Credit_Card_Fraud_Detection.ipynb

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    "colab": {
      "name": "Credit Card Fraud Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMijOe0gj6yv+bTLB0L7Nnp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ramtiin/Credit-Card-Fraud-Detection/blob/main/Credit_Card_Fraud_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1DaTxviGvHK"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import time, sys\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers, losses\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "go6TO4bPvOFT"
      },
      "source": [
        "Download the dataset from Kaggle. You need download the Kaggle API token (\"kaggle.json) first and put it in the main directory.\n",
        "\n",
        "\n",
        "Read [this](https://medium.com/unpackai/how-to-use-kaggle-datasets-in-google-colab-f9b2e4b5767c) article for more information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYIQ1N4uMg9j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afe26a49-75d3-4dc1-ca61-7e0687118cd8"
      },
      "source": [
        "! pip install -q kaggle\n",
        "\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets list\n",
        "\n",
        "! kaggle datasets download -d mlg-ulb/creditcardfraud\n",
        "! unzip creditcardfraud.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "ref                                                         title                                              size  lastUpdated          downloadCount  \n",
            "----------------------------------------------------------  ------------------------------------------------  -----  -------------------  -------------  \n",
            "gpreda/reddit-vaccine-myths                                 Reddit Vaccine Myths                              235KB  2021-09-15 20:16:36          12275  \n",
            "crowww/a-large-scale-fish-dataset                           A Large Scale Fish Dataset                          3GB  2021-04-28 17:03:01           7441  \n",
            "imsparsh/musicnet-dataset                                   MusicNet Dataset                                   22GB  2021-02-18 14:12:19           3035  \n",
            "dhruvildave/wikibooks-dataset                               Wikibooks Dataset                                   2GB  2021-07-03 18:37:20           2905  \n",
            "promptcloud/careerbuilder-job-listing-2020                  Careerbuilder Job Listing 2020                     42MB  2021-03-05 06:59:52           1914  \n",
            "nickuzmenkov/nih-chest-xrays-tfrecords                      NIH Chest X-rays TFRecords                         11GB  2021-03-09 04:49:23           1139  \n",
            "mathurinache/twitter-edge-nodes                             Twitter Edge Nodes                                342MB  2021-03-08 06:43:04            934  \n",
            "fatiimaezzahra/famous-iconic-women                          Famous Iconic Women                               838MB  2021-02-28 14:56:00           1374  \n",
            "simiotic/github-code-snippets                               GitHub Code Snippets                                7GB  2021-03-03 11:34:39            333  \n",
            "alsgroup/end-als                                            End ALS Kaggle Challenge                           12GB  2021-04-08 12:16:37            895  \n",
            "coloradokb/dandelionimages                                  DandelionImages                                     4GB  2021-02-19 20:03:47            824  \n",
            "mathurinache/the-lj-speech-dataset                          The LJ Speech Dataset                               3GB  2021-02-15 09:19:54            344  \n",
