credit_card_fraud_detection_dense_lay_classification.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "credit-card-fraud-detection-dense-lay-classification.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "7G3XZk8c1gsN",
        "8xxp-SG11mj5",
        "5MIxdTOb2J__",
        "Ua7lZ4T8NrvB",
        "rjhOiwgql5XJ",
        "eud5osD7xq5g"
      ],
      "mount_file_id": "1TlKd3CvBsCbR2zrx8Xca8QyOtmRLUHh_",
      "authorship_tag": "ABX9TyP/+KHpvxcM95YTt6VRk7Zo",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/komfysach/fraud-detection/blob/main/credit_card_fraud_detection_dense_lay_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7G3XZk8c1gsN"
      },
      "source": [
        "# Import Packages & Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbAl5o06remY"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "import tensorflow\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "np.random.seed(2)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIOgh1p7sctV",
        "outputId": "7382738f-dca3-499c-c7bf-1c690bf3db7f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt7TdDoJvBAX"
      },
      "source": [
        "path = '/content/drive/MyDrive/content/credit-card-detection/creditcard.csv'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UVZG9mzstXQ"
      },
      "source": [
        "data = pd.read_csv(path)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xxp-SG11mj5"
      },
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pBORHoMb1pVX",
        "outputId": "5e58a33e-fe36-41ac-be83-ffdbdee3f2be"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MIxdTOb2J__"
      },
      "source": [
        "# Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtEj_kQT2N6V"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "data['normalizedAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))\n",
        "data = data.drop(['Amount'],axis=1)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yG7wLQn62sWE",
        "outputId": "1468361d-ce5e-478c-b66d-e477fa37e6af"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Class</th>\n",
              "      <th>normalizedAmount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>0</td>\n",
              "      <td>0.244964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.342475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>0</td>\n",
              "      <td>1.160686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>0</td>\n",
              "      <td>0.140534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.073403</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2  ...       V28  Class  normalizedAmount\n",
              "0   0.0 -1.359807 -0.072781  ... -0.021053      0          0.244964\n",
              "1   0.0  1.191857  0.266151  ...  0.014724      0         -0.342475\n",
              "2   1.0 -1.358354 -1.340163  ... -0.059752      0          1.160686\n",
              "3   1.0 -0.966272 -0.185226  ...  0.061458      0          0.140534\n",
              "4   2.0 -1.158233  0.877737  ...  0.215153      0         -0.073403\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "L8JuT1X32vLD",
        "outputId": "d924f3ea-c43f-4d00-b4da-902a719e8b67"
      },
      "source": [
        "data = data.drop(['Time'],axis=1)\n",
        "data.head() "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Class</th>\n",
              "      <th>normalizedAmount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>0</td>\n",
              "      <td>0.244964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.342475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>0</td>\n",
              "      <td>1.160686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>0</td>\n",
              "      <td>0.140534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.073403</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         V1        V2        V3  ...       V28  Class  normalizedAmount\n",
              "0 -1.359807 -0.072781  2.536347  ... -0.021053      0          0.244964\n",
              "1  1.191857  0.266151  0.166480  ...  0.014724      0         -0.342475\n",
              "2 -1.358354 -1.340163  1.773209  ... -0.059752      0          1.160686\n",
              "3 -0.966272 -0.185226  1.792993  ...  0.061458      0          0.140534\n",
              "4 -1.158233  0.877737  1.548718  ...  0.215153      0         -0.073403\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOcLQDgz3Xgf"
      },
      "source": [
        "X = data.iloc[:, data.columns != 'Class']\n",
        "y = data.iloc[:, data.columns == 'Class']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4OR102j3mpI",
        "outputId": "a177e8d8-149e-4f77-dc26-a1476768c3be"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               V1         V2        V3  ...       V27       V28  normalizedAmount\n",
            "0       -1.359807  -0.072781  2.536347  ...  0.133558 -0.021053          0.244964\n",
            "1        1.191857   0.266151  0.166480  ... -0.008983  0.014724         -0.342475\n",
            "2       -1.358354  -1.340163  1.773209  ... -0.055353 -0.059752          1.160686\n",
            "3       -0.966272  -0.185226  1.792993  ...  0.062723  0.061458          0.140534\n",
            "4       -1.158233   0.877737  1.548718  ...  0.219422  0.215153         -0.073403\n",
            "...           ...        ...       ...  ...       ...       ...               ...\n",
            "284802 -11.881118  10.071785 -9.834783  ...  0.943651  0.823731         -0.350151\n",
            "284803  -0.732789  -0.055080  2.035030  ...  0.068472 -0.053527         -0.254117\n",
