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.0585</td>\n",
              "      <td>0.0640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>language_fr</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.2647</td>\n",
              "      <td>0.4412</td>\n",
              "      <td>0.0026</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.2697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>language_it</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0800</td>\n",
              "      <td>0.2712</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>0.0769</td>\n",
              "      <td>0.0831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>hasAnyApp</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.2642</td>\n",
              "      <td>0.4409</td>\n",
              "      <td>0.0026</td>\n",
              "      <td>0.2592</td>\n",
              "      <td>0.2693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>hasAndroidApp</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.0471</td>\n",
              "      <td>0.2118</td>\n",
              "      <td>0.0012</td>\n",
              "      <td>0.0447</td>\n",
              "      <td>0.0495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>hasIosApp</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.2185</td>\n",
              "      <td>0.4133</td>\n",
              "      <td>0.0024</td>\n",
              "      <td>0.2138</td>\n",
              "      <td>0.2232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>hasProfilePicture</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.9818</td>\n",
              "      <td>0.1337</td>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.9803</td>\n",
              "      <td>0.9833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Gender_Byte</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.7696</td>\n",
              "      <td>0.4211</td>\n",
              "      <td>0.0024</td>\n",
              "      <td>0.7648</td>\n",
              "      <td>0.7744</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b43dd7a-458c-4af8-b1b9-91f213f6488c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b43dd7a-458c-4af8-b1b9-91f213f6488c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b43dd7a-458c-4af8-b1b9-91f213f6488c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxk8AFwMpV-9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "451787db-4502-4262-af88-4369ff9c43ac"
      },
      "source": [
        "print(\"dfTEST Target Variable\")\n",
        "rp.summary_cont(dfTEST[TargetVariable])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dfTEST Target Variable\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Variable        N   Mean      SD      SE  95% Conf.  Interval\n",
              "0  transact  29562.0  0.068  0.2518  0.0015     0.0652    0.0709"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b6b9075-0150-446e-ae07-94978a086044\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Variable</th>\n",
              "      <th>N</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>SE</th>\n",
              "      <th>95% Conf.</th>\n",
              "      <th>Interval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>transact</td>\n",
              "      <td>29562.0</td>\n",
              "      <td>0.068</td>\n",
              "      <td>0.2518</td>\n",
              "      <td>0.0015</td>\n",
              "      <td>0.0652</td>\n",
              "      <td>0.0709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b6b9075-0150-446e-ae07-94978a086044')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b6b9075-0150-446e-ae07-94978a086044 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b6b9075-0150-446e-ae07-94978a086044');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo68CbqeEkAr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "936c8ce2-3c5d-449e-acfc-227168f486c7"
      },
      "source": [
        "\"\"\"Normally, random samples using 'from sklearn.model_selection import train_test_split'\n",
        "identify training and testing data but, for repeatablility,\n",
        "a static random stratified sample was made.\"\"\"\n",
        "#Training Data for X and y\n",
        "X_train = dfTrain.drop('transact',axis='columns')\n",
        "print(\"X_Train Shape:\\n\",X_train.shape, \"\\n\")\n",
        "print(\"X_train head:\\n\")\n",
        "print(X_train.head())\n",
        "y_train = dfTrain['transact']\n",
        "print(\"y_train shape: \\n\",y_train.shape, \"\\n\")\n",
        "print(\"y_train head:\\n\")\n",
        "print(y_train.head())\n",
        "\n",
        "# Testing Data for X and y\n",
        "X_test = dfTEST.drop('transact',axis='columns')\n",
        "print(\"\\nX_test shape: \\n\",X_test.shape,\"\\n\")\n",
        "print(\"X_test head:\\n\")\n",
        "print(X_test.head())\n",
        "\n",
        "y_test = dfTEST['transact']\n",
        "print(\"y_test Shape:\",y_test.shape,\"\\n\")\n",
        "print(\"y_test Head:\\n\")\n",
        "print(y_test.head())\n",
        "\n",
        "# Check shapes of training and testing datasets\n",
        "assert(X_train.shape==(68976, 18))\n",
        "assert(X_test.shape == (29562, 18))\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "\n",
        "#training data are rank one arrays that need to be reshaped \n",
        "y_train=y_train.values.reshape((68976,1))\n",
        "y_test=y_test.values.reshape((29562,1))\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_Train Shape:\n",
            " (68976, 18) \n",
            "\n",
            "X_train head:\n",
            "\n",
            "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
            "0            0.000947             0.0               0.0             0.0   \n",
            "1            0.000947             0.0               0.0             0.0   \n",
            "2            0.000936             0.0               0.0             0.0   \n",
            "3            0.000947             0.0               0.0             0.0   \n",
            "4            0.000947             0.0               0.0             0.0   \n",
            "\n",
            "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
            "0                1.0                0.0          0.06015                  0.0   \n",
            "1                1.0                0.0          0.06015                  0.0   \n",
            "2                1.0                0.0          0.06015                  0.0   \n",
            "3                1.0                0.0          0.06015                  0.0   \n",
            "4                1.0                0.0          0.06015                  0.0   \n",
            "\n",
            "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
            "0            0            1            0            0            0          0   \n",
            "1            0            0            0            1            0          1   \n",
            "2            0            0            0            1            0          1   \n",
            "3            0            1            0            0            0          0   \n",
            "4            0            1            0            0            0          0   \n",
            "\n",
            "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  \n",
            "0              0          0                  1            1  \n",
            "1              1          0                  1            1  \n",
            "2              1          0                  1            1  \n",
            "3              0          0                  1            1  \n",
            "4              0          0                  1            1  \n",
            "y_train shape: \n",
            " (68976,) \n",
            "\n",
            "y_train head:\n",
            "\n",
            "0    0\n",
            "1    0\n",
            "2    0\n",
            "3    0\n",
            "4    0\n",
            "Name: transact, dtype: int64\n",
            "\n",
            "X_test shape: \n",
            " (29562, 18) \n",
            "\n",
            "X_test head:\n",
            "\n",
            "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
            "0            0.000947             0.0               0.0             0.0   \n",
            "1            0.000947             0.0               0.0             0.0   \n",
            "2            0.000947             0.0               0.0             0.0   \n",
            "3            0.000787             0.0               0.0             0.0   \n",
            "4            0.000947             0.0               0.0             0.0   \n",
            "\n",
            "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
            "0                1.0                0.0          0.06015             0.000000   \n",
            "1                1.0                0.0          0.06015             0.000000   \n",
            "2                1.0                0.0          0.06015             0.000000   \n",
            "3                1.0                0.0          0.06015             0.000783   \n",
            "4                1.0                0.0          0.06015             0.000000   \n",
            "\n",
            "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
            "0            0            1            0            0            0          0   \n",
            "1            1            0            0            0            0          0   \n",
            "2            0            1            0            0            0          0   \n",
            "3            0            1            0            0            0          0   \n",
            "4            0            1            0            0            0          0   \n",
            "\n",
            "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  \n",
            "0              0          0                  1            1  \n",
            "1              0          0                  1            1  \n",
            "2              0          0                  1            1  \n",
            "3              0          0                  1            1  \n",
            "4              0          0                  1            1  \n",
            "y_test Shape: (29562,) \n",
            "\n",
            "y_test Head:\n",
            "\n",
            "0    0\n",
            "1    0\n",
            "2    0\n",
            "3    0\n",
            "4    0\n",
            "Name: transact, dtype: int64\n",
            "(68976, 18)\n",
            "(29562, 18)\n",
            "(68976, 1)\n",
            "(29562, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us6CL1lTz09M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "9543baa7-4de0-49a4-a6e2-6d0e69a3d581"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "X_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
              "0            0.000947             0.0               0.0             0.0   \n",
              "1            0.000947             0.0               0.0             0.0   \n",
              "2            0.000947             0.0               0.0             0.0   \n",
              "3            0.000787             0.0               0.0             0.0   \n",
              "4            0.000947             0.0               0.0             0.0   \n",
              "\n",
              "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
              "0                1.0                0.0          0.06015             0.000000   \n",
              "1                1.0                0.0          0.06015             0.000000   \n",
              "2                1.0                0.0          0.06015             0.000000   \n",
              "3                1.0                0.0          0.06015             0.000783   \n",
              "4                1.0                0.0          0.06015             0.000000   \n",
              "\n",
              "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
              "0            0            1            0            0            0          0   \n",
              "1            1            0            0            0            0          0   \n",
              "2            0            1            0            0            0          0   \n",
              "3            0            1            0            0            0          0   \n",
              "4            0            1            0            0            0          0   \n",
              "\n",
              "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  \n",
              "0              0          0                  1            1  \n",
              "1              0          0                  1            1  \n",
              "2              0          0                  1            1  \n",
