CortexSASFelipe.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "44509e0a",
      "metadata": {
        "id": "44509e0a"
      },
      "source": [
        "![Python_logo](https://www.python.org/static/community_logos/python-logo-master-v3-TM.png)\n",
        "\n",
        "\n",
        "   # **Cortex Game: Round1--Amount**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e6bf1264-7b5f-4f0e-a622-c308e40cb8be",
      "metadata": {
        "id": "e6bf1264-7b5f-4f0e-a622-c308e40cb8be"
      },
      "source": [
        "> Before playing the game, you need to connect to SASPy first.\n",
        ">\n",
        ">> If it is your first time, please follow the 4 steps mentioned below!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1a48e10-b624-4430-9cd1-25869d600d71",
      "metadata": {
        "id": "d1a48e10-b624-4430-9cd1-25869d600d71"
      },
      "source": [
        "***\n",
        "## **Connect to SASPy**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**0- Connect to your Google Drive folder**"
      ],
      "metadata": {
        "id": "wgW_D1kOY6d6"
      },
      "id": "wgW_D1kOY6d6"
    },
    {
      "cell_type": "code",
      "source": [
        "my_folder = \"/content/drive/MyDrive/CortexSAS\"\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Change the following code to set your Drive folder\n",
        "import os\n",
        "os.chdir(my_folder)\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqAV8e1WY45k",
        "outputId": "80f43863-9233-43fb-b81a-a99b0bc952d1"
      },
      "id": "EqAV8e1WY45k",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive/CortexSAS\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec889f32",
      "metadata": {
        "id": "ec889f32"
      },
      "source": [
        "**1- Make sure that your Python version is 3.3 or higher as well as your Java version is 1.8.0_162 or higher**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6d369a1a",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6d369a1a",
        "outputId": "053a8361-535d-47ce-e6c4-0afae74dbbd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python is at /usr/local/bin/python\n",
            "Python 3.8.15\n"
          ]
        }
      ],
      "source": [
        "!echo \"Python is at\" $(which python)\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!echo \"Java is at\" $(which java)\n",
        "!/usr/bin/java -version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNG5tchbZn5m",
        "outputId": "672524ce-a3e5-4d2a-ac98-51afe68bda02"
      },
      "id": "iNG5tchbZn5m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Java is at /usr/bin/java\n",
            "openjdk version \"11.0.17\" 2022-10-18\n",
            "OpenJDK Runtime Environment (build 11.0.17+8-post-Ubuntu-1ubuntu218.04)\n",
            "OpenJDK 64-Bit Server VM (build 11.0.17+8-post-Ubuntu-1ubuntu218.04, mixed mode, sharing)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10e73c6f",
      "metadata": {
        "id": "10e73c6f"
      },
      "source": [
        "**2- Install SASPy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c053af0",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1c053af0",
        "outputId": "1aa1512f-4773-4625-92a6-45f2b58e80a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: saspy in /usr/local/lib/python3.8/dist-packages (4.4.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install saspy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9fb501a5",
      "metadata": {
        "id": "9fb501a5"
      },
      "source": [
        "**3- Create the configuration file \"sascfg_personal.py\"**\n",
        "Please, check that your Home Region is correct, you can check it at [ODA-SAS](https://welcome.oda.sas.com/home)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c1a441c5-17c6-4818-841a-b87cf9a864f8",
      "metadata": {
        "collapsed": true,
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1a441c5-17c6-4818-841a-b87cf9a864f8",
        "outputId": "7a6621e2-9662-4263-d03c-12e499968671"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting sascfg_personal.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile sascfg_personal.py\n",
        "SAS_config_names=['oda']\n",
        "oda = {'java' : '/usr/bin/java',\n",
        "#US Home Region 1\n",
        "'iomhost' : ['odaws01-usw2.oda.sas.com','odaws02-usw2.oda.sas.com','odaws03-usw2.oda.sas.com','odaws04-usw2.oda.sas.com'],\n",
        "#US Home Region 2\n",
        "#'iomhost' : ['odaws01-usw2-2.oda.sas.com','odaws02-usw2-2.oda.sas.com'],\n",
        "#European Home Region 1\n",
        "#'iomhost' : ['odaws01-euw1.oda.sas.com','odaws02-euw1.oda.sas.com'],\n",
        "#Asia Pacific Home Region 1\n",
        "#'iomhost' : ['odaws01-apse1.oda.sas.com','odaws02-apse1.oda.sas.com'],\n",
        "#Asia Pacific Home Region 2\n",
        "#'iomhost' : ['odaws01-apse1-2.oda.sas.com','odaws02-apse1-2.oda.sas.com'],\n",
        "'iomport' : 8591,\n",
        "'authkey' : 'oda',\n",
        "'encoding' : 'utf-8'\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4- Create your .authinfo**"
      ],
      "metadata": {
        "id": "nQ_YzHeRaWH8"
      },
      "id": "nQ_YzHeRaWH8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "If there is no .authinfo file, you can create this"
      ],
      "metadata": {
        "id": "-RZAKpvL_eFC"
      },
      "id": "-RZAKpvL_eFC"
    },
    {
      "cell_type": "code",
      "source": [
        "#%%writefile .authinfo\n",
        "#oda user a01658002@tec.mx password PASSWORD"
      ],
      "metadata": {
        "id": "XZ4wlGJ2aT71"
      },
      "id": "XZ4wlGJ2aT71",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Copy this file to home"
      ],
      "metadata": {
        "id": "001tNy2D_gA0"
      },
      "id": "001tNy2D_gA0"
    },
    {
      "cell_type": "code",
      "source": [
        "!cp .authinfo ~/.authinfo"
      ],
      "metadata": {
        "id": "KeduVWzNSoLr"
      },
      "id": "KeduVWzNSoLr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "11efebd8",
      "metadata": {
        "id": "11efebd8"
      },
      "source": [
        "**5- Establish Connection (Need to do this step each time you use SASPy)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "413ecaba",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "413ecaba",
        "outputId": "3a9343f1-f16e-4b81-dbff-d4292f07eb0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using SAS Config named: oda\n",
            "SAS Connection established. Subprocess id is 2546\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Access Method         = IOM\n",
              "SAS Config name       = oda\n",
              "SAS Config file       = /content/drive/MyDrive/CortexSAS/sascfg_personal.py\n",
              "WORK Path             = /saswork/SAS_work615E00006C70_odaws01-usw2.oda.sas.com/SAS_work9DB500006C70_odaws01-usw2.oda.sas.com/\n",
              "SAS Version           = 9.04.01M6P11072018\n",
              "SASPy Version         = 4.4.1\n",
              "Teach me SAS          = False\n",
              "Batch                 = False\n",
              "Results               = Pandas\n",
              "SAS Session Encoding  = utf-8\n",
              "Python Encoding value = utf-8\n",
              "SAS process Pid value = 27760\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import saspy\n",
        "sas_session = saspy.SASsession(cfgfile=\"/content/drive/MyDrive/CortexSAS/sascfg_personal.py\")\n",
        "sas_session"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "719fb2db-4238-4892-aab6-757f696ccd57",
      "metadata": {
        "id": "719fb2db-4238-4892-aab6-757f696ccd57"
      },
      "source": [
        "***\n",
        "## Connect to Cortex Data Sets"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a9e40645",
      "metadata": {
        "id": "a9e40645"
      },
      "source": [
        "Load Cortex datasets from SAS Studio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ps = sas_session.submit(\"\"\"\n",
        "    libname cortex '~/my_shared_file_links/u39842936/Cortex Data Sets';\n",
        "    \"\"\")\n",
        "print(ps[\"LOG\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0OUk4FSX32H",
        "outputId": "dbb882f0-29a5-4dc9-eced-7ed737c5063e"
      },
      "id": "e0OUk4FSX32H",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "5                                                          The SAS System                      Friday, December  2, 2022 12:02:00 PM\n",
            "\n",
            "24         ods listing close;ods html5 (id=saspy_internal) file=_tomods1 options(bitmap_mode='inline') device=svg style=HTMLBlue;\n",
            "24       ! ods graphics on / outputfmt=png;\n",
            "25         \n",
            "26         \n",
            "27             libname cortex '~/my_shared_file_links/u39842936/Cortex Data Sets';\n",
            "28         \n",
            "29         \n",
            "30         \n",
            "31         ods html5 (id=saspy_internal) close;ods listing;\n",
            "32         \n",
            "\n",
            "6                                                          The SAS System                      Friday, December  2, 2022 12:02:00 PM\n",
            "\n",
            "33         \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For local Jupyter"
      ],
      "metadata": {
        "id": "CuFNXAEFYgyK"
      },
      "id": "CuFNXAEFYgyK"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a99acdd",
      "metadata": {
        "collapsed": true,
        "id": "5a99acdd"
      },
      "outputs": [],
      "source": [
        "#%%SAS sas_session\n",
        "#libname cortex '~/my_shared_file_links/u39842936/Cortex Data Sets';"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "907b9c04",
      "metadata": {
        "id": "907b9c04"
      },
      "source": [
        "## Transform cloud SAS dataset to Python dataframe (pandas)\n",
        "\n",
        "\n",
        "> **For reference**:\n",
        "\n",
        "> 1. [Pandas library](https://pandas.pydata.org/docs/user_guide/index.html)\n",
        "\n",
        "> 2. [sklearn.model_selection for data partition](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9ee94088",
      "metadata": {
        "id": "9ee94088"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data1 = sas_session.sasdata2dataframe(\n",
        "table='hist',\n",
        "libref='cortex'\n",
        ")\n",
        "\n",
        "data2 = sas_session.sasdata2dataframe(\n",
        "table='target_rd1',\n",
        "libref='cortex'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a776622a-bd69-46fe-a704-f84e750baae9",
      "metadata": {
        "id": "a776622a-bd69-46fe-a704-f84e750baae9"
      },
      "source": [
        "## Merge the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ee3751bb-a1e1-4287-ad3a-625e9140302f",
      "metadata": {
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        "id": "ee3751bb-a1e1-4287-ad3a-625e9140302f",
        "outputId": "fde4472b-8296-4ca5-f78e-77272b8394eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID   LastName FirstName  Woman   Age    Salary  \\\n",
              "588648  2588649.0      SERRE    WALTER    0.0  21.0    4900.0   \n",
              "619634  2619635.0     CHICAS     JAMES    0.0  26.0  242600.0   \n",
              "655728  2655729.0     HESLER   FRANCES    1.0  32.0   83500.0   \n",
              "670843  2670844.0      TIGHE    NELSON    0.0  33.0   53700.0   \n",
              "299865  2299866.0  TARANTINO    MARTIN    0.0  70.0   26200.0   \n",
              "\n",
              "                   Education      City  SeniorList  NbActivities  ...  \\\n",
              "588648           High School      City         8.0           0.0  ...   \n",
              "619634           High School     Rural         0.0           0.0  ...   \n",
              "655728           High School  Downtown         9.0           0.0  ...   \n",
              "670843  University / College      City         7.0           1.0  ...   \n",
              "299865  University / College  Downtown         2.0           0.0  ...   \n",
              "\n",
              "        Recency  Frequency  Seniority  TotalGift  MinGift  MaxGift  \\\n",
              "588648      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "619634      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "655728      2.0        2.0        6.0      760.0     10.0    750.0   \n",
              "670843      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "299865      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "\n",
              "        GaveLastYear  AmtLastYear  GaveThisYear  AmtThisYear  \n",
              "588648           0.0          0.0           0.0          0.0  \n",
              "619634           1.0         50.0           0.0          0.0  \n",
              "655728           0.0          0.0           0.0          0.0  \n",
              "670843           0.0          0.0           0.0          0.0  \n",
              "299865           0.0          0.0           0.0          0.0  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>LastName</th>\n",
              "      <th>FirstName</th>\n",
              "      <th>Woman</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Education</th>\n",
              "      <th>City</th>\n",
              "      <th>SeniorList</th>\n",
              "      <th>NbActivities</th>\n",
              "      <th>...</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Seniority</th>\n",
              "      <th>TotalGift</th>\n",
              "      <th>MinGift</th>\n",
              "      <th>MaxGift</th>\n",
              "      <th>GaveLastYear</th>\n",
              "      <th>AmtLastYear</th>\n",
              "      <th>GaveThisYear</th>\n",
              "      <th>AmtThisYear</th>\n",
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              "  </thead>\n",
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              "      <th>588648</th>\n",
              "      <td>2588649.0</td>\n",
              "      <td>SERRE</td>\n",
              "      <td>WALTER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>4900.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>City</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>619634</th>\n",
              "      <td>2619635.0</td>\n",
              "      <td>CHICAS</td>\n",
              "      <td>JAMES</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>242600.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Rural</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>655728</th>\n",
              "      <td>2655729.0</td>\n",
              "      <td>HESLER</td>\n",
              "      <td>FRANCES</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>83500.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Downtown</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>760.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>750.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>670843</th>\n",
              "      <td>2670844.0</td>\n",
              "      <td>TIGHE</td>\n",
              "      <td>NELSON</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>53700.0</td>\n",
              "      <td>University / College</td>\n",
              "      <td>City</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299865</th>\n",
              "      <td>2299866.0</td>\n",
              "      <td>TARANTINO</td>\n",
              "      <td>MARTIN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>26200.0</td>\n",
              "      <td>University / College</td>\n",
              "      <td>Downtown</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5092abf-0e9d-45f7-84e2-53e58533e790')\"\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-a5092abf-0e9d-45f7-84e2-53e58533e790 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-a5092abf-0e9d-45f7-84e2-53e58533e790');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "data_merge = pd.merge(data1, data2, on=[\"ID\"],how=\"right\")\n",
        "data_merge.sample(5)\n",
        "\n",
        "#data_merge.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_merge.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDwXtBuDkjXf",
        "outputId": "edb77a00-1bc8-4829-fb3e-c30543714be7"
      },
      "id": "qDwXtBuDkjXf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'LastName', 'FirstName', 'Woman', 'Age', 'Salary', 'Education',\n",
              "       'City', 'SeniorList', 'NbActivities', 'Referrals', 'Recency',\n",
              "       'Frequency', 'Seniority', 'TotalGift', 'MinGift', 'MaxGift',\n",
              "       'GaveLastYear', 'AmtLastYear', 'GaveThisYear', 'AmtThisYear'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Felipe"
      ],
      "metadata": {
        "id": "u0IgTf_s1O_c"
      },
      "id": "u0IgTf_s1O_c"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Poner mas alto  - Recency, Seniority\n",
        "\n",
        "\n",
        "1. Rellenar todos los nan (NO)\n",
        "\tSe puede predecir algunos nan con modelo NO\n",
        "2. TODO dataset predecir anterior año, target AmtLastYear -> AmtGiven\n",
        "3. Actualizar COPIA TODO dataset con anterior año GaveLastYear = 1, target AmtThisYear -> AmtGiven\n",
        "4. Juntar TODO y COPIA TODO ACTUALIZADA predecir AmtGiven\n",
        "\n",
        "\n",
        "\n",
        "Se puede crear un meanGift\n",
        "Se puede crear un GaveLastYear para todo dataset original con Recency = 1\n",
        "\n",
        "SeniorList sirve\n",
        "Referrals sirve\n",
        "\n",
        "Se puede hacer drop de Recency despu[es de procesar cuando = 1 y sacar media?\n",
        "Se puede dropear frequency ya que puede ser usado para probabilidad de donacion, no cantidad\n",
        "\"\"\" seniority lo mismo"
      ],
      "metadata": {
        "id": "ARgjdJUFAWgq"
      },
      "id": "ARgjdJUFAWgq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "QsHgUkTtmCbJ"
      },
      "id": "QsHgUkTtmCbJ"
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "giCZ21221QYj"
      },
      "id": "giCZ21221QYj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = data_merge.copy()\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "fPHWppcqqLX9",
        "outputId": "5abd7266-a66e-4664-9d05-b93f77a86b7a"
      },
      "id": "fPHWppcqqLX9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 ID           Woman             Age          Salary  \\\n",
              "count  1.000000e+06  1000000.000000  1000000.000000  1000000.000000   \n",
              "mean   2.500000e+06        0.516936       46.366170    65531.654000   \n",
              "std    2.886753e+05        0.499713       18.945324    61051.122343   \n",
              "min    2.000001e+06        0.000000       16.000000        0.000000   \n",
              "25%    2.250001e+06        0.000000       30.000000    20700.000000   \n",
              "50%    2.500000e+06        1.000000       46.000000    44000.000000   \n",
              "75%    2.750000e+06        1.000000       61.000000    91200.000000   \n",
              "max    3.000000e+06        1.000000       90.000000   250000.000000   \n",
              "\n",
              "           SeniorList    NbActivities       Referrals        Recency  \\\n",
              "count  1000000.000000  1000000.000000  1000000.000000  336334.000000   \n",
