Bank_Turnover_Customer_Churn_Using_ANN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jotjzj3cL0vx"
      },
      "source": [
        "Customer churn prediction is to measure why customers are leaving a business. In this tutorial we will be looking at customer churn in Bank business. We will build a deep learning model to predict the churn and use precision, recall, f1-score to measure performance of our model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wxHWtqssL0v1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHUPpGA6L0v3"
      },
      "source": [
        "LOAD THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ncIbWuWpL0v3",
        "outputId": "67a228fe-e3e6-41b0-c902-c457571163fb"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>15634602</td>\n",
              "      <td>Hargrave</td>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>15647311</td>\n",
              "      <td>Hill</td>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
              "4         79084.10       0  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('Churn_Modelling.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-EkWRJYL0v4"
      },
      "source": [
        "DROP UNNECCESSARY COLUMNS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tpi9J8CBL0v4",
        "outputId": "ec3a51d8-2dda-48fd-fe11-96f95dcca8e8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
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              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0          619    France  Female   42       2       0.00              1   \n",
              "1          608     Spain  Female   41       1   83807.86              1   \n",
              "2          502    France  Female   42       8  159660.80              3   \n",
              "3          699    France  Female   39       1       0.00              2   \n",
              "4          850     Spain  Female   43       2  125510.82              1   \n",
              "\n",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
              "3          0               0         93826.63       0  \n",
              "4          1               1         79084.10       0  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(['RowNumber','CustomerId','Surname'], axis= 'columns', inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPmLnS52L0v5"
      },
      "source": [
        "FEATURE ENGINEERING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqqV8oqTL0v5",
        "outputId": "6954b979-1fbb-4180-d953-696e402ca7d9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CreditScore          int64\n",
              "Geography           object\n",
              "Gender              object\n",
              "Age                  int64\n",
              "Tenure               int64\n",
              "Balance            float64\n",
              "NumOfProducts        int64\n",
              "HasCrCard            int64\n",
              "IsActiveMember       int64\n",
              "EstimatedSalary    float64\n",
              "Exited               int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xrDCJvJ2L0v6"
      },
      "outputs": [],
      "source": [
        "def print_unique_col_values(df):\n",
        "    for column in df:\n",
        "        if df[column].dtypes == 'object':\n",
        "            print(f'{column}: {df[column].unique()}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7QNJ4Nu5L0v6",
        "outputId": "5550d51c-133c-43c5-cc47-96ac0b30300c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Geography: ['France' 'Spain' 'Germany']\n",
            "Gender: ['Female' 'Male']\n"
          ]
        }
      ],
      "source": [
        "print_unique_col_values(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RGIKmUcL0v6"
      },
      "source": [
        "ONE HOT ENCODING CATEGORICAL VALUES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0brp4yhL0v6",
        "outputId": "14619b5a-574e-44b1-8a70-943e4130485a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
              "       'IsActiveMember', 'EstimatedSalary', 'Exited', 'Geography_France',\n",
              "       'Geography_Germany', 'Geography_Spain', 'Gender_Female', 'Gender_Male'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = pd.get_dummies(data=df, columns=['Geography', 'Gender'])\n",
        "df1.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SFLniOnjL0v7"
      },
      "outputs": [],
      "source": [
        "print_unique_col_values(df1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aoQM18r7L0v7",
        "outputId": "bd168880-ad70-4e79-c741-d1214a860676"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
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              "      <th>Gender_Male</th>\n",
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              "      <td>159660.80</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619   42       2       0.00              1          1   \n",
              "1          608   41       1   83807.86              1          0   \n",
              "2          502   42       8  159660.80              3          1   \n",
              "3          699   39       1       0.00              2          0   \n",
              "4          850   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
              "0               1        101348.88       1                 1   \n",
              "1               1        112542.58       0                 0   \n",
              "2               0        113931.57       1                 1   \n",
              "3               0         93826.63       0                 1   \n",
              "4               1         79084.10       0                 0   \n",
              "\n",
              "   Geography_Germany  Geography_Spain  Gender_Female  Gender_Male  \n",
              "0                  0                0              1            0  \n",
              "1                  0                1              1            0  \n",
              "2                  0                0              1            0  \n",
              "3                  0                0              1            0  \n",
              "4                  0                1              1            0  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkSRIgT0L0v7"
      },
      "source": [
        "SCALING THE DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BtVy4mdtL0v7"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rdswdiX1L0v7"
      },
      "outputs": [],
      "source": [
        "col_to_scale=['CreditScore', 'Age', 'Tenure','Balance', 'NumOfProducts', 'EstimatedSalary']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ffkM41PL0v8"
      },
      "outputs": [],
      "source": [
        "df1[col_to_scale] = scaler.fit_transform(df1[col_to_scale])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXxz3eatL0v8",
        "outputId": "a2ef3ea3-f4da-4ea5-cb5f-971ba1a5eaf8"
      },
      "outputs": [
        {
          "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>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.538</td>\n",
              "      <td>0.324324</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.506735</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.516</td>\n",
              "      <td>0.310811</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.334031</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.562709</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.304</td>\n",
              "      <td>0.324324</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.636357</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.569654</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.698</td>\n",
              "      <td>0.283784</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469120</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000</td>\n",
              "      <td>0.337838</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.500246</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.395400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore       Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
              "0        0.538  0.324324     0.2  0.000000       0.000000          1   \n",
              "1        0.516  0.310811     0.1  0.334031       0.000000          0   \n",
              "2        0.304  0.324324     0.8  0.636357       0.666667          1   \n",
              "3        0.698  0.283784     0.1  0.000000       0.333333          0   \n",
              "4        1.000  0.337838     0.2  0.500246       0.000000          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
              "0               1         0.506735       1                 1   \n",
              "1               1         0.562709       0                 0   \n",
              "2               0         0.569654       1                 1   \n",
              "3               0         0.469120       0                 1   \n",
              "4               1         0.395400       0                 0   \n",
              "\n",
              "   Geography_Germany  Geography_Spain  Gender_Female  Gender_Male  \n",
              "0                  0                0              1            0  \n",
              "1                  0                1              1            0  \n",
              "2                  0                0              1            0  \n",
              "3                  0                0              1            0  \n",
              "4                  0                1              1            0  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ja0CBEfwL0v8"
      },
      "outputs": [],
      "source": [
        "X = df1.drop('Exited',axis='columns')\n",
        "y = df1.Exited"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ng9wr-MIL0v8"
      },
      "source": [
        "SPLITTING THE DATASET INTO TRAINING AND TEST SET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iuimw4SJL0v8"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10, stratify=y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yjdZAQ6fL0v8",
        "outputId": "cdf42900-1acb-427f-9c6f-d6768a15b9e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(8000, 13)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYsurDH6L0v8",
        "outputId": "a2a3d84c-585d-4931-96af-b42b4aac9c4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2000, 13)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shapeb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27jw4D4zL0v9"
      },
      "source": [
        "IMPORTING TENSORFLOW LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLSGSduhL0v9"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqi5JA7wL0v9"
      },
      "source": [
        "BUILD THE MODEL(ANN)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ci3EnAwDL0v9"
      },
      "outputs": [],
      "source": [
        "def ANN(X_train, y_train, X_test, y_test, loss):\n",
        "    model = keras.Sequential([\n",
        "        keras.layers.Dense(13, input_dim=13, activation='relu'),\n",
        "        keras.layers.Dense(10, activation='relu'),\n",
        "        keras.layers.Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "    model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])\n",
        "\n",
        "\n",
        "    model.fit(X_train, y_train, epochs=100)\n",
        "\n",
        "    print(model.evaluate(X_test, y_test))\n",
        "\n",
        "    y_preds = model.predict(X_test)\n",
        "    y_preds = np.round(y_preds)\n",
        "\n",
        "    print(\"Classification Report: \\n\", classification_report(y_test, y_preds))\n",
        "\n",
        "    return y_preds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OF1FFsXzL0v9",