              "3              0          0                  1            1  \n",
              "4              0          0                  1            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7181a943-c953-4afa-a128-0514a9fd56fd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.06015</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</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>0.06015</td>\n",
              "      <td>0.000783</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>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7181a943-c953-4afa-a128-0514a9fd56fd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7181a943-c953-4afa-a128-0514a9fd56fd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7181a943-c953-4afa-a128-0514a9fd56fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5saHxAe27CsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "9243a9fd-9b2b-40a2-ef60-23cf4cc21b36"
      },
      "source": [
        "print(\"\\n X_train head: \\n\")\n",
        "X_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_train head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   daysSinceLastLogin  productsListed  productsPassRate  productsWished  \\\n",
              "0            0.000947             0.0               0.0             0.0   \n",
              "1            0.000947             0.0               0.0             0.0   \n",
              "2            0.000936             0.0               0.0             0.0   \n",
              "3            0.000947             0.0               0.0             0.0   \n",
              "4            0.000947             0.0               0.0             0.0   \n",
              "\n",
              "   seniorityAsMonths  socialNbFollowers  socialNbFollows  socialProductsLiked  \\\n",
              "0                1.0                0.0          0.06015                  0.0   \n",
              "1                1.0                0.0          0.06015                  0.0   \n",
              "2                1.0                0.0          0.06015                  0.0   \n",
              "3                1.0                0.0          0.06015                  0.0   \n",
              "4                1.0                0.0          0.06015                  0.0   \n",
              "\n",
              "   language_de  language_en  language_es  language_fr  language_it  hasAnyApp  \\\n",
              "0            0            1            0            0            0          0   \n",
              "1            0            0            0            1            0          1   \n",
              "2            0            0            0            1            0          1   \n",
              "3            0            1            0            0            0          0   \n",
              "4            0            1            0            0            0          0   \n",
              "\n",
              "   hasAndroidApp  hasIosApp  hasProfilePicture  Gender_Byte  \n",
              "0              0          0                  1            1  \n",
              "1              1          0                  1            1  \n",
              "2              1          0                  1            1  \n",
              "3              0          0                  1            1  \n",
              "4              0          0                  1            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b95b9233-1232-4eb8-9748-fc4a72879745\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</td>\n",
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              "    <tr>\n",
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              "      <td>0.000947</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000936</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>0.06015</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b95b9233-1232-4eb8-9748-fc4a72879745')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b95b9233-1232-4eb8-9748-fc4a72879745 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b95b9233-1232-4eb8-9748-fc4a72879745');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\n",
        "#Correlation Matrix\n",
        "#This is currently run on only the X variables but should include the whole dataset (i.e., include the Y variable...the dependent variable)\n",
        "start = time.time() #to meausure how long it takes\n",
        "#this takes a long time....consider commenting out once you see the results.\n",
        "sns.pairplot(data=X_train, hue=\"Gender_Byte\")\n",
        "plt.show() \n",
        "\n",
        "end = time.time()\n",
        "print(\"\\nThe time of execution of above program is :\", end-start,\"\\n\")\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "s6JKO8JpR9zz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "e9b98b97-9883-412e-cfc2-009bb3e5a50c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\n#Correlation Matrix\\n#This is currently run on only the X variables but should include the whole dataset (i.e., include the Y variable...the dependent variable)\\nstart = time.time() #to meausure how long it takes\\n#this takes a long time....consider commenting out once you see the results.\\nsns.pairplot(data=X_train, hue=\"Gender_Byte\")\\nplt.show() \\n\\nend = time.time()\\nprint(\"\\nThe time of execution of above program is :\", end-start,\"\\n\")\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezvTm7-YoDoM"
      },
      "source": [
        "# Activation Function Bias Analysis with FairLearn\n",
        "The Prupose of this code is to assess the technical bias resulting from the \n",
        "activation function used in the **hidden** layer of a Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RY5OvhNtBPYi"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l48i--sEBMXr"
      },
      "source": [
        "#Modeling\n",
        "import tensorflow as tf\n",
        "#\n",
        "# https://www.tensorflow.org/api_docs/python/tf/random/set_seed\n",
        "# Set the random seed for repeatable results (important to have numpy random seed set as well)\n",
        "tf.random.set_seed(1)\n",
        "from tensorflow import keras\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from fairlearn.metrics import MetricFrame\n",
        "from fairlearn.metrics import selection_rate,false_positive_rate,true_positive_rate,count\n",
        "\n",
        "#Identifying the initializer used in each layer; seed set to 4 to a Python integer. \n",
        "# \"An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.\" \n",
        "# https://keras.io/api/layers/initializers/#randomnormal-class\n",
        "initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1., seed=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNLh9cAiRx29"
      },
      "source": [
        "## Relu Activation Function\n",
        "rectified linear unit activation = ReLU\n",
        "\n",
        "https://keras.io/api/layers/activations/#relu-function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZVKwof-MZwN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "62b25385-f471-4632-8c88-653afb21e15b"
      },
      "source": [
        "# create ANN model https://thinkingneuron.com/how-to-use-artificial-neural-networks-for-classification-in-python/ \n",
        "### relu Activation function in all layers and sigmoid in output layer ###\n",
        "model_relu = Sequential()\n",
        "\n",
        "# Defining the Input layer and FIRST hidden layer, both are same!\n",
        "model_relu.add(Dense(19, input_shape=(18,), kernel_initializer=initializer, bias_initializer=\"zeros\", activation='relu'))\n",
        " \n",
        "# Defining the Second layer of the model\n",
        "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
        "model_relu.add(Dense(units=5, kernel_initializer=initializer, bias_initializer=\"zeros\", activation='relu'))\n",
        " \n",
        "# The output neuron is a single fully connected node \n",
        "#\n",
        "model_relu.add(Dense(1, kernel_initializer=initializer,activation='sigmoid'))\n",
        " \n",
        "# Compiling the model\n",
        "model_relu.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        " \n",
        "# Fitting the ANN to the Training set\n",
        "model_relu.fit(X_train, y_train, batch_size = 256, epochs = 30, verbose=1)\n",
        "\n",
        "# Generating Predictions on testing data\n",
        "Predictions_relu=model_relu.predict(X_test)\n",
        "\n",
        "TestingData=pd.DataFrame(data=X_test.values, columns=PredictorVariables)\n",
        "#TestingData.insert(len(df.columns), 'Churn', y_test.values)\n",
        "#TestingData.assign(Churn=y_test)\n",
        "TestingData[\"transact\"]=y_test\n",
        "TestingData['PredictedtransactRelu']=Predictions_relu\n",
        "\n",
        "#transform Predicted transact from decimal to 0 or 1 for Confusion Matrix & insert into Fairlearn\n",
        "TestingData[\"PredictedtransactReluClass\"]= np.where(TestingData['PredictedtransactRelu']>=0.5, 1, 0)\n",
        "#print(y_test[:10])\n",
        "print(y_test.dtype)\n",
        "print(TestingData.sample(11))\n",
        "model_relu.evaluate(X_test, y_test)\n",
        "\n",
        "#Fairlearn https://fairlearn.org/v0.7.0/quickstart.html\n",
        "gm_relu = MetricFrame(metrics=accuracy_score, y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactReluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "print(\"\\n\")\n",
        "print(\"Model score: \")\n",
        "print(gm_relu.overall)\n",
        "\n",
        "print(\"\\n\" \"Model score by category: \")\n",
        "print(gm_relu.by_group)\n",
        "print(' Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \\n')\n",
        "\n",
        "#FairLearn Metrics\n",
        "sr_relu = MetricFrame(metrics=selection_rate,y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactReluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "sr_relu.overall\n",
        "print(\"1.0==female, 0.0==male\")\n",
        "sr_relu.by_group\n",
        "#transform Sensative Varible from 0 or 1 to \"male\" or \"female\" for plotting\n",
        "TestingData[\"GenderClass\"]= np.where(TestingData['Gender_Byte']==1, \"female\",\"male\")\n",
        "#print(TestingData.head)\n",
        "metrics = {\n",
        "    'accuracy': accuracy_score,\n",
        "    'precision': precision_score,\n",
        "    'recall': recall_score,\n",
        "    'false positive rate': false_positive_rate,\n",
        "    'true positive rate': true_positive_rate,\n",
        "    'selection rate': selection_rate,\n",
        "    'count': count}\n",
        "metric_frame_relu = MetricFrame(metrics=metrics,\n",
        "                           y_true=TestingData['transact'],\n",
        "                           y_pred=TestingData['PredictedtransactReluClass'],\n",
        "                           sensitive_features=TestingData[\"GenderClass\"])\n",
        "metric_frame_relu.by_group.plot.bar(\n",
        "    subplots=True,\n",
        "    layout=[3, 3],\n",
        "    legend=False,\n",
        "    figsize=[12, 8],\n",
        "    title=\"Show all relu metrics\",\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 1.2121 - accuracy: 0.6804\n",
            "Epoch 2/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2928 - accuracy: 0.9297\n",
            "Epoch 3/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2618 - accuracy: 0.9392\n",
            "Epoch 4/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2456 - accuracy: 0.9399\n",
            "Epoch 5/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2320 - accuracy: 0.9401\n",
            "Epoch 6/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2223 - accuracy: 0.9404\n",
            "Epoch 7/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2161 - accuracy: 0.9405\n",
            "Epoch 8/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2100 - accuracy: 0.9409\n",
            "Epoch 9/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2051 - accuracy: 0.9414\n",
            "Epoch 10/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2024 - accuracy: 0.9419\n",
            "Epoch 11/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2004 - accuracy: 0.9421\n",
            "Epoch 12/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1989 - accuracy: 0.9423\n",
            "Epoch 13/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1976 - accuracy: 0.9427\n",
            "Epoch 14/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1965 - accuracy: 0.9432\n",
            "Epoch 15/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1957 - accuracy: 0.9433\n",
            "Epoch 16/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1948 - accuracy: 0.9435\n",
            "Epoch 17/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1940 - accuracy: 0.9436\n",
            "Epoch 18/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1932 - accuracy: 0.9435\n",
