DP_301_Deep_Learning_with_Tensorflow_Python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DP - 301 : Deep Learning with Tensorflow Python.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOohgyEnzL/ey7qYXHmt9vT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DeveloperAkram/Colab-Notebooks/blob/master/DP_301_Deep_Learning_with_Tensorflow_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xV2Rbs7EcuvS"
      },
      "source": [
        "# Import Dependencies\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# ML Pkgs\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "# DL Pkgs\n",
        "from sklearn.neural_network import MLPClassifier"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoeQRCtidoju",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5aa34b14-3fff-45d5-a90a-37b0aa070c3f"
      },
      "source": [
        "# DL tensorflow\n",
        "\n",
        "!pip install tensorflow\n",
        "!pip install tensorboard"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.7.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZhrUhGEd7ET",
        "outputId": "14469466-1cd6-4b44-f7f6-b964237355e7"
      },
      "source": [
        "# check for version\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4qQEQwCeLgs"
      },
      "source": [
        "# Load Dataset\n",
        "\n",
        "dataset = pd.read_csv('/content/bank-full.csv', sep=';')"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "mu-mlJlBe_ea",
        "outputId": "3effd408-734f-40df-c38e-79a0582734a5"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  marital  education  ... pdays  previous poutcome   y\n",
              "0   58    management  married   tertiary  ...    -1         0  unknown  no\n",
              "1   44    technician   single  secondary  ...    -1         0  unknown  no\n",
              "2   33  entrepreneur  married  secondary  ...    -1         0  unknown  no\n",
              "3   47   blue-collar  married    unknown  ...    -1         0  unknown  no\n",
              "4   33       unknown   single    unknown  ...    -1         0  unknown  no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4m39TyzKfEgh",
        "outputId": "9fe94703-7682-4961-b41d-c98344c63eb7"
      },
      "source": [
        "# check size of dataset\n",
        "\n",
        "dataset.shape"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkqTPE3LfWNb",
        "outputId": "fbcbfcc5-7538-4796-c1d6-8f93ce671673"
      },
      "source": [
        "# check Datatype\n",
        "\n",
        "dataset.dtypes"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           int64\n",
              "job          object\n",
              "marital      object\n",
              "education    object\n",
              "default      object\n",
              "balance       int64\n",
              "housing      object\n",
              "loan         object\n",
              "contact      object\n",
              "day           int64\n",
              "month        object\n",
              "duration      int64\n",
              "campaign      int64\n",
              "pdays         int64\n",
              "previous      int64\n",
              "poutcome     object\n",
              "y            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIPfs1mifmW0"
      },
      "source": [
        "# Feature Engineering: Encode Dataset\n",
        "\n",
        "# Get categorical\n",
        "\n",
        "cat_columns = dataset.select_dtypes(include= ['object', 'category']).columns.tolist()"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qa8mr8ANgP9-"
      },
      "source": [
        "cat_columns = ['job',\n",
        " 'marital',\n",
        " 'education',\n",
        " 'default',\n",
        " 'housing',\n",
        " 'loan',\n",
        " 'contact',\n",
        " 'month',\n",
        " 'poutcome']"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orPTIOszgUis"
      },
      "source": [
        "# Numerical Columns\n",
        "\n",
        "num_columns = dataset.select_dtypes(exclude= ['object', 'category']).columns.tolist()"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30qWdgdEg-eg",
        "outputId": "6748861f-dc34-4c68-ab9d-3d4d2b366c09"
      },
      "source": [
        "num_columns"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlcgYu6Jhayf",
        "outputId": "5e187389-0a7d-4eea-f3db-f1010607fdec"
      },
      "source": [
        "# Label Encoding Target\n",
        "\n",
        "dataset['y'].unique()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXglgZFYhrE9"
      },
      "source": [
        "# Label Encoding Target\n",
        "\n",
        "dataset['y'] = dataset['y'].map({'no':0, 'yes':1})"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtwFibNHh_kA"
      },
      "source": [
        "# Feature Encoding : Categorical\n",
        "\n",
        "ord_enc = OrdinalEncoder()"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLCHn-g2jEMn"
      },
      "source": [
        "# Unencoded\n",
        "\n",
        "dataset2 = dataset.copy()"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "GoNRKgnfjpEU",
        "outputId": "60fd9a09-426a-42b5-eefd-8cabdea392cc"
      },
      "source": [
        "dataset[cat_columns]"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>poutcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>may</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>may</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>may</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>may</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>may</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>retired</td>\n",
              "      <td>divorced</td>\n",
              "      <td>primary</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>success</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>nov</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                job   marital  education default  ... loan    contact month poutcome\n",
              "0        management   married   tertiary      no  ...   no    unknown   may  unknown\n",
              "1        technician    single  secondary      no  ...   no    unknown   may  unknown\n",
              "2      entrepreneur   married  secondary      no  ...  yes    unknown   may  unknown\n",
              "3       blue-collar   married    unknown      no  ...   no    unknown   may  unknown\n",
              "4           unknown    single    unknown      no  ...   no    unknown   may  unknown\n",
              "...             ...       ...        ...     ...  ...  ...        ...   ...      ...\n",
              "45206    technician   married   tertiary      no  ...   no   cellular   nov  unknown\n",
              "45207       retired  divorced    primary      no  ...   no   cellular   nov  unknown\n",
              "45208       retired   married  secondary      no  ...   no   cellular   nov  success\n",
              "45209   blue-collar   married  secondary      no  ...   no  telephone   nov  unknown\n",
              "45210  entrepreneur   married  secondary      no  ...   no   cellular   nov    other\n",
              "\n",
              "[45211 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PcvcypWjtKn"
      },
      "source": [
        "dataset[cat_columns] = ord_enc.fit_transform(dataset[cat_columns])"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "gwXdU8t-kEcz",
        "outputId": "6d190268-a95e-4f85-c6f8-510c77421dd8"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1506</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age   job  marital  education  ...  pdays  previous  poutcome  y\n",
              "0   58   4.0      1.0        2.0  ...     -1         0       3.0  0\n",
              "1   44   9.0      2.0        1.0  ...     -1         0       3.0  0\n",
              "2   33   2.0      1.0        1.0  ...     -1         0       3.0  0\n",
              "3   47   1.0      1.0        3.0  ...     -1         0       3.0  0\n",
              "4   33  11.0      2.0        3.0  ...     -1         0       3.0  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "UTd5x1a5tmOu",
        "outputId": "3fc6219a-c8a8-49e8-929a-f42489a599aa"
      },
      "source": [
        "dataset.tail()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
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              "      <td>3</td>\n",
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              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>668</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17</td>\n",
              "      <td>9.0</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2971</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>9.0</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  pdays  previous  poutcome  y\n",
              "45206   51  9.0      1.0        2.0  ...     -1         0       3.0  1\n",
              "45207   71  5.0      0.0        0.0  ...     -1         0       3.0  1\n",
              "45208   72  5.0      1.0        1.0  ...    184         3       2.0  1\n",
              "45209   57  1.0      1.0        1.0  ...     -1         0       3.0  0\n",
              "45210   37  2.0      1.0        1.0  ...    188        11       1.0  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gn87VxbRkIjw",
        "outputId": "0deb1922-ed90-4101-93cd-34ab8c5df9e5"
      },
      "source": [
        "# get Categories\n",
        "\n",
        "ord_enc.categories_"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management',\n",
              "        'retired', 'self-employed', 'services', 'student', 'technician',\n",
              "        'unemployed', 'unknown'], dtype=object),\n",
              " array(['divorced', 'married', 'single'], dtype=object),\n",
              " array(['primary', 'secondary', 'tertiary', 'unknown'], dtype=object),\n",
              " array(['no', 'yes'], dtype=object),\n",
              " array(['no', 'yes'], dtype=object),\n",
              " array(['no', 'yes'], dtype=object),\n",
              " array(['cellular', 'telephone', 'unknown'], dtype=object),\n",
              " array(['apr', 'aug', 'dec', 'feb', 'jan', 'jul', 'jun', 'mar', 'may',\n",
              "        'nov', 'oct', 'sep'], dtype=object),\n",
              " array(['failure', 'other', 'success', 'unknown'], dtype=object)]"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz2hyodBkSIj"
      },
      "source": [
        "# Data Visualization\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHHrxBrRklG9",
        "outputId": "c8afcfa0-3456-4ba2-f59e-7f9e5df72177"
      },
      "source": [
        "# Plot of Target Distribution\n",
        "\n",
        "dataset['y'].value_counts()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    39922\n",
              "1     5289\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "f682Z7G7k67q",
        "outputId": "ddfeafb9-0d9a-4654-f4aa-2c7abacdd91e"
      },
      "source": [
        "sns.countplot(x ='y', data = dataset)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f47202c9e10>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwtMbCe_lBhd",
        "outputId": "0ab5faed-5daf-4ed9-f9ab-a669c6f3b559"
      },
      "source": [
        "# Plot of Target Distribution\n",
        "\n",
        "dataset['y'].value_counts().div(dataset.shape[0]).mul(100)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    88.30152\n",
              "1    11.69848\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cUng16fslL6M"
      },
      "source": [
        "## Narrative \n",
        "\n",
        "*   Imbalanced dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "hgZWNk-FmeDq",
        "outputId": "cbce6a05-fb0b-4140-bffd-1fba67497459"
      },
      "source": [
        "# Distribution of Entire dataset\n",
        "\n",
        "#plt.figure(figsize=(20, 10))\n",
        "\n",
        "dataset.hist(figsize=(20, 10))\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FvgmcVFWfbPs6EXhT2/UZVXVBKz+CLmXB7nTpBF7X7gCXJElaNcOOCudQlJIkSQvbBpxWVYcCRwOnJjmUh/NUHgJ8pM3DI/NUnkKXg5KBPJVHAUcCb54dNIWH81TObrdhFY5LkiTpEYZtWHIoSkmSpAVU1d2zdxxV1deB2+m6+5unUpIkTZVFu8IluRhYD+yXZDPdVbMzgcuSnAx8AXhZW/0qulu4N9Ldxv0q6IaiTDI7FCU8eijK8+lu4/4wjggnSZKmSJK1wLPpRsBd9TyVw+SonLY8gNN2PIN5NUdhnK/NtL0303Y8krQUizYsVdXLF1jkUJSSJEnbkeSJwJ8Br6+qB7sbtDurladymByV05YHcNqO5+yLLn8or+YojDM357S9N9N2PJK0FMN2hZOksUtyXpJ7ktwyULZvkmuS3NH+7tPKk+SsJBuT3JTk8IFtTmzr39GS5M6WH5Hk5rbNWRn8RShJi0jyWLpGpYuq6s9bsXkqJUnSVLFhSdIkO59HJ6s1Ma6ksWsN0ecCt1fVOwYWmadSkiRNFRuWJE2sqvoYcO+cYhPjSuqDHwNeCTwnyafb41i6PJXPT3IH8Lw2D12eyjvp8lS+jy4HJS0n5Wyeyht4dJ7K97dtPod5KiVJ0hiMrnO2JPXDqifGheGS4446+SqMJgFrHxOP9jEmMK7l6GNMK6mq/gZYqPuseSolSdLUsGFJ0tRarcS47bmWnRx31MlXYTQJWPuYeLSPMYFxLUcfY5IkSdKOsyucpGljYlxJkiRJWiXesSRp2swmxj2TRyfGfW2SS+gSdT9QVXcnuRr43YGE3ccAb6yqe5M82JLoXk+XGPfs1TyQabX29CuXvc1ph23jpO1st+nMF+1ISJIkSZKGZMPSlBrmh9ti/OGmvklyMbAe2C/JZrrR3c4ELktyMvAF4GVt9auAY+mS3H4TeBV0iXGTzCbGhUcnxj0f2J0uKa6JcSVJkiRpgA1LkiZWVb18gUUmxpUkSZKkVWCOJUmSJEmSJA3FhiVJkiRJkiQNxa5wkiRJkiSpd4bNHby9gV/MHTx63rEkSZIkSZKkoXjHkpZs1CPN2VIsSZIkSdJk844lSZIkSZIkDcWGJUmSJEmSJA3FrnCSJOkhdnuWJEnScnjHkiRJkiRJkoZiw5IkSZIkSZKGYsOSJEmSJEmShmKOJUmSJEk7nVHnlAM4f8MeI9/nSkhyHvBi4J6qemYr2xe4FFgLbAJeVlX3JQnwbuBY4JvASVX1ybbNicCb2m7PqKoLWvkRwPnA7sBVwOuqqlbl4CStOu9YkiRJkqSdy/nAhjllpwMfqapDgI+0eYAXAoe0xynAe+Chhqg3A0cBRwJvTrJP2+Y9wKsHtpv7XJKmiA1LkiRJkrQTqaqPAffOKT4OuKBNXwC8ZKD8wupcB+ydZH/gBcA1VXVvVd0HXANsaMv2qqrr2l1KFw7sS9IUsiucJEmSJGlNVd3dpr8ErGnTBwB3Day3uZVtr3zzPOWPkuQUurugWLNmDTMzM4sGuXXr1iWttxpWM5bTDtu24LI1u29/+UJWIvZRvybDHBds/zVZ7c9PXz6zKxnHDjUsJdkEfB34DrCtqtaNsm+uJEmSJGl1VVUlWfGcSFV1DnAOwLp162r9+vWLbjMzM8NS1lsNqxnLSdvJCXbaYdt4+83L/2m/6RXrdyCi+Y36NdnecW/P9l6TlTju7enLZ3Yl4xhFV7ifqKpnVdW6Nj/KvrmSJEkTJ8l5Se5JcstA2b5JrklyR/u7TytPkrOSbExyU5LDB7Y5sa1/R7sQN1t+RJKb2zZntQt4krQjvty6sdH+3tPKtwAHDax3YCvbXvmB85RLmlIrkWNpJH1zVyAuSZKk1XI+JsaVNFmuAGYbsE8ELh8oP6E1gh8NPNC6zF0NHJNkn1Y3HQNc3ZY9mOTo1uh9wsC+JE2hHc2xVMBfttsk/6jdyjiqvrmPMkwf3PnsaN/CYft5rrRh+9aOy1Lfg770SR21aT0uSVKXGDfJ2jnFxwHr2/QFwAzwBgYuvgHXJZm9+LaedvENIMlsYtwZWmLcVj6bGPfDK3dEkqZJkovp6pj9kmyma8Q+E7gsycnAF4CXtdWvoktnspEupcmrAKrq3iS/A9zQ1nvLbH0FvIaugX13urrJ+kmaYjvasPRvq2pLku8Crkny94MLR903d5g+uPPZ0b6Fw/bzXGnD9q0dl6X2be1Ln9RRm9bj6gtzwEnqoVVPjAuTnxx3FKbteEZ9MXGcr80435uVuCA7KZ+1qnr5AoueO8+6BZy6wH7OA86bp/wTwDN3JEZJk2OHWiGqakv7e0+Sv6C7TfvLSfavqruX0Td3/ZzymR2JS5Kan6iqrw7Mz3ZDOTPJ6W3+DTyyG8pRdF1MjhrohrKO7g7NG5Nc0brtStLQVisxbnuuiU6OOwrTdjxnX3T5SC8mrnYi20HjfG9W4mLx+Rv2mKrPmiQtxdA5lpLskWTP2Wm6PrW3MKK+ucPGJUnbYQ44SeNkYlxJkjR1duRSxxrgL9ogJLsCf1JV/yfJDYyub64kDav3OeBWIi/aKG6/X+nb+Ic55sVeq3F1O+hrl4cdiWulPpN9fa1W2ezFtzN59MW31ya5hO6uyQfand9XA787kLD7GOCN7dzpwXah7nq6xLhnr+aBSJIkzRq6Yamq7gR+eJ7yrzGivrmStAN6nwNu1F0ZYDTdGVa6W8IwXQ8WyyE3rm4cfe1esyNxjbpryOx709fXaqWYGFeSJO0sJifTsyQtgzngJI2TiXElSdLOwoYlSVOn5X17TFV9fSAH3FsYUTeUVTwUSZIkDbh5ywOjv7v2zBeNdH/SzsaGJUnTyBxwkiRJkrQKbFiSNHXMAac+WrsCw1p7hVWSJEnjZsOSxmapP7JOO2zbkm939UeWJEmSJEmr5zHjDkCSJEmSJEmTyYYlSZIkSZIkDcWGJUmSJEmSJA3FhiVJkiRJkiQNxYYlSZIkSZIkDcVR4SRJkvQIN295YMkjsi6VI7dKkjSdvGNJkiRJkiRJQ7FhSZIkSZIkSUOxK5wkaeKtHXGXHbDbjiRJkrQU3rEkSZIkSZKkodiwJEmSJEmSpKHYsCRJkiRJkqShTH2Opfnybpx22LaRD6ErSZouS8nb5P+Txc2+jqN8rcx/JUmS1B9T37AkSTuTUSSxHmwA8Ae8JEmSpO2xYUmStKCVGG1NkiRJ0vaN+jz8/A17jHR/g8yxJEmSJEmSpKF4x5IkSRNqoStZ5n6SJEnSavGOJUmSJEmSJA2lN3csJdkAvBvYBXh/VZ055pA0gVYiH4