            "284804   1.919565  -0.301254 -3.249640  ...  0.004455 -0.026561         -0.081839\n",
            "284805  -0.240440   0.530483  0.702510  ...  0.108821  0.104533         -0.313249\n",
            "284806  -0.533413  -0.189733  0.703337  ... -0.002415  0.013649          0.514355\n",
            "\n",
            "[284807 rows x 29 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VelosgTM3pN8",
        "outputId": "758ad76b-25ae-42de-bb12-49d244ba34d9"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Class\n",
            "0           0\n",
            "1           0\n",
            "2           0\n",
            "3           0\n",
            "4           0\n",
            "...       ...\n",
            "284802      0\n",
            "284803      0\n",
            "284804      0\n",
            "284805      0\n",
            "284806      0\n",
            "\n",
            "[284807 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eM8_cbP0KSuY"
      },
      "source": [
        "data_head = data.head()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "e6Ck_xrlJ1Y_",
        "outputId": "bc3f8bc6-d45e-4a57-b566-c7417341296f"
      },
      "source": [
        "sns.pairplot(data_head, hue = 'Class', vars = ['V1', 'V2', 'V3', 'V4', 'V5'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7faabe96d610>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 942.25x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQRGFI6GIbAM"
      },
      "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 = 0.3, random_state=0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POCUSMoNI2ti",
        "outputId": "43171355-49b2-4673-ccf0-64ff759cbbe3"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199364, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKuhHvNiI768",
        "outputId": "a42aea51-0d32-4bc4-ef3e-ed63044f3547"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85443, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuT4BJCkQUXM"
      },
      "source": [
        "X_train_head = X_train[:5]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvzPN3JdQnOn"
      },
      "source": [
        "X_test_head = X_test[:5]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "k3pV9Eq3QrYS",
        "outputId": "3c94ffb5-e4ea-432e-f0c7-2a6e0cafc0fd"
      },
      "source": [
        "sns.heatmap(X_train_head, annot=False)\n",
        "\n",
        "plt.title(\"X train head\",fontsize=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2EMgz7VMSWig",
        "outputId": "896274fc-77ad-4c36-cad8-5880a1423791"
      },
      "source": [
        "sns.heatmap(X_test_head, annot=False)\n",
        "\n",
        "plt.title(\"X test head\",fontsize=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBm2D5rHI-PC"
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "X_test = np.array(X_test)\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ua7lZ4T8NrvB"
      },
      "source": [
        "# Deep neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqwOYDvXNwkl"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39rKT6QrbVAB"
      },
      "source": [
        "model = tensorflow.keras.Sequential()\n",
        "model.add(Dense(units=16, input_dim=29, activation='relu'))\n",
        "model.add(Dense(units=24, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(units=20, activation='relu'))\n",
        "model.add(Dense(units=24, activation='relu'))\n",
        "model.add(Dense(units=1, activation='sigmoid'))"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5KEpbmybr9M",
        "outputId": "334aa1e7-dd77-4087-8bf7-2190262d5431"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 16)                480       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 24)                408       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 20)                500       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 24)                504       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 25        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,917\n",
            "Trainable params: 1,917\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjhOiwgql5XJ"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nd7lcDG9cW1_",
        "outputId": "fd857776-013e-4275-e0f1-d8385208a203"
      },
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
        "model.fit(X_train,y_train,batch_size=15,epochs=5)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "13291/13291 [==============================] - 28s 2ms/step - loss: 0.0094 - accuracy: 0.9985\n",
            "Epoch 2/5\n",
            "13291/13291 [==============================] - 27s 2ms/step - loss: 0.0043 - accuracy: 0.9993\n",
            "Epoch 3/5\n",
            "13291/13291 [==============================] - 28s 2ms/step - loss: 0.0038 - accuracy: 0.9993\n",
            "Epoch 4/5\n",
            "13291/13291 [==============================] - 27s 2ms/step - loss: 0.0035 - accuracy: 0.9994\n",
            "Epoch 5/5\n",
            "13291/13291 [==============================] - 27s 2ms/step - loss: 0.0033 - accuracy: 0.9994\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7faab0020ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eud5osD7xq5g"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGZEi4QjpSOK",
        "outputId": "643657a3-3e7d-432b-bb03-1d47f587cb39"
      },
      "source": [
        "score = model.evaluate(X_test, y_test)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2671/2671 [==============================] - 4s 1ms/step - loss: 0.0030 - accuracy: 0.9994\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLt_Gx3HpizK",