        "outputId": "ea5d5adc-d54b-43f1-89cd-42b98c7a0835"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 2s 854us/step - loss: 0.5041 - accuracy: 0.7944\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 718us/step - loss: 0.4742 - accuracy: 0.7964\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 677us/step - loss: 0.4673 - accuracy: 0.7972\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 730us/step - loss: 0.4588 - accuracy: 0.7981\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 851us/step - loss: 0.4508 - accuracy: 0.8046\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 687us/step - loss: 0.4417 - accuracy: 0.8111\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 671us/step - loss: 0.4337 - accuracy: 0.8155\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 727us/step - loss: 0.4280 - accuracy: 0.8186\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 0s 667us/step - loss: 0.4237 - accuracy: 0.8158\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 0s 679us/step - loss: 0.4205 - accuracy: 0.8207\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 0s 695us/step - loss: 0.4176 - accuracy: 0.8224\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.4156 - accuracy: 0.8245\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.4126 - accuracy: 0.8265\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.4107 - accuracy: 0.8267\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.4077 - accuracy: 0.8285\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 0s 743us/step - loss: 0.4037 - accuracy: 0.8317\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 0s 683us/step - loss: 0.3994 - accuracy: 0.8313\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.3938 - accuracy: 0.8341\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 0s 727us/step - loss: 0.3872 - accuracy: 0.8369\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 0s 723us/step - loss: 0.3805 - accuracy: 0.8414\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 0s 685us/step - loss: 0.3739 - accuracy: 0.8450\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 0s 723us/step - loss: 0.3684 - accuracy: 0.8499\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 0s 759us/step - loss: 0.3622 - accuracy: 0.8508\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 0s 667us/step - loss: 0.3586 - accuracy: 0.8514\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 0s 848us/step - loss: 0.3571 - accuracy: 0.8549\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 0s 687us/step - loss: 0.3541 - accuracy: 0.8518\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 0s 683us/step - loss: 0.3522 - accuracy: 0.8553\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 0s 695us/step - loss: 0.3517 - accuracy: 0.8536\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.3505 - accuracy: 0.8537\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 0s 679us/step - loss: 0.3492 - accuracy: 0.8558\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 0s 735us/step - loss: 0.3481 - accuracy: 0.8549\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 0s 719us/step - loss: 0.3466 - accuracy: 0.8545\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 0s 687us/step - loss: 0.3468 - accuracy: 0.8560\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 0s 691us/step - loss: 0.3451 - accuracy: 0.8576\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 0s 692us/step - loss: 0.3452 - accuracy: 0.8575\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 0s 702us/step - loss: 0.3448 - accuracy: 0.8569\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 0s 783us/step - loss: 0.3430 - accuracy: 0.8564\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.3436 - accuracy: 0.8593\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 0s 700us/step - loss: 0.3430 - accuracy: 0.8568\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 0s 711us/step - loss: 0.3431 - accuracy: 0.8566\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3419 - accuracy: 0.8581\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 0s 735us/step - loss: 0.3418 - accuracy: 0.8593\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 0s 747us/step - loss: 0.3407 - accuracy: 0.8604\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 0s 745us/step - loss: 0.3406 - accuracy: 0.8624\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.3402 - accuracy: 0.8601\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 0s 727us/step - loss: 0.3397 - accuracy: 0.8605\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3393 - accuracy: 0.8594\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 0s 904us/step - loss: 0.3385 - accuracy: 0.8619\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 0s 711us/step - loss: 0.3384 - accuracy: 0.8601\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 763us/step - loss: 0.3387 - accuracy: 0.8611\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 0s 723us/step - loss: 0.3377 - accuracy: 0.8609\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.3368 - accuracy: 0.8599\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 0s 837us/step - loss: 0.3369 - accuracy: 0.8593\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.3373 - accuracy: 0.8596\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 723us/step - loss: 0.3361 - accuracy: 0.8599\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 0s 719us/step - loss: 0.3364 - accuracy: 0.8622\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.3360 - accuracy: 0.8597\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3350 - accuracy: 0.8622\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 731us/step - loss: 0.3356 - accuracy: 0.8608\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 711us/step - loss: 0.3342 - accuracy: 0.8618\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 0s 827us/step - loss: 0.3342 - accuracy: 0.8612\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 735us/step - loss: 0.3344 - accuracy: 0.8621\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 756us/step - loss: 0.3348 - accuracy: 0.8618\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 0s 747us/step - loss: 0.3345 - accuracy: 0.8614\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 726us/step - loss: 0.3337 - accuracy: 0.8620\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.3334 - accuracy: 0.8618\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 711us/step - loss: 0.3335 - accuracy: 0.8590\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 0s 683us/step - loss: 0.3337 - accuracy: 0.8622\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.3323 - accuracy: 0.8620\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 0s 727us/step - loss: 0.3326 - accuracy: 0.8625\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 691us/step - loss: 0.3320 - accuracy: 0.8635\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 679us/step - loss: 0.3332 - accuracy: 0.8629\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.3319 - accuracy: 0.8611\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 0s 731us/step - loss: 0.3318 - accuracy: 0.8611\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3315 - accuracy: 0.8614\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 0s 775us/step - loss: 0.3319 - accuracy: 0.8639\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 0s 751us/step - loss: 0.3314 - accuracy: 0.8626\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 0s 679us/step - loss: 0.3308 - accuracy: 0.8615\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 0s 699us/step - loss: 0.3313 - accuracy: 0.8626\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 0s 848us/step - loss: 0.3308 - accuracy: 0.8614\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3294 - accuracy: 0.8666\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 0s 711us/step - loss: 0.3307 - accuracy: 0.8635\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 736us/step - loss: 0.3299 - accuracy: 0.8636\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 698us/step - loss: 0.3303 - accuracy: 0.8615\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.3302 - accuracy: 0.8631\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 0s 730us/step - loss: 0.3299 - accuracy: 0.8596\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 695us/step - loss: 0.3294 - accuracy: 0.8641\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 703us/step - loss: 0.3296 - accuracy: 0.8633\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 715us/step - loss: 0.3299 - accuracy: 0.8627\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 730us/step - loss: 0.3291 - accuracy: 0.8655\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 0s 675us/step - loss: 0.3296 - accuracy: 0.8636\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 0s 727us/step - loss: 0.3286 - accuracy: 0.8634\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 0s 719us/step - loss: 0.3290 - accuracy: 0.8635\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 695us/step - loss: 0.3300 - accuracy: 0.8639\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 0s 747us/step - loss: 0.3295 - accuracy: 0.8634\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 702us/step - loss: 0.3286 - accuracy: 0.8643\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 707us/step - loss: 0.3292 - accuracy: 0.8654\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 723us/step - loss: 0.3282 - accuracy: 0.8648\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 716us/step - loss: 0.3284 - accuracy: 0.8645\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 735us/step - loss: 0.3289 - accuracy: 0.8639\n",
            "63/63 [==============================] - 0s 532us/step - loss: 0.3331 - accuracy: 0.8630\n",
            "[0.33314570784568787, 0.8629999756813049]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.97      0.92      1593\n",
            "           1       0.77      0.46      0.58       407\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.82      0.71      0.75      2000\n",
            "weighted avg       0.85      0.86      0.85      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_preds = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7-vJwlBL0v9"
      },
      "source": [
        "As We See, the precision, recall and f1 score of Class 1 is very low due to imbalanced dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76EKoy8SL0v-"
      },
      "source": [
        "Mitigating Skewdness of Data\n",
        "\n",
        "Method 1: Undersampling\n",
        "\n",
        "reference: https: // www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mko1Yf_aL0v-"
      },
      "outputs": [],
      "source": [
        "# Class count\n",
        "count_class_0, count_class_1 = df1.Exited.value_counts()\n",
        "\n",
        "# Divide by class\n",
        "df_class_0 = df1[df1['Exited'] == 0]\n",
        "df_class_1 = df1[df1['Exited'] == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YY99w2xvL0v-",
        "outputId": "c0e95099-77ef-45af-daf4-0209ba32f21d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random under-sampling:\n",
            "0    2037\n",
            "1    2037\n",
            "Name: Exited, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "df_class_0_under = df_class_0.sample(count_class_1)\n",
        "df_test_under = pd.concat([df_class_0_under, df_class_1], axis=0)\n",
        "\n",
        "print('Random under-sampling:')\n",
        "print(df_test_under.Exited.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f4fInek5L0v-"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = df_test_under.drop('Exited', axis='columns')\n",
        "y = df_test_under['Exited']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=15, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rSyntTLUL0v-",
        "outputId": "a40fb50d-b94f-4923-9f27-900c4469c20d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    1630\n",
              "0    1629\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oXCSbLR6L0v-",
        "outputId": "286385ad-6b66-4208-99c7-1c97d22ffcac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "102/102 [==============================] - 1s 2ms/step - loss: 0.6774 - accuracy: 0.5802\n",
            "Epoch 2/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.6464 - accuracy: 0.6349\n",
            "Epoch 3/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.6310 - accuracy: 0.6542\n",
            "Epoch 4/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6239 - accuracy: 0.6646\n",
            "Epoch 5/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6175 - accuracy: 0.6720\n",
            "Epoch 6/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.6120 - accuracy: 0.6744\n",
            "Epoch 7/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.6056 - accuracy: 0.6793\n",
            "Epoch 8/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5993 - accuracy: 0.6870\n",
            "Epoch 9/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5936 - accuracy: 0.6910\n",
            "Epoch 10/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5881 - accuracy: 0.7005\n",
            "Epoch 11/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5818 - accuracy: 0.7014\n",
            "Epoch 12/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5777 - accuracy: 0.7036\n",
            "Epoch 13/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5731 - accuracy: 0.7079\n",
            "Epoch 14/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5716 - accuracy: 0.7085\n",
            "Epoch 15/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5669 - accuracy: 0.7125\n",
            "Epoch 16/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5641 - accuracy: 0.7128\n",
            "Epoch 17/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5611 - accuracy: 0.7156\n",
            "Epoch 18/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5560 - accuracy: 0.7245\n",
            "Epoch 19/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5513 - accuracy: 0.7288\n",
            "Epoch 20/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5453 - accuracy: 0.7315\n",
            "Epoch 21/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5387 - accuracy: 0.7373\n",
            "Epoch 22/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5349 - accuracy: 0.7407\n",
            "Epoch 23/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5267 - accuracy: 0.7447\n",
            "Epoch 24/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5220 - accuracy: 0.7496\n",
            "Epoch 25/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5154 - accuracy: 0.7548\n",
            "Epoch 26/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.5104 - accuracy: 0.7579\n",
            "Epoch 27/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5026 - accuracy: 0.7668\n",
            "Epoch 28/100\n",
            "102/102 [==============================] - 0s 967us/step - loss: 0.5006 - accuracy: 0.7650\n",
            "Epoch 29/100\n",
            "102/102 [==============================] - 0s 812us/step - loss: 0.4968 - accuracy: 0.7683\n",
            "Epoch 30/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4941 - accuracy: 0.7683\n",
            "Epoch 31/100\n",
            "102/102 [==============================] - 0s 891us/step - loss: 0.4919 - accuracy: 0.7659\n",
            "Epoch 32/100\n",
            "102/102 [==============================] - 0s 891us/step - loss: 0.4883 - accuracy: 0.7671\n",
            "Epoch 33/100\n",
            "102/102 [==============================] - 0s 812us/step - loss: 0.4889 - accuracy: 0.7665\n",
            "Epoch 34/100\n",
            "102/102 [==============================] - 0s 901us/step - loss: 0.4852 - accuracy: 0.7665\n",
            "Epoch 35/100\n",
            "102/102 [==============================] - 0s 921us/step - loss: 0.4821 - accuracy: 0.7659\n",