              "mean         4.582967        0.561413        0.560556       3.039636   \n",
              "std          3.325844        0.996803        1.054027       2.120722   \n",
              "min          0.000000        0.000000        0.000000       1.000000   \n",
              "25%          2.000000        0.000000        0.000000       1.000000   \n",
              "50%          4.000000        0.000000        0.000000       2.000000   \n",
              "75%          7.000000        1.000000        1.000000       4.000000   \n",
              "max         10.000000       10.000000       16.000000      10.000000   \n",
              "\n",
              "           Frequency      Seniority      TotalGift        MinGift  \\\n",
              "count  336334.000000  336334.000000  336334.000000  336334.000000   \n",
              "mean        1.666882       4.474148     104.454055      43.130251   \n",
              "std         1.052090       2.588119     303.760477     150.249476   \n",
              "min         1.000000       1.000000      10.000000      10.000000   \n",
              "25%         1.000000       2.000000      20.000000      15.000000   \n",
              "50%         1.000000       4.000000      40.000000      20.000000   \n",
              "75%         2.000000       6.000000      90.000000      30.000000   \n",
              "max        10.000000      10.000000   15150.000000   10000.000000   \n",
              "\n",
              "             MaxGift    GaveLastYear     AmtLastYear    GaveThisYear  \\\n",
              "count  336334.000000  1000000.000000  1000000.000000  1000000.000000   \n",
              "mean       85.897932        0.122101        7.673015        0.122232   \n",
              "std       283.581066        0.327403       82.224854        0.327554   \n",
              "min        10.000000        0.000000        0.000000        0.000000   \n",
              "25%        20.000000        0.000000        0.000000        0.000000   \n",
              "50%        30.000000        0.000000        0.000000        0.000000   \n",
              "75%        75.000000        0.000000        0.000000        0.000000   \n",
              "max     10000.000000        1.000000    10000.000000        1.000000   \n",
              "\n",
              "          AmtThisYear  \n",
              "count  1000000.000000  \n",
              "mean         7.741080  \n",
              "std         83.110552  \n",
              "min          0.000000  \n",
              "25%          0.000000  \n",
              "50%          0.000000  \n",
              "75%          0.000000  \n",
              "max      10000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b02312a-c2cc-4960-ac3e-2eb6866c1974\">\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>ID</th>\n",
              "      <th>Woman</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>SeniorList</th>\n",
              "      <th>NbActivities</th>\n",
              "      <th>Referrals</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Seniority</th>\n",
              "      <th>TotalGift</th>\n",
              "      <th>MinGift</th>\n",
              "      <th>MaxGift</th>\n",
              "      <th>GaveLastYear</th>\n",
              "      <th>AmtLastYear</th>\n",
              "      <th>GaveThisYear</th>\n",
              "      <th>AmtThisYear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>336334.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.500000e+06</td>\n",
              "      <td>0.516936</td>\n",
              "      <td>46.366170</td>\n",
              "      <td>65531.654000</td>\n",
              "      <td>4.582967</td>\n",
              "      <td>0.561413</td>\n",
              "      <td>0.560556</td>\n",
              "      <td>3.039636</td>\n",
              "      <td>1.666882</td>\n",
              "      <td>4.474148</td>\n",
              "      <td>104.454055</td>\n",
              "      <td>43.130251</td>\n",
              "      <td>85.897932</td>\n",
              "      <td>0.122101</td>\n",
              "      <td>7.673015</td>\n",
              "      <td>0.122232</td>\n",
              "      <td>7.741080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.886753e+05</td>\n",
              "      <td>0.499713</td>\n",
              "      <td>18.945324</td>\n",
              "      <td>61051.122343</td>\n",
              "      <td>3.325844</td>\n",
              "      <td>0.996803</td>\n",
              "      <td>1.054027</td>\n",
              "      <td>2.120722</td>\n",
              "      <td>1.052090</td>\n",
              "      <td>2.588119</td>\n",
              "      <td>303.760477</td>\n",
              "      <td>150.249476</td>\n",
              "      <td>283.581066</td>\n",
              "      <td>0.327403</td>\n",
              "      <td>82.224854</td>\n",
              "      <td>0.327554</td>\n",
              "      <td>83.110552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000001e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.250001e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>20700.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.500000e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>44000.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.750000e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>91200.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>15150.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b02312a-c2cc-4960-ac3e-2eb6866c1974')\"\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-0b02312a-c2cc-4960-ac3e-2eb6866c1974 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-0b02312a-c2cc-4960-ac3e-2eb6866c1974');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGQCWlLKq_9l",
        "outputId": "12fcc952-b031-4fa4-e94f-d4facefb85a8"
      },
      "id": "zGQCWlLKq_9l",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1000000 entries, 0 to 999999\n",
            "Data columns (total 21 columns):\n",
            " #   Column        Non-Null Count    Dtype  \n",
            "---  ------        --------------    -----  \n",
            " 0   ID            1000000 non-null  float64\n",
            " 1   LastName      999977 non-null   object \n",
            " 2   FirstName     999996 non-null   object \n",
            " 3   Woman         1000000 non-null  float64\n",
            " 4   Age           1000000 non-null  float64\n",
            " 5   Salary        1000000 non-null  float64\n",
            " 6   Education     1000000 non-null  object \n",
            " 7   City          1000000 non-null  object \n",
            " 8   SeniorList    1000000 non-null  float64\n",
            " 9   NbActivities  1000000 non-null  float64\n",
            " 10  Referrals     1000000 non-null  float64\n",
            " 11  Recency       336334 non-null   float64\n",
            " 12  Frequency     336334 non-null   float64\n",
            " 13  Seniority     336334 non-null   float64\n",
            " 14  TotalGift     336334 non-null   float64\n",
            " 15  MinGift       336334 non-null   float64\n",
            " 16  MaxGift       336334 non-null   float64\n",
            " 17  GaveLastYear  1000000 non-null  float64\n",
            " 18  AmtLastYear   1000000 non-null  float64\n",
            " 19  GaveThisYear  1000000 non-null  float64\n",
            " 20  AmtThisYear   1000000 non-null  float64\n",
            "dtypes: float64(17), object(4)\n",
            "memory usage: 167.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df, x=\"Salary\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "pvNnNs4AzpbN",
        "outputId": "dd10ec10-72d0-4b77-ff24-0e914cf1bc63"
      },
      "id": "pvNnNs4AzpbN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f23c6815810>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('Salary', 'GaveLastYear', kind = 'scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LIHWvMqz51fS",
        "outputId": "0b017208-b9d1-48a1-ab45-bd23282b660d"
      },
      "id": "LIHWvMqz51fS",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f23c6811c50>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pd.plotting.scatter_matrix(df)"
      ],
      "metadata": {
        "id": "MEY8urIA7wAh"
      },
      "id": "MEY8urIA7wAh",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('Salary', 'AmtLastYear', kind = 'scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "hcs4gI6R6J2w",
        "outputId": "ac56c128-ec98-40d3-fc2e-f65853f5e505"
      },
      "id": "hcs4gI6R6J2w",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea7151690>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('Salary', 'AmtThisYear', kind = 'scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "bl89eaEq6w8T",
        "outputId": "a069fb1e-bcd0-4e75-ea1b-d4388649ba50"
      },
      "id": "bl89eaEq6w8T",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea7111550>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('AmtLastYear', 'AmtThisYear', kind = 'scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "KpQLmK4Z6408",
        "outputId": "f22c5eef-5880-493a-f868-54d63729cc38"
      },
      "id": "KpQLmK4Z6408",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea7074a10>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['GaveThisYear'], df['GaveLastYear']).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "QPGRvNRE91Cw",
        "outputId": "0b0253e0-a0f4-4176-9202-18d944665c2b"
      },
      "id": "QPGRvNRE91Cw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f23c5363950>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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IiOVpfR650HglXSoi/Xw1bd8EdMrbvybVitVrCtQp0oeZmZVBkyEREX8GNkr6YCpdCKwDFgD7ZijVAfPT8gJgVJrl1B/YkS4ZLQYGSmqTblgPBBanbW9I6p9mNY1qdKxCfZiZWRmU+mG664GfS6oCXgSuJBcwcySNAV4GLk9tFwJDgXrgrdSWiNgm6XZgRWo3ISK2peVrgWnAycDD6QVwZ0YfZmZWBiWFRESsBmoLbLqwQNsAxmUcZyowtUB9JdCzQH1roT7MzKw8/IlrMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy1RSSEh6SdIzklZLWplqbSUtkbQ+/WyT6pJ0j6R6SU9L6pN3nLrUfr2kurx633T8+rSvivVhZmblcTBnEp+JiN4Rse8xpuOBpRHRDVia1gGGAN3SaywwCXJv+MCtQD/gPODWvDf9ScCX8/Yb3EQfZmZWBodyuWk4MD0tTwcuzqvPiJxlQGtJHYBBwJKI2BYR24ElwOC07fSIWJaejz2j0bEK9WFmZmVQakgE8IikVZLGplr7iNiclv8MtE/LHYGNefs2pFqxekOBerE+zMysDFqU2O6/R8QmSWcCSyQ9l78xIkJSHP7hldZHCq6xAGedddaRHIaZ2QmlpDOJiNiUfr4K3E/unsIr6VIR6eerqfkmoFPe7jWpVqxeU6BOkT4aj29yRNRGRG11dXUp/yQzMytBkyEh6VRJp+1bBgYCa4AFwL4ZSnXA/LS8ABiVZjn1B3akS0aLgYGS2qQb1gOBxWnbG5L6p1lNoxodq1AfZmZWBqVcbmoP3J9mpbYAfhERiyStAOZIGgO8DFye2i8EhgL1wFvAlQARsU3S7cCK1G5CRGxLy9cC04CTgYfTC+DOjD7MzKwMmgyJiHgR6FWgvhW4sEA9gHEZx5oKTC1QXwn0LLUPMzMrD3/i2szMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLFPJISGpuaQ/SnowrXeRtFxSvaTZkqpS/X1pvT5t75x3jJtT/XlJg/Lqg1OtXtL4vHrBPszMrDwO5kziBuDZvPW7gLsjoiuwHRiT6mOA7al+d2qHpO7ASKAHMBj4UQqe5sBEYAjQHbgitS3Wh5mZlUFJISGpBrgI+GlaF3ABMC81mQ5cnJaHp3XS9gtT++HArIh4OyI2APXAeelVHxEvRsQuYBYwvIk+zMysDEo9k/g34JvAO2m9HfB6ROxJ6w1Ax7TcEdgIkLbvSO331xvtk1Uv1scBJI2VtFLSyi1btpT4TzIzs6Y0GRKS/gl4NSJWlWE870lETI6I2oiora6urvRwzMyOGy1KaHM+MEzSUKAlcDrw70BrSS3SX/o1wKbUfhPQCWiQ1AJoBWzNq++Tv0+h+tYifZiZWRk0eSYRETdHRE1EdCZ34/nRiPhn4NfApalZHTA/LS9I66Ttj0ZEpPrINPupC9ANeAJYAXRLM5mqUh8L0j5ZfZiZWRkcyuckbgK+Iame3P2DKak+BWiX6t8AxgNExFpgDrAOWASMi4i96SzhOmAxudlTc1LbYn2YmVkZlHK5ab+IeAx4LC2/SG5mUuM2O4HLMva/A7ijQH0hsLBAvWAfZmZWHv7EtZmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVySJiZWSaHhJmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVySJiZWSaHhJmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVqMiQktZT0hKSnJK2VdFuqd5G0XFK9pNnp+dSkZ1jPTvXlkjrnHevmVH9e0qC8+uBUq5c0Pq9esA8zMyuPUs4k3gYuiIheQG9gsKT+wF3A3RHRFdgOjEntxwDbU/3u1A5J3YGRQA9gMPAjSc0lNQcmAkOA7sAVqS1F+jAzszJoMiQi5y9p9aT0CuACYF6qTwcuTsvD0zpp+4WSlOqzIuLtiNgA1JN7fvV5QH1EvBgRu4BZwPC0T1YfZmZWBiXdk0h/8a8GXgWWAH8CXo+IPalJA9AxLXcENgKk7TuAdvn1Rvtk1dsV6cPMzMqgpJCIiL0R0RuoIfeX/4eO6KgOkqSxklZKWrlly5ZKD8fM7LhxULObIuJ14NfAx4HWklqkTTXAprS8CegEkLa3Arbm1xvtk1XfWqSPxuOaHBG1EVFbXV19MP8kMzMropTZTdWSWqflk4HPAc+SC4tLU7M6YH5aXpDWSdsfjYhI9ZFp9lMXoBvwBLAC6JZmMlWRu7m9IO2T1YeZmZVBi6ab0AGYnmYhNQPmRMSDktYBsyR9F/gjMCW1nwL8TFI9sI3cmz4RsVbSHGAdsAcYFxF7ASRdBywGmgNTI2JtOtZNGX2YmVkZNBkSEfE08LEC9RfJ3Z9oXN8JXJZxrDuAOwrUFwILS+3DzMzKw5+4NjOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy9RkSEjqJOnXktZJWivphlRvK2mJpPXpZ5tUl6R7JNVLelpSn7xj1aX26yXV5dX7Snom7XOPJBXrw8zMyqOUM4k9wP+KiO5Af2CcpO7AeGBpRHQDlqZ1gCFAt/QaC0yC3Bs+cCvQj9xzq2/Ne9OfBHw5b7/BqZ7Vh5mZlUGTIRERmyPiybT8JvAs0BEYDkxPzaYDF6fl4cCMyFkGtJbUARgELImIbRGxHVgCDE7bTo+IZRERwIxGxyrUh5mZlcFB3ZOQ1Bn4GLAcaB8Rm9OmPwPt03JHYGPebg2pVqzeUKBOkT4aj2uspJWSVm7ZsuVg/klmZlZEySEh6f3AL4F/iYg38relM4A4zGM7QLE+ImJyRNRGRG11dfWRHIaZ2QmlpJCQdBK5gPh5RPxHKr+SLhWRfr6a6puATnm716RasXpNgXqxPszMrAxKmd0kYArwbER8P2/TAmDfDKU6YH5efVSa5dQf2JEuGS0GBkpqk25YDwQWp21vSOqf+hrV6FiF+jAzszJoUUKb84EvAc9IWp1q/we4E5gjaQzwMnB52rYQGArUA28BVwJExDZJtwMrUrsJEbEtLV8LTANOBh5OL4r0YWZmZdBkSETE7wBlbL6wQPsAxmUcayowtUB9JdCzQH1roT7MzKw8/IlrMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy+SQMDOzTA4JMzPL5JAwM7NMDgkzM8vkkDAzs0wOCTMzy1TKM66nSnpV0pq8WltJSyStTz/bpLok3SOpXtLTkvrk7VOX2q+XVJdX7yvpmbTPPek515l9mJlZ+ZRyJjENGNyoNh5YGhHdgKVpHWAI0C29xgKTIPeGD9wK9APOA27Ne9OfBHw5b7/BTfRhZmZl0mRIRMR/AtsalYcD09PydODivPqMyFkGtJbUARgELImIbRGxHVgCDE7bTo+IZenZ2DMaHatQH2ZmVibv9Z5E+4jYnJb/DLRPyx2BjXntGlKtWL2hQL1YH39H0lhJKyWt3LJly3v455iZWSGHfOM6nQHEYRjLe+4jIiZHRG1E1FZXVx/JoZiZnVBavMf9XpHUISI2p0tGr6b6JqBTXruaVNsEfLpR/bFUrynQvlgfVk7faVXpETTtOzsqPYLjSufxD1V6CCV56c6LKj2EE8J7PZNYAOyboVQHzM+rj0qznPoDO9Ilo8XAQElt0g3rgcDitO0NSf3TrKZRjY5VqA8zMyuTJs8kJM0kdxZwhqQGcrOU7gTmSBoDvAxcnpovBIYC9cBbwJUAEbFN0u3AitRuQkTsuxl+LbkZVCcDD6cXRfowM7MyaTIkIuKKjE0XFmgbwLiM40wFphaorwR6FqhvLdSHmZmVjz9xbWZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaW6b0+mc7MrLKOhacmwjH/5ESfSZiZWSaHhJmZZXJImJlZpqM+JCQNlvS8pHpJ4ys9HjOzE8lRHRKSmgMTgSFAd+AKSd0rOyozsxPHUR0SwHlAfUS8GBG7gFnA8AqPyczshHG0T4HtCGzMW28A+jVuJGksMDat/kXS82UY2wlBcAbwWqXHUdRtqvQIrAKOif824Vj67/MDhYpHe0iUJCImA5MrPY7jkaSVEVFb6XGYNeb/NsvjaL/ctAnolLdek2pmZlYGR3tIrAC6SeoiqQoYCSyo8JjMzE4YR/XlpojYI+k6YDHQHJgaEWsrPKwTjS/j2dHK/22WgSKi0mMwM7Oj1NF+ucnMzCrIIWFmZpkcEmZmlskhYX9HUltJbSs9DjOrPIeEASDpLEmzJG0BlgNPSHo11TpXdnRmVikOCdtnNnA/8N8ioltEdAU6AA+Q+84ss4qT1F5Sn/RqX+nxnAg8BdYAkLQ+Irod7DazcpDUG/gx0Ip3v3WhBngduDYinqzU2I53DgkDQNIsYBswnXe/VLETUAecERGXV2psZpJWA1+JiOWN6v2Bn0REr8qM7PjnkDAA0teejCH3VewdU7kB+L/AlIh4u1JjM2viTLc+XR61I8AhYWZHPUn3AP8IzODAM91RwIaIuK5SYzveOSSsSZL+KSIerPQ47MQmaQgHnuluAhZExMLKjer455CwJkm6LSJurfQ4zKz8HBK2n6QPUfgvtWcrNyqz4iSNTQ8esyPAn5MwACTdRO7zEAKeSC8BMyWNr+TYzJpwzDwf9FjkMwkDQNILQI+I2N2oXgWs9eck7Ggl6cqIuK/S4zhe+UzC9nkH+IcC9Q5pm9nR6rZKD+B4dlQ/mc7K6l+ApZLW8+4Uw7OAroCnF1pFSXo6axPgr+c4gny5yfaT1Aw4jwNvXK+IiL2VG5UZSHoFGARsb7wJeDwiCp0F22HgMwnbLyLeAZZVehxmBTwIvD8iVjfeIOmx8g/nxOEzCTMzy+Qb12ZmlskhYWZmmRwSdsJID6z5haQXJa2S9AdJ/+MwHfsjklan1zZJG9LyryR9WlLB776S9FNJ3TO2fVnS7Lz10yX9SdLZh2PMZqVwSNgJQZLIPWXvPyPi7IjoC4wk9+CaQxYRz0RE74joDSwAbkzrn21iv6sjYl3G5p8CnSTtO8YEYGpEvPhexymp+Xvd105MDgk7UVwA7IqIH+8rRMTLEfEDSZ0l/VbSk+n1Ccg9iEnSRfvaS5om6VJJzSX9q6QVkp6W9JUS+n+/pHmSnpP08xRaSHpMUm065jRJayQ9I+nrkZtVcg3wb5JqgQuBf5X0PyU9kc5UfrLvjV/SJEkrJa2VtP8DZpJeknSXpCeByw7D79JOIJ4CayeKHkDWIy5fBT4XETsldQNmArXknvt9OfBQ+nqSC4Gvkns4046IOFfS+4DfS3okIjYU6f9jaQz/D/g9cD7wu7ztvYGOEdETQFJrgIh4WtJiYCm5L1/8R2AEcH5E7Jb0I+CfyT1n4VsRsS2FxlJJH42IfR9C2xoRfUr8XZnt5zMJOyFJmijpKUkrgJOAeyU9A8wF9t0jeBj4TAqCIeQuVf0NGAiMSo/UXA60A5r6bqsnIqIhfRZlNdC50fYXgbMl/UDSYOCNvG0TgU0R8Ri5oOoLrEj9Xwjsu0dxeTpb+CO5QMq/1zEbs/fAZxJ2olgLfGHfSkSMk3QGsBL4OvAK0IvcH047U5ud6YNag8j99T4r7S7g+ohYfBD95z/+dS+N/t+LiO2SeqW+riF3BnNV2vwO735/loDpEXFz/v6SugD/Gzg3HWsa0DKvyV8PYqxm+/lMwk4UjwItJX01r3ZK+tkK2Jz+yv8SkH9zdzZwJfBJYFGqLQa+KukkAEnnSDr1UAaXAqtZRPwS+DaQdWloKbxaWWYAAACrSURBVHCppDPTfm0lfQA4nVwQ7JDUntyZj9kh85mEnRAiIiRdDNwt6ZvAFnJvqjeRu1fxS0mjyAVB/l/djwA/A+ZHxK5U+ym5y0VPphvQW4CLD3GIHYH70vdnAdxcqFFErJP0beCR1HY3MC4ilkn6I/AcuS9o/P0hjscM8NdymJlZEb7cZGZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmf4/JPJZArvuSCgAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df, x='Education')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "H1Y7inX40Yyc",
        "outputId": "2031454d-24d8-429a-b33d-47ed28929f98"
      },
      "id": "H1Y7inX40Yyc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea702ba50>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('AmtThisYear', 'Education', kind = 'scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "bur_P-qz8Hhb",
        "outputId": "67571840-26e4-4713-e9c1-5fabe588fde3"
      },
      "id": "bur_P-qz8Hhb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6fb7f10>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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hsMltO8YcNnlMv+6YNGbEUC45eRrDhjQwcuhghg1p4JKTp9W8Y1JvvddVdUrqTzrp5XsBsDEiLk3XTP8qIh6SdBFwYkTs275TkqQ5wKURMa9kmSPSPPPS+DHAFan+T4EHI+IKSYMofpY0ipJOSek67oiIuEDSo8DfRcS9qX3bR8RZHZRfS406JYF7+bqXb225l2//DdNS9dzLt6NOSdX28v0+8MmIeCmNjwK+ka6l1pUuBOp7gO9SfNf/Ms1zaJWBOhK4HtiD4rmxr1K8f82SxgNXAbtTHIn+DfAClQN1OvBvwLbA08BHImJ9B+XXUsNANTMbyHIE6iMR8a7OyvoTSSMiYmMaPg/YMSI+2cfNysaBambWdR0FarXX6RokjYqI9WmBo7tQt14dL+mzFNv5DH6gupmZdaDaUPwG8ICkGwBR3HbwKzVr1TtARFxPcdrWzMysU1UFakRcJ6mZt357+mcRsbR2zTIzM6svVQWqpF2BjRQ3K9haFhHP1qphZmZm9aTaU7638davGhopbkH4JMW9fc3MzAa8ak/57lc6LmkG8Lc1aZGZmVkdqvZOSW1ExALgPZnbYmZmVreqvYb66ZLRBoqbuv9PTVpkZmZWh6q9hjqyZPgNimuqs/M3x8zMrD5Vew31wlo3xMzMrJ51GKiSfkbbe5a3EREnZm+RmZlZHersCPXS9PfPgAnAD9P4acCaWjXKzMys3nQYqK0PsJb0jXY3A/5ZunOSmZmZUf3PZoZL2r11RNIkYHhtmmRmZlZ/qu3lexYwT9LTFDfH3w04s2atMjMzqzPV9vK9XdIUYK9U9EREbK5ds8zMzOpLh6d8JZ1TMnpiRDyaXpslXVTjtpmZmdWNzq6hnloy/Nl2047N3BYzM7O61VmgqsJwuXEzM7MBq7NAjQrD5cbNzMwGrM46Je0v6RWKo9HGNEwaH1bTlpmZmdWRzm7sMKi3GmJmZlbPuvU8VDMzM2vLgWpmZpaBA9XMzCwDB6qZmVkGDlQzM7MMHKhmZmYZOFDNzMwycKCamZll4EA1MzPLwIFqZmaWgQPVzMwsAweqmZlZBg5UMzOzDByoZmZmGThQzczMMnCgmpmZZeBANTMzy8CBamZmloED1czMLAMHqpmZWQYOVDMzswwcqGZmZhk4UM3MzDJwoJqZmWXgQDUzM8vAgWpmZpaBA9XMzCwDB6qZmVkGDlQzM7MMHKhmZmYZOFDNzMwycKCamZll4EA1MzPLwIFqZmaWgQPVzMwsAweqmZlZBg5UMzOzDByoZmZmGThQzczMMnCgmpmZZeBANTMzy8CBamZmloED1czMLAMHqpmZWQYOVDMzswwcqGZmZhk4UM3MzDJwoJqZmWXgQDUzM8vAgWpmZpaBA9XMzCwDB6qZmVkGDlQzM7MMHKhmZmYZOFDNzMwycKCamZll4EA1MzPLwIFqZmaWgQPVzMwsAweqmZlZBg5UMzOzDByoZmZmGThQzczMMnCgmpmZZeBANTMzy8CBamZmloED1czMLAMHqpmZWQaDa7FQSROBORGxb0nZBcDGiLi0g3pNwOkR8fe1aFdax/0RcUhq4yER8eNuLGN+qru5pGwI8CXgZGADsBn4YkT8vIPlrASaIuJFSRsjYkRX29LbJp53W6+sZ8KIIewyupEFz73CjF224+FnX9k6beXFx5dtT2k5wOduepSfP7aG4/YZzw9/varsfIdedBfPv/I6O223Dff94x+XbUtH6+hMT+oee9lcnvjt79nrD7bl9k8f2aW6V85dxs2LXmDWtB0588gpXaoLcMylv2D5i5uYPLaR/z77qC7X7669z7+NTW9C4yB4/Ctde7966u6lq7lz6RpmTh3P0VMn9Np6f3j/Cm5Z9AInTduRDx0yqdfWu3zNBhY+9xLTd9mByeNH9tp6AdZt3Myq9ZvYeVQjY0YM7bX1ln4nfPnP9s++fEVE/oV2M1Azrn9wRLzRyTxHAGdHxAldXPYk4JsRcWK78ouBHYGPRcRmSeOB90bEf3SwrJX0UaA2NTVFc3Nzl+r0VphWY+XFx5dtT2toddbWzuq3qmaeSvqq7t6f+y82vfHWv+vGweLxL7+/qro9XXdP9NV6AWZePo+n1ry6dXzP8cO546wjar7e/S+4nZdfe3Pr+PbDBvHoBcfWfL2fv3kx1z347Nbx0w/elS+etF/N1wtwy8LnOXf2IoY0NLClpYVLTp7GidN3qvl6c32+JM2PiKZy0/rklK+keZK+JunXkp6SdFgqP0LSHEkNklZK2qGkzjJJ4yWNkzRb0sPpdWiafoGkH0i6D/iBpH3S8hdKWiRpSppvY1rkxcBhafpZku6RNL1kfb+SVO6/MMcCt7fbnm2BvwY+0XrUGhFrWsNU0mmSFktaIulrVbw/n0nbtkjShSXl/yTpydS2n0g6O5XvIel2SfMl3Stpr053Qhe9k8IUKrdn4nm38bmbHu12/UMvuquqdfSkfZ059rK5XSovdeXcZW3CFGDTG8GVc5d1WheKI9OulOey9/nl35dK5TndvXR1mzAFeHLNq9y9dHVN1/vD+1e0CVOAl197kx/ev6Km612+ZkObMAW47oFnWb5mQ03XC8WR6bmzF/HalhY2bH6D17a0cM7sRazbuLnzyj1Q6Tuhmu+KrujLa6iDI+JA4FPAF0onREQLcAvwpwCS3gM8ExFrgG8Cl0fEuylOr15dUnUqcExEnAZ8nOJIcjrQBKyirfOAeyNiekRcDlwDnJHW93+AYRFR7t1+W6ACk4FnI+KV9jNL+kPga8BRwHTg3ZJmVXpTJM0EpgAHpvkPkHS4pNbt3R84Lm1Tq6sowvwA4GzgXyos+2OSmiU1r127tlIT6t7PH1vT7brPv/J6xpZ0zxO//X2XykvdvOiFLpW3t/zFTV0qz2XTm10rz+nOpeU/L5XKc7mlwj6pVJ7Lwude6lJ5TqvWb2JIQ9vYGdLQwKr1tf18VfpO6Ml