            "landrykezebou/lvzhdr-tone-mapping-benchmark-dataset-tmonet  LVZ-HDR Tone Mapping Benchmark Dataset (TMO-Net)   24GB  2021-03-01 05:03:40            194  \n",
            "imsparsh/accentdb-core-extended                             AccentDB - Core & Extended                          6GB  2021-02-17 14:22:54            140  \n",
            "stuartjames/lights                                          LightS: Light Specularity Dataset                  18GB  2021-02-18 14:32:26            139  \n",
            "nickuzmenkov/ranzcr-clip-kfold-tfrecords                    RANZCR CLiP KFold TFRecords                         2GB  2021-02-21 13:29:51            129  \n",
            "datasnaek/youtube-new                                       Trending YouTube Video Statistics                 201MB  2019-06-03 00:56:47         150484  \n",
            "zynicide/wine-reviews                                       Wine Reviews                                       51MB  2017-11-27 17:08:04         143759  \n",
            "residentmario/ramen-ratings                                 Ramen Ratings                                      40KB  2018-01-11 16:04:39          26953  \n",
            "datasnaek/chess                                             Chess Game Dataset (Lichess)                        3MB  2017-09-04 03:09:09          21764  \n",
            "creditcardfraud.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  creditcardfraud.zip\n",
            "replace creditcard.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9r4DYGY_Ri9T"
      },
      "source": [
        "#Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DI432AOQG5MN"
      },
      "source": [
        "Data = pd.read_csv(\"creditcard.csv\",low_memory=False)\n",
        "Data = Data.drop([\"Time\"],axis=1)\n",
        "Data = Data.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "7T-_wOU4Rq6U",
        "outputId": "b46989c0-5712-44ca-b9fd-efa102e1786d"
      },
      "source": [
        "Data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.919560e-15</td>\n",
              "      <td>5.688174e-16</td>\n",
              "      <td>-8.769071e-15</td>\n",
              "      <td>2.782312e-15</td>\n",
              "      <td>-1.552563e-15</td>\n",
              "      <td>2.010663e-15</td>\n",
              "      <td>-1.694249e-15</td>\n",
              "      <td>-1.927028e-16</td>\n",
              "      <td>-3.137024e-15</td>\n",
              "      <td>1.768627e-15</td>\n",
              "      <td>9.170318e-16</td>\n",
              "      <td>-1.810658e-15</td>\n",
              "      <td>1.693438e-15</td>\n",
              "      <td>1.479045e-15</td>\n",
              "      <td>3.482336e-15</td>\n",
              "      <td>1.392007e-15</td>\n",
              "      <td>-7.528491e-16</td>\n",
              "      <td>4.328772e-16</td>\n",
              "      <td>9.049732e-16</td>\n",
              "      <td>5.085503e-16</td>\n",
              "      <td>1.537294e-16</td>\n",
              "      <td>7.959909e-16</td>\n",
              "      <td>5.367590e-16</td>\n",
              "      <td>4.458112e-15</td>\n",
              "      <td>1.453003e-15</td>\n",
              "      <td>1.699104e-15</td>\n",
              "      <td>-3.660161e-16</td>\n",
              "      <td>-1.206049e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 V1            V2  ...         Amount          Class\n",
              "count  2.848070e+05  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean   3.919560e-15  5.688174e-16  ...      88.349619       0.001727\n",
              "std    1.958696e+00  1.651309e+00  ...     250.120109       0.041527\n",
              "min   -5.640751e+01 -7.271573e+01  ...       0.000000       0.000000\n",