            "Epoch 36/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4819 - accuracy: 0.7668\n",
            "Epoch 37/100\n",
            "102/102 [==============================] - 0s 822us/step - loss: 0.4801 - accuracy: 0.7659\n",
            "Epoch 38/100\n",
            "102/102 [==============================] - 0s 891us/step - loss: 0.4806 - accuracy: 0.7693\n",
            "Epoch 39/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4788 - accuracy: 0.7699\n",
            "Epoch 40/100\n",
            "102/102 [==============================] - 0s 822us/step - loss: 0.4778 - accuracy: 0.7683\n",
            "Epoch 41/100\n",
            "102/102 [==============================] - 0s 901us/step - loss: 0.4784 - accuracy: 0.7671\n",
            "Epoch 42/100\n",
            "102/102 [==============================] - 0s 802us/step - loss: 0.4792 - accuracy: 0.7680\n",
            "Epoch 43/100\n",
            "102/102 [==============================] - 0s 871us/step - loss: 0.4770 - accuracy: 0.7689\n",
            "Epoch 44/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4756 - accuracy: 0.7732\n",
            "Epoch 45/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.4773 - accuracy: 0.7705\n",
            "Epoch 46/100\n",
            "102/102 [==============================] - 0s 772us/step - loss: 0.4747 - accuracy: 0.7714\n",
            "Epoch 47/100\n",
            "102/102 [==============================] - 0s 842us/step - loss: 0.4745 - accuracy: 0.7686\n",
            "Epoch 48/100\n",
            "102/102 [==============================] - 0s 802us/step - loss: 0.4742 - accuracy: 0.7720\n",
            "Epoch 49/100\n",
            "102/102 [==============================] - 0s 842us/step - loss: 0.4732 - accuracy: 0.7702\n",
            "Epoch 50/100\n",
            "102/102 [==============================] - 0s 832us/step - loss: 0.4742 - accuracy: 0.7732\n",
            "Epoch 51/100\n",
            "102/102 [==============================] - 0s 792us/step - loss: 0.4728 - accuracy: 0.7729\n",
            "Epoch 52/100\n",
            "102/102 [==============================] - 0s 832us/step - loss: 0.4717 - accuracy: 0.7723\n",
            "Epoch 53/100\n",
            "102/102 [==============================] - 0s 812us/step - loss: 0.4714 - accuracy: 0.7751\n",
            "Epoch 54/100\n",
            "102/102 [==============================] - 0s 822us/step - loss: 0.4709 - accuracy: 0.7739\n",
            "Epoch 55/100\n",
            "102/102 [==============================] - 0s 971us/step - loss: 0.4710 - accuracy: 0.7760\n",
            "Epoch 56/100\n",
            "102/102 [==============================] - 0s 911us/step - loss: 0.4699 - accuracy: 0.7717\n",
            "Epoch 57/100\n",
            "102/102 [==============================] - 0s 921us/step - loss: 0.4692 - accuracy: 0.7760\n",
            "Epoch 58/100\n",
            "102/102 [==============================] - 0s 822us/step - loss: 0.4696 - accuracy: 0.7739\n",
            "Epoch 59/100\n",
            "102/102 [==============================] - 0s 782us/step - loss: 0.4690 - accuracy: 0.7729\n",
            "Epoch 60/100\n",
            "102/102 [==============================] - 0s 831us/step - loss: 0.4694 - accuracy: 0.7739\n",
            "Epoch 61/100\n",
            "102/102 [==============================] - 0s 852us/step - loss: 0.4685 - accuracy: 0.7766\n",
            "Epoch 62/100\n",
            "102/102 [==============================] - 0s 881us/step - loss: 0.4696 - accuracy: 0.7736\n",
            "Epoch 63/100\n",
            "102/102 [==============================] - 0s 901us/step - loss: 0.4696 - accuracy: 0.7736\n",
            "Epoch 64/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4691 - accuracy: 0.7708\n",
            "Epoch 65/100\n",
            "102/102 [==============================] - 0s 860us/step - loss: 0.4681 - accuracy: 0.7717\n",
            "Epoch 66/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4678 - accuracy: 0.7714\n",
            "Epoch 67/100\n",
            "102/102 [==============================] - 0s 832us/step - loss: 0.4681 - accuracy: 0.7748\n",
            "Epoch 68/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4677 - accuracy: 0.7751\n",
            "Epoch 69/100\n",
            "102/102 [==============================] - 0s 871us/step - loss: 0.4696 - accuracy: 0.7766\n",
            "Epoch 70/100\n",
            "102/102 [==============================] - 0s 891us/step - loss: 0.4694 - accuracy: 0.7693\n",
            "Epoch 71/100\n",
            "102/102 [==============================] - 0s 782us/step - loss: 0.4669 - accuracy: 0.7751\n",
            "Epoch 72/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4668 - accuracy: 0.7769\n",
            "Epoch 73/100\n",
            "102/102 [==============================] - 0s 881us/step - loss: 0.4676 - accuracy: 0.7723\n",
            "Epoch 74/100\n",
            "102/102 [==============================] - 0s 821us/step - loss: 0.4669 - accuracy: 0.7729\n",
            "Epoch 75/100\n",
            "102/102 [==============================] - 0s 971us/step - loss: 0.4657 - accuracy: 0.7788\n",
            "Epoch 76/100\n",
            "102/102 [==============================] - 0s 852us/step - loss: 0.4666 - accuracy: 0.7736\n",
            "Epoch 77/100\n",
            "102/102 [==============================] - 0s 911us/step - loss: 0.4654 - accuracy: 0.7760\n",
            "Epoch 78/100\n",
            "102/102 [==============================] - 0s 910us/step - loss: 0.4656 - accuracy: 0.7757\n",
            "Epoch 79/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4645 - accuracy: 0.7797\n",
            "Epoch 80/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4667 - accuracy: 0.7782\n",
            "Epoch 81/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4678 - accuracy: 0.7754\n",
            "Epoch 82/100\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.4653 - accuracy: 0.7772\n",
            "Epoch 83/100\n",
            "102/102 [==============================] - 0s 881us/step - loss: 0.4658 - accuracy: 0.7763\n",
            "Epoch 84/100\n",
            "102/102 [==============================] - 0s 862us/step - loss: 0.4645 - accuracy: 0.7751\n",
            "Epoch 85/100\n",
            "102/102 [==============================] - 0s 852us/step - loss: 0.4639 - accuracy: 0.7748\n",
            "Epoch 86/100\n",
            "102/102 [==============================] - 0s 822us/step - loss: 0.4645 - accuracy: 0.7760\n",
            "Epoch 87/100\n",
            "102/102 [==============================] - 0s 911us/step - loss: 0.4648 - accuracy: 0.7785\n",
            "Epoch 88/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4649 - accuracy: 0.7766\n",
            "Epoch 89/100\n",
            "102/102 [==============================] - 0s 1ms/step - loss: 0.4642 - accuracy: 0.7754\n",
            "Epoch 90/100\n",
            "102/102 [==============================] - 0s 842us/step - loss: 0.4639 - accuracy: 0.7748\n",
            "Epoch 91/100\n",
            "102/102 [==============================] - 0s 842us/step - loss: 0.4627 - accuracy: 0.7775\n",
            "Epoch 92/100\n",
            "102/102 [==============================] - 0s 842us/step - loss: 0.4653 - accuracy: 0.7699\n",
            "Epoch 93/100\n",
            "102/102 [==============================] - 0s 921us/step - loss: 0.4634 - accuracy: 0.7791\n",
            "Epoch 94/100\n",
            "102/102 [==============================] - 0s 891us/step - loss: 0.4626 - accuracy: 0.7806\n",
            "Epoch 95/100\n",
            "102/102 [==============================] - 0s 921us/step - loss: 0.4642 - accuracy: 0.7800\n",
            "Epoch 96/100\n",
            "102/102 [==============================] - 0s 920us/step - loss: 0.4628 - accuracy: 0.7766\n",
            "Epoch 97/100\n",
            "102/102 [==============================] - 0s 951us/step - loss: 0.4662 - accuracy: 0.7714\n",
            "Epoch 98/100\n",
            "102/102 [==============================] - 0s 812us/step - loss: 0.4652 - accuracy: 0.7711\n",
            "Epoch 99/100\n",
            "102/102 [==============================] - 0s 812us/step - loss: 0.4626 - accuracy: 0.7788\n",
            "Epoch 100/100\n",
            "102/102 [==============================] - 0s 883us/step - loss: 0.4622 - accuracy: 0.7809\n",
            "26/26 [==============================] - 0s 1ms/step - loss: 0.4573 - accuracy: 0.7730\n",
            "[0.4572858512401581, 0.7730061411857605]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.78      0.77       408\n",
            "           1       0.78      0.77      0.77       407\n",
            "\n",
            "    accuracy                           0.77       815\n",
            "   macro avg       0.77      0.77      0.77       815\n",
            "weighted avg       0.77      0.77      0.77       815\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_preds = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_hdSFmyL0v-"
      },
      "source": [
        "As we see, there is considerable Improvement in the f1, recall and precision scores of Class 1 Value.\n",
        "The f1 score has improved from 0.58 to 0.77."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boWk9Ow_L0v_"
      },
      "source": [
        "Method2: Oversampling\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k0c73zkqL0v_",
        "outputId": "af7d3a4e-99dc-4818-9a83-2758f2b0ab51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random over-sampling:\n",
            "0    7963\n",
            "1    7963\n",
            "Name: Exited, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Oversample 1-class and concat the DataFrames of both classes\n",
        "df_class_1_over = df_class_1.sample(count_class_0, replace=True)\n",
        "df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)\n",
        "\n",
        "print('Random over-sampling:')\n",
        "print(df_test_over.Exitedb.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P5SZlqPGL0wF"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = df_test_over.drop('Exited', axis='columns')\n",
        "y = df_test_over['Exited']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=15, stratify=y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cSe4JNhOL0wF",
        "outputId": "77930c58-98d2-47f3-b0cf-884d20b5db64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "399/399 [==============================] - 1s 919us/step - loss: 0.6620 - accuracy: 0.6154\n",
            "Epoch 2/100\n",
            "399/399 [==============================] - 0s 920us/step - loss: 0.6135 - accuracy: 0.6735\n",
            "Epoch 3/100\n",
            "399/399 [==============================] - 0s 900us/step - loss: 0.5829 - accuracy: 0.7017\n",
            "Epoch 4/100\n",
            "399/399 [==============================] - 0s 857us/step - loss: 0.5554 - accuracy: 0.7208\n",
            "Epoch 5/100\n",
            "399/399 [==============================] - 0s 799us/step - loss: 0.5319 - accuracy: 0.7394\n",
            "Epoch 6/100\n",
            "399/399 [==============================] - 0s 747us/step - loss: 0.5101 - accuracy: 0.7530\n",
            "Epoch 7/100\n",
            "399/399 [==============================] - 0s 829us/step - loss: 0.4970 - accuracy: 0.7612\n",
            "Epoch 8/100\n",
            "399/399 [==============================] - 0s 862us/step - loss: 0.4893 - accuracy: 0.7596\n",
            "Epoch 9/100\n",
            "399/399 [==============================] - 0s 777us/step - loss: 0.4855 - accuracy: 0.7601\n",
            "Epoch 10/100\n",
            "399/399 [==============================] - 0s 792us/step - loss: 0.4815 - accuracy: 0.7644\n",
            "Epoch 11/100\n",
            "399/399 [==============================] - 0s 782us/step - loss: 0.4798 - accuracy: 0.7648\n",
            "Epoch 12/100\n",
            "399/399 [==============================] - 0s 782us/step - loss: 0.4781 - accuracy: 0.7655\n",
            "Epoch 13/100\n",
            "399/399 [==============================] - 0s 749us/step - loss: 0.4769 - accuracy: 0.7637\n",
            "Epoch 14/100\n",
            "399/399 [==============================] - 0s 792us/step - loss: 0.4751 - accuracy: 0.7664\n",
            "Epoch 15/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4737 - accuracy: 0.7661\n",
            "Epoch 16/100\n",
            "399/399 [==============================] - 0s 857us/step - loss: 0.4730 - accuracy: 0.7680\n",
            "Epoch 17/100\n",
            "399/399 [==============================] - 0s 819us/step - loss: 0.4720 - accuracy: 0.7670\n",
            "Epoch 18/100\n",
            "399/399 [==============================] - 0s 769us/step - loss: 0.4706 - accuracy: 0.7658\n",
            "Epoch 19/100\n",
            "399/399 [==============================] - 0s 782us/step - loss: 0.4701 - accuracy: 0.7694\n",
            "Epoch 20/100\n",
            "399/399 [==============================] - 0s 792us/step - loss: 0.4702 - accuracy: 0.7681\n",
            "Epoch 21/100\n",
            "399/399 [==============================] - 0s 779us/step - loss: 0.4693 - accuracy: 0.7703\n",
            "Epoch 22/100\n",
            "399/399 [==============================] - 0s 777us/step - loss: 0.4684 - accuracy: 0.7690\n",
            "Epoch 23/100\n",
            "399/399 [==============================] - 0s 781us/step - loss: 0.4680 - accuracy: 0.7703\n",
            "Epoch 24/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4680 - accuracy: 0.7701\n",
            "Epoch 25/100\n",
            "399/399 [==============================] - 0s 777us/step - loss: 0.4667 - accuracy: 0.7703\n",
            "Epoch 26/100\n",
            "399/399 [==============================] - 0s 784us/step - loss: 0.4654 - accuracy: 0.7710\n",