        "outputId": "7cb18f28-c3d0-4a1a-aba0-def7d9345b1d"
      },
      "source": [
        "print(score)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0029547973535954952, 0.9994499087333679]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAra8wfXxpgL"
      },
      "source": [
        "import itertools\n",
        "from sklearn import svm, datasets\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion Matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print(\"Confusion Matrix, without normalization\")\n",
        "      \n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        \n",
        "    plt.ylabel(\"True Label\")\n",
        "    plt.xlabel(\"Predicted Label\")\n",
        "    plt.tight_layout()\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmRpYl2gzqYM"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_test = pd.DataFrame(y_test)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4j8t5Qs0f-v"
      },
      "source": [
        "cnf_matrix = confusion_matrix(y_test, y_pred.round())"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9kIMRuN0mJ6",
        "outputId": "651e63d4-ac2f-462b-c8d1-aa8f15f1a04e"
      },
      "source": [
        "print(cnf_matrix)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[85286    10]\n",
            " [   37   110]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "drPOET9N0qDB",
        "outputId": "1f4ed2fe-7ef9-46ed-ff1d-2293f7f7a6bc"
      },
      "source": [
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[85286    10]\n",
            " [   37   110]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zgN6SeqVZhoeTzf1mZjWltke3W6wlGRHrJV1ENvllHTAmIua31OeZWeup0vhXkhZ9dzsiJpNNimlmtUrQroYHbjzBhZmVRdR2kPRriWZWNqm0rbSy1EnS3ZKek/SspEMkdZE0VdLC9LNzyitJ16W3+uZJOiinnBEp/0JJI3LS+0p6Ol1znZq4WeogaWZlq/DAza+AhyLiM8D+wLPAJcC0iOgNTEvHkL3R1zttI4Ffp/p0AUaRPZvdHxjVEFhTnm/kXDe0WGUcJM2sPCW2IkuJkZJ2AI4AbgKIiPcj4g2yt/XGpmxjgZPS/onAuMhMBzopW3L2WGBqRKyKiNXAVGBoOrd9RExP6+OMyykrLwdJMytL9pxkyS3JnSTNztlGNiquF7ACuFnSk5J+q2z1xK5pOViAV4GuaT/fm33dmkhfnCe9IA/cmFmZ1JyBm5VNvHHTHjgIuDgiZkj6FRu71kC2QqKklnlVMA+3JM2sbBW8J7kYWBwRM9Lx3WRB87XUVSb9bJj9pdCbfcXSu+dJL8hB0szKU8F7khHxKvCKpD4paTCwgOxtvYYR6hHAxLQ/CTg7jXIPBNakbvkUYIikzmnAZggwJZ1bK2lgGtU+O6esvNzdNrOyNNyTrKCLgd+l15lfBM4la9BNkHQesAhomAlnMnAcUA+8nfISEaskXUH2ejTA5RGxKu1fANwCdAQeTFtBDpJmVrZKxsiImAvku285OE/eAC4sUM4YYEye9NnAfqXWx0HSzMpWrZNXlMJB0szK43e3zcwKq/X5JB0kzaxM1TtXZCkcJM2sbDUcIx0kzax8bkmamRUgD9yYmRXnlqSZWRE1HCMdJM2sfG5JmpkV0oylGaqRg6SZlUV+TtLMrLg6j26bmRVWww1JB0kzK082oW7tRsmCQTJ3/dp8IuKJylfHzKpRDfe2i7Yk/6PIuQCOqnBdzKxKVbIlKell4O/ABmB9RPRL62jfCfQEXgZOjYjVaQmGX5HNTv42cE5DA07SCOCyVOyVETE2pfdl48zkk4HvpMl78yoYJCPiyE3+lmbWprRAb/vIiFiZc3wJMC0irpZ0STr+ITAM6J22AcCvgQEpqI4im+E8gDmSJqU1uH8NfAOYQRYkh1JkCYcmFwKT9AlJl0kanY57Szqhud/YzGqTgDqppK0MJwJj0/5Y4KSc9HGRmQ50SqspHgtMjYhVKTBOBYamc9tHxPTUehyXU1ZepayWeDPwPvD5dLwEuLL072ZmNa3E5WSb0SUP4GFJcySNTGld00qHAK8CXdN+N+CVnGsXp7Ri6YvzpBdUyuj2nhFxmqTTASLibdXyUJaZNVszIsJOkmbnHI+OiNGN8hwWEUsk7QJMlfRc7smICEkF7yFWWilB8n1JHcmiO5L2BN5r0VqZWdUQ0K70KLkyIvKthPihiFiSfi6XdC/QH3hN0m4RsSx1mZen7EuAPXIu757SlgCDGqU/ltK758lfUCnd7VHAQ8Aekn4HTAN+UMJ1ZtZGSKVtTZejT0rarmEfGAI8A0wCRqRsI4CJaX8ScLYyA4E1qVs+BRgiqbOkzqmcKencWkkDU4/47Jyy8mqyJRkRUyU9AQwk+0fjO41GncysDavwpLtdgXvTHb32wO0R8ZCkWcAESecBi4BTU/7JZI//1JM9AnQuQESsknQFMCvluzwiVqX9C9j4CNCDFBnZbqhEKb4AHEbW5d4KuLfE68ysDWhGd7uoiHgR2D9P+uvA4DzpAVxYoKwxwJg86bOB/UqtU5NBUtKNwF7AHSnpm5KOjoi8FTOztqeWR3JLaUkeBezd8ES6pLHA/BatlZlVlVp+4KWUgZt6oEfO8R4pzcwsjW6XtlWjYhNc3E92D3I74FlJM9PxAGDm5qmemW3xmvegeNUp1t3++WarhZlVtTa5pGxE/HFzVsTMqlNDd7tWlTLBxUBJsyS9Kel9SRskrd0clTOz6lDhd7e3KKUM3FwPnA4sJHv48nzghpaslJlVF5W4VaNSgiQRUQ/URcSGiLiZbP41M7PsjRuppK0alfKc5NuStgbmSroGWEaJwdXM2oYqjX8lKSXYnZXyXQS8Rfac5FdaslJmVl3atVNJWzUqZYKLRWn3XeDHAJLuBE5rwXqZWZUQ1duVLsWmLil7SEVrYWbVq8Rp0KrVFrXu9oF79+DxGde3djXMrJmq9fGeUmzKutsimy7NzAyo7ZHcTV13+7ki58ysDRFttCXpdbfNrFTta7gpWcNfzcw2h2z9msq9liipTtKTkh5Ix70kzZBUL+nO9Nw2kjqk4/p0vmdOGZem9OclHZuTPjSl1Uu6pJT6OEiaWdkqPJ/kd4Bnc45/CvwyIvYCVgPnpfTzgNUp/ZcpH5L2AYYD+5K9HXhjCrx1ZK9UDwP2AU5PeYt/t5KrbWZWQAVXS+wOHA/8Nh2LbHWEu1OWscBJaf/EdEw6PzjlPxEYHxHvRcRLZJOE909bfUS8GBHvA+NT3qJKmQVIks6U9KN03ENS/6a/rpm1BQ3rbpf47vZOkmbnbCMbFXct2ZLVH6TjHYE3ImJ9Ol4MdEv73YBXANL5NSn/h+mNrimUXlQpz0nemCp8FHA58HfgHuDgEq41szagrvSu9MqI6JfvhKQTgOURMUfSoApVrWylBMkBEXGQpCcBImJ1w41TMzNVboafQ4EvSToO2AbYHvgV0ElS+9Ra7A4sSfmXkM0lsVhSe2AH4PWc9Aa51xRKL6iUe5Lr0g3PhtUSd2ZjU9jMrCL3JCPi0ojoHhE9yQZeHomIM4BHgZNTthHAxLQ/KR2Tzj+SVnWdBAxPo9+9gN5k63LNAnqn0fKt02dMauq7ldKSvA64F9hF0lWpMpeVcJ2ZtREtPMHPD4Hxkq4EngRuSuk3AbdKqgdWkQU9ImK+pAnAAmA9cGFEbACQdBEwBagDxkREk8tjlzIL0O8kzQEGk92jPSkinm3iMjNrIxoGbiopIh4DHkv7L5KNTDfO8y5wSoHrrwKuypM+GZjcnLo0GSQl9QDeBu7PTYuIvzXng8ysdtXwW4kldbf/QHY/UmQ3U3sBz5M9qGlmbZ2groajZCnd7c/mHqfZgS5osRqZWVWp9SVlmz2fZEQ8IWlAS1TGzKpTmw6Skr6bc9gOOAhY2mI1MrOq0yanSsuxXc7+erJ7lPe0THXMrNq06e52eoh8u4j4/maqj5lVm7a6xk3Da0CSDt2cFTKz6iKgfQ03JYu1JGeS3X+cK2kScBfZutsARMTvW7huZlYl2mRLMsc2ZC+NH8XG5yUDcJA0M0C0o3ajZLEguUsa2X6GjcGxQbRorcysamQLgbV2LVpOsSBZB2wLef+JcJA0s0zzlmaoOsWC5LKIuHyz1cTMqpKAuhqOksWCZO1+azOrqErPArQlKRYkB2+2WphZVavhGFk4SEbEqs1ZETOrTqK2l12t5e9mZpuDsne3S9maLEraRtJMSU9Jmi/pxym9l6QZkuol3dmwzlZaouHOlD5DUs+csi5N6c9LOjYnfWhKq5d0SVN1cpA0s7KpxK0E7wFHRcT+wAHAUEkDgZ8Cv4yIvYDVwHkp/3nA6pT+y5QPSfuQLeewLzAUuFFSXXrV+gZgGLAPcHrKW5CDpJmVRWST7payNSUyb6bDrdIWZC+z3J3SxwInpf0T0zHp/GBlTdYTgfER8V5EvATUky0B0R+oj4gXI+J9YHzKW5CDpJmVrRKrJW4sS3WS5gLLganA/wFvpCVlARYD3dJ+N+AVgHR+DbBjbnqjawqlF9TsSXfNzD6qtPuNyU6SZuccj46I0bkZ0sqGB0jqRLZS62cqU89N4yBpZmVp5uj2yojoV0rGiHhD0qPAIUCnhpnJgO7AkpRtCbAHsFhSe2AHsrkmGtIb5F5TKD0vd7fNrGwVHN3eObUgkdQROAZ4FngUODllGwFMTPuT0jHp/CMRESl9eBr97gX0JpvZbBbQO42Wb002uDOpWJ3ckjSzslXwWfLdgLFpFLodMCEiHpC0ABgv6UrgSeCmlP8m4FZJ9cAqsqBHRMyXNAFYQLaiwoWpG4+ki4ApZPNTjImI+cUq5CBpZmVRBZeUjYh5wIF50l8kG5lunP4ucEqBsq4CrsqTPhmYXGqdHCTNrGxtfSEwM7OiajdEOkiaWQXUcEPSQdLMypM9AlS7UdJB0szK5pakmVlBarOT7pqZNcndbTOzYpoxeUU1cpA0s7I5SJqZFaEa7m57gosKevfddznskP70P2h/Dtp/X6748SgABg86nAF9D2BA3wPo1WN3TvnqSU2UZC3tm+d/nR6770LfA/b7MO2eu+/ioP335RNbt2PO7Nkfyf+zn/47+35mLz63bx+mPjxlc1d3i1bJSXe3RG5JVlCHDh14aOojbLvttqxbt46jvnAYQ44dxrTH/vxhnuGnfpUvfrHoRMi2GZw14hz+4YKLOP/rZ3+Ytu+++zF+wu+56IJvfiTvswsWcNed43niqfksW7qU44YezdMLXqCurm5zV3uLVaXxryRuSVaQJLbddlsA1q1bx/p16z7yTuvatWv546OP8MUT3ZJsbYcdfgRdunT5SNpn9t6bT/fp87G8D9w/kVNOG06HDh3o2asXe+65F7NmztxcVa0KKvG/auQgWWEbNmxgQN8D6LH7Lhx19DH0HzDgw3P3T7yPQUcNZvvtt2/FGlpzLVmyhO7dN87T2q1bd5YuLTpPa5sioJ1K26pRiwVJSWMkLZf0TEt9xpaorq6OGXPmUv/yYmbPmsn8ZzZ+/Ql33sGpp53eirUzawmltiOrM0q2ZEvyFrKlHNukTp068YVBR/Lwww8BsHLlSmbPmsmw445v5ZpZc3Xr1o3FizeuHbVkyWJ2373o2lFtS4mLgFXrfcsWC5IR8SeymYLbjBUrVvDGG28A8M477zDtf6bSp0+2htG999zNsONOYJtttmnNKtomOP6EL3HXneN57733ePmll6ivX8jB/T82/2ubVeuj261+T1LSSEmzJc1esXJFa1enLK8uW8bQo4/k4AM/x2GHHMzgo4/huONPAOCuCeM5dbi72luKs888nUGHH8ILzz/Pnj27c8uYm5h4373s2bM7M6b/la+ceDxfPO5YAPbZd1++esqpHPi5ffjSCUO59robPLLdiErcmixH2kPSo5IWSJov6TspvYukqZIWpp+dU7okXSepXtI8SQfllDUi5V8oaUROel9JT6drrlMTMwYrWzOnZUjqCTwQEfs1kRWAvn37xeMzZjed0cw2yaED+jFnzuyKNun2/uyBcfN9j5aU95C9Os8ptlqipN2A3SLiCUnbAXOAk4BzgFURcbWkS4DOEfFDSccBFwPHAQOAX0XEAEldgNlAPyBSOX0jYrWkmcC3gRlkyzhcFxEPFqpTq7ckzaz6VWrgJiKWRcQTaf/vZCsldgNOBMambGPJAicpfVxkppMtPbsbcCwwNSJWRcRqYCowNJ3bPiKmp1UVx+WUlZcfJjezsjXjduNOknK7i6MjYnT+MtWTbFGwGUDXiFiWTr0KdE373YBXci5bnNKKpS/Ok15QiwVJSXcAg8h+KYuBURFxU/GrzKwaNaP/vrJYd/vD8qRtgXuAf4yItbm3DSMiJLXcfcJGWixIRoRHKczaAFHZ1RIlbUUWIH8XEb9Pya9J2i0ilqUu8/KUvgTYI+fy7iltCVkjLTf9sZTePU/+gnxP0szKU8HnJNNI803AsxHxi5xTk4CGEeoRwMSc9LPTKPdAYE3qlk8BhkjqnEbChwBT0rm1kgamzzo7p6y8fE/SzMpWweHyQ4GzgKclzU1p/wxcDUyQdB6wCDg1nZtMNrJdD7wNnAsQEaskXQHMSvkuj4iG57YvIHvZpSPwYNoKcpA0s/JVKEpGxF+KlDY4T/4ALixQ1hhgTJ702UBJjyWCg6SZla1638suhYOkmZWlYRagWuUgaWblc5A0MyvM3W0zsyKqdIKfkjhImlnZajhGOkiaWZlKnQetSjlImllZstHt2o2SDpJmVrbaDZEOkmZWCTUcJR0kzaxsfgTIzKyIGr4l6SBpZuWr4RjpIGlm5an0pLtbGgdJMytPiRPqVisHSTMrWw3HSAdJM6uAGo6SXuPGzMpU6qrbTUdSSWMkLZf0TE5aF0lTJS1MPzundME5Lv8AAAckSURBVEm6TlK9pHmSDsq5ZkTKv1DSiJz0vpKeTtdcpxJupjpImllZGibdLWUrwS3A0EZplwDTIqI3MC0dAwwDeqdtJPBryIIqMAoYAPQHRjUE1pTnGznXNf6sj3GQNLPyqcStCRHxJ2BVo+QTgbFpfyxwUk76uMhMBzql5WaPBaZGxKqIWA1MBYamc9tHxPS0Ns64nLIK8j1JMytbM9642UnS7Jzj0RExuolruqalYAFeBbqm/W7AKzn5Fqe0YumL86QX5SBpZmVrxiNAKyOi36Z+TkSEpNjU6zeFu9tmVrYK9bYLeS11lUk/l6f0JcAeOfm6p7Ri6d3zpBflIGlm5UkPk5eybaJJQMMI9QhgYk762WmUeyCwJnXLpwBDJHVOAzZDgCnp3FpJA9Oo9tk5ZRXk7raZlaWSryVKugMYRHbvcjHZKPXVwARJ5wGLgFNT9snAcUA98DZwLkBErJJ0BTAr5bs8IhoGgy4gG0HvCDyYtqIcJM2sbJV6ljwiTi9wanCevAFcWKCcMcCYPOmzgf2aUycHSTMrm9/dNjMrwpPumpkVU7sx0kHSzMpXwzHSQdLMyiN5SVkzs+JqN0Y6SJpZ+Wo4RjpImln5ari37SBpZuUqbULdauUgaWZlyV5LbO1atBwHSTMrm4OkmVkR7m6bmRXidbfNzAorc0LdLZ6DpJmVr4ajpIOkmZXNryWamRVRuyHSQdLMKqGGo6SDpJmVrZYfAVK2TMSWQdIKsoV+as1OwMrWroQ1S63+mX0qInauZIGSHiL7fZViZUQMreTnt7QtKkjWKkmzy1mQ3TY//5lZA6+7bWZWhIOkmVkRDpKbx+jWroA1m//MDPA9STOzotySNDMrwkHSzKwIB0kzsyIcJFuIpD6SDpG0laS61q6PlcZ/VtaYB25agKSvAD8BlqRtNnBLRKxt1YpZQZI+HREvpP26iNjQ2nWyLYNbkhUmaSvgNOC8iBgMTAT2AH4oaftWrZzlJekEYK6k2wEiYoNblNbAQbJlbA/0Tvv3Ag8AWwFfk2p44r0qJOmTwEXAPwLvS7oNHChtIwfJCouIdcAvgK9IOjwiPgD+AswFDmvVytnHRMRbwNeB24HvA9vkBsrWrJttGRwkW8afgYeBsyQdEREbIuJ2YHdg/9atmjUWEUsj4s2IWAl8E+jYECglHSTpM61bQ2tNnk+yBUTEu5J+BwRwafpL9h7QFVjWqpWzoiLidUnfBH4m6TmgDjiylatlrchBsoVExGpJvwEWkLVO3gXOjIjXWrdm1pSIWClpHjAMOCYiFrd2naz1+BGgzSANAES6P2lbOEmdgQnA9yJiXmvXx1qXg6RZHpK2iYh3W7se1vocJM3MivDotplZEQ6SZmZFOEiamRXhIGlmVoSDZJWQtEHSXEnPSLpL0ifKKOsWSSen/d9K2qdI3kGSPr8Jn/GypI+txVwovUAZ50i6vhKfa7apHCSrxzsRcUBE7Ae8D/xD7klJm/RiQEScHxELimQZBDQ7SJrVCgfJ6vRnYK/UyvuzpEnAAkl1kn4maZakeen1OpS5XtLzkv4H2KWhIEmPSeqX9odKekLSU5KmSepJFoz/KbViD5e0s6R70mfMknRounZHSQ9Lmi/pt0DJsx1J6i/pr5KelPS/kvrknN4j1XGhpFE515wpaWaq1397xh5rKX4tscqkFuMw4KGUdBCwX0S8JGkksCYiDpbUAXhc0sPAgUAfYB+y98cXAGMalbsz8BvgiFRWl4hYJem/gDcj4ucp3+3ALyPiL5J6AFOAvYFRwF8i4nJJxwPnNeNrPQccHhHrJR1NNmHxV9O5/sB+wNvALEl/AN4im7Pz0IhYJ+lG4AxgXDM+06wkDpLVo6OkuWn/z8BNZN3gmRHxUkofAnyu4X4jsAPZvJZHAHekqb+WSnokT/kDgT81lBURqwrU42hgn5xpMbeXtG36jK+ka/8gaXUzvtsOwFhJvckmBdkq59zUiHgdQNLvyaabWw/0JQuaAB2B5c34PLOSOUhWj3ci4oDchBQg3spNAi6OiCmN8h1XwXq0AwY2fmWvzLmErwAejYgvpy7+YznnGr8SFmTfc2xEXFrOh5qVwvcka8sU4FtpCQkkfTrNvP0n4LR0z3I38k/9NR04QlKvdG2XlP53YLucfA8DFzccSGoI3H8CvpbShgGdm1HvHcjWAgI4p9G5YyR1kdQROAl4HJgGnCxpl4a6SvpUMz7PrGQOkrXlt2T3G5+Q9Azw32S9hXuBhencOOCvjS+MiBXASOD3kp4C7kyn7ge+3DBwA3wb6JcGhhawcZT9x2RBdj5Zt/tvReo5T9LitP0CuAb4d0lP8vHezUzgHmAecE9EzE6j8ZcBD6cpzaYCu5X4OzJrFk9wYWZWhFuSZmZFOEiamRXhIGlmVoSDpJlZEQ6SZmZFOEiamRXhIGlmVsT/BzKuylLRUXZKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "V3C-3nZY1MRn",
        "outputId": "64e311ae-2524-4bcc-bf07-4a4938ca61ff"
      },
      "source": [
        "y_pred = model.predict(X)\n",
        "y_expected = pd.DataFrame(y)\n",
        "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[284284     31]\n",