              "25%   -9.203734e-01 -5.985499e-01  ...       5.600000       0.000000\n",
              "50%    1.810880e-02  6.548556e-02  ...      22.000000       0.000000\n",
              "75%    1.315642e+00  8.037239e-01  ...      77.165000       0.000000\n",
              "max    2.454930e+00  2.205773e+01  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6TpaAkfReMD"
      },
      "source": [
        "#Data distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "IK1cywY4Mshb",
        "outputId": "3948e466-b671-49c2-f44a-a697ae0df4f8"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "plt.rc('font', size=14)          # controls default text sizes\n",
        "Data['Class'].value_counts().plot(kind = 'pie',autopct='%1.2f%%')\n",
        "plt.axis('equal')\n",
        "plt.show()\n",
        "print(\" Target Result \\n\",Data['Class'].value_counts(),\"\\n\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Target Result \n",
            " 0    284315\n",
            "1       492\n",
            "Name: Class, dtype: int64 \n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhIN-tO9RIra"
      },
      "source": [
        "Our data is highly imbalanced!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4niQnwApRV5u"
      },
      "source": [
        "#Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "H6exNxjnNTVz",
        "outputId": "612fe52d-037f-43f9-b50e-7e7ff599e64a"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "sns.heatmap(Data.corr())\n",
        "plt.show()\n",
        "\n",
        "print(\"Note :-A correlation of 0 means that no relationship exists between the two variables, whereas a correlation of 1 indicates a perfect positive relationship.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note :-A correlation of 0 means that no relationship exists between the two variables, whereas a correlation of 1 indicates a perfect positive relationship.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GGOZ2qmPhJx"
      },
      "source": [
        "#Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9P7w0dVlXfc"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(Data.iloc[:,:-1], Data.iloc[:,-1], test_size = 0.2, random_state = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2C-cb-KrVDTC"
      },
      "source": [
        "#Classic machine learning methods with Catboost algorithm "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGteWvbNRwY_"
      },
      "source": [
        "##Dealing with imbalanced data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMdPoPVdSHU6"
      },
      "source": [
        "One approach to addressing the problem of class imbalance is to randomly resample the training dataset. The two main approaches to randomly resampling an imbalanced dataset are to delete examples from the majority class, called undersampling, and to duplicate examples from the minority class, called oversampling.\n",
        "\n",
        "Here, we will use both approaches provided by the imbalanced-learn Python library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84wE42gSSmv3",
        "outputId": "9defa534-426e-4a24-814d-2b280e0b981a"
      },
      "source": [
        "!pip install --upgrade imbalanced-learn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.0)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (0.24.2)\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: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.1)\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: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn) (2.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsyhKxOpSpA5",
        "outputId": "285042ed-f1a8-4943-99f0-8d97e3c1774b"
      },
      "source": [
        "# check version number\n",