zJi2X9JKmvrJ8k9ZX1k7Tz6EXDUpJdgD8Ang9sBm5IckVV3TbeyCQbq3Z21k+S+sr6SVJfWT9JO5deNCwBRwIbq+pOgCSXAMcBVjyaSmtPv3KkOVBsqFpR1k+S+sr6SVJfWT9JO5FU1bhjIMlLgQ1V9fNt/pXAUVX12jnrnQKc0ma/H/jskE+5H/DVIbftM49rskzLcT2tqp4y7iBWygrXT339DPQxrj7GBMa1HOOIyfqJqaufhuXx9Nc0HQss/Xisn5iK+qkvsfQlDuhPLH2JA/oTy3LiWFYd1Zc7lpakqs4BztnR/ST5RFWtG0FIveJxTZZpPa6d1TD1U18/A32Mq48xgXEtRx9j2llMU/00LI+nv6bpWGD6jmelTXr91JdY+hIH9CeWvsQB/YllJePoy6hwW4CDBuYPbGWSNG7WT5L6yvpJUl9ZP0k7kb40LN0AHJLk4CS7AccDV4w5JkkC6ydJ/WX9JKmvrJ+knUgvusJV1bYkrwWuphuO8ryqunUFn3KHu9P1lMc1Wab1uKbKCtdPff0M9DGuPsYExrUcfYxpou2k9dOwPJ7+mqZjgek7nqHsRPVTX2LpSxzQn1j6Egf0J5YVi6MXybslSZIkSZI0efrSFU6SJEmSJEkTxoYlSZIkSZIkDWXqG5aSHJTk2iS3Jbk1yeta+b5JrklyR/u7z7hjXa4kuyT5VJIPtfmDk1yfZGOSS1uivImSZO8kH0zy90luT/KjU/Je/Ur7/N2S5OIkj5+G90vDS7IhyWfb+396D+KZt67si7n1XR/MV1/1IKZH1TVjiuO8JPckuWWgbOLr8mm0WF2U5HHtf9TG9j9r7epHuXRLOJ6Tknwlyafb4+fHEedSzPc9mrM8Sc5qx3pTksNXO8blWMLxrE/ywMB785urHeNSLeV/5qS9P33Ul/qpL/VKn+qEvnyf+/JdXGIcq/WaPD7Jx5N8psXy2/OsM/rvTlVN9QPYHzi8Te8J/ANwKPB7wOmt/HTgbeOOdYhj+1XgT4APtfnLgOPb9HuBXxp3jEMc0wXAz7fp3YC9J/29Ag4APg/sPvA+nTQN75ePoT8TuwCfA763fc4/Axw65pjmrSvH/VoNxPeI+q4Pj/nqqzHHM29dM6ZYfhw4HLhloGyi6/JpfCylLgJeA7y3TR8PXDruuHfweE4Cfn/csS7xeB71PZqz/Fjgw0CAo4Hrxx3zDh7P+j7V8Yscy6L/Myft/enboy/1U5/qlT7VCX35Pvflu7jEOFbrNQnwxDb9WOB64Og564z8uzP1dyxV1d1V9ck2/XXgdrqT7+PofhTQ/r5kPBEOJ8mBwIuA97f5AM8BPthWmcRjehJdJXUuQFV9u6ruZ8Lfq2ZXYPckuwJPAO5mwt8v7ZAjgY1VdWdVfRu4hO5zPjbbqSvHbm591wfbqa/GbW5d88VxBFFVHwPunVM8DXX5tFlKXTT4vn0QeG475+ij3tWtO2KB79Gg44ALq3MdsHeS/VcnuuVbwvFMjCX+z5yo96eH+lI/9aZe6VOd0Jfvc1++i306j27HubXNPrY95o7YNvLvztQ3LA1qt3g9m67Vbk1V3d0WfQlYM6awhvUu4NeAf23zTwbur6ptbX4zq/BhTnJ+kjOWsN73t1v+vp7klxdY7WDgK8D/TNfl5f1J9uDR79XTknxgNEew8qpqC/A/gH+ka1B6ALiRMbxf6o0DgLsG5nv1/s+pK/tgbn33kCSbkjxvuTtMMrODt6ovVF+NzXx1TVX95ThjmmPS/+9Oo6XURQ+t0/5nPUB3ztFHS61bf7p1h/hgkoNWKphh66dlmHu8T6f7ETzJfrR13/hwkh8cdzBLsZ3/mb3+Xz8B+lI/9apeWcSKfOZ2oC5b1e9zX76Li5xHr8prki6FxKeBe4BrqmrB12RU352dpmEpyROBPwNeX1UPDi6r7h6wua14vZXkxcA9VXXjuGNZhl8Drq2qPavqrAXW2ZXulsr3VNWzgW/QdZd4yNz3KsnaJNWuzvdSujwix9H9EH0qsAewYaxBSQvYXl05pnj6Wt8tWl+ttvnqmiQ/N86YFjJp/3c1Vf43sLaqfgi4hoev2E6Dvwa2jDuIHfBJ4GlV9cPA2cD/GnM8i+rb/0yNzTTXK8Na1e9zX76Li8Sxaq9JVX2nqp4FHAgcmeSZK/Vcs3aKhqUkj6V7gy+qqj9vxV+evQWu/b1nXPEN4ceAn0qyie52zOcA76a7rW+2geVA+nVy8TTg1kXW2QxsHmhR/SDdD7e579U3VizKlfE84PNV9ZWq+hfgz+newz6/X1pZW4DBq1m9eP8XqCvH7VH1XU/uWFyovhqn+eqafzPmmAZN8v/dabWUuuihddr/rCcBX1uV6JZv0eOpqq9V1bfa7PuBI1YptpXQy/8lw6qqB2e7b1TVVcBjk+w35rAWtIT/mVP1/oxBX+qnSapXevOZW83vc1++i4vFMY46rqVpuJZH39Qw8u/O1Dcstb6C5wK3V9U7BhZdAZzYpk8ELl/t2IZVVW+sqgOrai1dsq2PVtUr6D40L22rrcgxJXl2kk+2Lm2XAo8fWPbi1t3t/iR/l+SHWvlHgZ8Afj/J1iTfl+RFrfvIg0nuSvJbVfUl4K4kr0yyGXgucBvde3VbuwXzROCzAyF9rP29v+177KMyzeMfgaOTPKF9HmePa8XfL/XWDcAh6UYG3I3ue3zFOAPaTl05VgvUd3PvwvmRdKNw3Jfkf6YbDWOfJB9KN0rLfW36wPmeI8nTk3w0ydeSfDXJRUn2Hli+Kcl/bbe4P9Dqvvvp6qvvT3Ic8KfA85J8LsmGtt2Tkpyb5KmRlLEAACAASURBVO4kW5KckWSXlXidmvnqmttX8PmWa2L/706xpdRFg+/bS+m+g32922zR48kj82r8FCv/HXnWYN2RNlJjklenG43n3iRXJHlqK5/vTuyD07ruJnlGkr9O8gDd8b0jnaOB76O7K3o2VcEfJLmynbNdn+TpsztMcky6Ua4eSPKHbZ9jHSEvyXe3uoskR9L9TullI+YS/2deAZww8P48MNAdWIvrS/3Ux3plISv6mUs3kti7knwxyReB36RLFM08513XzJ53te/zdwOvS/K3rU76y4ygUaUv38WlxLFadVySp8yexybZHXg+8PdzVhv9d6dWOCv5uB/Av6W73f4m4NPtcSxdH8KPAHcAfwXsO+5Yhzy+9Tw8Ktz3Ah8HNtL9yHnciJ9rN+ALwK/QJQF7KfAvwBl0/UjvAY6iGz3hRGDTbAzADG30pIG4D6P7Qv0Q8GW6RK7Poms4+jbd7YH7tPfqn+juEPgr4G3AB9p+1rb3d9dxvxeLvHa/TfeFvgX4Y+BxK/1++ej3o9VD/0A30sh/70E889aV445rTowP1XcDZZva9+ogYF/gb1ud9GTgp+kSWO/ZvmP/a2C7h+ok4Bl0/3QfBzyFrsH6XXOe4+N03cv2pTth/MVWX90GfKc97750fdZ/oG33F8Af0f3Q+662j19Y4dfoUXXNmN6ri+nyPP1Lq7tPZkr+707bY766CHgL8FNt+vHt+7OxfYa/d9wx7+Dx/L90d1B/hu4Czw+sYCwL1R3PAb5Kd5fj4+i6RHysbbO21cW7DnyP/pUuSe7JdD9yL6c7f3o83Z2JnwNubts9o+3nfLofLEe2fV0EXNKW7Qc8CPzHtux17bv68yv1WrTnna9e+EXgF9vy1w68N9cB/2bcn6ftHMtCvy8GjyfAHwy8P+vGHfekPfpSP/WlXlnCd2hFPnOtLnteO+br6M5p/pzu99p3Wiy/THfO81/ozrs+2eqZ2e/zJ1tc3wfsTnceduYIYuvFd3GJcaxKHUf32/pTLZZbgN9cje9O2o6lRSX5cbquKAfU7Lc0+Tvgo3Q/GL5aVb8xsP5ngVOq6q+TzNA1Bs07qlOSd9Gl3fiVJOvbugcOLN9Ed8LzV0l+i+7E6efSJUf7PPDYejgRtqSdRKsbzqyq97b5Y4Gzq+rpc9Z7Fl2et33a/AwL1ElJXgK8ubrcSbPP8aaq+kCb/z1gr6r6xSR/BHyzqn5lzj7W0N1BtHdV/VMrezldnfgTozp+Sf21UN1Bd3Hua1X1a638icB9wCFt00ec1wzWV0kuBP4ZeEtVbZ7zfAUcUlUbk5wPbKuq2TudjgXeUVU/kOQE4Jeq6kfbstDVV7+90HmapJ3X7O8wWsNRdd24SPIC4I+qu6t87jbznXf9VVWd0eZfQ9fIYd7ZKTH1XeE0Uk8Ftsw2KjVfaH+fBpyWrhvc/Unup7uD4Knz7SjJUUmubbdLPkDXmtvbfvSSem1wpI8vAE9N1x3sj5J8IcmDdHch7Z15uqIlWZPkknTd1R4EPsCj66MvDUx/E3himz6I7grYXE+j+/F490Cd+Ed0V/kk7TzmqzueysPnT1SXc+NrLG2Uol+ju/r+8SS3JvnPy3xu2vM/VG+287pHNFJJ0jweUXe16dluvEs571qoTtIUsGFJy3E3cMBs39Dme9rfu4C3VtXeA48nVNXFC+zrT+j6dh5UVU8C3kvro0uXnPsJsyu2CukpC+zHW+4kDSZk/B7gi8BpwPcDR1XVXsCPt+Xh0X6Xri45rK37cwusN5+76Ib4nq/8W8B+A3XiXlU1EcNnS1pRX6RrfAYgyR50d35v4eEBSp4wsP53z05U1Zeq6tVV9VTgF4A/TPKMZT7/3XTJa2efP4PzkrSAR9RdPHzOBcs779IUsmFJy/F/gW3ALyd5bJL/SNd3H+B9wC+2O5GSZI90Cbr3XGBfewL3VtU/t+RlPzuw7B+Ax7ftHwu8iS4HwXy+Qpd74Ht38NgkTa5TkxyYZF/gvwOX0tUx/0SX2H9f4M3b2X5PYCvwQJIDgP+2jOc+F3hVkucmeUySA5L8QHVJIf8SeHuSvdqypyf598McoKSpcjFdvfGsJI+ja9y+vqo2VdVX6BqYfi7JLu2OpMGk2z+ThwciuI+uUfxfl/n8VwKHJXlJuiThpzLQeCVJC7gYeFNLDr0fXfLu2ZF6l3PepSlkw5KWrKq+TZfo8SS6JJL/iS5xG1X1CeDVwO/TnehsbOst5DXAW5J8na5SumzgeR5oy9/Pw1fv5r1Fu6q+CbwV+NvW3eTooQ9Q0qT6E7pGnDvpuqWdAbyLLjnkV+kSJP6f7Wz/23RJdB+g+8E131C186qqjwOvAt7Ztv9rHr6adwLdoAe30dWLHwT2n2c3knYiVfVXwG/QDUt9N13D0fEDq7yaroH7a8APAn83sOxHgOuTbKW78/t1VXXnMp//q8DPAL/XnuNQ4BN0d1lK0kLOoKsrbqJLgv3JVgbLO+/SFDJ5tyRJkrSTSvIYugt4r6iqa8cdjyRp8njHkiRJkrQTSfKCJHu3rni/TpcH5boxhyVJmlA2LEmSJEk7lx+l6zr8VeAngZdU1T+NNyRJ0qSyK5wkSZIkSZKG4h1LkiRJkiRJGsqu4w5gWPvtt1+tXbt20fW+8Y1vsMcee6x8QDtgEmKEyYhzEmKEyYhzqTHeeOONX62qp6xCSAtKsgvdKBVbqurFSQ4GLgGeDNwIvLKqvt1ySVwIHEE3Es5/qqpNbR9vBE4GvgP8clVd3co3AO8GdgHeX1VnLhbPUuunHdHXz1Af4+pjTNDPuPoYEwwfVx/qp76ZpvMnmIw4jXF0JiHOSTp/6hvrp9VnjKMzCXEuJ8Zl11FVNZGPI444opbi2muvXdJ64zQJMVZNRpyTEGPVZMS51BiBT9SY6wPgV+mGnP9Qm78MOL5Nvxf4pTb9GuC9bfp44NI2fSjwGeBxwMF0eSd2aY/PAd9LN2z8Z4BDF4tnqfXTjujrZ6iPcfUxpqp+xtXHmKqGj6sP9VPfHtN0/lQ1GXEa4+hMQpyTdP7Ut4f10+ozxtGZhDiXE+Ny6yi7wkmaaEkOBF4EvL/NB3gO8MG2ygXAS9r0cW2etvy5bf3jgEuq6ltV9XlgI3Bke2ysqjur6tt0d0Edt/JHJUmSJEmTYWK7wklS8y7g14A92/yTgfuralub3wwc0KYPAO4CqKptSR5o6x/AI4dZHtzmrjnlR80XRJJTgFMA1qxZw8zMzPBHtARbt25d8ecYRh/j6mNM0M+4+hgT9DcuSZIk7QQNSzdveYCTTr9ypPvcdOaLRro/ScNJ8mLgnqq6Mcn6ccZSVecA5wCsW7eu1q9fPJy1O1A3nXbYd3j733zjUeXjrp9mZmZYyrGvpj7GBP2Mq48xQX/jkjTZduT/8ELO39DvHCvTwN93Uv9MfcOSpKn2Y8BPJTkWeDywF12i7b2T7NruWjoQ2NLW3wIcBGxOsivwJLok3rPlswa3WahckiRJknZ65liSNLGq6o1VdWBVraVLxv3RqnoFcC3w0rbaicDlbfqKNk9b/tGWnO4K4Pgkj2sjyh0CfBy4ATgkycFJdmvPccUqHJokSZIkTQTvWJI0jd4AXJLkDOBTwLmt/Fzgj5NsBO6layiiqm5NchlwG7ANOLWqvgOQ5LXA1XQjxJ1XVbeu6pFIkiRJUo/ZsCRpKlTVDDDTpu+kG9Ft7jr/DPzMAtu/FXjrPOVXAVeNMFRJkiRJmhp2hZMkSZIkSdJQltywlGSXJJ9K8qE2f3CS65NsTHJpyz9Cy1FyaSu/PsnagX28sZV/NskLBso3tLKNSU4f3eFJkiRJkiRppSznjqXXAbcPzL8NeGdVPQO4Dzi5lZ8M3NfK39nWI8mhdPlMfhDYAPxha6zaBfgD4IXAocDL27qSJEmSJEnqsSU1LCU5EHgR8P42H+A5wAfbKhcAL2nTx7V52vLntvWPAy6pqm9V1eeBjXQ5UI4ENlbVnVX1beCStq4kSZIkSZJ6bKnJu98F/BqwZ5t/MnB/VW1r85uBA9r0AcBdAFW1LckDbf0DgOsG9jm4zV1zyo+aL4gkpwCnAKxZs4aZmZlFA1+zO5x22LZF11uOpTzvcmzdunXk+1wJkxDnJMQIkxHnJMQoSZIkSRqvRRuWkrwYuKeqbkyyfuVDWlhVnQOcA7Bu3bpav37xcM6+6HLefvNoB7/b9IrFn3c5ZmZmWMqxjNskxDkJMcJkxDkJMUqSJEmSxmspLS4/BvxUkmOBxwN7Ae8G9k6ya7tr6UBgS1t/C3AQsDnJrsCTgK8NlM8a3GahckmSJEmSJPXUojmWquqNVXVgVa2lS7790ap6BXAt8NK22onA5W36ijZPW/7RqqpWfnwbNe5g4BDg48ANwCFtlLnd2nNcMZKjkyRJkiRJ0orZkT5ibwAuSXIG8Cng3FZ+LvDHSTYC99I1FFFVtya5DLgN2AacWlXfAUjyWuBqYBfgvKq6dQfikiRJkiRJ0ipY0qhws6pqpqpe3KbvrKojq+oZVfUzVfWtVv7Pbf4ZbfmdA9u/taqeXlXfX1UfHii/qqq+ry1766gOTpIkaRySHJTk2iS3Jbk1yeta+b5JrklyR/u7TytPkrOSbExyU5LDB/Z1Ylv/jiQnDpQfkeTmts1ZbRReSZKkVbWshiVJkiQtyTbgtKo6FDgaODXJocDpwEeq6hDgI20e4IV0aQIOoRsB9z3QNUQBb6YbMfdI4M2zjVFtnVcPbLdhFY5L0oRL8vgkH0/ymdbw/dut/OAk17fG6ktbmhJaKpNLW/n1SdYO7OuNrfyzSV4wUL6hlW1McvrcGCRNFxuWJEmSRqyq7q6qT7bprwO3AwcAxwEXtNUuAF7Spo8DLqzOdXSDpOwPvAC4pqrurar7gGuADW3ZXlV1XctleeHAviRpe74FPKeqfhh4Fl2dcjTwNuCdVfUM4D7g5Lb+ycB9rfydbT1aY/nxwA/SNWz/YZJdkuwC/AFdg/mhwMvbupKm1I7kWJIkSdIi2tX9ZwPXA2uq6u626EvAmjZ9AHDXwGabW9n2yjfPUz7f859CdxcUa9asYWZmZtGY77n3Ac6+6PJF11uOww540kj3B7B169YlHc84GePojDrO0w7bNrJ9zZqE17I1Rm9ts49tjwKeA/xsK78A+C26OyOPa9MAHwR+v3W9PQ64pKVE+XzLsXtkW2/jbEqUJJe0dW9buaOSNE42LEmSJK2QJE8E/gx4fVU9OJgGqaoqSa10DFV1DnAOwLp162r9+vWLbnP2RZfz9ptHe5q46RWLP+9yzczMsJTjGSdjHJ1Rx3nS6VeObF+zzt+wx0S8lu2uohuBZ9DdXfQ54P6qmm1tG2ysfqiBu6q2JXkAeHIrv25gt4PbzG0QP2qBOJbd8L1m99E3Cq5EY+AkNDIa4+hMQpwrGaMNS5IkSSsgyWPpGpUuqqo/b8VfTrJ/Vd3durPd08q3AAcNbH5gK9sCrJ9TPtPKD5xnfUlaVBud+1lJ9gb+AviBMcVhw/cYGePoTEKcKxmjOZYkSZJGrHUTORe4vareMbDoCmB2ZLcTgcsHyk9oo8MdDTzQusxdDRyTZJ+WtPsY4Oq27MEkR7fnOmFgX5K0JFV1P3At8KN0ud1mW2wGG6sfavhuy58EfI3tN4jPVy5pStmwJEmSNHo/BrwSeE6ST7fHscCZwPOT3AE8r80DXAXcCWwE3ge8BqCq7gV+B7ihPd7SymjrvL9t8zngw6txYJImW5KntDuVSLI78Hy6AQauBV7aVpvb8D3bIP5S4KMtT9MVwPFt1LiD6Uan/DhdXXVIG2VuN7oE31es/JFJGhe7wkmSJI1YVf0NkAUWP3ee9Qs4dYF9nQecN0/5J4Bn7kCYknZO+wMXtDxLjwEuq6oPJbkNuCTJGcCn6O66pP3945ac+166hiKq6tYkl9El5d4GnNq62JHktXR3XO4CnFdVt67e4UlabTYsSZIkSdJOoqpuohupcm75nTw8qttg+T8DP7PAvt4KvHWe8qvo7sSUtBOwK5wkSZIkSZKGYsOSJEmSJEmShmLDkiRJkiRJkoZiw5IkSZIkSZKGYsOSJEmSJEmShmLDkiRJkiRJkoZiw5IkSZIkSZKGYsOSJEmSJEmShmLDkiRJkiRJkoZiw5IkSZIkSZKGYsOSJEmSJEmShmLDkiRJkiRJkoZiw5IkSZIkSZKGYsOSJEmSJO0kkhyU5NoktyW5NcnrWvm+Sa5Jckf7u08rT5KzkmxMclOSwwf2dWJb/44kJw6UH5Hk5rbNWUmy+kcqabXYsCRJkiRJO49twGlVdShwNHBqkkOB04GPVNUhwEfaPMALgUPa4xTgPdA1RAFvBo4CjgTePNsY1dZ59cB2G1bhuCSNiQ1LkiaWV9wkSZKWp6rurqpPtumvA7cDBwDHARe01S4AXtKmjwMurM51wN5J9gdeAFxTVfdW1X3ANcCGtmyvqrquqgq4cGBfkqbQruMOQJJ2wOwVt08m2RO4Mck1wEl0V9zOTHI63RW3N/DIK25H0V1NO2rgits6oNp+rmgnSbNX3K4HrqK74vbhVTxGSZKkFZFkLfBsuvOcNVV1d1v0JWBNmz4AuGtgs82tbHvlm+cpn+/5T6G7C4o1a9YwMzOzaMxrdofTDtu26HrLsZTnXa6tW7euyH5HyRhHZxLiXMkYbViSNLHayc/dbfrrSQavuK1vq10AzNA1LD10xQ24LsnsFbf1tCtuAK1xakOSGdoVt1Y+e8XNhiVJkjTRkjwR+DPg9VX14OBN2VVVSWqlY6iqc4BzANatW1fr169fdJuzL7qct9882p+xm16x+PMu18zMDEs5nnEyxtGZhDhXMkYbliRNhUm84rYjV9sWulo37islfbxa08eYoJ9x9TEm6G9ckjSpkjyWrlHpoqr681b85ST7V9Xd7cLbPa18C3DQwOYHtrItPHwhb7Z8ppUfOM/6kqbUog1LSQ6i6xe7hq6LyDlV9e7WdeRSYC2wCXhZVd3X8o+8GzgW+CZw0mwf3pa35E1t12dU1QWt/AjgfGB3uq4mr2t3FEjSoib1ittJp1859POddti2ea/WrcQVt+Xo49WaPsYE/YyrjzFBf+OSpEnUfq+dC9xeVe8YWHQFcCJwZvt7+UD5a5NcQpdK4IHW+HQ18LsDCbuPAd5YVfcmeTDJ0XQX/E4Azl7xA5M0NktJ3u2oAZJ6a3tX3NrypV5xW6jcK26SJGma/BjwSuA5ST7dHsfSNSg9P8kdwPPaPHQX/u8ENgLvA14D0FII/A5wQ3u8ZTatQFvn/W2bz2EaAWmqLXrHkjlMJPWVV9wkSZKWp6r+BlholNvnzrN+AacusK/zgPPmKf8E8MwdCFPSBFlWjqVJzGEyCaMGTEruiEmIcxJihMmIcxJi5OErbjcn+XQr+3W6BqXLkpwMfAF4WVt2FV033Y10XXVfBd0VtySzV9zg0VfczqfrqvthbPSWJEmSpIcsuWFpUnOYTMKoAZOSO2IS4pyEGGEy4pyEGL3iJkmSJEnjtZQcS+YwkSRJWoYk5yW5J8ktA2X7JrkmyR3t7z6tPEnOSrIxyU1JDh/Y5sS2/h1tEJTZ8iOS3Ny2OSuDV/wkSZJW0aINS0vIYQKPzmFyQjtJOpqWwwS4GjgmyT7tROoY4Oq27MEkR7fnOmFgX5IkSZPofB49GIkDn0iSpKmzlDuWHDVAkiRpGarqY8C9c4qPoxvwhPb3JQPlF1bnOmB24JMX0AY+qar7gNmBT/anDXzSuvheOLAvSZKkVbWUUeHMYSJJkrTjVn3gE5jewU9gMgaaMMbRGXWco/6Mw+S8lpI0SqPNai1JkqRFrdbAJ+25pnLwE5iMgSaMcXRGHedJp185sn3NOn/DHhPxWkrSKC0pebckSZJ2mAOfSJKkqWPDkiRJ0upw4BNJkjR17AonSZI0YkkuBtYD+yXZTDe625nAZUlOBr4AvKytfhVwLN0gJt8EXgXdwCdJZgc+gUcPfHI+sDvdoCcOfCJJksbChiVJkqQRq6qXL7DIgU8kSdJUsSucJEmSJEmShmLDkiRJkiRJkoZiw5IkSZIk7USSnJfkniS3DJTtm+SaJHe0v/u08iQ5K8nGJDclOXxgmxPb+nckOXGg/IgkN7dtzmoDDUiaUjYsSZIkSdLO5Xxgw5yy04GPVNUhwEfaPMALgUPa4xTgPdA1RNENTHAUcCTw5tnGqLbOqwe2m/tckqaIDUuSJEmStBOpqo8B984pPg64oE1fALxkoPzC6lwH7J1kf+AFwDVVdW9V3QdcA2xoy/aqquva4AQXDuxL0hRyVDhJkiRJ0pqqurtNfwlY06YPAO4aWG9zK9te+eZ5yh8lySl0d0GxZs0aZmZmFg9ydzjtsG2LrrccS3ne5dq6deuK7HeUjHF0JiHOlYzRhiVJkiRJ0kOqqpLUKjzPOcA5AOvWrav169cvus3ZF13O228e7c/YTa9Y/HmXa2ZmhqUczzgZ4+hMQpwrGaNd4SRJkiRJX27d2Gh/72nlW4CDBtY7sJVtr/zAecolTSkbliRJkiRJVwCzI7udCFw+UH5CGx3uaOCB1mXuauCYJPu0pN3HAFe3ZQ8mObqNBnfCwL4kTSG7wkmSJEnSTiTJxcB6YL8km+lGdzsTuCzJycAXgJe11a8CjgU2At8EXgVQVfcm+R3ghrbeW6pqNiH4a+hGntsd+HB7SJpSNixJkiRJ0k6kql6+wKLnzrNuAacusJ/zgPPmKf8E8MwdiVHS5LBhSZIkSZIkqUfWnn7lSPd3/oY9Rrq/QeZYkiRJkiRJ0lBsWJIkSZIkSdJQbFiSJEmSJEnSUGxYkiRJkiRJ0lBM3i1JU2TUSf42nfmike5PkiRJ0nTxjiVJkiRJkiQNxYYlSZIkSZIkDcWGJUmSJEmSJA3FhiVJkiRJkiQNxYYlSZIkSZIkDcWGJUmSJEmSJA3FhiVJkiRJkiQNpTcNS0k2JPlsko1JTh93PJI0y/pJUl9ZP0nqK+snaeex67gDAEiyC/AHwPOBzcANSa6oqtvGG5k0GdaefuXI93n+hj1Gvs9JZP0kqa+snyT1lfWTtHPpRcMScCSwsaruBEhyCXAcYMUjadx26vppuY2Wpx22jZMW2WbTmS/akZAkPWynrp8k9Zr1k7QT6UvD0gHAXQPzm4Gj5q6U5BTglDa7Nclnl7Dv/YCv7nCEg3G8bZR7A1YgxhUyCXFOQowwAXH+xNuWHOPTVjqWMVvJ+mlov9zTz9BS4lqBOnQxvXyt6GdcfYwJho/L+ompPn+C/n5mBxnj6PQ+Ts+fHmL9NAGfV4xxlHof5zLqJ1hmHdWXhqUlqapzgHOWs02ST1TVuhUKaSQmIUaYjDgnIUaYjDgnIcY+GaZ+2hF9fX/6GFcfY4J+xtXHmKC/cU2KaT1/gsmI0xhHZxLinIQY+8T6abyMcXQmIc6VjLEvybu3AAcNzB/YyiRp3KyfJPWV9ZOkvrJ+knYifWlYugE4JMnBSXYDjgeuGHNMkgTWT5L6y/pJUl9ZP0k7kV50hauqbUleC1wN7AKcV1W3jmj3q9Y1ZQdMQowwGXFOQowwGXFOQowrboXrpx3R1/enj3H1MSboZ1x9jAn6G9dYef4ETEacxjg6kxDnJMS44qyfgMmI0xhHZxLiXLEYU1UrtW9JkiRJkiRNsb50hZMkSZIkSdKEsWFJkiRJkiRJQ5mahqUkG5J8NsnGJKfPs/xxSS5ty69PsraHMf5qktuS3JTkI0me1rcYB9b76SSVZCxDKi4lziQva6/nrUn+pG8xJvmeJNcm+VR7z48dQ4znJbknyS0LLE+Ss9ox3JTk8NWOUY+21O/pDuz/oPbZnP3+vK6V75vkmiR3tL/7tPIFPydJTmzr35HkxIHyI5Lc3LY5K0mWGNsu7TvzoTZ/cKvTN7Y6frdWvmCdn+SNrfyzSV4wUD7U65pk7yQfTPL3SW5P8qPjfq2S/Ep7725JcnGSx4/jtZqvjlmN12ah59CjLfZebu/z0aMYx37+tJQ4B9Yb2znUUmKM509LidHzp1Vg/bR6cQ6sZ/20iL7XUWOrn6pq4h90CeE+B3wvsBvwGeDQOeu8Bnhvmz4euLSHMf4E8IQ2/Ut9jLGttyfwMeA6YF1P3+9DgE8B+7T57+phjOcAv9SmDwU2jeG1/HHgcOCWBZYfC3wYCHA0cP1qx+hj+Z+tETzH/sDhbXpP4B/aZ/T3gNNb+enA27b3OQH2Be5sf/dp07PfyY+3ddO2feESY/tV4E+AD7X5y4Dj2/R7B75T89b57Tg+AzyO/5+9e4+Xs6rvvv/5QjjEcAbvXUyiwYpaEBXIzaH4eOcBhQBqaGsRRUkoym0BizVWQrUFEX1CW0RAiqWQEhANiFhShGKA7Nub1gAGkXAQ2UCQxECQhEBAwODv+WOtIVd2ZvaemcyeuWb29/16zWtfs67T77pm9po1a9YBdsv3cvNNua/AXOCTeXlLYIdO3itgPPAYMLZwj2Z04l5RJY9px72pdQ4/Gs9Par0/ShZjR8tP9caZt+tYGarOe+nyU31xuvxUjveC86cWxZm3c/7Umjg7mkd1Kn/qlRZL+wEDEfFoRLwCzAOmDdpmGqnwD3AtcEjll82yxBgRCyPixfx0ETChjfHVFWP2FeAc4KV2BldQT5yfAi6KiNUAEbGyhDEGsF1e3h74dRvjSwFE/BhYNcQm04ArIlkE7CBp1/ZEZzXU+3/atIhYERF35+XngQdJlRXFfHQucFRervU+OQxYEBGr8v/iAmBqXrddRCyK9Al3ReFYNUmaABwJXJqfCziYlKdXi6lanj8NmBcRL0fEY8AA6Z42dV8lbU/6AL8s369XIuLZTt8r0qyvYyWNAV4HrOjEvaqRx7Tj3tQ6h23I5afW6YYylMtPLeLyU1s4f2od50+tU/o8qlP5U69ULI0Hnig8X5bTFbaD/QAAIABJREFUqm4TEeuANcDObYlu0PmzajEWnUCqSWynYWPMTeUmRsQP2xnYIPXcy7cCb5X0X5IWSZratuiSemI8E/i4pGXAjcBn2hNaQxp939rIa+trkpuV7w3cAfRFxIq86kmgb5iYhkpfViV9ON8AvgD8Pj/fGXg25+mDj1Mrz68W09eAmTViHc5uwNPAv+Umz5dKGkcH71VELAf+CfgVqUJpDbCY1tyroWKtVzvuTa1z2IZcfmqd0pahJJ0p6du4/NROLj9tOudPrTNknJIul3QpbcifJB0r6UeNxph1On+C3sijRiR/6pWKpZ4i6ePAZOAfOx1LkaTNgK+TvoC14niVgk4r/ImkTxaejyE1l5wCfBT4V0k7tOhcrfJR4PKImEBqknhlvsdmpSBpG+D7wGcj4rniutxCJNoYyweAlRGxuF3nrNMYUnPjiyNib+AFUter13TgXu1I+jVqN+ANwDigE4WvYbXj3rT7/lvnlLX8BK0vQw1xnin5y0yzXH4yGwFlzp+y99Pi/EnSpDxe05hKWkRcFRGHNnnIbsifYJTmUb1ygcuBiYXnE3Ja1W3ym3t74Jm2RDfo/Fm1GJH0PuCLwIci4uU2xVYxXIzbAu8A+iUtJfXJnN+Bwd2qxTn4Xi0D5kfE73IXjl+SMqJ2qef1PoE05gkR8RNga2CXtkRXv7ret9ZWbXlNJG1BqlS6KiKuy8lPVZrK5r+VJsi1YhoqfUKV9KEcBHwo5z3zSN26zic1360UWIrHqZXnV4vpReD5GrEOZxmwLCLuyM+vJVU0dfJevQ94LCKejojfAdeR7l8r7tVQsdarHfem1jlsQy4/tU43lKHquZcuP7WGy0+bzvlT6wwX5xbA62kwf5K0eRtjhM7nT9AbedTI5E/R5gGvRuJBqr18lPTrbGUQrT0HbXMyGw7udk0JY9ybNBjY7mW8j/kNeB2p20flS8f/BL4EPE4quF8BbJ+3n0T6lXg6qUvGb4Av5nVTgVeA3wFrgZ/n9ONJ47k8n2P534NinAbcQ+oK8zjwAeAp4FVSf+C1wDfz8efmfXYhNffbuSz3Mm9zEzAjL/8Rqf+tOvC6T6L24G5HsuHgbnd24r3pR2PvrRacQ/l/+RuD0v+RDQdE/oeh3iekwZYfIw24vGNe3imvGzzo8hENxDeF9YN3f48NB6Q+KS9XzfOBPYGHSIM/rs2Pq4GvAkuBW0l53DqgH5iQ9/tzYPGgOD4HXA/8X+BtOe3MfJ86dq+A/YH7SWMriTT+xGeavFfFwbsfJQ1a2dB7kEF5TDvuTa1z+NF4flLr/VGyGDtafmogzqXA3wD3ksot15O6ad5EKvfcwvpBaT+U/4+fJeVFfzToOJ/Px1lDysO2JrVO/C2pjFTJ395AypeuAa7M636Z/2+qxejyU/2xTsLlp06/F5w/NRfnQ8ADOd+5mvSD3dmkiT5uz3nO5LxvAG/Jy5cDF5O6d71A+iHrSFKZ6rmcX5xZOO+v8v6V/OjAyjkK2/wxcFfOy14C/qxwL+8kjfv0XznWxaSxHzuSPzXwmnc8j+pE/tSRN/cI3bwjSB+Uj7C+8uIsUs0