3RTm1CtRK55FLy29Kf+cDE8vMez3wgTR8ahoHOAb4tqSFwK3AdpJaT5XeGhGte+YB4B8lnQvsVlJeyQ3ACela6F8C17afQdI2wM4R8XQnyyr1bmBeRKxNp6F/BBzewfwz0+sRYAGwF0XAHgrcEhGvRcQG4GepTSOAQ4Ab0ntyJcWp57eJiKsioikimsaNG9eFTagvx+0zvtt1d9pum4wt6Z69/mDbLpWXmjWt7K6vWN7e5LGNXSrPpXFQ18pzmjm1/OelUnkuJ1XYJ5XKc5m+yw5dKs9p51GNbGlpaVO2paWFnUfV9vNV6TuhJ98V5dQqUNcBo9qVjQZeLBlvPcZ/k/Kdox4AJksaB8zirQBuAA5KR5bTI2KniGg9jbv1vE3qbHQisAn4L0kd9qyIiN8DdwEnAX9BEXztHQb8qkz5cmBXSdt1tI4qCfhqyfZNjohrOpi/AXipZP7pEbF3hna00VvXsqpVqT0rLz6+qs4GleqXdkzqaB09aV9nKnVAqqZj0plHTqFxsNqUNQ5W1R2TKnVAqnXHpEodkHqjY9LRUyew5/jhbcr2HD+85h2TPnTIJLYf1vZ/DNsPG1TzjkmTx4/k9IN3bVN2+sG79krHpDEjhnLJydMYNqSBkUMHM2xIA5ecPK3mHZMqfSfk7phUk05JAJKagXMi4heSRgMPAsdFxG8kzaPoENQsaSzQHBET23cUkvR1YAIwJiLen8p+DDwSEV9P49MjYmH7Tk+SdgdWRERIuhRYFRFXtHb+kXQAcFlEvLekzQdQHPndGxGtR8el2/R14O6IaH/KF0mXAOOAMyPi9fQfgSMoAvhB4ABgPXAH8M8RcUu5TknplO+XgKMjYqOknYAtwG4UR5+HUPwHZAFwVURcKul+itPgN0gSMK3C6eqtutMpCdzL1718a8u9fN3Lt5Zy9PLtqFMSEVGTF8X1zLnAwvT6YMm0eRRBAjAWWJmGj6DoHdw6XxPFaeIPl5SNpTj9uwhYCvxbKr+AIoxb5zsPeCyt+3ZgdCrfmP4OAX4BPAqcVVLvCeDYCtv0MNBYYdo2wCUUR6tLgIeA96VppwGLU/nXSuqsBMaWtisNfzLNv5jiSH2Pkm18CrgXmA38dSqflLbx0fSefL6z/XPAAQeEmZl1DcUBYNnv1Zododaj1IFoHrBXFB2jSqftDHw3Io7ri7alNoyI4qh1W+Aeip/oLOjOsrp7hGpmNpC94342804k6XSKo8rz24cpQESs6sswTa5KHY8WALO7G6ZmZpZfTe6UVI8i4jrgur5uR0ci4v/2dRvMzKw8H6GamZll4EA1MzPLwJ2SBihJa4Fnull9LG1/UzwQeJsHhoG2zQNte6Hn27xbRJS9M44D1bpMUnOlXm79lbd5YBho2zzQthdqu80+5WtmZpaBA9XMzCwDB6p1x1V93YA+4G0eGAbaNg+07YUabrOvoZqZmWXgI1QzM7MMHKhmZmYZOFCtapKOlfSkpOWSzuvr9vSEpF0kzZW0VNJjkj6ZykdLukvSsvR3VCqXpG+lbV8kaUbJsj6c5l8m6cN9tU3VkjRI0iOS5qTxSZIeStt2vaRtUvnQNL48TZ9YsozPpvInJb2vb7akOpJ2kHSjpCckPS7p4P6+nyWdlT7XSyT9RNKw/rafJf27pN9KWlJSlm2/SjpA0uJU51vp0Zgdq/QYGr/8Kn0Bg4DfALtTPKruUWBqX7erB9uzIzAjDY+keCzeVIpH8J2Xys8jPW4PeD/wc4oHwB8EPJTKRwNPp7+j0vCovt6+Trb908CPSY9KBP4DODUN/xvwN2n4b3nr8YinAten4alp/w+leHTgb4BBfb1dHWzv94GPpuFtgB36834GdgJWkB41mfbvGf1tPwOHAzOAJSVl2fYr8Os0r1Ld4zprk49QrVoHAssj4umIeB34KXBSH7ep2yLihUhP64mIDcDjFF9EJ1F8AZP+zkrDJwHXReFBYAdJOwLvA+6KiN9FxHrgLuDYXtyULkmPITweuDqNCzgKuDHN0n6bW9+LG4Gj0/wnAT+NiM0RsYLiGcAH9s4WdI2k7Sm+eK8BiIjXI+Il+vl+pnjwSaOkwcC2wAv0s/0cEfcAv2tXnGW/pmnbRcSDUaTrdSXLqsiBatXaCXiuZHxVKqt76RTXuyge3zc+Il5Ik1YD49Nwpe2vt/flCuAcoPURhWOAlyLijTRe2v6t25amv5zmr6dtngSsBb6XTnNfLWk4/Xg/R8TzwKXAsxRB+jIwn/69n1vl2q87peH25R1yoNqAJmkEMBv4VES8Ujot/c+03/yuTNIJwG8jYn5ft6UXDaY4LfivEfEu4FWKU4Fb9cP9PIriiGwS8IfAcN7ZR9M10Rf71YFq1Xoe2KVkfOdUVrckDaEI0x9FxE2peE063UP6+9tUXmn76+l9ORQ4UdJKilP2RwHfpDj91fps5NL2b922NH17YB31tc2rgFUR8VAav5EiYPvzfj4GWBERayNiC3ATxb7vz/u5Va79+nwabl/eIQeqVethYErqKbgNReeFW/u4Td2WrhFdAzweEZeVTLoVaO3p92HglpLy01NvwYOAl9OppTuAmZJGpSODmansHSciPhsRO0fERIr994uI+CAwFzglzdZ+m1vfi1PS/JHKT029QycBUyg6cLzjRMRq4DlJe6aio4Gl9OP9THGq9yBJ26bPees299v9XCLLfk3TXpF0UHoPTy9ZVmV93VPLr/p5UfSUe4qit9/5fd2eHm7LH1GcDloELEyv91NcO7obWAb8NzA6zS/gO2nbFwNNJcv6S4oOG8uBj/T1tlW5/UfwVi/f3Sm+KJcDNwBDU/mwNL48Td+9pP756b14kip6P/bxtk4HmtO+vpmiN2e/3s/AhcATwBLgBxQ9dfvVfgZ+QnGNeAvFmYi/yrlfgab0/v0G+DbpzoIdvXzrQTMzswx8ytfMzCwDB6qZmVkGDlQzM7MMHKhmZmYZOFDNzMwycKCaWUWSZkkKSXt1o97UNPwdSQtVPNlnUxpeKOkUSfMkNZWp3yTpWxWWPUzFk2P2Kyn7jKQru7p9Zjn5ZzNmVpGk6yluX/eLiPhCF+pdS/E71xtLyiamsn1LyuYBZ0dEcxfbdSzFbyQPT+27h+K3heu7spyS5Q2Ot+5za9YtPp1YbBcAAAJLSURBVEI1s7LSfY7/iOIH86emsiMk/VLSLZKelnSxpA9K+nV6duQekg4BTgS+no5E9+hkVX+e6j8l6bCS9bQ+r/W9JUe1j0gaGRG3U/yo/3TgcuACYLCk2ZIeTq9DU/0DJT2Q6t7fetckSWdIulXSLyhuBmDWI4M7n8XMBqiTgNsj4ilJ6yQdkMr3B/ameHTW08DVEXGgioe0fyIiPiXpVtodoXZgcKr/fuALFPeiLXU28P8j4r4U8q+l8k9R3NlnWUT8QNKPgcsj4leSdqW4rdzeFHcMOiwi3pB0DHARcHJaxgxgWkS0fwyYWZc5UM2sktMobp4Pxc30TwPmAA9HekSWpN8Ad6Z5FgNHdmM9rQ8mmA9MLDP9PuAyST8CboqIVQAR8T/p6HJOmu8YYGpx61UAtksBvD3wfUlTKG43OaRk2Xc5TC0XB6qZvY2k0RRPo9lPUgCDKMLoNmBzyawtJeMtdO87pbX+m+XqR8TFkm6juNfyfZLeFxFPlKyz9dmuDcBBEfFaaX1J3wbmRsSfpuu480omv9qN9pqV5WuoZlbOKcAPImK3iJgYEbsAK4DDqqy/ARiZoyGS9oiIxRHxNYqnHlXqcXwn8ImSetPT4Pa89eitM3K0yawcB6qZlXMa8J/tyman8mr8FPhM6gjUWaekznxK0hJJiyieLPLzCvP9PdAkaZGkpcDHU/klwFclPYLPylkN+WczZmZmGfgI1czMLAMHqpmZWQYOVDMzswwcqGZmZhk4UM3MzDJwoJqZmWXgQDUzM8vgfwE6YfNbd7k3agAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Education'], df['GaveThisYear']).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "zl3AfcVXAdZs",
        "outputId": "81974213-69a7-4297-be9d-f5f59b579028"
      },
      "id": "zl3AfcVXAdZs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6fc0c50>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Education'], df['GaveLastYear']).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "Hgb9n9QPAq5Y",
        "outputId": "726df21f-e5f2-4922-b2fd-a5f19640309f"
      },
      "id": "Hgb9n9QPAq5Y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6f022d0>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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aFkkaV9Resg8zM6uMco4k1gCDI6Iv0A8YKukw4ArgqojoCbwBnJn2PxN4I7VflfZDUi9gJNAbGAr8TFJrSa2Ba4FjgV7AaWlfmunDzMwqoMWQiIJ30mrb9ApgMDA5tU8ETkzLJ6R10vbPSVJqvysi1kTEYmARcGh6LYqIlyNiLXAXcEJ6T1YfZmZWAWVdk0j/458DvApMB/4IvBkR69Muy4BuabkbsBQgbV8NdC5ub/KerPbOzfRhZmYVUFZIRMSGiOgHNFD4n/+nPtKqNpOksyTNkjRr5cqVeZdjZrbd2KzRTRHxJvBb4HCgk6Q2aVMDsDwtLwf2BEjbOwKritubvCerfVUzfTSt64aIGBARA7p06bI5fyQzM2tGOaObukjqlJZ3AL4APE8hLE5Ou40G7k/LU9I6afujERGpfWQa/dQD2Ad4GpgJ7JNGMrWjcHF7SnpPVh9mZlYBbVreha7AxDQKqRVwd0Q8IOk54C5JlwJ/ACak/ScAt0laBLxO4Zc+EbFA0t3Ac8B64NyI2AAg6TxgGtAauCkiFqTPuiijDzMzq4AWQyIi5gEHlWh/mcL1iabt7wOnZHzWZcBlJdqnAlPL7cPMzCrDd1ybmVmmck43mW2Xuo97sKL9Lbl8WEX7M9sWfCRhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaWySFhZmaZHBJmZpbJIWFmZpkcEmZmlskhYWZmmRwSZmaWySFhZmaZHBJmZpapxZCQtKek30p6TtICSWNT+66SpktamL7uktol6WpJiyTNk9S/6LNGp/0XShpd1H6wpGfTe66WpOb6MDOzyijnSGI98P8iohdwGHCupF7AOOCRiNgHeCStAxwL7JNeZwHXQeEXPnAJMBA4FLik6Jf+dcDXit43NLVn9WFmZhXQYkhExIqIeCYtvw08D3QDTgAmpt0mAiem5ROAW6PgKaCTpK7AEGB6RLweEW8A04GhadvOEfFURARwa5PPKtWHmZlVwGZdk5DUHTgImAHsEREr0qY/AXuk5W7A0qK3LUttzbUvK9FOM300ressSbMkzVq5cuXm/JHMzKwZZYeEpI8D9wLfiIi3irelI4DYxrVtork+IuKGiBgQEQO6dOnyUZZhZlZXygoJSW0pBMTtEfHL1PzndKqI9PXV1L4c2LPo7Q2prbn2hhLtzfVhZmYVUM7oJgETgOcj4sqiTVOAxhFKo4H7i9pHpVFOhwGr0ymjacAxknZJF6yPAaalbW9JOiz1NarJZ5Xqw8zMKqBNGfscCXwFeFbSnNT2HeBy4G5JZwKvAKembVOB44BFwLvA6QAR8bqkHwAz037jI+L1tHwOcAuwA/BQetFMH2ZmVgEthkREPAEoY/PnSuwfwLkZn3UTcFOJ9llAnxLtq0r1YWZmleE7rs3MLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsk0PCzMwyOSTMzCyTQ8LMzDI5JMzMLJNDwszMMjkkzMwsU4shIekmSa9Kml/Utquk6ZIWpq+7pHZJulrSIknzJPUves/otP9CSaOL2g+W9Gx6z9WS1FwfZmZWOeUcSdwCDG3SNg54JCL2AR5J6wDHAvuk11nAdVD4hQ9cAgwEDgUuKfqlfx3wtaL3DW2hDzMzq5AWQyIi/ht4vUnzCcDEtDwROLGo/dYoeAroJKkrMASYHhGvR8QbwHRgaNq2c0Q8FREB3Nrks0r1YWZmFbKl1yT2iIgVaflPwB5puRuwtGi/ZamtufZlJdqb68PMzCpkqy9cpyOA2Aa1bHEfks6SNEvSrJUrV36UpZiZ1ZUtDYk/p1NFpK+vpvblwJ5F+zWktubaG0q0N9fHX4mIGyJiQEQM6NKlyxb+kczMrKktDYkpQOMIpdHA/UXto9Iop8OA1emU0TTgGEm7pAvWxwDT0ra3JB2WRjWNavJZpfowM7MKadPSDpLuBI4GdpO0jMIopcuBuyWdCbwCnJp2nwocBywC3gVOB4iI1yX9AJiZ9hsfEY0Xw8+hMIJqB+Ch9KKZPszMrEJaDImIOC1j0+dK7BvAuRmfcxNwU4n2WUCfEu2rSvVhZmaV4zuuzcwsk0PCzMwytXi6yczMgO91rHB/qyvbXwYfSZiZWSaHhJmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVySJiZWSaHhJmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVySJiZWSaHhJmZZfJU4WaVUqdTTVtt85GEmZllckiYmVkmh4SZmWVySJiZWSZfuN5K3cc9WNH+llw+rKL9mVl985GEmZllckiYmVkmh4SZmWVySJiZWSaHhJmZZar6kJA0VNKLkhZJGpd3PWZm9aSqQ0JSa+Ba4FigF3CapF75VmVmVj+q/T6JQ4FFEfEygKS7gBOA53KtKk+eJM7MKqjaQ6IbsLRofRkwsOlOks4Czkqr70h6sQK15UKwG/BaxTr8virW1fbOP7vaVgc/v71KNVZ7SJQlIm4Absi7jkqQNCsiBuRdh20+/+xqW73+/Kr6mgSwHNizaL0htZmZWQVUe0jMBPaR1ENSO2AkMCXnmszM6kZVn26KiPWSzgOmAa2BmyJiQc5l5a0uTqttp/yzq211+fNTRORdg5mZValqP91kZmY5ckiYmVkmh4SZmWWq6gvXViCpdURsyLsOK4+kk5rbHhG/rFQttuUk7QtcB+wREX0kHQgMj4hLcy6tonzhugZIehm4F7g5Iup3SpIaIenmZjZHRJxRsWJsi0l6HLgQ+HlEHJTa5kdEn3wrqywfSdSGvhTuEfmFpFbATcBdEfFWvmVZKRFxet412DbRISKeljaZHmN9XsXkxdckakBEvB0RN0bEEcBFwCXACkkTJfXMuTzLIKmjpCslzUqv/5BU4RkabSu8JumTQABIOhlYkW9JlefTTTUgTZk+DDgd6A7cBtwOHAX8a0Tsm191lkXSvcB8YGJq+grQNyKavWZh1UHS3hRuoDsCeANYDHw5IpbkWVelOSRqQLom8VtgQkQ82WTb1RFxQT6VWXMkzYmIfi21WXWTtCPQKiLezruWPPiaRJVLRxG3RMT4UtsdEFXtPUmfjognACQdCbyXc01WJknfbLIOsBqYHRFzcikqBz6SqAGSno6IQ/OuwzaPpH4UTjV1BAS8DoyOiHm5FmZlkXQHMAD4r9T0d8A8Cqd874mIH+ZUWkU5JGqApKuAtsAk4C+N7RHxTG5FWdkk7Qzg0Wi1RdJ/A8dFxDtp/ePAg8BQCkcTdfEoZZ9uqg2N57CLTzkFMDiHWqxMaSTTJcCgtP44MD4i/EzY2rA7sKZofR2FG+vek7Qm4z3bHYdEDYiIz+Zdg22RmyiMbjo1rX8FuBnw6KbacDswQ9L9af144I50Ibtubmr16aYaIWkY0Bto39iWdTHbqoNHN9U+SQOAI9Pq/0TErDzryYNvpqsBkq4HRgDnU7gAegoZDy23qvKepE83rnh0U01qD7wVET8BXpHUI++CKs1HEjVA0ryIOLDo68eBhyLiqLxrs2yS+gK3sunopjERMTfXwqwski6hMLppv4jYV9LfUhjVdGQLb92u+JpEbWj83+e76S/qKqBrjvVYGVIY9PXoppr198BBwDMAEfG/knbKt6TKc0jUhgckdQL+ncJf2AB+kW9J1hJJHwO+SGFcfZvGieJ8LalmrI2IkNQ4d9OOeReUB59uqgGSPhYRaxqXKZwnfb+xzaqTpIdJd+gCG58HEhH/kVtRVjZJ/wzsA3wB+DfgDOCOiLgm18IqzCFRAyQ9ExH9W2qz6lKPzx7Y3kj6AnAMhWtK0yJies4lVZxPN1UxSX8DdAN2kHQQhb+oADsDHXIrzMr1pKQDIuLZvAuxLZNCoe6CoZiPJKqYpNHAGAojLIrHZ79NYdI/PwazCkl6lsJ1ozYUTle8TOHOXVF4Mt2BOZZnLZD0NukZEk03Ufj57VzhknLlkKgBkr4YEffmXYeVR1Kz97BExCuVqsVsazkkakDTUTKN7R4lU90kHQYsaHwOQRoKu39EzMi3MmuOpF2b2x4Rr1eqlmrgkKgBHiVTmyT9Aegf6R9Zej75LA84qG6SFlM43aQSmyMi9q5wSbnyheva0BARQ/Muwjabouh/YRHxgST/m6tyEVF3U280x3M31YYnJR2QdxG22V6WdIGktuk1lsJFbKsRkoZL+lF6/V3e9eTBp5tqgKTngJ4UHsTuUTI1QtLuwNV8+NyP3wDfiIhX86vKyiXpcuAQClOGA5wGzIyI7+RXVeU5JGpA1mgZj5Ix++hImgf0i4gP0npr4A/19p8zn26qASkM9gQGp+V38c+uakn6mqR90rIk3SRptaR5knzRurZ0KlrumFsVOfJFtBpQPGUxhSebtQX+kw8fhmLVZSxwS1o+DegL7E1hRtGfAJ7ivTb8G/AHSb+lcIp3EDAu35Iqz6ebaoCkOaQpiyPioNQ2r94Oe2tF8dPnJN0BzEgPrfGcWzVGUlcK1yUAno6IP+VZTx58yqI2rE1DKet6yuIa8oGkrpLaA5+jcMG60Q451WRlkjRE0skAEbEiIqZExBTg02nCv7rikKgNd0v6OdBJ0tco/NK5MeeaLNvFFObaWgJMiYgFAJI+g4fA1oKLgcdLtD8G1N0sBz7dVCM8ZXFtSTfN7RQRbxS17Ujh39w7+VVmLZE0KyIGZGyru9O8Dokakub+KZ67qa7mkDGrBEkvAb0iYn2T9rbAcxGxTz6V5cOnm2qApK9L+hMwj8JpjNlsOnW4mW07vwRuLL72J+njwPVpW13xkUQNkLQQODwiXsu7FrPtXTpVeCnwVaDxhtVPABOA/x8R6/KqLQ8OiRqQZoE9KSLezbsW2zySugF7selpwv/OryIrl6QdKEyHA7AoIt7Ls568OCRqQHp06c3ADApzNwEQERfkVpS1SNIVwAjgOT6c4j0iYnh+VZltHt9xXRt+DjwKPAt8kHMtVr4Tgf0iYk2Le5pVKYdEbWgbEd/MuwjbbC9TmELFIVFDJLWtt+sOzfHpphog6V8p3Jj1X2x6uslDYKuQpGso3B3fjcK8TY/g04Q1Q9IsYBnwMPBwRCzJt6J8OSRqQHqcYlN19xjFWiFpdHPbI2JipWqxLSOpOzA0vboBTwAPAY/X2+lDh4SZWTPSTXRHUQiMo4GVETEs16IqyCFRAyR1AL4JfCIizkrPKtgvIh7IuTRrhqRnSZMyFllN4UbISyNiVeWrsq0lqVtELM+7jkpxSNQASZMo3GU9KiL6pNB4snE6aqtOkn5IYejrHalpJNAB+BPw6Yg4Pq/azMrl0U214ZMRMULSaQAR8a4k5V2UtejzTZ4d8Wzj8yQkfTm3qsw2g+duqg1r092fjc+T+CQeVlkLWks6tHFF0iFA67S6vvRbrFpIOiDvGqqBjyRqwyUUhuPtKel2Co8tHZNrRVaOrwI3pcnhBLwFfDVNHPdvuVZm5fiZpI9ReBTt7RGxOud6cuFrEjVCUmfgMAq/bJ7yZH+1Q1JHgHr9JVPL0iCRM4BTgKeBm+vtWS4OiSomqdlnIUfEM5Wqxcon6csR8Z+SSt4lHxFXVrom23KSWlOYYuVqCkeDAr4TEXUxbbhPN1W3/2hmWwCDK1WIbZbG5xDslGsVtlUkHQicDgwDpgPHR8Qzkv4W+D118mwJH0mYmZUg6XHgF8DkptOES/pKRNyWT2WV5ZCoYpK+FRE/TMunRMQ9Rdv+NSK+k191lkXS1c1t99xNtUHSNyLix03axkbET/KqKQ8eAlvdRhYtf7vJtqGVLMQ2y+yi1/Am67NzrMs2z6gSbWMqXUTefE2iuiljudS6VYniCfzS/0Y9oV8NSTetfgnoIWlK0aadgLqbedkhUd0iY7nUulUn/5xqz5PACmA3Nh088jYwL5eKcuRrElVM0gbgLxSOGnYAGl1UeCEAAAURSURBVJ9xLaB9RLTNqzYrT+M0HHnXYbalHBJm25ikt/nwCKIDm4Z7RMTOuRRmZZH0RER8usnPEer05+eQMDOzTB7dZGZWgqRPprmbkHS0pAskdcq7rkpzSJiZlXYvsEFST+AGYE8+fDZI3XBImJmV9kFErAf+HrgmIi4EuuZcU8U5JMzMSluX7pkYDTQ+KrjuRhQ6JMzMSjsdOBy4LCIWS+oB1MV8TcU8usnMrIk0PfitEfEPedeSNx9JmJk1EREbgL0ktcu7lrx5Wg4zs9JeBv4nzd/0l8bGentolEPCzKy0P6ZXK+r4AVK+JmFm1gxJHSLi3Zb33D75moSZWQmSDpf0HPBCWu8r6Wc5l1VxDgkzs9J+DAwBVgFExFxgUK4V5cAhYWaWISKWNmnakEshOfKFazOz0pZKOgIISW2BscDzOddUcb5wbWZWgqTdgJ8An6fwLIlfA2MjYlWuhVWYQ8LMrARJXSJiZd515M3XJMzMSvsfSb+WdGY9PkeikUPCzKyEiNgX+C7QG3hG0gOSvpxzWRXn001mZi1I1yeuBP4hIlrnXU8l+UjCzKwESTtLGi3pIeBJYAVwaM5lVZyPJMzMSpC0GLgPuDsifp93PXlxSJiZlSBJ4V+QvpnOzKyYpB9HxDeAKZL+KiQiYngOZeXGIWFmtqnGR5T+KNcqqoRPN5mZWSYfSZiZlSDpSOB7wF4UflcKiIjYO8+6Ks1HEmZmJUh6AfgnYDZFs7/W29xNPpIwMyttdUQ8lHcRefORhJlZCZIuB1oDvwTWNLZHxDO5FZUDh4SZWQmSfluiOSJicMWLyZFDwszMMvmahJlZEUnfbNIUwGvAExGxOIeScuUJ/szMNrVTk9fOwADgIUkj8ywsDz7dZGZWBkm7Ar+JiP5511JJPpIwMytDRLxO4Ya6uuKQMDMrg6TPAm/kXUel+cK1mVkRSc9SuFhdbFfgf4FRla8oX74mYWZWRNJeTZoCWBURf8mjnrw5JMzMLJOvSZiZWSaHhJmZZXJImCWSNkiaU/QaV2KfoyU9sI37PVrSEUXrZ0uquwukVp08usnsQ+9FRL8c+j0aeAd4EiAirs+hBrOSfCRh1gJJQyW9IOkZ4KSi9u9J+uei9fmSuqflUZLmSZor6bbUdrykGZL+IOk3kvZI+58N/FM6ejmq+HMl9ZP0VPqsX0naJbU/JukKSU9LeknSURX6dlidcUiYfWiHJqebRkhqD9wIHA8cDPxNSx8iqTfwXWBwRPQFxqZNTwCHRcRBwF3AtyJiCXA9cFVE9IuI3zX5uFuBiyLiQOBZ4JKibW0i4lDgG03azbYZn24y+9BfnW6S1A9YHBEL0/p/Ame18DmDgXsi4jXYOJ0DQAMwSVJXoB3Q7IyikjoCnSLi8dQ0EbinaJdfpq+zge4t1GS2RXwkYbbl1rPpv6H2Lex/DfDTiDgA+HoZ+7ek8WlpG/B/+Owj4pAwa94LQHdJn0zrpxVtWwL0B5DUH+iR2h8FTpHUOW3bNbV3BJan5dFFn/M2hSmpNxERq4E3iq43fAV4vOl+Zh8lh4TZh5pek7g8It6ncHrpwXTh+tWi/e8FdpW0ADgPeAkgIhYAlwGPS5oLXJn2/x5wj6TZFB5i0+i/gL9vvHDdpKbRwL9Lmgf0A8Zvyz+wWUs8LYeZmWXykYSZmWVySJiZWSaHhJmZZXJImJlZJoeEmZllckiYmVkmh4SZmWVySJiZWab/A0fNczztsMM2AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df, x='City')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ZOeER60Z40LL",
        "outputId": "cf486669-8b22-4990-81e6-de77302351a0"
      },
      "id": "ZOeER60Z40LL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6e85110>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('SeniorList', 'Seniority', kind = 'scatter') # Años desde primera donación"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "4AU8lho8CQso",
        "outputId": "be5bb61d-6218-41ca-9d59-e54fd8709d09"
      },
      "id": "4AU8lho8CQso",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6b734a50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('SeniorList', 'Recency', kind = 'scatter') # Años desde última donación"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "1bmeBdmYC8n4",
        "outputId": "f740ff8a-a367-4036-9aa8-c3bd59956560"
      },
      "id": "1bmeBdmYC8n4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6d419d0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('SeniorList', 'Frequency', kind = 'scatter') # Numero de donaciones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zmSo_SyzD2Ff",
        "outputId": "3866c4fe-6ba0-48ef-ef04-a4bedfd0552a"
      },
      "id": "zmSo_SyzD2Ff",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6cfc510>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot('SeniorList', 'AmtThisYear', kind = 'scatter') # Numero de donaciones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "kUDUeZ3zEOYL",
        "outputId": "d52eef69-0142-4d85-bb3e-0739988e9f94"
      },
      "id": "kUDUeZ3zEOYL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6c9bb50>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['SeniorList'] == 0].sample(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DZQ-7tX4G0zW",
        "outputId": "814e0c19-8e90-4f53-d5e1-317bd957c9d1"
      },
      "id": "DZQ-7tX4G0zW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID   LastName FirstName  Woman   Age    Salary  \\\n",
              "968845  2968846.0     ORTEGA  SAMANTHA    1.0  44.0   23000.0   \n",
              "411634  2411635.0    HAMMOND    CURTIS    0.0  16.0   25600.0   \n",
              "854276  2854277.0    CLAUSEN    CHERYL    1.0  30.0   12100.0   \n",
              "392235  2392236.0      GIBBS    GEORGE    0.0  34.0  234500.0   \n",
              "145592  2145593.0     BAILEY    ROBERT    0.0  58.0   12800.0   \n",
              "399718  2399719.0     MARINO   RICHARD    0.0  67.0   23500.0   \n",
              "807676  2807677.0    JOHNSON   CATHRYN    1.0  16.0   26300.0   \n",
              "496544  2496545.0  OSTERBERG    ANGELA    1.0  62.0   43000.0   \n",
              "165608  2165609.0   GAUTHIER   LILLIAN    1.0  62.0   30400.0   \n",
              "997261  2997262.0       SHAW    GERALD    0.0  24.0    5300.0   \n",
              "488615  2488616.0     ABBOTT      DANA    0.0  87.0   38500.0   \n",
              "271005  2271006.0   DAHLHEIM   NICHOLE    1.0  35.0   34700.0   \n",
              "468256  2468257.0     THOMAS     JERRY    0.0  26.0  108200.0   \n",
              "485501  2485502.0   STEPHENS     DEENA    1.0  21.0    3600.0   \n",
              "89373   2089374.0   MORRISON     JASON    0.0  49.0   24300.0   \n",
              "94443   2094444.0    SESSOMS      EMMA    1.0  48.0  111800.0   \n",
              "302967  2302968.0   MORRISON    HORACE    0.0  44.0    1600.0   \n",
              "283188  2283189.0     NGUYEN   BEVERLY    1.0  35.0   16900.0   \n",
              "935577  2935578.0     WATSON       JON    0.0  57.0  105000.0   \n",
              "21794   2021795.0     PATKAR   CHARLES    0.0  29.0   16100.0   \n",
              "\n",
              "                   Education      City  SeniorList  NbActivities  ...  \\\n",
              "968845            Elementary  Suburban         0.0           0.0  ...   \n",
              "411634           High School  Suburban         0.0           0.0  ...   \n",
              "854276           High School  Downtown         0.0           0.0  ...   \n",
              "392235  University / College      City         0.0           0.0  ...   \n",
              "145592  University / College  Suburban         0.0           0.0  ...   \n",
              "399718  University / College      City         0.0           0.0  ...   \n",
              "807676  University / College      City         0.0           0.0  ...   \n",
              "496544  University / College      City         0.0           0.0  ...   \n",
              "165608           High School  Downtown         0.0           0.0  ...   \n",
              "997261  University / College  Suburban         0.0           0.0  ...   \n",
              "488615           High School      City         0.0           0.0  ...   \n",
              "271005  University / College      City         0.0           0.0  ...   \n",
              "468256  University / College     Rural         0.0           0.0  ...   \n",
              "485501           High School     Rural         0.0           0.0  ...   \n",
              "89373            High School      City         0.0           0.0  ...   \n",
              "94443   University / College  Suburban         0.0           0.0  ...   \n",
              "302967  University / College      City         0.0           0.0  ...   \n",
              "283188           High School  Downtown         0.0           0.0  ...   \n",
              "935577  University / College  Suburban         0.0           0.0  ...   \n",
              "21794   University / College      City         0.0           0.0  ...   \n",
              "\n",
              "        Recency  Frequency  Seniority  TotalGift  MinGift  MaxGift  \\\n",
              "968845      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "411634      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "854276      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "392235      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "145592      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "399718      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "807676      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "496544      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "165608      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "997261      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "488615      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "271005      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "468256      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "485501      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "89373       NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "94443       NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "302967      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "283188      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "935577      NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "21794       NaN        NaN        NaN        NaN      NaN      NaN   \n",
              "\n",