        "import imblearn\n",
        "print(imblearn.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGWhmKh6jaII"
      },
      "source": [
        "# import the Random Under Sampler object.\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "# create the object.\n",
        "under_sampler = RandomUnderSampler()\n",
        "over_sampler = RandomOverSampler()\n",
        "\n",
        "# fit the object to the training data.\n",
        "X_train_under, y_train_under = under_sampler.fit_resample(X_train, y_train)\n",
        "X_train_over, y_train_over = over_sampler.fit_resample(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiqkvoyRwLBX"
      },
      "source": [
        "Our data now is balanced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "_S0Sc9U1dWqC",
        "outputId": "363f7bdd-0090-4e12-fee9-5aaaeec3da17"
      },
      "source": [
        "plt.figure(figsize=(7,4))\n",
        "plt.rc('font', size=14)          # controls default text sizes\n",
        "y_train_over.value_counts().plot(kind = 'pie',autopct='%1.2f%%')\n",
        "plt.axis('equal')\n",
        "plt.show()\n",
        "print(\" Target Result \\n\",y_train_over.value_counts(),\"\\n\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qO4BIAGm7CKhcRkwXAC/g7Zed6brup9nHDwT+5Hcw8ZU+XxLZmraLgMrlPKaNZCdx3eLxn/uaSPJB7wxFtqbtIqB26B2DMWY0ULb5Y67rfuhLIskHvTMU2Zq2i4Dq83+MMaYGuAHvMPGybXxLsV+hxF9vls9qH0Dzp9v/TpHoaKC8A1bbjiHbkMs7hl/hXdPnBOAvwCy8Q8gvAn7sfzTxS7VpctERSCLdVNPUsf3vEhtyKaajgX91XXe+MaYdWOy67n3GmI+Bc4E5eUkofmizHUAkgLRdBFQuR+UNxptVHCADDMveXwBM8zOU+E4boMjWtF0EVC7F9D6we/b+EuA7xhiDdzVbfX4RbK22A4gEkLaLgMqlmO4C9sveT+LtvmsBrgGu9jeW+CxjO4BIAGm7CKhczmP6zWb3nzXG7A0cDLzruq4uFBhsH9sOIBJA2i4CaoeP48+et6Rzl8KhFuggtxGySCHrQMeKB5ZxXbfnJ435UV8X5Lrutb4kkvxwamrRNZlENqnFyWjW/YDa3ohpqymIeuACKqZg+xgVk8gm2o0XYL0Wk+u6u/VXEMm7VcABtkOIBMQq2wGkZ9v9zMEYc7QxJm2MGbSN52qyz83ITzzxkd4hinTR9hBgffkw/ELgmuzs4t24rpvBO1T8Yr+Die/0DlGki7aHAOtLMU0Gnu7l+Wfx5tCTYNM7RJEu2h4CrC/FNALv0MqeuHRNTyTB9ZHtACIBohFTgPWlmFbSNePDtuyHfumFgU6CFunyuu0A0rO+FFMKuNIYU7nlE8aYAcB/Zb9HgszJfIDmNBQB+BQnk7YdQnrWl5kfrgJmAu8YY/4fsDT7+D7A9wED/CI/8cRnrwCH2w4hYtli2wGkd9stJtd1PzHGTAN+i1dAZtNTwBPABa7ramqPcFiEiklkke0A0rs+zZXnuu5y4BhjzBBgD7xyetd13fX5DCe+0ztFEW0HgZfTJK7ZIno5T1kk/7RBimg7CLxeJ3GVAuTUrAOG2o4hYsk6nMxw2yGkd7oMQvTo3aJEmdb/EFAxRY82TIkyrf8hoGKKnudtBxCxSOt/CKiYoucZoMF2CBEL6vHm9pSAUzFFjZNpAp6yHUPEgqey678EnIopmubaDiBigdb7kFAxRdNf6X3GeJFC04G33ksIqJiiyMl8Aiy0HUOkH72Ek1ljO4T0jYopuh6xHUCkH2l9DxEVU3Rpf7tEidb3ENGURFHm1LwHTLQdQyTP3sPJ7Gk7hPSdRkzR9hfbAUT6gdbzkFExRdtteNfVEilULt56LiGiYooyJ/MeOtlWCtuTOJn3bYeQ3KiY5GbbAUTySOt3CKmY5K/Ah7ZDiOTBcnRSbSipmKLOybQDt9qOIZIHt+JkNMNJCKmYBOB3QIvtECI+asFbryWEVEyyaYqiB2zHEPHRHE1BFF4qJtlEHxJLIdH6HGKa+UG6ODX/AKbYjiGyk17GyXzRdgjZcRoxyeb+w3YAER9cZjuA7BwVk3RxMk/jXXpdJKyexsloHQ45FZNsKYGmKZJwcvHWXwk5FZN052QWAXNsxxDZAX/GySy2HUJ2nopJtuVnQJvtECI5aMNbb6UAqJhka07mHeBO2zFEcnAHTuZd2yHEHyom6ckVQKPtECJ90Ii3vkqBUDHJtjmZVcD1tmOI9MH1OJmPbYcQ/6iYpDf/DSyzHUKkF8vw1lMpICom6ZmTqQdmocPHJZhcYFZ2PZUComKS3jmZeWjeMQmmm7LrpxSYEtsBJBQuBY4GdrcdJJ+cvzVxxbzuV/8YVWWovWQgAK7rcsW8Zm5b3Mr6Jpep44q56ZgKJo0s7nW5D/yzlcufa+b99R1MHFLEVV8v51/2Ke18fnvLbW5z+d4jTTy8tJXR1UXcHK/g8N27Nt0bF7awYGUb93xrgF8/ijBYhk6mLVgaMcn2RWiX3l7Divj4x9WdtzfOq+p87v++0MKvF7Rw49EVvHx2FSOrDDP+2MBnzT3/WBasaOPkOY2cOrmUV8+t4tTJpXz7z40sXNnW5+XetriVxavaWXBWFeccVMopDzSyafLlFZkOfr2gmeuOqsjTTySQtAuvwKmYpG8iskuvpAhGVxd13kZUeZuI67pct7CFxCHlfGvfUj4/spjZJ1TyWYvLPW+09ri86xa2cNhuxfx0ejn7jPD+PDRWzHULW/q83CVr2zlurxImjSzmgillrGlwWdvgFdP5jzbhHFrOyKpIbcrahVfgIrU2y067lAI/Sm/Z+g7G/vozdrv+M74zp4Fl670rc3+wwaW2zuWIiV270CpLDdN3LeHFle09Lm/BinaO2L37HvMjJ5bw4or2Pi93/1HFPP9hO42tLk+838aYasPwAYb732qlsdXlzAPKfPv3h4B24UWAikn6rsB36U0dV8xdJ1Ty+GkDuP3YSmrrXKbdUc+6hg5q67yCGlVtur1mVJXpfG5bauvcrV9Tbaitc7PPb3+5s75Qyv6jitj35jqumt/M/d+uJNMMiaebuOWblVzxt2Y+d2Md039fz9