wpA/c75EGH70TeHMJY7yFVElyT37ML1GMR+V/nPNIBZetcyZyVr6nbwa2IVU8XZn3m5Qzk38FxgLvIrUs+qO8/kzg24POfyTwh/mN/r9ILQkqs1PtlzOd9+ftHs0Z1hdJGeB/FO6lSE3OHwCWkL9MleReVmLcI2eUP8+v96EdiPG7pDFYfkf6FeAE4NPApwv38aJ8DUvowCyAftT33mrx8d+T/3fvLeRHR5DGLbgVeDjnV5Uv9zXfJ8Bf5DxiADi+kD4ZuC/v800a+MBlw4qlN5Py9AFSHr9VTq+a55MKAatJFd0PkSqUfkcqUH2E9OH/KPDlvP+/5/22Ig2EWPxy9zNSAejdwE/z/fp3UuVHR+9Vjv8Xeb8rc/wN3au87ov5vA9RmI2u3vcg1fOYEb83tc7hR335CS4/jVScS0mD9/YB/03KU+4mffHcGrgNOIM0jsgLpPLOFqSx5QaALQvHuZNUabQT6Qe5yuf2FFIrymJcZ5K+sB1B+kFuVX5eLUaXn+qL0eWncrwXnD81F+dq4K9JZZ/ZrC8HzWD4iqU1pFbQm+X7PwXYKz9/Z77Wo/L2k/L+YwpxzCBXLOX8azXwCVKFzTmkSvfHWP/9blX+3xqbn9/Zyfypzte8o3lUp/KnSgHMbEiSDgTmA7vG+sFfkXQr8P2I+Of8/G2kQv5YUrO6x0gDwS3L6+8Evh4R8ySdScqoPj7Eef8dWBgR50v6F+DFiPjrKtv1kyqpLm3JBZtZT5L0XtIvc+Oj8ukq/TdwW0R8adC27yblPzvm5xcDqyLii5L2JBW+/iA606zdzLpM7mLyxYi4Kj//PmnsuL/Mzz8DHEL6VX6viDg6p29G+mX+2IiodFX5UkR8O6//B9IMiZ+WNIVUHppQOO+ZwHsi4n35+R6kFphjR/6qzaxMhioHkSroPhkR7ylsH6TWVgOSLgc2i4jjhjj+N0jDG/51ngDmMWCLyvdHSTMq55D0CeAzEbFfYf+fAP8SEZfn73e3RMTZed1JpMqbUo4bOdr1yhhLNvImAo8XK5WyN5C6pFU8TqpxLs7A82Rh+UVSy6aqJB2eR/lfJelZUo1wpU/qRFLNqplZs94ALK8UprLHASS9TtK/SHpc0nPAj0njElXGEJgLfCxPZfwJUpN7VyqZWSOeKiz/tsrzbRhUtoqI35Mqloqz9tRdtqqx/dbFAXXNbNSoWQ6qU3E2MSTtL2mhpKclrSG1jKl3PKHB3yMrsWxKXmcd4oolq9cTwBurFEJ+Dbyp8PyNpLFJnmJ4GzSXk7QVabDgfyJNFb0DqQ+vCjH8YT3HMjOrYQUwPlcOVbwx/50JvA3YPyK2A96b0wUQEYtIY8P9P8DHSF3MzMxabYOyVc6vJlLf4KouD5nZUIYqB71AGp8RAEl/UGX/wXnMd0i9WiZGxPakMRxVY9vBBn+PrMTige67kCuWrF53kjKi2ZLGSdpa0kGkPpx/LWm3PDX514Crq7RsquYpYFJh+sUtSeOAPA2sk3Q4UJyO8jLgeEmHSNpM0nhJby8c682bfJVm1ut+Qqr8/itJW0j6U9L4bZBmbfot8KyknUhjnQx2BWksn99FxO3tCNjMRp1rgCNzeWcLUqX3y6RxmYbzFLCzpO1HMkAz61pDlYN+Duwp6d2StiaNzzacbUnDBLwkaT/SD28VT5MmDKj1He1G4K2SPiZpjKSPkMYnuqHhq7KOc8WS1SUiXgU+CLyFNGD2MtJAt3NIv9r/mNSH9iXS7EP1+F7++4ykuyPieeCvSAWq1aSMaX4hhjtJs8adRxo47v+wvpb7fODDklZLuqDJyzSzHhcRrwB/Sho8chUpH7sur/4GaXy435AG2P3PKoe4kjRl+LdHOlYzG50i4iHg48CFpPzog8AHc/413L6/IP3o96ikZyW9YUSDNbOuMlQ5KCJ+SRqE+hbS5Bf1/IB2EnCWpOeBvyd9j6uc60XSJCn/lfOjAwbF8gxpQoGZpBnHvwB8ICJ+swmXaB3iwbvNzMzqJGkssJI0W+XDnY7HzMzMzKzT3GLJzMysfn8J3OVKJTMzMzOzxLNBmJmZ1SFP8S3gqA6HYmZmZmZWGu4KZ2ZmZmZmZmZmTRm2K1ye/etOST+XdL+kL+f03STdIWlA0tWStszpW+XnA3n9pMKxTs/pD0k6rJA+NacNSJrV+ss0MzMzMzMzM7NWG7bFkiQB4yJibZ7y9HbgVOBzwHURMU/St4CfR8TFkk4C3hkRn5Z0DPAnEfERSXuQZqnYD3gDabT5t+bT/BJ4P2mmsbuAj0bEA0PFtcsuu8SkSZOGvcAXXniBcePGDbtdt+il6+mla4HRez2LFy/+TUS8vg0hdY1i/lSm90VZYilLHFCeWMoSB5QnllbE4fxpY91efipjXGWMCRxXIzoRk/OnjXV7/jSY42ytbokTuifWoeJsOI+KiLofwOuAu4H9SdOfjsnpBwI35+WbgQPz8pi8nYDTgdMLx7o57/favjl9g+1qPfbdd9+ox8KFC+varlv00vX00rVEjN7rAX4aDeQjo+FRzJ/K9L4oSyxliSOiPLGUJY6I8sTSijicPw2dPw2lLO+DwcoYVxljinBcjehETGXIn4DNgZ8BN+TnuwF3AAPA1cCWOX2r/Hwgr59UOMbpOf0h4LBC+tScNgDMqieebs+fBnOcrdUtcUZ0T6xDxdloHlXX4N2SNgcWA28BLgIeAZ6NiHV5k2XA+Lw8HngiV1qtk7QG2DmnLyoctrjPE4PS968Rx4nAiQB9fX309/cPG/vatWvr2q5b9NL19NK1gK/HzMzMzLrKqcCDwHb5+TnAebG+R8oJwMX57+qIeEvukXIOUOmRcgywJ7lHiqRKj5SLKPRIkTQ/humRYmbdq66KpYh4FXi3pB2AHwBvH9GoasdxCXAJwOTJk2PKlCnD7tPf308923WLXrqeXroW8PWYmZmZWXeQNAE4Evgq8Lk8/MnBwMfyJnOBM0kVS9PyMsC1wDfz9tOAeRHxMvCYpAHSsCcAAxHxaD7XvLytK5bMelRdFUsVEfGspIWk7ms7SBqTWy1NAJbnzZYDE4FlksYA2wPPFNIrivvUSt9kS5avYcasH7bqcAAsnX1kS49nZma9b9IQn0Uz91rX1GeVP4/MrKxq5XnN5nfgPK/FvgF8Adg2P9+ZLumRsnLVGi686vpht2vEXuO3b+nxoHta/zvO1uuWWFsZ57AVS5JeD/wuVyqNJTVpPAdYCHwYmAdMByr/3fPz85/k9bdFREiaD3xH0tdJTSV3B+4kjb+0u6TdSBVKx7C+ptzMzMzMzMxaRNIHgJURsVjSlE7G0kyPlAuvup5zlzTUPmJYS48d/ryN6pbW/46z9bol1lbGWc9/5K7A3DzO0mbANRFxg6QHgHmSziYN+nZZ3v4y4MrcFHIVqaKIiLhf0jWkJpDrgJNzFzsknUIazHtzYE5E3N+SqzMzMzMzM7Oig4APSToC2Jo0xtL5dEmPFDMrn2ErliLiXmDvKumPsr4PbTH9JeDPaxzrq6R+vIPTbwRurCNeMzMzMzMza1JEnE6azY3cYunzEXGspO/hHilm1oTNOh2AmdlIkLSDpGsl/ULSg5IOlLSTpAWSHs5/d8zbStIFkgYk3Stpn8JxpuftH5Y0vXNXZGZmZjaiTiMN5D1AGkOp2CNl55z+OWAWpB4pQKVHyn+Se6TkFk+VHikPknq8uEeKWQ9rbedUM7PyOB/4z4j4sKQtgdcBfwvcGhGzJc0iFYxOAw4n/cq2O2lwyYuB/SXtBJwBTAYCWJyny13d/ssxMzMza62I6Af687J7pJhZU9xiycx6jqTtgfeSf2mLiFci4lnSVLdz82ZzgaPy8jTgikgWkcYY2BU4DFgQEatyZdICYGobL8XMzMzMzKzU3GLJzHrRbsDTwL9JehewGDgV6IuIFXmbJ4G+vPzaNLpZZbrcWukbqTVdbpmmGy1LLGWJA9oby8y91tVc1zd26PW1jETsZXl9yhKHmZmZmQ3NFUtm1ovGAPsAn4mIOySdTx4PoCIPOhmtOmGt6XLLNN1oWWIpSxzQ3lhmzPphzXUz91rX1NTJvTw9clniMDMzM7OhuSucmfWiZcCyiLgjP7+WVNH0VO7iRv67Mq+vNV3uUNPompmZmZmZjXpusWRmPScinpT0hKS3RcRDwCGkGUseIE2XO5uNp9E9RdI80uDdayJihaSbga9VZo8DDiVPz2tm9Zk0REutoczca13NVl5LZx+5KSGZmZmZWQu5YsnMetVngKvyjHCPAseTWmleI+kE4HHg6LztjcARwADwYt6WiFgl6SvAXXm7syJiVfsuwTqp2QoRMzMzM7PRxBVLZtaTIuIeYHKVVYdU2TaAk2scZw4wp7XRmZmZmZmZ9QaPsWRmZmZmZmZmZk1xxZKZmZmZmZmZmTXFFUtmZmZmZmZmZtYUj7FkZtZDhhpweqhZtmrx7FtmZmZmZjYUVyyZmVlNIzEz2uVTx7X8mGZmZmZm1hmuWDIzs67XbAVYM624zMzMzMxsPY+xZGZmZmZmZmZmTXHFkpmZmVmLSZojaaWk+wppO0laIOnh/HfHnC5JF0gakHSvpH0K+0zP2z8saXohfV9JS/I+F0hSe6/QzMzMLHHFkpmZmVnrXQ5MHZQ2C7g1InYHbs3PAQ4Hds+PE4GLIVVEAWcA+wP7AWdUKqPyNp8q7Df4XGZmZmZt4YolMzMzsxaLiB8DqwYlTwPm5uW5wFGF9CsiWQTsIGlX4DBgQUSsiojVwAJgal63XUQsiogArigcy8zMzKytPHi3mZmZWXv0RcSKvPwk0JeXxwNPFLZbltOGSl9WJb0qSSeSWkLR19dHf3//sIGuXbu2ru3arYxxlTEm6HxcM/daVzW9b2ztdcMZqevp9L0yM+t2w1YsSZpI+iWsDwjgkog4PzfPvhqYBCwFjo6I1bmP//nAEcCLwIyIuDsfazrwpXzosyNibk7fl9RkfCxwI3Bq/gXOzMzMrOdEREhqS1knIi4BLgGYPHlyTJkyZdh9+vv7qWe7ditjXGWMCTofV60ZN2futY5zlzT32/bSY6dsQkS1dfpemZl1u3q6wq0DZkbEHsABwMmS9sDjBJiZmZk14qncjY38d2VOXw5MLGw3IacNlT6hSrqZmZlZ2w1bsRQRKyotjiLieeBBUnNrjxNgZmZmVr/5QGVmt+nA9YX04/LscAcAa3KXuZuBQyXtmH+MOxS4Oa97TtIBuaX4cYVjmZmZmbVVQ+1QJU0C9gbuoAPjBDQzRsCm9OOupZN9sHupD3gvXQv4eszMbD1J3wWmALtIWkZqtT0buEbSCcDjwNF58xtJQwgMkIYROB4gIlZJ+gpwV97urIioDAh+EuuHEbgpP8zMzMzaru6KJUnbAN8HPhsRz6UfyJJ2jRPQzBgBF151fdP9uGsZqf7d9eilPuC9dC3g6zEzs/Ui4qM1Vh1SZdsATq5xnDnAnCrpPwXesSkxDmXJ8jU1x8hp1tLZR7b0eGZmZlYO9YyxhKQtSJVKV0XEdTnZ4wSYmZmZmZmZmY1iw1Ys5b77lwEPRsTXC6s8ToCZlZqkzSX9TNIN+fluku6QNCDpaklb5vSt8vOBvH5S4Rin5/SHJB3WmSsxMzMzMzMrp3paLB0EfAI4WNI9+XEEaZyA90t6GHhffg5pnIBHSeME/CtpDADymACVcQLuYuNxAi7N+zyCxwkws9Y4lTThQMU5wHkR8RZgNXBCTj8BWJ3Tz8vbkWfAPAbYkzRb5T9L2rxNsZuZmZmZmZXesIMPRcTtgGqs7opxAsxs9JE0ATgS+Crwudwi8mDgY3mTucCZwMWk2SzPzOnXAt/M208D5kXEy8BjkgaA/YCftOkyzMzMzMzMSq21o1qbmZXHN4AvANvm5zsDz0ZEZZrI4gyUr81aGRHrJK3J248HFhWO2fCsle2eXW+oWTBHYpbMZozEPWn2uspyT5qNYyTeW61+fUbitfGMlWZmZmbl4YolM+s5kj4ArIyIxZKmtOOctWatbPfsekPN4jRzr3UtnyWzGZdPHdfye9Ls7FVluSfNxjESs5S2+j07Eq9NJ2dnNTMzM7MNdb40bWbWegcBH8rjwW0NbAecD+wgaUxutVScgbIya+UySWOA7YFnqD2bpZmZmZmZmVHf4N1mZl0lIk6PiAkRMYk0+PZtEXEssBD4cN5s8GyWlVkuP5y3j5x+TJ41bjdgd+DONl2GmZmZmZlZ6bnFkpmNJqcB8ySdDfwMuCynXwZcmQfnXkWqjCIi7pd0DfAAsA44OSJebX/YZmZmZmZm5eSKJTPraRHRD/Tn5UdJs7oN3uYl4M9r7P9V0sxy1iJLlq9petwdMzMzMzMrF3eFMzMzMzMzGyUkTZS0UNIDku6XdGpO30nSAkkP57875nRJukDSgKR7Je1TONb0vP3DkqYX0veVtCTvc4Ektf9KzaxdXLFkZmZmZmY2eqwDZkbEHsABwMmS9gBmAbdGxO7Arfk5wOGkcSZ3B04ELoZUEQWcAexPahF+RqUyKm/zqcJ+U9twXWbWIa5YMjMzMzMzGyUiYkVE3J2XnwceBMYD04C5ebO5wFF5eRpwRSSLSLPs7gocBiyIiFURsRpYAEzN67aLiEV5MpQrCscysx7kMZbMzMzMzEbIpBEYU27p7CNbfkwbnSRNAvYG7gD6ImJFXvUk0JeXxwNPFHZbltOGSl9WJb3a+U8ktYKir6+P/v7+YWPuGwsz91o37HaNqOe8jVq7du2IHLfVHGfrdUusrYzTFUtmZmZmZmajjKRtgO8Dn42I54rDIEVESIqRjiEiLgEuAZg8eXJMmTJl2H0uvOp6zl3S2q+xS48d/ryN6u/vp57r6TTH2XrdEmsr43RXODMzMzMzs1FE0hakSqWrIuK6nPxU7sZG/rsypy8HJhZ2n5DThkqfUCXdzHqUK5bMzMzMzMxGiTxD22XAgxHx9cKq+UBlZrfpwPWF9OPy7HAHAGtyl7mbgUMl7ZgH7T4UuDmve07SAflcxxWOZWY9yF3hzMzM7DVLlq9hxgiMCWNmZqVxEPAJYImke3La3wKzgWsknQA8Dhyd190IHAEMAC8CxwNExCpJXwHuytudFRGr8vJJwOXAWOCm/DCzHuWKJbMeMBIDg14+dVzLj2lmZmZmnRURtwOqsfqQKtsHcHKNY80B5lRJ/ynwjk0I08y6iLvCmZmZmZmZmZlZU9xiycysQ0aipZmZmZmZmVk7ucWSmZmZmZmZmZk1xS2WzMzMzNpI0lLgeeBVYF1ETJa0E3A1MAlYChwdEavzjErnkwbOfRGYERF35+NMB76UD3t2RMxt53VY59TT4nXmXuvqHoh/6ewjNzUkMzMbxYZtsSRpjqSVku4rpO0kaYGkh/PfHXO6JF0gaUDSvZL2KewzPW//cC4IVdL3lbQk73NBLkCZmZmZ9bL/NyLeHRGT8/NZwK0RsTtwa34OcDiwe36cCFwMqSwGnAHsD+wHnFEpj5mZmZm1Uz1d4S4Hpg5Ka2Xh52LgU4X9Bp/LzMzMrNdNAyotjuYCRxXSr4hkEbCDpF2Bw4AFEbEqIlYDC3AZyszMzDpg2K5wEfFjSZMGJU8DpuTluUA/cBqFwg+wSFKl8DOFXPgBkLQAmCqpH9guF5SQdAWpIHXTplyUmZmZWYkF8CNJAfxLRFwC9EXEirz+SaAvL48Hnijsuyyn1UrfiKQTST/40dfXR39//7AB9o1NXalaqZ7zDmft2rUtOU4rDRdTq+9jvRp5DUfintY696a8t0bqtS/j+8rMrJs0O8ZSqwo/4/Py4PSqeqlg1Kxe+uDrpWuBzl7PSBRau/n1kTQRuIKUNwVwSUSc7zFMrNeMxMyCM/dq+SFtY++JiOWS/gewQNIviisjInKlU0vkiqtLACZPnhxTpkwZdp8Lr7qec5e0dijOpccOf97h9Pf3U0/87TRcTPWOc9RqM/daV/dr2IrXZrBa191IXIONRJxQzveVmVk32eQSQ6sLP8Ocq2cKRs3qpQ++XroW6Oz1jESh9fKp47r59VkHzIyIuyVtCyzOLSVnkLrxzpY0i9SN9zQ27Ma7P6mL7v6FbryTSRVUiyXNz91OzMyaEhHL89+Vkn5AGibgKUm7RsSK3Np7Zd58OTCxsPuEnLac9a3HK+n9Ixy6mZmZ2UbqGWOpmqdyoYcGCj+10idUSTcza1pErKi0OIqI54EHSa0hPYaJmXWUpHG5whtJ44BDgfuA+UBlcpPpwPV5eT5wXJ4g5QBgTW41fjNwqKQd87iVh+Y0MzMzs7ZqtilPpfAzm40LP6dImkf61X9N/uXtZuBrhQG7DwVOj4hVkp7LBaU7gOOAC5uMycxsI3mMuL1JeUzbxzAZqkthu8fdGImuwd0cB5QnlrLEAeWJZag4urWbbtYH/CBPgjsG+E5E/Keku4BrJJ0APA4cnbe/kdRNd4DUVfd4gFyG+gpwV97urMpYlmZmZmbtNGzFkqTvkppa7yJpGalbyGxaV/g5iTTz3FjSoN0euNvMWkLSNsD3gc9GxHP5ixzQvjFMhuoi2e5xNzZlXItejAPKE0tZ4oDyxDJUHJ3skr6pIuJR4F1V0p8BDqmSHsDJNY41B5jT6hjNzMzMGlHPrHAfrbGqJYWfiPgp8I7h4jAza4SkLUiVSldFxHU52WOYmJmZdalWT1iwdPaRLT2emdlo1ewYS2ZmpZVnebsMeDAivl5Y5TFMzMzMzMzMWqjzbd3NzFrvIOATwBJJ9+S0v6W13XjNzMzMzMxGPVcsmVnPiYjbAdVY7TFMzMzMzMzMWsRd4czMzMzMzMzMrCmuWDIzMzMzMzMzs6a4YsnMzMzMzMzMzJriiiUzMzMzMzMzM2uKB+82MzMzs640adYPW3q8pbOPbOnxzMzMRgO3WDIzMzMzMzMzs6a4YsnMzMzMzMzMzJriiiUzMzMzMzMzM2uKK5bMzMzMzMzMzKwprlgyMzMzMzMzM7OmuGLJzMzMzMzMzMyaMqbTAZiZmZmZlcGkWT9seJ+Ze61jRhP7mVl5NPO/P5zLp45r+THNysoVS9YxS5avaXlBbOnsI1t6PDMzMzPrTZXKhFZWDrosamajkbvCmZmZmZmZmZlZU1yxZGZmZmZmZmZmTXHFkpmZmZmZmZmZNcUVS2ZmZmZmZmZm1pTSDN4taSpwPrA5cGlEzO5wSGZmgPMnMysv50/WCiMxI5aZ8yez0aMUFUuSNgcuAt4PLAPukjQ/Ih7obGRmNto5fzKzsnL+ZGZl5fyp9TNge8ZBK7NSVCwB+wEDEfEogKR5wDRg1GQ8ZlZazp/MrKy6Kn9qRauYVk4Lb2Yjqqvyp24wEi0LXVllraKI6HQMSPowMDVruIG6AAAgAElEQVQiPpmffwLYPyJOGbTdicCJ+enbgIfqOPwuwG9aGG6n9dL19NK1wOi9njdFxOtHOphOaUH+VKb3RVliKUscUJ5YyhIHlCeWVsTh/ImeKz+VMa4yxgSOqxGdiMn5Ez2XPw3mOFurW+KE7ol1qDgbyqPK0mKpLhFxCXBJI/tI+mlETB6hkNqul66nl64FfD2jXa38qUz3sSyxlCUOKE8sZYkDyhNLWeLoBb1UfipjXGWMCRxXI8oY02jRS/nTYI6ztbolTuieWFsZZ1lmhVsOTCw8n5DTzMw6zfmTmZWV8yczKyvnT2ajSFkqlu4Cdpe0m6QtgWOA+R2OycwMnD+ZWXk5fzKzsnL+ZDaKlKIrXESsk3QKcDNpOso5EXF/iw7fUNPKLtBL19NL1wK+np7UgvypTPexLLGUJQ4oTyxliQPKE0tZ4iitUVp+KmNcZYwJHFcjyhhTVxul+dNgjrO1uiVO6J5YWxZnKQbvNjMzMzMzMzOz7lOWrnBmZmZmZmZmZtZlXLFkZmZmZmZmZmZN6ZmKJUlTJT0kaUDSrCrrt5J0dV5/h6RJ7Y+yPnVcywxJT0u6Jz8+2Yk46yVpjqSVku6rsV6SLsjXe6+kfdodY73quJYpktYUXpu/b3eMjZA0UdJCSQ9Iul/SqVW26ZrXp2yG+19uYxxLJS3J78mftvncG/3PSNpJ0gJJD+e/O3YojjMlLS/8vx4x0nHk81b9v2v3fRkijrbfF0lbS7pT0s9zLF/O6bvlz+yB/Bm+5UjHYuXJuwrxDPtZ1UmSNpf0M0k3dDqWCkk7SLpW0i8kPSjpwBLE9Nf59btP0nclbd2hOErxuWTN6XT+1Mj7Z6gytKTpefuHJU0fgTgbKmt0KtZGP/81xHd6Safn9IckHdbKOAvn2CC/L2OcqlLub8vrHhFd/yANCPcI8GZgS+DnwB6DtjkJ+FZePga4utNxb8K1zAC+2elYG7im9wL7APfVWH8EcBMg4ADgjk7HvAnXMgW4odNxNnA9uwL75OVtgV9Web91zetTpkc9/8ttjGUpsEuHzr3R/wzwD8CsvDwLOKdDcZwJfL4D96Tq/12778sQcbT9vuT8ZZu8vAVwR85vrgGOyenfAv6y3a/XaHuUKe8qxDTsZ1WH4/sc8J0yff4Dc4FP5uUtgR06HM944DFgbH5+DTCjQ7GU4nPJj6Zeu47nT428f2qVoYGdgEfz3x3z8o4tjrOhskanYm30858a3+nztf0c2ArYLb9PNh+B13+D/L6McVKl3N+O171XWiztBwxExKMR8QowD5g2aJtppA9ZgGuBQySpjTHWq55r6SoR8WNg1RCbTAOuiGQRsIOkXdsTXWPquJauEhErIuLuvPw88CCp8FfUNa9PyfTc/3IzavzPFPPjucBRHYqjI4b4v2vrfanz/78tcv6yNj/dIj8COJj0mQ1teq9Y+fKuMr1XB5M0ATgSuLTTsVRI2p705fcygIh4JSKe7WxUQJqNeqykMcDrgF93IoiyfC5ZUzqePzX4/qlVhj4MWBARqyJiNbAAmNriOBsta3Qk1iY+/2t9p58GzIuIlyPiMWCA9H5pmcH5fT5v6eKsYcRf916pWBoPPFF4voyNCxyvbRMR64A1wM5tia4x9VwLwJ/l5mrXSprYntBGTF3XLGmtpDe3LarmHZibc94kac9OB1Ov3ERzb9IvBUX1vidtQ2W6bwH8SNJiSSd2KIaivohYkZefBPo6GMspkkLS99SBrg+D/u86dl+q/P+fkj9j5rTrvuTm5fcAK0kFmEeAZ/NnNjjvaZcy5V0bGeKzqlO+AXwB+H27TzxEuWg34Gng33KXjUsljWtzeBuIiOXAPwG/AlYAayLiR52MaZAyfS5ZbWXNn2q9f2rF29brqLOs0bFYG/z8r/Wdvh33dHB+v3NJ46xW7h/x171XKpZGm/8AJkXEO0n/fHOH2b4nRMQ2EfFop+MYxt3AmyLiXcCFwL93OJ66SNoG+D7w2Yh4rtPxWMu9JyL2AQ4HTpb03k4HVBGpvW106PQXA3+Yl58Gzm3nyYf6v2vnfakSR+W+vJv0BbAt9yUiXo2IdwMTSL/evb0d57XuUbbPKkkfAFZGxOJOnH+IctEYUlediyNib+AFUteHjskV1NNIlV5vAMZJ+ngnY6qlw59L1uXK9v4ZobLGZEm3tyK+HEfpP/87nd83aMhy/0i9R3ulYmk5UGy1MyGnVd0mN8HdHnimLdE1ZthriYhnIuLl/PRSYN82xTZS6nn9ukJEPFdpzhkRNwJbSNqlw2ENSdIWpA+cqyLiuiqb9Mzr02aluW/5l2IiYiXwA9rT5HYoT1W6U+a/KzsRREQ8FRGv5qdX08b7UuP/ru33pVoclfsSEb8H/pU2v19yl52FwIGkJtlj8irnPe1RmryrqI7Pqk44CPiQpKWkLjkHS/p2vTsX3tuttgxYFhGVVl3XkiqaOul9wGMR8XRE/A64DvjjDsdUVIrPJRtWKfMnar9/asXblutosKzRSKyrWx0r1P35X+s7/Ujf043ye+D8EsZZq9w/4u/RXqlYugvYPY/KviVpgKz5g7aZD1RGM/8wcFuurSubYa9l0Pg2HyL1md1kSrMHXKc049wzkr4p6Q8l3Zaf/0bSVZJ2KOyzVNLf5C4TL0i6TFJf7gb2vKRbCt0otshdTk6U9GtJKyR9Pl/fcZL2k7SE9Ev53fn8WxbOFZLekpd3lvQfkp6TdJeks4s153nbTyuNYv+spItyv9YRJekPKueRtB/pf6yMFZjAa32DLwMejIiv19is8vpI0gGk5usramxr69WTL404SeMkbVtZBg4Fqs5q2EbF/Hg6cH2rT5DzptOVZkNZLenfSAMlkvOsFZKelPQXeZdDgfskHZm7jjwn6QlJZxaO+UNJnxl0nnsl/Un+/zhPaaaY55Rm43hHjdhq/d+N+H2pJ45BnzF/QhveL5JeX/lskTQWeD/ps20h6TMb2nBPDChJ3lVU52dV20XE6RExISImke7TbRHx8Wr5j9LMR1MkLZN0mqQnSV3VNpM0S9Ijuax1jaSdAHJZ6pTiOZW62v9pXi6Wi7aXdIWkp8ndBCVVfvX/CjCpcIxJed8x+fkMSY/mcttjko4dgdv1K+AASa/Lr+chtKj82iJtzX+taaXLn7Ja759aZeibgUMl7Zi/Jx2a01qmkG8eCGxVyY+AV4AT8mbfBP6HpFXAu4BP5Vj/BHgrqTV3JdbbJf0VaYyh40lDf6yV9Gw+31hJ50p6XGmG7Nvz5zmSPqQ049uzkvol/VEhzl9J+rtcnnqBNOj1CuB3wPOSbgE+Vbin9wI/yOd9BFiSv9PPB45Rmo1tN2B34M5W3c8a+f2x1C6n1Kp7GNE4hyj3j/x7NFo8UnqnHqQRzX9JeoN9MaedBXwoL28NfI80QNadwJs7HfMmXMv/B9xPGlF+IfD2Fpxz83y884Bx+X69B3gLqYC/FfB64MfANwr7LQUWkfppjifVft5N6se7NXAbsIT1GUSQCjx/la/padKvWBeR+nEOAPuTCkAPkpptVs4VwFvy8rz8eB1pdP0ngNsHbXsDsAPwxnyeqS24T98tXMsyUsb8aeDTef0phddmEfDHnX4/DXM978n36l7gnvw4YtA1Kb8+j+TXcnKn4+6WR7X/5Q7E8Ob8fvx5fm+2NY4a/zM7A7cCDwO3ADuNwHmXkj5IJ5JmtHgaeB5YB7wK/B1pVo9n8//ALaQZVKYAe5Eqhd8JPAUclY95NIVZEUmFsGdIM9McBizOeY6APwJ2rRFbrf+7Eb8vdcZxZf5fv5dU4Kh6HS2O5Z3Az/I57wP+vvD+vZP02fA9YKt2vn9H66MMedegeKq+Vzsd16AYp7B+lqDB+c9/AWfnbdYB55DKVWOBU0nlhQk57V+A7+bjHAf8V+Ece+Q8a6v8vFguuoL0RWFbUhnq8RzHvcAvgGsKx5mU9x1DKvM9B7wtr9sV2HOE7tGXcyz35XymI//PdOhzyY+WvX4dzZ8aef8wRBka+Iv82TYAHD8CcVbyzVeA35LKgEeTvoc9lmN/JedLW5GG8Ph1jvUXlTyiEOtvSd/zjifNUH77oPNdBPSTvg9uTmqRuBWpguoF0vfJLUhjFA0AW+b9luf1D+TzriV9l/wAqSLxxRz7VvnYzwD/Nx/jF6TWU6/Px/pijv8h4PARfA9MYX1+X7WcwhB1DyMZJzXK/e14j3Y0Y/CjPA9SbfbTlQxkiO2OAn5WeL4UOLbw/PukPv2V558B/j0vT8qZ1NsL6/8BuKzGuT4L/KDwPEgVXZuTMvO3FdadzcYVS+8pPL+GPMWiH3740fuPnDd9uvD8iPyhOQeYXUh/K4UvZ1WO8w3gvLy8dS7A7J6f/xPwz3n5YFJB9wBgs05fvx9++NG5xxD5zxTSF7mtC+seBA4pPN81l3HGkCqJXiCN3QjwVWBOYdtiuegVCtOuA/8b6M/LZwLfLqyrlMcqFUvPAn8GjO30vfPDDz9a+xgiP7oM+IdC+jY575lUzCMK6/uBT+blGYO+d21Gqnh6V5Xz/x0bVmxvRqpMmlKIr97vkqcBVw46/s3A9E7fZz+iZ7rC2aabCDwe60e1B0CpW9s8ScslPQd8Gxg8ZtBTheXfVnm+zaDtiyPMP04axBFJb5V0g1L3lOeAr1U5F6SWU2MGHeeJKts9WVh+sUocZtbbquU1b6iS/hpJ+0taqNQleA2p9d4uABHxEmkspo9L2gz4KOlXdyLiNlKT8ouAlZIukbTdyFyWmXWBqmUd4Omcl1S8idytI3fteJDUqrIv0hThPyR1u4CU51xV5Vy7kFoCFPOzx6ljpqGIeAH4CCmvW6HU5bd0A+ea2SapVR56Lc+INEbsMzQ3Q9kupB/fHqmybvB5fp/jKZ6n3u+SbwL+vJJf5jzzPaQKeeswVyxZxRPAG7XxQJJfI9VY7xUR2wEfJzWZ2xTFgcDeSGp2CWkmol+QWgNsB/xtjXM9TWpKPqHGMc3MoHpes6JKetF3SN2/JkbE9sC32DAfmgscSxof5MWI+EllRURcEBH7krqrvBX4mxZdh5l1n1plnRi03ROkrhA7FB5bRx58ldTt5qOSDiR9cVtY5Vy/IbU0eNOgc1aO8QJp6ICKPyjuHBE3R8T7SV/OfkEatN/Meke1/OjXFPKMPB7Pzqzvmga1843B+dhvgJdYP9Nu0eDzKMfTzIDVT5BaLBXzy3ERMbuJY1mLuWLJKu4kfeGanQf92lrSQaRm2GuBNZLG05ovSn+XB2/ck9RP9+qcvi2pn//a/GvZX1bbOdIsTtcBZ+bjvJ00DoGZWdHJkibkgXC/SMprrgFmSNpD0uuAMwbtsy2wKiJeUhqA/2PFlbki6ffAueTWSgCS/mdu7bQFqUD2Ut7OzEanavlPNd8CvirpTfDaQPbTCutvJH0pOwu4Ov/av4FcLromH2fbfKzPkVqZQxqT6r2S3ihpe+D0yr65Zfq0/KXyZVKZz3mXWW+plh99Fzhe0rslbUVqTHBHRCyNiKdJFT8fl7S50kQnxUqjp4AJeQD1SiukOcDXJb0h73NgPu41wJGSDsllpJmkvOa/m7iObwMflHRYPkdlUoQJw+5pI84VSwa8Vij5IKmv/q9IA7p9hDTQ4j7AGlJz7FZM8ft/SIOA3Qr8U0T8KKd/nvQl7nnSr2W1CmGQBsnentTd7UpS5vhyC2Izs97xHeBHwKOk5tlnR8RNpHGTbiPlQ7cN2uck4CxJzwN/TyoQDXYFaYDv4rTi25HyrdWkJt/PAP/Ysisxs26zUf5TY7vzSa0kf5TznUWkSUwAiIiXSWWv9+Vj1vIZUqX2o8Dteds5+RgLSGWqe0mTDNxQ2G8zUiXUr4FVwP+ixg97Zta1qpWHbiGNf/R9UuOCP2R9t1tIM7H9Dak8sycbVgTdRhoY+klJv8lpnycN/nwXKS85hzTm5EOkHi8Xklo2fRD4YES80uhFRMQTwDRSr5anSS2Y/gbXaZSCIga3ZDMbGZImkWYh2GLwWE4tOPY5wB9ExPRWHtfMupOkpaRBJm8ZgWMfB5wYEe9p9bHNrPuNZP5jZtYI50fWLq7ds64k6e2S3qlkP9JUnz/odFxm1tty97mTgEs6HYuZmZmZWRm4Ysm61bakpuEvkJp3nwtc39GIzKynSTqM1PT6KYbukmJmZmZmNmq4K5yZmZmZmZmZmTXFLZbMzMzMzMzMzKwpYzodQLN22WWXmDRpUtV1L7zwAuPGjWtvQI6h1HGUIYayxNHqGBYvXvybiHh9yw7YA4bKn4rK8H7YFN0cfzfHDt0dfztjd/60sW7Pn8oYVxljAsfViE7E5PxpY92YP5UllrLEAeWJpSxxQHliaSSOhvOoiOjKx7777hu1LFy4sOa6dnEM65UhjjLEEFGOOFodA/DTKEGeUKbHUPlTURneD5uim+Pv5tgjujv+dsbu/Kn38qcyxlXGmCIcVyM6EZPzp97In8oSS1niiChPLGWJI6I8sTQSR6N5lLvCmZmZmZmZmZlZU1yxZGZmZmZmZmZmTenaMZbqNWnWD1t+zKWzj2z5Mc1s9FmyfA0zWpxHOX8ys1Zw/mRmZtZZra7LuHzqyI3z5BZLZmZmZmZmZmbWFFcsmZmZmZmZmZlZU+quWJK0uaSfSbohP99N0h2SBiRdLWnLnL5Vfj6Q108qHOP0nP6QpMMK6VNz2oCkWa27PDMzMzMzMzMzGymNtFg6FXiw8Pwc4LyIeAuwGjghp58ArM7p5+XtkLQHcAywJzAV+OdcWbU5cBFwOLAH8NG8rZmZmZmZmZmZlVhdFUuSJgBHApfm5wIOBq7Nm8wFjsrL0/Jz8vpD8vbTgHkR8XJEPAYMAPvlx0BEPBoRrwDz8rZmZmZmZmZmZlZi9c4K9w3gC8C2+fnOwLMRsS4/XwaMz8vjgScAImKdpDV5+/HAosIxi/s8MSh9/2pBSDoROBGgr6+P/v7+qsGuXbv2tXUz91pXdZtNUeu8tWLolDLEUJY4yhBDWeIoQwxmZmZmZmbWG4atWJL0AWBlRCyWNGXkQ6otIi4BLgGYPHlyTJlSPZz+/n4q61o9VS7A0mOrn7dWDJ1ShhjKEkcZYihLHGWIwcxstMhd/n8KLI+ID0jajdQ6e2dgMfCJiHhF0lbAFcC+wDPARyJiaT7G6aShBl4F/ioibs7pU4Hzgc2BSyNidlsvzszMzIz6usIdBHxI0lJSQehgUiFmB0mViqkJwPK8vByYCJDXb08qIL2WPmifWulmZmZm3c5jVJqZmVlPG7ZiKSJOj4gJETGJVLC5LSKOBRYCH86bTQeuz8vz83Py+tsiInL6MXnWuN2A3YE7gbuA3fMsc1vmc8xvydWZmZmZdYjHqDQzM7PRoN4xlqo5DZgn6WzgZ8BlOf0y4EpJA8AqUkUREXG/pGuAB4B1wMkR8SqApFOAm0lNuedExP2bEJeZmZlZGXTVGJVFfWNbP05lK8b3K+M4gWWMCRxXI8oYk5lZN2moYiki+oH+vPwo6deywdu8BPx5jf2/Cny1SvqNwI2NxGJmZmZWVt04RmXRhVddz7lLNuX3x43VM0blcMo4TmAZYwLH1YgyxmRm1k1aW2IwMzMzM1g/RuURwNbAdhTGqMytlqqNUbmszjEqGSLdzMzMrG3qGbzbzMzMzBrgMSrNrKwkbS3pTkk/l3S/pC/n9N0k3SFpQNLVOW8h5z9X5/Q7JE0qHOv0nP6QpMMK6VNz2oCkWe2+RjNrL1csmVnXcsHIzLrQacDn8liUO7PhGJU75/TPAbMgjVEJVMao/E/yGJW5xVNljMoHgWs8RqWZ1ell4OCIeBfwbmCqpAPwrJVm1iR3hTOzblYpGK2VtAVwu6SbSF/KzouIeZK+RSoQXUyhYCTpGFLB6CODCkZvAG6R9NZ8jouA95MGxr1L0vyIeKCdF2lm3c1jVJpZmeTWkGvz0y3yI0izVn4sp88FziSVn6blZUizVn5z8KyVwGO5YrySvw3k/A5JlVkrXX4y61GuWDKzruWCkZmZmVnjcquixcBbSD+iPUKXzFpZpln8yhJLWeKA8sRSljig+VhaPTvrSN4TVyyZWVfr5oJRWafzrleZPrAb1c2xQ3fH382xm5n1ioh4FXi3pB2AHwBv71AcDc9aWaZZ/MoSS1nigPLEUpY4oPlYZsz6YUvjuHzquBG7J65YMrOu1s0Fo7JO512vMn1gN6qbY4fujr+bYzcz6zUR8aykhcCBeNZKM2uSB+82s54QEc+SZlt6rWCUV1UrGFFnwWioApOZmZlZ15H0+vyDHJLGksaSfBDPWmlmTXLFkpl1LReMzMzMzBq2K7BQ0r2kss6CiLgBz1ppZk1yVzgz62a7AnPzOEubkQouN0h6AJgn6WzgZ2xYMLoyF4xWkSqKiIj7JVUKRuvIBSMASZWC0ebAHBeMzMzMrJtFxL3A3lXSPWulmTXFFUtm1rVcMDIzMzMzM+ssd4UzMzMzMzMzM7OmuGLJzMzMzMzMzMya4oolMzMzMzMzMzNriiuWzMzMzMzMzMysKa5YMjMzMzMzMzOzprhiyczMzMzMzMzMmuKKJTMzMzMzMzMza4orlszMzMzMzMzMrCmuWDIzMzMzMzMzs6a4YsnMzMzMzMzMzJriiiUzMzMzMzMzM2uKK5bMzMzMzMzMzKwprlgyMzMzMzMzM7OmuGLJzLqWpImSFkp6QNL9kk7N6TtJWiDp4fx3x5wuSRdIGpB0r6R9Cseanrd/WNL0Qvq+kpbkfS6QpPZfqZmZmVlruPxkZq3miiUz62brgJkRsQdwAHCypD2AWcCtEbE7cGt+DnA4sHt+nAhcDKkgBZwB7A/sB5xRKUzlbT5V2G9qG67LzMzMbKS4/GRmLeWKJTPrWhGxIiLuzsvPAw8C44FpwNy82VzgqLw8DbgikkXADpJ2BQ4DFkTEqohYDSwApuZ120XEoogI4IrCsczMzMy6jstPZtZqYzodgJlZK0iaBOwN3AH0RcSKvOpJoC8vjweeKOy2LKcNlb6sSnq1859I+hWPvr4++vv7h425byzM3GvdsNs1op7ztsratWvber5W6ubYobvj7+bYzcx6TTeWn8r0OVKWWMoSB5QnlrLEAc3H0urvCSN5T1yxZGZdT9I2wPeBz0bEc8Vu/BERkmKkY4iIS4BLACZPnhxTpkwZdp8Lr7qec5e0Nhteeuzw522V/v5+6rnOMurm2KG74+/m2M3Mekm3lp/K9DlSlljKEgeUJ5ayxAHNxzJj1g9bGsflU8eN2D1xVzgz62qStiAViq6KiOty8lO5GTb578qcvhyYWNh9Qk4bKn1ClXQzMzOzruXyk5m10rAVS541wMzKKucVlwEPRsTXC6vmA5U8ZjpwfSH9uJxPHQCsyU2+bwYOlbRjzssOBW7O656TdEA+13GFY5mZmZl1HZefzKzV6mmx5FkDzKysDgI+ARws6Z78OAKYDbxf0sPA+/JzgBuBR4EB4F+BkwAiYhXwFeCu/Dgrp5G3uTTv8whwUzsuzMzMzGyEuPxkZi017OAeucZ5RV5+XlJx1oApebO5QD9wGoVZA4BFkiqzBkwhzxoAIKkya0A/edaAnF6ZNcCZj5kNKSJuB2q1cDykyvYBnFzjWHOAOVXSfwq8YxPCNLNRSNJE0kxIfUAAl0TE+fmHtquBScBS4OiIWJ1/1T8fOAJ4EZhRmbUpt/L+Uj702RExN6fvC1wOjCV98Ts153NmZjW5/GRmrdbQqLHdMmtAcbTzVo+kDvXNulSGUejLEENZ4ihDDGWJowwxmJmNApUW33dL2hZYnH9Um0Fq8T1b0ixSi+/T2LDF9/6k1tz7F1p8TyZVUC2WND9P7V1p8X0HqWJpKv5hzszMzNqs7oqlbpo1oDjqeqtHUof6Zl0qwyj0ZYihLHGUIYayxFGGGMzMep1bfJuZmdloUVfF0lCzBkTEigZmDZgyKL0fzxpgZmZmPaxbWnwX9Y1tfavvVrSWLWOr2zLGBI6rEWWMycysmwxbsVTHrAGz2XjWgFMkzSM15V6TK59uBr5WGLD7UOD0iFgl6bk8w8AdpFkDLmzBtZmZmZl1VDe1+C668KrrOXdJQyMmDKueFt/DKWOr2zLGBI6rEWWMycysm9RTYqjMGrBE0j057W9JFUrXSDoBeBw4Oq+7kTTw5ABp8MnjIc0aIKkyawBsPGvA5aTBJ2/CzbjNzMysy7nFt5mZmY0G9cwK51kDzMzMzBrgFt9mZmY2WrS2jbOZmZmZgVt8m5mZ2SjhiiUzMzOzFnOLbzMzMxstNut0AGZmZmZmZmZm1p1csWRmZmZmZmZmZk1xxZKZmZmZmZmZmTXFFUtmZmZmZmZmZtYUVyyZmZmZmZmZmVlTXLFkZmZmZmY2ikiaI2mlpPsKaTtJWiDp4fx3x5wuSRdIGpB0r6R9CvtMz9s/LGl6IX1fSUvyPhdIqjVLppn1AFcsmVnXcqHIzMzMrCmXA1MHpc0Cbo2I3YFb83OAw4Hd8+NE4GJIZS7gDGB/YD/gjEq5K2/zqcJ+g89lZj3EFUtm1s0ux4UiMzMzs4ZExI+BVYOSpwFz8/Jc4KhC+hWRLAJ2kLQrcBiwICJWRcRqYAEwNa/bLiIWRUQAVxSOZWY9aEynAzAza1ZE/FjSpEHJ04ApeXku0A+cRqFQBCySVCkUTSEXigAkVQpF/eRCUU6vFIpuGrkrMjMzM+uYvohYkZefBPry8njgicJ2y3LaUOnLqqRvRNKJpB/86Ovro7+/f9gg165dW9d27VCWWMoSB5QnlrLEAc3HMnOvdaWIox6uWDKzXtP2QhE0VzDqG9v6D4x2foCW6QO7Ud0cO3R3/N0c+//f3v0HX1bX9x1/vrPLryACuumG7m5cTDZJV7CCO7DWNN2IIQs6rJ1YC4MBHJKdVGg02qZQO9FoMwFbNEKJdoEN4BCREO5tVMgAABoUSURBVCvbCLOlyo7TTkAWQX4G+QZQdousii4BJ+Lqu3+cz1euX74/7j37vefH9/t8zNz53nvOufe87ufe8znnvr/nhyQtFpmZEZENzGcLsAVg3bp1uWHDhjmfs2PHDoaZrgldydKVHNCdLF3JAfWznHPB5+Y1x9UbDx1bm1hYkrRgNbVRVOY18obRZdfdxCX3zW83/PiZc893vnRphT2qPmeHfufvc3ZJWuCeioijMvPJslf3njJ8N7BqYLqVZdhuXthLfHL4jjJ85TTTS1qgPMeSpIXmqbIxxAgbRTMNd6NIkiQtFtuAyYuYnA3cNDD8rHIhlPXA3rJ3+Hbg5Ig4spyf8mRgexn3TESsLxc+OWvgtSQtQBaWJC00bhRJkiTNIiI+BfwN8EsRsSsizgUuAn49Ih4B3lgeA9wMPApMAFcA7wQo56f8EHBnuX1w8pyVZZory3P+Ds9RKS1oHgonqbfKRtEGYFlE7KK6uttFwA1lA+lrwNvK5DcDp1Jt4HwPeAdUG0URMblRBC/eKLoaOIRqg8iNIkmS1HuZecYMo06aZtoEzpvhdbYCW6cZvhM4Zn8ySuoPC0uSesuNIkmSJElql4fCSZIkSZIkqRYLS5IkSZIkSarFwpIkSZIkSZJqsbAkSZIkSZKkWiwsSZIkSZIkqRavCidJkiRJ6oX7du/lnAs+N6+v+fhFb5rX15MWG/dYkiRJkiRJUi0WliRJkiRJklSLhSVJkiRJkiTVYmFJkiRJkiRJtVhYkiRJkiRJUi1eFU6SFpDV83yVlNm899h9ta7K4pVXJEmSpIXDwpIkqVHjKH5ZrJIkSQvZbNtPC/mffXW3G2drkz68777pTGEpIjYCHwOWAFdm5kUtR5rRMF/uURZuv9hSt/Wpf1qsRt3oGKaPtm9WH9g/Seoq+ydp8ejEOZYiYglwOXAKsBY4IyLWtptKkuyfJHWX/ZOkrrJ/khaXruyxdAIwkZmPAkTE9cAm4MFWUzVkXOdEqbtL5Ez8770WqUXdP0nqNPsnSV1l/yQtIl0pLK0Anhh4vAs4cepEEbEZ2FwePhsRD8/wesuAb81rwhH93gLMEBfXfmrrbdGRDNCNHPOd4RXz+FpdNN/906AufB9q60I/V9cw2fejz2tCb9ueZrPbP9Gd/mmelqkufve7mAnMNYo2Mtk/sSD6p058n+tuk41pW6fzbdLCNl4n2uTXLh4px0h9VFcKS0PJzC3Alrmmi4idmbmugUhm6EmOLmToSo4uZFiIhu2fBvX9s+hz/j5nh37n73P2vlpI/VMXc3UxE5hrFF3MtFj0vX/qSpau5IDuZOlKDuhOlnHm6MQ5loDdwKqBxyvLMElqm/2TpK6yf5LUVfZP0iLSlcLSncCaiDg6Ig4ETge2tZxJksD+SVJ32T9J6ir7J2kR6cShcJm5LyLOB7ZTXY5ya2Y+sB8vOdLulGNihhd0IUcXMkA3cnQhQ2+MoX8a1PfPos/5+5wd+p2/z9k7ZZH2T13M1cVMYK5RdDFTry2i/qkrWbqSA7qTpSs5oDtZxpYjMnNcry1JkiRJkqQFrCuHwkmSJEmSJKlnLCxJkiRJkiSplgVVWIqIjRHxcERMRMQFDc53a0TsiYj7B4a9LCJujYhHyt8jx5xhVUTcFhEPRsQDEfGupnNExMER8aWI+ErJ8Edl+NERcUf5XD5dTuA3VhGxJCLujoi/bjHD4xFxX0TcExE7y7CmvxdHRMSNEfG3EfFQRLyu6QyaXlv91bBG7VOicml5P/dGxPHtvoPh+4GIOKg8nijjV7eZu2QaetntWttHxO+X78z9EfGpsm7oTdurO/1TF9ajZZ5Db+c1uTzOkOsDEbG7tNk9EXHqwLgLS66HI+I3xpSpk+uOWXK12l6a3Vx9UVPrkCFynBMR3xz4Hv32mHK8aJmfMr7V/mfK+A0RsXegTf5wTDmmXbanTDP2dhkyR1NtMu1v8inTzP+yk5kL4kZ1Uri/A14JHAh8BVjb0Lx/FTgeuH9g2IeBC8r9C4CLx5zhKOD4cv8w4KvA2iZzAAG8pNw/ALgDWA/cAJxehn8C+DcNfCbvAf4C+OvyuI0MjwPLpgxr+ntxDfDb5f6BwBFNZ/A27efSWn81QsaR+hTgVOCW0g+sB+7owHsYqh8A3gl8otw/Hfh0B7IPvex2qe2BFcBjwCEDbX5On9p+sd+61D91YT1a5jP0dl6Ty+MMuT4A/Ltppl1bPsuDgKPLZ7xkDJk6ue6YJVer7eVt1s9szr6oiXXIkDnOAf5bA23yomV+yvhW+58p4zdQtr/G3CbTLttNt8uQOZpqk2l/k0+ZZt6XnYW0x9IJwERmPpqZzwPXA5uamHFmfhF4esrgTVQ/DCh/3zLmDE9m5pfL/b8HHqLawG8sR1aeLQ8PKLcE3gDc2EQGgIhYCbwJuLI8jqYzzKKxzyMiDqfq9K8CyMznM/O7TWbQjFrrr4ZVo0/ZBFxb+oHbgSMi4qiGY//YiP3A4Hu6ETipTN+KGstup9qe6oqzh0TEUuCngSfpSdsL6H7/1Pg6bMTtvMaWxxlyzWQTcH1mfj8zHwMmqD7r+c7UyXXHLLlm0kh7aVbD9EVNrEM60ycOscx3tf8ZmyGX7bG3S40+Zmxm+U0+aN6XnYVUWFoBPDHweBctfZjF8sx8stz/BrC8qRmXXdmOo6pONpojqkNP7gH2ALdSVfi/m5n7yiRNfC5/CvwB8KPy+OUtZIBqAf5fEXFXRGwuw5r8PI4Gvgn8eVSHA10ZEYc2nEHT61p/Nash+5SuvadR+oEfZy/j95bp2zLqstuZts/M3cB/Bb5OVVDaC9xFf9peHfo+0f56dDZdXh7PL4d7bI0XDhVsPFdX1x1TckFH2ksvMsxn0MQ6ZNjvwm+W79GNEbFqnjMMq2vf29eVw7FuiYhXjXtm0yzbkxptl1lyQENtMvU3eWbO2CbztewspMJSZ2W1j9nUKuFYRMRLgL8C3p2ZzzSdIzN/mJmvAVZSVfh/eZzzmyoi3gzsycy7mpzvDH4lM48HTgHOi4hfHRzZwOexlGoX1Y9n5nHAc1S7nzeZQT3Xdp9SR8f6gTp6u+yWH2WbqIpj/xg4FNjYaij1Wdvr0aF0JUfxceDngddQFXcvaSNEV9cd0+TqRHup9/4nsDozX031j/Vr5ph+Mfgy8IrM/KfAZcBnxzmz2fqcJs2Ro7E2mfqbPCKOGde8Ji2kwtJuYLA6vLIMa8tTk7vYlb97xj3DiDiA6ot8XWZ+pq0cAOWwjduA11Htbri0jBr35/J64LSIeJxqV9U3AB9rOAPw4//ck5l7gP9BVWhr8vPYBewaqFDfSPVjtZXvhH5C1/qraY3Yp3TpPY3aD/w4exl/OPDtJgNPMeqy26W2fyPwWGZ+MzN/AHyG6vPoS9urQ9+nDqxHZ9PJ5TEznyo/KH4EXMELh281lqur647pcnWhvTSjYT6DJtYhc+bIzG9n5vfLwyuB185zhmF15nubmc9MHo6VmTcDB0TEsnHMa4Y+Z1Aj7TJXjibbZGCek7/Jp/6Tb96XnYVUWLoTWBPVlWcOpDoJ1bYW82wDzi73zwZuGufMyjGRVwEPZeZH2sgRET8TEUeU+4cAv051fOltwFvLZB8B/tGU510aER+bjwyZeWFmrszM1VTfgS9k5plTMjTxeRwaEYdN3gdOBu6nwc8jM78BPBERv1QGnQQ82GQGzahr/dWL1OhTtgFnRWU9sHfgsIdG1egHBt/TW8v0re19UGPZ7UzbUx0Ctz4ifrp8hyaz96LtBXSkf+rCenQOnVwe4yfPG/IvqdpsMtfpUV0J6GhgDfClMcy/k+uOmXK13V6a1TB9URPrkDlzTPkenUb1+6cNndkeiIifLcsdEXECVd1h3v9xNEufM2js7TJMjgbbZLrf5H87ZbL5X3ZyzGclb/JGdcb3r1Kd1+d9Dc73U1S7z/6A6j/N51Ido/h54BHgfwMvG3OGX6Harfhe4J5yO7XJHMCrgbtLhvuBPyzDX0m1Mp6g2lX0OeCIMm4p1X+tXjuGPBt44WpQgxn+EjhozJ/HK6muGvEV4IHJ72ML34vXADvLZ/JZ4MimM3ib8bNppb8aId9IfQrVFSguL+/nPmBd2++h5JqzHwAOLo8nyvhXdiD30Mtu19oe+COqDZj7gU9SXVWpN23vrRv9U1fWo2WeQ2/nNbk8zpDrk2W+91L9cDhqYPr3lVwPA6eMKVMn1x2z5Gq1vbzN+bm9qC8CPgicVu43sg4ZIseflH7qK1T/SPnlMeWYbpn/XeB3y/i2+5/BLOcPtMntwD8bU46Zlu1G22XIHE21yUy/yce67ER5YakxEXEL8JnMvCKqc6F8ODPXtp1LkiRJkiSNZiEdCqf+uAZ4e7n/dqr/FkmSJEmSpJ5xjyU1LiIOptp18p9T7Qa4NjO/3m4qSZIkSZI0KgtLakVEXAGcCHwrM9/Qdh5JkiRJkjQ6D4VTW64BjsXD4CRJkiRJ6i33WFIrIuLnqK4a9LOZ+UzbeSRJkiRJ0ujcY0mNi4ifAt4DXG9RSZIkSZKk/lradgAtLhFxKPAU8DVgY8txJEmSJEnSfvBQOEmSJEmSJNXioXCSJEmSJEmqpbeHwi1btixXr14953TPPfcchx566PgD7Yc+ZIR+5OxDRuhHzmEz3nXXXd/KzJ9pIJIkSZIkqWOGLixFxBJgJ7A7M98cEUcD1wMvB+4Cfiszn4+Ig4BrgdcC3wb+dWY+Xl7jQuBc4IfA72Xm9jJ8I/AxYAlwZWZeNFee1atXs3Pnzjlz79ixgw0bNgz7NlvRh4zQj5x9yAj9yDlsxoj42vjTSJIkSZK6aJRD4d4FPDTw+GLgo5n5C8B3qApGlL/fKcM/WqYjItYCpwOvojpp859FxJJSsLocOAVYC5xRppUkSZIkSVKHDVVYioiVwJuAK8vjAN4A3FgmuQZ4S7m/qTymjD+pTL+J6vLy38/Mx4AJ4IRym8jMRzPzeaq9oDbt7xuTJEmSJEnSeA17KNyfAn8AHFYevxz4bmbuK493ASvK/RXAEwCZuS8i9pbpVwC3D7zm4HOemDL8xOlCRMRmYDPA8uXL2bFjx5zB9zy9l8uuu2nO6UZx7IrD5/X1nn322aHeS9v6kLMPGaEfOfuQUZIkSZLUrjkLSxHxZmBPZt4VERvGH2lmmbkF2AKwbt26HOb8L5dddxOX3De/5yh//My55zuKPpxvB/qRsw8ZoR85+5BRkiRJktSuYSourwdOi4hTgYOBl1KdaPuIiFha9lpaCewu0+8GVgG7ImIpcDjVSbwnh08afM5MwyVJkiRJktRRc55jKTMvzMyVmbma6uTbX8jMM4HbgLeWyc4GJo8321YeU8Z/ITOzDD89Ig4qV5RbA3wJuBNYExFHR8SBZR7b5uXdSZIkSZIkaWz25xix/wBcHxH/GbgbuKoMvwr4ZERMAE9TFYrIzAci4gbgQWAfcF5m/hAgIs4HtgNLgK2Z+cB+5JIkSZIkSVIDRiosZeYOYEe5/yjVFd2mTvMPwL+a4fl/DPzxNMNvBm4eJYskSZIkSZLaNeehcJIkSZIkSdJ0LCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRa5iwsRcSqiLgtIh6MiAci4l1l+Msi4taIeKT8PbIMj4i4NCImIuLeiDh+4LXOLtM/EhFnDwx/bUTcV55zaUTEON6sJEmSJEmS5s8weyztA96bmWuB9cB5EbEWuAD4fGauAT5fHgOcAqwpt83Ax6EqRAHvB04ETgDeP1mMKtP8zsDzNu7/W5MkSZIkSdI4zVlYyswnM/PL5f7fAw8BK4BNwDVlsmuAt5T7m4Brs3I7cEREHAX8BnBrZj6dmd8BbgU2lnEvzczbMzOBawdeS5IkSZIkSR21dJSJI2I1cBxwB7A8M58so74BLC/3VwBPDDxtVxk22/Bd0wyfbv6bqfaCYvny5ezYsWPOzMsPgfceu2/O6UYxzHxH8eyzz877a45DH3L2ISP0I2cfMkqSJEmS2jV0YSkiXgL8FfDuzHxm8DRImZkRkWPI9xMycwuwBWDdunW5YcOGOZ9z2XU3ccl9I9XP5vT4mXPPdxQ7duxgmPfStj7k7ENG6EfOPmSUJEmSJLVrqKvCRcQBVEWl6zLzM2XwU+UwNsrfPWX4bmDVwNNXlmGzDV85zXBJkiRJkiR12DBXhQvgKuChzPzIwKhtwOSV3c4GbhoYfla5Otx6YG85ZG47cHJEHFlO2n0ysL2MeyYi1pd5nTXwWpIkSZIkSeqoYY4Rez3wW8B9EXFPGfYfgYuAGyLiXOBrwNvKuJuBU4EJ4HvAOwAy8+mI+BBwZ5nug5n5dLn/TuBq4BDglnKTJEmSJElSh81ZWMrM/wPEDKNPmmb6BM6b4bW2AlunGb4TOGauLJIkSZIkSeqOoc6xJEmSJEmSJE1lYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNViYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNViYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNViYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNViYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNViYUmSJEmSJEm1WFiSJEmSJElSLRaWJEmSJEmSVIuFJUmSJEmSJNWytO0Akvbf6gs+N++vefXGQ+f9NSVJkiRJC4t7LEmSJEmSJKkWC0uSJEmSJEmqxcKSJEmSJEmSarGwJEmSJEmSpFosLEmSJEmSJKkWC0uSJEmSJEmqxcKSJEmSJEmSaulMYSkiNkbEwxExEREXtJ1HkiRJkiRJs+tEYSkilgCXA6cAa4EzImJtu6kkSZIkSZI0m04UloATgInMfDQznweuBza1nEmSJEmSJEmzWNp2gGIF8MTA413AiVMniojNwOby8NmIeHiI114GfGu/Ew7muHg+Xw0YQ8Yx6UPOPmSEHuT8tYuHzviKcWeRJEmSJHVTVwpLQ8nMLcCWUZ4TETszc92YIs2LPmSEfuTsQ0boR84+ZJQkSZIktasrh8LtBlYNPF5ZhkmSJEmSJKmjulJYuhNYExFHR8SBwOnAtpYzSZIkSZIkaRadOBQuM/dFxPnAdmAJsDUzH5inlx/p0LmW9CEj9CNnHzJCP3L2IaMkSZIkqUWRmW1nkCRJkiRJUg915VA4SZIkSZIk9YyFJUmSJEmSJNWyYApLEbExIh6OiImIuGCa8QdFxKfL+DsiYnUHM74nIh6MiHsj4vMR8YquZRyY7jcjIiOilcvRD5MzIt5W2vOBiPiLrmWMiJ+LiNsi4u7ymZ/aQsatEbEnIu6fYXxExKXlPdwbEcc3nVGSJEmS1F0LorAUEUuAy4FTgLXAGRGxdspk5wLfycxfAD4KXNzBjHcD6zLz1cCNwIc7mJGIOAx4F3BHk/kG5j9nzohYA1wIvD4zXwW8u2sZgf8E3JCZx1FdCfHPmsxYXA1snGX8KcCactsMfLyBTJIkSZKknlgQhSXgBGAiMx/NzOeB64FNU6bZBFxT7t8InBQR0aWMmXlbZn6vPLwdWNlgvqEyFh+iKsz9Q5PhBgyT83eAyzPzOwCZuaeDGRN4abl/OPD/GsxXBcj8IvD0LJNsAq7Nyu3AERFxVDPpJEmSJEldt1AKSyuAJwYe7yrDpp0mM/cBe4GXN5JuyvyL6TIOOhe4ZayJXmzOjOVQqFWZ+bkmg00xTFv+IvCLEfF/I+L2iJhtr5xxGCbjB4C3R8Qu4Gbg3zYTbSSjfm8lSZIkSYvI0rYD6MUi4u3AOuBftJ1lUET8FPAR4JyWowxjKdXhWxuo9vz6YkQcm5nfbTXVTzoDuDozL4mI1wGfjIhjMvNHbQeTJEmSJGkYC2WPpd3AqoHHK8uwaaeJiKVUhx59u5F0U+ZfTJeRiHgj8D7gtMz8fkPZJs2V8TDgGGBHRDwOrAe2tXAC72HachewLTN/kJmPAV+lKjQ1ZZiM5wI3AGTm3wAHA8saSTe8ob63kiRJkqTFaaEUlu4E1kTE0RFxINWJkLdNmWYbcHa5/1bgC5mZXcoYEccB/52qqNT0OYHmzJiZezNzWWauzszVVOeBOi0zd3YpZ/FZqr2ViIhlVIfGPdqxjF8HTioZ/wlVYembDWYcxjbgrHJ1uPXA3sx8su1QkiRJkqRuWBCHwmXmvog4H9gOLAG2ZuYDEfFBYGdmbgOuojrUaILqZMWndzDjfwFeAvxlOa/41zPztI5lbN2QObcDJ0fEg8APgX+fmY3toTZkxvcCV0TE71OdyPuchoudRMSnqApwy8q5nt4PHFDewyeozv10KjABfA94R5P5JEmSJEndFg3/jpUkSZIkSdICsVAOhZMkSZIkSVLDLCxJkiRJkiSpFgtLkiRJkiRJqsXCkiRJkiRJkmqxsCRJkiRJkqRaLCxJkiRJkiSpFgtLkiRJkiRJquX/AyNesnd3jb3tAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "8Edy9Lxmm12y",
        "outputId": "96971d65-9a67-4127-dbe5-6bdeaccecb79"
      },
      "source": [
        "# Correlation Plot\n",
        "\n",
        "corr_mat = dataset.corr()\n",
        "corr_mat"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021868</td>\n",
              "      <td>-0.403240</td>\n",
              "      <td>-0.106807</td>\n",
              "      <td>-0.017879</td>\n",
              "      <td>0.097783</td>\n",
              "      <td>-0.185513</td>\n",
              "      <td>-0.015655</td>\n",
              "      <td>0.026221</td>\n",
              "      <td>-0.009120</td>\n",
              "      <td>-0.042357</td>\n",
              "      <td>-0.004648</td>\n",
              "      <td>0.004760</td>\n",
              "      <td>-0.023758</td>\n",
              "      <td>0.001288</td>\n",
              "      <td>0.007367</td>\n",
              "      <td>0.025155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <td>-0.021868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062045</td>\n",
              "      <td>0.166707</td>\n",
              "      <td>-0.006853</td>\n",
              "      <td>0.018232</td>\n",
              "      <td>-0.125363</td>\n",
              "      <td>-0.033004</td>\n",
              "      <td>-0.082063</td>\n",
              "      <td>0.022856</td>\n",
              "      <td>-0.092870</td>\n",
              "      <td>0.004744</td>\n",
              "      <td>0.006839</td>\n",
              "      <td>-0.024455</td>\n",
              "      <td>-0.000911</td>\n",
              "      <td>0.011010</td>\n",
              "      <td>0.040438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <td>-0.403240</td>\n",