            "Epoch 19/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1927 - accuracy: 0.9436\n",
            "Epoch 20/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1923 - accuracy: 0.9436\n",
            "Epoch 21/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1917 - accuracy: 0.9438\n",
            "Epoch 22/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1912 - accuracy: 0.9437\n",
            "Epoch 23/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1909 - accuracy: 0.9438\n",
            "Epoch 24/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1903 - accuracy: 0.9440\n",
            "Epoch 25/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1902 - accuracy: 0.9439\n",
            "Epoch 26/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1896 - accuracy: 0.9439\n",
            "Epoch 27/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1896 - accuracy: 0.9437\n",
            "Epoch 28/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1892 - accuracy: 0.9440\n",
            "Epoch 29/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1889 - accuracy: 0.9437\n",
            "Epoch 30/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1882 - accuracy: 0.9439\n",
            "int64\n",
            "       daysSinceLastLogin  ...  PredictedtransactReluClass\n",
            "17011            0.000928  ...                           0\n",
            "24985            0.000947  ...                           0\n",
            "27025            0.000697  ...                           0\n",
            "18038            0.000802  ...                           0\n",
            "792              0.000422  ...                           0\n",
            "21966            0.000946  ...                           0\n",
            "21212            0.000909  ...                           0\n",
            "27156            0.000929  ...                           0\n",
            "28032            0.000837  ...                           0\n",
            "24406            0.000939  ...                           0\n",
            "29015            0.000923  ...                           0\n",
            "\n",
            "[11 rows x 21 columns]\n",
            "924/924 [==============================] - 1s 1ms/step - loss: 0.1948 - accuracy: 0.9422\n",
            "\n",
            "\n",
            "Model score: \n",
            "0.9422231242811717\n",
            "\n",
            "Model score by category: \n",
            "Gender_Byte\n",
            "0.0    0.932913\n",
            "1.0    0.945011\n",
            "Name: accuracy_score, dtype: object\n",
            " Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \n",
            "\n",
            "1.0==female, 0.0==male\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9ea6850>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9ddd650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9ea6490>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9d51c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9d0bf10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9cc5d90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9c8c1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9c42490>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a9c424d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7kuw0U7FL892MJNIT+Ca9R5LLk3y3fZt+xUzEJUnSFmB/YHVVramqB4ALgMN6K1TV5VX1y/b0CmBRmz4YuLSqNlTVncClwPIZilua96Y9kZ7gN+m3AxdV1bOAI4EPTXdckiRtIXYH1vY8X9fmjeYY4IuTWTbJsUlWJlm5fv36zQxXGhwzcUZ63G/SQAGPbdM7ALfNQFySJM0rSV4LLAPeN5nlqurMqlpWVcsWLlw4PcFJ89BMJNIT+TZ8KvDaJOuAi4H/OtKK/MYsSRpAtwKLe54vavMeJsnLgLcBh1bV/ZNZVlJ/5spgw6OAs6tqEfAK4LwkvxWb35glSQPoamCfJHsl2YauC+SK3gpJngV8hC6Jvr2n6BLgoCQ7tUGGB7V5kqbAVjOwjYl8Gz6GNvihqr6dZDtgV+B2JEkaYFW1MckJdAnwAuCsqlqV5DRgZVWtoOvK8RjgU0kAflxVh1bVhiTvpEvGAU6rqg2zsBuag/Y7Z7/ZDmFK/ODoH8zatmcikd70TZougT4SeM2wOj8GDgTOTvJUYDvAvhvSLEiyHPh7ugP2x6rq3SPU+X26LlkFfK+qhrdpSVOoqi6m6/rYO++UnumXjbHsWcBZ0xedNLimPZGe4DfpE4GPJvlvdAfm11dVTXdskh6u5yo7L6cbz3B1khVVdV1PnX2Ak4EXVNWdSR43O9FKkjS7ZuKM9ES+SV8HvGAmYpE0pk1X2QFIMnSVnet66rwJOKNdk5Zh/TElSRoYc2WwoaS5YSJX2Xky8OQk30xyResKIknSwJmRM9KS5pWtgH2AA+gGD389yX5VdVdvpSTHAscC7LHHHjMdoyRJ084z0pJ6TeQqO+uAFVX166q6Cfg3usT6YbxcpSRpvjORltRr3OvVAp+jOxtNkl3punqsmckgJUmaC0ykJW1SVRuBoavsXA9cNHSVnSSHtmqXAHckuQ64HPjzqrpjdiKWJGn22Eda0sNM4Co7BbylPSRJGliekZYkSZL6YCItSZIk9cFEWpIkSeqDibQkSZLUBxNpSZIkqQ8m0pIkSVIfTKQlSZKkPphIS5IkSX0wkZYkSZL6YCItSdIcl2R5khuSrE5y0gjlL07ynSQbkxw+rOzBJNe2x4qZi1qa/6Y9kR6v8bc6v5/kuiSrkpw/3TFJkrSlSLIAOAM4BFgKHJVk6bBqPwZeD4x0DP1VVT2zPQ6d1mClAbPVdK68p/G/HFgHXJ1kRVVd11NnH+Bk4AVVdWeSx01nTJIkbWH2B1ZX1RqAJBcAhwGbjqVVdXMre2g2ApQG1XSfkd7U+KvqAWCo8fd6E3BGVd0JUFW3T3NMkiRtSXYH1vY8X9fmTdR2SVYmuSLJq6c2NGmwTXciPZHG/2TgyUm+2Rr58mmOSZKkQbJnVS0DXgN8IMmThldIcmxLtleuX79+5iOUtlBzYbDhVsA+wAHAUcBHk+w4UkUbuiRpAN0KLO55vqjNm5CqurX9XQN8FXjWCHXOrKplVbVs4cKFmxetNECmO5GeSONfB6yoql9X1U3Av9El1r/Fhi5JGkBXA/sk2SvJNsCRwISuvpFkpyTbtuldgRfQ07da0uaZ7kR6Io3/c3Rno4ca+ZOBNdMclyRJW4Sq2gicAFwCXA9cVFWrkpyW5FCAJM9Nsg44AvhIklVt8acCK5N8D7gceHfvgH9Jm2dar9pRVRuTDDX+BcBZQ40fWFlVK1rZQUmuAx4E/ryq7pjOuCRJ2pJU1cXAxcPmndIzfTXdr77Dl/sWsN+0BygNqGlNpGFCjb+At7SHJEmStEWYC4MNJUmSpC2OibQkSZLUBxNpSZIkqQ8m0pIkSVIfTKQlPUyS5UluSLI6yUlj1Pu9JJVk2UzGJ0nSXGEiLWmTJAuAM4BDgKXAUUmWjlBve+DNwJUzG6EkSXOHibSkXvsDq6tqTVU9AFwAHDZCvXcC7wHum8ngJEmaS0ykJfXaHVjb83xdm7dJkmcDi6vqCzMZmCRJc42JtKQJS/II4O+AEydQ99gkK5OsXL9+/fQHJ0nSDDORltTrVmBxz/NFbd6Q7YGnAV9NcjPwfGDFSAMOq+rMqlpWVcsWLlw4jSFLkjQ7TKQl9boa2CfJXkm2AY4EVgwVVtXdVbVrVS2pqiXAFcChVbVydsKVJGn2mEhL2qSqNgInAJcA1wMXVdWqJKclOXR2o5MkaW7ZarYDkDS3VNXFwMXD5p0ySt0DZiImSZLmIs9IS5IkSX0wkZYkSZL6YCItSZIk9cFEWpKkOS7J8iQ3JFmd5KQRyl+c5DtJNiY5fFjZ0Ul+1B5Hz1zU0vw3I4n0eB8APfV+L0mNdE1aSZIGUZIFwBnAIcBS4KgkS4dV+zHweuD8YcvuDLwDeB6wP/COJDtNd8zSoJj2RHqCHwAk2R54M3DldMckSdIWZH9gdVWtqaoHgAuAw3orVNXNVfV94KFhyx4MXFpVG6rqTuBSYPlMBC0Ngpk4Iz3uB0DzTuA9wH0zEJMkSVuK3YG1Pc/XtXnTvaykccxEIj1uI07ybGBxVX1hBuKRJEk9khybZGWSlevXr5/tcKQtxqwPNkzyCODvgBMnUNeGLkkaNLcCi3ueL2rzpmzZqjqzqpZV1bKFCxf2Hag0aGYikR6vEW8PPA34apKbgecDK0YacGhDlyQNoKuBfZLslWQb4EhgxQSXvQQ4KMlObZDhQW2epCkwE4n0mB8AVXV3Ve1aVUuqaglwBXBoVa2cgdgkSZrTqmojcAJdAnw9cFFVrUpyWpJDAZI8N8k64AjgI0lWtWU30I1Buro9TmvzJE2BraZ7A1W1McnQB8AC4KyhDwBgZVVN9Fu1JEkDqaouBi4eNu+Unumr6X7xHWnZs4CzpjVAaUBNeyIN438ADJt/wEzEJEmSJG2OWR9sKEmSJG2JTKQlSZKkPphIS5IkSX0wkZYkSZL6YCItSZIk9cFEWpIkSeqDibQkSZLUBxNpSZIkqQ8m0pIkSVIfTKQlSZKkPphIS5IkSX0wkZb0MEmWJ7khyeokJ41Q/pYk1yX5fpJ/TrLnbMQpSdJsM5GWtEmSBcAZwCHAUuCoJEuHVfsusKyqng58GnjvzEYpSdLcYCItqdf+wOqqWlNVDwAXAIf1Vqiqy6vql+3pFcCiGY5RkqQ5wURaUq/dgbU9z9e1eaM5BvjiSAVJjk2yMsnK9evXT2GIkiTNDSbSkvqS5LXAMuB9I5VX1ZlVtayqli1cuHBmg5MkaQaYSEvqdSuwuOf5ojbvYZK8DHgbcGhV3T9DsUkDawKDgLdNcmErvzLJkjZ/SZJfJbm2PT4807FL89m0J9JeAUDaolwN7JNkryTbAEcCK3orJHkW8BG6JPr2WYhRGigTHAR8DHBnVe0NvB94T0/ZjVX1zPY4bkaClgbEtCbSXgFA2rJU1UbgBOAS4HrgoqpaleS0JIe2au8DHgN8qp3hWjHK6iRNjXEHAbfn57TpTwMHJskMxigNpK2mef2bGj9AkqHGf91Qhaq6vKf+FcBrpzkmSWOoqouBi4fNO6Vn+mUzHpQ02EYaBPy80epU1cYkdwO7tLK9knwXuAd4e1X9yzTHKw2M6U6kJ9L4e416BQBJkjRpPwH2qKo7kjwH+FySfavqnt5KSY4FjgXYY489ZiFMacs0ZwYbjncFgFbHy2lJkgbNRAYBb6qTZCtgB+COqrq/qu4AqKprgBuBJw/fgFfZkfoz3Yn0lF4BwIYuSRpA4w4Cbs+PbtOHA5dVVSVZ2MYrkeSJwD7AmhmKW5r3prtrx6bGT5dAHwm8prdCzxUAlnsFAEmSHq71eR4aBLwAOGtoEDCwsqpWAB8HzkuyGthAd7wFeDFwWpJfAw8Bx1XVhpnfC2l+mtZEeoKNv/cKAAA/rqpDR12pJEkDZgKDgO8Djhhhuc8An5n2AKUBNd1npL0CgCRJkualOTPYUJIkSdqSmEhLkiRJfTCRliRJkvpgIi1JkiT1wURakiRJ6oOJtCRJktQHE2lJkiSpDybSkiRJUh9MpCVJkqQ+mEhLkiRJfTCRliRJkvpgIi1JkiT1wURakiRJ6oOJtCRJktQHE2lJkiSpDybSkiRJUh9MpCVJkqQ+zEginWR5khuSrE5y0gjl2ya5sJVfmWTJTMQl6bfZXqW5Z3PaZZKT2/wbkhw8k3FL8920J9JJFgBnAIcAS4GjkiwdVu0Y4M6q2ht4P/Ce6Y5L0m+zvUpzz+a0y1bvSGBfYDnwobY+SVNgJs5I7w+srqo1VfUAcAFw2LA6hwHntOlPAwcmyQzEJunhbK/S3LM57fIw4IKqur+qbgJWt/VJmgJbzcA2dgfW9jxfBzxvtDpVtTHJ3cAuwM96KyU5Fji2Pb0vyappiXjm7MqwfZxqmf5zhTsAd0/7VqbXfPg/AOwzBeuwvY5u2t8n/PWMfB+xzY5nZv4Pk2mvm9MudweuGLbs7sM3MA/bK0zz+ySvt71O0PQfY6f/fzFqe52JRHrKVNWZwJkASc6sqmPHWWROS7KyqpbNdhybw//D3JHkzNmOoZftdW7yfzE32F6n33x5n2zp/4v58n8YrWwmunbcCizueb6ozRuxTpKt6L6B3THOev/fVAWozeL/Ye6Yiv+F7XX+838xN0zm/7A57XIiy25ObJpe/i/mhlH/DzORSF8N7JNkryTb0A16WDGszgrg6DZ9OHBZVdVYK60q31xzgP+HuWOK/he213nO/8XcMMn/w+a0yxXAke2qHnvR/UR91RTGpmnk/2JuGOv/MO1dO1pfrROAS4AFwFlVtSrJacDKqloBfBw4L8lqYAPdh8QgmFM/7Q0w/w+N7XVMvk/mjoH6X2xOu2z1LgKuAzYCx1fVg7OyIzNvoN4nc9i8/j9knBNJkiRJkkbgnQ0lSZKkPphIS5IkSX0wkZYkSZL6YCItSZIk9cFEegYleUqSa5Pcm+RPx6m7JEm164HOaUlWJTlgjPIvJjl6tHJJ0y/Jz5M8cYzyMduxpM50HZ+T7NHa6YKpXK+ml1ftmEFJPg7cU1X/bQJ1lwA3AVtX1cZpDm3KJDkV2LuqXjvbsQw3l2PT3JXkZuCNVfWV2Y5lqiQ5G1hXVW+f7ViGm8uxSTB1x+e5/Nni8XLiPCM9s/YEVs12EPPRlnDmXvOT772J87WSZp/tcIpVlY8ZeACXAQ8C9wE/B54MvBL4LnAPsBY4taf+EqCArdrz1wNrgHvpvgn/YU/d/wxcD9xJd8H+PUeJYWidxwK3AT8B3tpTvi3wgVZ2W5vetpXtCnweuIvuYv//Ajyild0MvAxYDjwA/Lrt4/da+VeBN7b13wU8rWebC4FfAY9rz18FXNvqfQt4+hivaQHHAz8Cbmrz/r69lvcA1wAvavNHi20HuhsZ/ITutrnvAhbM9vvFx9x4AOcBD7X36M+B/97Tjo4Bfgx8HTiA7ixq77I3Ay9r048ATgJupLtt80XAzqNs8wBgHfCXwM/aenrb+w7AucB64Bbg7T1tcW/ga8DdbdkLe5arVn5sawcPtH36f73xAru1/d25Z9lntfVt3Z5P9jNn02vV5n8K+GmL8+vAvm3+aLHtBnym7fNNwJ/O9nvDx/x4AH/RPvvvBW4ADmzzR22z/PbxeczjCPCm1l7upbsxzrPH+WwZWu9udHem3ACsBt7Us85TW0zntvWuApaNsZ8eL6frPTTbAQzSg5ZQ9jw/ANivNdinA/8OvLqVbWpQwKPbG/0prewJPQeew1oDe2qr+3bgW6Nsf2idn2zr3K8dmIYO9qcBVwCPo0twvwW8s5X9DfBhYOv2eBG/6Rp0c886TgU+Mdp+A2cBp/eUHQ98qU0/C7gdeB7d3buObuvedpT9KeBSYGfgkW3ea4Fd2mtxIt3BersxYvss8JH2ejyO7ta5fzzb7xUfc+fR+/5uz4fa0bntffNIxk+k39za1iK6L5QfAT45yvYOoLsD3d+1ui8BftHT/s8F/gnYvsXyb8AxreyTwNvoPlO2A17Ys96i+6kW4GzgXWPEexkPP2i/D/hwm+7nM2fTa9Xm/+cW/9CX92t7lnlYbG1frgFOAbYBnkh3UuHg2X5v+NiyH8BT6BLJ3drzJcCT2vSobZbfTnhHPY4AR9Alnc8FQvdlds9WtqnNjbLerwMfam35mXTH6w9OghwAACAASURBVJe2slPpTsy9gu54+TfAFWPsq8fL6XofzXYAg/RgWCI9QvkHgPe36U0Nqr1p7wJ+b6gB9CzzRdpBtD1/BPBLRjhD1LPO3+mZ917g4236RuAVPWUHAze36dPoDt57j7DeTR8GozS+TftNd8brxp6ybwKva9P/QEvce8pvAF4yyutVQx8qY7ymdwLPGCk24PHA/b2vKXAUcPlsv1d8zJ0Hox/sntgz7wDGTqSvp53pas+fQHe2Z6sRtncAXSL96J55FwF/RXfAfABY2lP2x8BX2/S5dLfjXTTCeieTSL8RuKxNhy7ZeHF73s9nzhOHl/XU2bHV2WGk2Oi+WP942DInA/842+8NH1v2gy6pvb0dl7YeVjZqm+Xhx+cxjyN0v9i8eZTtj/bZshWwmO5X7O17yv8GOLtNnwp8padsKfCrMfbV4+U0PewjPYuSPC/J5UnWJ7kbOI6uC8XDVNUvgD9o5T9J8oUkv9OK9wT+PsldSYa6XQTYfYxNr+2ZvoXu5yPa31tGKXsf3VmoLydZk+Skyexrj8uBR7V9X0L3LfuzPfty4tC+tP1Z3BPDePtCkrcmuT7J3W35HRjhNe3Z3tZ0r+nQ9j5C901bGs/a8atssifw2Z732fV0B8nHj1L/ztbuhwy1xV3p3rPD2+lQe//vdO3/qnYVjv88iRh7fQb4/5I8AXgx3U/Q/9KzL31/5iRZkOTdSW5Mcg9dMgFjt9Pdhn0u/CWjv3bShFTVauDP6JLG25NckGToeDPRNjvecWQx3UmqydoN2FBV9/bM623r0J1BHvJLYLtx+j97vJwGJtKz63y6/k+Lq2oHuq4TGaliVV1SVS+n+1b8Q+CjrWgt3U8rO/Y8HllV3xpju4t7pveg6w9N+7vnSGVVdW9VnVhVTwQOBd6S5MCRQh1ju1TVg3Rn145qj8/3fFCspev20bsvj6qqT461yqGJJC+iSyR+H9ipqnak64OZ4XV7tnc/sGvP9h5bVfuOtQ8aOKO9p3vn/wJ41NCTdvmqhT3la4FDhr23t6uqW0dZ905JHt3zfKgt/ozurNjwdnorQFX9tKreVFW70Z2p/lCSvSexT7T13Al8me4L/GuAC6qdgqK/z5ze7b2GrnvIy+gO3Eva/LHa6U3Dtrd9Vb1irH2QJqKqzq+qF9K1qQLe04om2mbHO46sBZ402ubHCO02YOck2/fM29TW++TxchqYSM+u7em+cd6XZH+6A8xvSfL4JIe1A+v9dJ3/H2rFHwZOTrJvq7tDkiPG2e5fJXlUW+YNwIVt/ieBtydZmGRXuj6Jn2jrfVWSvZOErrE92BNDr38HliQZ6711Pt0B+g/b9JCPAse1s9VJ8ugkrxz2QTKW7el+El8PbJXkFOCxo8VWVT+hSxb+Z5LHJnlEkicleckEt6fB8O90/XLH8m90Z4NemWRrun7D2/aUfxg4PcmeAK2NHTbOOv86yTbtgPcq4FM9X0RPT7J9W99b+E07PSLJorb8nXQHw9Ha6Xj7dD7wOuBwHt5O+/nM6bU93efYHXRfPv7HOLFdBdyb5C+SPLKd0X5akudOYpvSb0l3b4eXJtmWrr/xr3j4sXXcNjuB48jHgLcmeU47ru09tE7GaIdVtZZunNLfJNkuydPpBu1+Ykp23uPllDGRnl1/ApyW5F66pPWiUeo9gu5geRvdz6gvAf4LQFV9lu4b9AXtZ9J/BQ4ZZ7tfo+um8c/A31bVl9v8dwErge8DPwC+0+YB7AN8hS6J/zbwoaq6fIR1f6r9vSPJd0baeFVdSXcGbze6/pZD81fSjW7+IF0SsJruaiUTdQnwJbqk5ha6D8ben7JGiu11dAOYrmvb/DTdWX9pyN/QfcG8K8lbR6pQVXfTteeP0Z0x+gXdlTeG/D3dr09fbu39Crq+v6P5Kd378Tbg/wDHVdUPW9l/betfA3yDLsk9q5U9F7gyyc/b9t5cVWtGWP/HgaVtnz43Sgwr6Nr9T6vqez372s9nTq9z6drnrXTt7oqxYmtfHl5F1w3sJrqz8h+jO5stbY5tgXfTvad+StdN4eRWNpk2O+pxpKo+BZxO107vBT5HN+APxv9sOYruF5vb6LpAvqOm7prTHi+niDdkGSDZQm/yIg2SdHcX/ERVLRqvriRpdnlGWpIkSeqDibQkSZLUB7t2SJIkSX3wjLQkSZLUBxNpSZIkqQ9j3QFnTtt1111ryZIlsx2GNGdcc801P6uqhePXnHm2V+nhbK/SlmOs9rrFJtJLlixh5cqVsx2GNGckuWX8WrPD9io9nO1V2nKM1V7t2iFJkiT1wURakqQ5IsnyJDckWZ3kpBHKt01yYSu/st1oiyQvT3JNkh+0vy/tWeY5bf7qJP8rSWZuj6T5zURakqQ5IMkC4Ay6W64vBY5KsnRYtWOAO6tqb+D9dLdrh+421/+xqvYDjgbO61nmH4A30d3yfR9g+bTthDRgTKQlSZob9gdWV9WaqnoAuAA4bFidw4Bz2vSngQOTpKq+W1W3tfmrgEe2s9dPAB5bVVdUd+OIc4FXT/+uSINhix1sqMFx/e88dbZD2GxP/eH1sx3CvHfGcZfNdgib7fgPv3T8SprPdgfW9jxfBzxvtDpVtTHJ3cAudGekh/we8J2quj/J7m09vevcffiGkxwLHAuwxx57bOZujO9//sGrpn0b0+3ECz8/2yFoDjCRHsV8SN7ABE6SBkmSfem6exw0meWq6kzgTIBly5Z5y+MBMR++0MDsfqmZdNeOfgdCtLKT2/wbkhzcM//mNhDi2iRec0eSNIhuBRb3PF/U5o1YJ8lWwA7AHe35IuCzwOuq6sae+ovGWaekPk0qkd6cgRCt3pHAvnQDHT7U1jfkd6vqmVW1rK89kSRpy3Y1sE+SvZJsQ3fMXDGszgq6wYQAhwOXVVUl2RH4AnBSVX1zqHJV/QS4J8nz29U6Xgf803TviDQoJntGuu+BEG3+BVV1f1XdBKxu65MkaeBV1UbgBOAS4HrgoqpaleS0JIe2ah8HdkmyGngLMPTL8AnA3sAp7dfda5M8rpX9CfAxuuPujcAXZ2aPpPlvsn2kN2cgxO7AFcOWHRrwUMCXkxTwkdZXS5KkgVJVFwMXD5t3Ss/0fcARIyz3LuBdo6xzJfC0qY1UEsydwYYvrKpb27fnS5P8sKq+PrzSTI8qliRJkkYz2a4dmzMQYtRlq2ro7+10AyVG7PJRVWdW1bKqWrZw4cJJhi5JkiRNnckm0n0PhGjzj2xX9diL7u5KVyV5dJLtAZI8mu6SPf/a3+5IkiRJM2NSXTtan+ehgRALgLOGBkIAK6tqBd1AiPPaQIgNdMk2rd5FwHXARuD4qnowyeOBz3bjEdkKOL+qvjRF+ydJkiRNi0n3ke53IEQrOx04fdi8NcAzJhuHJEmSNJsmfUMWSZIkSSbSkiRJUl9MpKUBlWR5khuSrE5y0gjlb0lyXZLvJ/nnJHv2lB2d5EftcfTwZSVJGgQm0tIASrIAOAM4BFgKHJVk6bBq3wWWVdXT6e5S+t627M7AO+huxrQ/8I4kO81U7JIkzRUm0tJg2h9YXVVrquoB4ALgsN4KVXV5Vf2yPb2C7trvAAcDl1bVhqq6E7gUWD5DcUuSNGeYSEuDaXdgbc/zdW3eaI4BvtjnspIkzUtz5RbhkuaoJK8FlgEvmeRyxwLHAuyxxx7TEJkkSbPLM9LSYLoVWNzzfFGb9zBJXga8DTi0qu6fzLJVdWZVLauqZQsXLpyywCVJmitMpKXBdDWwT5K9kmxDdwfSFb0VkjwL+AhdEn17T9ElwEFJdmqDDA9q8yRJGih27ZAGUFVtTHICXQK8ADirqlYlOQ1YWVUrgPcBjwE+lQTgx1V1aFVtSPJOumQc4LSq2jALu6E56IzjLpvtEDbb8R9+6WyHIGkLYSItDaiquhi4eNi8U3qmXzbGsmcBZ01fdJIkzX127ZAkSZL6YCItSZIk9cFEWpIkSerDpBPpJMuT3JBkdZKTRijfNsmFrfzKJEt6yk5u829IcvCw5RYk+W6Sz/ezI5IkSdJMmlQinWQBcAZwCLAUOCrJ0mHVjgHurKq9gfcD72nLLqW7xNa+dLcT/lBb35A3A9f3sxOSJEnSTJvsGen9gdVVtaaqHgAuAA4bVucw4Jw2/WngwHTXzjoMuKCq7q+qm4DVbX0kWQS8EvhYf7shSdKWrd9ffJPskuTyJD9P8sFhy3y1rfPa9njczOyNNBgmm0jvDqzteb6uzRuxTlVtBO4Gdhln2Q8A/x14aJLxSJK0xducX3yB+4C/At46yur/sKqe2R63j1JHUh9mfbBhklcBt1fVNROoe2ySlUlWrl+/fgaikyRpRvT9i29V/aKqvkGXUEuaQZNNpG8FFvc8X9TmjVgnyVbADsAdYyz7AuDQJDfTfXC8NMknRtp4VZ1ZVcuqatnChQsnGbokSXPW5vziO55/bN06/qp1tZQ0RSabSF8N7JNkryTb0A0eXDGszgrg6DZ9OHBZVVWbf2Tr47UXsA9wVVWdXFWLqmpJW99lVfXaPvdHkiT9xh9W1X7Ai9rjj0aq5C++Un8mlUi3b8AnAJfQXWHjoqpaleS0JIe2ah8HdkmyGngLcFJbdhVwEXAd8CXg+Kp6cGp2Q5KkLdrm/OI7qqq6tf29FzifNsh/hHr+4iv1YavJLlBVFwMXD5t3Ss/0fcARoyx7OnD6GOv+KvDVycYkSdIWbtMvvnQJ85HAa4bVGfrF99s8/BffEbVke8eq+lmSrYFXAV+ZjuClQTXpRFqSJE2tqtqYZOgX3wXAWUO/+AIrq2oF3S++57VffDfQJdsAtHFGjwW2SfJq4CDgFuCSlkQvoEuiPzqDuyXNeybSkiTNAZv5i++SUVb7nKmKT9Jvm/XL30mSJElbIhNpSZIkqQ8m0pIkSVIfTKQlSZKkPphISwMoyfIkNyRZneSkEcpfnOQ7STYmOXxY2YPtLmnXJhl+QyZJkgaGV+2QBkySBcAZwMvpbkN8dZIVVXVdT7UfA68H3jrCKn5VVc+c9kAlSZrjTKSlwbM/sLqq1gAkuQA4jO6uowBU1c2t7KHZCFCSpC2BXTukwbM7sLbn+bo2b6K2S7IyyRXtxg8jSnJsq7dy/fr1/cYqSdKcZSItabL2rKpldLcv/kCSJ41UqarOrKplVbVs4cKFMxuhJEkzwERaGjy3Aot7ni9q8yakqm5tf9cAXwWeNZXBSZK0pTCRlgbP1cA+SfZKsg1wJDChq28k2SnJtm16V+AF9PStliRpkJhISwOmqjYCJwCXANcDF1XVqiSnJTkUIMlzk6wDjgA+kmRVW/ypwMok3wMuB9497GofkiQNDK/aIQ2gqroYuHjYvFN6pq+m6/IxfLlvAftNe4CSJG0BJn1GegI3ctg2yYWt/MokS3rKTm7zb0hycJu3XZKrknwvyaokf705OyRJkiTNhEkl0j03cjgEWAoclWTpsGrHAHdW1d7A+4H3tGWX0vXF3BdYDnyore9+4KVV9QzgmcDyJM/vf5ckSZKk6TfZM9KbbuRQVQ8AQzdy6HUYcE6b/jRwYJK0+RdU1f1VdROwGti/Oj9v9bduj+pjXyRJkqQZM9lEeiI3cthUpw1quhvYZaxlkyxIci1wO3BpVV050sa9wYMkSZLmijlx1Y6qerCqnkk3uGn/JE8bpZ43eJAkSdKcMNlEeiI3cthUJ8lWwA7AHRNZtqruoruk1vJJxiVJkiTNqMkm0hO5kcMK4Og2fThwWVVVm39ku6rHXsA+wFVJFibZESDJI4GXAz/sb3ckSZKkmTGp60hX1cYkQzdyWACcNXQjB2BlVa0APg6cl2Q1sIEu2abVu4juLmgbgeOr6sEkTwDOaVfweATdzSE+P1U7KEmSJE2HSd+QZQI3criP7m5oIy17OnD6sHnfB5412TgkSZpvkiwH/p7uZNXHqurdw8q3Bc4FnkPXbfIPqurmJLvQXSnrucDZVXVCzzLPAc4GHkl3/H5z+6VY0maaE4MNJUkadJtzrwbgPuCvgLeOsOp/AN5E16VyHxyHJE0ZE2lJkuaGvu/VUFW/qKpv0CXUm7Tuk4+tqivaWehzgVdP615IA8REWpKkuWFz7tUw1jrXjbNOSX0ykZYkacB5wzOpPybSkiTNDZtzr4ax1rlonHV6wzOpTybSkiTNDZtzr4YRVdVPgHuSPD9JgNcB/zT1oUuDadKXv5MkSVNvc+7VAJDkZuCxwDZJXg0cVFXXAX/Cby5/98X2kDQFTKQlSZojNvNeDUtGmb8SeNrURSlpiF07pAGVZHmSG5KsTnLSCOUvTvKdJBuTHD6s7OgkP2qPo4cvK0nSIDCRlgbQBG/88GPg9cD5w5bdGXgH8Dy6696+I8lO0x2zJElzjYm0NJjGvfFDVd1cVd8HHhq27MHApVW1oaruBC7FO6VJkgaQibQ0mCZy44fNWtbr0kqS5jsTaUnTwuvSSpLmOxNpaTBN5MYP07GsJEnzxqQT6QmM9N82yYWt/MokS3rKTm7zb0hycJu3OMnlSa5LsirJmzdnhyRNyERu/DCaS4CDkuzUBhke1OZJkjRQJpVIT3Ck/zHAnVW1N/B+4D1t2aV0B+t96QYmfaitbyNwYlUtBZ4PHD/COiVNoaraCAzd+OF64KKhGz8kORQgyXOTrKO7Zu1Hkqxqy24A3kmXjF8NnNbmSZI0UCZ7Q5ZNI/0BkgyN9L+up85hwKlt+tPAB9ttSQ8DLqiq+4Gb2l2Z9q+qbwM/Aaiqe5NcTzdwqXedkqbYBG78cDVdt42Rlj0LOGtaA5QkaY6bbNeOiYzW31SnnfW6G9hlIsu2biDPAq4caeNeBUCSJElzxZwZbJjkMcBngD+rqntGquNVACRJkjRXTDaRnsho/U11kmwF7ADcMdaySbamS6L/T1X930nGJEmSJM24ySbSExnpvwI4uk0fDlxWVdXmH9mu6rEXsA9wVes//XHg+qr6u353RJIkSZpJkxpsWFUbkwyN9F8AnDU00h9YWVUr6JLi89pgwg10yTat3kV0gwg3AsdX1YNJXgj8EfCDJNe2Tf1lGwglSZIkzUmTvWrHREb630d3uayRlj0dOH3YvG8AmWwckiRJ0myaM4MNJUmSpC2JibQkSZLUBxNpSZIkqQ8m0pIkSVIfTKQlSZKkPphIS5IkSX0wkZYkSZL6YCItSdIckWR5khuSrE5y0gjl2ya5sJVfmWRJT9nJbf4NSQ7umX9zkh8kuTbJypnZE2kwTPqGLJIkaeolWQCcAbwcWAdcnWRFVV3XU+0Y4M6q2jvJkcB7gD9IspTuTsL7ArsBX0ny5Kp6sC33u1X1sxnbGWlAeEZakqS5YX9gdVWtqaoHgAuAw4bVOQw4p01/GjgwSdr8C6rq/qq6CVjd1idpGplIS5I0N+wOrO15vq7NG7FOVW0E7gZ2GWfZAr6c5Jokx4604STHJlmZZOX69es3e0ekQWEiLQ2ofvtiJlmS5Fetv+W1ST4807FLmpQXVtWzgUOA45O8eHiFqjqzqpZV1bKFCxfOfITSFspEWhpAPX0xDwGWAke1Ppa9NvXFBN5P1xdzyI1V9cz2OG5Ggpbmv1uBxT3PF7V5I9ZJshWwA3DHWMtW1dDf24HPYpcPacqYSEuDaXP6YkqaHlcD+yTZK8k2dIMHVwyrswI4uk0fDlxWVdXmH9l+SdoL2Ae4Ksmjk2wPkOTRwEHAv87AvkgDYdKJ9DRdmuesJLcnsXFLM2Nz+mIC7JXku0m+luRF0x2sNAhaOzsBuAS4HrioqlYlOS3Joa3ax4FdkqwG3gKc1JZdBVwEXAd8CTi+XbHj8cA3knwPuAr4QlV9aSb3S5rPJnX5u2m8NM/ZwAeBczd3hyRNu58Ae1TVHUmeA3wuyb5VdU9vpTao6ViAPfbYYxbClLY8VXUxcPGweaf0TN8HHDHKsqcDpw+btwZ4xtRHKgkmf0Z6Wi7NU1VfBzb0uQ+SJq/vvpitDd8BUFXXADcCTx6+AQcvSZLmu8km0tN1aR5JM6vvvphJFrZfp0jyRLq+mGtmKG5JkuaMLerOhv5ULE2NqtqYZKgv5gLgrKG+mMDKqlpB1xfzvNYXcwNdsg3wYuC0JL8GHgKOqyp/UZIkDZzJJtKT+Tl43UQvzTNRVXUmcCbAsmXLalKRS3qYfvtiVtVngM9Me4CSJM1xk+3aMeWX5uk/dEmSJGn2TCqRnqZL85Dkk8C3gackWZfkmM3fNUmSJGn6TLqP9FRfmqfNP2qycUiSJEmzyTsbSpIkSX0wkZYkSZL6YCItSZIk9cFEWpIkSeqDibQkSZLUBxNpSZIkqQ8m0pIkSVIfTKQlSZKkPphIS5IkSX0wkZYkSZL6YCItSZIk9cFEWpIkSeqDibQkSZLUBxNpSZIkqQ+TTqSTLE9yQ5LVSU4aoXzbJBe28iuTLOkpO7nNvyHJwRNdp6SpNx1tWdLm8RgrbVkmlUgnWQCcARwCLAWOSrJ0WLX/v717D7ejru89/v4QsFxsgZaUKoEGNdIDSLlE5LRaUfsgahGtPFY4LWg5xip4Ob0caW0LR2y9VfsUq2i0OQURKcdaDYimFLTaIoUAKSQITQpSgijBCF6o3Po9f8xsXG73TnYm2WvWznq/nmc9e813Zn7z3Zn92+ub2b/5zanAt6rqKcCfA+9s9z0QeAVwEHAs8IEk82bYpqRtaDb68rByl7ZXfsZKc8+WXpE+ElhXVbdV1UPARcDxk7Y5Hjivff8J4HlJ0sYvqqoHq+p2YF3b3kzalLRtzUZflrR1/IyV5pgtLaT3Ae4cWF7fxqbcpqoeAe4HfmoT+86kTUnb1mz0ZUlbx89YaY7Zse8EtkSSJcCSdvH7Sdb0mc82sBdw76weIZnV5oHdaX6Rz2Xbw3kAWDSMg8yU/XXLnf6h2Wz9MfbZzRjSebC/zr5Z/Tn53YuH8nvd/joDQzgX0/bXLS2k7wL2HVhe0Mam2mZ9kh1pfgi+uZl9N9cmAFW1FFgKkGRpVS2Zaru5IsnKqlrcdx5bw/MwOpIs3YLNZ6svP8b+Opo8F6Nhmv7a22fs9tZfYfv5OZnr52J7OQ/TrdvSoR3XAouS7J/kcTQ3NiyftM1y4JT2/QnAlVVVbfwV7R3H+9NU99fMsM2pXLKFuWt2eB5Gx5aci9noy9sqN80uz8VomOo8jMpnrD8jo8NzMRqmPQ9bdEW6qh5JcjqwApgHLKuqNUneCqysquXAXwEfTbIO2EjTaWm3uxi4GXgEOK2qHgWYqs0Z5OIP1wjwPIyOLTkXs9WXt0Vuml2ei9Ew1XkYlc9Yf0ZGh+diNGzqPKT5j6z6kGRJ++c09cjzoJnw52R0eC40E/6cjIbt/TxYSEuSJEkd+IhwSZIkqQMLaUmSJKkDC2lJkiSpAwtpSdKckGSXJAf0nYekzRuX/mohLUnTSPLUJFckWd0uH5LkD/vOaxwlOQ5YBXyuXT40yUyeOaAxYX8dHePUXy2kNXRJvprkl/vOQ5qBDwO/DzwMUFU30s7bq6E7CzgSuA+gqlYB+/eZkEaO/XV0nMWY9FcLaUma3q5VNfmpjY/0kokerqr7J8Wcv1WD7K+jY2z6q4W0SLJvkk8m2ZDkm0n+MskOSf4wyR1J7klyfpLd2+2PTrJ+UhuPXWVOclaSi9t9vpNkTZLF7bqPAvsBlyT5bpL/PezvV9oC9yZ5Mu0HQJITgLv7TWlsrUlyEjAvyaIk7wOu6jspjRT76+gYm/5qIT3mkswDLgXuABYC+wAXAa9sX88BngQ8HvjLLWj6xW07ewDLJ/atqt8A/gM4rqoeX1Xv2gbfhjRbTgM+BPxckruANwGv7TelsfV64CDgQeDjwLdpzoc0wf46Osamv/pkwzGX5L/TFLpPqKpHBuJXAH9bVR9olw8AVgO7AM8ELqiqBQPbfxX4n1X1D0nOAp5ZVRNXqA8ErquqXSZvO/vfobT1kuwG7FBV3+k7F0mbZn/VMO3YdwLq3b7AHYNFdOuJNFepJ9xB8/Oy9wzb/frA+weAnZPsOMVxpJGT5LeniQNQVe8dakJjLMklbGJsZVW9eIjpaATZX0fHOPZXC2ndCew3RZH7NeBnB5b3o7lp4xs0RfauEyva4SHzt+CY/hlEo+7H+05Aj/mzvhPQyLO/jo6x668W0rqG5maMdyQ5E3gUOIJmTNObk3wW2AD8KfA3VfVIkn+jucL8IuDvgT8AfmwLjvkNmnHX0kiqqv/Tdw5qVNU/9p2DRpv9dXSMY3+1kB5zVfVoO3H6OTQ3ARZwIc1NAU8EvgjsDKyguXmAqro/yeuAjwDzgHcB63+09Wm9HXhfkncBb6uqsfsfrOaGJDsDp9LcNLPzRLyqfrO3pMZUkkU0vzsO5IfPhf8pF2B/HSXj1F+92VCSppHk/wG3ACcBbwX+B/CVqnpjr4mNoST/BJwJ/DlwHPAqmhvK/rjXxDQy7K+jY5z6q4W0JE0jyQ1VdViSG6vqkCQ7AV+qqqP6zm3cJLmuqo5IclNVPW0w1nduGg3219ExTv3VoR2SNL2H26/3JTmYZjaan+4xn3H2YJIdgLVJTgfuopnfXppgfx0dY9NffSCLJE1vaZI9gT+imW/9Zpp7AjR8b6SZLegNNDdE/zpwcq8ZadTYX0fH2PRXh3ZIkkZeksXAW2im5dypDVdVHdJfVpKmMk79dc4W0nvttVctXLiw7zSkkXHdddfdW1VbMp+3NiPJHjRXURYyMBSuqt7QV07jKsmtwO8BNwH/NRGvqjum3Uljxf46Osapv87ZMdILFy5k5cqVfachjYwk290vqBFwGXA1kz4M1IsNVbW87yQ00uyvo2Ns+uucLaQlaQh2rqopHz+soTszyUeAK4AHJ4JV9cn+UtKIsb+OjrHprxbSkjS9jyZ5NXApP/xhsLG/lMbWq4CfoxlvOXG1sYDt7oNZndlfR8fY9FcLaUma3kPADLkuPwAAFfBJREFUu2lumpm4oaTwEfd9eHpVHdB3Ehpp9tfRMTb91UJakqb3O8BTqurevhMRVyU5sKpu7jsRjSz76+gYm/5qIT2N9Wd8qe8UtokF73hW3ylIc9k64IG+kxAARwGrktxO82f7sJ1Op6XO7K+jY2z6q4W0JE3vezQfBp/nh8dcOp3W8B3bdwIaefbX0TE2/dVCWpKm96n2pZ5tj/PPapuzv46IceqvFtKSNI2qOi/JLsB+VXVr3/lImp79VX3Yoe8EJGlUJTkOWAV8rl0+NMlYPGRAmmvsr+qDhbQkTe8s4EjgPoCqWoVTaUmj6izsrxoyC2lJmt7DVXX/pJiPHpZGk/1VQ+cYaUma3pokJwHzkiwC3gBc1XNOkqZmf9XQeUVakiZJ8tH27b8DB9FMpfVx4NvAm/rKS9KPsr+qT16RlqQfdUSSJwK/BjwHeM/Aul2B7/eSlaSp2F/VGwtpSfpRHwSuoLlRaeVAPEDhDUzSKLG/qjcO7ZCkSarqnKr6b8CyqnrSwGv/qvJDWRoh9lf1abOFdJJ9k3w+yc1J1iR5Yxv/ySSXJ1nbft2zjSfJOUnWJbkxyeEDbZ3Sbr82ySkD8SOS3NTuc06SzMY3K0lboqpe23cOkmbG/qo+zOSK9CPA71TVgcBRwGlJDgTOAK6oqkU0f1I5o93+BcCi9rUEOBeawhs4E3gGzTyPZ04U3+02rx7Yb2ye0S5JkqS5abOFdFXdXVXXt++/A3wF2Ac4Hjiv3ew84CXt++OB86txNbBHkicAzwcur6qNVfUt4HLg2HbdT1TV1VVVwPkDbUmSJEkjaYvGSCdZCBwG/Auwd1Xd3a76OrB3+34f4M6B3da3sU3F108Rn+r4S5KsTLJyw4YNW5K6JI2MJHsnuTDJbUmuS/LlJC/dBu0eneTSDvs9Ncll7bC765Nc3ObYqT1pe2Of1XRmXEgneTzwt8Cbqurbg+vaK8m1jXP7EVW1tKoWV9Xi+fPnz/bhJGmba+8B+RTwxfaGqCOAVwALeshlxyQ7A58Bzq2qRVV1OPABwF+yEvZZbdqMCukkO9EU0R+rqk+24W+0wzJov97Txu8C9h3YfUEb21R8wRRxSdoePRd4qKo+OBGoqjuq6n1J5iV5d5Jr25u1XwOPXbX6QpJPJLklyccmbspOcmwbux741Yk2k+yWZFmSa5LckOT4Nv7KJMuTXElzf8tJwJer6pKBfL5QVasHk05yZHsV7oYkVyU5oI0f1B5jVZvzovbYn0nyr0lWJ/m1WfvXlGaffVbTmsmsHQH+CvhKVb13YNVyYGLmjVOATw/ET07jKOD+dgjICuCYJHumucnwGGBFu+7bSY5qj3XyQFuStL05CLh+mnWn0vzOfDrwdODVSfZv1x1G85S2A2nmxf3F9srUh4HjgCOAnxlo6y3AlVV1JM1DKt6dZLd23eHACVX1bOBg4LoZ5H0L8KyqOgz4Y+BP2/hvAX9RVYcCi2mG5x0LfK2qfr6qDgY+N4P2pVFln9W0ZvJAll8EfgO4KcmqNvYHwDuAi5OcCtwBvLxddxnwQmAd8ADwKoCq2pjkbODadru3VtXG9v3rgL8GdgE+274kabuX5P3AM4GHaH6XHpLkhHb17jQzGT0EXFNV69t9VgELge8Ct1fV2jZ+Ac1sSdBcrHhxkt9tl3cG9mvfXz7w+3emdgfOS7KIZijfTm38y8BbkiwAPllVa5PcBLwnyTuBS6vqS1t4LGlk2Wc1aLOFdFX9E83TgabyvCm2L+C0adpaBiybIr6S5n9YkrS9WwO8bGKhqk5LshfNE9n+A3h9Va0Y3CHJ0cCDA6FH2fzv7wAvq6pbJ7X1DOB7k/J59gzyPhv4fFW9NM2N519o878wyb8ALwIuS/KaqroyzTMEXgi8LckVVfXWGRxDGkX2WU3LJxtK0nBdCeycZPDhEbu2X1cAr01zX8rEnfm7TW5gwC3AwiRPbpdPHFi3Anj9wLjMw6Zp40LgF5K8aCKQ5JeSTL64sTs/uH/llQPbPgm4rarOoRmWd0iSJwIPVNUFwLtp/iwtzVX2WU3LQlqShqj9q91LgGcnuT3JNTRz8b8Z+AhwM3B9ktXAh9jEVayq+j7Nn4U/0964dM/A6rNp/pR7Y5I17fJUbfwn8Cs0H+Brk9xMM9xu8hyj7wLenuSGSTm9HFjd/un6YJpnATwNuKaNnQm8bTP/LNLIss9qU9L8fMw9ixcvrpUrV85a++vP2D6GBy14x7P6TkFDkuS6qlrcdx6SJI0Lr0hLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1stpBOsizJPUlWD8TOSnJXklXt64UD634/yboktyZ5/kD82Da2LskZA/H9k/xLG/+bJI/blt+gJEmSNBtmckX6r4Fjp4j/eVUd2r4uA0hyIPAK4KB2nw8kmZdkHvB+4AXAgcCJ7bYA72zbegrwLeDUrfmGJEmSpGHYbCFdVV8ENs6wveOBi6rqwaq6HVgHHNm+1lXVbVX1EHARcHySAM8FPtHufx7wki38HiRJkqSh25ox0qcnubEd+rFnG9sHuHNgm/VtbLr4TwH3VdUjk+KSJEnSSOtaSJ8LPBk4FLgbeM82y2gTkixJsjLJyg0bNgzjkJIkSdKUOhXSVfWNqnq0qv4L+DDN0A2Au4B9BzZd0Mami38T2CPJjpPi0x13aVUtrqrF8+fP75K6JEmStE10KqSTPGFg8aXAxIwey4FXJPmxJPsDi4BrgGuBRe0MHY+juSFxeVUV8HnghHb/U4BPd8lJkiRJGqYdN7dBko8DRwN7JVkPnAkcneRQoICvAq8BqKo1SS4GbgYeAU6rqkfbdk4HVgDzgGVVtaY9xJuBi5K8DbgB+Ktt9t1JkiRJs2SzhXRVnThFeNpit6r+BPiTKeKXAZdNEb+NHwwNkSRJkuYEn2woSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1sNlCOsmyJPckWT0Q+8kklydZ237ds40nyTlJ1iW5McnhA/uc0m6/NskpA/EjktzU7nNOkmzrb1KSJEna1mZyRfqvgWMnxc4ArqiqRcAV7TLAC4BF7WsJcC40hTdwJvAM4EjgzIniu93m1QP7TT6WJEmSNHI2W0hX1ReBjZPCxwPnte/PA14yED+/GlcDeyR5AvB84PKq2lhV3wIuB45t1/1EVV1dVQWcP9CWJEmSNLK6jpHeu6rubt9/Hdi7fb8PcOfAduvb2Kbi66eIS5IkSSNtq282bK8k1zbIZbOSLEmyMsnKDRs2DOOQkiRJ0pS6FtLfaIdl0H69p43fBew7sN2CNrap+IIp4lOqqqVVtbiqFs+fP79j6pIkSdLW61pILwcmZt44Bfj0QPzkdvaOo4D72yEgK4BjkuzZ3mR4DLCiXfftJEe1s3WcPNCWJEmSNLJ23NwGST4OHA3slWQ9zewb7wAuTnIqcAfw8nbzy4AXAuuAB4BXAVTVxiRnA9e22721qiZuYHwdzcwguwCfbV+SJEnSSNtsIV1VJ06z6nlTbFvAadO0swxYNkV8JXDw5vKQJEmSRolPNpQkSZI6sJCWJEmSOrCQliRJkjqwkJYkSZI6sJCWJEmSOrCQliRJkjqwkJYkSZI6sJCWJEmSOrCQliRJkjqwkJYkSZI6sJCWJEmSOrCQliRJkjrYse8EpM1Zf8aX+k5hqy14x7P6TkGSJG1jXpGWJEmSOrCQliRJkjqwkJYkSZI6sJCWJEmSOrCQliRJkjrYqkI6yVeT3JRkVZKVbewnk1yeZG37dc82niTnJFmX5MYkhw+0c0q7/dokp2zdtyRJkiTNvm1xRfo5VXVoVS1ul88ArqiqRcAV7TLAC4BF7WsJcC40hTdwJvAM4EjgzIniW5IkSRpVszG043jgvPb9ecBLBuLnV+NqYI8kTwCeD1xeVRur6lvA5cCxs5CXJEmStM1sbSFdwN8nuS7Jkja2d1Xd3b7/OrB3+34f4M6Bfde3senikiRJ0sja2icbPrOq7kry08DlSW4ZXFlVlaS28hiPaYv1JQD77bfftmpWkiRJ2mJbdUW6qu5qv94D/B3NGOdvtEM2aL/e025+F7DvwO4L2th08amOt7SqFlfV4vnz529N6pIkSdJW6VxIJ9ktyY9PvAeOAVYDy4GJmTdOAT7dvl8OnNzO3nEUcH87BGQFcEySPdubDI9pY5IkSdLI2pqhHXsDf5dkop0Lq+pzSa4FLk5yKnAH8PJ2+8uAFwLrgAeAVwFU1cYkZwPXttu9tao2bkVekiRJ0qzrXEhX1W3Az08R/ybwvCniBZw2TVvLgGVdc5EkSZKGzScbSpIkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHVhIS5IkSR1YSEuSJEkdWEhLkiRJHYxMIZ3k2CS3JlmX5Iy+85EkSZI2ZSQK6STzgPcDLwAOBE5McmC/WUmSJEnTG4lCGjgSWFdVt1XVQ8BFwPE95yRJkiRNa8e+E2jtA9w5sLweeMbkjZIsAZa0i99PsmYIuc2mvYB7Z/UI75zV1gF2B+6f9aPMru3hPAAsGspRJEkSMDqF9IxU1VJgKUCSpVW1ZDO7jLQkK6tqcd95bA3Pw+hIsrTvHCRJGiejMrTjLmDfgeUFbWxTLpm9dLQFPA+jw3MhSdIQjUohfS2wKMn+SR4HvAJYvqkdqsqiYQR4HkaH50KSpOEaiaEdVfVIktOBFcA8YFlVzfXxzzPhn+JHg+dBkiRtsVRV3zlIkiRJc86oDO2QJEmS5hQLaUmSJKkDC2lJkiSpAwtpja0kuyQ5oO88JEnS3GQhPURJnprkiiSr2+VDkvxh33mNoyTHAauAz7XLhybZ5JSLkiRJgyykh+vDwO8DDwNU1Y00c2Zr+M4CjgTuA6iqVcD+fSYkSZLmFgvp4dq1qq6ZFHukl0z0cFXdPynmXJCSJGnGRuKBLGPk3iRPpi3YkpwA3N1vSmNrTZKTgHlJFgFvAK7qOSdJkjSH+ECWIUryJJqn6P0C8C3gduDXq+qrfeY1jpLsCrwFOAYIzVM1z66q7/eamCRJmjMspHuQZDdgh6r6Tt+5SJIkqRsL6SFI8tubWl9V7x1WLuMuySVsYix0Vb14iOlIkqQ5zDHSw/HjfSegx/xZ3wlIkqTtg1ekJUmSpA68Ij1ESXYGTgUOAnaeiFfVb/aW1JhqZ+p4O3AgP3wuntRbUpIkaU5xHunh+ijwM8DzgX8EFgDecNiP/wucSzOP93OA84ELes1IkiTNKQ7tGKIkN1TVYUlurKpDkuwEfKmqjuo7t3GT5LqqOiLJTVX1tMFY37lJkqS5waEdw/Vw+/W+JAcDXwd+usd8xtmDSXYA1iY5HbgLeHzPOUmSpDnEoR3DtTTJnsAfAcuBm4F39ZvS2HojsCvNEw2PAH4dOLnXjCRJ0pzi0A6NpSSLaZ5s+LPATm24quqQ/rKSJElziYX0ECXZg+aq50IGhtVU1Rv6ymlcJbkV+D3gJuC/JuJVdUdvSUmSpDnFMdLDdRlwNZOKN/ViQ1Ut7zsJSZI0d3lFeoiSXF9Vh/edhyDJ84ATgSuAByfiVfXJ3pKSJElzioX0ECX5X8B3gUv54eJtY29JjakkFwA/B6zhB38dKB+OI0mSZspCeoiSnAb8CXAfMPEPXz5Nb/iS3FpVB/SdhyRJmrscIz1cvwM8paru7TsRcVWSA6vq5r4TkSRJc5OF9HCtAx7oOwkBcBSwKsntNMNsgtPfSZKkLWAhPVzfoynePs8Pj5F2+rvhO7bvBCRJ0txmIT1cn2pf6pnzRUuSpK3lzYZDlmQXYL+qurXvXCRJktTdDn0nME6SHAesAj7XLh+axIeCSJIkzUEW0sN1FnAkzfR3VNUqwKnvJEmS5iAL6eF6uKrunxTzUeGSJElzkDcbDteaJCcB85IsAt4AXNVzTpIkSerAK9JDkOSj7dt/Bw6imfru48C3gTf1lZckSZK6c9aOIUhyM/DLwGeB50xeX1Ubh56UJEmStopDO4bjg8AVNDcWrhyIByi84VCSJGnO8Yr0ECU5t6pe23cekiRJ2noW0pIkSVIH3mwoSZIkdWAhLUmSJHVgIb2dSLJ3kguT3JbkuiRfTvLSbdDu0Uku7bDfU5NclmRtkuuTXNzm2Kk9SZKkUeOsHduBJAE+BZxXVSe1sZ8FXtxDLjvS/Fx9BvjtqrqkjR8NzB92PpIkSbPFK9Lbh+cCD1XVBycCVXVHVb0vybwk705ybZIbk7wGHrvS/IUkn0hyS5KPtQU5SY5tY9cDvzrRZpLdkixLck2SG5Ic38ZfmWR5kitppvk7CfjyRBHd5vOFqlo9mHSSI9sr5zckuSrJAW38oPYYq9qcF7XH/kySf02yOsmvzdq/piRJ0gx4RXr7cBBw/TTrTgXur6qnJ/kx4J+T/H277rB2368B/wz8YpKVwIdpivN1wN8MtPUW4Mqq+s0kewDXJPmHdt3hwCFVtTHJe4HrZpD3LcCzquqRJL8M/CnwMuC3gL+oqo8leRwwD3gh8LWqehFAkt1n0L4kSdKssZDeDiV5P/BM4CHgDuCQJCe0q3cHFrXrrqmq9e0+q4CFwHeB26tqbRu/AFjS7nsM8OIkv9su7wzs176/vMMTGncHzkuyiObBNDu18S8Db0myAPhkVa1NchPwniTvBC6tqi9t4bEkSZK2KYd2bB/W0FwRBqCqTgOeRzMmOcDrq+rQ9rV/VU1ckX5woI1H2fx/rAK8bKCt/arqK+26703K54gZ5H028PmqOhg4jqYwp6oupBnf/Z/AZUmeW1X/1n6PNwFvS/LHM2hfkiRp1lhIbx+uBHZOMvjUxF3bryuA1ybZCR6bTWO3TbR1C7AwyZPb5RMH1q0AXj8wlvqwadq4EPiFJC+aCCT5pSQHT9pud+Cu9v0rB7Z9EnBbVZ0DfJrmivoTgQeq6gLg3Qz8x0GSJKkPFtLbgWoeT/kS4NlJbk9yDXAe8GbgI8DNwPVJVgMfYhNXnqvq+zRDOT7T3mx4z8Dqs2mGX9yYZE27PFUb/wn8Ck3RvTbJzcDrgA2TNn0X8PYkN0zK6eXA6na4ycHA+cDTaMZkrwLOBN62mX8WSZKkWeUjwiVJkqQOvCItSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHVgIS1JkiR1YCEtSZIkdWAhLUmSJHXw/wFPbIhr52frUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "uwpCTh8VBdfh",