            "Epoch 27/100\n",
            "399/399 [==============================] - 0s 852us/step - loss: 0.4655 - accuracy: 0.7713\n",
            "Epoch 28/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4648 - accuracy: 0.7710\n",
            "Epoch 29/100\n",
            "399/399 [==============================] - 0s 807us/step - loss: 0.4643 - accuracy: 0.7685\n",
            "Epoch 30/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4637 - accuracy: 0.7703\n",
            "Epoch 31/100\n",
            "399/399 [==============================] - 0s 798us/step - loss: 0.4636 - accuracy: 0.7724\n",
            "Epoch 32/100\n",
            "399/399 [==============================] - 0s 751us/step - loss: 0.4633 - accuracy: 0.7703\n",
            "Epoch 33/100\n",
            "399/399 [==============================] - 0s 907us/step - loss: 0.4629 - accuracy: 0.7712\n",
            "Epoch 34/100\n",
            "399/399 [==============================] - 0s 784us/step - loss: 0.4625 - accuracy: 0.7706\n",
            "Epoch 35/100\n",
            "399/399 [==============================] - 0s 761us/step - loss: 0.4611 - accuracy: 0.7717\n",
            "Epoch 36/100\n",
            "399/399 [==============================] - 0s 854us/step - loss: 0.4618 - accuracy: 0.7718\n",
            "Epoch 37/100\n",
            "399/399 [==============================] - 0s 814us/step - loss: 0.4605 - accuracy: 0.7714\n",
            "Epoch 38/100\n",
            "399/399 [==============================] - 0s 865us/step - loss: 0.4623 - accuracy: 0.7724\n",
            "Epoch 39/100\n",
            "399/399 [==============================] - 0s 832us/step - loss: 0.4598 - accuracy: 0.7728\n",
            "Epoch 40/100\n",
            "399/399 [==============================] - 0s 807us/step - loss: 0.4599 - accuracy: 0.7733\n",
            "Epoch 41/100\n",
            "399/399 [==============================] - 0s 845us/step - loss: 0.4597 - accuracy: 0.7693\n",
            "Epoch 42/100\n",
            "399/399 [==============================] - 0s 807us/step - loss: 0.4594 - accuracy: 0.7724\n",
            "Epoch 43/100\n",
            "399/399 [==============================] - 0s 784us/step - loss: 0.4579 - accuracy: 0.7737\n",
            "Epoch 44/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4596 - accuracy: 0.7746\n",
            "Epoch 45/100\n",
            "399/399 [==============================] - 0s 812us/step - loss: 0.4583 - accuracy: 0.7734\n",
            "Epoch 46/100\n",
            "399/399 [==============================] - 0s 803us/step - loss: 0.4580 - accuracy: 0.7729\n",
            "Epoch 47/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4571 - accuracy: 0.7718\n",
            "Epoch 48/100\n",
            "399/399 [==============================] - 0s 790us/step - loss: 0.4575 - accuracy: 0.7744\n",
            "Epoch 49/100\n",
            "399/399 [==============================] - 0s 792us/step - loss: 0.4562 - accuracy: 0.7751\n",
            "Epoch 50/100\n",
            "399/399 [==============================] - 0s 769us/step - loss: 0.4578 - accuracy: 0.7732\n",
            "Epoch 51/100\n",
            "399/399 [==============================] - 0s 955us/step - loss: 0.4555 - accuracy: 0.7756\n",
            "Epoch 52/100\n",
            "399/399 [==============================] - 0s 821us/step - loss: 0.4559 - accuracy: 0.7737\n",
            "Epoch 53/100\n",
            "399/399 [==============================] - 0s 772us/step - loss: 0.4552 - accuracy: 0.7757\n",
            "Epoch 54/100\n",
            "399/399 [==============================] - 0s 777us/step - loss: 0.4564 - accuracy: 0.7733\n",
            "Epoch 55/100\n",
            "399/399 [==============================] - 0s 925us/step - loss: 0.4552 - accuracy: 0.7746\n",
            "Epoch 56/100\n",
            "399/399 [==============================] - 0s 925us/step - loss: 0.4566 - accuracy: 0.7725\n",
            "Epoch 57/100\n",
            "399/399 [==============================] - 0s 882us/step - loss: 0.4534 - accuracy: 0.7751\n",
            "Epoch 58/100\n",
            "399/399 [==============================] - 0s 809us/step - loss: 0.4548 - accuracy: 0.7769\n",
            "Epoch 59/100\n",
            "399/399 [==============================] - 0s 814us/step - loss: 0.4548 - accuracy: 0.7768\n",
            "Epoch 60/100\n",
            "399/399 [==============================] - 0s 910us/step - loss: 0.4530 - accuracy: 0.7785\n",
            "Epoch 61/100\n",
            "399/399 [==============================] - 0s 859us/step - loss: 0.4530 - accuracy: 0.7760\n",
            "Epoch 62/100\n",
            "399/399 [==============================] - 0s 847us/step - loss: 0.4538 - accuracy: 0.7757\n",
            "Epoch 63/100\n",
            "399/399 [==============================] - 0s 849us/step - loss: 0.4532 - accuracy: 0.7784\n",
            "Epoch 64/100\n",
            "399/399 [==============================] - 0s 862us/step - loss: 0.4529 - accuracy: 0.7778\n",
            "Epoch 65/100\n",
            "399/399 [==============================] - 0s 837us/step - loss: 0.4523 - accuracy: 0.7780\n",
            "Epoch 66/100\n",
            "399/399 [==============================] - 0s 827us/step - loss: 0.4519 - accuracy: 0.7797\n",
            "Epoch 67/100\n",
            "399/399 [==============================] - 0s 811us/step - loss: 0.4523 - accuracy: 0.7795\n",
            "Epoch 68/100\n",
            "399/399 [==============================] - 0s 857us/step - loss: 0.4524 - accuracy: 0.7793\n",
            "Epoch 69/100\n",
            "399/399 [==============================] - 0s 821us/step - loss: 0.4514 - accuracy: 0.7799\n",
            "Epoch 70/100\n",
            "399/399 [==============================] - 0s 799us/step - loss: 0.4510 - accuracy: 0.7781\n",
            "Epoch 71/100\n",
            "399/399 [==============================] - 0s 799us/step - loss: 0.4513 - accuracy: 0.7810\n",
            "Epoch 72/100\n",
            "399/399 [==============================] - 0s 839us/step - loss: 0.4514 - accuracy: 0.7802\n",
            "Epoch 73/100\n",
            "399/399 [==============================] - 0s 808us/step - loss: 0.4508 - accuracy: 0.7809\n",
            "Epoch 74/100\n",
            "399/399 [==============================] - 0s 975us/step - loss: 0.4497 - accuracy: 0.7790\n",
            "Epoch 75/100\n",
            "399/399 [==============================] - 0s 800us/step - loss: 0.4497 - accuracy: 0.7845\n",
            "Epoch 76/100\n",
            "399/399 [==============================] - 0s 812us/step - loss: 0.4505 - accuracy: 0.7788\n",
            "Epoch 77/100\n",
            "399/399 [==============================] - 0s 804us/step - loss: 0.4490 - accuracy: 0.7824\n",
            "Epoch 78/100\n",
            "399/399 [==============================] - 0s 789us/step - loss: 0.4487 - accuracy: 0.7797\n",
            "Epoch 79/100\n",
            "399/399 [==============================] - 0s 978us/step - loss: 0.4500 - accuracy: 0.7812\n",
            "Epoch 80/100\n",
            "399/399 [==============================] - 0s 930us/step - loss: 0.4484 - accuracy: 0.7812\n",
            "Epoch 81/100\n",
            "399/399 [==============================] - 0s 761us/step - loss: 0.4482 - accuracy: 0.7815\n",
            "Epoch 82/100\n",
            "399/399 [==============================] - 0s 724us/step - loss: 0.4480 - accuracy: 0.7828\n",
            "Epoch 83/100\n",
            "399/399 [==============================] - 0s 816us/step - loss: 0.4485 - accuracy: 0.7816\n",
            "Epoch 84/100\n",
            "399/399 [==============================] - 0s 807us/step - loss: 0.4482 - accuracy: 0.7827\n",
            "Epoch 85/100\n",
            "399/399 [==============================] - 0s 739us/step - loss: 0.4488 - accuracy: 0.7827\n",
            "Epoch 86/100\n",
            "399/399 [==============================] - 0s 807us/step - loss: 0.4481 - accuracy: 0.7830\n",
            "Epoch 87/100\n",
            "399/399 [==============================] - 0s 832us/step - loss: 0.4478 - accuracy: 0.7827\n",
            "Epoch 88/100\n",
            "399/399 [==============================] - 0s 764us/step - loss: 0.4480 - accuracy: 0.7811\n",
            "Epoch 89/100\n",
            "399/399 [==============================] - 0s 772us/step - loss: 0.4470 - accuracy: 0.7835\n",
            "Epoch 90/100\n",
            "399/399 [==============================] - 0s 757us/step - loss: 0.4479 - accuracy: 0.7845\n",
            "Epoch 91/100\n",
            "399/399 [==============================] - 0s 727us/step - loss: 0.4488 - accuracy: 0.7809\n",
            "Epoch 92/100\n",
            "399/399 [==============================] - 0s 754us/step - loss: 0.4474 - accuracy: 0.7830\n",
            "Epoch 93/100\n",
            "399/399 [==============================] - 0s 749us/step - loss: 0.4478 - accuracy: 0.7835\n",
            "Epoch 94/100\n",
            "399/399 [==============================] - 0s 754us/step - loss: 0.4468 - accuracy: 0.7841\n",
            "Epoch 95/100\n",
            "399/399 [==============================] - 0s 668us/step - loss: 0.4463 - accuracy: 0.7827\n",
            "Epoch 96/100\n",
            "399/399 [==============================] - 0s 731us/step - loss: 0.4473 - accuracy: 0.7814\n",
            "Epoch 97/100\n",
            "399/399 [==============================] - 0s 725us/step - loss: 0.4470 - accuracy: 0.7829\n",
            "Epoch 98/100\n",
            "399/399 [==============================] - 0s 634us/step - loss: 0.4473 - accuracy: 0.7820\n",
            "Epoch 99/100\n",
            "399/399 [==============================] - 0s 769us/step - loss: 0.4469 - accuracy: 0.7840\n",
            "Epoch 100/100\n",
            "399/399 [==============================] - 0s 734us/step - loss: 0.4470 - accuracy: 0.7829\n",
            "100/100 [==============================] - 0s 465us/step - loss: 0.4378 - accuracy: 0.7897\n",
            "[0.4378308355808258, 0.7897049784660339]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.79      0.79      1593\n",
            "           1       0.79      0.79      0.79      1593\n",
            "\n",
            "    accuracy                           0.79      3186\n",
            "   macro avg       0.79      0.79      0.79      3186\n",
            "weighted avg       0.79      0.79      0.79      3186\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_preds = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6gDw5xjL0wF"
      },
      "source": [
        "f1-score for minority class 1 improved from 0.58 to 0.79.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1BaG4gML0wF"
      },
      "source": [
        "Method3: SMOTE\n",
        "\n",
        "To install imbalanced-learn library use pip install imbalanced-learn command"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0SpAs7RwL0wG"
      },
      "outputs": [],
      "source": [
        "X = df1.drop('Exited', axis='columns')\n",
        "y = df1['Exited']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WnRre_BML0wG",
        "outputId": "b32cbdd2-dbcd-4e9c-9d80-2d42c99b04b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    7963\n",
              "1    7963\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE(sampling_strategy='minority')\n",
        "X_sm, y_sm = smote.fit_resample(X, y)\n",
        "\n",
        "y_sm.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MThoLFVUL0wG"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size=0.2, random_state=15, stratify=y_sm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q5xHr6-mL0wG",
        "outputId": "9319955e-a369-422d-db13-081ca00c51ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "399/399 [==============================] - 1s 611us/step - loss: 0.6671 - accuracy: 0.5758\n",
            "Epoch 2/100\n",
            "399/399 [==============================] - 0s 621us/step - loss: 0.6068 - accuracy: 0.6684\n",
            "Epoch 3/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.5796 - accuracy: 0.6971\n",
            "Epoch 4/100\n",
            "399/399 [==============================] - 0s 596us/step - loss: 0.5640 - accuracy: 0.7059\n",
            "Epoch 5/100\n",
            "399/399 [==============================] - 0s 596us/step - loss: 0.5538 - accuracy: 0.7171\n",
            "Epoch 6/100\n",
            "399/399 [==============================] - 0s 679us/step - loss: 0.5424 - accuracy: 0.7272\n",
            "Epoch 7/100\n",
            "399/399 [==============================] - 0s 641us/step - loss: 0.5301 - accuracy: 0.7365\n",
            "Epoch 8/100\n",
            "399/399 [==============================] - 0s 597us/step - loss: 0.5195 - accuracy: 0.7414\n",
            "Epoch 9/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.5094 - accuracy: 0.7480\n",
            "Epoch 10/100\n",
            "399/399 [==============================] - 0s 638us/step - loss: 0.5004 - accuracy: 0.7567\n",
            "Epoch 11/100\n",
            "399/399 [==============================] - 0s 709us/step - loss: 0.4935 - accuracy: 0.7600\n",
            "Epoch 12/100\n",
            "399/399 [==============================] - 0s 590us/step - loss: 0.4861 - accuracy: 0.7643\n",
            "Epoch 13/100\n",
            "399/399 [==============================] - 0s 583us/step - loss: 0.4803 - accuracy: 0.7680\n",
            "Epoch 14/100\n",
            "399/399 [==============================] - 0s 590us/step - loss: 0.4759 - accuracy: 0.7720\n",
            "Epoch 15/100\n",
            "399/399 [==============================] - 0s 585us/step - loss: 0.4722 - accuracy: 0.7761\n",
            "Epoch 16/100\n",