            " [   127    365]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PpJ/dUrkkXZaeYJwrqV9OW6NS/Wckjcop30vS42mfy5QuxDZ3jJY4eZpZyco0YLQKODMi+gADgTHpqcSzgWkR0RuYlj5D9vRi77SMBsalWLoDY8nuK+8PjM1JhuOAU3P2G5LKmztGs5w8zaw0efY6W8udEbE4Ih5N628BT5I9bDMcmJCqTQBGpPXhwMTIPAJ0lbQtcDgwNSJei4jXganAkLStS0Q8EtkN7hMbtdXUMZrla55mVpLsPs+8r3n2kDQr5/OVEXHlOm1KOwJ7AtOBbSJicdr0ErBNWm/qKcbtWilf2EQ5LRyjWU6eZlYiFTJgtLS1J4wkdQZuB74aEW/mJuaICEmVeSyywGP4tN3MSlaum+QlbUSWOG+IiIY5GV9Op9ykn6+k8uaeYmypvGcT5S0do1lOnmZWmjJd80wj39cAT0bEpTmb7gYaRsxHAXfllJ+YRt0HAm+kU+/JwGBJ3dJA0WBgctr2pqSB6VgnNmqrqWM0y6ftZlaSAq95tmQ/4ATgcUkNczd+C/ghcKukk4EXgIbZgyYBRwALgHeBkwAi4jVJ55M9Ig7w/Yh4La2fDlwLdAJ+nxZaOEaznDzNrGTlyJ0R8RDNv+nokCbqBzCmmbbGA+ObKJ8F7N5E+atNHaMlTp5mVrL2NulHPpw8zaw0frbdzKxwtTqfp5OnmZWo/c3VmQ8nTzMrWQ3mTidPMyude55mZgWSB4zMzIrjnqeZWRFqMHc6eZpZ6dzzNDMrVB6TfrRHTp5mVhL5Pk8zs+LUebTdzKxwNdjxdPI0s9JkEx3XXvZsNnnmvgO5KQ1vuTMzq8Gz9hZ7npe0sC2Ag8sci5m1Ue555oiIg9ZnIGbWdtVg7mz9BXCSNpX0HUlXps+9JQ2rfGhm1hYIqJPyWtqTfN6e+WvgPWDf9HkRcEHFIjKztiXP1w63t1P7fJLnRyPiR8BKgIh4l+Zf0mRmNagcrx5ua/K5Vek9SZ3IBomQ9FFgRUWjMrM2Q0CH9pYZ85BP8hwL3AdsL+kGsncrf6GSQZlZ21KDubP15BkRUyU9Cgwk+yXzlYhYWvHIzKxN8GTILTsQ2J/s1H0j4M6KRWRmbY5P25sg6RfAzsBNqeg0SYdGxJiKRmZmbUbtpc78ep4HAx+PiIYBownA/IpGZWZtSnu7DSkf+dyqtADolfN5+1RmZpZG2/Nb2pOWJgb5Hdk1zs2BJyXNSJ8HADPWT3hmtsFrhzfA56Ol0/aL11sUZtamlWu0XdJ4YBjwSkTsnsrOBU4FlqRq34qISWnbOcDJwGrgfyJiciofAvwMqAOujogfpvKdgJuBLYHZwAkR8Z6kjsBEYC/gVeCYiHi+pVhbmhjkzwV/czOrOQ2n7WVyLXA5WSLL9ZOI+ECHTlIf4FhgN+DDwB8k7ZI2XwEcBiwEZkq6OyKeAC5Kbd0s6ZdkiXdc+vl6ROws6dhU75iWAs1nYpCBkmZKelvSe5JWS3qztf3MrHaU69n2iHgAeC3Pww4Hbo6IFRHxHNlYTP+0LIiIZyPiPbKe5nBlARwM3Jb2nwCMyGlrQlq/DThErQScz4DR5cBxwDNAJ+AUsqxuZgZkvc98FqCHpFk5y+g8D3GGpLmSxkvqlsq2A17MqbMwlTVXviXw74hY1aj8A22l7W+k+s3KJ3kSEQuAuohYHRG/Bobks5+ZtX9SdpN8PguwNCL2zlmuzOMQ44CPAn2BxbQ8Uft6k899nu9K2hiYI+lHZMHnlXTNrDZUcrA9Il5+/zi6CrgnfVxEdutkg56pjGbKXwW6SqpPvcvc+g1tLZRUD2yR6jcrnyR4Qqp3BvBOOsBn8tjPzGpEhw7KaymGpG1zPh4JzEvrdwPHSuqYRtF7k91GORPoLWmn1PE7Frg7PejzJ2Bk2n8UcFdOW6PS+kjgjw0PBjUnn4lBXkiry4Hz0pe5hVZGosysNgiV7dl2STcBg8iujS4km9VtkKS+ZPeZPw+cBhAR8yXdCjwBrALGRMTq1M4ZwGSyW5XGR0TDU5FnATdLugD4G3BNKr8GuE7SArIBq2Nbi7XYVw/vU+R+ZtbelHGi44g4ronia5ooa6h/IXBhE+WTgElNlD9LNhrfuHw5cFQhsW5Q723f8+O9eHj65dUOw8wK5CeMcrTw3naRTUtnZgbU5ghyse9tf6rcgZhZ2yTc8/wAv7fdzPJVX4Ndzw3qmqeZtT3ZmzHd8zQzK1h7m6szH06eZlayGux45jWrkiR9XtL30udekta5T8rMalPDe9vzfLa93cjnMu8vyG6Kb7h59S08q5KZ5ahTfkt7ks9p+4CI6CfpbwAR8Xp6XtTMDLXDXmU+8kmeKyXVkT1XiqStgDUVjcrM2pQazJ15Jc/LgDuBrSVdSDbjyHcqGpWZtSkebW9CRNwgaTZwCNm14RER8WTFIzOzNqFhwKjWtJo8JfUC3gV+l1sWEf+sZGBm1nbUYO7M67T9XrLrnQI2AXYCniZ7Y52Z1TpBXQ1mz3xO2z+R+znNtnR6xSIyszalzK8ebjMKfsIoIh6VNKASwZhZ2+Tk2QRJ/5vzsQPQD/hXxSIyszbHE4M0bfOc9VVk10Bvr0w4ZtbW+LS9Cenm+M0j4uvrKR4za2vK+A6jtqSl13DUR8QqSfutz4DMrG0RUF+DXc+Wep4zyK5vzpF0N/Absve2AxARd1Q4NjNrI9zzbNomwKvAwbx/v2cATp5mBogO1F72bCl5bp1G2ufxftJsEBWNyszajOwFcNWOYv1rKXnWAZ2hyV8pTp5mlpFH2xtbHBHfX2+RmFmbJKCuBrNnS8mz9v40zKwonlXpgw5Zb1GYWZtWg7mz+eQZEa+tz0DMrG0S+b0Mrb2pxe9sZuWk7Nn2fJZWm5LGS3pF0rycsu6Spkp6Jv3slsol6TJJCyTNTTO+NewzKtV/RtKonPK9JD2e9rlMKajmjtESJ08zK5nyXPJwLTCkUdnZwLSI6A1MS58BhgK90zIaGAdZIgTGAgOA/sDYnGQ4Djg1Z78hrRyjWU6eZlYSkU2GnM/Smoh4AGh8yXA4MCGtTwBG5JRPjMwjQFdJ2wKHA1Mj4rWIeB2YCgxJ27pExCMREcDERm01dYxmFTyfp5lZYxUeMNomIhan9ZeAbdL6dsCLOfUWprKWyhc2Ud7SMZrl5GlmJcrvembSQ9KsnM9XRsSV+e4cESGpog/p5HsMJ08zK0mBo+1LI2LvAg/xsqRtI2JxOvV+JZUvArbPqdczlS0CBjUqvz+V92yifkvHaJaveZpZyco12t6Mu4GGEfNRwF055SemUfeBwBvp1HsyMFhStzRQNBiYnLa9KWlgGmU/sVFbTR2jWe55mlnJynXJU9JNZL3GHpIWko2a/xC4VdLJwAvA0an6JOAIYAHZ69FPguwedUnnAzNTve/n3Ld+OtmIfifg92mhhWM0y8nTzEqiMr56OCKOa2bTOk88phHzMc20Mx4Y30T5LGD3JspfbeoYLXHyNLOS+QVwZmZFqL3U6eRpZmVQgx1PJ08zK012q1LtZU8nTzMrmXueZmYFkydDNjMrlE/bzcyKIZ+2m5kVxcnTzKwIqsHTdk8MUgannfJFen14a/bq+/5TX+ec9Q322P1j/Meen+TokUfy73//G4CbbryBAXv1XbtsunEHHpszp1qh16Tly5ez/z796d9vD/rtsRvnnzcWgIhg7He/zSf67ELfT3ycK35+GQAP/Pl+ttlyi7V/Z/93gd/InauckyG3Je55lsEJo77Af59+Bqd88cS1ZYccehjnX/gD6uvr+fY5Z/Hji37AhT+4iOM+dzzHfe54AOY9/jhHjxzBHn37Viv0mtSxY0fum/pHOnfuzMqVKzn4wP0ZfPhQnn7qSRa++CKPzXuKDh068Mor789Ktt/+B3DHXfdUMeoNWzvLi3lxz7MM9j/gU3Tv3v0DZYceNpj6+ux3U/8BA1m0cOE6+916y00cdfSx6yVGe58kOnfuDMDKlStZtXIlkrjyV+P41ne+R4cO2T+LrbfeupphtinK87/2xMlzPZh47XgOHzJ0nfLbfnMLRx/T3CQyVkmrV69mwF596fXhrTn40MPoP2AAzz37D277zS3sN2Bvhg8byoJnnllbf/ojf6V/vz0YPmwoT8yfX8XINzwCOii/pT2pWPJs6hWiteiiH1xIXX09x6ZT9QYzpk9n006bstvu68yOZetBXV0d02fPYcHzC5k1cwbz581jxYoVdNxkEx6ePouTTj6V0079IgB99+zH0/94gRmPPsaXxnyZo0e2+m6wGpNvv7N9Zc9K9jyvZd1XiNaU6yZcy6R77+HaiTesM2XXb269maOPda+z2rp27cqBgw5iypT72K5nT0aM+AwAw0ccybzH5wLQpUuXtaf5Q4YewcqVK1m6dGnVYt7gpPs881nak4olz2ZeIVozpky+j0sv+RG33Xk3m2666Qe2rVmzhttvu9XXO6tkyZIla+9+WLZsGdP+MJVdd/0Y//lfI/jz/X8C4MEH/szOvXcB4KWXXiKbdxdmzpjBmjVr2HLLLasT/AbIo+1VImk02Qvr2b5XrypHU5wTP38cD/75fpYuXcpHd+zJd793Hj/+0Q9YsWIFw4YcBmSDRj//xS8BeOjBB+jZc3t2+shHqhl2zXpp8WJO/eIoVq9ezZpYw2dHHs0Rnx7Gvvvtz0knHs/Pf/YTNuvcmXG/uhqAO2+/jauuHEd9XT2bdOrExOtvrsnJf1tSi38aaviNWpHGpR2BeyIirwt7e+21dzw8fVbrFc2sKPsN2JvZs2eVNdd9/BN7xq9/+6e86u6zc7fZRbw9c4NU9Z6nmbV97W0wKB9OnmZWslq8ilHJW5VuAv4K7CppYXqlp5m1Q8pzaU8q1vNs4RWiZtaOCL8908yscO3wHs58OHmaWclqMHc6eZpZGdRg9nTyNLMStb/n1vPh5GlmJWmYVanWOHmaWemcPM3MCleLp+2eDNnMSlauKekkPS/pcUlzJM1KZd0lTZX0TPrZLZVL0mWSFkiaK6lfTjujUv1nJI3KKd8rtb8g7Vt01nfyNLOSlfkJo4Miom/OBCJnA9MiojcwLX0GGAr0TstoYBxkyRYYCwwA+gNjGxJuqnNqzn5Fzzns5Glmpck3cxZ/Zj8cmJDWJwAjcsonRuYRoKukbYHDgakR8VpEvA5MBYakbV0i4pHIppObmNNWwZw8zawk2Wi78lqAHpJm5SyjGzUXwBRJs3O2bRMRi9P6S8A2aX074MWcfRemspbKFzZRXhQPGJlZyQroVC5tZT7P/SNikaStgamSnsrdGBEhqXKTEBfAPU8zK12ZTtsjYlH6+QpwJ9k1y5fTKTfp5yup+iJg+5zde6aylsp7NlFeFCdPMytZOd6eKWkzSZs3rAODgXnA3UDDiPko4K60fjdwYhp1Hwi8kU7vJwODJXVLA0WDgclp25uSBqZR9hNz2iqYT9vNrGRlmlVpG+DOdPdQPXBjRNwnaSZwa5oT+AXg6FR/EnAEsAB4FzgJICJek3Q+MDPV+35ENLyM8nSyN/t2An6flqI4eZpZycqROyPiWWCPJspfBQ5pojyAMc20NR4Y30T5LCCvd6q1xsnTzEriyZDNzIrhyZDNzIpTg7nTydPMyqAGs6eTp5mVyJMhm5kVzJMhm5kVy8nTzKxwPm03MyuCb1UyMytCDeZOJ08zK5FvkjczK5wfzzQzK1LtpU4nTzMrgxrseDp5mlnpfKuSmVkxai93OnmaWelqMHc6eZpZaSQaXitcU5w8zax0tZc7nTzNrHQ1mDudPM2sdDV41u7kaWal8mTIZmYFyx7PrHYU65+Tp5mVzMnTzKwIPm03MyuUp6QzMyuc8K1KZmbFqcHs6eRpZiXz45lmZkWovdTp5Glm5VCD2dPJ08xKVou3Kikiqh3DWpKWAC9UO44K6AEsrXYQVpD2+ne2Q0RsVc4GJd1H9ueVj6URMaScx6+WDSp5tleSZkXE3tWOw/LnvzNrTYdqB2Bm1hY5eZqZFcHJc/24stoBWMH8d2Yt8jVPM7MiuOdpZlYEJ08zsyI4eZqZFcHJs0Ik7SppH0kbSaqrdjyWH/9dWb48YFQBkj4D/B+wKC2zgGsj4s2qBmbNkrRLRPw9rddFxOpqx2QbNnUO8ZwAAARYSURBVPc8y0zSRsAxwMkRcQhwF7A9cJakLlUNzpokaRgwR9KNABGx2j1Qa42TZ2V0AXqn9TuBe4CNgM9JNTjx4QZM0mbAGcBXgfckXQ9OoNY6J88yi4iVwKXAZyQdEBFrgIeAOcD+VQ3O1hER7wBfBG4Evg5skptAqxmbbdicPCvjQWAKcIKkT0XE6oi4EfgwsEd1Q7PGIuJfEfF2RCwFTgM6NSRQSf0kfay6EdqGyPN5VkBELJd0AxDAOekf3wpgG2BxVYOzFkXEq5JOA34s6SmgDjioymHZBsjJs0Ii4nVJVwFPkPVmlgOfj4iXqxuZtSYilkqaCwwFDouIhdWOyTY8vlVpPUgDD5Guf9oGTlI34FbgzIiYW+14bMPk5GnWBEmbRMTyasdhGy4nTzOzIni03cysCE6eZmZFcPI0MyuCk6eZWRGcPNsISaslzZE0T9JvJG1aQlvXShqZ1q+W1KeFuoMk7VvEMZ6XtM67vJsrb6aNL0i6vBzHNSs3J8+2Y1lE9I2I3YH3gP/O3SipqAceIuKUiHiihSqDgIKTp1l75+TZNj0I7Jx6hQ9Kuht4QlKdpB9LmilpbnrMEGUul/S0pD8AWzc0JOl+SXun9SGSHpX0mKRpknYkS9JfS73eAyRtJen2dIyZkvZL+24paYqk+ZKuBvKePUpSf0l/lfQ3SX+RtGvO5u1TjM9IGpuzz+clzUhx/cozINn65scz25jUwxwK3JeK+gG7R8RzkkYDb0TEf0jqCDwsaQqwJ7Ar0Ifs+fongPGN2t0KuAr4VGqre0S8JumXwNsRcXGqdyPwk4h4SFIvYDLwcWAs8FBEfF/Sp4GTC/haTwEHRMQqSYeSTST92bStP7A78C4wU9K9wDtkc6buFxErJf0COB6YWMAxzUri5Nl2dJI0J60/CFxDdjo9IyKeS+WDgU82XM8EtiCbV/RTwE1pirV/SfpjE+0PBB5oaCsiXmsmjkOBPjnTknaR1Dkd4zNp33slvV7Ad9sCmCCpN9lkKhvlbJsaEa8CSLqDbFq/VcBeZMkUoBPwSgHHMyuZk2fbsSwi+uYWpMTxTm4R8OWImNyo3hFljKMDMLDxo4slzvF8PvCniDgyXSq4P2db40fggux7ToiIc0o5qFkpfM2zfZkMfCm9CgRJu6SZ0h8AjknXRLel6SnWHgE+JWmntG/3VP4WsHlOvSnAlxs+SGpI6A8An0tlQ4FuBcS9Bdm7ngC+0GjbYZK6S+oEjAAeBqYBIyVt3RCrpB0KOJ5ZyZw825erya5nPippHvArsrOLO4Fn0raJwF8b7xgRS4DRwB2SHgNuSZt+BxzZMGAE/A+wdxqQeoL3R/3PI0u+88lO3//ZQpxzJS1My6XAj4AfSPob654NzQBuB+YCt0fErHR3wHeAKWnquKnAtnn+GZmVhScGMTMrgnueZmZFcPI0MyuCk6eZWRGcPM3MiuDkaWZWBCdPM7MiOHmamRXh/wGlbayZ8s9aXAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhXO2-uwqqnm"
      },
      "source": [
        "# Improving our model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4z2U4Biqu7z"
      },
      "source": [
        "Undersampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFy4fBdlqqIP",
        "outputId": "e1a02108-493f-4479-cdf5-a773380c7bcc"
      },
      "source": [
        "fraud_indices = np.array(data[data.Class == 1].index)\n",
        "number_records_fraud = len(fraud_indices)\n",
        "print(number_records_fraud)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "492\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze1rmDROrU8G"
      },
      "source": [
        "normal_indices = data[data.Class == 0].index"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrKUg87irbqI",
        "outputId": "c886747e-bb33-472a-a53d-b335e1c1dc43"
      },
      "source": [
        "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace=False)\n",
        "random_normal_indices = np.array(random_normal_indices)\n",
        "print(len(random_normal_indices))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "492\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pWgWpDGr_hC",
        "outputId": "db333b3e-d439-406d-de13-a6b6710ee95a"
      },
      "source": [
        "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
        "print(len(under_sample_indices))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "984\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1caP9pEsMfJ"
      },
      "source": [
        "under_sample_data = data.iloc[under_sample_indices,:]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnncowEesTmF"
      },
      "source": [
        "Splitting our data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odTqYV9gsW6J"
      },
      "source": [
        "X_undersample = under_sample_data.iloc[:,under_sample_data.columns != 'Class']\n",
        "y_undersample = under_sample_data.iloc[:,under_sample_data.columns == 'Class']"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNd-FFZlsna_"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_undersample, y_undersample, test_size=0.3)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OTOlUwPsyQ8"
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "X_test = np.array(X_test)\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJhtu2Cps-zR",
        "outputId": "f22a70ca-a44d-4723-c1d6-5271a9abfb10"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 16)                480       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 24)                408       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 20)                500       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 24)                504       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 25        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,917\n",
            "Trainable params: 1,917\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2l4Plk_OtJut"
      },
      "source": [
        "# Re-Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eNvTQBBtTas",
        "outputId": "4aed9096-a534-4bac-a6a7-6347c2103f11"
      },
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
        "model.fit(X_train,y_train,batch_size=15,epochs=5)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "46/46 [==============================] - 1s 3ms/step - loss: 0.4028 - accuracy: 0.8895\n",
            "Epoch 2/5\n",
            "46/46 [==============================] - 0s 2ms/step - loss: 0.1928 - accuracy: 0.9128\n",
            "Epoch 3/5\n",
            "46/46 [==============================] - 0s 2ms/step - loss: 0.1604 - accuracy: 0.9302\n",
            "Epoch 4/5\n",
            "46/46 [==============================] - 0s 2ms/step - loss: 0.1421 - accuracy: 0.9404\n",
            "Epoch 5/5\n",
            "46/46 [==============================] - 0s 2ms/step - loss: 0.1292 - accuracy: 0.9448\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7faaa795e6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Fr7zqAnHtW6E",
        "outputId": "dd5bef7f-2b3d-41dc-92a0-34e2c75f8d77"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_expected = pd.DataFrame(y_test)\n",
        "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[143   0]\n",
            " [ 12 141]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "9tQRe3N5tzOo",