        "outputId": "e822bc30-7fa0-4086-fd12-6d0224748060"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-deb7ca2f-14ad-4031-9377-e098d68905e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035623</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.057118</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035623</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</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>0.06015</td>\n",
              "      <td>0.000783</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036469</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035623</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-deb7ca2f-14ad-4031-9377-e098d68905e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-deb7ca2f-14ad-4031-9377-e098d68905e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-deb7ca2f-14ad-4031-9377-e098d68905e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   daysSinceLastLogin  productsListed  ...  PredictedtransactReluClass  GenderClass\n",
              "0            0.000947             0.0  ...                           0       female\n",
              "1            0.000947             0.0  ...                           0       female\n",
              "2            0.000947             0.0  ...                           0       female\n",
              "3            0.000787             0.0  ...                           0       female\n",
              "4            0.000947             0.0  ...                           0       female\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZLxrL8LR5SZ"
      },
      "source": [
        "## Tanh Activation Function\n",
        "\n",
        "Hyperbolic tangent activation function.\n",
        "\n",
        "https://keras.io/api/layers/activations/#tanh-function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybATBIihsDgT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "906b71c4-09b4-49bc-b790-0fbd6962a9eb"
      },
      "source": [
        "# create ANN model https://thinkingneuron.com/how-to-use-artificial-neural-networks-for-classification-in-python/ \n",
        "### tanh Activation function in all layers and sigmoid in output layer ###\n",
        "model_tanh = Sequential()\n",
        " \n",
        "# Defining the Input layer and FIRST hidden layer, both are same!\n",
        "model_tanh.add(Dense(19, input_shape=(18,), kernel_initializer=initializer, bias_initializer=\"zeros\", activation='relu'))\n",
        " \n",
        "# Defining the Second layer of the model\n",
        "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
        "model_tanh.add(Dense(units=5, kernel_initializer=initializer, bias_initializer=\"zeros\",activation='tanh'))\n",
        " \n",
        "# The output neuron is a single fully connected node \n",
        "#\n",
        "model_tanh.add(Dense(1, kernel_initializer=initializer,activation='sigmoid'))\n",
        " \n",
        "# Compiling the model\n",
        "model_tanh.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        " \n",
        "# Fitting the ANN to the Training set\n",
        "model_tanh.fit(X_train, y_train ,batch_size = 256, epochs = 30, verbose=1)\n",
        "\n",
        "# Generating Predictions on testing data\n",
        "Predictions_tanh=model_tanh.predict(X_test)\n",
        "\n",
        "#TestingData=pd.DataFrame(data=X_test.values, columns=PredictorVariables)\n",
        "#TestingData['transact']=y_test.values\n",
        "TestingData['PredictedtransactTanh']=Predictions_tanh\n",
        "\n",
        "#transform Predicted transact from decimal to 0 or 1 for Confusion Matrix & insert into Fairlearn\n",
        "TestingData[\"PredictedtransactTanhClass\"]= np.where(TestingData['PredictedtransactTanh']>=0.5, 1, 0)\n",
        "print(y_test.dtype)\n",
        "print(TestingData.sample(11))\n",
        "model_tanh.evaluate(X_test, y_test)\n",
        "\n",
        "#Fairlearn https://fairlearn.org/v0.7.0/quickstart.html\n",
        "gm_tanh = MetricFrame(metrics=accuracy_score, y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactTanhClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "print(\"\\n\")\n",
        "print(\"Model score: \")\n",
        "print(gm_tanh.overall)\n",
        "\n",
        "print(\"\\n\" \"Model score by category: \")\n",
        "print(gm_tanh.by_group)\n",
        "print(' Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \\n')\n",
        "\n",
        "#FairLearn Metrics\n",
        "sr_tanh = MetricFrame(metrics=selection_rate,y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactTanhClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "sr_tanh.overall\n",
        "print(\"1.0==female, 0.0==male\")\n",
        "sr_tanh.by_group\n",
        "#transform Sensative Varible from 0 or 1 to \"male\" or \"female\" for plotting\n",
        "#TestingData[\"GenderClass\"]= np.where(TestingData['gender']>=1, \"female\",\"male\")\n",
        "#print(TestingData.head)\n",
        "metrics = {\n",
        "    'accuracy': accuracy_score,\n",
        "    'precision': precision_score,\n",
        "    'recall': recall_score,\n",
        "    'false positive rate': false_positive_rate,\n",
        "    'true positive rate': true_positive_rate,\n",
        "    'selection rate': selection_rate,\n",
        "    'count': count}\n",
        "metric_frame_tanh = MetricFrame(metrics=metrics,\n",
        "                           y_true=TestingData['transact'],\n",
        "                           y_pred=TestingData['PredictedtransactTanhClass'],\n",
        "                           sensitive_features=TestingData[\"GenderClass\"])\n",
        "metric_frame_tanh.by_group.plot.bar(\n",
        "    subplots=True,\n",
        "    layout=[3, 3],\n",
        "    legend=False,\n",
        "    figsize=[12, 8],\n",
        "    title=\"Show all tanh metrics\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 1.1264 - accuracy: 0.4428\n",
            "Epoch 2/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2482 - accuracy: 0.9332\n",
            "Epoch 3/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2269 - accuracy: 0.9366\n",
            "Epoch 4/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2214 - accuracy: 0.9382\n",
            "Epoch 5/30\n",
            "270/270 [==============================] - 1s 3ms/step - loss: 0.2175 - accuracy: 0.9392\n",
            "Epoch 6/30\n",
            "270/270 [==============================] - 1s 3ms/step - loss: 0.2133 - accuracy: 0.9401\n",
            "Epoch 7/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2096 - accuracy: 0.9407\n",
            "Epoch 8/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2062 - accuracy: 0.9414\n",
            "Epoch 9/30\n",
            "270/270 [==============================] - 0s 2ms/step - loss: 0.2030 - accuracy: 0.9418\n",
            "Epoch 10/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1998 - accuracy: 0.9417\n",
            "Epoch 11/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1969 - accuracy: 0.9420\n",
            "Epoch 12/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1944 - accuracy: 0.9423\n",
            "Epoch 13/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1919 - accuracy: 0.9424\n",
            "Epoch 14/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1897 - accuracy: 0.9427\n",
            "Epoch 15/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1880 - accuracy: 0.9430\n",
            "Epoch 16/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1865 - accuracy: 0.9432\n",
            "Epoch 17/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1853 - accuracy: 0.9435\n",
            "Epoch 18/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1840 - accuracy: 0.9435\n",
            "Epoch 19/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1830 - accuracy: 0.9440\n",
            "Epoch 20/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1821 - accuracy: 0.9439\n",
            "Epoch 21/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1816 - accuracy: 0.9441\n",
            "Epoch 22/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1807 - accuracy: 0.9441\n",
            "Epoch 23/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1803 - accuracy: 0.9443\n",
            "Epoch 24/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1796 - accuracy: 0.9444\n",
            "Epoch 25/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1791 - accuracy: 0.9445\n",
            "Epoch 26/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1786 - accuracy: 0.9444\n",
            "Epoch 27/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1782 - accuracy: 0.9445\n",
            "Epoch 28/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1779 - accuracy: 0.9445\n",
            "Epoch 29/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1775 - accuracy: 0.9444\n",
            "Epoch 30/30\n",
            "270/270 [==============================] - 0s 2ms/step - loss: 0.1769 - accuracy: 0.9445\n",
            "int64\n",
            "       daysSinceLastLogin  ...  PredictedtransactTanhClass\n",
            "20229            0.000205  ...                           0\n",
            "5616             0.000285  ...                           0\n",
            "6025             0.000939  ...                           0\n",
            "10868            0.000355  ...                           0\n",
            "20167            0.000924  ...                           0\n",
            "503              0.000604  ...                           0\n",
            "9128             0.000077  ...                           0\n",
            "28294            0.000908  ...                           0\n",
            "20850            0.000923  ...                           0\n",
            "17931            0.000771  ...                           0\n",
            "4751             0.000844  ...                           0\n",
            "\n",
            "[11 rows x 24 columns]\n",
            "924/924 [==============================] - 2s 2ms/step - loss: 0.1818 - accuracy: 0.9420\n",
            "\n",
            "\n",
            "Model score: \n",
            "0.9420201610175225\n",
            "\n",
            "Model score by category: \n",
            "Gender_Byte\n",
            "0.0    0.932325\n",
            "1.0    0.944923\n",
            "Name: accuracy_score, dtype: object\n",
            " Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \n",
            "\n",
            "1.0==female, 0.0==male\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5d10850>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5cc9d90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5d10e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a98e4050>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a990f9d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5be2950>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5c14d50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5bc2b90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a5b5a090>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWYRSgbVFpjz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "2ffc078f-92f9-4762-9f3e-f5f41a114578"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9fe26aa5-1dc3-474b-a08d-4cc015b36f1f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "      <th>PredictedtransactTanh</th>\n",