            "399/399 [==============================] - 0s 583us/step - loss: 0.4704 - accuracy: 0.7756\n",
            "Epoch 17/100\n",
            "399/399 [==============================] - 0s 580us/step - loss: 0.4666 - accuracy: 0.7784\n",
            "Epoch 18/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.4627 - accuracy: 0.7780\n",
            "Epoch 19/100\n",
            "399/399 [==============================] - 0s 626us/step - loss: 0.4618 - accuracy: 0.7786\n",
            "Epoch 20/100\n",
            "399/399 [==============================] - 0s 598us/step - loss: 0.4591 - accuracy: 0.7789\n",
            "Epoch 21/100\n",
            "399/399 [==============================] - 0s 558us/step - loss: 0.4582 - accuracy: 0.7820\n",
            "Epoch 22/100\n",
            "399/399 [==============================] - 0s 583us/step - loss: 0.4562 - accuracy: 0.7818\n",
            "Epoch 23/100\n",
            "399/399 [==============================] - 0s 578us/step - loss: 0.4553 - accuracy: 0.7835\n",
            "Epoch 24/100\n",
            "399/399 [==============================] - 0s 614us/step - loss: 0.4538 - accuracy: 0.7834\n",
            "Epoch 25/100\n",
            "399/399 [==============================] - 0s 580us/step - loss: 0.4530 - accuracy: 0.7795\n",
            "Epoch 26/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.4510 - accuracy: 0.7847\n",
            "Epoch 27/100\n",
            "399/399 [==============================] - 0s 593us/step - loss: 0.4511 - accuracy: 0.7840\n",
            "Epoch 28/100\n",
            "399/399 [==============================] - 0s 591us/step - loss: 0.4509 - accuracy: 0.7867\n",
            "Epoch 29/100\n",
            "399/399 [==============================] - 0s 573us/step - loss: 0.4505 - accuracy: 0.7830\n",
            "Epoch 30/100\n",
            "399/399 [==============================] - 0s 578us/step - loss: 0.4483 - accuracy: 0.7868\n",
            "Epoch 31/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.4475 - accuracy: 0.7863\n",
            "Epoch 32/100\n",
            "399/399 [==============================] - 0s 606us/step - loss: 0.4474 - accuracy: 0.7856\n",
            "Epoch 33/100\n",
            "399/399 [==============================] - 0s 571us/step - loss: 0.4466 - accuracy: 0.7863\n",
            "Epoch 34/100\n",
            "399/399 [==============================] - 0s 576us/step - loss: 0.4463 - accuracy: 0.7861\n",
            "Epoch 35/100\n",
            "399/399 [==============================] - 0s 580us/step - loss: 0.4460 - accuracy: 0.7866\n",
            "Epoch 36/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.4448 - accuracy: 0.7891\n",
            "Epoch 37/100\n",
            "399/399 [==============================] - 0s 576us/step - loss: 0.4438 - accuracy: 0.7878\n",
            "Epoch 38/100\n",
            "399/399 [==============================] - 0s 563us/step - loss: 0.4435 - accuracy: 0.7885\n",
            "Epoch 39/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.4423 - accuracy: 0.7895\n",
            "Epoch 40/100\n",
            "399/399 [==============================] - 0s 673us/step - loss: 0.4433 - accuracy: 0.7874\n",
            "Epoch 41/100\n",
            "399/399 [==============================] - 0s 566us/step - loss: 0.4426 - accuracy: 0.7900\n",
            "Epoch 42/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4418 - accuracy: 0.7889\n",
            "Epoch 43/100\n",
            "399/399 [==============================] - 0s 555us/step - loss: 0.4407 - accuracy: 0.7921\n",
            "Epoch 44/100\n",
            "399/399 [==============================] - 0s 560us/step - loss: 0.4404 - accuracy: 0.7924\n",
            "Epoch 45/100\n",
            "399/399 [==============================] - 0s 560us/step - loss: 0.4387 - accuracy: 0.7916\n",
            "Epoch 46/100\n",
            "399/399 [==============================] - 0s 555us/step - loss: 0.4392 - accuracy: 0.7907\n",
            "Epoch 47/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.4389 - accuracy: 0.7911\n",
            "Epoch 48/100\n",
            "399/399 [==============================] - 0s 551us/step - loss: 0.4382 - accuracy: 0.7933\n",
            "Epoch 49/100\n",
            "399/399 [==============================] - 0s 555us/step - loss: 0.4382 - accuracy: 0.7931\n",
            "Epoch 50/100\n",
            "399/399 [==============================] - 0s 558us/step - loss: 0.4375 - accuracy: 0.7938\n",
            "Epoch 51/100\n",
            "399/399 [==============================] - 0s 563us/step - loss: 0.4368 - accuracy: 0.7929\n",
            "Epoch 52/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4363 - accuracy: 0.7940\n",
            "Epoch 53/100\n",
            "399/399 [==============================] - 0s 568us/step - loss: 0.4368 - accuracy: 0.7943\n",
            "Epoch 54/100\n",
            "399/399 [==============================] - 0s 573us/step - loss: 0.4358 - accuracy: 0.7959\n",
            "Epoch 55/100\n",
            "399/399 [==============================] - 0s 560us/step - loss: 0.4354 - accuracy: 0.7944\n",
            "Epoch 56/100\n",
            "399/399 [==============================] - 0s 573us/step - loss: 0.4349 - accuracy: 0.7949\n",
            "Epoch 57/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4330 - accuracy: 0.7943\n",
            "Epoch 58/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4344 - accuracy: 0.7972\n",
            "Epoch 59/100\n",
            "399/399 [==============================] - 0s 570us/step - loss: 0.4323 - accuracy: 0.7980\n",
            "Epoch 60/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.4322 - accuracy: 0.7959\n",
            "Epoch 61/100\n",
            "399/399 [==============================] - 0s 598us/step - loss: 0.4323 - accuracy: 0.7973\n",
            "Epoch 62/100\n",
            "399/399 [==============================] - 0s 581us/step - loss: 0.4319 - accuracy: 0.7929\n",
            "Epoch 63/100\n",
            "399/399 [==============================] - 0s 593us/step - loss: 0.4299 - accuracy: 0.7976\n",
            "Epoch 64/100\n",
            "399/399 [==============================] - 0s 593us/step - loss: 0.4306 - accuracy: 0.7955\n",
            "Epoch 65/100\n",
            "399/399 [==============================] - 0s 601us/step - loss: 0.4291 - accuracy: 0.7957\n",
            "Epoch 66/100\n",
            "399/399 [==============================] - 0s 573us/step - loss: 0.4280 - accuracy: 0.7990\n",
            "Epoch 67/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.4281 - accuracy: 0.7977\n",
            "Epoch 68/100\n",
            "399/399 [==============================] - 0s 580us/step - loss: 0.4261 - accuracy: 0.7985\n",
            "Epoch 69/100\n",
            "399/399 [==============================] - 0s 699us/step - loss: 0.4259 - accuracy: 0.8013\n",
            "Epoch 70/100\n",
            "399/399 [==============================] - 0s 588us/step - loss: 0.4251 - accuracy: 0.8013\n",
            "Epoch 71/100\n",
            "399/399 [==============================] - 0s 585us/step - loss: 0.4243 - accuracy: 0.8002\n",
            "Epoch 72/100\n",
            "399/399 [==============================] - 0s 603us/step - loss: 0.4250 - accuracy: 0.8009\n",
            "Epoch 73/100\n",
            "399/399 [==============================] - 0s 568us/step - loss: 0.4227 - accuracy: 0.8014\n",
            "Epoch 74/100\n",
            "399/399 [==============================] - 0s 588us/step - loss: 0.4215 - accuracy: 0.8008\n",
            "Epoch 75/100\n",
            "399/399 [==============================] - 0s 581us/step - loss: 0.4213 - accuracy: 0.8016\n",
            "Epoch 76/100\n",
            "399/399 [==============================] - 0s 597us/step - loss: 0.4208 - accuracy: 0.8036\n",
            "Epoch 77/100\n",
            "399/399 [==============================] - 0s 591us/step - loss: 0.4206 - accuracy: 0.8020\n",
            "Epoch 78/100\n",
            "399/399 [==============================] - 0s 574us/step - loss: 0.4198 - accuracy: 0.8039\n",
            "Epoch 79/100\n",
            "399/399 [==============================] - 0s 576us/step - loss: 0.4186 - accuracy: 0.8054\n",
            "Epoch 80/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4171 - accuracy: 0.8048\n",
            "Epoch 81/100\n",
            "399/399 [==============================] - 0s 568us/step - loss: 0.4168 - accuracy: 0.8049\n",
            "Epoch 82/100\n",
            "399/399 [==============================] - 0s 565us/step - loss: 0.4155 - accuracy: 0.8040\n",
            "Epoch 83/100\n",
            "399/399 [==============================] - 0s 569us/step - loss: 0.4149 - accuracy: 0.8068\n",
            "Epoch 84/100\n",
            "399/399 [==============================] - 0s 571us/step - loss: 0.4161 - accuracy: 0.8037\n",
            "Epoch 85/100\n",
            "399/399 [==============================] - 0s 562us/step - loss: 0.4146 - accuracy: 0.8043\n",
            "Epoch 86/100\n",
            "399/399 [==============================] - 0s 566us/step - loss: 0.4146 - accuracy: 0.8073\n",
            "Epoch 87/100\n",
            "399/399 [==============================] - 0s 577us/step - loss: 0.4127 - accuracy: 0.8082\n",
            "Epoch 88/100\n",
            "399/399 [==============================] - 0s 568us/step - loss: 0.4124 - accuracy: 0.8115\n",
            "Epoch 89/100\n",
            "399/399 [==============================] - 0s 593us/step - loss: 0.4115 - accuracy: 0.8087\n",
            "Epoch 90/100\n",
            "399/399 [==============================] - 0s 603us/step - loss: 0.4115 - accuracy: 0.8064\n",
            "Epoch 91/100\n",
            "399/399 [==============================] - 0s 601us/step - loss: 0.4104 - accuracy: 0.8081\n",
            "Epoch 92/100\n",
            "399/399 [==============================] - 0s 588us/step - loss: 0.4123 - accuracy: 0.8082\n",
            "Epoch 93/100\n",
            "399/399 [==============================] - 0s 595us/step - loss: 0.4109 - accuracy: 0.8078\n",
            "Epoch 94/100\n",
            "399/399 [==============================] - 0s 591us/step - loss: 0.4092 - accuracy: 0.8104\n",
            "Epoch 95/100\n",
            "399/399 [==============================] - 0s 593us/step - loss: 0.4084 - accuracy: 0.8115\n",
            "Epoch 96/100\n",
            "399/399 [==============================] - 0s 603us/step - loss: 0.4086 - accuracy: 0.8115\n",
            "Epoch 97/100\n",
            "399/399 [==============================] - 0s 590us/step - loss: 0.4076 - accuracy: 0.8104\n",
            "Epoch 98/100\n",
            "399/399 [==============================] - 0s 586us/step - loss: 0.4085 - accuracy: 0.8100\n",
            "Epoch 99/100\n",
            "399/399 [==============================] - 0s 596us/step - loss: 0.4088 - accuracy: 0.8094\n",
            "Epoch 100/100\n",
            "399/399 [==============================] - 0s 696us/step - loss: 0.4089 - accuracy: 0.8114\n",
            "100/100 [==============================] - 0s 445us/step - loss: 0.4078 - accuracy: 0.8142\n",
            "[0.4077799916267395, 0.8141870498657227]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.83      0.82      1593\n",
            "           1       0.82      0.80      0.81      1593\n",
            "\n",
            "    accuracy                           0.81      3186\n",
            "   macro avg       0.81      0.81      0.81      3186\n",
            "weighted avg       0.81      0.81      0.81      3186\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_preds = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWdkLwvzL0wH"
      },
      "source": [
        "SMOT Oversampling increases f1 score of minority class 1 from 0.58 to 0.81.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iusy57qRL0wH"
      },
      "source": [
        "Method4: Use of Ensemble with undersampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TwYhojeFL0wH",
        "outputId": "1673d3ab-9a9d-4561-d5e0-ae68201428f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    7963\n",
              "1    2037\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.Exited.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UgwqqhH1L0wH"
      },
      "outputs": [],
      "source": [
        "X = df1.drop('Exited', axis='columns')\n",
        "y = df1['Exited']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BHJtlftKL0wH"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=15, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mbr70IH4L0wI"
      },
      "outputs": [],
      "source": [
        "df2 = X_train.copy()\n",
        "df2['Exited'] = y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kv5N7QVcL0wI",
        "outputId": "864b51ee-ab41-46fd-8fd4-6a1418dd2ab3"
      },
      "outputs": [
        {
          "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>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5710</th>\n",
              "      <td>0.856</td>\n",
              "      <td>0.216216</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.554265</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.339721</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3745</th>\n",
              "      <td>0.852</td>\n",
              "      <td>0.256757</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.371163</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.980432</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5429</th>\n",
              "      <td>0.664</td>\n",
              "      <td>0.405405</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.325318</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>0.648</td>\n",