K1PZdkAdAuvIjQwQ+SGyczD6fm58B/2Y7it6P3LO329ZfGF7P79XXMfq2VL43v/QCHfCotNtwUr+z22NlzGznnoDKWrm3nvrdaWXxOFX96s5XTH2zk5bOrLSXNu//ULrxo0IhJcudkrgTutx0j36rLDJNGFvHuug5GV3ubyuq67oPF1fVu53PbMrrabP2aOpfR2RHSjix3XrqNRR+3c8m0Mp79oJ34niUMLDecOrmURas6ej0YI8Tux8noRNqIUDHJjvo/wP/aDpFPTW0uS9d2MGZgEbsNNoyuNjy1rK3b8/OXtzGtl9HUl3cp7vYagKeWtTFtF+81uS63uc3lvFQTt32zkpIiQ4cLrdk9iS3ZvXjthddLr+CtbxIRKibZMU6mATge+MR2FL9c8mQT89JtfLC+g4Ur25h5fyP1LS7f3b8UYwwXTy3j6hea+cuSVt78pJ0zH2qkusxwyuSuXYDf+EM9lz3d1Pn1RVO9UU3y+WaWrm3nl/ObeS7dzsVTvQMW+rrcTa78ezNHTixhyjivtL4yoZgHlrTyam0717zYzKQRRQyuMFu9LsRWAydk1zeJCH3GJDvOyazAqTkReBYI/aFhKzd28K8PNLK2wWVEleFL44t56XtV7DrYe//274eU0djmcsGjTaxvdJk6vpgnTx/AwPKuInj/0w52GdT1fm/aLiXcO7OSnz3bzH8+18zEoUXcN7OSqeO7Nr2+LBfgzU/aue+tNl49t+vcqhP3KWH+8lIOm13PuIFFzD6h+2dRIdcCfAsns8J2EOlfZtOJeiI7zKk5C/id7RhScM7Cyei6YBGkXXmy85zMHcCNtmNIQblBpRRdKibxyw+BR22HkILwKPAj2yHEHu3KE/84NRVACvi67SgSWs8A38TJNG33O6VgqZjEX05NFfA48BXbUSR05gNH6Qg8UTGJ/5yagcBTwFTbUSQ0FgIzcDKf2Q4i9ukzJvGf98vlSOAl21EkFBYAR6qUZBMVk+SHk8kAM4C/244igTYPOCK7vogAKibJJydTh3fl26dtR5FAego4OrueiHRSMUl+eR9kHwvMsR1FAmUOcCxOptF2EAkeFZPkn3fo70mAQ4Fey0n6zAV+DpyEk2m2HUaCSUflSf/y5tb7A1C1vW+VglMHnIGTedB2EAk2FZP0P6dmP+BhIGY5ifSfNHAcTuYN20Ek+LQrT/qfk3kdmIJ3RJYUvr8BU1RK0lcqJrHDyazFO5z8FttRJK9+i3fi7FrbQSQ8tCtP7HNqzgF+AwywHUV80wD8ECdzm+0gEj4qJgkGp2YP4Pdojr1CMB+YhZN5z3YQCSftypNg8H6JfQ24GO/dtoRPA97/39dUSrIzNGKS4NHoKYw0ShLfaMQkwaPRU5holCS+04hJgs0bPd0JfNV2FNmKRkmSFxoxSbB1jZ5OAt6xnEY87+D9f2iUJHmhEZOEh1NTAszCm2ttrOU0UbQKuAK4EyfTZjuMFC4Vk4SPU1MJXARcCgy2nCYKNgBJ4AbNBi79QcUk4eXUDAESwIVApeU0hagRuAG4Giez3nYYiQ4Vk4SfUzMOb/T0XWCQ5TSFYCMwG6+QPrIdRqJHxSSFw6mpAk4Dzgf2s5wmjF7Dm9vubpxMve0wEl0qJilMTs0heAU1EyiznCbIWvCuJnszTuYF22FEQMUkhc6pGQmcBZwL7Go5TZAsB24FfoeTWWM7jMjmVEwSDU5NEXAYcDxwLNG8SGEamJu9PYeT6bAbR2TbVEwSTU7NZOC47G0KYOwGygsXeJlNZaQL9UlIqJhEnJrRwDfxSupwwn3oeSPwNF4Z/RUnU2s5j0jOVEwim3NqSoFJwMHAQdnbfkC5zVg9aAZeBxYDi7J/voWTabWaSmQnqZhEtqerrA6iq7D2AIb0Y4r1wHuohCQCVEwiO8qpqQDGZG9jt3F/NFABlAIl2Vsx0A60ZW+tQBNQC3yMNx/dln/W4mSa+uufJWKbiklERAJFl70QEZFAUTGJiEigqJhERCRQVEwiIhIoKiaRPDHGnG+M+cAY02SMWWyM+artTCJhoGISyQNjzMnA9cAvgC8ALwKPGWMmWA0mEgI6XFwkD4wxC4HXXdc9e7PH3gXmuK57mb1kIsGnEZOIz4wxZXizQzy5xVNPAtP6P5FIuKiYRPw3HG+Gh9VbPL4abzYIEemFiklERAJFxSTiv7V48+GN2uLxUXhz4olIL1RMIj5zXbcFb/bvGVs8NQPv6DwR6UWJ7QAiBepa4I/GmH8ALwD/hjfr+C1WU4mEgIpJJA9c173PGDMM+BneJTDeBI5xXXe53WQiwafzmEREJFD0GZOIiASKiklERAJFxSQiIoGiYhIRkUBRMYmISKComEREJFBUTCIiEigqJhERCZT/Dw5P4yzZJlsQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Target Result \n",
            " 1    227440\n",
            "0    227440\n",
            "Name: Class, dtype: int64 \n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WdfXPwHTWoG",
        "outputId": "fc67efd3-87e0-4acf-8d3f-e9f315141010"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.7/dist-packages (0.26.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLH8w12pIQ1E"
      },
      "source": [
        "from catboost import CatBoostClassifier\n",
        "CLF_over = CatBoostClassifier(silent=True)\n",
        "CLF_under = CatBoostClassifier(silent=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSKD6pTgtY6q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8fd758a-3834-4731-e379-b3b2ee4a9ca4"
      },
      "source": [
        "# Fit the data to the model\n",
        "CLF_under.fit(X_train_under, y_train_under)\n",
        "CLF_over.fit(X_train_over, y_train_over)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f17dde0ea50>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGis2OtlRcin"
      },
      "source": [
        "y_pred_under = CLF_under.predict(X_test)\n",
        "y_pred_over = CLF_over.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K59Lw180XuLr"
      },
      "source": [
        "##Oversampling results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1v39r7TsU0B6",