        "outputId": "13dfb2d3-60e6-45f7-ddbd-6e01a6247163"
      },
      "source": [
        "y_pred = model.predict(X)\n",
        "y_expected = pd.DataFrame(y)\n",
        "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[283111   1204]\n",
            " [    46    446]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vA6LLM244gN"
      },
      "source": [
        "# Improving our model part 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fK2fWril4-EU"
      },
      "source": [
        "SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RiSYFHhI3sdh",
        "outputId": "b13a02ab-3609-452a-e1cb-a4b315531372"
      },
      "source": [
        "!pip install -U imbalanced-learn"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.1.0)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn) (3.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJv2xbk43yLv"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jrq5PY8z5BEp"
      },
      "source": [
        "Resample our data sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjCUX-0I34nT"
      },
      "source": [
        "X_resample, y_resample = SMOTE().fit_resample(X,y.values.ravel())"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O49qE0ye4ijy"
      },
      "source": [
        "y_resample = pd.DataFrame(y_resample)\n",
        "X_resample = pd.DataFrame(X_resample)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WltbNQrd5DR_"
      },
      "source": [
        "Splitting our data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vt8_pTc-4tCY"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_resample,y_resample,test_size=0.3)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxTtHcQn5Gbf"
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "X_test = np.array(X_test)\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYdVY8mZ5Lvx"
      },
      "source": [
        "# Re-Training part 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwYGLeJm5OsT",
        "outputId": "6fbfcd30-e2d4-4c25-ddc5-1b08539e30ac"
      },
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
        "model.fit(X_train,y_train,batch_size=15,epochs=5)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "26537/26537 [==============================] - 55s 2ms/step - loss: 0.0302 - accuracy: 0.9894\n",
            "Epoch 2/5\n",
            "26537/26537 [==============================] - 54s 2ms/step - loss: 0.0127 - accuracy: 0.9966\n",
            "Epoch 3/5\n",
            "26537/26537 [==============================] - 54s 2ms/step - loss: 0.0102 - accuracy: 0.9973\n",
            "Epoch 4/5\n",
            "26537/26537 [==============================] - 55s 2ms/step - loss: 0.0089 - accuracy: 0.9979\n",
            "Epoch 5/5\n",
            "26537/26537 [==============================] - 54s 2ms/step - loss: 0.0084 - accuracy: 0.9980\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7faaa3fdec10>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "y0yWj0bR5WeY",
        "outputId": "083a65e7-5059-44c1-8337-a1f8c2aee7a2"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_expected = pd.DataFrame(y_test)\n",
        "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[84990   253]\n",
            " [   11 85335]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "tHJcYau35XVW",
        "outputId": "b326f0ea-e346-4032-e972-00a21fedc3bc"
      },
      "source": [
        "y_pred = model.predict(X)\n",
        "y_expected = pd.DataFrame(y)\n",
        "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
        "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix, without normalization\n",
            "[[283587    728]\n",
            " [     1    491]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bVyv0VqkWn21XFDIkXErF0qHA0Ig4OX0+DtgxIk5v6pgOq/aNrgMPr0g8VhkfTbqq2iFYEXbecXumTJncopmu65cGRL9jRhW078wr9p8SEdu35PmrpZItzznA+jmf+6UyM2tHBLSzHnlBKnnNcxIwQNJGkroARwLjKng+M6uK2hxtr1jLMyLqJJ0OTAA6AqMj4pVKnc/Mqqed5cWCVPQm+YgYD4yv5DnMrMoEHWpwwMiPZ5pZWUSWPAtZ8tYjrS/pCUmvSnpF0vdSeW9JEyVNTz97pXJJGpUewnlR0nY5dQ1L+0+XNCynfLCkl9Ixo5SuJTR1jnycPM2sbFJhSzPqgDMjYgtgCDAiPVhzDvBYRAwAHkufIXsAZ0BahgPXZrGoNzCS7NbIHYCROcnwWuCUnOOGpvKmztEkJ08zK1tLDBhFxNyIeD6t/wt4jex+8QOBMWm3McBBaf1AYGxkngF6SloH2BeYGBEfRsRHwERgaNrWIyKeiewezbEN6mrsHE3yxCBmVp7CWpXFVSltCGwLPAusHRFz06Z3gfrnaRt7EGe9ZspnN1JOnnM0ycnTzMqS3edZcPbsIyl3YoDrIuK6FeqTVgfuAc6IiAW5dUdESKroLDmFnsPJ08zK1PxgUI55+Z4wktSZLHHeEhH1M4K/J2mdiJibut7vp/KmHsSZA+zeoPzJVN6vkf3znaNJvuZpZmVriWueaeT7euC1iLgiZ9M4oH7EfBhwf0758WnUfQjwcep6TwD2kdQrDRTtA0xI2xZIGpLOdXyDuho7R5Pc8jSz8rTcNc+dgeOAlyRNTWU/Bn4J3CnpJOAtoH4CjPHA/sAMYBFwIkBEfCjpfLKnHAHOi4gP0/ppwI1AN+DhtJDnHE1y8jSzshR5zbNJEfEUTb+sY69G9g9gRBN1jQZGN1I+GdiykfL5jZ0jHydPMyubH880MytBe5v0oxBOnmZWnhp9tt3J08zKUqvzeTp5mlmZ2t9cnYVw8jSzstVg7nTyNLPyueVpZlYkecDIzKw0bnmamZWgBnOnk6eZlc8tTzOzYlVgMuS2wMnTzMoi3+dpZlaajh5tNzMrXg02PJ08zaw82WuFay97Npk8c18g35j6V4SamdVgrz1vy/PyPNsC2LOFYzGzNsotzxwRscfKDMTM2q4azJ3Nvz1T0qqSfirpuvR5gKQDKh+ambUFAjpKBS3tSSGvHr4BWAJ8LX2eA1xQsYjMrG0p8LXD7a1rX0jy3DgiLgE+A4iIRTT9hjszq0FSYUt7UsitSkskdSMbJELSxsDiikZlZm2GgA7tLTMWoJDkORJ4BFhf0i1kL6Y/oZJBmVnbUoO5s/nkGRETJT0PDCH7R+Z7ETGv4pGZWZvgyZDz+zqwC1nXvTNwX8UiMrM2x932Rki6BtgEuC0VnSrpGxExoqKRmVmbUXups7CW557A5hFRP2A0BnilolGZWZvS3m5DKkQhtyrNAPrnfF4/lZmZpdH2wpb2JN/EIA+QXePsDrwm6bn0eUfguZUTnpm1eu3wBvhC5Ou2X7bSojCzNq2lRtsljQYOAN6PiC1T2c+BU4AP0m4/jojxadu5wEnAUuC/I2JCKh8K/BroCPwhIn6ZyjcCbgfWBKYAx0XEEkldgbHAYGA+cEREvJkv1nwTg/y/or+5mdWc+m57C7kRuIoskeW6MiJWaNBJ2gI4EvgKsC7wJ0mbps1XA3sDs4FJksZFxKvAxamu2yX9lizxXpt+fhQRm0g6Mu13RL5AC5kYZIikSZL+LWmJpKWSFjR3nJnVjpZ6tj0i/gx8WOBpDwRuj4jFETGLbCxmh7TMiIiZEbGErKV5oLIA9gTuTsePAQ7KqWtMWr8b2EvNBFzIgNFVwFHAdKAbcDJZVjczA7LWZyEL0EfS5JxleIGnOF3Si5JGS+qVytYD3s7ZZ3Yqa6p8TeCfEVHXoHyFutL2j9P+TSokeRIRM4COEbE0Im4AhhZynJm1f1J2k3whCzAvIrbPWa4r4BTXAhsDg4C55J+ofaUp5D7PRZK6AFMlXUIWfEFJ18xqQyUH2yPivc/Po98DD6aPc8hunazXL5XRRPl8oKekTql1mbt/fV2zJXUC1kj7N6mQJHhc2u90YGE6wSEFHGdmNaJDBxW0lELSOjkfDwZeTuvjgCMldU2j6APIbqOcBAyQtFFq+B0JjEsP+jwBHJqOHwbcn1PXsLR+KPB4/YNBTSlkYpC30uqnwP+mL3MHzYxEmVltEGqxZ9sl3QbsTnZtdDbZrG67SxpEdp/5m8CpABHxiqQ7gVeBOmBERCxN9ZwOTCC7VWl0RNQ/FXk2cLukC4AXgOtT+fXATZJmkA1YHdlcrKW+eninEo8zs/amBSc6joijGim+vpGy+v0vBC5spHw8ML6R8plko/ENyz8FDism1lb13vZtN+/P089eVe0wzKxIfsIoR573totsWjozM6A2R5BLfW/76y0diJm1TcItzxX4ve1mVqhONdj0bFXXPM2s7cnejOmWp5lZ0drbXJ2FcPI0s7LVYMOzoFmVJOlYST9Ln/tL+sJ9UmZWm+rf217gs+3tRiGXea8huym+/ubVf+FZlcwsR0cVtrQnhXTbd4yI7SS9ABARH6XnRc3MUDtsVRaikOT5maSOZM+VImktYFlFozKzNqUGc2dByXMUcB/QV9KFZDOO/LSiUZlZm+LR9kZExC2SpgB7kV0bPigiXqt4ZGbWJtQPGNWaZpOnpP7AIuCB3LKI+EclAzOztqMGc2dB3faHyK53ClgF2AiYRvbGOjOrdYKONZg9C+m2b5X7Oc22dFrFIjKzNqWFXz3cZhT9hFFEPC9px0oEY2Ztk5NnIyT9IOdjB2A74J2KRWRmbY4nBmlc95z1OrJroPdUJhwza2vcbW9Eujm+e0T8cCXFY2ZtTQu+w6gtyfcajk4RUSdp55UZkJm1LQI61WDTM1/L8zmy65tTJY0D7iJ7bzsAEXFvhWMzszbCLc/GrQLMB/bk8/s9A3DyNDNAdKD2sme+5Nk3jbS/zOdJs15UNCozazOyF8BVO4qVL1/y7AisDo3+k+LkaWYZebS9obkRcd5Ki8TM2iQBHWswe+ZLnrX3p2FmJfGsSivaa6VFYWZtWg3mzqaTZ0R8uDIDMbO2SRT2MrT2pha/s5m1JGXPtheyNFuVNFrS+5JezinrLWmipOnpZ69ULkmjJM2Q9GKa8a3+mGFp/+mShuWUD5b0UjpmlFJQTZ0jHydPMyubClwKcCMwtEHZOcBjETEAeCx9BtgPGJCW4cC1kCVCYCSwI7ADMDInGV4LnJJz3NBmztEkJ08zK4vIJkMuZGlORPwZaHjJ8EBgTFofAxyUUz42Ms8APSWtA+wLTIyIDyPiI2AiMDRt6xERz0REAGMb1NXYOZpU9HyeZmYNFTFg1EfS5JzP10XEdc0cs3ZEzE3r7wJrp/X1gLdz9pudyvKVz26kPN85muTkaWZlKux6ZjIvIrYv9UwREZIq+pBOoedwt93MylI/2l7IUqL3Upeb9PP9VD4HWD9nv36pLF95v0bK852jSU6eZla2lhptb8I4oH7EfBhwf0758WnUfQjwcep6TwD2kdQrDRTtA0xI2xZIGpJG2Y9vUFdj52iSu+1mVraWukde0m3A7mTXRmeTjZr/ErhT0knAW8DhaffxwP7ADLLXo58I2T3qks4HJqX9zsu5b/00shH9bsDDaSHPOZrk5GlmZVELvno4Io5qYtMXnnhMI+YjmqhnNDC6kfLJwJaNlM9v7Bz5OHmaWdn8AjgzsxLUXup08jSzFlCDDU8nTzMrT3arUu1lTydPMyubW55mZkWTJ0M2MyuWu+1mZqWQu+1mZiVx8jQzK4FqsNvuiUEq6NSTv0X/dfsyeNAXngazVmDp0qUM2X5bDjnwAACefOJxdvrqdgwetCUnnziMuro6AKa9/jpf32Un1litK1decVk1Q26VWnIy5LbEybOCjht2Avc/+Ei1w7AmXDXq1wzcfHMAli1bxsnfGsbYW25nytSX6b/BBtw8NptYvFfv3lx+5SjO+MEPqxluqyYVtrQnTp4VtMuuu9G7d+9qh2GNmD17No88/BAnfutkAObPn0+XLl0YsOmmAOz5jb354333ANC3b1+2/+pX6dy5c9Xibe1U4H/tiZOn1aQfnXkGF150CR06ZH8F+vTpQ11dHVMmZ2+IuO+eu5n99tv5qrBEQAcVtrQnFUuejb1C1Kw1GP/Qg/Rdqy/bDR68vEwSY2++nbN++H122WkHunfvTseOHasYZVtSaLuzfWXPSo623whcRfaGOrNW429/fZoHHxzHI4+MZ/Gnn7JgwQJOPP5Ybhh7M489+RcA/jTxUaZPf6PKkbYR7fB6ZiEq1vJs4hWiZlV3/oUX8X9vzmbajDcZe8vt7L7Hntww9mbefz97bc3ixYu5/NKLOWX4t6scadvg0fYqkTRc0mRJkz+Y90G1w2lRxx97FLvvuhNvTJvGxhv248bR11c7JMvjyssvZdBWm/PV7bZm/2/+B7vvsScA7777Lhtv2I9Rv7qCi39xARtv2I8FCxZUOdrWRQUu7YmymewrVLm0IfBgRBR0o+PgwdvH089Obn5HMyvJzjtuz5Qpk1s0j22+1bZxwx+fKGjfnTbpNaWcVw+3Jn7CyMzK1t4Ggwrh5GlmZWtnlzMLUslblW4D/gYMlDQ7vdLTzNqhWrzmWbGWZ55XiJpZOyL89kwzs+LV6H2eTp5mVrYazJ1OnmbWAmowezp5mlmZ2t9z64Vw8jSzstTPqlRrnDzNrHxOnmZmxavFbnvVJwYxs7avpV7DIelNSS9JmippcirrLWmipOnpZ69ULkmjJM2Q9KKk7XLqGZb2ny5pWE754FT/jHRsyVnfydPMytbCTxjtERGDciYQOQd4LCIGAI+lzwD7AQPSMhy4FrJkC4wEdgR2AEbWJ9y0zyk5xw0t+ssmTp5mVp5CM2fpPfsDgTFpfQxwUE752Mg8A/SUtA6wLzAxIj6MiI+AicDQtK1HRDwT2XRyY3PqKpqTp5mVJRttV0EL0Kd+/t60DG9QXQCPSpqSs23tiJib1t8F1k7r6wG5L5qancrylc9upLwkHjAys7IV0aic18x8nrtExBxJfYGJkl7P3RgRIalykxAXwS1PMytfC3XbI2JO+vk+cB/ZNcv3Upeb9PP9tPscYP2cw/ulsnzl/RopL4mTp5mVrSXenilpNUnd69eBfYCXgXFA/Yj5MOD+tD4OOD6Nug8BPk7d+wnAPpJ6pYGifYAJadsCSUPSKPvxOXUVzd12MytbC82qtDZwX7p7qBNwa0Q8ImkScGeaE/gt4PC0/3hgf2AGsAg4ESAiPpR0PjAp7XdeRNS/jPI0sjf7dgMeTktJnDzNrGwtkTsjYiawTSPl84G9GikPYEQTdY0GRjdSPhko6J1qzXHyNLOyeDJkM7NSeDJkM7PS1GDudPI0sxZQg9nTydPMyuTJkM3MiubJkM3MSuXkaWZWPHfbzcxK4FuVzMxKUIO508nTzMrkm+TNzIrnxzPNzEpUe6nTydPMWkANNjydPM2sfL5VycysFLWXO508zax8NZg7nTzNrDwS9a8VrilOnmZWvtrLnU6eZla+GsydTp5mVr4a7LU7eZpZuTwZsplZ0bLHM6sdxcrn5GlmZXPyNDMrgbvtZmbF8pR0ZmbFE75VycysNDWYPZ08zaxsfjzTzKwEtZc6nTzNrCXUYPZ08jSzstXirUqKiGrHsJykD4C3qh1HBfQB5lU7CCtKe/2dbRARa7VkhZIeIfvzKsS8iBjakuevllaVPNsrSZMjYvtqx2GF8+/MmtOh2gGYmbVFTp5mZiVw8lw5rqt2AFY0/84sL1/zNDMrgVueZmYlcPI0MyuBk6eZWQmcPCtE0kBJO0nqLKljteOxwvh3ZYXygFEFSDoE+AUwJy2TgRsjYkFVA7MmSdo0It5I6x0jYmm1Y7LWzS3PFiapM3AEcFJE7AXcD6wPnC2pR1WDs0ZJOgCYKulWgIhY6haoNcfJszJ6AAPS+n3Ag0Bn4GipBic+bMUkrQacDpwBLJF0MziBWvOcPFtYRHwGXAEcImnXiFgGPAVMBXapanD2BRGxEPgWcCvwQ2CV3ARazd94ay0AAAPkSURBVNisdXPyrIy/AI8Cx0naLSKWRsStwLrANtUNzRqKiHci4t8RMQ84FehWn0AlbSdps+pGaK2R5/OsgIj4VNItQADnpr98i4G1gblVDc7yioj5kk4FLpX0OtAR2KPKYVkr5ORZIRHxkaTfA6+StWY+BY6NiPeqG5k1JyLmSXoR2A/YOyJmVzsma318q9JKkAYeIl3/tFZOUi/gTuDMiHix2vFY6+TkadYISatExKfVjsNaLydPM7MSeLTdzKwETp5mZiVw8jQzK4GTp5lZCZw82whJSyVNlfSypLskrVpGXTdKOjSt/0HSFnn23V3S10o4x5uSvvAu76bKm6jjBElXtcR5zVqak2fb8UlEDIqILYElwLdzN0oq6YGHiDg5Il7Ns8vuQNHJ06y9c/Jsm/4CbJJahX+RNA54VVJHSZdKmiTpxfSYIcpcJWmapD8BfesrkvSkpO3T+lBJz0v6u6THJG1IlqS/n1q9u0paS9I96RyTJO2cjl1T0qOSXpH0B6Dg2aMk7SDpb5JekPRXSQNzNq+fYpwuaWTOMcdKei7F9TvPgGQrmx/PbGNSC3M/4JFUtB2wZUTMkjQc+DgiviqpK/C0pEeBbYGBwBZkz9e/CoxuUO9awO+B3VJdvSPiQ0m/Bf4dEZel/W4FroyIpyT1ByYAmwMjgaci4jxJ3wROKuJrvQ7sGhF1kr5BNpH0f6VtOwBbAouASZIeAhaSzZm6c0R8Juka4BhgbBHnNCuLk2fb0U3S1LT+F+B6su70cxExK5XvA2xdfz0TWINsXtHdgNvSFGvvSHq8kfqHAH+urysiPmwijm8AW+RMS9pD0urpHIekYx+S9FER320NYIykAWSTqXTO2TYxIuYDSLqXbFq/OmAwWTIF6Aa8X8T5zMrm5Nl2fBIRg3ILUuJYmFsEfDciJjTYb/8WjKMDMKTho4tlzvF8PvBERBycLhU8mbOt4SNwQfY9x0TEueWc1KwcvubZvkwAvpNeBYKkTdNM6X8GjkjXRNeh8SnWngF2k7RROrZ3Kv8X0D1nv0eB79Z/kFSf0P8MHJ3K9gN6FRH3GmTvegI4ocG2vSX1ltQNOAh4GngMOFRS3/pYJW1QxPnMyubk2b78gex65vOSXgZ+R9a7uA+YnraNBf7W8MCI+AAYDtwr6e/AHWnTA8DB9QNGwH8D26cBqVf5fNT/f8mS7ytk3fd/5InzRUmz03IFcAlwkaQX+GJv6DngHuBF4J6ImJzuDvgp8GiaOm4isE6Bf0ZmLcITg5iZlcAtTzOzEjh5mpmVwMnTzKwETp5mZiVw8jQzK4GTp5lZCZw8zcxK8P8BvomX1kX3BWgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}