              "        GaveLastYear  AmtLastYear  GaveThisYear  AmtThisYear  \n",
              "968845           0.0          0.0           0.0          0.0  \n",
              "411634           0.0          0.0           0.0          0.0  \n",
              "854276           0.0          0.0           0.0          0.0  \n",
              "392235           0.0          0.0           0.0          0.0  \n",
              "145592           0.0          0.0           0.0          0.0  \n",
              "399718           0.0          0.0           0.0          0.0  \n",
              "807676           0.0          0.0           0.0          0.0  \n",
              "496544           0.0          0.0           0.0          0.0  \n",
              "165608           0.0          0.0           0.0          0.0  \n",
              "997261           0.0          0.0           0.0          0.0  \n",
              "488615           0.0          0.0           0.0          0.0  \n",
              "271005           0.0          0.0           0.0          0.0  \n",
              "468256           0.0          0.0           0.0          0.0  \n",
              "485501           0.0          0.0           0.0          0.0  \n",
              "89373            0.0          0.0           0.0          0.0  \n",
              "94443            0.0          0.0           0.0          0.0  \n",
              "302967           0.0          0.0           0.0          0.0  \n",
              "283188           1.0         75.0           0.0          0.0  \n",
              "935577           0.0          0.0           0.0          0.0  \n",
              "21794            0.0          0.0           1.0         30.0  \n",
              "\n",
              "[20 rows x 21 columns]"
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              "      <th>968845</th>\n",
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              "      <td>ORTEGA</td>\n",
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              "      <th>411634</th>\n",
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              "      <th>854276</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12100.0</td>\n",
              "      <td>High School</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>34.0</td>\n",
              "      <td>234500.0</td>\n",
              "      <td>University / College</td>\n",
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              "      <td>58.0</td>\n",
              "      <td>12800.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>488615</th>\n",
              "      <td>2488616.0</td>\n",
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              "      <td>DANA</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>271005</th>\n",
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              "      <td>University / College</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>49.0</td>\n",
              "      <td>24300.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>University / College</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>302967</th>\n",
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              "      <td>1600.0</td>\n",
              "      <td>University / College</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>283188</th>\n",
              "      <td>2283189.0</td>\n",
              "      <td>NGUYEN</td>\n",
              "      <td>BEVERLY</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>16900.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Downtown</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>935577</th>\n",
              "      <td>2935578.0</td>\n",
              "      <td>WATSON</td>\n",
              "      <td>JON</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>105000.0</td>\n",
              "      <td>University / College</td>\n",
              "      <td>Suburban</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21794</th>\n",
              "      <td>2021795.0</td>\n",
              "      <td>PATKAR</td>\n",
              "      <td>CHARLES</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>16100.0</td>\n",
              "      <td>University / College</td>\n",
              "      <td>City</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 21 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['SeniorList'] == 0].info()"
      ],
      "metadata": {
        "colab": {
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        "id": "KrWYuP2-Hoa2",
        "outputId": "85f58dfe-98fa-4c5b-9e40-79d34cbc7255"
      },
      "id": "KrWYuP2-Hoa2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 166199 entries, 6 to 999998\n",
            "Data columns (total 21 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   ID            166199 non-null  float64\n",
            " 1   LastName      166197 non-null  object \n",
            " 2   FirstName     166199 non-null  object \n",
            " 3   Woman         166199 non-null  float64\n",
            " 4   Age           166199 non-null  float64\n",
            " 5   Salary        166199 non-null  float64\n",
            " 6   Education     166199 non-null  object \n",
            " 7   City          166199 non-null  object \n",
            " 8   SeniorList    166199 non-null  float64\n",
            " 9   NbActivities  166199 non-null  float64\n",
            " 10  Referrals     166199 non-null  float64\n",
            " 11  Recency       0 non-null       float64\n",
            " 12  Frequency     0 non-null       float64\n",
            " 13  Seniority     0 non-null       float64\n",
            " 14  TotalGift     0 non-null       float64\n",
            " 15  MinGift       0 non-null       float64\n",
            " 16  MaxGift       0 non-null       float64\n",
            " 17  GaveLastYear  166199 non-null  float64\n",
            " 18  AmtLastYear   166199 non-null  float64\n",
            " 19  GaveThisYear  166199 non-null  float64\n",
            " 20  AmtThisYear   166199 non-null  float64\n",
            "dtypes: float64(17), object(4)\n",
            "memory usage: 27.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['SeniorList'] > 0].info()"
      ],
      "metadata": {
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        },
        "id": "af1fs439nxHz",
        "outputId": "dca55c71-bca5-409a-abff-cc45b4c58848"
      },
      "id": "af1fs439nxHz",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 833801 entries, 0 to 999999\n",
            "Data columns (total 21 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   ID            833801 non-null  float64\n",
            " 1   LastName      833780 non-null  object \n",
            " 2   FirstName     833797 non-null  object \n",
            " 3   Woman         833801 non-null  float64\n",
            " 4   Age           833801 non-null  float64\n",
            " 5   Salary        833801 non-null  float64\n",
            " 6   Education     833801 non-null  object \n",
            " 7   City          833801 non-null  object \n",
            " 8   SeniorList    833801 non-null  float64\n",
            " 9   NbActivities  833801 non-null  float64\n",
            " 10  Referrals     833801 non-null  float64\n",
            " 11  Recency       336334 non-null  float64\n",
            " 12  Frequency     336334 non-null  float64\n",
            " 13  Seniority     336334 non-null  float64\n",
            " 14  TotalGift     336334 non-null  float64\n",
            " 15  MinGift       336334 non-null  float64\n",
            " 16  MaxGift       336334 non-null  float64\n",
            " 17  GaveLastYear  833801 non-null  float64\n",
            " 18  AmtLastYear   833801 non-null  float64\n",
            " 19  GaveThisYear  833801 non-null  float64\n",
            " 20  AmtThisYear   833801 non-null  float64\n",
            "dtypes: float64(17), object(4)\n",
            "memory usage: 140.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['MinGift'].isna()].sample(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 386
        },
        "id": "hqoRGCuoIEh8",
        "outputId": "f4ca9c1e-00c7-41b1-d379-5cda2f85b06a"
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      "id": "hqoRGCuoIEh8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID    LastName  FirstName  Woman   Age    Salary  \\\n",
              "953593  2953594.0     STRACHE   JERMAINE    0.0  55.0  109400.0   \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-2edfe537-e1b0-4571-b9fe-c65c0ab99f0c 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-2edfe537-e1b0-4571-b9fe-c65c0ab99f0c');\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": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data preprocessing"
      ],
      "metadata": {
        "id": "H-YaNyX5mmSI"
      },
      "id": "H-YaNyX5mmSI"
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_cols(df):\n",
        "  return df.drop(columns=['LastName', 'FirstName'])"
      ],
      "metadata": {
        "id": "J88dG03Tmlne"
      },
      "id": "J88dG03Tmlne",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dummies(df):\n",
        "  df = pd.concat([df, pd.get_dummies(df[\"Education\"])], axis = 1)\n",
        "  df = df.drop('Education', axis = 1)\n",
        "\n",
        "  df = pd.concat([df, pd.get_dummies(df[\"City\"])], axis = 1)\n",
        "  return df.drop('City', axis = 1)"
      ],
      "metadata": {
        "id": "HLQ-HVCv4PHS"
      },
      "id": "HLQ-HVCv4PHS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Llenar NA Values"
      ],
      "metadata": {
        "id": "baRgti-NzzKu"
      },
      "id": "baRgti-NzzKu"
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_na(df, MAX_FILL):\n",
        "  df.loc[df['Recency'].isna(), 'Recency'] = MAX_FILL\n",
        "  df.loc[df['Frequency'].isna(), 'Frequency'] = 0\n",
        "  df.loc[df['Seniority'].isna(), 'Seniority'] = MAX_FILL\n",
        "  df.loc[df['TotalGift'].isna(), 'TotalGift'] = 0\n",
        "  df.loc[df['MinGift'].isna(), 'MinGift'] = 0\n",
        "  df.loc[df['MaxGift'].isna(), 'MaxGift'] = 0\n",
        "  return df"
      ],
      "metadata": {
        "id": "O-fQQiKp0ixT"
      },
      "id": "O-fQQiKp0ixT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rename_df(df):\n",
        "  return df.rename(columns = {\"AmtLastYear\" : \"AmtGiven\", \"GaveLastYear\": \"Gave\"})"
      ],
      "metadata": {
        "id": "C8AK5M4VojAG"
      },
      "id": "C8AK5M4VojAG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def update_df2(df2, MAX_FILL):\n",
        "  df2.loc[df2['Gave'] == 1, 'Recency'] = 1\n",
        "  df2.loc[(df2['Gave'] == 0) & (df2['Recency'] < MAX_FILL), 'Recency'] += 1\n",
        "  df2.loc[df2['Gave'] == 1, 'Frequency'] += 1\n",
        "  df2.loc[(df2['Gave'] == 1) & (df2['Seniority'] == MAX_FILL), 'Seniority'] = 1\n",
        "  df2.loc[df2['Seniority'] < MAX_FILL, 'Seniority'] += 1\n",
        "  df2['TotalGift'] += df2['AmtGiven']\n",
        "  df2.loc[df2['Gave'] == 1 & ((df2['MinGift'] > df2['AmtGiven']) | (df2['MinGift'] == 0)), 'MinGift'] = df2['AmtGiven']\n",
        "  df2.loc[(df2['MaxGift'] < df2['AmtGiven']), 'MaxGift'] = df2['AmtGiven']\n",
        "  df2 = df2.drop(['AmtGiven', 'Gave'], axis=1)\n",
        "  return df2"
      ],
      "metadata": {
        "id": "Po61z7fzqb3c"
      },
      "id": "Po61z7fzqb3c",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rename_df2(df2):\n",
        "    return df2.rename(columns = {\"AmtThisYear\" : \"AmtGiven\", \"GaveThisYear\": \"Gave\"})"
      ],
      "metadata": {
        "id": "trd-BJnB1Ryg"
      },
      "id": "trd-BJnB1Ryg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_df(df):\n",
        "  return df.drop(['AmtThisYear', 'GaveThisYear'], axis=1)"
      ],
      "metadata": {
        "id": "TZ4LcQsq45MV"
      },
      "id": "TZ4LcQsq45MV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_vars(data):\n",
        "  data['JustGave'] = 0\n",
        "  data.loc[data['Recency'] == 1, 'JustGave'] = 1\n",
        "  data['MeanGift'] = data['TotalGift'] / data['Frequency']\n",
        "  data.loc[data['MeanGift'].isna(), 'MeanGift'] = 0\n",
        "  return data  "
      ],
      "metadata": {
        "id": "g2XLVGTxYE1E"
      },
      "id": "g2XLVGTxYE1E",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(data, train):\n",
        "  df = data.copy()\n",
        "  df = drop_cols(df)\n",
        "  df = dummies(df)\n",
        "  MAX_FILL = 100\n",
        "  df = fill_na(df, MAX_FILL)\n",
        "  df = rename_df(df)\n",
        "  df2 = df.copy()\n",
        "  df2 = update_df2(df2, MAX_FILL)\n",
        "\n",
        "  if train == True:\n",
        "    df2 = rename_df2(df2)\n",
        "    df = drop_df(df)\n",
        "    double_data = df.append(df2)\n",
        "    double_data = add_vars(double_data)\n",
        "    return double_data\n",
        "  else:\n",
        "    df2 = add_vars(df2)\n",
        "    return df2"
      ],
      "metadata": {
        "id": "28nRsX3W5teq"
      },
      "id": "28nRsX3W5teq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "double_data = preprocess(data_merge, train = True)\n",
        "double_data.to_csv('PreprocessedDouble.csv', index=False)"
      ],
      "metadata": {
        "id": "Z_N6Sb69AaVk"
      },
      "id": "Z_N6Sb69AaVk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data_merge = drop_cols(data_merge)\n",
        "# data_merge.dropna(inplace =True)\n",
        "# data_merge.info()\n",
        "# data_merge.to_csv('DataMergeOriginalNotNull.csv', index=False)\n",
        "# data_merge.sort_values([\"Recency\"], axis = 0, ascending = True,\n",
        "#                  inplace = True, na_position ='last')\n",
        "# data_merge.head(50)"
      ],
      "metadata": {
        "id": "j1BeLSCUc3pY"
      },
      "id": "j1BeLSCUc3pY",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,14))\n",
        "sns.heatmap(double_data.corr(), cmap=\"RdYlBu\", \n",
        "    annot=True, square=True,\n",
        "    vmin=-1, vmax=1, fmt=\"+.3f\")\n",
        "plt.title(\"Correlation matrix for the Cortex Fundraising dataset\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljN3KOqnve2P",
        "outputId": "f1b0eeb4-2c51-4934-9890-9ea16d6b34f8"
      },
      "id": "ljN3KOqnve2P",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation matrix for the Cortex Fundraising dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x1008 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Models Felipe"
      ],
      "metadata": {
        "id": "cP5XyA-AKlrh"
      },
      "id": "cP5XyA-AKlrh"
    },
    {
      "cell_type": "markdown",
      "id": "11f80a12-8482-4d72-8c10-656f5d1fe657",
      "metadata": {
        "id": "11f80a12-8482-4d72-8c10-656f5d1fe657"
      },
      "source": [
        "## Treat Missing Values\n",
        "\n",
        "> Please be aware that deleting all missing values can induce a selection bias. \n",
        "Some missing values are very informative. For example, when MinGift is missing, it means that the donor never gave in the past 10 years (leading to but excluding last year). Instead of deleting this information, replacing it by 0 is more appropriate!\n",
        "\n",
        "> A good understanding of the business case and the data can help you come up with more appropriate strategies to deal with missing values.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b861046-5968-4601-bd4b-b6510f1bf5b9",
      "metadata": {
        "id": "9b861046-5968-4601-bd4b-b6510f1bf5b9"
      },
      "source": [
        "## Data Partition"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Only donors\n",
        "double_data = double_data[double_data['Gave'] == 1]\n",
        "double_data.to_csv('PreprocessedDoubleGave.csv', index=False)"
      ],
      "metadata": {
        "id": "y8guJkSFzoN8"
      },
      "id": "y8guJkSFzoN8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f4bf6498-b18c-400d-9202-bb466d1272df",
      "metadata": {
        "collapsed": true,
        "id": "f4bf6498-b18c-400d-9202-bb466d1272df"
      },
      "outputs": [],
      "source": [
        "# All data doubled\n",
        "# double_data = pd.read_csv('PreprocessedDouble.csv')\n",
        "double_data = pd.read_csv('PreprocessedDoubleGave.csv')\n",
        "from sklearn.model_selection import train_test_split\n",
        "train, validation = train_test_split(double_data, test_size=0.1, random_state=12345)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3396591b",
      "metadata": {
        "id": "3396591b"
      },
      "source": [
        "## Prebuilt Models to Predict donation amount\n",
        "***\n",
        "\n",
        "### **Linear Regression Model**\n",
        "\n",
        "\n",
        "> The [sk-learn library](https://scikit-learn.org/stable/index.html ) offers more advanced models.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "double_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZT1SzC6FoUx",
        "outputId": "05ee98dd-80dc-41b3-b952-4bdd0578a0c1"
      },
      "id": "ZZT1SzC6FoUx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Woman', 'Age', 'Salary', 'SeniorList', 'NbActivities',\n",
              "       'Referrals', 'Recency', 'Frequency', 'Seniority', 'TotalGift',\n",
              "       'MinGift', 'MaxGift', 'Gave', 'AmtGiven', 'Elementary', 'High School',\n",
              "       'University / College', 'Downtown', 'Rural', 'Suburban', 'JustGave',\n",
              "       'MeanGift'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0833fb2d-554c-4e3b-933c-9a35828fd912",
      "metadata": {
        "collapsed": true,
        "id": "0833fb2d-554c-4e3b-933c-9a35828fd912"
      },
      "outputs": [],
      "source": [
        "from sklearn import linear_model, preprocessing\n",
        "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "# from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostRegressor\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "import xgboost as xgb\n",
        "\n",
        "\n",
        "# predictors = ['Woman', 'Age', 'Salary', 'SeniorList', 'NbActivities',\n",
        "#        'Referrals', 'Recency', 'Frequency', 'Seniority', 'TotalGift',\n",
        "#        'MinGift', 'MaxGift', 'Elementary', 'High School',\n",
        "#        'University / College', 'Downtown', 'Rural', 'Suburban', 'JustGave',\n",
        "#        'MeanGift']\n",
        "\n",
        "# predictors = ['Age', 'Referrals', 'SeniorList','MinGift', 'Woman', 'NbActivities', 'JustGave', 'Frequency']\n",
        "\n",
        "# Predictors only Gave = 1\n",
        "predictors = ['Woman', 'Age', 'Salary', 'SeniorList', 'NbActivities',\n",
        "       'Referrals', 'Recency', 'Frequency', 'Seniority', 'TotalGift',\n",
        "       'MinGift', 'MaxGift', 'Rural', 'JustGave', 'MeanGift']\n",
        "\n",
        "X_train = train[predictors] \n",
        "Y_train = train['AmtGiven']\n",
        "X_valid = validation[predictors] \n",
        "Y_valid = validation['AmtGiven']\n",
        "\n",
        "# Sacler\n",
        "# scaler = preprocessing.StandardScaler().fit(X_train)\n",
        "# X_train = pd.DataFrame(scaler.transform(X_train))\n",
        "# X_valid = pd.DataFrame(scaler.transform(X_valid))\n",
        "\n",
        "# Models tried\n",
        "# regr = linear_model.LinearRegression()\n",
        "regr = DecisionTreeRegressor(max_depth=7)\n",