              "      <td>0.062045</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.108576</td>\n",
              "      <td>-0.007023</td>\n",
              "      <td>0.002122</td>\n",
              "      <td>-0.016096</td>\n",
              "      <td>-0.046893</td>\n",
              "      <td>-0.039201</td>\n",
              "      <td>-0.005261</td>\n",
              "      <td>-0.006991</td>\n",
              "      <td>0.011852</td>\n",
              "      <td>-0.008994</td>\n",
              "      <td>0.019172</td>\n",
              "      <td>0.014973</td>\n",
              "      <td>-0.016850</td>\n",
              "      <td>0.045588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>-0.106807</td>\n",
              "      <td>0.166707</td>\n",
              "      <td>0.108576</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010718</td>\n",
              "      <td>0.064514</td>\n",
              "      <td>-0.090790</td>\n",
              "      <td>-0.048574</td>\n",
              "      <td>-0.110928</td>\n",
              "      <td>0.022671</td>\n",
              "      <td>-0.057304</td>\n",
              "      <td>0.001935</td>\n",
              "      <td>0.006255</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>0.017570</td>\n",
              "      <td>-0.019361</td>\n",
              "      <td>0.066241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default</th>\n",
              "      <td>-0.017879</td>\n",
              "      <td>-0.006853</td>\n",
              "      <td>-0.007023</td>\n",
              "      <td>-0.010718</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.066745</td>\n",
              "      <td>-0.006025</td>\n",
              "      <td>0.077234</td>\n",
              "      <td>0.015404</td>\n",
              "      <td>0.009424</td>\n",
              "      <td>0.011486</td>\n",
              "      <td>-0.010021</td>\n",
              "      <td>0.016822</td>\n",
              "      <td>-0.029979</td>\n",
              "      <td>-0.018329</td>\n",
              "      <td>0.034898</td>\n",
              "      <td>-0.022419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>balance</th>\n",
              "      <td>0.097783</td>\n",
              "      <td>0.018232</td>\n",
              "      <td>0.002122</td>\n",
              "      <td>0.064514</td>\n",
              "      <td>-0.066745</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.068768</td>\n",
              "      <td>-0.084350</td>\n",
              "      <td>-0.027273</td>\n",
              "      <td>0.004503</td>\n",
              "      <td>0.019777</td>\n",
              "      <td>0.021560</td>\n",
              "      <td>-0.014578</td>\n",
              "      <td>0.003435</td>\n",
              "      <td>0.016674</td>\n",
              "      <td>-0.020967</td>\n",
              "      <td>0.052838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <td>-0.185513</td>\n",
              "      <td>-0.125363</td>\n",
              "      <td>-0.016096</td>\n",
              "      <td>-0.090790</td>\n",
              "      <td>-0.006025</td>\n",
              "      <td>-0.068768</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.041323</td>\n",
              "      <td>0.188123</td>\n",
              "      <td>-0.027982</td>\n",
              "      <td>0.271481</td>\n",
              "      <td>0.005075</td>\n",
              "      <td>-0.023599</td>\n",
              "      <td>0.124178</td>\n",
              "      <td>0.037076</td>\n",
              "      <td>-0.099971</td>\n",
              "      <td>-0.139173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan</th>\n",
              "      <td>-0.015655</td>\n",
              "      <td>-0.033004</td>\n",
              "      <td>-0.046893</td>\n",
              "      <td>-0.048574</td>\n",
              "      <td>0.077234</td>\n",
              "      <td>-0.084350</td>\n",
              "      <td>0.041323</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010873</td>\n",
              "      <td>0.011370</td>\n",
              "      <td>0.022145</td>\n",
              "      <td>-0.012412</td>\n",
              "      <td>0.009980</td>\n",
              "      <td>-0.022754</td>\n",
              "      <td>-0.011043</td>\n",
              "      <td>0.015458</td>\n",
              "      <td>-0.068185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contact</th>\n",
              "      <td>0.026221</td>\n",
              "      <td>-0.082063</td>\n",
              "      <td>-0.039201</td>\n",
              "      <td>-0.110928</td>\n",
              "      <td>0.015404</td>\n",
              "      <td>-0.027273</td>\n",
              "      <td>0.188123</td>\n",
              "      <td>-0.010873</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.027936</td>\n",
              "      <td>0.361145</td>\n",
              "      <td>-0.020839</td>\n",
              "      <td>0.019614</td>\n",
              "      <td>-0.244816</td>\n",
              "      <td>-0.147811</td>\n",
              "      <td>0.272214</td>\n",
              "      <td>-0.148395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>-0.009120</td>\n",
              "      <td>0.022856</td>\n",
              "      <td>-0.005261</td>\n",
              "      <td>0.022671</td>\n",
              "      <td>0.009424</td>\n",
              "      <td>0.004503</td>\n",
              "      <td>-0.027982</td>\n",
              "      <td>0.011370</td>\n",
              "      <td>-0.027936</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.006028</td>\n",
              "      <td>-0.030206</td>\n",
              "      <td>0.162490</td>\n",
              "      <td>-0.093044</td>\n",
              "      <td>-0.051710</td>\n",
              "      <td>0.083460</td>\n",
              "      <td>-0.028348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>-0.042357</td>\n",
              "      <td>-0.092870</td>\n",
              "      <td>-0.006991</td>\n",
              "      <td>-0.057304</td>\n",
              "      <td>0.011486</td>\n",
              "      <td>0.019777</td>\n",
              "      <td>0.271481</td>\n",
              "      <td>0.022145</td>\n",
              "      <td>0.361145</td>\n",
              "      <td>-0.006028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006314</td>\n",
              "      <td>-0.110031</td>\n",
              "      <td>0.033065</td>\n",
              "      <td>0.022727</td>\n",
              "      <td>-0.033038</td>\n",
              "      <td>-0.024471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>-0.004648</td>\n",
              "      <td>0.004744</td>\n",
              "      <td>0.011852</td>\n",
              "      <td>0.001935</td>\n",
              "      <td>-0.010021</td>\n",
              "      <td>0.021560</td>\n",
              "      <td>0.005075</td>\n",
              "      <td>-0.012412</td>\n",
              "      <td>-0.020839</td>\n",
              "      <td>-0.030206</td>\n",
              "      <td>0.006314</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084570</td>\n",
              "      <td>-0.001565</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>0.010925</td>\n",
              "      <td>0.394521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <td>0.004760</td>\n",
              "      <td>0.006839</td>\n",
              "      <td>-0.008994</td>\n",
              "      <td>0.006255</td>\n",
              "      <td>0.016822</td>\n",
              "      <td>-0.014578</td>\n",
              "      <td>-0.023599</td>\n",
              "      <td>0.009980</td>\n",
              "      <td>0.019614</td>\n",
              "      <td>0.162490</td>\n",
              "      <td>-0.110031</td>\n",
              "      <td>-0.084570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.088628</td>\n",
              "      <td>-0.032855</td>\n",
              "      <td>0.101588</td>\n",
              "      <td>-0.073172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pdays</th>\n",
              "      <td>-0.023758</td>\n",
              "      <td>-0.024455</td>\n",
              "      <td>0.019172</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>-0.029979</td>\n",
              "      <td>0.003435</td>\n",
              "      <td>0.124178</td>\n",
              "      <td>-0.022754</td>\n",
              "      <td>-0.244816</td>\n",
              "      <td>-0.093044</td>\n",
              "      <td>0.033065</td>\n",
              "      <td>-0.001565</td>\n",
              "      <td>-0.088628</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.454820</td>\n",
              "      <td>-0.858362</td>\n",
              "      <td>0.103621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous</th>\n",
              "      <td>0.001288</td>\n",
              "      <td>-0.000911</td>\n",
              "      <td>0.014973</td>\n",
              "      <td>0.017570</td>\n",
              "      <td>-0.018329</td>\n",
              "      <td>0.016674</td>\n",
              "      <td>0.037076</td>\n",
              "      <td>-0.011043</td>\n",
              "      <td>-0.147811</td>\n",
              "      <td>-0.051710</td>\n",
              "      <td>0.022727</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>-0.032855</td>\n",
              "      <td>0.454820</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.489752</td>\n",
              "      <td>0.093236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome</th>\n",
              "      <td>0.007367</td>\n",
              "      <td>0.011010</td>\n",
              "      <td>-0.016850</td>\n",
              "      <td>-0.019361</td>\n",
              "      <td>0.034898</td>\n",
              "      <td>-0.020967</td>\n",
              "      <td>-0.099971</td>\n",
              "      <td>0.015458</td>\n",
              "      <td>0.272214</td>\n",
              "      <td>0.083460</td>\n",
              "      <td>-0.033038</td>\n",
              "      <td>0.010925</td>\n",
              "      <td>0.101588</td>\n",
              "      <td>-0.858362</td>\n",
              "      <td>-0.489752</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.077840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>0.025155</td>\n",
              "      <td>0.040438</td>\n",
              "      <td>0.045588</td>\n",
              "      <td>0.066241</td>\n",
              "      <td>-0.022419</td>\n",
              "      <td>0.052838</td>\n",
              "      <td>-0.139173</td>\n",
              "      <td>-0.068185</td>\n",
              "      <td>-0.148395</td>\n",
              "      <td>-0.028348</td>\n",
              "      <td>-0.024471</td>\n",
              "      <td>0.394521</td>\n",
              "      <td>-0.073172</td>\n",
              "      <td>0.103621</td>\n",
              "      <td>0.093236</td>\n",
              "      <td>-0.077840</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age       job   marital  ...  previous  poutcome         y\n",
              "age        1.000000 -0.021868 -0.403240  ...  0.001288  0.007367  0.025155\n",
              "job       -0.021868  1.000000  0.062045  ... -0.000911  0.011010  0.040438\n",
              "marital   -0.403240  0.062045  1.000000  ...  0.014973 -0.016850  0.045588\n",
              "education -0.106807  0.166707  0.108576  ...  0.017570 -0.019361  0.066241\n",
              "default   -0.017879 -0.006853 -0.007023  ... -0.018329  0.034898 -0.022419\n",
              "balance    0.097783  0.018232  0.002122  ...  0.016674 -0.020967  0.052838\n",
              "housing   -0.185513 -0.125363 -0.016096  ...  0.037076 -0.099971 -0.139173\n",
              "loan      -0.015655 -0.033004 -0.046893  ... -0.011043  0.015458 -0.068185\n",
              "contact    0.026221 -0.082063 -0.039201  ... -0.147811  0.272214 -0.148395\n",
              "day       -0.009120  0.022856 -0.005261  ... -0.051710  0.083460 -0.028348\n",
              "month     -0.042357 -0.092870 -0.006991  ...  0.022727 -0.033038 -0.024471\n",
              "duration  -0.004648  0.004744  0.011852  ...  0.001203  0.010925  0.394521\n",
              "campaign   0.004760  0.006839 -0.008994  ... -0.032855  0.101588 -0.073172\n",
              "pdays     -0.023758 -0.024455  0.019172  ...  0.454820 -0.858362  0.103621\n",
              "previous   0.001288 -0.000911  0.014973  ...  1.000000 -0.489752  0.093236\n",
              "poutcome   0.007367  0.011010 -0.016850  ... -0.489752  1.000000 -0.077840\n",
              "y          0.025155  0.040438  0.045588  ...  0.093236 -0.077840  1.000000\n",
              "\n",
              "[17 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "f03Q_rc6oIxJ",
        "outputId": "02d82905-dc0f-42d7-f3ac-20a3aaaf02e7"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "sns.heatmap(corr_mat, annot= True)\n",
        "plt.show()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4s-KAyDTor32"
      },
      "source": [
        "# Feature Selection\n",
        "\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urM7LjRupgbZ"
      },
      "source": [
        "def select_best_features(xfeatures, ylabels, score_fxn = chi2):\n",
        "  skb =SelectKBest(score_func= score_fxn, k = 10)\n",
        "  best_feature_fit = skb.fit(xfeatures, ylabels)\n",
        "  best_feat_dataset = pd.DataFrame({'feature_scores' :best_feature_fit.scores_, 'feature_name': xfeatures.columns})\n",
        "  return best_feat_dataset"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNdQH4WYrIss",
        "outputId": "b3f9bacf-3de3-412b-e4e4-b3087be51c45"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
              "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VCG7LBtrSXj"
      },
      "source": [
        "Xfeatures = dataset[['age', 'job', 'marital', 'education', 'default', 'housing',\n",
        "       'loan', 'contact', 'day', 'month', 'duration', 'campaign',\n",
        "       'previous', 'poutcome']]"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxtNZ_strgYn"
      },
      "source": [
        "ylabels = dataset['y']"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7YIVXVvCt_qQ",
        "outputId": "8c30d419-2d16-42ab-edb4-47ef5efa6e70"
      },
      "source": [