              "      <td>0.391892</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.426077</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.010339</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8967</th>\n",
              "      <td>0.970</td>\n",
              "      <td>0.094595</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.417230</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CreditScore       Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
              "5710        0.856  0.216216     0.5  0.554265       0.333333          0   \n",
              "3745        0.852  0.256757     0.1  0.371163       0.333333          1   \n",
              "5429        0.664  0.405405     0.7  0.000000       0.333333          1   \n",
              "551         0.648  0.391892     0.6  0.426077       0.000000          1   \n",
              "8967        0.970  0.094595     0.7  0.000000       0.333333          1   \n",
              "\n",
              "      IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
              "5710               0         0.339721                 1                  0   \n",
              "3745               1         0.980432                 0                  1   \n",
              "5429               0         0.325318                 1                  0   \n",
              "551                1         0.010339                 0                  1   \n",
              "8967               1         0.417230                 1                  0   \n",
              "\n",
              "      Geography_Spain  Gender_Female  Gender_Male  Exited  \n",
              "5710                0              0            1       0  \n",
              "3745                0              1            0       0  \n",
              "5429                0              1            0       0  \n",
              "551                 0              0            1       1  \n",
              "8967                0              0            1       0  "
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQCTR-rzL0wI"
      },
      "outputs": [],
      "source": [
        "df2_class0 = df2[df2.Exited == 0]\n",
        "df2_class1 = df2[df2.Exited == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUEhTvjjL0wI"
      },
      "outputs": [],
      "source": [
        "def get_train_batch(df_majority, df_minority, start, end):\n",
        "    df_train = pd.concat([df_majority[start:end], df_minority], axis=0)\n",
        "\n",
        "    X_train = df_train.drop('Exited', axis='columns')\n",
        "    y_train = df_train.Exited\n",
        "    return X_train, y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MSrGPo0cL0wI",
        "outputId": "a42c9b7e-9e46-463a-edab-b2f595e529c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "98/98 [==============================] - 1s 866us/step - loss: 0.6631 - accuracy: 0.6243\n",
            "Epoch 2/100\n",
            "98/98 [==============================] - 0s 907us/step - loss: 0.6369 - accuracy: 0.6493\n",
            "Epoch 3/100\n",
            "98/98 [==============================] - 0s 804us/step - loss: 0.6259 - accuracy: 0.6518\n",
            "Epoch 4/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.6186 - accuracy: 0.6605\n",
            "Epoch 5/100\n",
            "98/98 [==============================] - 0s 773us/step - loss: 0.6107 - accuracy: 0.6650\n",
            "Epoch 6/100\n",
            "98/98 [==============================] - 0s 815us/step - loss: 0.6043 - accuracy: 0.6765\n",
            "Epoch 7/100\n",
            "98/98 [==============================] - 0s 804us/step - loss: 0.5949 - accuracy: 0.6854\n",
            "Epoch 8/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5863 - accuracy: 0.6906\n",
            "Epoch 9/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5769 - accuracy: 0.7050\n",
            "Epoch 10/100\n",
            "98/98 [==============================] - 0s 835us/step - loss: 0.5658 - accuracy: 0.7152\n",
            "Epoch 11/100\n",
            "98/98 [==============================] - 0s 2ms/step - loss: 0.5557 - accuracy: 0.7235\n",
            "Epoch 12/100\n",
            "98/98 [==============================] - 0s 2ms/step - loss: 0.5450 - accuracy: 0.7322\n",
            "Epoch 13/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5345 - accuracy: 0.7389\n",
            "Epoch 14/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.5268 - accuracy: 0.7418\n",
            "Epoch 15/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5189 - accuracy: 0.7494\n",
            "Epoch 16/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.5133 - accuracy: 0.7501\n",
            "Epoch 17/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.5083 - accuracy: 0.7514\n",
            "Epoch 18/100\n",
            "98/98 [==============================] - 0s 877us/step - loss: 0.5048 - accuracy: 0.7549\n",
            "Epoch 19/100\n",
            "98/98 [==============================] - 0s 989us/step - loss: 0.4989 - accuracy: 0.7626\n",
            "Epoch 20/100\n",
            "98/98 [==============================] - 0s 918us/step - loss: 0.4991 - accuracy: 0.7549\n",
            "Epoch 21/100\n",
            "98/98 [==============================] - 0s 2ms/step - loss: 0.4967 - accuracy: 0.7635\n",
            "Epoch 22/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4951 - accuracy: 0.7578\n",
            "Epoch 23/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4935 - accuracy: 0.7590\n",
            "Epoch 24/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4908 - accuracy: 0.7661\n",
            "Epoch 25/100\n",
            "98/98 [==============================] - 0s 991us/step - loss: 0.4900 - accuracy: 0.7603\n",
            "Epoch 26/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4883 - accuracy: 0.7654\n",
            "Epoch 27/100\n",
            "98/98 [==============================] - 0s 976us/step - loss: 0.4885 - accuracy: 0.7661\n",
            "Epoch 28/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4865 - accuracy: 0.7667\n",
            "Epoch 29/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.4901 - accuracy: 0.7658\n",
            "Epoch 30/100\n",
            "98/98 [==============================] - 0s 969us/step - loss: 0.4865 - accuracy: 0.7622\n",
            "Epoch 31/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.4863 - accuracy: 0.7654\n",
            "Epoch 32/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.4856 - accuracy: 0.7667\n",
            "Epoch 33/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4836 - accuracy: 0.7699\n",
            "Epoch 34/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4839 - accuracy: 0.7696\n",
            "Epoch 35/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.4817 - accuracy: 0.7683\n",
            "Epoch 36/100\n",
            "98/98 [==============================] - 0s 846us/step - loss: 0.4830 - accuracy: 0.7651\n",
            "Epoch 37/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4816 - accuracy: 0.7696\n",
            "Epoch 38/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.4807 - accuracy: 0.7693\n",
            "Epoch 39/100\n",
            "98/98 [==============================] - 0s 975us/step - loss: 0.4807 - accuracy: 0.7712\n",
            "Epoch 40/100\n",
            "98/98 [==============================] - 0s 835us/step - loss: 0.4808 - accuracy: 0.7674\n",
            "Epoch 41/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4801 - accuracy: 0.7664\n",
            "Epoch 42/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4795 - accuracy: 0.7690\n",
            "Epoch 43/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.4799 - accuracy: 0.7699\n",
            "Epoch 44/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.4793 - accuracy: 0.7683\n",
            "Epoch 45/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4782 - accuracy: 0.7706\n",
            "Epoch 46/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.4782 - accuracy: 0.7693\n",
            "Epoch 47/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.4786 - accuracy: 0.7715\n",
            "Epoch 48/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4776 - accuracy: 0.7683\n",
            "Epoch 49/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.4772 - accuracy: 0.7734\n",
            "Epoch 50/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4772 - accuracy: 0.7686\n",
            "Epoch 51/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4762 - accuracy: 0.7683\n",
            "Epoch 52/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4761 - accuracy: 0.7702\n",
            "Epoch 53/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4746 - accuracy: 0.7686\n",
            "Epoch 54/100\n",
            "98/98 [==============================] - 0s 947us/step - loss: 0.4748 - accuracy: 0.7693\n",
            "Epoch 55/100\n",
            "98/98 [==============================] - 0s 970us/step - loss: 0.4757 - accuracy: 0.7718\n",
            "Epoch 56/100\n",
            "98/98 [==============================] - 0s 868us/step - loss: 0.4753 - accuracy: 0.7677\n",
            "Epoch 57/100\n",
            "98/98 [==============================] - 0s 801us/step - loss: 0.4750 - accuracy: 0.7693\n",
            "Epoch 58/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.4752 - accuracy: 0.7693\n",
            "Epoch 59/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4743 - accuracy: 0.7725\n",
            "Epoch 60/100\n",
            "98/98 [==============================] - 0s 907us/step - loss: 0.4736 - accuracy: 0.7715\n",
            "Epoch 61/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.4737 - accuracy: 0.7696\n",
            "Epoch 62/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4733 - accuracy: 0.7728\n",
            "Epoch 63/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.4726 - accuracy: 0.7747\n",
            "Epoch 64/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.4726 - accuracy: 0.7763\n",
            "Epoch 65/100\n",
            "98/98 [==============================] - 0s 907us/step - loss: 0.4716 - accuracy: 0.7715\n",
            "Epoch 66/100\n",
            "98/98 [==============================] - 0s 846us/step - loss: 0.4721 - accuracy: 0.7738\n",
            "Epoch 67/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.4714 - accuracy: 0.7728\n",
            "Epoch 68/100\n",
            "98/98 [==============================] - 0s 918us/step - loss: 0.4713 - accuracy: 0.7712\n",
            "Epoch 69/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.4704 - accuracy: 0.7734\n",
            "Epoch 70/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4718 - accuracy: 0.7725\n",
            "Epoch 71/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.4718 - accuracy: 0.7754\n",
            "Epoch 72/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.4713 - accuracy: 0.7706\n",
            "Epoch 73/100\n",
            "98/98 [==============================] - 0s 794us/step - loss: 0.4721 - accuracy: 0.7715\n",
            "Epoch 74/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4711 - accuracy: 0.7744\n",
            "Epoch 75/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4712 - accuracy: 0.7725\n",
            "Epoch 76/100\n",
            "98/98 [==============================] - 0s 969us/step - loss: 0.4706 - accuracy: 0.7757\n",
            "Epoch 77/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.4707 - accuracy: 0.7725\n",
            "Epoch 78/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4696 - accuracy: 0.7738\n",
            "Epoch 79/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4707 - accuracy: 0.7757\n",
            "Epoch 80/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.4711 - accuracy: 0.7699\n",
            "Epoch 81/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4700 - accuracy: 0.7770\n",
            "Epoch 82/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.4700 - accuracy: 0.7699\n",
            "Epoch 83/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4727 - accuracy: 0.7722\n",
            "Epoch 84/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.4700 - accuracy: 0.7731\n",
            "Epoch 85/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.4688 - accuracy: 0.7738\n",
            "Epoch 86/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.4695 - accuracy: 0.7760\n",
            "Epoch 87/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4699 - accuracy: 0.7744\n",
            "Epoch 88/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4690 - accuracy: 0.7741\n",
            "Epoch 89/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.4694 - accuracy: 0.7731\n",
            "Epoch 90/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4711 - accuracy: 0.7747\n",
            "Epoch 91/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4679 - accuracy: 0.7763\n",
            "Epoch 92/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4685 - accuracy: 0.7722\n",
            "Epoch 93/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4696 - accuracy: 0.7744\n",
            "Epoch 94/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4707 - accuracy: 0.7709\n",
            "Epoch 95/100\n",
            "98/98 [==============================] - 0s 969us/step - loss: 0.4691 - accuracy: 0.7731\n",
            "Epoch 96/100\n",
            "98/98 [==============================] - 0s 949us/step - loss: 0.4685 - accuracy: 0.7725\n",
            "Epoch 97/100\n",
            "98/98 [==============================] - 0s 877us/step - loss: 0.4684 - accuracy: 0.7766\n",
            "Epoch 98/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.4672 - accuracy: 0.7754\n",
            "Epoch 99/100\n",