        "outputId": "9613f612-c609-4089-b4b2-edfba47e6442"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_pred=y_pred_over, y_true=y_test)\n",
        "print(cm)\n",
        "print(\"Accuracy:\",accuracy_score(y_pred=y_pred_over, y_true=y_test))\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_score\n",
        "print(\"f1:\",f1_score(y_test, y_pred_over))\n",
        "print(\"Recall:\",recall_score(y_pred=y_pred_over, y_true=y_test))\n",
        "print(\"Precision:\",precision_score(y_pred=y_pred_over, y_true=y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56861    14]\n",
            " [   16    71]]\n",
            "Accuracy: 0.9994733330992591\n",
            "f1: 0.8255813953488372\n",
            "Recall: 0.8160919540229885\n",
            "Precision: 0.8352941176470589\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0H6C4BhYC0z"
      },
      "source": [
        "##Undersampling results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0t7VPD1tRlDI",
        "outputId": "64cfbd2e-b3c4-4fe6-b907-0d7c5aa7c2d4"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_pred=y_pred_under, y_true=y_test)\n",
        "print(cm)\n",
        "print(\"Accuracy:\",accuracy_score(y_pred=y_pred_under, y_true=y_test))\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_score\n",
        "print(\"f1:\",f1_score(y_test, y_pred_under))\n",
        "print(\"Recall:\",recall_score(y_pred=y_pred_under, y_true=y_test))\n",
        "print(\"Precision:\",precision_score(y_pred=y_pred_under, y_true=y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[56134   741]\n",
            " [   11    76]]\n",
            "Accuracy: 0.9867982163547628\n",
            "f1: 0.168141592920354\n",
            "Recall: 0.8735632183908046\n",
            "Precision: 0.09302325581395349\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gfq00UkYPMk"
      },
      "source": [
        "Overall, the oversampling method give us better result. However, If detecting frauds is our first priority, no matter how it costs, we can consider using undersampling method.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGz2bRp-akm-"
      },
      "source": [
        "##Importance of each features on model output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvtlqJNuZOk-",
        "outputId": "e258f041-c69e-4a3c-f9a4-4e1dcce97638"
      },
      "source": [
        "!pip install shap"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting shap\n",
            "  Downloading shap-0.39.0.tar.gz (356 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.19.5)\n",
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            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
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            "Building wheels for collected packages: shap\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.39.0-cp37-cp37m-linux_x86_64.whl size=491640 sha256=c2a490b0de43c18045908e34cf9d32cc79764019af4bb901b649bdecc9419ec5\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/25/8f/6ae5df62c32651cd719e972e738a8aaa4a87414c4d2b14c9c0\n",
            "Successfully built shap\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.39.0 slicer-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWW21YXqHHhA"
      },
      "source": [
        "import shap\n",
        "explainer = shap.TreeExplainer(CLF_over)\n",
        "shap_values = explainer.shap_values(X_train_over)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "m7m-IaOaIomw",
        "outputId": "efe1913d-8c6e-47c4-9ac4-6fc02d815dcd"
      },
      "source": [
        "shap.summary_plot(shap_values, X_train_over)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x684 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O54RruYZwh30"