              "      <th>PredictedtransactTanhClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.057107</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.036658</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</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>0.06015</td>\n",
              "      <td>0.000783</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036482</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.026779</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fe26aa5-1dc3-474b-a08d-4cc015b36f1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9fe26aa5-1dc3-474b-a08d-4cc015b36f1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "   daysSinceLastLogin  ...  PredictedtransactTanhClass\n",
              "0            0.000947  ...                           0\n",
              "1            0.000947  ...                           0\n",
              "2            0.000947  ...                           0\n",
              "3            0.000787  ...                           0\n",
              "4            0.000947  ...                           0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1RyYbiZSdQ4"
      },
      "source": [
        "## Selu Activation Function\n",
        "\n",
        "Scaled Exponential Linear Unit (SELU).\n",
        "\n",
        "https://keras.io/api/layers/activations/#selu-function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWfUGDVxsOmQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "de4418a2-f017-4807-9f6e-ac0e5915c416"
      },
      "source": [
        "# create ANN model https://thinkingneuron.com/how-to-use-artificial-neural-networks-for-classification-in-python/\n",
        "### Selu Activation function in all layers and sigmoid in output layer ###\n",
        "model_selu = Sequential()\n",
        " \n",
        "# Defining the Input layer and FIRST hidden layer, both are same!\n",
        "model_selu.add(Dense(19, input_shape=(18,), kernel_initializer=initializer, bias_initializer=\"zeros\", activation='relu'))\n",
        " \n",
        "# Defining the Second layer of the model\n",
        "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
        "model_selu.add(Dense(units=5, kernel_initializer=initializer, bias_initializer=\"zeros\", activation='selu'))\n",
        " \n",
        "# The output neuron is a single fully connected node \n",
        "#\n",
        "model_selu.add(Dense(1, kernel_initializer=initializer,activation='sigmoid'))\n",
        " \n",
        "# Compiling the model\n",
        "model_selu.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        " \n",
        "# Fitting the ANN to the Training set\n",
        "model_selu.fit(X_train, y_train ,batch_size = 256, epochs = 30, verbose=1)\n",
        "\n",
        "# Generating Predictions on testing data\n",
        "Predictions_selu=model_selu.predict(X_test)\n",
        "\n",
        "#TestingData=pd.DataFrame(data=X_test.values, columns=PredictorVariables)\n",
        "#TestingData['transact']=y_test.values\n",
        "TestingData['PredictedtransactSelu']=Predictions_selu\n",
        "\n",
        "#transform Predicted transact from decimal to 0 or 1 for Confusion Matrix & insert into Fairlearn\n",
        "TestingData[\"PredictedtransactSeluClass\"]= np.where(TestingData['PredictedtransactSelu']>=0.5, 1, 0)\n",
        "print(y_test.dtype)\n",
        "print(TestingData.sample(11))\n",
        "model_selu.evaluate(X_test, y_test)\n",
        "\n",
        "#Fairlearn https://fairlearn.org/v0.7.0/quickstart.html\n",
        "gm_selu = MetricFrame(metrics=accuracy_score, y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactSeluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "print(\"\\n\")\n",
        "print(\"Model score: \")\n",
        "print(gm_selu.overall)\n",
        "\n",
        "print(\"\\n\" \"Model score by category: \")\n",
        "print(gm_selu.by_group)\n",
        "print(' Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \\n')\n",
        "\n",
        "#FairLearn Metrics\n",
        "sr_selu = MetricFrame(metrics=selection_rate,y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactSeluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "sr_selu.overall\n",
        "print(\"1.0==female, 0.0==male\")\n",
        "sr_selu.by_group\n",
        "#transform Sensative Varible from 0 or 1 to \"male\" or \"female\" for plotting\n",
        "#TestingData[\"GenderClass\"]= np.where(TestingData['Gender_Byte']>=1, \"female\",\"male\")\n",
        "#print(TestingData.head)\n",
        "metrics = {\n",
        "    'accuracy': accuracy_score,\n",
        "    'precision': precision_score,\n",
        "    'recall': recall_score,\n",
        "    'false positive rate': false_positive_rate,\n",
        "    'true positive rate': true_positive_rate,\n",
        "    'selection rate': selection_rate,\n",
        "    'count': count}\n",
        "metric_frame_selu = MetricFrame(metrics=metrics,\n",
        "                           y_true=TestingData['transact'],\n",
        "                           y_pred=TestingData['PredictedtransactSeluClass'],\n",
        "                           sensitive_features=TestingData[\"GenderClass\"])\n",
        "metric_frame_selu.by_group.plot.bar(\n",
        "    subplots=True,\n",
        "    layout=[3, 3],\n",
        "    legend=False,\n",
        "    figsize=[12, 8],\n",
        "    title=\"Show all selu metrics\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.5320 - accuracy: 0.8750\n",
            "Epoch 2/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2718 - accuracy: 0.9281\n",
            "Epoch 3/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2322 - accuracy: 0.9334\n",
            "Epoch 4/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2243 - accuracy: 0.9358\n",
            "Epoch 5/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2182 - accuracy: 0.9367\n",
            "Epoch 6/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2135 - accuracy: 0.9380\n",
            "Epoch 7/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2094 - accuracy: 0.9391\n",
            "Epoch 8/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2058 - accuracy: 0.9403\n",
            "Epoch 9/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2024 - accuracy: 0.9411\n",
            "Epoch 10/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2000 - accuracy: 0.9418\n",
            "Epoch 11/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1979 - accuracy: 0.9425\n",
            "Epoch 12/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1958 - accuracy: 0.9429\n",
            "Epoch 13/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1940 - accuracy: 0.9431\n",
            "Epoch 14/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1923 - accuracy: 0.9434\n",
            "Epoch 15/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1910 - accuracy: 0.9436\n",
            "Epoch 16/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1897 - accuracy: 0.9437\n",
            "Epoch 17/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1886 - accuracy: 0.9437\n",
            "Epoch 18/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1875 - accuracy: 0.9437\n",
            "Epoch 19/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1865 - accuracy: 0.9438\n",
            "Epoch 20/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1857 - accuracy: 0.9439\n",
            "Epoch 21/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1850 - accuracy: 0.9442\n",
            "Epoch 22/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1843 - accuracy: 0.9442\n",
            "Epoch 23/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1837 - accuracy: 0.9443\n",
            "Epoch 24/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1830 - accuracy: 0.9442\n",
            "Epoch 25/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1826 - accuracy: 0.9443\n",
            "Epoch 26/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1819 - accuracy: 0.9442\n",
            "Epoch 27/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1816 - accuracy: 0.9443\n",
            "Epoch 28/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1811 - accuracy: 0.9443\n",
            "Epoch 29/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1809 - accuracy: 0.9444\n",
            "Epoch 30/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1802 - accuracy: 0.9443\n",
            "int64\n",
            "       daysSinceLastLogin  ...  PredictedtransactSeluClass\n",
            "10276            0.000935  ...                           0\n",
            "11813            0.000391  ...                           0\n",
            "9480             0.000936  ...                           0\n",
            "24921            0.000938  ...                           0\n",
            "25321            0.000737  ...                           0\n",
            "6484             0.000939  ...                           0\n",
            "12056            0.000871  ...                           0\n",
            "17140            0.000928  ...                           0\n",
            "16532            0.000031  ...                           0\n",
            "12474            0.000887  ...                           0\n",
            "16821            0.000928  ...                           0\n",
            "\n",
            "[11 rows x 26 columns]\n",
            "924/924 [==============================] - 1s 1ms/step - loss: 0.1839 - accuracy: 0.9429\n",
            "\n",
            "\n",
            "Model score: \n",
            "0.9429334957039442\n",
            "\n",
            "Model score by category: \n",
            "Gender_Byte\n",
            "0.0    0.934234\n",
            "1.0    0.945538\n",
            "Name: accuracy_score, dtype: object\n",
            " Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \n",
            "\n",
            "1.0==female, 0.0==male\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a98bef50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0f83210>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0fc5550>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a17ecf50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0edcb90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0f0ff10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0ed1350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0e8a610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0e8a650>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xL5Z3qGhFt7z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "c7e56a07-8aaf-4af6-a53c-26059634106a"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-da8e5e99-b40e-4857-887e-a2d66832f606\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "      <th>PredictedtransactTanh</th>\n",
              "      <th>PredictedtransactTanhClass</th>\n",
              "      <th>PredictedtransactSelu</th>\n",
              "      <th>PredictedtransactSeluClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.057107</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.036658</td>\n",
              "      <td>0</td>\n",
              "      <td>0.043551</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</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>0.06015</td>\n",
              "      <td>0.000783</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036482</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.026779</td>\n",
              "      <td>0</td>\n",
              "      <td>0.028357</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da8e5e99-b40e-4857-887e-a2d66832f606')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-da8e5e99-b40e-4857-887e-a2d66832f606 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-da8e5e99-b40e-4857-887e-a2d66832f606');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "   daysSinceLastLogin  ...  PredictedtransactSeluClass\n",
              "0            0.000947  ...                           0\n",
              "1            0.000947  ...                           0\n",
              "2            0.000947  ...                           0\n",
              "3            0.000787  ...                           0\n",
              "4            0.000947  ...                           0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7tekLGXSjQn"
      },
      "source": [
        "## Elu Activation Function\n",
        "\n",
        "Exponential Linear Unit.\n",
        "\n",
        "https://keras.io/api/layers/activations/#elu-function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NH51L12sbFP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "df6662ec-bc27-42a3-c37e-c4afc69e6854"
      },
      "source": [
        "# create ANN model https://thinkingneuron.com/how-to-use-artificial-neural-networks-for-classification-in-python/\n",
        "### elu Activation function in all layers and sigmoid in output layer ###\n",
        "model_elu = Sequential()\n",
        " \n",