        "# regr = MLPRegressor(random_state=1, max_iter=500, early_stopping=True)\n",
        "# regr = linear_model.BayesianRidge()\n",
        "# regr = MultinomialNB() # Malo\n",
        "# regr = RandomForestClassifier(n_estimators=30, max_depth=None, min_samples_split=2, random_state=0, max_features=None)\n",
        "# regr = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=2)\n",
        "# regr = xgb.XGBRegressor(objective=\"reg:linear\", random_state=42)\n",
        "\n",
        "regr.fit(X_train,Y_train)\n",
        "regr_predict=regr.predict(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation[\"Pred\"] = regr_predict\n",
        "validation[[\"AmtGiven\", \"Pred\"]].sample(10)\n",
        "# great = validation.loc[validation[\"AmtGiven\"] > 400]\n",
        "# great[[\"AmtGiven\", \"Pred\"]].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "kuPJzn-CGGzf",
        "outputId": "1981a21f-3cac-4443-fda6-8806742d7c78"
      },
      "id": "kuPJzn-CGGzf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        AmtGiven        Pred\n",
              "114733      30.0   53.127382\n",
              "38098       10.0   62.185843\n",
              "225433      25.0   62.678150\n",
              "1715        20.0   37.775828\n",
              "142192      40.0  121.176658\n",
              "217366      25.0   44.358454\n",
              "156853      20.0   62.185843\n",
              "84108       25.0   47.963204\n",
              "226317      30.0   47.963204\n",
              "49678       50.0   48.135236"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AmtGiven</th>\n",
              "      <th>Pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114733</th>\n",
              "      <td>30.0</td>\n",
              "      <td>53.127382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38098</th>\n",
              "      <td>10.0</td>\n",
              "      <td>62.185843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225433</th>\n",
              "      <td>25.0</td>\n",
              "      <td>62.678150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1715</th>\n",
              "      <td>20.0</td>\n",
              "      <td>37.775828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142192</th>\n",
              "      <td>40.0</td>\n",
              "      <td>121.176658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217366</th>\n",
              "      <td>25.0</td>\n",
              "      <td>44.358454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156853</th>\n",
              "      <td>20.0</td>\n",
              "      <td>62.185843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84108</th>\n",
              "      <td>25.0</td>\n",
              "      <td>47.963204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226317</th>\n",
              "      <td>30.0</td>\n",
              "      <td>47.963204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49678</th>\n",
              "      <td>50.0</td>\n",
              "      <td>48.135236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a01d8489-1e5e-4fa1-b8b9-e49b308d68cf')\"\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",
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              "      padding: 0 0 0 0;\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a01d8489-1e5e-4fa1-b8b9-e49b308d68cf 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-a01d8489-1e5e-4fa1-b8b9-e49b308d68cf');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors = sns.color_palette(\"colorblind\")\n",
        "X = np.arange(30)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(X, Y_valid[:30], color=colors[0], label=\"AmtGiven\")\n",
        "plt.plot(X, regr_predict[:30], color=colors[1], label=\"Prediction\", linewidth=2)\n",
        "plt.xlabel(\"data\")\n",
        "plt.ylabel(\"$\")\n",
        "plt.title(\"Decision Tree Regression\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "dUQpAddWL8W9",
        "outputId": "742a8c66-c00b-4d70-bf1f-1ccfa7a23a69"
      },
      "id": "dUQpAddWL8W9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d47987df",
      "metadata": {
        "id": "d47987df",
        "outputId": "407bc3ca-9106-45a9-caf3-53285d006bde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "61.153547656056006\n",
            "42162.036696581345\n",
            "205.33396381646497\n"
          ]
        }
      ],
      "source": [
        "#MAE\n",
        "print(metrics.mean_absolute_error(Y_valid,regr_predict))\n",
        "#MSE\n",
        "print(metrics.mean_squared_error(Y_valid,regr_predict))\n",
        "#RMSE\n",
        "print(np.sqrt(metrics.mean_squared_error(Y_valid,regr_predict)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_tree(regr, filled = True, feature_names=predictors)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "HEMIlZdAfEM7",
        "outputId": "612c5118-8c12-44e6-b306-aa917addebb7"
      },
      "id": "HEMIlZdAfEM7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# xgb.plot_importance(regr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "ariywdrihKXB",
        "outputId": "6b462b4e-7317-489a-8de0-5b969746e2a9"
      },
      "id": "ariywdrihKXB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c68a29d30>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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t5s+fz8EHH8ygQYNYuHBhtqM6l/tnWmm0AKYAR5nZ25LuA35hZuMk/Ro4wsxWh3WvMrPPJDUB5kjqa2avx/bVm6ij+5YMjnsy8LiZlZ31nZNyrHIpQ5Nw7X6bt/Gl1q+OLaMzgqRIUt5czFpQUFA+PWTIEAYMGFA+FElpaSlr166lqKiIm266iWXLlnHyySfzwAMPICl7odPYsGFDhdeS65KUNyezmlliHsBo4Drg+di8o4BHw3QxsHNs2QVEvQNfJ/oc7KwwvwDoT1SI/hFb/yqgEPgwPO8CLE2dTnesyh49evSwpJg3b162I9RIkvImKeuVV15pl1xyiR133HE2d+7c8vldu3a1Tz/9NIvJ0kvSe2uWrLzZzAossjS/UxN5eTATkvYCLic6I+sLPEF0lhb3JtAvjFaMmd1o0ZlU23oN61yWld1kUTYUydFHH80pp5zCvHnR1e+3336bb775hp133jmbMZ1L5OXBUqCLpL3N7F3gv4lGFAZYD+wArCYqPBuBtZI6AicQnWGVM7N3JS0C/iDpGjMrldSCaOys6sSP5VyinX766axZs6Z8KJImTZowcuRIRo4cSZ8+fdh+++2ZOnVqzl0adI1PEovWV8A5wN8lNQUWAneGZXcBT0n60MyOkPQa0YjD7wMvVLK/84hueX9X0hqgBMjkCykVjrXtL8e57Js/f36F5wUFBWy//fZMmzYtS4mcSy9RRcvMRsee7p9m+URgYuz5iEr2kx+bXgf8vJL1ioE+qdPpjuWcc67uNdjPtJxzzjU8XrScc84lhhct55xzieFFyznnXGJ40XKunixfvpy8vLzyR9u2bSt0VL/llluQxOrV/i0K5yqT2KIlySRNiz1vKmmVpMfD85MljcpgP90lPS7p/yQtljRP0sDUfUjaRdIroefh4ZL+X129Ntcw9ezZk8LCQgoLC1m8eDGtWrXi1FNPBeD999/nmWeeoXPnzllO6VxuS2zRIvricB9JLcPzY4APyhaa2Swzu6mqHYQvEj9BNMRINzM7ELgI6JpmH0cBRWa2P9H3vrxouW02Z84cunXrxp577gnAr371K26++Wb/8q5z1UjU97TSeBI4CZgBDAMeBA4HkDQC6G9mF0qaAqwj6je4K3CFmc0AhgMvmdmssh2a2VJgaXwfRMOX3Ay0lNQfWA50k1QIzDaz31QW0IcmqTtJyVs2BEjcQw89xLBhwwCYOXMmnTp1ol+/fvUdzbnESXrRegi4NlwS7AvcSyhaaewGHAb0AmYRFbreRA11q2RmhZKu5dsi2AXoHfoUbsW7vNePpOQtKCio0C1706ZNPPLIIwwePJinnnqKUaNG8ec//5mCggK++uorXnjhBdq1a5fVzDnZ3bsSScoKycqbi1kTXbTM7PVQQIYRnXVV5TGLhiB5M/Qi3IqkfwDdgbfN7LTvkOsuojZPdO66t91SlIy3+bL9NpOUrJCcvMXD8ykoKCA/Px+IzqwOPvhgTjvtNIqKilizZg0XXnghAKtXr+aiiy5iwYIF7LrrrlnLHM+b65KUFZKVNxez5v6/+OrNAsYC+cBOVaz3dWy67IODN4CBZTPN7NRw+a+ykZFrrGWzJixPc3koFxUUFFA8PD/bMTKWtLxlHnzwwfJLg/vtt1+FYey7dOnCokWLvJu6c5VI8o0YZe4Frjezom3Y9gHgh5JOjs1rlcF2ZR3enauRjRs3Mnv2bE47bZtP5J1r1BJ/pmVmK4EJ27htiaTBwF8kjQM+ISpIf6hmuzWSXpC0FPjfqm7EcC6udevWrFmzptLlxcXF9RfGuQRKbNEyszZp5hUQxswysynAlDA9orJtzWwZcGIlx4jvo3w6PP+vbc3unHNu2zSEy4POOecaCS9azjnnEsOLlnPOucTwouWccy4xvGg555xLDC9aLpFKS0s5//zzGTx4MACHH354+ZAfu+++O6ecckqWEzrn6kKdFa0wdMgtseeXSxodpqdIGlrFtuMkfSCpynySdowPESJpd0kzqtkmPtzIKZL2jS27QdLR1b44l3Xjx4+vMIzH/Pnzy4f9GDBggH9517kGqi6/p/U1cJqkMWaW8ah2oVCdSjT8xyBgXhWr70g0RMj/AJjZh0ClxTCsM4uo9RPAKcDjwJth2bWZ5syUd3mvHfFO6StXruSJJ57gpJNOYu7cuRXWW7duHXPnzmXy5Mn1HdE5Vw/q8vLgZqKmsb+qZPnRkhZJejt0pSiTT9QT8A6iRrgASOoo6R+SloTHocBNhCFCJP1ZUpfQpQJJL0vqHdu+QFJ/SSMk3Ra2Pxn4c9i+W/wMUNKBkp4LA0M+LWm3MP9iSW9Kel3SQ7X1ZrnMXXrppdx8881st93Wf30fe+wxjjrqKNq2bZuFZM65ulbXHTFuB16XdHOaZV2Ag4BuwDxJe5vZV3w7LtZM4I+SmpnZJqJWTc+FprZNgDbAKKBP2RAhoeN7menAGcB1oeDsZmaLJPUBMLMXJc0CHg9ja5UPwCepGTARGGJmqySdCdwIjAzH3MvMvpa0Y7oX7UOT1L6y4RFeeuklNm3axPr16ykpKWHNmjUVhk64/fbbOfHEE3NuOIVcHOKhKknKm6SskKy8OZnVzOrkAWwIP28ArgEuB0aHeVOAkbF1nwfygO2JRh/eIcx/FBgcplcBzVOO0QVYmu450Al4I0xfAtwYpkcAt8VyDI1tP4Xo8mIfokEjC8OjCHgmrPMU0VhcPwHaVPc+9OjRw5Ji3rx52Y5QrVGjRlmnTp1szz33tO9973vWsmVLGz58uJmZrVq1ytq3b28lJSVZTrm1JLy3cUnKm6SsZsnKm82swCJL8zu1Pu4eHAecC7ROmW9pnh9H9DlVkaRiokEbh7ENzOwDYI2kvsCZRGdemRJRwcsLj/3M7Niw7CSiM8gDgIWSEtu/MYnGjBnDypUrKS4u5tprr+XII49k2rRpAMyYMYPBgwfTokWLLKd0ztWVOi9aZvYZ8DBR4Yr7saTtJHUDuhINYT8MOM/MuphZF2Av4BhJrYA5wC8AJDWR1I7qhwiZDlwBtDOz19Msr2z75cAukgaE4zWT1DvcJPJ9M5sH/BZoR3SZ0uWA+BD2zrmGqb6+p3ULkDqq3X+ABcD/AheELMcD5bevmdlG4F/Aj4gu8R0hqQhYDOxrZmuAFyQtlfTnNMedAZxFVDTTeQj4jaTXQvEsO+43RJcJ/yRpCdElwkOBJsC0kOE1YIKZfZH52+BqU15eHo8//nj584KCAo4//vgsJnLO1bU6u7RlFYf/+ITY4IqWMlRITPs0+4l/4WZImuWpQ4T0STlu05T1p/DtcCMvAPvGFo+IrVdIbFTjmMMqye6cc66OeUcM55xzieFFyznnXGJ40XLOOZcYXrScc84lhhctl3NKS0vZf//9yzu4Dx8+nJ49e9KnTx9GjhzJpk2bspzQOZctiStakm6VdGns+dOSJsWe3yLp19lJ52rD+PHj2WeffcqfDx8+nGXLllFUVERJSQmTJk2qYmvnXEOWuKIFvED0namyjvA7A71jyw8FXsxCLlcLyjq4n3feeeXzTjzxRCQhiYMOOoiVK1dmMaFzLpuS2ILoReDWMN0bWArsJul7wJfAPkA7Sa8Rvb6FwC8sanBbTNSM9wSiLvQ/A8YAewN/NrM7JbUhatb7PaAZcLWZzQzNeP+X6MvOhxL1SBxiZiVVhfWhSaoXH3akrIP7+vXrt1pv06ZN3H///YwfP74+4znnckjiipaZfShps6TORMXjJaLmuAOAtcA7wCTgKDN7W9J9RO2fxoVd/MfM8iTdSvQl4x8CLYiK353AV8CpZrZO0s7Ay6EbPEB3YJiZnS/pYeB0YFpqRu/yXjPpOrgXFhZu1cF97NixdO3aldLSUgoKCnKzA3UlkpQVkpU3SVkhWXlzMWviilbwIlHBOhT4C1HROpSoaK0ESszs7bDuVOCXfFu0ygpQEVGX9vXAekllQ41sJBoSZSCwJey7Y9hmReiUAVErqS7pwpnZXURjidG56952S1Ey3ubL9ttMNrIWD88H4Omnn2bx4sWMGDGCr776inXr1jFp0iSmTZvG9ddfT9OmTXn44YfLx9EqKCggPz+/3vNuiyRlhWTlTVJWSFbeXMyajN+mWyv7XGs/ojOk94HLiIYTKSA6A6rM1+Hnlth02fOmwHBgF+BAM9sULim2SNkWoBRoWV3Qls2asDx2+SuXFRQUlBeQbBgzZgxjxowpzzJ27FimTZvGpEmTePrpp5kzZ07agR+dc41HRr8Bwqi+zcN0fhi9N+0AiPXkRWAw8JmZlYZO8jsSXSJ8BOgiae+w7n8Dz9Vg3+2AT0PBOgLYsxZzu21wwQUX8MknnzBgwADy8vK44YYbsh3JOZclmZ5pPQL0D4XgLqIbFR4ATqyrYNUoIrpr8IGUeW3MbKWkc4C/h7GuFhJ9VpWpvwH/DJ3cFwHLaimzq4H8/PzyyxKbNyfjM0HnXN3LtGhtMbPNkk4FJprZxHB3XlaYWSnQNmXeiNj0HGD/NNt1iU1PIXR7T11GdMaWTryD/NiaZHbOOffdZfoBwSZJw4CzgbIBjJrVTSTnnHMuvUyL1jlEZx83mtkKSXsB99ddLOecc25rGV0eNLM3Jf0W6ByerwD+VJfBnHPOuVSZ3j34I6Ih558Kz/NiX7h1zjnn6kWmlwdHAwcBX0D5UPRd6yiTc845l1bGN2KY2dqUeVtqO4xLvq+++oqDDjqIfv360bt3b6677joAzj33XPr160ffvn0ZOnQoGzZsyHJS51wSZVq03pD0X0ATSd0lTaQOO6lLMknTYs+bSlol6fGqttvGYzWV9EdJ70gqDI+rYstfjE3/WdIb4ecISbvXdp6ka968OXPnzmXJkiUUFhby1FNP8fLLL3PrrbeyZMkSXn/9dTp37sxtt92W7ajOuQTK9HtaFwFXEbUxegB4GvhDXYUi6v/XR1LL0EX9GKKu6nXhD8CuwH5m9pWkHYhaQgFgZofG1v0Z0N7MSiUVELWQ+rCqnTeGLu/xLu2SaNOmDRB1Zd+0aROSaNs2+lqdmVFSUoKk2gntnGtUqj3TktQEeMLMrjKzH4TH1Wb2VR1nexIo+204jGhIkbJMrSXdK2mBpNckDQnzu0iaL+nV8CgbdytfUoGkGZKWSfqbIq2A84GLyl6Pma03s9GxY20IP2cBbYDFks4E+gN/C2dm1fYgbExKS0vJy8ujQ4cOHHPMMRx88MEAnHPOOey6664sW7aMiy66KMspnXNJJDOrfiVpDnBams+16kQoFIcC1wI/AV4GLgUuN7PBkv4IvGlm00IPxAVEHTCMqHvHV5K6Aw+aWX9J+UStp3oTnRm9APyGqMHuVDPbqntGPIuZtUkzXRDyLEqzTXxokgOvHXf3d35P6kPHlvBJlaODpbdfp3Zp52/YsIFrrrmGiy++mL322guICtqECRPo1asXJ5xwwneJy4YNG8rP6nJdkrJCsvImKSskK282sx5xxBGLzax/6vxMLw9uAIokzSa6dAeAmV1cS/m2Ymavh4EXhxGddcUdC5ws6fLwvAXRd8g+BG6TlEfUhb1HbJsFZrYSQFIh0bAir8d3GnoWXgLsBBxqZu9vY/byoUl69uxpFw0fsi27qXcFBQWcUcvDELz66qusWbOGc845p3xes2bNuPnmm/nTn77bV/1ycdiEyiQpKyQrb5KyQrLy5mLWTG/EeBS4BnieaBypskddmwWMJXZpMBBwupnlhUdnM3sL+BXwCdCP6PLd9rFtUocVaQq8C3QOn2NhZpPNLI9oXK4mdfGCGrpVq1bxxRdfAFBSUsLs2bPp2bMn7777LhB9pjVr1ix69eqVzZjOuYTKtCPG1LoOUol7gS/MrChc4ivzNHCRpIvMzCTtb2avEQ0rstLMtkg6m2oKj5l9KekeorOzn4fLik2oWOwqsx7YYZteVQP20UcfcfbZZ1NaWsqWLVs444wzOOmkkzj88MNZt24dZka/fv244447sh3VOZdAGRUtSSuIPi+qwMzq9AvG4XLehDSLfk80EvHrkrYDVhCNr/U/wCOSfkrUvWNjmm1TXRX2t1TSeqCEaLTjKu8KJOoQf6ekEmBAuMux0evbty+vvbb1AAAvvPBCFlYCdQcAAB8NSURBVNI45xqaTD/Tin8Y1gL4MdC+9uNEym52SJlXQDQqMaFA/DzNOu8AfWOzfpu6bXh+YWx6EzAqPKrMkjL9CNE4Y8455+pJRp9pmdma2OMDMxvHt7ejO+ecc/Ui08uDB8Sebkd05pXpWZpzzjlXKzItPLfEpjcTfYZ0Ru3Hcc455yqXadE618zei88IA0E655xz9SbT72nNyHCea6Qq6+4+fPhwevbsSZ8+fRg5ciSbNm3KclLnXJJVWbQk9ZJ0OtBO0mmxxwiiuwhrhaSrQvf010Mvv4O3YR/9JaW7Pb6qbfLTdY6XNEnSvlVs5x3eU1TW3X348OEsW7aMoqIiSkpKmDRpUrajOucSrLrLgz2Jvv+0I/Cj2Pz1RI1mvzNJA8IxDjCzryXtTGZf7q0g9ADcqg9gFcet9LWb2XnVbD6CDDq8NyaVdXc/8cQTy9c56KCDWLlyZbYiOucagCqLlpnNBGZKGmBmL9VRht2A1Wb2dTjmagBJBwJ/IeqsvhoYYWYfhUa1rwBHEBXTc81sfuiYUdZQtz1RN42uwJfAz0Ivw9FAtzD/P8Bf0wUqa4YLvAbcQ3S3pIV9vs+3Hd6r/WJxQx6aJD4kCUTNcA888EDeffddfvnLX5Z3d4eokN1///2MHz++1vI65xqfTLu8twDOJeqSXn5Z0MxGfucAUhvgX0Ar4FlgOtEAk88BQ8xsVRgK5DgzGxkKymIzu0zSicCvzezolKI1kagQXi/pSOAvZpYXitaPgMPMrCS+TUqmAqKiZcBNZnZMmL+jmX1RVYf3sF6j6PJek+7uY8eOpUWLFlx44YVpt9kW3i277iQpb5KyQrLyJrnL+/3AMuA44AZgOPBWbQQzsw3hrOpworOn6UQDM/YBZofBApsAH8U2ezT8XEzUrT3VYcDpYf9zJe0kqW1YNqsGLZfeA7qGIvgE8EyGr6m8y3vnrnvbLUXJ+ErbZfttpiZZi4fnV7os3t39+uuvp2nTpjz88MNst12m9/5ULxc7UFcmSVkhWXmTlBWSlTcXs2b6G2pvM/uxpCFmNlXSA8D82gphZqVEbZYKJBUBvwTeMLMBlWxS1rG9rFt7TWTSj7As1+eS+hEV6wuIvptWo7PLls2asPymZDQPKSgoqLIQVWXVqlU0a9aMHXfcsby7+29/+1smTZrE008/zZw5c2q1YDnnGqdMf+GX3af8haQ+wMdAh9oIIKkn0cCN74RZeURncceWfZYmqRnQw8zeyHC384nOBn8fLgGuNrN1NR3iPdwU8o2ZPSJpOTAtLPIO7ynSdXcfPHgwTZs2Zc8992TAgOj/H6eddhrXXnttltM655Iq06J1l6TvEY2pVTbsfG395mkDTAwjEG8mGuPqZ0SX1yZIahdyjgMyLVqjgXslvU50I8bZVax7lKT4LW0/jk13AiaHTvIAV4afU/AO7xVU1t198+bNWUjjnGuoMh1Pq+zLNc8R3XlXa8xsMXBomkWrgYFp1s+PTa8mfKaV0gX+M+CUNNuOTnleALRMc+z82PQBqQu9w7tzzmVHRh8ySOoo6R5J/xue7yvp3LqN5pxzzlWU6SfjU4hGCy7rAvE2cGldBHLOOecqk2nR2tnMHga2AJjZZqI795xzzrl6k2nR2ihpJ6Iv2yLpEGBtnaVyzjnn0sj07sFfE9012E3SC8AuwNA6S+Wcc86lUV2X984AZvYqMIjoLr+fA73N7PW6j+dqy8iRI+nQoQN9+vQpn1dYWMghhxxCXl4e/fv35623aqXJiXPO1ZnqLg8+FpuebmZvmNlSM6u3QZEklYbhSpZK+mf4PperoREjRvDUU09VmHfFFVdw3XXXUVhYyA033MBf/5q2f7BzzuWM6i4PxltI1Or3s2qgxMzyACRNJWrxdGOWstRYNru8x7uwDxw4kOLi4grLJbFu3ToA1q5dy0477VSf8ZxzrsaqO9OySqaz5SWiLhVI6ibpKUmLJc2X1CvM7yjpH5KWhMehYf5PJC0IZ21/ldQkzN8g6caw7suSOla2H0k3SCq/1T9sd0m9vwu1ZNy4cfzmN7/h+9//Ppdffjnnn18rQ6Q551ydqXJoEkmlRA1mRdQ54suyRYCZWdvKtq21gNIGM2sTisxDwD1m9pSkOcAFZvZOGOl4jJkdKWk68JKZjQvbtCH6ftnNwGlmtknS/wAvm9l9kgw42cz+KelmYJ2Z/aGS/XwPeNTMDgitnd4BDjKzNSmZc2JoktShQz7++GOuvPJKJk+eDMCECRPo168fgwYNYt68ecycOZNx48ZlI+o28SEe6k6S8iYpKyQrby4OTYKZ5fSD6PtghcAq4HmiYUraACVhftnjrbD+KqB5yj4uJBpluGzd5cDosOxrvi3eZwKTKttPmD8b2B84HphRXf4ePXpYrlixYoX17t27/Hnbtm1ty5YtZma2ZcsWa9WqVbaibZN58+ZlO0LGkpTVLFl5k5TVLFl5s5kVWGRpfqcmYaCnEosGcGxF1JXjl0QdOr6w8FlXBgRMNbMr0yzbFN4gyGyok0nACGBXopGME2v33XfnueeeIz8/n7lz59KpU6dsR3LOuSolZoAjM/sSuBi4jOgy5QpJPwZQpF9YdQ7wizC/SegSPwcYKqlDmN9e0p7VHDLdfgD+QXSW9QOiIpoIw4YNY8CAASxfvpw99tiDe+65h7vvvpvLLruMfv368bvf/Y7LLrss2zGdc65KSTjTKmdmr4XhRoYRjZd1h6SrgWZEn3ctAS4hGkrlXKIzp19YNCbX1cAz4bOoTURnbP+u4nBb7YfoM65vJM0jOtNLTCurBx98MO38xYsXl08XFBTUUxrnnNs2OV+0zKxNyvMfxZ4en2b9T4AhaeZPB6ZXtX8zmwHMqGo/oegdQsVxt5xzztWDxFwezAWS9iUapHKOfTvSsnPOuXqS82daucTM3iR7X7J2zrlGz8+0nHPOJYYXLeecc4nhRauBS9fd/cwzzyQvL4+8vDy6dOlCXl6mX3dzzrnsavBFqy67xEvaUFv7qivpurtPnz6dwsJCCgsLOf300znttNOylM4552qmwRctQkcNM+sDfEb0/ayMSEr8jSoDBw6kffv2aZeZGQ8//DDDhg2r51TOObdtEv9LuYZeAvoCSCoALjezRZJ2Jupz1UXSCOA0ov6GTSSdBMwkapbbDLjazGZmesBsDE0SH5KkKvPnz6djx4507969jhM551ztaDRFK3RqPwq4J4PVDwD6mtln4WzrVDNbF4rby5JmxfoVpjtWvMs71+63uRZeQeZSO1t8/PHHbNy4cav5t956KwcddFD5/A0bNiSqK0aS8iYpKyQrb5KyQrLy5mLWxlC0WkoqJBqH6y2iLu3VmW1mn4VpAX+UNBDYEvbTEfi4so3N7C7gLoDOXfe2W4rq920uHp5f8XlxMa1btyY//9v5mzdv5swzz2Tx4sXsscceQFTs4uvkuiTlTVJWSFbeJGWFZOXNxayNoWil6xI/AdjMt5/ptUjZZmNsejiwC3CgRWNxFadZv1ItmzVheYaX6+rTs88+S69evcoLlnPOJUFjuBEDqNglPlzyKwYODIuHVrFpO+DTULCOAKrrDp9T0nV3B3jooYf8BgznXOI0hjOtcild4scCD4fPn6q6U+JvwD8lFQGLgGV1n7T2VNbdfcqUKfUbxDnnakGDL1rVdInvG5u+OiyfQjTIZNn6q4EBmezbOedc3Wo0lwedc84lnxct55xzieFFyznnXGJ40XLOOZcYXrScc84lhhetBizdsCQAEydOpFevXvTu3ZsrrrgiS+mcc67mcrJo1XQ4EUm7SHpF0muSDq+DPKMlXV7b+61r6YYlmTdvHjNnzmTJkiW88cYbXH554l6Wc64Ry9XvaZWYWR6ApKlErZdurGL9o4AiMzsv0wNIamJmpZU9ry313eU93uF94MCBFBcXV1h+xx13MGrUKJo3bw5Ahw4d6i2bc859Vzl5ppXiJaImtUjqJukpSYslzZfUS1IecDMwJJydtZR0rKSXJL0q6e+S2oTtiyX9SdKrwI/TPD9f0kJJSyQ9EvoVViDpYklvSnpd0kP1+D7Uirfffpv58+dz8MEHM2jQIBYuXJjtSM45l7FcPdMC0g4nchdwgZm9I+lg4H/M7EhJ1wL9zezCMHzI1cDRZrZR0m+BXwM3hH2sMbMDwv5vSnm+k5ndHab/AJwLTEyJNQrYy8y+ruyyZTaHJqluWJK1a9dSVFTETTfdxLJlyzj55JN54IEHkJSTwxBUJUl5k5QVkpU3SVkhWXlzMquZ5dwDKAUKgVXA80ATokEZS8L8ssdbYf0RwG1hejCwOrbOm8A9YVkxsGfsOKnPBwHzgSJgBXBnmD+aaMBIgKeAGcBPgDbVvZYePXpYNq1YscJ69+5d/vy4446zuXPnlj/v2rWrffrpp2ZmNm/evPqO950kKW+SspolK2+SspolK282sxINzLvV79RcvTxY9pnWnkTjWf2S6FLmF2aWF3vsk2ZbEY2HVbbOvmZ2bmz5xpT148+nABea2X7A9aQfguQk4HaigSIXho7xiXHKKacwb948ILpU+M0337DzzjtnOZVzzmUmV4sWUHE4EeBLYIWkHwMo0i/NZi8DP5S0d1ivtaQeGR5yB+AjSc2IxtGqQNJ2wPfNbB7wW6JhS3K2aW66YUlGjhzJe++9R58+fTjrrLOYOnUqkrId1TnnMpLzZwlWcTiR4cAdkq4GmgEPAUtS1l8laQTwoKTmYfbVwNsZHO4a4BWiy5KvEBWxuCbANEntiM7oJpjZF9v0wupBZcOSTJs2rZ6TOOdc7cjJomVVDydyfJr1p1BxOJG5wA/SrNelmud3AHek2W507OlhlSd3zjlXl3L68qBzzjkX50XLOedcYnjRcs45lxhetJxzziWGF62ESde5/bPPPuOYY46he/fuHHPMMXz++edZTOicc3UnZ4uWpKskvRF6/BWGtk013Ud/SRO2dRtJ+ZIOrelx61K6zu033XQTRx11FO+88w5HHXUUN910U5bSOedc3crJoiVpAFE7pgPMrC9wNPB+TfdjZovM7OIaHLdpyjb5QE4VrYEDB9K+ffsK82bOnMnZZ58NwNlnn81jjz2WjWjOOVfncvJ7WsBuwGoz+xrAzFYDSDoQ+AtRF4rVwAgz+0hSAdGXgY8AdgTONbP5kvKJegYOltQeuBfoStRd42dm9rqk0UC3MP8/kv4KXA5cCFwAlEr6CXARcB/Qw8w2SWpL9MXmHma2qbIXUhtDk8SHG0nnk08+YbfddgNg11135ZNPPvlOx3POuVyVq0XrGeBaSW8DzwLTgReJOq4PCV0vziQaY2tk2KapmR0k6UTgOqKzs7jrgdfM7BRJRxIVoLywbF/gMDMrCYUOMyuWdCewwczGAoTieBLwGHAW8Gi6glXbXd6r69y+efPmCuuUlpZuU2fmnOzoXIUk5U1SVkhW3iRlhWTlzcWsOVm0zGxDOKs6nOjsaTrwB6APMDv0ymsCfBTb7NHwczHQJc1uDwNOD/ufK2mncLYEMMvMSjKINgm4gqhonQOcX0n+u4iGUaFz173tlqLv9jYXD8+v+Ly4mNatW5OfH83v1KkTPXv2ZLfdduOjjz5i9913L19WEwUFBdu0XbYkKW+SskKy8iYpKyQrby5mzcmiBWDRKMIFQIGkIqJO72+Y2YBKNvk6/Cyl5q8rtfN7ZZlekNQlnI01MbOl1W3TslkTlldzee+7Ovnkk5k6dSqjRo1i6tSpDBkypE6P55xz2ZKrN2L0lNQ9NisPeAvYJdykgaRmknrXYLfzCZ3bQ9FZbWbrqtlmPVs3zb0PeACYXINj15p0ndtHjRrF7Nmz6d69O88++yyjRo3KRjTnnKtzuXqm1QaYGEYG3gy8S/QZ0V3AhNBlvSkwDngjw32OBu4NHeO/BM7OYJt/AjMkDQEuMrP5wN+ILlWmb6Fexyrr3D5nzpx6TuKcc/UvJ4uWmS0m/a3mq4GBadbPj02vJnymZWYFRJcYMbPPgFPSbDs65Xl8m7eBvimbHAbMyOUhSZxzrqHKyaKVqyRNBE4ATsx2Fueca4y8aNWAmV2U7QzOOdeY5eSNGM4551w6XrScc84lhhct55xzieFFK4ctX76cvLy88kfbtm0ZN25ctmM551zWJKpoSTJJ02LPm0paJenx77DP4yUtkLQsDIEyXVLnsOwGSUeH6cPDUCmFkgaEHod1qmfPnhQWFlJYWMjixYtp1aoVp556al0f1jnnclbS7h7cCPSR1DL0CjwG+GBbdyapD1ET3pPN7K0w72Si73n9x8yuja0+HBhjZtMkjQD6A09Wd4yadnmvrKP7nDlz6NatG3vuuWfG+3LOuYYmUWdawZNEndYBhhHrTCHpIEkvSXpN0ouSeob5v5J0b5jeT9JSSa2A3wJ/LCtYAGY2y8yeD+tOkTRU0nnAGcDvJT0I3ACcGc66zqyH18xDDz3EsGHD6uNQzjmXs2Rm2c6QMUkbiDplXAv8BHgZuJRvx8xqC3xpZpvDZb1fmNnpkrYj6nJxK3AVcElofvsqcI6ZLankeFOAx81sRsr0CKC/mV1YyXbxoUkOvHbc3Rm/xv06tdtq3qZNmxg6dCiTJ0/eagDI2rRhwwbatGlTZ/uvbUnKm6SskKy8ScoKycqbzaxHHHHEYjPrnzo/aZcHCQM3diE6y0q9PNcOmBqa7RrQLGyzJRSa14G/mtkLqfuVtBMwB2gF3FU2htY2ZiwfmqRnz5520fDv1nV95syZHHzwwZx22mnfaT/VycVhCKqSpLxJygrJypukrJCsvLmYNYmXBwFmAWPZumnt74F5ZtYH+BHQIrasO7AB2D027w3gAAAzW2NmeUTFJqf+G/Tggw/6pUHnnCO5Rete4HozK0qZ345vb8wYUTYzdIWfQNRsdydJQ8Oim4GrJO0T20erDI6fbsiSOrFx40Zmz55d52dZzjmXBIksWma20swmpFl0MzBG0mtUvPR5K3B76Np+LnCTpA6h6F0C3CdpuaQXgH2Ixsuqyjxg3/q4EaN169asWbOGdu22/qzLOecam0R9pmVmW122SxlK5CWgR2zx1WH+yNj67wN7x54/AaS9J93MRlQy/Rnwg215Dc4557ZdIs+0nHPONU5etJxzziWGFy3nnHOJ4UXLOedcYnjRyiGlpaXsv//+DB48ONtRnHMuJ2WtaEkqDbeMlz26ZCtLrhg/fjz77LNP9Ss651wjlc0zrRIzy4s9issWKNKozgJXrlzJE088wXnnnZftKM45l7Ny5nta4UzraeAV4EDgRElnEHVXbw78w8yuC+teBZwNfAq8Dyw2s7GSCoia5y6StDOwyMy6SGoC3ATkh33dbmZ/lZQPjAZWA32AxcBPzMwk/QAYD7QGvgaOIvo+18VmVhhy/Av4ZWUNd6HqoUniw5Bceuml3Hzzzaxfv74G75pzzjUu2SxaLSUVhukVwK+I+gOebWYvSzo2PD8IEDBL0kCiMbXOAvKI8r9KVGyqci6w1sx+IKk58IKkZ8Ky/YHewIfAC8APJS0ApgNnmtnC0D2+BLiHqD3UpZJ6AC3SFayULu9cu9/mtKEKCgoAeOmll9i0aRPr16+nsLCQNWvWlC+rTxs2bMjKcbdVkvImKSskK2+SskKy8uZi1mwWrZLQoBYoP9P6t5m9HGYdGx6vhedtiIrYDkRnXV+G7WZlcKxjgb6xnoPtwr6+ARaY2cqwr0KiASDXAh+Z2UIAM1sXlv8duEbSb4CRwJR0B4t3ee/cdW+7pSj921w8PB+Ap59+msWLFzNixAi++uor1q1bx6RJk5g2bVra7epKLnZ0rkqS8iYpKyQrb5KyQrLy5mLWnLk8GGyMTYtopOC/xleQdGkV22/m28/p4h3eBVxkZk+n7Cuf6NJfmVKqeE/M7EtJs4EhRJctD6wiCwAtmzVheSWjEZcZM2YMY8aMAaK/JGPHjq33guWcc0mQyzc7PA2MlNQGQFInSR2A54FTJLWUtAPRECRlivm2kAxN2dcvJDUL++ohqXUVx14O7BY+10LSDpLKitkkoo7xC83s8+/0Cp1zztVIrp1plTOzZ8KQIS9JgmgsrJ+Y2auSpgNLiG7EWBjbbCzwcPhMKX73wySiy36vKtrZKuCUKo79TejePlFSS6LPs44GNpjZYknrgMm19FIryM/Pz7nTceecyxVZK1qpHdvDLe99UuaNJ7qDL3XbG4EbASSNjs1fBvSNrVrW5X0L8LvwiCsIj7LtL4xNLwQOST22pN2JzlCfSV3mnHOubuXy5cGcI+mnRLfkXxUKoXPOuXqUs5cHM2Vmo+vxWPcB99XX8ZxzzlXkZ1rOOecSw4uWc865xPCi5ZxzLjG8aDnnnEsML1rOOecSw4uWc865xPCi5ZxzLjFkZtnO0KBJWk/UyzAJdiYaWywpkpQ3SVkhWXmTlBWSlTebWfc0s11SZyb+y8UJsNzM+mc7RCYkLUpKVkhW3iRlhWTlTVJWSFbeXMzqlwedc84lhhct55xzieFFq+7dle0ANZCkrJCsvEnKCsnKm6SskKy8OZfVb8RwzjmXGH6m5ZxzLjG8aDnnnEsML1p1RNLxkpZLelfSqGznSSXp+5LmSXpT0huSLgnzR0v6QFJheJyY7awAkoolFYVMi8K89pJmS3on/PxetnMCSOoZe/8KJa2TdGmuvLeS7pX0qaSlsXlp30tFJoS/x69LOiBH8v5Z0rKQ6R+Sdgzzu0gqib3Hd+ZA1kr/3CVdGd7b5ZKOq8+sVeSdHstaLKkwzM/qe1vOzPxRyw+gCfB/QFdge2AJsG+2c6Vk3A04IEzvALwN7AuMBi7Pdr40eYuBnVPm3QyMCtOjgD9lO2clfxc+BvbMlfcWGAgcACyt7r0ETgT+FxBwCPBKjuQ9Fmgapv8Uy9slvl6OZE375x7+vS0BmgN7hd8ZTbKdN2X5LcC1ufDelj38TKtuHAS8a2bvmdk3wEPAkCxnqsDMPjKzV8P0euAtoFN2U9XYEGBqmJ4KnJLFLJU5Cvg/M/t3toOUMbPngc9SZlf2Xg4B7rPIy8COknarn6SRdHnN7Bkz2xyevgzsUZ+ZKlPJe1uZIcBDZva1ma0A3iX63VFvqsorScAZwIP1mak6XrTqRifg/djzleRwQZDUBdgfeCXMujBcdrk3Vy65AQY8I2mxpJ+FeR3N7KMw/THQMTvRqnQWFf/R5+J7C5W/l0n4uzyS6GywzF6SXpP0nKTDsxUqRbo/91x/bw8HPjGzd2Lzsv7eetFq5CS1AR4BLjWzdcAdQDcgD/iI6PJALjjMzA4ATgB+KWlgfKFF1y9y6vsbkrYHTgb+Hmbl6ntbQS6+l5WRdBWwGfhbmPUR0NnM9gd+DTwgqW228gWJ+HNPYxgV/8OVE++tF6268QHw/djzPcK8nCKpGVHB+puZPQpgZp+YWamZbQHupp4vV1TGzD4IPz8F/kGU65OyS1Xh56fZS5jWCcCrZvYJ5O57G1T2Xubs32VJI4DBwPBQaAmX2taE6cVEnxP1yFpIqvxzz+X3tilwGjC9bF6uvLdetOrGQqC7pL3C/7bPAmZlOVMF4Xr1PcBbZvaX2Pz45xWnAktTt61vklpL2qFsmuhD+KVE7+nZYbWzgZnZSVipCv9TzcX3Nqay93IW8NNwF+EhwNrYZcSskXQ8cAVwspl9GZu/i6QmYbor0B14LzspyzNV9uc+CzhLUnNJexFlXVDf+SpxNLDMzFaWzciZ9zbbd4I01AfRXVdvE/1v5Kps50mT7zCiS0CvA4XhcSJwP1AU5s8CdsuBrF2J7rJaArxR9n4COwFzgHeAZ4H22c4ay9waWAO0i83LifeWqJB+BGwi+hzl3MreS6K7Bm8Pf4+LgP45kvddos+Dyv7u3hnWPT38HSkEXgV+lANZK/1zB64K7+1y4IRceG/D/CnABSnrZvW9LXt4GyfnnHOJ4ZcHnXPOJYYXLeecc4nhRcs551xieNFyzjmXGF60nHPOJUbTbAdwztWcpFKi26jLnGJmxVmK41y98VvenUsgSRvMrE09Hq+pfdug1rms8cuDzjVAknaT9HwY92hpWXNTReO8vSppiaQ5YV57SY+Fhq4vS+ob5o+WdL+kF4D7Q0eERyQtDI8fZvElukbKLw86l0wtywbnA1aY2akpy/8LeNrMbgytd1pJ2oWo991AM1shqX1Y93rgNTM7RdKRwH1EzV0hGvPpMDMrkfQAcKuZ/UtSZ+BpYJ86fI3ObcWLlnPJVGJmeVUsXwjcG5oiP2ZmhZLygectGrsJMysbR+kwohY9mNlcSTvFunfPMrOSMH00sG/UthKAtpLamNmG2ntZzlXNi5ZzDZCZPR+GbzkJmCLpL8Dn27CrjbHp7YBDzOyr2sjo3Lbwz7Sca4Ak7Uk0gN/dwCSiIdVfBgaGjuLELg/OB4aHefnAaovGVkv1DHBR7BhVnek5Vyf8TMu5hikf+I2kTcAG4KdmtiqM+vyopO2Ixsw6BhhNdCnxdeBLvh2iJNXFwO1hvabA88AFdfoqnEvht7w755xLDL886JxzLjG8aDnnnEsML1rOOef+f3t1QAIAAAAg6P/rdgR6wg1pAbAhLQA2pAXAhrQA2Aif1FyPRTDxagAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Probability of Giving**"