      },
      "source": [
        "V14 has the most impact on the model output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DI0WxPjjbBLF"
      },
      "source": [
        "#Deep learning and autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1m6MlgA9wuSL"
      },
      "source": [
        "AutoEncoder is an unsupervised Artificial Neural Network that attempts to encode the data by compressing it into the lower dimensions (bottleneck layer or code) and then decoding the data to reconstruct the original input. The bottleneck layer (or code) holds the compressed representation of the input data. The number of hidden units in the code is called code size.\n",
        "\n",
        "AutoEncoders are widely used in anomaly detection. The reconstruction errors are used as the anomaly scores.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jC_BKyGUxYD2"
      },
      "source": [
        "To detect anomalies in a high-dimension dataset using autoencoders, the following steps will be performed:\n",
        "\n",
        "\n",
        "1.   During the training, input is only normal transactions. The bottleneck layer will learn the latent representation of the normal input data.\n",
        "2.   The Decoder will use the bottleneck layers output to reconstruct the normal transactions of the original input data.\n",
        "3.   There are differences between a fraudulent transaction and a normal transaction. The Autoencoder will have difficulty reconstructing the fraudulent transaction, resulting in a high reconstruction error.\n",
        "\n",
        "4.   You can flag a new transaction is fraudulent based on a specified threshold value for the reconstruction error."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ahi4qj7yThy"
      },
      "source": [
        "![1_ce89U6z-MhgGRln9VRAMxQ 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "musFCUTC0NRU"
      },
      "source": [
        "##Normalizing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oU29SDaeoj6Q"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler()\n",
        "train_data = sc.fit_transform(X_train)\n",
        "test_data = sc.transform(X_test)\n",
        "train_labels = y_train\n",
        "test_labels = y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9Yp_9I80Vs8"
      },
      "source": [
        "##Splitting normal and fraud data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjzn_p380aqM"
      },
      "source": [
        "We train the model using only normal data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNsn7x63o4jq",
        "outputId": "11242d5a-f7e4-40cb-b5b4-44cec00f4bb7"
      },
      "source": [
        "train_labels = train_labels.astype(bool)\n",
        "test_labels = test_labels.astype(bool)\n",
        "#creating normal and fraud datasets\n",
        "normal_train_data = train_data[~train_labels]\n",
        "normal_test_data = test_data[~test_labels]\n",
        "anomalous_train_data = train_data[train_labels]\n",
        "anomalous_test_data = test_data[test_labels]\n",
        "print(\" No. of records in Fraud Train Data=\",len(anomalous_train_data))\n",
        "print(\" No. of records in Normal Train data=\",len(normal_train_data))\n",
        "print(\" No. of records in Fraud Test Data=\",len(anomalous_test_data))\n",
        "print(\" No. of records in Normal Test data=\",len(normal_test_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " No. of records in Fraud Train Data= 405\n",
            " No. of records in Normal Train data= 227440\n",
            " No. of records in Fraud Test Data= 87\n",
            " No. of records in Normal Test data= 56875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvEptL4r0iRZ"
      },
      "source": [