        "Xfeatures.head(50)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>35</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>28</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>42</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>380</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>58</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>43</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>222</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>53</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>517</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>58</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>57</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>174</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>51</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>353</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>98</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>57</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>60</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>219</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>33</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>262</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>56</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>164</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>32</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>342</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>40</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>181</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>172</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>39</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>296</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>52</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>127</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>46</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>255</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>36</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>348</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>57</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>49</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>230</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>208</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>59</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>51</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>336</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>57</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>365</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>53</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>577</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>37</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>44</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>50</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>180</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>54</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1492</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>58</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>616</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>58</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>355</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>44</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>225</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>55</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>29</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>363</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age   job  marital  education  ...  duration  campaign  previous  poutcome\n",
              "0    58   4.0      1.0        2.0  ...       261         1         0       3.0\n",
              "1    44   9.0      2.0        1.0  ...       151         1         0       3.0\n",
              "2    33   2.0      1.0        1.0  ...        76         1         0       3.0\n",
              "3    47   1.0      1.0        3.0  ...        92         1         0       3.0\n",
              "4    33  11.0      2.0        3.0  ...       198         1         0       3.0\n",
              "5    35   4.0      1.0        2.0  ...       139         1         0       3.0\n",
              "6    28   4.0      2.0        2.0  ...       217         1         0       3.0\n",
              "7    42   2.0      0.0        2.0  ...       380         1         0       3.0\n",
              "8    58   5.0      1.0        0.0  ...        50         1         0       3.0\n",
              "9    43   9.0      2.0        1.0  ...        55         1         0       3.0\n",
              "10   41   0.0      0.0        1.0  ...       222         1         0       3.0\n",
              "11   29   0.0      2.0        1.0  ...       137         1         0       3.0\n",
              "12   53   9.0      1.0        1.0  ...       517         1         0       3.0\n",
              "13   58   9.0      1.0        3.0  ...        71         1         0       3.0\n",
              "14   57   7.0      1.0        1.0  ...       174         1         0       3.0\n",
              "15   51   5.0      1.0        0.0  ...       353         1         0       3.0\n",
              "16   45   0.0      2.0        3.0  ...        98         1         0       3.0\n",
              "17   57   1.0      1.0        0.0  ...        38         1         0       3.0\n",
              "18   60   5.0      1.0        0.0  ...       219         1         0       3.0\n",
              "19   33   7.0      1.0        1.0  ...        54         1         0       3.0\n",
              "20   28   1.0      1.0        1.0  ...       262         1         0       3.0\n",
              "21   56   4.0      1.0        2.0  ...       164         1         0       3.0\n",
              "22   32   1.0      2.0        0.0  ...       160         1         0       3.0\n",
              "23   25   7.0      1.0        1.0  ...       342         1         0       3.0\n",
              "24   40   5.0      1.0        0.0  ...       181         1         0       3.0\n",
              "25   44   0.0      1.0        1.0  ...       172         1         0       3.0\n",
              "26   39   4.0      2.0        2.0  ...       296         1         0       3.0\n",
              "27   52   2.0      1.0        1.0  ...       127         1         0       3.0\n",
              "28   46   4.0      2.0        1.0  ...       255         2         0       3.0\n",
              "29   36   9.0      2.0        1.0  ...       348         1         0       3.0\n",
              "30   57   9.0      1.0        1.0  ...       225         1         0       3.0\n",
              "31   49   4.0      1.0        2.0  ...       230         1         0       3.0\n",
              "32   60   0.0      1.0        1.0  ...       208         1         0       3.0\n",
              "33   59   1.0      1.0        1.0  ...       226         1         0       3.0\n",
              "34   51   4.0      1.0        2.0  ...       336         1         0       3.0\n",
              "35   57   9.0      0.0        1.0  ...       242         1         0       3.0\n",
              "36   25   1.0      1.0        1.0  ...       365         1         0       3.0\n",
              "37   53   9.0      1.0        1.0  ...      1666         1         0       3.0\n",
              "38   36   0.0      0.0        1.0  ...       577         1         0       3.0\n",
              "39   37   0.0      2.0        1.0  ...       137         1         0       3.0\n",
              "40   44   7.0      0.0        1.0  ...       160         1         0       3.0\n",
              "41   50   4.0      1.0        1.0  ...       180         2         0       3.0\n",
              "42   60   1.0      1.0        3.0  ...        22         1         0       3.0\n",
              "43   54   5.0      1.0        1.0  ...      1492         1         0       3.0\n",
              "44   58   5.0      1.0        3.0  ...       616         1         0       3.0\n",
              "45   36   0.0      2.0        0.0  ...       242         1         0       3.0\n",
              "46   58   6.0      1.0        2.0  ...       355         1         0       3.0\n",
              "47   44   9.0      1.0        1.0  ...       225         2         0       3.0\n",
              "48   55   9.0      0.0        1.0  ...       160         1         0       3.0\n",
              "49   29   4.0      2.0        2.0  ...       363         1         0       3.0\n",
              "\n",
              "[50 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UMt13ayrlX0"
      },
      "source": [
        "best_features = select_best_features(Xfeatures, ylabels)"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "kt_yPcFlunax",
        "outputId": "4b7876aa-7fd2-4fd3-885f-fcb91f867fb3"
      },
      "source": [
        "best_features"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_scores</th>\n",
              "      <th>feature_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.879958e+01</td>\n",
              "      <td>age</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.824523e+02</td>\n",
              "      <td>job</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.976607e+01</td>\n",
              "      <td>marital</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.061772e+01</td>\n",
              "      <td>education</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.231387e+01</td>\n",
              "      <td>default</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.889497e+02</td>\n",
              "      <td>housing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.765161e+02</td>\n",
              "      <td>loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.253814e+03</td>\n",
              "      <td>contact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.592004e+02</td>\n",
              "      <td>day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.432191e+01</td>\n",
              "      <td>month</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.807711e+06</td>\n",
              "      <td>duration</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8.405821e+02</td>\n",
              "      <td>campaign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3.593220e+03</td>\n",
              "      <td>previous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.046775e+02</td>\n",
              "      <td>poutcome</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    feature_scores feature_name\n",
              "0     7.879958e+01          age\n",
              "1     1.824523e+02          job\n",
              "2     2.976607e+01      marital\n",
              "3     9.061772e+01    education\n",
              "4     2.231387e+01      default\n",
              "5     3.889497e+02      housing\n",
              "6     1.765161e+02         loan\n",
              "7     1.253814e+03      contact\n",
              "8     1.592004e+02          day\n",
              "9     4.432191e+01        month\n",
              "10    1.807711e+06     duration\n",
              "11    8.405821e+02     campaign\n",
              "12    3.593220e+03     previous\n",
              "13    1.046775e+02     poutcome"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "G3Hsf8C5y5WH",
        "outputId": "0ebec0e0-ac60-41a5-dbf3-f3e8a2545942"
      },
      "source": [
        "best_features.nlargest(12, 'feature_scores')"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_scores</th>\n",
              "      <th>feature_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.807711e+06</td>\n",
              "      <td>duration</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3.593220e+03</td>\n",
              "      <td>previous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.253814e+03</td>\n",
              "      <td>contact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8.405821e+02</td>\n",
              "      <td>campaign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.889497e+02</td>\n",
              "      <td>housing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.824523e+02</td>\n",
              "      <td>job</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.765161e+02</td>\n",
              "      <td>loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.592004e+02</td>\n",
              "      <td>day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.046775e+02</td>\n",
              "      <td>poutcome</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.061772e+01</td>\n",
              "      <td>education</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.879958e+01</td>\n",
              "      <td>age</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.432191e+01</td>\n",
              "      <td>month</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    feature_scores feature_name\n",
              "10    1.807711e+06     duration\n",
              "12    3.593220e+03     previous\n",
              "7     1.253814e+03      contact\n",
              "11    8.405821e+02     campaign\n",
              "5     3.889497e+02      housing\n",
              "1     1.824523e+02          job\n",
              "6     1.765161e+02         loan\n",
              "8     1.592004e+02          day\n",
              "13    1.046775e+02     poutcome\n",
              "3     9.061772e+01    education\n",
              "0     7.879958e+01          age\n",
              "9     4.432191e+01        month"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2FslDbDzcII",
        "outputId": "9dc3c870-1601-451e-bfe6-d74653011e53"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4X3JTQuAzxEH",
        "outputId": "37457a3a-271d-48a5-8996-aaae09d85387"
      },
      "source": [
        "best_features.nlargest(15, 'feature_scores')['feature_name'].tolist()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['duration',\n",
              " 'previous',\n",
              " 'contact',\n",
              " 'campaign',\n",
              " 'housing',\n",
              " 'job',\n",
              " 'loan',\n",
              " 'day',\n",
              " 'poutcome',\n",
              " 'education',\n",
              " 'age',\n",
              " 'month',\n",
              " 'marital',\n",
              " 'default']"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7zBEnFW0gJ6"
      },
      "source": [
        "selected_features = best_features.nlargest(15, 'feature_scores')['feature_name'].tolist()"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIjwKGozz_sm"
      },
      "source": [
        "# Selected Features\n",
        "\n",
        "Xselected = dataset[selected_features]"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "buhHYURf0qtj",
        "outputId": "967d536a-8cd5-49ed-80ba-67d316fb7e95"
      },
      "source": [
        "Xselected"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>previous</th>\n",
              "      <th>contact</th>\n",
              "      <th>campaign</th>\n",
              "      <th>housing</th>\n",
              "      <th>job</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>education</th>\n",
              "      <th>age</th>\n",
              "      <th>month</th>\n",
              "      <th>marital</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>58</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>151</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>44</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>33</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>47</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>198</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>977</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>456</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>1127</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>72</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>508</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>361</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       duration  previous  contact  campaign  ...  age  month  marital  default\n",