      ],
      "metadata": {
        "id": "D2l92DoXjK0K"
      },
      "id": "D2l92DoXjK0K"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Decision Tree**"
      ],
      "metadata": {
        "id": "SqH6XCuCi31D"
      },
      "id": "SqH6XCuCi31D"
    },
    {
      "cell_type": "code",
      "source": [
        "double_data = pd.read_csv('PreprocessedContact.csv')\n",
        "\n",
        "gave = double_data[double_data['Gave'] == 1]\n",
        "n = gave['ID'].count()\n",
        "didnt_give = double_data[double_data['Gave'] == 0].sample(n)\n",
        "double_data = gave.append(didnt_give)\n",
        "\n",
        "train, validation = train_test_split(double_data, test_size=0.1, random_state=12345)"
      ],
      "metadata": {
        "id": "kaXd_h2Jke8e"
      },
      "id": "kaXd_h2Jke8e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "predictors2 = ['Age','JustGave','Contact','Woman', 'NbActivities',\n",
        "              'TotalGift', 'Frequency', 'Recency', 'Referrals']\n",
        "\n",
        "X_train = train[predictors2] \n",
        "Y_train = train['Gave']\n",
        "X_valid = validation[predictors2] \n",
        "Y_valid = validation['Gave']\n",
        "\n",
        "DT_model_prob = DecisionTreeClassifier(max_depth=4,criterion=\"entropy\")\n",
        "DT_model_prob.fit(X_train,Y_train)\n",
        "DT_predict_proba = DT_model_prob.predict_proba(X_valid) #Predictions on Testing data\n",
        "DT_predict_prob = DT_model_prob.predict(X_valid) #Predictions on Testing data\n",
        "DT_probs = DT_model_prob.predict_proba(X_valid)[:, 1]\n",
        "print(DT_probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B39aSQC8i7lv",
        "outputId": "3d924efe-d27b-439a-cc36-5cc50bbd14f5"
      },
      "id": "B39aSQC8i7lv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.31971499 0.31971499 0.31971499 ... 0.75558836 0.31971499 0.75006851]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_valid, DT_predict_prob))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z65M6jnJnEyq",
        "outputId": "6a8bb9c0-4a8b-4181-dba5-ca70b53afebe"
      },
      "id": "z65M6jnJnEyq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.63      0.73      0.68     14978\n",
            "         1.0       0.68      0.58      0.62     14914\n",
            "\n",
            "    accuracy                           0.65     29892\n",
            "   macro avg       0.66      0.65      0.65     29892\n",
            "weighted avg       0.65      0.65      0.65     29892\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "import matplotlib.pyplot as plt \n",
        "plt.rc(\"font\", size=14)\n",
        "\n",
        "DT_roc_auc = roc_auc_score(Y_valid, DT_model_prob.predict(X_valid))\n",
        "fpr, tpr, thresholds = roc_curve(Y_valid, DT_model_prob.predict_proba(X_valid)[:,1])\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, label='Decision tree (area = %0.2f)' % DT_roc_auc)\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.savefig('DT_ROC')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "w-L-cjgmnMZt",
        "outputId": "4ed3da23-a60b-409e-cc50-02406e21b111"
      },
      "id": "w-L-cjgmnMZt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12c58819",
      "metadata": {
        "id": "12c58819"
      },
      "source": [
        "### **Tensorflow**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "double_data = pd.read_csv('PreprocessedDouble.csv')\n",
        "gave = double_data[double_data['Gave'] == 1]\n",
        "n = gave['ID'].count()\n",
        "didnt_give = double_data[double_data['Gave'] == 0].sample(n)\n",
        "double_data = gave.append(didnt_give)\n",
        "\n",
        "train, validation = train_test_split(double_data, test_size=0.1, random_state=12345)"
      ],
      "metadata": {
        "id": "N_Gqn6rckgIv"
      },
      "id": "N_Gqn6rckgIv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7fa631af",
      "metadata": {
        "id": "7fa631af"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras import optimizers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictors = ['Woman', 'Age', 'Salary', 'SeniorList', 'NbActivities',\n",
        "#        'Referrals', 'Recency', 'Frequency', 'Seniority', 'TotalGift',\n",
        "#        'MinGift', 'MaxGift', 'Rural', 'JustGave', 'MeanGift']\n",
        "\n",
        "# Predictors Gave\n",
        "predictorsTF = ['SeniorList', 'NbActivities', 'Referrals', 'Recency', 'Frequency', 'Seniority', 'TotalGift',\n",
        "       'MinGift', 'High School', 'Downtown', 'Rural', 'Suburban', 'JustGave', 'MeanGift']\n",
        "\n",
        "X_train = train[predictors] \n",
        "Y_train = train['Gave']\n",
        "X_valid = validation[predictors] \n",
        "Y_valid = validation['Gave']\n",
        "\n",
        "input_dim = X_train.shape[1]\n",
        "input_dim"
      ],
      "metadata": {
        "id": "DmD42YCW3Pmv"
      },
      "id": "DmD42YCW3Pmv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.Input(shape=(input_dim,)),\n",
        "    Dense(units=30, activation=\"relu\", kernel_initializer=tf.keras.initializers.HeUniform()),\n",
        "    Dense(units=1, activation=\"sigmoid\")\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdxXamwv26pZ",
        "outputId": "8b708c86-9b98-417e-f92c-332118b5c65d"
      },
      "id": "QdxXamwv26pZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 30)                450       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 481\n",
            "Trainable params: 481\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(label_smoothing=0.1), optimizer=optimizers.Adam(), metrics=[tf.keras.metrics.BinaryAccuracy() ,tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])"
      ],
      "metadata": {
        "id": "NClKMRE37Igo"
      },
      "id": "NClKMRE37Igo",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, Y_train, epochs=6, validation_data=(X_valid, Y_valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OD7BwdB87VS5",
        "outputId": "101972f1-f767-474a-9c29-36297b0b2d39"
      },
      "id": "OD7BwdB87VS5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "13744/13744 [==============================] - 29s 2ms/step - loss: 0.7907 - binary_accuracy: 0.5941 - recall: 0.5517 - precision: 0.6025 - val_loss: 0.6716 - val_binary_accuracy: 0.6216 - val_recall: 0.5447 - val_precision: 0.6461\n",
            "Epoch 2/6\n",
            "13744/13744 [==============================] - 28s 2ms/step - loss: 0.7513 - binary_accuracy: 0.6002 - recall: 0.5643 - precision: 0.6078 - val_loss: 0.7008 - val_binary_accuracy: 0.6057 - val_recall: 0.6041 - val_precision: 0.6081\n",
            "Epoch 3/6\n",
            "13744/13744 [==============================] - 28s 2ms/step - loss: 0.7240 - binary_accuracy: 0.6055 - recall: 0.5675 - precision: 0.6139 - val_loss: 0.6762 - val_binary_accuracy: 0.6092 - val_recall: 0.7779 - val_precision: 0.5831\n",
            "Epoch 4/6\n",
            "13744/13744 [==============================] - 36s 3ms/step - loss: 0.6976 - binary_accuracy: 0.6118 - recall: 0.5646 - precision: 0.6232 - val_loss: 0.6718 - val_binary_accuracy: 0.6195 - val_recall: 0.4674 - val_precision: 0.6749\n",
            "Epoch 5/6\n",
            "13744/13744 [==============================] - 28s 2ms/step - loss: 0.6840 - binary_accuracy: 0.6157 - recall: 0.5566 - precision: 0.6310 - val_loss: 0.6806 - val_binary_accuracy: 0.6266 - val_recall: 0.5586 - val_precision: 0.6488\n",
            "Epoch 6/6\n",
            "13744/13744 [==============================] - 27s 2ms/step - loss: 0.6729 - binary_accuracy: 0.6185 - recall: 0.5626 - precision: 0.6331 - val_loss: 0.6599 - val_binary_accuracy: 0.6293 - val_recall: 0.5754 - val_precision: 0.6472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1, 3, 1)\n",
        "plt.title(\"Binary Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.plot(history.history[\"binary_accuracy\"], label=\"Train\")\n",
        "plt.plot(history.history[\"val_binary_accuracy\"], label=\"Validation\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.title(\"Recall\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.plot(history.history[\"recall\"], label=\"Train\")\n",
        "plt.plot(history.history[\"val_recall\"], label=\"Validation\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.title(\"Precision\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.plot(history.history[\"precision\"], label=\"Train\")\n",
        "plt.plot(history.history[\"val_precision\"], label=\"Validation\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1JWFh3LEAo6",
        "outputId": "6f1a231c-81e5-4bc0-9ea6-0a858022c8c9"
      },
      "id": "H1JWFh3LEAo6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f430dc311f0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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vRn8mrdNivyoFKJAQaNdeb6sq9riMbcGvagKnpqYqs8CECyjcB+9Ph4KdcNF/IeUahw4XkfVKKZfFNrbWr9u2bWPYsGGuupz95G7TCkA16D97APlFbH2nQdOvrqK2EvIyoXMf6NAVcjMhJAS6e/d34kjfmiagYCN7vY70qauGaz6F/md4WyLfRCn9HUXGaFNZTbm3JTLxNSyzwlAj2ieyE5Qd1r+XUN+PAALTBBRcZCyEuRdoU89NX5s3/5aoqwYUhEVCSFirJiCTIKTe8AuFGOPo9p31sx+ZgUwFEAwoBT8/Cx9eBz1Hwk3fQuwQb0vl21gigMIiIaSdNgM11HtXJhPfwjIoCDFG+2GR2mRYVeQ9mRzENAEFOvV1sPg+WP8WDL8ELn35mMPKpHmsFYDltSXiw8QEDAUgx34TIhDZGcrzjN+K799ezRlAIFNVop2969+CU++By98yb/72UlelR3YhocdGePUth4KaBBn1tfomb50qpX1nQOn/nh/g+yrKxDmKDsD7V+oohYuehbGzvC2Rf1FXpUf/cGwkZ/oBTKyxNcpv10EPGKqKdWSQj2POAAKRhgaYdzkUH4BrPnb45r906VKGDBnCwIEDeeKJJ2w16SUiG43HdhFpNHqKSL3VvqZ1I/wDSwSQsS5i8rlT+Wr5iuMWgz3zzDPcfvvtNg+fNGkSlvDHqVOnUlR0ok34scce46mnnmpRjAULFpCRcaz09iOPPMKyZcsc/jgmtpk8eTJfffXVcdsc6tfCghOifR7761956rV0qC7R/0Mb+FK/mgogENm+VI/8L3waBpzp0KH19fXccccdLFmyhIyMDObPn3/cj9XggFIqWSmVDDwHfGq1r9KyTyk1remBfkF9jXb6GjOAmTNnkP75V8fNANLT05k5c2arp1q8eDGdO3d2SoymN4o5c+Zw9tlnO3UukxOZOXMm6enpx21zqF+jO9i284dF6t9PtW0zkC/1q6kAApEVz0Kn3trp6yBr1qxh4MCB9O/fn/DwcGbMmMHnn7dYrG0mMN9ZUX0Sy3J+QwFcfsV0Fn3zEzWVuujH3r17OXjwIPPnzyc1NZURI0bw6KOP2jxV3759yc/PB+Dvf/87gwcP5tRTT21MKwzw2muvkZaWxujRo/nNb35DRUUFK1asYOHChfzxj38kOTmZXbt2MXv2bD7++GMAvvnmG1JSUhg1ahQ33HAD1dXVjdd79NFHGTNmDKNGjSIzM9MtX1EgcPnll7No0aLG4i+O92sehIad2K9hESChvPbKyz7fr6YPINA4sBb2r4TznoBQx7s3JyeHXr2OlYBOSkpi9erVNtuKSB+gH/Ct1eZIEVkH1AFPKKUWNHPsLcAtAL1797ZfwCUPwuEtrbdzhJ6j4HwrU5d1BBDQtWtXxqWcxJL/LePiawaQnp7O9OnTefjhh+natSv19fWcddZZbN68mZNOsln/iPXr15Oens7GjRupq6tjzJgxjB07FoDLLruMm2++GYA///nPvPHGG9x1111MmzaNCy+8kMsvv/y4c1VVVTF79my++eYbBg8ezHXXXcdLL73E3XffDUD37t3ZsGEDL774Ik899RSvv/66K78t9+CJfm1C165dGTduHEuWLOHiiy92ol8V6zdl2O7XyE5cds5Ebv79/SAhPtuv5gwg0Fj5nF6R6GB6ByeZAXyslLIOkO9jLDm/CnhGRAbYOlAp9apSKlUplRobG+sJWe2nrkpP7a0U6MzfXET6p18Ax8wEH374IWPGjCElJYWtW7faMpU18uOPP3LppZfSoUMHYmJimDbtmHXs119/5bTTTmPUqFHMmzePrVu3tiheVlYW/fr1Y/BgnXJg1qxZ/PDDD437L7vsMgDGjh3L3r17Hf74wYS1GcixftUpdH5cudZ2v0Z24tfM7Zx26qk+3a/mDCCQOLobtn0Bp/weIjo6dYrExEQOHDjQ+D47O7sxG6INZgB3WG9QSuUYz7tFZDmQgq757BpaGNG5jFqrCCCDi6eexz1//gcbNmygoqKCrl278tRTT7F27Vq6dOnC7NmzqaqqauaELTN79mwWLFjA6NGjmTt3LsuXL2+T+JaU0/6Ultgj/WqDiy++mHvuucfxfrWkUAtpZgwd0ZHZ9zzKgnlvMvq0KT7br3bNAETkPBHJEpGdIvJgM22mi0iGiGwVkfeNbckistLYtllErrRqP1dE9lhFjCS75BMFM6te0qmLx93q9CnS0tLYsWMHe/bsoaamhvT09ONGqxZEZCjQBVhpta2LiEQYr7sDpwDND4t9EaWMENCI4zZHd+rE5Imp3HDDDcycOZOSkhKioqLo1KkTR44cYcmSJS2e9vTTT2fBggVUVlZSWlrKF1980bivtLSU+Ph4amtrmTdvXuP2jh07UlpaesK5hgwZwt69e9m5cycA7777LmecYab1cIbo6GgmT57sRL9qDdBsv4aEUlpeSXyXSGprany2X1udAYhIKPACcA6QDawVkYVKqQyrNoOAh4BTlFKFIhJn7KoArlNK7RCRBGC9iHyllLLExf1RKfWxKz9Q0FJxFH55D066EmLinT5NWFgYzz//PFOmTKG+vp4bbriBESNG8Mgjj5CammqtDGYA6er4dLLDgFdEpAE9uHjC+nfiFzTUgao/YQZASBgzL5nCpTf+gfT0dIYOHUpKSgpDhw6lV69enHLKKS2edsyYMVx55ZWMHj2auLg40tLSGvc9/vjjjB8/ntjYWMaPH994c5gxYwY333wzzz77bKOTECAyMpK33nqLK664grq6OtLS0rjttttc9x0EGTNnzuTSSy91rl/Hjmu+Xx95mPFTryK2RzzjT57gm/2qlGrxAUwAvrJ6/xDwUJM2/wfcZMe5NgGDjNdzgctbO8b6MXbsWGXSDN//n1KPxih1JMPtlwLWKQf6rbVHa/2akeH+z9RIVYlSORuUqiw+fnt5vt5eW+U5WdyIre80oPvV1RTn6N9DQ0Pzbeprlcr5RamibM/JpRzrW3tMQInAAav32cY2awYDg0XkZxFZJSLnNT2JiIwDwjneHvx3wzT0tMV0YOO4W0RknYisy8vLs0PcIKS2Cla/CgPPgTg/zq/uC1gigJoWx7GkgzBXA5tAYy3g49JANCUkDCKi9apgH6274qoooDBgEDAJHRf+mog0rn4RkXjgXeB6pZRledxDwFAgDegKPGDrxMqXo0V8hc0fQHkuTLzL25L4P7VVusJTSJN87pYFP2Y+IBNorAXcKpGdob76+PrSPoQ9CiAH6GX1PsnYZk02sFApVauU2gNsRysERCQGWAT8SSm1ynKAUuqQMTupBt4Cxjn/MYKYhgZY+Tz0PAn6ne5tadyG8tQIypIDqOnILjRw8gF57Lu0A1+SxSEa6uxbZxPZST97KEW0o9+nPQpgLTBIRPqJSDja+dc0x8sC9OjfEv0xGNhttP8MeEc1cfYaswJERIBLgF8dktxEs+N/kL8dJv6u5emoHxMZGUlBQYFnbhZ1J4aAAlYJ4fx7BqCUoqCggMhI79d/9mi/upr62hNnibYIbQfhUVDp/iIxzvRtqypMKVUnIncCXwGhwJtKqa0iMgftWFho7DtXRDKAenR0T4GIXAOcDnQTkdnGKWcrpTYC80QkFhBgI2CGMTjDiucgJglGOJ72wV9ISkoiOzsbt/uAVAMUH4TICjhcceL+4nwIr4D2/lPwwxaRkZEkJSV5WwzP9as7KDqo19rY+p00pboUKgshr9btNQIc7ltbnmFffZhRQE3IXqcjf35+zqOXxcPRIh5j3yr9fWYusb3/uTSl0q/2rEwuZsmSJWrw4MFqwIAB6p///Odx+yz9CkxHr9/YCryvVGMUX2/gf8A2Y39f5Q/96moqjjr2vzu6x2j/rFvFaonm/rNmKgh/ZsXzEBEDY67ztiSBQb6RoC12sO390XFQ5oejVQN7Mr02WdMzArjbavc7wL+UUsPQPrtcT8nuU1h+A9FxLbez0KWv9tFt+6LVpp7GVAD+SuE+yFgAY2dDZIy3pQkM8rK0/b9zH9v7o+N0tJWfYmem15uBF5RShQBKqVwAERkOhCmlvja2lyml7LB/BCCW34C9CgBg2EVwYDWUHnaPTE5iKgB/ZdVLOlzxZNvFK0ycIC8Lug1qvu5vlH/PAGxles3JaRrQ1+yansFAkYh8KiK/iMi/jCwBxxEU63bKDAUQ5aACAMhc5Hp52oCpAPyRykLY8A6MugJiErwtTeCQnwWxQ5rfHx0LNaVQE9AD3+bW9IQBpwH3odfu9AdmNz1YBcO6nTInZgCxQ6HrAJ8zA5kKwB9Z9ybUlsOEO70tSeBQUw5F+1tWAJYRn5+agezM9Nrcmp5sYKNSardSqg4d+j3GM5L7GOW5Ouliewdq/oroWcDeH/UAzkcwFYC/UVcNq1/RpR57jvS2NIFD/g793L0ZBzBAdA/97KdmIDszvdpc04NeD9TZCN0GOBN/y/TqKspyIap786mgm2PYNL2AbPtXrbf1EGY9AH9jy0dQdgQufdnbkgQWeZYIoKHNt4k27n1+OgNoLdOrgc01PQAich/wjbF4cz3wmjc+h9cpz3PM/GMhIQU6Jmgz0OgZrpfLCUwF4E8opRd+9RgJ/Sd7W5rAIj9LT+u79m++jcUEVOafCgBg6tSpTJ069bhtc+bMaXxtxIzfazyOw4gAsl3zMpgoy3XMAWwhJASGXQgb3tUmx/Ao18vmqEjeFsDEAXYug7xMnfQtQNM+eI28LOg2AMLCm28TZcwA/FgBmLgAZ2cAAEMvhLpK2PmNa2VyElMB+BMrntVTyBGXeVuSwCMvq2X7P2jlENnZb01AJi5AKW2CjXIywqnPKdC+C2R+6Vq5nMRUAP7CwY2w5wc4+baWR6kmjlNXo+sptxQBZCG6hzkDCGaqiqG+xvkZQGgYDLkAspbq352XMRWAv7DyeQjvqFf+mriWo7t0GciWHMAWouO0CcAkOLH0vTM+AAvDLoTqYh0S6mX8VwGU58Oub70thWco2g+/fgpjZx3LL27iOiwRQK2ZgEBP/c0ZQPDizCKwpvSfDO2ifGJRmP8qgA3vwLuX+tSiCrex6mXt9DXTPriHvCxA7FMA0XGmAghmnMkD1JR2kTDoHJ0WoqHeNXI5if8qgIQU/Xxok3flcDeVRbDhbe347eT9HO4BSX4WdO4F4R1abxtlpIOorXS/XCa+hzN5gGwx7CKtTLLXtl2mNuC/CiB+tH4++It35XA36+dCTRlMNNM+uI287fbZ/+HYyM+cBQQnZbk6CWMHB9JA2GLQuRAa7nUzkP8qgA5ddZ7tgxu9LYn7qKuB1S9DvzOOKTwT19JQr0tq2mP+gWPpIExHcHBSngsdujefMdZeImOg/yStALxYEtN/FQBAfHJgzwB+/QRKD+l6vx5k6dKlDBkyhIEDB/LEE0/YatJLRDYaj+0i0lgjUURmicgO4zHLc1I7SdE+qK+2LwQUzMVgwU5Z3rFBQFsZdpH+/R3e4przOYF/K4CEFP0FVhz1tiSux5L2IW44DDzLY5e1p2oUcEAplayUSgaeAz4FEJGuwKPAeHTFqEdFpIvHhHcGe3IAWdNoAjriHnlMfJvy3GM5odrKkKnanORFM5BdCkBEzhORLBHZKSIPNtNmuohkiMhWEXnf2JYsIiuNbZtF5Eqr9v1EZLVxzg9ExPHVTQnJ+vlQAJqBdn0LuVt1ymcPpn2ws2qUNTOB+cbrKcDXSqmjRkWpr4Hzmj3SF3AkBBSOzQBME1BwUpbXdgewhaju0HuiV1cFt6oAjKo/LwDnA8OBmUZ5OOs2zdURrQCuM7adBzxjFJcAeBJ4Wik1ECgEbnRY+kB2BK94FqJ7wqjLPXpZO6tGASAifYB+gGVBRiJwwKpJtrHN1rG+UTkqL0t/z+07t94WICxCr8UwTUDBhyUNhKtmAKAXheVmQMEu153TAeyZAYwDdhqFIGqAdODiJm1s1hFVSm1XSu0wXh9EF5GONdLJngl8bBz/NnCJw9K37wJd+gWeI/jQZti9HMbfqm84vssM4GOllMPBzD5TOSo/q/ki8M0R3cPMBxSMVJdof5GrZgCgk8OB18xA9igAe0Z1zdURbURExgHhwC6gG1BkVBZq7pyW40mOVLEAACAASURBVFoeKSakBJ4CWPk8hEdD6g0ev7SdVaMszOCY+QcgB+hl9T7J2OabKOVYCKgFP68NbOIklj53lRMY9PqThBSfVgD20FwdUQBEJB54F7heKdXgyIlbHSkmJEPxfigvaIP4PkRxto7+GXOd/WYJF2Jn1ShEZCjQBVhptdlSTKSL4fw919jmm5Qc1Iu67LX/W4iONWcAwUjjKmAXz1iHXgg56/Tv0cPYowDsGdU1V0cUEYkBFgF/UkqtMtoXoMvLhbVwTvtoXBEcIH6A1S/rken427xyeeuqUcOGDWP69OmNVaMWLlxo3XQGkG4UEAFAKXUUeBxdPnAtMMfY5pvkWyKA7AwBtRBlpoMISly1Crgpw4wBVuYi157XDuypCLYWGCQi/dA36RnAVU3aLECP/N+yriNqRPZ8BryjlLLY+1FKKRH5Drgc7VOYBbQYatIsjY7gjTDwbKdO4TM0NMCWj2HI+dClj9fEaK1qFIBS6jFbxyql3gTedJdsLsXREFAL0bHaHlxbpfO6mAQHrkgEZ4vYwXoWuuUjPRvo2NNjkX+tKgClVJ2I3ImeyocCbyqltorIHGCdUmohzdQRFZFrgNOBbiIy2zjlbKXURuABIF1E/gb8Arzh1CeI7ARdBwRGJNDBX/TCr2EXeVuS4CAvSxd4cbS4R+Nq4Fzo3Nv1cpn4JuWWNBDdXH/u0TPgmznwn6H69xg/Wj96nqSfu/R1i1KwqyawUmoxsLjJtkesXtusI6qUeg94r5lz7kZHGLWdhBQ4sNolp/IqmV/qurSDzvW2JMFBXpYe/Tv6x2qsDZxnKoBgoixX3/zbmgbCFqfeq9cEHNqkH4c3w67vdJ0KgIhOEH/SMYUQfxJ0G6QLzLSBwCgKn5AMv35sLNP2YkhhW8laDH1PaXuiKRP7yM+CoRc4fpzlN2Y6goOLchemgWiKCPSZoB8Waqv0GgGLQji0Cda9AXVVen9Ye+g58nilEDfcodDxAFEAFkfwRp1n2x8p2KULvo+93tuSHIdSCgnEAvTl+VBR4Lj9H6xmAGY6iKCiLNf5WsDO0C4SEsfoh4X6Op280KIQDm3WvoN1hgU9JEznSLtpmV0z28BQAD1P0s8H/VgBWCIAhk5tuZ0HKauu4873NzB1ZDzT03q1foA/0ZgCwsEIILBKCGeuBQgqynKh2wDvyhAaBj2G68foGXpbQwMU7T2mEGrK7TZrBoYCiIzR9jB/dgRnLoKeo3zGpnyouJIb5q5j+5FSpozo6W1xXE9jCKiDawBAj8wiOpkmIEcoy4VVL+rCRvEneVsax1FK97cnZwD2EhICXfvrx4hLHTo0MBQAaD/AvhXelsI5yvK0E3uSzTx7HufXnGJufHst5dX1vDk7jTMG++CPvq3kZem6rDFOVlkzS0M6hoTCT0/rqCt/VADVpdr27uoQUC/j3+mgrUlIgZIc//xTbl8CKJ0e1st8m3mE6a+sJESEj26bEJg3f9AKoPsgPXpyhug4MyOoI0R10yPUnHXelsQ5yt2QBsIHCBwFEG+khvbHvECZi6BTb20C8iLvrNzLTW+vo39sFAvuOIVh8TFelcetWEJAnSUq1nQCO0piKhxY69UKWE7TuAo4sAZEAaQATgLE//wA1WU63nfoBR7N+29NfYNizhcZPPL5Vs4cGseHt06gR0wAr3CtKoHSg87Z/y1EmwnhHCYpDcoO65m6v1HuplXAXiZwfAARHfWU3t+Kw+z6VqeY9VL0T0VNHb9P38jXGUeYPbEvf7lwOKEhARj2aU3+Dv3cphlAHFQXm+kgHCEpVT9nr4VOTvpevIW78gB5mcCZAYCRGtrPZgBZi7VjrPdEj186t6SKK19ZxTfbjvDoRcN5bNqIwL/5g15vAc6FgFqwjARNP4D99BgJYZGQ7Yd+gLJcQNyTBsKLBJYCiE/WuXRKD3tbEvuor4PtS2HweW1e0u0oWYdLufTFFezMLePVa1O5/pR+Hr2+V8nPgtBwnV/FWRoVgB8GHXiLsHD9H81e621JHKc8V5dw9PD/1N0ElgKwrAj2F0fw/pVQWehcOoI28MP2PC5/aQW19Q18dNsEzh4eWJENrZKXBd0Gtu3PbJ0PyMR+klL1/7OuxtuSOIYrawH7EIGlAHqOwq8cwZmLIDQCBpzpsUumr9nP9XPXktilPQvuOIWRiZ08dm2fIS/L8SIwTbHkAzIjgRwjKU37vI5s8bYkjlGe6995xpohsBRARLQu7uEPjmCltAIYMFnL7WYaGhRPLs3kwU+3cMrA7nx02wQSOrd3+3V9jtpKKNzbNgcwHBsNmiYgx0hK08/+5gcoO2LOAPyC+GT/mAEc+VWXsvTA4q+q2nrumv8LLy3fxVXje/PmrFQ6RrZz+3V9koKdgGpbCCgY6SBi/M4EtHTpUoYMGcLAgQN54oknbLYRkekikiEiW0Xk/Sb7YkQkW0Sed0qATonQMcG//ABKGZmGA08BBJZHA7QfYHM6lByCmHhvS9M8mYsA0dW/3EhBWTU3v7OODfuLeHjqUG4+rX9gZve0l7YkgWtKdJxfzQDq6+u54447+Prrr0lKSiItLY1p06YxfPjwxjYiMgh4CDhFKVUoIk3veo8DP7RJkKRU/1IANWVQVxmQCiDwZgCNjmAfnwVkLoJe4936o9qZW8alL65g68ESXrp6DLecPiC4b/6gFYCEaCdwW4nyr8Vga9asYeDAgfTv35/w8HBmzJjB55+fUIn1ZuAFpVQhgFKqUcOJyFigB/C/NgmSlKbNcP7y3QXoGgAIRAXQc5T+g/uyH6Bov87n7cbFX6t2F3DZiz9TUVNH+i0nc/4oH54NeZL8LB3+6YrFW9H+lQ4iJyeHXr2OpfVOSkoiJ+eEVbmDgcEi8rOIrBKR8wBEJAT4N3BfS9cQkVtEZJ2IrMvLa+YGb/ED+EteoMY8QKYT2PcJ76AdfL48A8haop+HXuiW02ceLuGGuWuJ7RjBZ789hZTeXdxyHb+krTmArInyLxOQnYQBg4BJwEzgNRHpDPwWWKyUym7pYKXUq0qpVKVUamxsMzfM+NG6cIm/mIGCfQYgIueJSJaI7BQRmzmLm3McichSESkSkS+btJ8rIntEZKPxSG7bR7EiPlnHGvtq0qnML7UN2g3FJYoqarjlnfVERYTx/s0n06trB5dfw2+pr9WV19oaAmohOg6qiqGu2jXnczOJiYkcOHCg8X12djaJiYlNm2UDC5VStUqpPcB2tEKYANwpInuBp4DrRMS2F7k1wjvoVcF+owCMWV4w+gBEJBR4ATgfGA7MFJHhTdpYO45GAHdb7f4XcG0zp/+jUirZeLjOZpOQokdmJQdddkqXUVkIe392y+KvuvoG7pr/C4eLq3j5mrFOJ3RrS6SIiNRbKfWFzn0SN3F0DzTUum4G4GfpINLS0tixYwd79uyhpqaG9PR0pk2b1rTZAvToHxHpjjYJ7VZKXa2U6q2U6os2A72jlHK+gEVSGuRsgIZ6p0/hMcrz0GkguntbEpdjzwxgHLBTKbVbKVUDpAMXN2nTrONIKfUNUOoiee0jwZhM+KIfYPv/QNW7RQE8uTSTH3fk8/glIxjbxzmzjyVSZMmSJWRkZDB//nwyMjKaNougeYVfaaXUT7i7eJW2VAGzReNqYP8wA4WFhfH8888zZcoUhg0bxvTp0xkxYgSPPPIICxc26uqvgAIRyQC+Qw/SClwuTFKajq6x5GXyZcpydQ6gAEsDAfaFgSYCB6zeZwPjm7QZDCAiPwOhwGNKqaV2nPvvIvII8A3woFLqhLm0iNwC3ALQu7ed5RJ7jNQViA7+4vE0C62S+SVE94SEMa23dYAFv+Tw2o97uG5CH65Mc76spHWkCNAYKWIdKgjEAn+2pfB9msYQUBeagMBvFADA1KlTmTr1+OCDOXPmNL5WSingXuNhE6XUXGBumwSxzgzaY0SbTuV2ygNzDQC4zgncnOOoJR4ChgJpQFfgAVuN7HIqNcVXHcG1VbDzGx3942wlKhtsyS7mgU82M75fV/5y4fDWD2gBOyNFIrARKWIQaUSBrBKRS9okjKvJy9IlICM6uuZ8luIggecIdj9d+0P7rv7hByjz0VrALsCeu1AO0MvqfZKxzZrmHEfNopQ6pDTVwFtoU5PrSEjxPUfwnu+hthyGuG5WkldazS3vrqN7dAQvXj2GdqEeCewSmlf4fZRSqcBVwDMiYtPTbVe4oKvJz3Kd+Qf8cgbgM4hoM5A/pIQoOxLUM4C1wCAR6Sci4cAMoKlzz6bjqKWTiki88SzAJcCvDkneGgnJUJEPxS1GrXmWzEUQ3hH6neaS09XUNfDbeesprKjhlWvH0i06os3ntDNSpIZmFL5SKsd43g0sB1JsXcepmV1baGiAvO2ucwADtGuv00H4iRPY50hK1bOyqmJvS9Iy5YGZCRTsUABKqTrgTrRzaBvwoVJqq4jMERGLk69Zx5GI/Ah8BJxl5BCZYhwzT0S2AFuA7sDfXPnBGlcE+4ojuKFBx/8POhvC2n6jBvjrF1tZu7eQJ39zksuyetoZKVKEDYUvIl1EJMJq+ynACR5kr1B8QC/nd5X930JUrDkDcJakVEDpaCBfpboMaisCdgZgl1tbKbUYWNxk2yNWr5t1HCmlbA53lVLuzYHcY8QxR/Cwi9x6KbvIWadtxS5a/PX+6v3MW72fW8/oz8XJJ4zQncY6UqS+vp4bbrihMVIkNTXVogxKOKbw6zEUvohMBF4RkQb04OIJpZRvKACLA9iVMwAwagObCsApEscCos1AAyZ7WxrbBGgtYAuBF9dkoV17iBvuO8VhMr/Uqx8Hnt3mU63be5RHF/7KGYNjuX+Ki29otB4pAqCUOkHhK6VWAKNcLpAraAwBdUESOGuiYv0jlNEXieyk+8OXHcGWfEXBagLyaxJG6xmALziCMxdB39OgfWvBUS1zqLiS297bQGLn9jw7IyU4avi6grwsvZCnQ1fXntecAbQNS2ZQX/iP2qJxBhC8UUD+S0IKVB7V9l9vkrdd56Fv45qEqtp6bn13PZU1dbx2XSqdOgRpTn9ncGUOIGui4qCqyP9KHPoKSWn6P3q0xZgR72FJA2HOAPwQX0kNnWmkQWpD8RelFA9/uoXN2cU8fWUyg3q4KJY9GFDK9SGgFvwsHYTP4esVwsqMNBBRvp0GoqFBkXm4hDd/2sO/vrLfJBm4PgCAuBHa7n5wIwxvmr3Cg2Qt1sqok/PO2jd/3sunv+Rwz9mDOXdETxcKFwSUHdGhhu6YATQqgNw29W/QEjsUwqO1GWj0ld6W5kTKc7XZMNS3ZttKKfbkl7NiVwErdxWwancBBeV6Fjq4RzT3njPELvNwYCuAdpGGI9iLM4DSw/rHPfnPTp/i5535/GPxNqaM6MFdZ7qgkEmw4eoUENb4WT4gnyMkFBLH+K4juCzXZ8w/OUWVrNiZz8pdBazYVcDhkioAesZEcsaQWCYO6M6EAd1IdKDWd2ArANALwjIWajOAN6phNeb+d87+v7+ggjve38CA2Cj+PT2ZENPp6zj52/WzW2YAhnPQVADOk5QGP/8Xaip0GhdfojzPaw7gvNJqVu4uYOWufFbsKmBfQQUA3aLCOXlANyYO6MbEAd3p262D05X+gkABpMCGd6Bon64E5WkyF+nrxg1z+NDy6jpueXcdSsFr16USHRH43eUW8jL1it2ObjCdRVmZgEycIykNGurg0CboM8Hb0hxP2RFITPXIpYoqali1+ygrd+WzcncB24+UAdAxMoyT+3dj9sS+TBzQncE9ol1W2jXw7yjxRmrog794XgFUl+r8P+NucXj2oZTijx9vYvuRUt6+YRx9ukW5ScggIC9Lm3/cMQMM76DTe/hLfVtfJNEqM6jPKQD3ZwJVSvHk0ixe+WEXSkH7dqGk9evKZWOSmDigGyMSOrkt3DvwFUCPERDSTjuCR1zq2WvvXAb1NU5F/7zw3U4WbznMn6YO47RBgRmD7DHysmDQue47f3SsOQNoC9GxenDma36AmnKdvNGNCqC+QfHnBVuYv+YAl49NYkZaL05K6kx4mGcCNANfAYRFQA8vOYIzF+lCEr2alk9omW+2HeHfX2/n0pREbjqtn5uECxIqjuqbsztCQC1EmYvB2kxSmq6U50u4uRZwbX0D93ywkS83H+KuMwdy7zmDXWbasZfAXgdgISFFJ4Xz5GrD+lpd/Wvw+Q5VEtqZW8bv0zcyMqET/7xslMd/EAGHOx3AFqLNhHBtJikNSg9C8Qm1J7yHZW2HG2YAlkWdX24+xEPnD+UP5w7xyn89OBRAfLKOAy/c47lr7v0Jqot18Rc7UUpx74cbiWwXwivXjiWyXagbBQwS3BkCaiEqzjQBtRXrCmG+QuMqYNeaYEurapn15hq+y8rl75eO5NYzbJbM8AjBoQAaVwR7MDFc1mIIaw/97c9yuGxbLpuzi3ngvKEkOBDLa9IC+dt1P3R2vkxmq0T3gMpCPeszcY4eoyA0wscUgOszgRaW13DN66tZt6+QZ65M5urxfVx2bmcIDgUQNxxCwz3nB1AKMhfDgDPtjmtuaFD85+vt9O3WgUtTzBWlLiMvE7oP1AuO3IUlTtxMB+E8YeF6zY4vpYSw9KeLZgC5JVXMeHUV2w6X8so1Y12axt1ZgkMBhIXraCBPFYc5tAlKsh1a/PW/jMNsO1TC788eRJhnyjoGB3nbobuLU0A3pXE18BH3XifQSUrT/1FfSaxXlqvrFrsgDcSBoxVc8cpKDhRWMPf6NM4e3sMFArad4LnTxCfDwU26Mpe7yVwEEgKDz2u9LXr0//TXO+gfG8W00d4fFQQM1WVQvN+9DmCwqg1szgDaROJYqKuCI66tDus05bkuMf/szC3jipdXUlRRy7ybxjNxgO8klgseBZCQop2ynnAEZy6C3hMgqptdzRf/eoisI6X8/qxBZn5/V1KwQz+7MwQUjpkITEdw27BkBs1Z7105LJTltdn882tOMdNfWUldgyL9lpNJ6d3FRcK5huBSAOB+P8DRPZC71W7zT32D4r/LdjAoLpoLT0pwr2zBhrvKQDYl2kwI5xI6JUF0T99xBJcdadMMYN3eo8x8bRXt24Xy0W0TGBYf40LhXINdCkBEzhORLBHZKSIPNtNmuohkiMhWEXnfavtSESkSkS+btO8nIquNc34gIuFt+yitEDdMRxm4WwFkGaWT7Vz9++Xmg+zILePuswebo39Xk5el04F37e/e64RH6ZTGphO4bYgcqxDmC5Tn6QgvJ/hhex7XvrGG2OgIPrptAv26+2Yql1YVgIiEAi8A5wPDgZkiMrxJm0HAQ8ApSqkRwN1Wu/8FXGvj1E8CTyulBgKFwI1OfQJ7CW0HPUdqB607yVys6xB0bX0Fb119A/9dtoOhPTty/kgzx7/Lyd+ub/6eyOUeZS4GcwlJabo6WHmBd+WoqYCaMqdMQEt/PcRNb6+jb/coPrh1gk+HdNszAxgH7FRK7VZK1QDpQNPqKjcDLyilCgGUUo3/BKXUN0CpdWPRS97OBD42Nr0NXOLUJ3CE+GS9FsBdjuDyAti/wu7FXws3HWR3fjl3nz3YTPPsDvIyXV8Evjmi48woIFfQ6AfwcjhouXNrAD5Zn81v521gZGIM6TefTGzHCDcI5zrsUQCJgHVR3WxjmzWDgcEi8rOIrBKR1sJfugFFSqm6Fs4JgIjcIiLrRGRdXl4bp9gJKVBT6r76ozu+AtVgl/2/rr6B/36zg+HxMUwZ4RshYQFFXbX2x7g7BNRCVKxpAnIFCckgod43A1kiuhzIA/TOyr384aNNTBzQnXdvHO8XNbtd5QQOAwYBk4CZwGsi0tkVJ1ZKvaqUSlVKpcbGtnFBRoJVamh3kLkIYhKPpaBugU9/yWFfQQX3eCEBVFBQsAtUvfsdwBaizYRwLiE8Sq/Z8boCMGZzdhSDUUrxwnc7eeTzrZwzvAevz0olyk9qd9ijAHKAXlbvk4xt1mQDC5VStUqpPcB2tEJojgKgs4hYviVb53Q9sUMhLNI9C8JqKmDnN9r528oNvba+gee+3cGoxE6cPcw3ys1Zs3TpUoYMGcLAgQN54oknbLZpwek/S0R2GI9ZHhO6KUe26md3h4BaiO4BlUfNdBCuICkNstdDQ733ZCi3LxOoUoonlmbyr6+yuDQlkRevHuNXObzsUQBrgUFG1E44MANY2KTNAvToHxHpjjYJNWtnUUop4DvgcmPTLOBzhyR3htB20GOke2YAu5dDXaVd5p9P1mdz4GilV9K/tkZ9fT133HEHS5YsISMjg/nz55ORkdG0WQQ2nP4i0hV4FBiP9h09KiLeCXxe96aejcUNb72tK2hcC5DvmesFMklp2lRryeTqDcrsSwPx5NIsXvl+N9ec3Jt/XzGadn62ir9VaQ07/Z3AV8A24EOl1FYRmSMi04xmXwEFIpKBvrH/USlVACAiPwIfAWeJSLaITDGOeQC4V0R2on0Cb7jygzVLQoqOBHKlI7i+Fla/DBGdoO+pLTatqWvguW93ktyrM5OG+F6hlzVr1jBw4ED69+9PeHg4M2bM4PPPT9DNsdh2+k8BvlZKHTX2fQ3YtxzalexfpZ3xE+/yTAQQWK0FMB3BbcbiCPamGag8F9p30WlkmuHNn/bw8ve7uObk3jx+8Ui/DOSwy1CllFoMLG6y7RGr1wq413g0Pfa0Zs65Gz1K9CwJybD2NSjY6RrzgFKw8He69ONF/231hvPhugPkFFXyDx/N9Z+Tk0OvXscsfklJSaxevbppswgMpz8QCjymlFqKfQEDgHbuA7cA9O7t4kydP/5H53AZc51rz9sSjbWBTUdwm+k2ACI7awXgyT60piy3RfPPl5sP8viiDM4b0ZO/Thvpk/9le/Cv+YorsKwIdpUfYNmjsOl9mPQwjJ3dYtPqunpe+G4nY/t04fRBvpMPxAmENjr9Xerct+bwrzoa6+TbtUPRU1ichaYjuO2IGH4AL4aCljdfC3jFrnzu/WATaX268syMZL9ewBl8CqD7EJ0f3hV+gBXPw8//hdQb4Yz7W23+wdoDHCqu8knbv4XExEQOHDg2iM/OziYx8YRBfA22nf72BAy4l5+e1qtyx93s0csemwH4tgJw1sEvIskistLYtllErnSroElpkLsNqkrceplmKTti0/6fcbCEW99ZT9/uHXjtulS/cvjaIvgUQGgY9BzVdgWw+UP4359g2DSY+q9WI3+qavXof1y/rkwcYF+SOG+QlpbGjh072LNnDzU1NaSnpzNt2rSmzYqw7fT/CjhXRLoYzt9zjW2e4ehu2PoppF6v7beeJCIa2kX5dEZQexz8LazqrwCuM7adBzzjqlBvmySlAgoObnDbJVqk7MQ0ENmFFcx+aw1REWHMvX6cX8T5t0bwKQDQfoBDm50PM9u5DBbcDn1Pg8tes6vYyPur93OkpJp7zvbd0T9AWFgYzz//PFOmTGHYsGFMnz6dESNG8Mgjj7BwYWPwVwk2nP5KqaPA4+jIsbXAHGObZ/j5WZ375+Q7PHbJ44iO9WknsJ0Ofpur+pVS25VSO4zXB4FcdDCAe0gcq5+94QiurdRRSFZrAArLa7juzTVU1dbzzo3jfDq9gyP4x2oFV5OQAmtehfwdEOfgQqHs9fDBdRA7DGbMg3aRrR5SWVPPi8t3MaF/Nyb48OjfwtSpU5k69fh0FnPmzDnuvVKqOaf/m8Cb7pTPJiWHYOM8SL4KYuI9fnnA52sD2+ngHwxgw8HfiIiMA8KBXU0Pdplzv31nba71hh+g7Pg1AJU19dz49lqyCyt578bxDO7R0fMyuYkgnQE46QjO3wHzLoeo7nDNJxDZya7D3lu1j/yyau45x0OLkoKRVS9AQx2c8nvvyRAd59MmIDtpcVW/iMQD7wLXK6VOiKV2qXM/KU3PAJRq23kcxRLJFR1HXX0Dd83fwC8Hinh2RjLj+nX1rCxuJjgVQPfB0K6DY36AkoPw7qW60te1n0FH+/L3VNTU8fL3uzhtUPeA+/H4DJWFsO4tGHGZ+1M/t0RUrE/PAOx08De7ql9EYoBFwJ+UUqvcLnBSKlQUeKaIkzWGGU9FxfKXz39l2bZc5kwbwXkjvTSzdCPBqQBCQqHnSTozqD1UFsF7v9E3mms+1nHKdvLOyn0UlNdw99nm6N9trHlNp+499R7vyhHdAyp8Nx2EnQ5+m6v6jSwAnwHvKKU+bnqQW0hK1c+eNgMZJqDXfyln/poD3Dl5INdO6OtZGTxEcCoA0I7gw3Y4gmsrYf5Mbf658r1j5iM7KKuu45Xvd3HG4FjG9vGtUnABQ005rHoJBk3R9R68SXQsoHw2HYSdDv7mVvVPB04HZovIRuPRetbDthA7TEdWedoRbJiA/u+no1wxNok/nBu4g7fgdAKDvpGvflnnG4kbZrtNfR18chPsXwmXvwEDJjt0ibdX7KWwota0/buTDe/oJGynneCP9jzWawG85YhuhdYc/M2t6ldKvQe85wERjxEaBoljPD4D2L9/LzEqilOHxPvsin1XEbwzgPhWUkMrBYvugcwv4fwnYeRvHDp9SVUtr/6wm7OGxpHcy33h0kFNXQ2seA56T4TeJ3tbGqt8QH7vCPYdklL1TL220iOXW7f3KBk7dlIa1pUXrh7jd8ndHCWwP11LdB+kp5fN+QG++7seXZ52H4y/1eHTv/XTXoorzdG/W9nyIZTk+MboH6wygvquI9jvSErT0V2HNrv9UjtzS7nx7XUkhJXSM74XHcID30ASvAogJBTiT7I9A1j9KvzwL0i5Fs78s8OnLq6s5fWfdnPu8B6MTLQvVNTEQRrqddqHnqNg4NnelkZjWTlq5gNyHYkWR7B7/QCHi6u47o01tAsNYVhMFe06BUeVvuBVAKD9AIe3aFu/hV8/hSX368IuFz7TaooHW7zx0x5Kq+rMyB93su0LndH11Hud6iO3EBGtw4vNFfN1NQAAIABJREFUjKCuo2MP6NzbrQqguLKW2W+toaSqjrnXp9GuMv+ENBCBSnArgPhkXcQlP0u/370cPr1F25Mvf1M7oRykqKKGN3/aw/kjezI8Ica18ppolIKf/gNdB8Dwi70tzfFE+XY6CEDPnta+oR/+gBszg1bV1nPLO+vYlVfGy9eMZWRcBFSXtFoIJlAIbgVgCek8+Iv2BaRfrX0DM+dDO+dyfbz2427Ka8zRv1vZ9a0u6nPK7+3Kw+RRfL028P7V8OokWHSvzmnl6VW2zpCUBiXZejGmC6lvUNz74UZW7znKU1eM5tRB3Y/5b5pJBR1oBLcC6DZQpw7OWKhTPLTvolM8OJlJ8mh5DXN/3ssFo+IZ0jNw8oX4HD89DR3jYfQMb0tyIlFxvmkCKsuFz26HN8/V6xQufwtmvO875rOWaKwQ5rpZgFKKx7/MYPGWw/xp6jAuTjZWRDeWgjQVQOATEgLxo3UBkYZ6uOZTiElw+nSv/rCbitp67j57kAuFNDmOA2th748w4U4Ii/C2NCfiazOA+lpY+SI8Nxa2fKR9JneuhZGX+cfNH7SjPzTcZX6A8uo67vtoM3NX7OWmU/tx8+lW6UMs5rvo4DABBX6cU2v0nqBNQFd/1KYSkfll1by9Yi/TRicwMM4c/buNn/6jZ2itVF/zGtFxOn9NfZ1TPiSXsudHHdCQm6Ejpc57EroP9K5MzhAWoQdqLpgBZBws4c75G9iTX87vzhx4oqm20QQUHE5gu36hInIe8F90etjXlVInlBISkenAY4ACNimlrjK2zwIssZR/U0q9bWxfDsQDlhUe51oVF/ccZzygywdGta1E44vf7aK6rp7fnWWO/t3GkQzIWgxnPKgjbtxAeXUdB4sqOVhcxaGiSg4WVVJaXUdcx0h6xETQMyaSHp0i6RETSXSEjb9PlJEOoiIfOvZ0i4ytUpwDX/8Ffv1ER9DMeF9HtfnLiN8WSWk64V99bat1t22hlOLdVfv426JtdG7fjnk3jWfiABv/+UYTkDkDAEBEQoEXgHPQmQLXishCpVSGVRvrKkKFIhJnbO8KPAoY5X1YbxxbaBx6tVLKi4U/gbBwCGvbzX/boRLeXrmXK9N6MSDWPTcmE7Ttv12UUwvzAGrqGjhSUmXc4Cs5WKRfHyo2thVVUlJVd9wxItChXSjlNSfmjIqOCCPOUAo9YyKJi4lkfCVMBrbt3ElMv07EdYzw3GrSumpY9SJ8/y9Q9TDpIe0odzKgwadIStWf7chWncfLAYorarn/k018tfUIk4bE8tQVo+ke3Yz5sDxXp3n3RfOiG7BnBjAO2KmU2g0gIunAxYB1LTmbVYSAKcDXlqpQIvI1upzcfNeI730aGhR/XvArndq34/4pDhaXMbGfwr16RHvy7dCh9bTav+wvZPGWQ2QXHhvN55VVnxD00rlDOxI6tSepS3vG9etKfKf2JHSOJKFze+KNkX670BDKq+s4UlLFkZJqjpRUcbikynivt63ec5Tc0irWNZQwOQL++dEP/NBQjAh0i4qgR0wE/bpHcfqgWM4YEkuPmNYLCTnEzmWw5AG9NmLohTDl79Clr2uv4U0aHcFrHVIA6/cd5XfzN3KkpIo/TR3Gjaf2I6SlIu5luUHjAAb7FEAicMDqfTYwvkmb5qoI2TrWOgH5WyJSD3yCNg/5QUza8Xy8Ppv1+wr5v8tPoktUuLfFCVx+flbXYpjQfLnH+gbF1xmHee3HPazfV0hEWAiJXdqT2Lk9Q4bEktC5PQmd2hNvdYO3d7l/VEQY/WOj6d/CDK+hQVGc0w/emMODp3Xj/G6jGpXE4eIq1u49ypebDwEwLD6GyUNimTQkjjG9OxPm7CyhcC989Seds6rrALj6ExjU/MpopRTZhZX8cqCI8FDxnxz3nXppu3z2Ohh3c6vNGxoUL32/i/98vZ2EzpF8fPtE+3JyleUGTQgouM4JbF1FKAn4QURGtXLM1UqpHBHpiFYA1wLvNG3kshJzbqCwvIZ/LtlGap8uXD4mydviBC6lR+CX9yB5ps0orYqaOj5al82bP+9hX0EFvbq259GLhjM9tRdRtuz0biIkROgSp38Hw2OqGD7u+N+rUorMw6Usz8rju6xcXvlhNy8u30XHyDBOG9SdSYPj7J8d1FbCz//VZjEJgbMe1cqxiemiqKKGTdnFbNxfxKbsIjYdKKKgvAaA1D5d/EcBiByrENYKuaVV3PvBJn7amc8FJ8Xzz8tGERNpp9+gPFdHHQUJ9vw7coBeVu+TjG3WZAOrlVK1wB4RsVQRysEoLmF17HIApVSO8VwqIu+jTU0nKACl1KvAqwCpqak+NUN4cmkmJVV1/O3SkS1PK03axqoXoaEWTrn7uM25JVXMXbGXeav3U1xZS0rvzjxw3lCmjOhJqLf6IzwawtrbDAUVEYbFxzAsPobbJw2gpKqWn3fkszwrj+Xbc1m85TAAw+NjmNTc7EAp7Qhf+iAU7ddV0M79G3RKpLqunm0Hiti4v1Df9A8UsSe/3Lg2DIyN5syhcST37szopM7+t1YlKVXPdCqONmsG/GF7Hvd+uJGy6jqeuGwUV6b1ciydc1meaQJqwlpgkIj0Q9/QZwBXNWmzAF0/9C3rKkLootH/EBHLyqpzgYdEJAzorJTKF5F2wIXAsjZ/Gg+yfl8h6WsPcPNp/Rja00z54DYqi3TKguEXN1Zi23aohNd/3MPCTTn8f3vnHl9Vde377yBPSHjmAZgEAgYCSQWEBB+oQLWiYHlYVKg9hWOrp1SL1EOtbS0iwtXT2l6v1XKLD/R6rMGq5aEBixSQWxUIGiEkBBAQEoGE8MgD8tg78/yx1g6bsJPsJCvZjzW/n8/6ZO2111p77MxkjTnHHPM3HPWKSWn9uP+mQYwZ6AclN0WMHHIv1gL0iAzj9qv6c/tV/VseHaTGM2FoHPE5f4CPf4eKG8aJ6X/jM5VG7pYz5BYdoeCbcmqdRpne+O4RjErqxcwxiVyd1IurEnvS3dtesL/iviBs6K2XvFXnrOePG/ezfMtXDO0bzV/vv7b1xdvrqqHmnG3WAIAXDkAp5RCRhzAqBYUAryql9orIEiBHKbXWfO9Ws4qQk4tVhBCRpzCcCMASpdRpEYkCPjQf/iEYD/+XrP5yHYXDWc/jq/Po1yNSSz50NDtfhtoK1LgFfLy/lJe3HWLbgVN0DQvh+2MHcN8NgxgYE+VrKy8lKr7VktDejA66UM+uriv4OnIsPyr9OWVZdcCXdAsP4aqEnvz7uGRGJfVi1IBe9OsRGXyFTPqPMsJdRTsvcQDHTp9nftYXfHH0LLPHDmDRHWl0DW+DREiVvVYBg5dzAEqpbCC70bFFbvseqwiZ770KvNroWBUwpg32+gWvf/o1BcfLWX7v6E6NMduO2vOoz5ZzIu5G5q4qp/BkMfHdI/jFpFTuvWYAvbr56aR7dDyc+bpdt/A0OijY8RG9vzjLX0LG852rEhmV1IuRSb0YEh/d9knkQCIiGuLTL5kH2JB3nEff2Y1S8KfZV/PdkW1fyW83HSDQK4FbzYlz1fzxH4VMSI3jtm/5aKFPB7NhwwYefvhhnE4nP/7xj3nssccanxIjIqVcnAt6QSn1MoCZ1bXHPH5UKXVZ1XFvOFNVy573/sBN508x/8xEpC88e9dIpo68gvBQP3/YRcdbKl/cMDrolgtdQnnsoYegq02rzCVmQN67VNfWsSy7kDc++5qRiT350+zRDIjp1r57V2oHoGmBpz7Ix1GveHJqevANsQGn08mDDz7Ixo0bSUxMJDMzk6lTp5KWltb41FVKqYc83OKCUqrNxcKPnT7Pio8P8fddh9nQ5TX2R6Yz/94fckNKbOD8vqNMOYh6p7VqpfuyIfkG+z78wZgH2LWS+X9axT9Ke/PATYNZeGuqNZ0ClwOwUQjIz7tS/sXH+0v5YPdxHpyY4n9xZ4vYsWMHKSkpDB48mPDwcGbNmsWaNWs67fOLzlxg1c5jPD5gL4lyiqHfe4Ibh8QFzsMfjB6kqjdUN63i1AEoOwCpU6y7Z4DhrFesOjUQgNTKHaycm8mvJw+3bkToCgHZRAYCtAPwmuo6J4vW5DEoNor/GD+45QsClOLiYpKSLmb9JiYmUlzcOOsXgO+JyG4ReUdE3NOEI0UkR0Q+E5HpTX2OiDxgnpdTWnpRPvnawX345LEJzKp5D/p+C4bc2tQt/JeOqA287wPjZ+rt1t0zgMgrPseMP/+LX246x5HwIfys7x4mDrO4p15ZChE9IcziVdp+jHYAXvJ/t37FkbLzLJmWTkSonxUh6XzOAslKqRHARuB1t/cGKqUyMFKFnxORKz3dQCm1QimVoZTKiIu72OMSEWKLPjKqtN3w88AUMHPFkK2UhS5cD/1GQK+kls8NIi7UOnk6u4BpL/6Lb85W88L3r2bgjd8n/MTncPZYyzdoDVUltkoBBe0AvOLIqSr+vOUr7hjRnxuHBPcfSEJCAseOXfzHKioqIiEhofFpTqVUjbn/Mm4ZXW4L/A5hLPq7ulUGKAXb/mjo2KQ1OYDwb1xSwlYVhqkshWPbYZi9wj9b95dy63Nb+cvHh7g7I5FNj4znjhFXIOnm30W+xaFJm+kAgXYALaKUYtHavYSHdOG3d1w2ERp0ZGZmcuDAAQ4fPkxtbS1ZWVlMnXpZIo/7iqKpQAGAiPQWkQhzPxYYx6WigS1zeCt887mhYulrPf224goBWTUC2L8BUIaksw04VVnDgqwvmPPqDsJCurDqgWt5+s4R9Oxm/tn1GWyMhvJXW/vBNtMBAp0F1CLr807w8f5SFt2RZr2Cox8SGhrKCy+8wKRJk3A6ndx3332kp6ezaNEiMjIyXM4gXkT2Ag7gNDDXvHw48BcRqcfoXDzjLhvuFbFDjapVIxsvNg8gIrpDaKR1xeELsw0xtCDXqFFK8c6uIpZlF1BV42D+zUP46YQriQzzEHJNnw6blhhhIKvCYlUlED3RmnsFCNoBNENljYMl6/JJ69+DH1430NfmdBqTJ09m8uRLe5tLlixxf1lsxvkvQSn1CdC+p1SPK+CWJ9p1C58jYl1t4Nrz8NVmGP1vgTkf4iWHT1Xx6/f28OmhMjIG9ubpO69iSHNSDmmmA8hfA9d7ykZuJY4aqD5nuxCQdgDN8NzG/ZysqObPPxhtj5WWGuvwUg+oRQ5tBseFoA3/1DrqeWnbIf7PpgNEhHRh2YxvMTtzQMviijFXXgwDWeEAXM7aZpPA2gE0QcHxclZ+coRZmQMYPaB3yxdoNO5ExcM5C7JU9mUbqYnJN7T/Xn7G50fP8Kt391B4soLJV/Xjie+mty7M6goDnSuCnu2UY7fhIjDQk8Aeca/y9cvbUn1tjiYQiY5v/wig3mlMAA/5Tpvq4PorFdV1LFqTx/eWf0J5dR0v/TCDP987pvVzbGkWZgNV2qsYvAs9AvDA33YdY9fXZ/j9zBH+Kzim8W+i443C8O2RgyjaadxjWPCEfz7ce4In1uzlZEU1c65LZuGkVKLbKqjoCgPt/XuzleK8okEIToeAbM3pqlqeXr+Pscl9mDlGV/nStJEoUw7ifFnbUwv3fQBdwiDlO9ba5gNKKqr57eo8Ptx7kmH9urP8B6O52orQqlVhIB0C0gD81/p9VFY7eGr6twJLf0bjX0RbsBagMBsG3QiRwVFw6IujZ3n0tlTW/ewGax7+YF0YqKoUInrYSgYCtAO4hF1fn2ZVzjF+dMOgwCuXp/EvXD3JtuoBle6HsoNBk/0T3z2Sjx+dyE8npBBmZUZdzJXG+oi97VwUVlliKxE4F9oBmDic9fzm73lc0TOS+TcP8bU5mkDHNZlY2ca1AIUdI/62YcMGUlNTSUlJ4ZlnnvF4jojcLSL5IrLXrNftOj5HRA6Y25zWfrbHBV1WkDYdinYYYaC2Ulliuwlg0A6ggdc+OcK+ExUs+m66rvKlaT8NIaA2rgbelw39R7Y/vdENV62H9evXk5+fz1tvvUV+/qULtUVkCPArYJxSKh1YYB7vAzwBXAOMBZ5wq/XtW9JnGD/bEwayoRAcaAcAwPFzF/jfG/czMTWOSen26wVoOoCIHhAS0bYQUGWJkQFksfa/l7Ue7gdeVEqdAVBKub7AJGCjUuq0+d5G4DZLDWwrVoSBbCgEB146ABG5TUQKReSgiFxWH9A8p1XDRhEZIyJ7zHs+Lz6ccV36foFZ5UtP/GosQsRcC9CGEFDhekBZnv7pZa2HocBQEfmXWdPB9ZBPANxXthWZx/yD9oSBHLVQfdZ2QnDghQMQkRDgReB2IA2YLSJpjc5py7BxOUZvY4i5+aQ38fH+Uj7Yc5yHJqa0v6aoRuNOVFzbRgCF2dBzgFEQp/MJxfh/nADMBl4SEa9rUDZV6KfDaQgDrW39tS4ZCD0J7JGxwEGl1CGlVC2QBUxrdE6rho0i0h/ooZT6TCmlgP8HdLr4e3294un1+xgY040HgrjKl8ZHRPdt/QigtgoObTF6/xaPRr2s9VAErFVK1SmlDgP7MRxCMeAuu5loHruEpgr9dDgxV0Lfq4xFYa3FNU+jRwAe8Wbo19phY4K539w9O5z1eScoOF7Oz28Zqqt8aawnug0jgK82g6O6Q9I/vaz1sBqj9++q6TAUOAR8CNxq1nzoDdxqHvMf0tsYBmoQgrPf/J9Vk8DtGjY2R0cNKZ31ij9uLGRIfDTfHXmFZffVaBpwSULXO72/pjAbInvCwOstN8e91sPw4cO5++67G2o9rF3bEDr5ECgTkXxgM/ALpVSZUuo08BSw09yWmMf8h7aGgRpWAdsvBORNvqM3Q78iYLtSqg44LCLuw8YJja7dYh5PbHTcY+VxpdQKYAVARkaG8sJer1iTW8xXpVUsv3c0IS1Jz2o0bSHaJQdx2rsUwwbxt1s7TPytpVoPZkj2EXO7BKXUq8CrHWKYFbjCQPmr4bqfen9dgw6QDgF5YicwREQGiUg4MAto7GJbNWxUSh0HykXkWjP754eAxQU+m6bOWc9zHx0g/YoeTErv11kfq7Ebrh6lt2GgY9sN7aAgWf3rE9KnGb/H1oSBKkshvDuEde04u/yUFh2AUsoBPITxMC8A3lZK7RWRJSLiCiC2Zdj4U4yC4geBr4D1Fn6vZnlnVxFHT5/nP28d2nLhCY2mrbh6lN7qATWIv93ScTYFO2ltCAPZdBEYeKkGqpTKBrIbHVvktt/qYaNSKgfo9Dy3GoeTP206wKikXkxMtd+QT9OJuCYVvSkNqZQp/nZT0Ii/+YTYlNaHgWwqAwFBIAddV1dHUVER1dXVXp1fWePgyfG9iY0OZ9++fR1sXeARGRlJYmIiYWHBU4DEZ0S1Qg6itBBOH2q/rr3GCAP9cymcK4aeXiQXVpZAnD0LPwW8AygqKqJ79+4kJye3uIq3vl6x72QFfUK7MDg2Sq/6bYRSirKyMoqKihg0aJCvzQl8IntCSLh3IaBCc4Ct4//tJ22G4QAK1sK181o+v6rEkN22IQGvBVRdXU1MTIxXD/Oyqhocznr69YjUD38PiAgxMTFej6Y0LSByMRW0JQqz4YqroYdOSW43sSnGKmpvFoU56+DCGVvqAEEQOADAq4e5s15RWlFD98gwrfbZDNoxWkx0XMsjgIqTUJRjufibrUmbbmYDecwuv0jDIjB7TgIHhQPwhlOVNTjqFX17RPjaFL9n27ZtLWnGx4hIqYjkmtuPXW+0VzM+6Iju23Ia6P6OEX+zNemmskxBC9lADTIQ9pwEtoUDcDjrOVVZQ4/IMLqFW9v7LysrY9SoUYwaNYp+/fqRkJDQ8Lq2trbZa3Nycpg/f76l9rQXp9PJ0qVLm9WMN1mllBplbi+Dn2vG+4ooL0YA+7Kh1wCIT2v+PI33xA4xw0AtSES7tJpsGgKyRSzkVGUtznpF3x7W1/uMiYkhNzcXgMWLFxMdHc3ChQsb3nc4HISGev41Z2RkkJGRYblN7WHHjh0MGDCAwYMNcTyXZnxamlcPpwbxPwARcWnGv9VR9vo90fFQdQrq66GLh/5WTaUh/pZxn+Xib7YnbTpsbiEbqGEVsD1DQEHlAJ5ct5f8b8ovOaaA87UOQrtImwTf0q7owRPfTW/VNXPnziUyMpIvvviCcePGMWvWLB5++GGqq6vp2rUrK1euJDU1lS1btvDss8/y/vvvs3jxYo4ePcqhQ4c4evQoCxYs8MnooLi4mH79Lq6OTkxMZPv27Z5O/Z6I3IShFvlzpdQxWqEZLyIPAA8ADBgwwBrj/ZGoeFBOuHAaomIvf/+rf4KzRod/OoJ00wE0lw3UoAOkRwBBSZ2zHhTWFqL2gqKiIj755BNCQkIoLy9n27ZthIaG8tFHH/HrX/+ad99997Jr9u3bx+bNm6moqCA1NZV58+b5az7+WeBapVSNiPwH8Drw7dbcoKM0nvyOhtKQJZ4dQGE2RPaCAdaLv9ke9zBQUw6gqhTCoyHcnrVAgsoBNO6p1znrKTxRQc+uYST16dwGvuuuuwgJMUYc586dY86cORw4cAARoa6uzuM1U6ZMISIigoiICOLj4zl58iSJidbVhPWGhIQETpw40fC6Cc14p1Kqxtx/Gfidud+U+J99cfUsq0ow6im54XQY4m9DJ0FIUP0r+g+uMFD5N55TbCtLbKkC6iKoJ4FLKmpQCuJ9kPkTFRXVsP/b3/6WiRMnkpeXx7p165rMs4+IuGhnSEgIDoejw+1sTGZmJl9//XVLmvHuw5KpGBpREAia8Z2NK7vEU2GYY9uNHHS9+KvjcGUDNaUNVHnSthlAEMQOoNbh5HRVLb2jwnxe7OXcuXMNvejXXnvNp7a0RGhoKL/5zW9a0oyPN2s/fwnMB+YCBIRmfGcT3YwcRGG2sVI45ebOtclOxA6B+PSmF4VVldp2AhiCLATkTkmFEaGI72595k9refTRR5kzZw5Lly5lyhT/X+wzfvx4fvKTn1xyzF0zHihWSnlMX/J7zfjOJrKX8ZBvvBZAKUP9c9B4iOjuG9vsQvp02LzMcxiosgQGjvONXX5AUDqAmjonZ6rqiIkOJzy08wY5ixcv9nj8uuuuY//+/Q2vly5dCsCECROYMGGCx2vz8vI6wkRNZyNirgVoFAIq3QdnDsP1P/ONXXYizXQA+WvhWreOjbPOyM6yYSEYF0EZAiqpqEEE4rrrVb8aPyDKQ23gfR8YP3X8v+OJG2qEgfIbLQqrOmX81JPAwUN1nZMz52uJiQ7v9NRPjcYj0X0vXw1cmA1XjIYe/X1jk91Inw5HPzXCQC5sLgMBQegATpZX00WEuGjd+9f4CY0F4cqPQ/EuvfirM0nzkA3UIASnQ0BBwYVaB+cu1BEbHUGo7v1r/AWXJHR9vfF6v1n9VKt/dh5xQw2tJfcwUMMqYB0CCgpOltcQ0kWI7R7ua1M0motEu+QgzhivC9dD72SIH+5Ts2xH+gw4+pkxAgM3HSA9AmgWEblNRApF5KCIPObh/bnNyAP/l4jkmds9bsdfE5HDbteMas8XOV/joLy6jrjoCEI9iW5pNL7C1cOsKjHF37YavX8t/ta5pE0H1EWJ6MpSCIuC8KhmLwtmWnxSikgI8CJwO8Za9tki4kka0pM88BRgNDAKQyJ4oYi4V7z+hds1ue35IifKqwnt0oWYTo79T5w4kQ8/vHSx63PPPce8eZ61RyZMmEBOTg4AkydP5uzZs5eds3jxYp599tlmP3f16tWXyDQvWrSIjz76qLXmazqDhtXAJfDVJi3+5itcYSDXorDKk7bu/YN3I4CxwEGl1CGlVC2QBUzz8v5pwMdKKYdSqgrYjSEPbCmVNQ4qaxzEdY8gpEvn9qpmz55NVlbWJceysrKYPXt2i9dmZ2fTq1evNn1uYwewZMkSbrnlljbdS9PBuB4ylSWG9n/X3pB0rW9tsitp0y+GgapKbO8AvFkI5kni9xoP53mSB/4SoyjIH4BuwETAvbrIMhFZBGwCHnMTGGsVJ89VExbShdj/vwhOWLyAqt9VcLvHqlgAzJw5k8cff5za2lrCw8M5cuQI33zzDW+99RaPPPIIFy5cYObMmTz55JOXXZucnExOTg6xsbEsW7aM119/nfj4eJKSkhgzZgwAL730EitWrKC2tpaUlBTeeOMNcnNzWbt2LVu3bmXp0qW8++67PPXUU9xxxx3MnDmTTZs2sXDhQhwOB5mZmSxfvpyIiAiSk5OZM2cO69ato66ujr/97W8MGzbM2t+X5nJcIaCKb+DAhzBEi7/5jPTpsOV/GWGgylKIudLXFvkUq4Ll64BkpdQIYCOGPDBKqX8A2cAnGEVBPgWc5jW/AoYBmUAf4JeebiwiD4hIjojklJZeLqhVXeekqtbo/QudH1Pt06cPY8eOZf16I7MjKyuLu+++m2XLlpGTk8Pu3bvZunUru3fvbvIeu3btIisri9zcXLKzs9m5c2fDe3feeSc7d+7kyy+/ZPjw4bzyyitcf/31TJ06ld///vfk5uZy5ZUX/4irq6uZO3cuq1atYs+ePTgcDpYvX97wfmxsLJ9//jnz5s1rMcyksYiuvaFLGBSsMyaCdfjHd8SlmmGg1XoEgHcjgGIgye11onmsAaVUmdtLd3lglFLLgGUAIvJXjBECSilzKp4aEVkJLMQDzenGK6Uor66jb0gX+kSFN9tT70hcYaBp06aRlZXFK6+8wttvv82KFStwOBwcP36c/Px8RowY4fH6bdu2MWPGDLp1MySr3dU38/LyePzxxzl79iyVlZVMmjSpWVsKCwsZNGgQQ4cOBWDOnDm8+OKLLFiwADAcCsCYMWN477332v3dNV7gkoMo2gkhEXClFn/zKWnTYcvTgLJtIRgX3owAdgJDRGSQiIQDs4BLtFVFxH05Y4M8sIiEiEiMuT8CGAH8w/0aERFgOtDq2M2mghJqHYr4HhF08WFGxbRp09i0aROff/4558+fp0+fPjz77LNs2rSJ3bt3M2WsavatAAAILklEQVTKlCYloFti7ty5vPDCC+zZs4cnnniizfdx4ZKc9pXctG1xKU4OHg8R0b61xe6km9lAYGslUPDCASilHMBDGLruBcDbSqm9IrJERFxd1fme5IExdOO3iUg+Ri/+B+b9AN4UkT3AHiAWWNoaw+vrFX/YuJ/QLkKvbr7N+4+OjmbixIncd999zJ49m/LycqKioujZsycnT55sCA81xU033cTq1au5cOECFRUVrFu3ruG9iooK+vfvT11dHW+++WbD8e7du1NRUXHZvVJTUzly5AgHDx4E4I033mD8+PEWfVNNm3FlAmntH98Tlwpx5hoMG8tAgJdqoEqpbIxYvvuxRW77v8KI6Te+rprLyiA1vNeqEoKNWZ93goLj5fTo2sunvX8Xs2fPZsaMGWRlZTFs2DCuvvpqhg0bRlJSEuPGNS83O3r0aO655x5GjhxJfHw8mZmZDe899dRTXHPNNcTFxXHNNdc0PPRnzZrF/fffz/PPP88777zTcH5kZCQrV67krrvuapgEbiztrPEBrlBD6u2+tUNjkD4DthTYPgQkSgVOOdaMjAzlyqHfXFjCX7cf5ecZUaSl6RWVVlJQUMDw4U3/TkVkV1P1ANqCe7sGLcd2QFEOXPdTX1vSJLZq18pS+NdzcPMiCA1+3bCm2jZgc9EmpsYzMTWegoKClk/WaHxN0lhj0/gH0XEwaZmvrfA5WjNBo9FobEpQOIBACmP5O/p3Gdxs2LCB1NRUUlJSeOaZy9OmW9D1+p2Z7FEgIs+bGXyaACbgHUBkZCRlZWX6wWUBSinKysqIjPR9HWWN9TidTh588EHWr19Pfn4+b7311iVyIm540vW6HhiHkcr9LYwFnDq9LMAJ2DkAF4mJiRQVFeFplbCm9URGRpKYmOhrMzQdwI4dO0hJSWHw4MGAkUm2Zs0a0tI8Juo1RgGRQDggGCneJzvKVk3nEPAOICwsjEGDBvnaDI3G7ykuLiYp6eKi/sTERLZv3+7p1Mt0vZRSn4rIZuA4hgN4QSmlMzACnIAPAWk0GkvxqOslIinAcAwpmATg2yJyY+OLW9Lu0vgX2gFoNDYhISGBY8cuCvsWFRWRkJBwyTlKqTI3Vd6XgTHm/gzgM6VUpVKqElgPXNf4M5RSK5RSGUqpjLg4e8ssBALaAWg0NiEzM5MDBw5w+PBhamtrycrKukR4EJrW9QKOAuNFJFREwjAmgHUIKMAJqJXAIlIKfO12KBY45SNzPGEXewYqpSzr3ul2bTXtsacnF9V9TwEngCuAKqAXRq9/KuAATgPzlFL7zMqAfwZuwpgQ3qCUeqS5D/LQru213Wr8yRboWHs8/s8GlANojIjkWLl0vb1oe6zB3+zW9liHP9nuT7aAb+zRISCNRqOxKdoBaDQajU0JdAewwtcGNELbYw3+Zre2xzr8yXZ/sgV8YE9AzwFoNBqNpu0E+ghAo9FoNG1EOwCNRqOxKQHrAETkNhEpFJGDIvKYj21JEpHNIpJvyuU+7Et7TJtCROQLEXnf17a0Bt2uLdqk27X9tvhdu4Jv2jYgHYC5KOVF4HaMmsOzRcQrScMOwgH8p1IqDbgWeNDH9gA8TICt1NTt6hW6XduPP7Yr+KBtA9IBAGOBg0qpQ0qpWiALmOYrY5RSx5VSn5v7FRiNmND8VR2HiCQCUzBWdQYSul2bQberNfhbu4Lv2jZQHUACcMztdRE+bkAXIpIMXA141NntJJ4DHgXqfWhDW9Dt2jy6XS3GT9oVfNS2geoA/BIRiQbeBRYopcp9ZMMdQIlSapcvPj8Y0e0anPhDu5p2+KxtA9UBFHNR0AoMjfJiH9kCgKmQ+C7wplLqPR+aMg6YKiJHMIba3xaR//ahPa1Bt2vT6Ha1ED9qV/Bh2wbkQjARCcWoVnQzxh/STuD7Sqm9PrJHMApnnFZKLfCFDZ4QkQnAQqXUHb62xRt0u3qHbtd22+OX7Qqd37YBOQJQSjmAh4APMSZw3vbVH5PJOODfMDx3rrlN9qE9AYlu1+BEt6v/EpAjAI1Go9G0n4AcAWg0Go2m/WgHoNFoNDZFOwCNRqOxKdoBaDQajU3RDkCj0WhsinYArUBEnG5pY7lWqhqKSLKI5Fl1P4336HYNXnTbNk+orw0IMC4opUb52giN5eh2DV502zaDHgFYgIgcEZHficgeEdkhIinm8WQR+aeI7BaRTSIywDzeV0T+LiJfmtv15q1CROQlU6P8HyLS1Tx/vqldvltEsnz0NW2HbtfgRbetiVJKb15ugBPIddvuMY8fAX5j7v8QeN/cXwfMMffvA1ab+6swBKgAQoCeQDKGTvko8/jbwA/M/W+ACHO/l69/D8G26XYN3k23bfObXgncCkSkUikV7eH4EeDbSqlDpsjUCaVUjIicAvorperM48eVUrEiUgokKqVq3O6RDGxUSg0xX/8SCFNKLRWRDUAlsBrjD7Kyg7+qrdDtGrzotm0eHQKyDtXEfmuocdt3cnGOZgpGRaXRwE4xxLU0nYNu1+DF9m2rHYB13OP281Nz/xNglrl/L7DN3N8EzIOGOqA9m7qpiHQBkpRSm4FfYgw9L+vRaDoM3a7Bi+3b1i+9kh/TVURy3V5vUEq50sp6i8hujB7BbPPYz4CVIvILoBT4d/P4w8AKEfkRRq9hHnC8ic8MAf7b/IMT4Hml1FnLvpEGdLsGM7ptm0HPAViAGU/MUEqd8rUtGuvQ7Rq86LY10CEgjUajsSl6BKDRaDQ2RY8ANBqNxqZoB6DRaDQ2RTsAjUajsSnaAWg0Go1N0Q5Ao9FobMr/ACGbfHzy+NEMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn = model.predict(X_valid)\n",
        "validation[\"Prob\"] = y_pred_nn\n",
        "validation[[\"Gave\", \"Prob\"]].sample(30)"
      ],
      "metadata": {
        "colab": {
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              "         Gave      Prob\n",
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          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "20aed00e",
      "metadata": {
        "id": "20aed00e"
      },
      "source": [
        "# Scoring New Data\n",
        "\n",
        "## Prepare data for scoring"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d04c91b",
      "metadata": {
        "id": "0d04c91b"
      },
      "outputs": [],
      "source": [
        "data3 = sas_session.sasdata2dataframe(\n",
        "table='score_rd1',\n",
        "libref='cortex'\n",
        ")\n",
        "data4 = sas_session.sasdata2dataframe(\n",
        "table='score',\n",
        "libref='cortex'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e961f185-38c6-4d2a-b1f5-1b142c9b6d30",
      "metadata": {
        "id": "e961f185-38c6-4d2a-b1f5-1b142c9b6d30"
      },
      "source": [
        " ### Score new data based on your champion model\n",
        " \n",
        "> Pick your champion model from previous steps and use it to predict next year donations. \n",
        " \n",
        "> In this case, the linear regression model performed better than the regression tree based on the MSE criterion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f6cf3e45",
      "metadata": {
        "collapsed": true,
        "id": "f6cf3e45"
      },
      "outputs": [],
      "source": [
        "scoring_data = pd.merge(data3, data4, on=[\"ID\"],how=\"right\")\n",
        "scoring_data = preprocess(scoring_data, train = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predict Amount with tree"
      ],
      "metadata": {
        "id": "eMUdrtisFUeo"
      },
      "id": "eMUdrtisFUeo"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "05b14516",
      "metadata": {
        "collapsed": true,
        "id": "05b14516"
      },
      "outputs": [],
      "source": [
        "X = scoring_data[predictors] \n",
        "regr_predict_end=regr.predict(X)\n",
        "\n",
        "scoring_data['Prediction'] = regr_predict_end\n",
        "scoring_data.sort_values(by=['Prediction'], inplace=True,ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predict with Neural Network"
      ],
      "metadata": {
        "id": "vckVRftgFXKV"
      },
      "id": "vckVRftgFXKV"
    },
    {
      "cell_type": "code",
      "source": [
        "Xtf = scoring_data[predictorsTF] \n",
        "y_pred_nn = model.predict(Xtf)\n",
        "scoring_data['Probability'] = y_pred_nn"
      ],
      "metadata": {
        "id": "NTnCUP9sFJfK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ce516ee-2405-41cf-bd86-979ed29bf986"
      },
      "id": "NTnCUP9sFJfK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31250/31250 [==============================] - 37s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scoring_data.head(5)"
      ],
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          "height": 317
        },
        "id": "Fgt_gQwzGcX-",
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      "id": "Fgt_gQwzGcX-",
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "               ID  Woman   Age    Salary  SeniorList  NbActivities  Referrals  \\\n",
              "527000  2527001.0    0.0  54.0  148400.0        10.0           4.0        2.0   \n",
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              "        Recency  Frequency  Seniority  ...  Elementary  High School  \\\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>16.666667</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.656000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420890</th>\n",
              "      <td>2420891.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>104200.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286775</th>\n",
              "      <td>2286776.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>249000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7000.000000</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184873</th>\n",
              "      <td>2184874.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>144900.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>8000.0</td>\n",
              "      <td>0.371514</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25934159-8252-44fc-b138-0c69f9f32cea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Result = scoring_data.loc[scoring_data['Probability'] > 0.4, 'ID']\n",
        "\n",
        "Result= Result.astype(int)"
      ],
      "metadata": {
        "id": "EDNq2BTg03OJ"
      },
      "id": "EDNq2BTg03OJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(Result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHwe7S3y12WR",
        "outputId": "7a8a232b-a75f-446e-febe-d8603fdf344e"
      },
      "id": "cHwe7S3y12WR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "752164"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gk5Rc_FH29Ff",
        "outputId": "c62c500e-2149-4b02-b1ea-c599b871fd68"
      },
      "id": "Gk5Rc_FH29Ff",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "527000    2527001\n",
            "9566      2009567\n",
            "420890    2420891\n",
            "286775    2286776\n",
            "982303    2982304\n",
            "           ...   \n",
            "240226    2240227\n",
            "691173    2691174\n",
            "292451    2292452\n",
            "41679     2041680\n",
            "588352    2588353\n",
            "Name: ID, Length: 752164, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d86a874a",
      "metadata": {
        "id": "d86a874a"
      },
      "source": [
        "## Exporting Results to a CSV File"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Result= scoring_data[['ID','Prediction']]\n",
        "Result[Result['Prediction'] > 75].info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anUEx5s-KZJ3",
        "outputId": "7db7641a-1c25-48ff-969b-22495b0ccdf7"
      },
      "id": "anUEx5s-KZJ3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 156328 entries, 9566 to 58785\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count   Dtype  \n",
            "---  ------      --------------   -----  \n",
            " 0   ID          156328 non-null  float64\n",
            " 1   Prediction  156328 non-null  float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 3.6 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "669e8465",
      "metadata": {
        "id": "669e8465"
      },
      "outputs": [],
      "source": [
        "Result= scoring_data['ID'].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21e1beed-b3ee-4c65-af88-fdaa5b5f0ce7",
      "metadata": {
        "id": "21e1beed-b3ee-4c65-af88-fdaa5b5f0ce7"
      },
      "outputs": [],
      "source": [
        "# Define your cutoff and choose a number of rows to submit to the leaderboard\n",
        "NB = 155000\n",
        "submission = Result.head(NB)\n",
        "submission.to_csv('Tree7DoubleOnlyDonors155.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scoring Data With Contact"
      ],
      "metadata": {
        "id": "Uhx0R2vppFs-"
      },
      "id": "Uhx0R2vppFs-"
    },
    {
      "cell_type": "code",
      "source": [
        "data3 = sas_session.sasdata2dataframe(\n",
        "table='score',\n",
        "libref='cortex'\n",
        ")\n",
        "data4 = sas_session.sasdata2dataframe(\n",
        "table='score_rd2_contact',\n",
        "libref='cortex'\n",
        ")\n",
        "data5 = sas_session.sasdata2dataframe(\n",
        "table='score_rd2_nocontact',\n",
        "libref='cortex'\n",
        ")"
      ],
      "metadata": {
        "id": "l10ZE4p3pIDR"
      },
      "id": "l10ZE4p3pIDR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predict 'amount' | Prob for members who were contacted"
      ],
      "metadata": {
        "id": "GkIHIS0BpdHa"
      },
      "id": "GkIHIS0BpdHa"
    },
    {
      "cell_type": "code",
      "source": [
        "scoring_data_contact = pd.merge(data3, data4, on=[\"ID\"],how=\"right\")\n",
        "scoring_data_contact = preprocess(scoring_data_contact, train = False)\n",
        "X_amt = scoring_data_contact[predictors]\n",
        "X_prob = scoring_data_contact[predictors2] \n",
        "\n",
        "\n",
        "regr_predict_amt=regr.predict(X_amt)\n",
        "scoring_data_contact['Amt'] = regr_predict_amt\n",
        "\n",
        "regr_predict_prob=DT_model_prob.predict_proba(X_prob)[:,1]\n",
        "scoring_data_contact['ProbContact'] = regr_predict_prob\n",
        "scoring_data_contact= scoring_data_contact[['ID','Amt', 'ProbContact']]"
      ],
      "metadata": {
        "id": "Breg9lA_pXnG"
      },
      "id": "Breg9lA_pXnG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scoring_data_contact.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P_k_o2C1uu9M",
        "outputId": "551f38cc-a833-4bbf-cee2-a23c06e07a92"
      },
      "id": "P_k_o2C1uu9M",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID         Amt  ProbContact\n",
              "481499  2481500.0   48.206071     0.751117\n",
              "7770    2007771.0   52.312829     0.537045\n",
              "117414  2117415.0   47.963204     0.878486\n",
              "535241  2535242.0  126.071248     0.903185\n",
              "833511  2833512.0   44.358454     0.750069"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Amt</th>\n",
              "      <th>ProbContact</th>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predict 'prob given' for members who were not contacted"
      ],
      "metadata": {
        "id": "MVDjSj7EphO5"
      },
      "id": "MVDjSj7EphO5"
    },
    {
      "cell_type": "code",
      "source": [
        "scoring_data_nocontact = pd.merge(data3, data5, on=[\"ID\"],how=\"right\")\n",
        "scoring_data_nocontact = preprocess(scoring_data_nocontact, train = False)\n",
        "\n",
        "X_prob = scoring_data_nocontact[predictors2] \n",
        "\n",
        "regr_predict_nocontact=DT_model_prob.predict_proba(X_prob)[:,1]\n",
        "\n",
        "scoring_data_nocontact['ProbNoContact'] = regr_predict_nocontact\n",
        "\n",
        "scoring_data_nocontact= scoring_data_nocontact[['ID','ProbNoContact']]"
      ],
      "metadata": {
        "id": "xjzk_4k-phqT"
      },
      "id": "xjzk_4k-phqT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scoring_data_nocontact.sample(5)"
      ],
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        "colab": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID  ProbNoContact\n",
              "268319  2268320.0       0.319715\n",
              "541220  2541221.0       0.550744\n",
              "284169  2284170.0       0.319715\n",
              "604112  2604113.0       0.550744\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_Amt = pd.merge(scoring_data_contact, scoring_data_nocontact, on=[\"ID\"],how=\"right\")\n",
        "result_Amt.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "9YoeSkSzpnY4",
        "outputId": "49d9dac0-f87d-4047-d6af-a167602bf163"
      },
      "id": "9YoeSkSzpnY4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ID        Amt  ProbContact  ProbNoContact\n",
              "0  2000001.0  72.852538     0.751117       0.550744\n",
              "1  2000002.0  41.843750     0.903185       0.492087\n",
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              "                                                     [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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          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# def Calc_Uplift(raw_data):\n",
        "#     return ((raw_data['Amt']*raw_data['ProbContact']*1.25) - (raw_data['Amt']*raw_data['ProbNoContact']))\n",
        "\n",
        "# result_Amt[\"Uplift\"] = result_Amt.apply(lambda row: Calc_Uplift(row), axis=1)\n",
        "\n",
        "# # Sorting data by descending Uplift value\n",
        "# result_Amt.sort_values(by=['ProbContact', 'Uplift'], ascending=False, inplace=True)"
      ],
      "metadata": {
        "id": "xcCPKs4us1QS"
      },
      "id": "xcCPKs4us1QS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# result_Amt[result_Amt['Uplift'] > 50].info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpCncSFcwJfT",
        "outputId": "9aee06f3-c03d-47ee-d208-6cc70c3df942"
      },
      "id": "JpCncSFcwJfT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 142464 entries, 755699 to 127186\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   ID             142464 non-null  float64\n",
            " 1   Amt            142464 non-null  float64\n",
            " 2   ProbContact    142464 non-null  float64\n",
            " 3   ProbNoContact  142464 non-null  float64\n",
            " 4   Uplift         142464 non-null  float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 6.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_Amt[result_Amt['ProbContact'] > 0.90].info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnYx5c3I3rbN",
        "outputId": "3d8d6558-d85d-4ba9-89f9-e8a365e8f2bd"
      },
      "id": "GnYx5c3I3rbN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 202841 entries, 755699 to 292451\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   ID             202841 non-null  float64\n",
            " 1   Amt            202841 non-null  float64\n",
            " 2   ProbContact    202841 non-null  float64\n",
            " 3   ProbNoContact  202841 non-null  float64\n",
            " 4   Uplift         202841 non-null  float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 9.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_Amt.sample(3)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Hdx_elUL6V2q",
        "outputId": "3f1aaa0e-6d64-45e6-f891-24be1a3d1120"
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      "id": "Hdx_elUL6V2q",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ID        Amt  ProbContact  ProbNoContact     Uplift\n",
              "2685    2002686.0  35.425798     0.751117       0.319715  21.935006\n",
              "163392  2163393.0  41.997483     0.875000       0.550744  22.804902\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f767f2b-2c30-42bd-954d-726501d2fbb5')\"\n",
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              "      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",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f767f2b-2c30-42bd-954d-726501d2fbb5 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-4f767f2b-2c30-42bd-954d-726501d2fbb5');\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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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Result = result_Amt[(result_Amt['Uplift'] > 50) | ((result_Amt['Amt'] > 100) & (result_Amt['ProbContact'] > 0.85))]\n",
        "# Result= Result['ID'].astype(int)"
      ],
      "metadata": {
        "id": "tR3q_yT28fla"
      },
      "id": "tR3q_yT28fla",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(Result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kt4Ygc-D4wMM",
        "outputId": "06e45c2d-9eb0-499a-e037-fdb9f6494ff2"
      },
      "id": "Kt4Ygc-D4wMM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "143806"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_Amt.sort_values(by=['ProbContact', 'Amt'], ascending=False, inplace=True)\n",
        "Result= result_Amt['ID'].astype(int)"
      ],
      "metadata": {
        "id": "V6dxF3PPpxRZ"
      },
      "id": "V6dxF3PPpxRZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NB = 160000\n",
        "submission = Result.head(NB)\n",
        "submission.to_csv('Tree7OnlyDonorsTree4ProbContAmt160.csv', index=False)"
      ],
      "metadata": {
        "id": "jZ0DIyTHpztf"
      },
      "id": "jZ0DIyTHpztf",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
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    "colab": {
      "provenance": [],
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