        "# Defining the Input layer and FIRST hidden layer, both are same!\n",
        "model_elu.add(Dense(19, input_shape=(18,), kernel_initializer=initializer, bias_initializer=\"zeros\", activation='relu'))\n",
        " \n",
        "# Defining the Second layer of the model\n",
        "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
        "model_elu.add(Dense(units=5, kernel_initializer=initializer, bias_initializer=\"zeros\", activation='elu'))\n",
        " \n",
        "# The output neuron is a single fully connected node \n",
        "#\n",
        "model_elu.add(Dense(1, kernel_initializer=initializer, activation='sigmoid'))\n",
        " \n",
        "# Compiling the model\n",
        "model_elu.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        " \n",
        "# Fitting the ANN to the Training set\n",
        "model_elu.fit(X_train, y_train ,batch_size = 256, epochs = 30, verbose=1)\n",
        "\n",
        "# Generating Predictions on testing data\n",
        "Predictions_elu=model_elu.predict(X_test)\n",
        "\n",
        "#TestingData=pd.DataFrame(data=X_test.values, columns=PredictorVariables)\n",
        "#TestingData['transact']=y_test.values\n",
        "TestingData['PredictedtransactElu']=Predictions_elu\n",
        "\n",
        "#transform Predicted transact from decimal to 0 or 1 for Confusion Matrix & insert into Fairlearn\n",
        "TestingData[\"PredictedtransactEluClass\"]= np.where(TestingData['PredictedtransactElu']>=0.5, 1, 0)\n",
        "print(y_test.dtype)\n",
        "print(TestingData.sample(11))\n",
        "model_elu.evaluate(X_test, y_test)\n",
        "\n",
        "#Fairlearn https://fairlearn.org/v0.7.0/quickstart.html\n",
        "gm_elu = MetricFrame(metrics=accuracy_score, y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactEluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "print(\"\\n\")\n",
        "print(\"Model score: \")\n",
        "print(gm_elu.overall)\n",
        "\n",
        "print(\"\\n\" \"Model score by category: \")\n",
        "print(gm_elu.by_group)\n",
        "print(' Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \\n')\n",
        "\n",
        "#FairLearn Metrics\n",
        "sr_elu = MetricFrame(metrics=selection_rate,y_true=TestingData['transact'], y_pred=TestingData['PredictedtransactEluClass'], sensitive_features=TestingData['Gender_Byte'])\n",
        "sr_elu.overall\n",
        "print(\"1.0==female, 0.0==male\")\n",
        "sr_elu.by_group\n",
        "#transform Sensative Varible from 0 or 1 to \"male\" or \"female\" for plotting\n",
        "#TestingData[\"GenderClass\"]= np.where(TestingData['Gender_Byte']>=1, \"female\",\"male\")\n",
        "#print(TestingData.head)\n",
        "metrics = {\n",
        "    'accuracy': accuracy_score,\n",
        "    'precision': precision_score,\n",
        "    'recall': recall_score,\n",
        "    'false positive rate': false_positive_rate,\n",
        "    'true positive rate': true_positive_rate,\n",
        "    'selection rate': selection_rate,\n",
        "    'count': count}\n",
        "metric_frame_elu = MetricFrame(metrics=metrics,\n",
        "                           y_true=TestingData['transact'],\n",
        "                           y_pred=TestingData['PredictedtransactEluClass'],\n",
        "                           sensitive_features=TestingData[\"GenderClass\"])\n",
        "metric_frame_elu.by_group.plot.bar(\n",
        "    subplots=True,\n",
        "    layout=[3, 3],\n",
        "    legend=False,\n",
        "    figsize=[12, 8],\n",
        "    title=\"Show all elu metrics\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 1.2942 - accuracy: 0.6166\n",
            "Epoch 2/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.3323 - accuracy: 0.9117\n",
            "Epoch 3/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2365 - accuracy: 0.9381\n",
            "Epoch 4/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2204 - accuracy: 0.9399\n",
            "Epoch 5/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2135 - accuracy: 0.9411\n",
            "Epoch 6/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2095 - accuracy: 0.9417\n",
            "Epoch 7/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2066 - accuracy: 0.9423\n",
            "Epoch 8/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2043 - accuracy: 0.9424\n",
            "Epoch 9/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2023 - accuracy: 0.9426\n",
            "Epoch 10/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.2005 - accuracy: 0.9427\n",
            "Epoch 11/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1988 - accuracy: 0.9428\n",
            "Epoch 12/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1974 - accuracy: 0.9431\n",
            "Epoch 13/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1959 - accuracy: 0.9432\n",
            "Epoch 14/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1945 - accuracy: 0.9434\n",
            "Epoch 15/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1933 - accuracy: 0.9434\n",
            "Epoch 16/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1923 - accuracy: 0.9433\n",
            "Epoch 17/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1912 - accuracy: 0.9436\n",
            "Epoch 18/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1902 - accuracy: 0.9436\n",
            "Epoch 19/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1893 - accuracy: 0.9436\n",
            "Epoch 20/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1886 - accuracy: 0.9438\n",
            "Epoch 21/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1879 - accuracy: 0.9438\n",
            "Epoch 22/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1872 - accuracy: 0.9438\n",
            "Epoch 23/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1865 - accuracy: 0.9439\n",
            "Epoch 24/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1858 - accuracy: 0.9437\n",
            "Epoch 25/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1852 - accuracy: 0.9438\n",
            "Epoch 26/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1845 - accuracy: 0.9440\n",
            "Epoch 27/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1840 - accuracy: 0.9440\n",
            "Epoch 28/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1835 - accuracy: 0.9440\n",
            "Epoch 29/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1830 - accuracy: 0.9440\n",
            "Epoch 30/30\n",
            "270/270 [==============================] - 1s 2ms/step - loss: 0.1822 - accuracy: 0.9440\n",
            "int64\n",
            "       daysSinceLastLogin  ...  PredictedtransactEluClass\n",
            "7501             0.000938  ...                          0\n",
            "17178            0.000928  ...                          0\n",
            "28883            1.000000  ...                          0\n",
            "15082            0.000924  ...                          0\n",
            "15522            0.000929  ...                          0\n",
            "13832            0.000889  ...                          0\n",
            "4899             0.000075  ...                          0\n",
            "7252             0.000938  ...                          0\n",
            "720              0.000004  ...                          0\n",
            "7418             0.000938  ...                          0\n",
            "5338             0.000179  ...                          0\n",
            "\n",
            "[11 rows x 28 columns]\n",
            "924/924 [==============================] - 1s 1ms/step - loss: 0.1858 - accuracy: 0.9426\n",
            "\n",
            "\n",
            "Model score: \n",
            "0.9425613963872539\n",
            "\n",
            "Model score by category: \n",
            "Gender_Byte\n",
            "0.0    0.931885\n",
            "1.0    0.945758\n",
            "Name: accuracy_score, dtype: object\n",
            " Remember, from data wrangling, IF [Gender_Byte]=\"female\" THEN \"1\" ELSE \"0\" ENDIF\" \n",
            "\n",
            "1.0==female, 0.0==male\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0b6fa50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0b25250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0b6f750>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0b25850>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0b05c10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0abcf90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0a7d3d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0a34690>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23a0a346d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKTwruYIF3EQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "3c9611c4-2ca0-4011-96cf-0cede4614a35"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ae042a12-2829-4a7a-90e8-b5b3e92b2b65\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "      <th>PredictedtransactTanh</th>\n",
              "      <th>PredictedtransactTanhClass</th>\n",
              "      <th>PredictedtransactSelu</th>\n",
              "      <th>PredictedtransactSeluClass</th>\n",
              "      <th>PredictedtransactElu</th>\n",
              "      <th>PredictedtransactEluClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "      <td>0.034608</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.057107</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.036658</td>\n",
              "      <td>0</td>\n",
              "      <td>0.043551</td>\n",
              "      <td>0</td>\n",
              "      <td>0.049436</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "      <td>0.034608</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</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>0.06015</td>\n",
              "      <td>0.000783</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036482</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.026779</td>\n",
              "      <td>0</td>\n",
              "      <td>0.028357</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035453</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "      <td>0.034608</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae042a12-2829-4a7a-90e8-b5b3e92b2b65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae042a12-2829-4a7a-90e8-b5b3e92b2b65 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae042a12-2829-4a7a-90e8-b5b3e92b2b65');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   daysSinceLastLogin  ...  PredictedtransactEluClass\n",
              "0            0.000947  ...                          0\n",
              "1            0.000947  ...                          0\n",
              "2            0.000947  ...                          0\n",
              "3            0.000787  ...                          0\n",
              "4            0.000947  ...                          0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVD8J5nMnqVT"
      },
      "source": [
        "# Fairlearn Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rseY_tMOSxHp"
      },
      "source": [
        "## Overall Model Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RV10lI91Dy_4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81c35317-3125-454a-987d-319b944caed4"
      },
      "source": [
        "print (\"Relu\\n\", metric_frame_relu.overall, \n",
        "      \"\\n\\nTanh\\n\", metric_frame_tanh.overall,\n",
        "      \"\\n\\nSelu\\n\", metric_frame_selu.overall,\n",
        "      \"\\n\\nElu\\n\", metric_frame_elu.overall,\n",
        "      \"\\n\\nExponential\\n\", metric_frame_exponential.overall,\n",
        "      \"\\n\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relu\n",
            " accuracy                 0.942223\n",
            "precision                0.755481\n",
            "recall                   0.222775\n",
            "false positive rate    0.00526297\n",
            "true positive rate       0.222775\n",
            "selection rate          0.0200595\n",
            "count                       29562\n",
            "dtype: object \n",
            "\n",
            "Tanh\n",
            " accuracy                  0.94202\n",
            "precision                0.711238\n",
            "recall                   0.248633\n",
            "false positive rate    0.00736815\n",
            "true positive rate       0.248633\n",
            "selection rate          0.0237805\n",
            "count                       29562\n",
            "dtype: object \n",
            "\n",
            "Selu\n",
            " accuracy                  0.9429\n",
            "precision               0.729078\n",
            "recall                  0.255594\n",
            "false positive rate    0.0069326\n",
            "true positive rate      0.255594\n",
            "selection rate         0.0238482\n",