              "0           261         0      2.0         1  ...   58    8.0      1.0      0.0\n",
              "1           151         0      2.0         1  ...   44    8.0      2.0      0.0\n",
              "2            76         0      2.0         1  ...   33    8.0      1.0      0.0\n",
              "3            92         0      2.0         1  ...   47    8.0      1.0      0.0\n",
              "4           198         0      2.0         1  ...   33    8.0      2.0      0.0\n",
              "...         ...       ...      ...       ...  ...  ...    ...      ...      ...\n",
              "45206       977         0      0.0         3  ...   51    9.0      1.0      0.0\n",
              "45207       456         0      0.0         2  ...   71    9.0      0.0      0.0\n",
              "45208      1127         3      0.0         5  ...   72    9.0      1.0      0.0\n",
              "45209       508         0      1.0         4  ...   57    9.0      1.0      0.0\n",
              "45210       361        11      0.0         2  ...   37    9.0      1.0      0.0\n",
              "\n",
              "[45211 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiqFWfLX1N3Y"
      },
      "source": [
        "# Split Dataset Before : Prevent Data Leakasge\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(Xselected, ylabels, test_size = 0.3, random_state = 42)"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAHM4ZeW08mh"
      },
      "source": [
        "**Scaling Dataset**\n",
        "\n",
        "\n",
        "*   StandardScaler\n",
        "*   MinMaxScaler Normalize\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2Bxsq--0t9U"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CC4NiZyBRaS"
      },
      "source": [
        "sc = MinMaxScaler()"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERIXUfAeBjUT",
        "outputId": "ac22974c-1f80-4322-d95b-7c380418b656"
      },
      "source": [
        "# Fit\n",
        "\n",
        "sc.fit(X_train)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler(copy=True, feature_range=(0, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pN7WM5IqBTgb"
      },
      "source": [
        "X_train_scaled = sc.transform(X_train)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDTGKvxLCCSk"
      },
      "source": [
        "# connect to df\n",
        "\n",
        "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns.to_list())"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Y2qV1JoGBt3h",
        "outputId": "85f66f02-e0c6-44ef-bcbd-1f9e760272b0"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>previous</th>\n",
              "      <th>contact</th>\n",
              "      <th>campaign</th>\n",
              "      <th>housing</th>\n",
              "      <th>job</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>education</th>\n",
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              "      <th>month</th>\n",
              "      <th>marital</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>9125</th>\n",
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              "      <td>20</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       duration  previous  contact  campaign  ...  age  month  marital  default\n",
              "10747       153         0      2.0         4  ...   36    6.0      2.0      0.0\n",
              "26054       312         0      0.0         3  ...   56    9.0      1.0      0.0\n",
              "9125         83         0      2.0         2  ...   46    6.0      1.0      0.0\n",
              "41659       302         5      0.0         1  ...   41   10.0      0.0      0.0\n",
              "4443         90         0      2.0         1  ...   38    8.0      1.0      0.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "bRzyUmCDBzP1",
        "outputId": "ae0fcd96-b319-4859-b112-f6ad8c4ff9f7"
      },
      "source": [
        "# scaled dataset\n",
        "\n",
        "X_train_scaled.head()\n"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>previous</th>\n",
              "      <th>contact</th>\n",
              "      <th>campaign</th>\n",
              "      <th>housing</th>\n",
              "      <th>job</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>education</th>\n",
              "      <th>age</th>\n",
              "      <th>month</th>\n",
              "      <th>marital</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.031110</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.048387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.233766</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.063440</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.493506</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.016877</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.016129</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.061407</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.298701</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.018300</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.259740</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration  previous  contact  campaign  ...       age     month  marital  default\n",
              "0  0.031110  0.000000      1.0  0.048387  ...  0.233766  0.545455      1.0      0.0\n",
              "1  0.063440  0.000000      0.0  0.032258  ...  0.493506  0.818182      0.5      0.0\n",
              "2  0.016877  0.000000      1.0  0.016129  ...  0.363636  0.545455      0.5      0.0\n",
              "3  0.061407  0.018182      0.0  0.000000  ...  0.298701  0.909091      0.0      0.0\n",
              "4  0.018300  0.000000      1.0  0.000000  ...  0.259740  0.727273      0.5      0.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4RJJdwlCpip"
      },
      "source": [
        "# Apply on Test Data\n",
        "\n",
        "X_test_scaled = sc.transform(X_test)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LECytuxSD17M"
      },
      "source": [
        "# convert to df\n",
        "\n",
        "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns.to_list())"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9nsqbbAEfI8"
      },
      "source": [
        "**Model Building**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIyu-TKUES5v",
        "outputId": "ec866918-34ce-4416-84eb-660a9e944d2f"
      },
      "source": [
        "# Requirement\n",
        "\n",
        "X_train_scaled.shape\n",
        "print(y_train.unique())  # binary classification, loss_fxn : binary_crossentropy, output 1"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLm7GIKdEz-P"
      },
      "source": [
        "input_dim = X_train_scaled.shape[1]\n",
        "output_dim = 1"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOXRe-5LGO99"
      },
      "source": [
        "**Define the Model Arch**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBetk440Gv_Y"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpqJOkdaHotV"
      },
      "source": [
        "# define model \n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(20, activation='relu', kernel_initializer= 'he_normal', input_shape = (input_dim, )))\n",
        "model.add(Dense(16, activation='relu', kernel_initializer= 'he_normal'))\n",
        "model.add(Dense(output_dim, activation='softmax'))"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YHvJxLoEI9L",
        "outputId": "923605ca-7b06-4f5e-b8ba-34181e6fec2e"
      },
      "source": [
        "# Fix error for tuple\n",
        "\n",
        "print((1,))\n",
        "print(type(1))\n",
        "print(type((1,)))"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1,)\n",
            "<class 'int'>\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuKXJWXUF8Pk"
      },
      "source": [
        "def get_hidden_layers_neurons(sample_size, num_input, num_output, alpha = 2):\n",
        "  num_hidden_layers = sample_size/(alpha * (num_input + num_output))\n",
        "  return num_hidden_layer"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFttd1WwUL8s"
      },
      "source": [
        "# Compile Model\n",
        "\n",
        "model.compile(optimizer= 'adam', loss= 'binary_crossentropy', metrics= ['accuracy'])"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ieQvNv0nDPdC",
        "outputId": "ed78b4b7-4428-4922-b79d-434e8c98af8f"
      },
      "source": [
        "# Summary model\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 20)                300       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                336       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 653\n",
            "Trainable params: 653\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "Ta-H1FtPDc7W",
        "outputId": "998118e1-9d7a-4089-97b2-7228a1d0c047"
      },
      "source": [
        "# Visualize the model\n",
        "\n",
        "plot_model(model, to_file= 'bank_term_prediction_model.png', show_shapes=True)"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eMhiNn6ElzQ"
      },
      "source": [
        "### Fit Model\n",
        "\n",
        "\n",
        "> Identify patterns between features and label\n",
        "\n",
        "\n",
        "> Learn the relationship\n",
        "\n",
        "\n",
        "> Training\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRRbXbTvFOYh"
      },
      "source": [
        "#### Params\n",
        "\n",
        "1.   epoch\n",
        "2.   batch_size\n",
        "3.   verbose\n",
        "      - 0 : no display\n",
        "      - 1 and 2 : show\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WonpliZEUaa",
        "outputId": "cf2c22fd-7b72-43d5-f603-91f10b44ec79"
      },
      "source": [
        "# Fit the model\n",
        "\n",
        "model.fit(X_train_scaled, y_train, epochs= 50, batch_size= 32, verbose= 2)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "989/989 - 2s - loss: 0.3311 - accuracy: 0.1166 - 2s/epoch - 2ms/step\n",
            "Epoch 2/50\n",
            "989/989 - 1s - loss: 0.2740 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 3/50\n",
            "989/989 - 1s - loss: 0.2567 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 4/50\n",
            "989/989 - 1s - loss: 0.2501 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 5/50\n",
            "989/989 - 1s - loss: 0.2458 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 6/50\n",
            "989/989 - 1s - loss: 0.2432 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 7/50\n",
            "989/989 - 1s - loss: 0.2414 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 8/50\n",
            "989/989 - 1s - loss: 0.2403 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 9/50\n",
            "989/989 - 1s - loss: 0.2395 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 10/50\n",
            "989/989 - 1s - loss: 0.2384 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 11/50\n",
            "989/989 - 1s - loss: 0.2377 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 12/50\n",
            "989/989 - 1s - loss: 0.2365 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 13/50\n",
            "989/989 - 1s - loss: 0.2368 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 14/50\n",
            "989/989 - 1s - loss: 0.2360 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 15/50\n",
            "989/989 - 1s - loss: 0.2352 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 16/50\n",
            "989/989 - 1s - loss: 0.2345 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 17/50\n",
            "989/989 - 1s - loss: 0.2345 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 18/50\n",
            "989/989 - 1s - loss: 0.2341 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 19/50\n",
            "989/989 - 1s - loss: 0.2333 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 20/50\n",
            "989/989 - 1s - loss: 0.2332 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 21/50\n",
            "989/989 - 1s - loss: 0.2324 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 22/50\n",
            "989/989 - 1s - loss: 0.2321 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 23/50\n",
            "989/989 - 1s - loss: 0.2314 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 24/50\n",
            "989/989 - 1s - loss: 0.2318 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 25/50\n",
            "989/989 - 1s - loss: 0.2308 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 26/50\n",
            "989/989 - 1s - loss: 0.2301 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 27/50\n",
            "989/989 - 1s - loss: 0.2300 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 28/50\n",
            "989/989 - 1s - loss: 0.2294 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 29/50\n",
            "989/989 - 1s - loss: 0.2292 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 30/50\n",
            "989/989 - 1s - loss: 0.2293 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 31/50\n",
            "989/989 - 1s - loss: 0.2292 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 32/50\n",
            "989/989 - 1s - loss: 0.2288 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 33/50\n",
            "989/989 - 1s - loss: 0.2285 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 34/50\n",
            "989/989 - 1s - loss: 0.2279 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 35/50\n",
            "989/989 - 1s - loss: 0.2279 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 36/50\n",
            "989/989 - 1s - loss: 0.2278 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 37/50\n",
            "989/989 - 1s - loss: 0.2273 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 38/50\n",
            "989/989 - 1s - loss: 0.2270 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 39/50\n",
            "989/989 - 1s - loss: 0.2266 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 40/50\n",
            "989/989 - 1s - loss: 0.2268 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 41/50\n",
            "989/989 - 1s - loss: 0.2262 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 42/50\n",
            "989/989 - 1s - loss: 0.2259 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 43/50\n",
            "989/989 - 1s - loss: 0.2256 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 44/50\n",
            "989/989 - 1s - loss: 0.2255 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 45/50\n",
            "989/989 - 1s - loss: 0.2261 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 46/50\n",
            "989/989 - 1s - loss: 0.2248 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 47/50\n",
            "989/989 - 1s - loss: 0.2255 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 48/50\n",
            "989/989 - 1s - loss: 0.2246 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 49/50\n",
            "989/989 - 1s - loss: 0.2248 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 50/50\n",
            "989/989 - 1s - loss: 0.2245 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f472036add0>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntUl4zpFGmiE"
      },
      "source": [