            "98/98 [==============================] - 0s 876us/step - loss: 0.4672 - accuracy: 0.7731\n",
            "Epoch 100/100\n",
            "98/98 [==============================] - 0s 877us/step - loss: 0.4683 - accuracy: 0.7702\n",
            "63/63 [==============================] - 0s 549us/step - loss: 0.4943 - accuracy: 0.7660\n",
            "[0.4943360388278961, 0.765999972820282]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.76      0.84      1593\n",
            "           1       0.46      0.77      0.57       407\n",
            "\n",
            "    accuracy                           0.77      2000\n",
            "   macro avg       0.69      0.77      0.71      2000\n",
            "weighted avg       0.83      0.77      0.78      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "X_train, y_train = get_train_batch(df2_class0, df2_class1, 0, 1495)\n",
        "\n",
        "y_pred1 = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wc4-2rA5L0wJ",
        "outputId": "2ffea23d-dca6-43b8-f3e5-883125bbf801"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "98/98 [==============================] - 1s 1ms/step - loss: 0.6884 - accuracy: 0.5488\n",
            "Epoch 2/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.6573 - accuracy: 0.6077\n",
            "Epoch 3/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.6390 - accuracy: 0.6320\n",
            "Epoch 4/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.6256 - accuracy: 0.6406\n",
            "Epoch 5/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.6156 - accuracy: 0.6630\n",
            "Epoch 6/100\n",
            "98/98 [==============================] - 0s 918us/step - loss: 0.6066 - accuracy: 0.6749\n",
            "Epoch 7/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.5996 - accuracy: 0.6835\n",
            "Epoch 8/100\n",
            "98/98 [==============================] - 0s 876us/step - loss: 0.5937 - accuracy: 0.6906\n",
            "Epoch 9/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5910 - accuracy: 0.6893\n",
            "Epoch 10/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.5856 - accuracy: 0.6944\n",
            "Epoch 11/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.5827 - accuracy: 0.6970\n",
            "Epoch 12/100\n",
            "98/98 [==============================] - 0s 949us/step - loss: 0.5818 - accuracy: 0.7037\n",
            "Epoch 13/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.5801 - accuracy: 0.6989\n",
            "Epoch 14/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.5788 - accuracy: 0.7062\n",
            "Epoch 15/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5761 - accuracy: 0.6992\n",
            "Epoch 16/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.5750 - accuracy: 0.7050\n",
            "Epoch 17/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.5746 - accuracy: 0.7024\n",
            "Epoch 18/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.5745 - accuracy: 0.7014\n",
            "Epoch 19/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.5756 - accuracy: 0.7088\n",
            "Epoch 20/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.5719 - accuracy: 0.7066\n",
            "Epoch 21/100\n",
            "98/98 [==============================] - 0s 815us/step - loss: 0.5709 - accuracy: 0.7059\n",
            "Epoch 22/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.5708 - accuracy: 0.7091\n",
            "Epoch 23/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.5694 - accuracy: 0.7075\n",
            "Epoch 24/100\n",
            "98/98 [==============================] - 0s 949us/step - loss: 0.5684 - accuracy: 0.7059\n",
            "Epoch 25/100\n",
            "98/98 [==============================] - 0s 804us/step - loss: 0.5691 - accuracy: 0.7088\n",
            "Epoch 26/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.5674 - accuracy: 0.7110\n",
            "Epoch 27/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.5671 - accuracy: 0.7158\n",
            "Epoch 28/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.5667 - accuracy: 0.7123\n",
            "Epoch 29/100\n",
            "98/98 [==============================] - 0s 856us/step - loss: 0.5650 - accuracy: 0.7101\n",
            "Epoch 30/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.5656 - accuracy: 0.7072\n",
            "Epoch 31/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.5637 - accuracy: 0.7155\n",
            "Epoch 32/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5634 - accuracy: 0.7114\n",
            "Epoch 33/100\n",
            "98/98 [==============================] - 0s 907us/step - loss: 0.5629 - accuracy: 0.7155\n",
            "Epoch 34/100\n",
            "98/98 [==============================] - 0s 918us/step - loss: 0.5616 - accuracy: 0.7174\n",
            "Epoch 35/100\n",
            "98/98 [==============================] - 0s 835us/step - loss: 0.5621 - accuracy: 0.7149\n",
            "Epoch 36/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5610 - accuracy: 0.7165\n",
            "Epoch 37/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5595 - accuracy: 0.7206\n",
            "Epoch 38/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5600 - accuracy: 0.7133\n",
            "Epoch 39/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5589 - accuracy: 0.7168\n",
            "Epoch 40/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5578 - accuracy: 0.7194\n",
            "Epoch 41/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5574 - accuracy: 0.7178\n",
            "Epoch 42/100\n",
            "98/98 [==============================] - 0s 990us/step - loss: 0.5568 - accuracy: 0.7194\n",
            "Epoch 43/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5566 - accuracy: 0.7136\n",
            "Epoch 44/100\n",
            "98/98 [==============================] - 0s 815us/step - loss: 0.5557 - accuracy: 0.7200\n",
            "Epoch 45/100\n",
            "98/98 [==============================] - 0s 895us/step - loss: 0.5547 - accuracy: 0.7184\n",
            "Epoch 46/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5556 - accuracy: 0.7178\n",
            "Epoch 47/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5550 - accuracy: 0.7149\n",
            "Epoch 48/100\n",
            "98/98 [==============================] - 0s 969us/step - loss: 0.5529 - accuracy: 0.7206\n",
            "Epoch 49/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5528 - accuracy: 0.7216\n",
            "Epoch 50/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.5525 - accuracy: 0.7194\n",
            "Epoch 51/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5509 - accuracy: 0.7226\n",
            "Epoch 52/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5508 - accuracy: 0.7200\n",
            "Epoch 53/100\n",
            "98/98 [==============================] - 0s 918us/step - loss: 0.5505 - accuracy: 0.7165\n",
            "Epoch 54/100\n",
            "98/98 [==============================] - 0s 942us/step - loss: 0.5497 - accuracy: 0.7229\n",
            "Epoch 55/100\n",
            "98/98 [==============================] - 0s 798us/step - loss: 0.5483 - accuracy: 0.7235\n",
            "Epoch 56/100\n",
            "98/98 [==============================] - 0s 951us/step - loss: 0.5500 - accuracy: 0.7216\n",
            "Epoch 57/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.5475 - accuracy: 0.7219\n",
            "Epoch 58/100\n",
            "98/98 [==============================] - 0s 846us/step - loss: 0.5464 - accuracy: 0.7296\n",
            "Epoch 59/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5467 - accuracy: 0.7306\n",
            "Epoch 60/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.5474 - accuracy: 0.7232\n",
            "Epoch 61/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5451 - accuracy: 0.7242\n",
            "Epoch 62/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5450 - accuracy: 0.7210\n",
            "Epoch 63/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.5444 - accuracy: 0.7270\n",
            "Epoch 64/100\n",
            "98/98 [==============================] - 0s 876us/step - loss: 0.5427 - accuracy: 0.7251\n",
            "Epoch 65/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.5419 - accuracy: 0.7226\n",
            "Epoch 66/100\n",
            "98/98 [==============================] - 0s 846us/step - loss: 0.5418 - accuracy: 0.7235\n",
            "Epoch 67/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.5396 - accuracy: 0.7293\n",
            "Epoch 68/100\n",
            "98/98 [==============================] - 0s 907us/step - loss: 0.5386 - accuracy: 0.7293\n",
            "Epoch 69/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5326 - accuracy: 0.7322\n",
            "Epoch 70/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5276 - accuracy: 0.7376\n",
            "Epoch 71/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.5246 - accuracy: 0.7398\n",
            "Epoch 72/100\n",
            "98/98 [==============================] - 0s 846us/step - loss: 0.5207 - accuracy: 0.7443\n",
            "Epoch 73/100\n",
            "98/98 [==============================] - 0s 815us/step - loss: 0.5147 - accuracy: 0.7488\n",
            "Epoch 74/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.5093 - accuracy: 0.7587\n",
            "Epoch 75/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5058 - accuracy: 0.7584\n",
            "Epoch 76/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.5028 - accuracy: 0.7571\n",
            "Epoch 77/100\n",
            "98/98 [==============================] - 0s 938us/step - loss: 0.4994 - accuracy: 0.7600\n",
            "Epoch 78/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4975 - accuracy: 0.7658\n",
            "Epoch 79/100\n",
            "98/98 [==============================] - 0s 959us/step - loss: 0.4960 - accuracy: 0.7606\n",
            "Epoch 80/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.4948 - accuracy: 0.7658\n",
            "Epoch 81/100\n",
            "98/98 [==============================] - 0s 928us/step - loss: 0.4922 - accuracy: 0.7661\n",
            "Epoch 82/100\n",
            "98/98 [==============================] - 0s 926us/step - loss: 0.4896 - accuracy: 0.7670\n",
            "Epoch 83/100\n",
            "98/98 [==============================] - 0s 813us/step - loss: 0.4897 - accuracy: 0.7626\n",
            "Epoch 84/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.4876 - accuracy: 0.7738\n",
            "Epoch 85/100\n",
            "98/98 [==============================] - 0s 854us/step - loss: 0.4850 - accuracy: 0.7642\n",
            "Epoch 86/100\n",
            "98/98 [==============================] - 0s 844us/step - loss: 0.4861 - accuracy: 0.7690\n",
            "Epoch 87/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4853 - accuracy: 0.7680\n",
            "Epoch 88/100\n",
            "98/98 [==============================] - 0s 937us/step - loss: 0.4825 - accuracy: 0.7680\n",
            "Epoch 89/100\n",
            "98/98 [==============================] - 0s 897us/step - loss: 0.4810 - accuracy: 0.7709\n",
            "Epoch 90/100\n",
            "98/98 [==============================] - 0s 980us/step - loss: 0.4800 - accuracy: 0.7715\n",
            "Epoch 91/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.4777 - accuracy: 0.7731\n",
            "Epoch 92/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4788 - accuracy: 0.7664\n",
            "Epoch 93/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4750 - accuracy: 0.7718\n",
            "Epoch 94/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4737 - accuracy: 0.7722\n",
            "Epoch 95/100\n",
            "98/98 [==============================] - 0s 1ms/step - loss: 0.4717 - accuracy: 0.7766\n",
            "Epoch 96/100\n",
            "98/98 [==============================] - 0s 969us/step - loss: 0.4713 - accuracy: 0.7766\n",
            "Epoch 97/100\n",
            "98/98 [==============================] - 0s 876us/step - loss: 0.4707 - accuracy: 0.7722\n",
            "Epoch 98/100\n",
            "98/98 [==============================] - 0s 887us/step - loss: 0.4694 - accuracy: 0.7731\n",
            "Epoch 99/100\n",
            "98/98 [==============================] - 0s 866us/step - loss: 0.4695 - accuracy: 0.7741\n",
            "Epoch 100/100\n",
            "98/98 [==============================] - 0s 825us/step - loss: 0.4687 - accuracy: 0.7738\n",
            "63/63 [==============================] - 0s 790us/step - loss: 0.4739 - accuracy: 0.7680\n",
            "[0.4739205837249756, 0.7680000066757202]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.77      0.84      1593\n",
            "           1       0.46      0.75      0.57       407\n",
            "\n",
            "    accuracy                           0.77      2000\n",
            "   macro avg       0.69      0.76      0.70      2000\n",
            "weighted avg       0.83      0.77      0.79      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "X_train, y_train = get_train_batch(df2_class0, df2_class1, 1495, 2990)\n",
        "\n",
        "y_pred2 = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bCzRtKo0L0wJ",
        "outputId": "f146fdae-dfe2-47f0-cce7-ad87757939a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 1s 861us/step - loss: 0.6886 - accuracy: 0.5347\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 0s 839us/step - loss: 0.6398 - accuracy: 0.6484\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.6135 - accuracy: 0.6704\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 0s 885us/step - loss: 0.5996 - accuracy: 0.6827\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.5903 - accuracy: 0.6870\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 0s 849us/step - loss: 0.5821 - accuracy: 0.6971\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.5762 - accuracy: 0.6982\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 0s 2ms/step - loss: 0.5701 - accuracy: 0.7079\n",