            "count                      29562\n",
            "dtype: object \n",
            "\n",
            "Elu\n",
            " accuracy                 0.942528\n",
            "precision                0.730088\n",
            "recall                   0.246146\n",
            "false positive rate    0.00664223\n",
            "true positive rate       0.246146\n",
            "selection rate          0.0229348\n",
            "count                       29562\n",
            "dtype: object \n",
            "\n",
            "Exponential\n",
            " accuracy                0.923415\n",
            "precision               0.310912\n",
            "recall                  0.103431\n",
            "false positive rate    0.0167326\n",
            "true positive rate      0.103431\n",
            "selection rate         0.0226304\n",
            "count                      29562\n",
            "dtype: object \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8F3FclPS4Ji"
      },
      "source": [
        "## By Group Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJaQGxVkXXAE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74bcb9c1-8ad4-4fce-add9-6987259acf8e"
      },
      "source": [
        "print(\"Relu       =\", metric_frame_relu.by_group.to_dict(),\"\\n\")\n",
        "#print(\"Sigmoid    =\", metric_frame_sigmoid.by_group.to_dict(),\"\\n\")\n",
        "#print(\"Softmax    =\", metric_frame_softmax.by_group.to_dict(),\"\\n\")\n",
        "#print(\"Softplus   =\", metric_frame_softplus.by_group.to_dict(),\"\\n\")\n",
        "#print(\"Softsign   =\", metric_frame_softsign.by_group.to_dict(),\"\\n\")\n",
        "print(\"Tanh       =\", metric_frame_tanh.by_group.to_dict(),\"\\n\")\n",
        "print(\"Selu       =\", metric_frame_selu.by_group.to_dict(),\"\\n\")\n",
        "print(\"Elu        =\", metric_frame_elu.by_group.to_dict(),\"\\n\")\n",
        "print(\"Exponential=\", metric_frame_exponential.by_group.to_dict(),\"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relu       = {'accuracy': {'female': 0.945010989010989, 'male': 0.9329125073399882}, 'precision': {'female': 0.7554585152838428, 'male': 0.7555555555555555}, 'recall': {'female': 0.232996632996633, 'male': 0.19391634980988592}, 'false positive rate': {'female': 0.005266870444392194, 'male': 0.005249761374482978}, 'true positive rate': {'female': 0.232996632996633, 'male': 0.19391634980988592}, 'selection rate': {'female': 0.02013186813186813, 'male': 0.019817968291250734}, 'count': {'female': 22750, 'male': 6812}} \n",
            "\n",
            "Tanh       = {'accuracy': {'female': 0.9448791208791208, 'male': 0.9324721080446271}, 'precision': {'female': 0.7103825136612022, 'male': 0.7142857142857143}, 'recall': {'female': 0.26262626262626265, 'male': 0.20912547528517111}, 'false positive rate': {'female': 0.007477075005878204, 'male': 0.006999681832643971}, 'true positive rate': {'female': 0.26262626262626265, 'male': 0.20912547528517111}, 'selection rate': {'female': 0.024131868131868132, 'male': 0.022607163828537873}, 'count': {'female': 22750, 'male': 6812}} \n",
            "\n",
            "Selu       = {'accuracy': {'female': 0.9455384615384615, 'male': 0.9340869054609513}, 'precision': {'female': 0.7303370786516854, 'male': 0.7251461988304093}, 'recall': {'female': 0.26262626262626265, 'male': 0.23574144486692014}, 'false positive rate': {'female': 0.006771690571361392, 'male': 0.007476932866687878}, 'true positive rate': {'female': 0.26262626262626265, 'male': 0.23574144486692014}, 'selection rate': {'female': 0.023472527472527472, 'male': 0.025102759835584264}, 'count': {'female': 22750, 'male': 6812}} \n",
            "\n",
            "Elu        = {'accuracy': {'female': 0.9457142857142857, 'male': 0.9318849089841457}, 'precision': {'female': 0.7314814814814815, 'male': 0.7246376811594203}, 'recall': {'female': 0.265993265993266, 'male': 0.19011406844106463}, 'false positive rate': {'female': 0.006818716200329179, 'male': 0.006045179764556157}, 'true positive rate': {'female': 0.265993265993266, 'male': 0.19011406844106463}, 'selection rate': {'female': 0.023736263736263738, 'male': 0.020258367586611863}, 'count': {'female': 22750, 'male': 6812}} \n",
            "\n",
            "Exponential= {'accuracy': {'female': 0.9362637362637363, 'male': 0.8805049911920141}, 'precision': {'female': 0.5650557620817844, 'male': 0.14}, 'recall': {'female': 0.10235690235690235, 'male': 0.10646387832699619}, 'false positive rate': {'female': 0.005501998589231131, 'male': 0.05472478523703468}, 'true positive rate': {'female': 0.10235690235690235, 'male': 0.10646387832699619}, 'selection rate': {'female': 0.011824175824175824, 'male': 0.058719906048150326}, 'count': {'female': 22750, 'male': 6812}} \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQgS0toTQJw_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "f6fd5843-de3a-46f3-924b-33ea075f417c"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-62c56921-648c-4485-a185-2fb2922a3673\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "      <th>PredictedtransactTanh</th>\n",
              "      <th>PredictedtransactTanhClass</th>\n",
              "      <th>PredictedtransactSelu</th>\n",
              "      <th>PredictedtransactSeluClass</th>\n",
              "      <th>PredictedtransactElu</th>\n",
              "      <th>PredictedtransactEluClass</th>\n",
              "      <th>PredictedtransactExponential</th>\n",
              "      <th>PredictedtransactExponentialClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
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              "      <td>0.057107</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.028357</td>\n",
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              "      <td>0.035453</td>\n",
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              "      <td>0.073418</td>\n",
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              "      <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>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.035636</td>\n",
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              "      <td>0.026793</td>\n",
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              "      <td>0.034608</td>\n",
              "      <td>0</td>\n",
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              "          const element = document.querySelector('#df-62c56921-648c-4485-a185-2fb2922a3673');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   daysSinceLastLogin  ...  PredictedtransactExponentialClass\n",
              "0            0.000947  ...                                  0\n",
              "1            0.000947  ...                                  0\n",
              "2            0.000947  ...                                  0\n",
              "3            0.000787  ...                                  0\n",
              "4            0.000947  ...                                  0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sf2VqkOZcQe"
      },
      "source": [
        "# Gender variable DoubleCheck\n",
        "Compare to make sure Gender Variable is properly identified.  The count should mat counted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gux-f8Wz9Xvb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "cb509bc2-7cec-4566-aa8e-ec0cba243e40"
      },
      "source": [
        "rp.summary_cont(TestingData[\"Gender_Byte\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>N</th>\n",
              "      <th>Mean</th>\n",
              "      <th>SD</th>\n",
              "      <th>SE</th>\n",
              "      <th>95% Conf.</th>\n",
              "      <th>Interval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Gender_Byte</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aad9ba3a-46db-482d-8c5a-5190204f56b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "      Variable        N    Mean      SD      SE  95% Conf.  Interval\n",
              "0  Gender_Byte  29562.0  0.7696  0.4211  0.0024     0.7648    0.7744"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nt1lTAvfOmLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "22141c40-e842-4b8e-cd21-4ec30520a1a0"
      },
      "source": [
        "rp.summary_cat(TestingData[\"Gender_Byte\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Variable</th>\n",
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              "      <th>Count</th>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ac7b057-1f92-439a-8ce2-43ed04f8c511 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ac7b057-1f92-439a-8ce2-43ed04f8c511');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Variable  Outcome  Count  Percent\n",
              "0  Gender_Byte      1.0  22750    76.96\n",
              "1                   0.0   6812    23.04"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4g_jpsLGFetE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "d633e338-3e94-4893-fb15-9900b6dc5e00"
      },
      "source": [
        "print(\"\\n X_test head: \\n\")\n",
        "TestingData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " X_test head: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9fd4962b-92d2-4497-bd80-35fae6df22d4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>daysSinceLastLogin</th>\n",
              "      <th>productsListed</th>\n",
              "      <th>productsPassRate</th>\n",
              "      <th>productsWished</th>\n",
              "      <th>seniorityAsMonths</th>\n",
              "      <th>socialNbFollowers</th>\n",
              "      <th>socialNbFollows</th>\n",
              "      <th>socialProductsLiked</th>\n",
              "      <th>language_de</th>\n",
              "      <th>language_en</th>\n",
              "      <th>language_es</th>\n",
              "      <th>language_fr</th>\n",
              "      <th>language_it</th>\n",
              "      <th>hasAnyApp</th>\n",
              "      <th>hasAndroidApp</th>\n",
              "      <th>hasIosApp</th>\n",
              "      <th>hasProfilePicture</th>\n",
              "      <th>Gender_Byte</th>\n",
              "      <th>transact</th>\n",
              "      <th>PredictedtransactRelu</th>\n",
              "      <th>PredictedtransactReluClass</th>\n",
              "      <th>GenderClass</th>\n",
              "      <th>PredictedtransactTanh</th>\n",
              "      <th>PredictedtransactTanhClass</th>\n",
              "      <th>PredictedtransactSelu</th>\n",
              "      <th>PredictedtransactSeluClass</th>\n",
              "      <th>PredictedtransactElu</th>\n",
              "      <th>PredictedtransactEluClass</th>\n",
              "      <th>PredictedtransactExponential</th>\n",
              "      <th>PredictedtransactExponentialClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
              "      <td>0.034608</td>\n",
              "      <td>0</td>\n",
              "      <td>0.073250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000947</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>0.06015</td>\n",
              "      <td>0.000000</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.057107</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.036658</td>\n",
              "      <td>0</td>\n",
              "      <td>0.043551</td>\n",
              "      <td>0</td>\n",
              "      <td>0.049436</td>\n",
              "      <td>0</td>\n",
              "      <td>0.023727</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000947</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
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              "      <td>0.026793</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.073250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000787</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.026779</td>\n",
              "      <td>0</td>\n",
              "      <td>0.028357</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0</td>\n",
              "      <td>female</td>\n",
              "      <td>0.025040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.026793</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fd4962b-92d2-4497-bd80-35fae6df22d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9fd4962b-92d2-4497-bd80-35fae6df22d4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9fd4962b-92d2-4497-bd80-35fae6df22d4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   daysSinceLastLogin  ...  PredictedtransactExponentialClass\n",
              "0            0.000947  ...                                  0\n",
              "1            0.000947  ...                                  0\n",
              "2            0.000947  ...                                  0\n",
              "3            0.000787  ...                                  0\n",
              "4            0.000947  ...                                  0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    }
  ]
}