        "# Evaluate the model\n",
        "\n",
        "loss, acc = model.evaluate(X_test_scaled, y_test, verbose= 0)"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Z4VMbueHjeh",
        "outputId": "8131ee9a-28bc-4575-caa1-c69ad6ffdd66"
      },
      "source": [
        "acc"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11781185120344162"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SztGJpd9HpNk",
        "outputId": "ea20218c-9c8b-4795-b069-2d2bddfb2c1e"
      },
      "source": [
        "loss"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.23279233276844025"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5nkRJyIHzBu"
      },
      "source": [
        "### Make Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcnNPQbMHsJW",
        "outputId": "7be92de7-aef3-4e55-9668-4903de3fdfd8"
      },
      "source": [
        "exl = X_test_scaled.iloc[0]\n",
        "print(exl)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "duration     0.039040\n",
            "previous     0.000000\n",
            "contact      1.000000\n",
            "campaign     0.000000\n",
            "housing      1.000000\n",
            "job          0.090909\n",
            "loan         0.000000\n",
            "day          0.500000\n",
            "poutcome     1.000000\n",
            "education    0.333333\n",
            "age          0.285714\n",
            "month        0.727273\n",
            "marital      0.500000\n",
            "default      0.000000\n",
            "Name: 0, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3VYmzMwILhM",
        "outputId": "ffcb4826-d1e6-489d-c069-5263419b93ad"
      },
      "source": [
        "# Expected Value\n",
        "\n",
        "y_test.iloc[0]"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MGPbev6Iqg1"
      },
      "source": [
        "# predict\n",
        "# Reshape dataset\n",
        "\n",
        "sample1 = np.array(exl).reshape(1, -1)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4RvnamvJFSP",
        "outputId": "4547c32f-38d7-4d64-aa7e-0a77ecdf4a00"
      },
      "source": [
        "print(sample1)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.03904026 0.         1.         0.         1.         0.09090909\n",
            "  0.         0.5        1.         0.33333333 0.28571429 0.72727273\n",
            "  0.5        0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKwh03r-JIUZ"
      },
      "source": [
        "# Probabilities\n",
        "\n",
        "probabilities = model.predict(sample1)"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khxe84FEMz0j",
        "outputId": "607db631-05dc-43fe-df7e-c7c70046eac2"
      },
      "source": [
        "probabilities"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnZxu9ogM2K1",
        "outputId": "385e3ec7-413d-4334-eea9-ff61a6e72b56"
      },
      "source": [
        "# Actual Prediction\n",
        "\n",
        "np.argmax(probabilities)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVFsR7-fM_zZ"
      },
      "source": [
        "# Save Model\n",
        "\n",
        "model.save('bank_term_predictor_model_2022.h5')"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fguvk68ZNZSO"
      },
      "source": [
        "# Load Model\n",
        "\n",
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFBFPDufNltv"
      },
      "source": [
        "new_model = load_model('bank_term_predictor_model_2022.h5')"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4WmtVx0Ns3A",
        "outputId": "5b47d14d-d9e7-4ece-fa2e-efbce6c6dfad"
      },
      "source": [
        "# Summary Model\n",
        "\n",
        "new_model.summary()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 20)                300       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                336       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 653\n",
            "Trainable params: 653\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RaykoW5UOE4G"
      },
      "source": [
        "### Visualize Learning & Loss Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8z1RnyiN1tB"
      },
      "source": [
        "history = model.fit(Xselected, ylabels, epochs= 50, batch_size= 32, verbose= 0, validation_split= 0.3)"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbOQ8U00O0eC",
        "outputId": "9d4965d6-c678-4a76-8ba0-400b1519777e"
      },
      "source": [
        "history.history['loss']"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.98720645904541,\n",
              " 1.067325234413147,\n",
              " 0.8262556195259094,\n",
              " 0.8270912170410156,\n",
              " 0.8533859848976135,\n",
              " 0.8039456009864807,\n",
              " 0.7179124355316162,\n",
              " 0.8686813116073608,\n",
              " 0.8597317337989807,\n",
              " 0.8026564717292786,\n",
              " 0.9013038277626038,\n",
              " 0.7901955842971802,\n",
              " 0.7147078514099121,\n",
              " 0.6747174263000488,\n",
              " 0.8051089644432068,\n",
              " 0.848821222782135,\n",
              " 0.7702367901802063,\n",
              " 0.7526018619537354,\n",
              " 0.6700690388679504,\n",
              " 0.993401050567627,\n",
              " 0.717009961605072,\n",
              " 0.7606467008590698,\n",
              " 0.727463960647583,\n",
              " 0.6775700449943542,\n",
              " 0.7228813767433167,\n",
              " 0.7893612384796143,\n",
              " 0.7097356915473938,\n",
              " 0.8263192772865295,\n",
              " 0.8067881464958191,\n",
              " 0.6907002925872803,\n",
              " 0.7467142343521118,\n",
              " 0.6475639939308167,\n",
              " 0.8793703317642212,\n",
              " 0.8069664835929871,\n",
              " 0.6632394194602966,\n",
              " 0.6832268238067627,\n",
              " 0.6975122690200806,\n",
              " 0.8787582516670227,\n",
              " 0.6278089284896851,\n",
              " 0.6759061217308044,\n",
              " 0.8411092758178711,\n",
              " 0.591240644454956,\n",
              " 0.6845636367797852,\n",
              " 0.8572507500648499,\n",
              " 0.7661881446838379,\n",
              " 0.6213012933731079,\n",
              " 0.5680961608886719,\n",
              " 0.6384711861610413,\n",
              " 0.7900723218917847,\n",
              " 0.7869771718978882]"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z_zF9Hg8PbhJ",
        "outputId": "c38495d0-5f3f-4f92-9f38-97cc73c59e23"
      },
      "source": [
        "# Plot \n",
        "\n",
        "plt.title('Learning Curve')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.plot(history.history['loss'], label = 'train')\n",
        "plt.plot(history.history['val_loss'], label = 'val_loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcF-yHJWZzdm",
        "outputId": "c5c1a2ae-4780-40d6-e03b-933ce2a94db1"
      },
      "source": [
        "# using Dropout on visible Input Layer\n",
        "\n",
        "# define model \n",
        "\n",
        "model2 = Sequential()\n",
        "model2.add(Dropout(0.2, input_shape = (input_dim, )))\n",
        "model2.add(Dense(20, activation='relu', kernel_initializer= 'he_normal'))\n",
        "model2.add(Dense(16, activation='relu', kernel_initializer= 'he_normal'))\n",
        "model2.add(Dense(output_dim, activation='softmax'))\n",
        "\n",
        "# Compile Model\n",
        "model2.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "# Fit the Model\n",
        "model2.fit(X_train_scaled, y_train, epochs = 50, batch_size = 32, verbose = 2)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "989/989 - 2s - loss: 0.3521 - accuracy: 0.1166 - 2s/epoch - 2ms/step\n",
            "Epoch 2/50\n",
            "989/989 - 1s - loss: 0.3061 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 3/50\n",
            "989/989 - 1s - loss: 0.2932 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 4/50\n",
            "989/989 - 1s - loss: 0.2909 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 5/50\n",
            "989/989 - 1s - loss: 0.2889 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 6/50\n",
            "989/989 - 1s - loss: 0.2867 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 7/50\n",
            "989/989 - 1s - loss: 0.2871 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 8/50\n",
            "989/989 - 1s - loss: 0.2861 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 9/50\n",
            "989/989 - 1s - loss: 0.2824 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 10/50\n",
            "989/989 - 1s - loss: 0.2819 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 11/50\n",
            "989/989 - 1s - loss: 0.2812 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 12/50\n",
            "989/989 - 1s - loss: 0.2799 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 13/50\n",
            "989/989 - 1s - loss: 0.2804 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 14/50\n",
            "989/989 - 1s - loss: 0.2790 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 15/50\n",
            "989/989 - 1s - loss: 0.2803 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 16/50\n",
            "989/989 - 1s - loss: 0.2780 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 17/50\n",
            "989/989 - 1s - loss: 0.2763 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 18/50\n",
            "989/989 - 1s - loss: 0.2774 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 19/50\n",
            "989/989 - 1s - loss: 0.2782 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 20/50\n",
            "989/989 - 1s - loss: 0.2758 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 21/50\n",
            "989/989 - 1s - loss: 0.2753 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 22/50\n",
            "989/989 - 1s - loss: 0.2746 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 23/50\n",
            "989/989 - 1s - loss: 0.2745 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 24/50\n",
            "989/989 - 1s - loss: 0.2730 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 25/50\n",
            "989/989 - 1s - loss: 0.2723 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 26/50\n",
            "989/989 - 1s - loss: 0.2734 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 27/50\n",
            "989/989 - 1s - loss: 0.2712 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 28/50\n",
            "989/989 - 1s - loss: 0.2723 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 29/50\n",
            "989/989 - 1s - loss: 0.2722 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 30/50\n",
            "989/989 - 1s - loss: 0.2707 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 31/50\n",
            "989/989 - 1s - loss: 0.2707 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 32/50\n",
            "989/989 - 1s - loss: 0.2724 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 33/50\n",
            "989/989 - 1s - loss: 0.2694 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 34/50\n",
            "989/989 - 1s - loss: 0.2698 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 35/50\n",
            "989/989 - 1s - loss: 0.2714 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 36/50\n",
            "989/989 - 1s - loss: 0.2695 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 37/50\n",
            "989/989 - 1s - loss: 0.2712 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 38/50\n",
            "989/989 - 1s - loss: 0.2686 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 39/50\n",
            "989/989 - 1s - loss: 0.2739 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 40/50\n",
            "989/989 - 1s - loss: 0.2712 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 41/50\n",
            "989/989 - 1s - loss: 0.2673 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 42/50\n",
            "989/989 - 1s - loss: 0.2692 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 43/50\n",
            "989/989 - 1s - loss: 0.2719 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 44/50\n",
            "989/989 - 1s - loss: 0.2694 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 45/50\n",
            "989/989 - 1s - loss: 0.2681 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 46/50\n",
            "989/989 - 1s - loss: 0.2682 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 47/50\n",
            "989/989 - 1s - loss: 0.2679 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 48/50\n",
            "989/989 - 1s - loss: 0.2668 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 49/50\n",
            "989/989 - 1s - loss: 0.2682 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n",
            "Epoch 50/50\n",
            "989/989 - 1s - loss: 0.2662 - accuracy: 0.1166 - 1s/epoch - 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4734463c90>"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM-YZBpZsTzA"
      },
      "source": [
        "# Evaluate the Model\n",
        "\n",
        "loss2, acc2 = model2.evaluate(X_test_scaled, y_test, verbose=0)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4qcnXXoskVh",
        "outputId": "ab175356-90b3-47f4-f7cf-4267e01e03c2"
      },
      "source": [
        "print(loss2)\n",
        "print(acc2)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2706003785133362\n",
            "0.11781185120344162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDwcWACprnx3"
      },
      "source": [
        "# Deep Learning with Scikit-Learn\n",
        "\n",
        "## MLPClassifier"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omACQ4ersLUQ"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tpk4GELlswQJ"
      },
      "source": [
        "# Init\n",
        "\n",
        "mlp_nn = MLPClassifier(max_iter= 50, random_state= 42)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zOYhHPvs0XB",
        "outputId": "dc11d2f8-11ef-4296-96a5-5bd08ace0016"
      },
      "source": [
        "mlp_nn.fit(X_train_scaled, y_train)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
              "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
              "              hidden_layer_sizes=(100,), learning_rate='constant',\n",
              "              learning_rate_init=0.001, max_fun=15000, max_iter=50,\n",
              "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
              "              power_t=0.5, random_state=42, shuffle=True, solver='adam',\n",
              "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
              "              warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZHClbKutHBn",
        "outputId": "c89a388b-7990-4fb8-efad-20901ecbe7a3"
      },
      "source": [
        "# accuracy\n",
        "\n",
        "mlp_nn.score(X_test_scaled, y_test)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8990710704806841"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhSnUbSztTap",
        "outputId": "261f070c-ff6c-49c6-f0b9-4a46a67e4342"
      },
      "source": [
        "mlp_nn.predict(sample1)"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx99Zvzetr0a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}