            "Epoch 9/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.5646 - accuracy: 0.7094\n",
            "Epoch 10/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.5586 - accuracy: 0.7130\n",
            "Epoch 11/100\n",
            "87/87 [==============================] - 0s 942us/step - loss: 0.5530 - accuracy: 0.7227\n",
            "Epoch 12/100\n",
            "87/87 [==============================] - 0s 930us/step - loss: 0.5481 - accuracy: 0.7242\n",
            "Epoch 13/100\n",
            "87/87 [==============================] - 0s 954us/step - loss: 0.5451 - accuracy: 0.7292\n",
            "Epoch 14/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.5407 - accuracy: 0.7354\n",
            "Epoch 15/100\n",
            "87/87 [==============================] - 0s 960us/step - loss: 0.5383 - accuracy: 0.7372\n",
            "Epoch 16/100\n",
            "87/87 [==============================] - 0s 873us/step - loss: 0.5322 - accuracy: 0.7451\n",
            "Epoch 17/100\n",
            "87/87 [==============================] - 0s 838us/step - loss: 0.5296 - accuracy: 0.7484\n",
            "Epoch 18/100\n",
            "87/87 [==============================] - 0s 919us/step - loss: 0.5273 - accuracy: 0.7516\n",
            "Epoch 19/100\n",
            "87/87 [==============================] - 0s 933us/step - loss: 0.5268 - accuracy: 0.7509\n",
            "Epoch 20/100\n",
            "87/87 [==============================] - 0s 898us/step - loss: 0.5219 - accuracy: 0.7552\n",
            "Epoch 21/100\n",
            "87/87 [==============================] - 0s 847us/step - loss: 0.5166 - accuracy: 0.7542\n",
            "Epoch 22/100\n",
            "87/87 [==============================] - 0s 922us/step - loss: 0.5120 - accuracy: 0.7599\n",
            "Epoch 23/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.5076 - accuracy: 0.7614\n",
            "Epoch 24/100\n",
            "87/87 [==============================] - 0s 897us/step - loss: 0.5035 - accuracy: 0.7635\n",
            "Epoch 25/100\n",
            "87/87 [==============================] - 0s 919us/step - loss: 0.4997 - accuracy: 0.7646\n",
            "Epoch 26/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4954 - accuracy: 0.7668\n",
            "Epoch 27/100\n",
            "87/87 [==============================] - 0s 941us/step - loss: 0.4922 - accuracy: 0.7708\n",
            "Epoch 28/100\n",
            "87/87 [==============================] - 0s 815us/step - loss: 0.4893 - accuracy: 0.7733\n",
            "Epoch 29/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.4871 - accuracy: 0.7708\n",
            "Epoch 30/100\n",
            "87/87 [==============================] - 0s 854us/step - loss: 0.4844 - accuracy: 0.7758\n",
            "Epoch 31/100\n",
            "87/87 [==============================] - 0s 965us/step - loss: 0.4810 - accuracy: 0.7765\n",
            "Epoch 32/100\n",
            "87/87 [==============================] - 0s 954us/step - loss: 0.4797 - accuracy: 0.7718\n",
            "Epoch 33/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4784 - accuracy: 0.7747\n",
            "Epoch 34/100\n",
            "87/87 [==============================] - 0s 919us/step - loss: 0.4763 - accuracy: 0.7787\n",
            "Epoch 35/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4742 - accuracy: 0.7747\n",
            "Epoch 36/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4732 - accuracy: 0.7791\n",
            "Epoch 37/100\n",
            "87/87 [==============================] - 0s 826us/step - loss: 0.4727 - accuracy: 0.7773\n",
            "Epoch 38/100\n",
            "87/87 [==============================] - 0s 884us/step - loss: 0.4714 - accuracy: 0.7744\n",
            "Epoch 39/100\n",
            "87/87 [==============================] - 0s 826us/step - loss: 0.4708 - accuracy: 0.7780\n",
            "Epoch 40/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4690 - accuracy: 0.7827\n",
            "Epoch 41/100\n",
            "87/87 [==============================] - 0s 884us/step - loss: 0.4697 - accuracy: 0.7733\n",
            "Epoch 42/100\n",
            "87/87 [==============================] - 0s 942us/step - loss: 0.4692 - accuracy: 0.7798\n",
            "Epoch 43/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4669 - accuracy: 0.7780\n",
            "Epoch 44/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4667 - accuracy: 0.7816\n",
            "Epoch 45/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4659 - accuracy: 0.7798\n",
            "Epoch 46/100\n",
            "87/87 [==============================] - 0s 849us/step - loss: 0.4659 - accuracy: 0.7809\n",
            "Epoch 47/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4653 - accuracy: 0.7783\n",
            "Epoch 48/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.7830\n",
            "Epoch 49/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4635 - accuracy: 0.7845\n",
            "Epoch 50/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4634 - accuracy: 0.7801\n",
            "Epoch 51/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4622 - accuracy: 0.7805\n",
            "Epoch 52/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4614 - accuracy: 0.7841\n",
            "Epoch 53/100\n",
            "87/87 [==============================] - 0s 965us/step - loss: 0.4618 - accuracy: 0.7848\n",
            "Epoch 54/100\n",
            "87/87 [==============================] - 0s 884us/step - loss: 0.4610 - accuracy: 0.7866\n",
            "Epoch 55/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4600 - accuracy: 0.7888\n",
            "Epoch 56/100\n",
            "87/87 [==============================] - 0s 826us/step - loss: 0.4597 - accuracy: 0.7888\n",
            "Epoch 57/100\n",
            "87/87 [==============================] - 0s 814us/step - loss: 0.4592 - accuracy: 0.7884\n",
            "Epoch 58/100\n",
            "87/87 [==============================] - 0s 826us/step - loss: 0.4595 - accuracy: 0.7823\n",
            "Epoch 59/100\n",
            "87/87 [==============================] - 0s 872us/step - loss: 0.4579 - accuracy: 0.7845\n",
            "Epoch 60/100\n",
            "87/87 [==============================] - 0s 849us/step - loss: 0.4577 - accuracy: 0.7841\n",
            "Epoch 61/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.4591 - accuracy: 0.7884\n",
            "Epoch 62/100\n",
            "87/87 [==============================] - 0s 872us/step - loss: 0.4572 - accuracy: 0.7866\n",
            "Epoch 63/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.4564 - accuracy: 0.7859\n",
            "Epoch 64/100\n",
            "87/87 [==============================] - 0s 954us/step - loss: 0.4555 - accuracy: 0.7906\n",
            "Epoch 65/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.4573 - accuracy: 0.7848\n",
            "Epoch 66/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4561 - accuracy: 0.7874\n",
            "Epoch 67/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4558 - accuracy: 0.7866\n",
            "Epoch 68/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4565 - accuracy: 0.7884\n",
            "Epoch 69/100\n",
            "87/87 [==============================] - 0s 814us/step - loss: 0.4548 - accuracy: 0.7874\n",
            "Epoch 70/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4553 - accuracy: 0.7913\n",
            "Epoch 71/100\n",
            "87/87 [==============================] - 0s 886us/step - loss: 0.4534 - accuracy: 0.7921\n",
            "Epoch 72/100\n",
            "87/87 [==============================] - 0s 859us/step - loss: 0.4544 - accuracy: 0.7838\n",
            "Epoch 73/100\n",
            "87/87 [==============================] - 0s 882us/step - loss: 0.4542 - accuracy: 0.7856\n",
            "Epoch 74/100\n",
            "87/87 [==============================] - 0s 966us/step - loss: 0.4532 - accuracy: 0.7866\n",
            "Epoch 75/100\n",
            "87/87 [==============================] - 0s 899us/step - loss: 0.4529 - accuracy: 0.7892\n",
            "Epoch 76/100\n",
            "87/87 [==============================] - 0s 921us/step - loss: 0.4533 - accuracy: 0.7899\n",
            "Epoch 77/100\n",
            "87/87 [==============================] - 0s 931us/step - loss: 0.4522 - accuracy: 0.7917\n",
            "Epoch 78/100\n",
            "87/87 [==============================] - 0s 901us/step - loss: 0.4530 - accuracy: 0.7888\n",
            "Epoch 79/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4522 - accuracy: 0.7863\n",
            "Epoch 80/100\n",
            "87/87 [==============================] - 0s 849us/step - loss: 0.4514 - accuracy: 0.7884\n",
            "Epoch 81/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4505 - accuracy: 0.7888\n",
            "Epoch 82/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4519 - accuracy: 0.7906\n",
            "Epoch 83/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4535 - accuracy: 0.7892\n",
            "Epoch 84/100\n",
            "87/87 [==============================] - 0s 861us/step - loss: 0.4501 - accuracy: 0.7892\n",
            "Epoch 85/100\n",
            "87/87 [==============================] - 0s 814us/step - loss: 0.4506 - accuracy: 0.7917\n",
            "Epoch 86/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4494 - accuracy: 0.7910\n",
            "Epoch 87/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4497 - accuracy: 0.7863\n",
            "Epoch 88/100\n",
            "87/87 [==============================] - 0s 1ms/step - loss: 0.4497 - accuracy: 0.7888\n",
            "Epoch 89/100\n",
            "87/87 [==============================] - 0s 896us/step - loss: 0.4491 - accuracy: 0.7899\n",
            "Epoch 90/100\n",
            "87/87 [==============================] - 0s 989us/step - loss: 0.4485 - accuracy: 0.7906\n",
            "Epoch 91/100\n",
            "87/87 [==============================] - 0s 930us/step - loss: 0.4484 - accuracy: 0.7845\n",
            "Epoch 92/100\n",
            "87/87 [==============================] - 0s 837us/step - loss: 0.4517 - accuracy: 0.7823\n",
            "Epoch 93/100\n",
            "87/87 [==============================] - 0s 884us/step - loss: 0.4489 - accuracy: 0.7888\n",
            "Epoch 94/100\n",
            "87/87 [==============================] - 0s 872us/step - loss: 0.4485 - accuracy: 0.7877\n",
            "Epoch 95/100\n",
            "87/87 [==============================] - 0s 965us/step - loss: 0.4484 - accuracy: 0.7895\n",
            "Epoch 96/100\n",
            "87/87 [==============================] - 0s 872us/step - loss: 0.4479 - accuracy: 0.7881\n",
            "Epoch 97/100\n",
            "87/87 [==============================] - 0s 977us/step - loss: 0.4491 - accuracy: 0.7881\n",
            "Epoch 98/100\n",
            "87/87 [==============================] - 0s 989us/step - loss: 0.4484 - accuracy: 0.7935\n",
            "Epoch 99/100\n",
            "87/87 [==============================] - 0s 977us/step - loss: 0.4480 - accuracy: 0.7834\n",
            "Epoch 100/100\n",
            "87/87 [==============================] - 0s 907us/step - loss: 0.4470 - accuracy: 0.7903\n",
            "63/63 [==============================] - 0s 597us/step - loss: 0.5767 - accuracy: 0.7020\n",
            "[0.5767270922660828, 0.7020000219345093]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.66      0.78      1593\n",
            "           1       0.39      0.86      0.54       407\n",
            "\n",
            "    accuracy                           0.70      2000\n",
            "   macro avg       0.67      0.76      0.66      2000\n",
            "weighted avg       0.84      0.70      0.73      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "X_train, y_train = get_train_batch(df2_class0, df2_class1, 2990, 4130)\n",
        "\n",
        "y_pred3 = ANN(X_train, y_train, X_test, y_test, 'binary_crossentropy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00OKagIOL0wJ"
      },
      "outputs": [],
      "source": [
        "y_pred_final = y_pred1.copy()\n",
        "for i in range(len(y_pred1)):\n",
        "    n_ones = y_pred1[i] + y_pred2[i] + y_pred3[i]\n",
        "    if n_ones > 1:\n",
        "        y_pred_final[i] = 1\n",
        "    else:\n",
        "        y_pred_final[i] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p8cV80vAL0wJ",
        "outputId": "ccb7f579-d116-41f3-f936-dd32f57f1670"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.73      0.82      1593\n",
            "           1       0.44      0.81      0.57       407\n",
            "\n",
            "    accuracy                           0.75      2000\n",
            "   macro avg       0.69      0.77      0.69      2000\n",
            "weighted avg       0.83      0.75      0.77      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "cl_rep = classification_report(y_test, y_pred_final)\n",
        "print(cl_rep)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUUv3lwsL0wJ"
      },
      "source": [
        "f1-score for minority class 1 is 0.57"
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "e94dd2023f21a93510fb04e380baf504272aa09c40641b7e8c2a7346f5fbf161"
    },
    "kernelspec": {
      "display_name": "Python 3.9.5 64-bit",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.5"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": []
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}