        "##Model structure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dm7he5RXpUrf"
      },
      "source": [
        "nb_epoch = 50\n",
        "batch_size = 128\n",
        "input_dim = normal_train_data.shape[1] #num of columns, 30\n",
        "encoding_dim = 14\n",
        "hidden_dim_1 = int(encoding_dim / 2) #\n",
        "hidden_dim_2=4  \n",
        "learning_rate = 1e-7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvR8PMCcpdy7",
        "outputId": "b90dbdb6-da6b-4b89-c950-e37406eb2156"
      },
      "source": [
        "#input Layer\n",
        "input_layer = tf.keras.layers.Input(shape=(input_dim, ))\n",
        "#Encoder\n",
        "encoder = tf.keras.layers.Dense(encoding_dim, activation=\"tanh\", \n",
        "                                activity_regularizer=tf.keras.regularizers.l2(learning_rate))(input_layer)\n",
        "encoder=tf.keras.layers.Dropout(0.2)(encoder)\n",
        "encoder = tf.keras.layers.Dense(hidden_dim_1, activation='relu')(encoder)\n",
        "encoder = tf.keras.layers.Dense(hidden_dim_2, activation=tf.nn.leaky_relu)(encoder)\n",
        "# Decoder\n",
        "decoder = tf.keras.layers.Dense(hidden_dim_1, activation='relu')(encoder)\n",
        "decoder=tf.keras.layers.Dropout(0.2)(decoder)\n",
        "decoder = tf.keras.layers.Dense(encoding_dim, activation='relu')(decoder)\n",
        "decoder = tf.keras.layers.Dense(input_dim, activation='tanh')(decoder)\n",
        "#Autoencoder\n",
        "autoencoder = tf.keras.Model(inputs=input_layer, outputs=decoder)\n",
        "autoencoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_9 (InputLayer)         [(None, 29)]              0         \n",
            "_________________________________________________________________\n",
            "dense_36 (Dense)             (None, 14)                420       \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 14)                0         \n",
            "_________________________________________________________________\n",
            "dense_37 (Dense)             (None, 7)                 105       \n",
            "_________________________________________________________________\n",
            "dense_38 (Dense)             (None, 4)                 32        \n",
            "_________________________________________________________________\n",
            "dense_39 (Dense)             (None, 7)                 35        \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 7)                 0         \n",
            "_________________________________________________________________\n",
            "dense_40 (Dense)             (None, 14)                112       \n",
            "_________________________________________________________________\n",
            "dense_41 (Dense)             (None, 29)                435       \n",
            "=================================================================\n",
            "Total params: 1,139\n",
            "Trainable params: 1,139\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyek8bflpjsP"
      },
      "source": [
        "cp = tf.keras.callbacks.ModelCheckpoint(filepath=\"best_model.h5\",\n",
        "                               mode='min', monitor='val_loss', verbose=2, save_best_only=True)\n",
        "# define our early stopping\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=0.0001,\n",
        "    patience=10,\n",
        "    verbose=1, \n",
        "    mode='min',\n",
        "    restore_best_weights=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-LYZ6njpmIU"
      },
      "source": [
        "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqPD-36a0wZ8"
      },
      "source": [
        "##Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3a6dhN3pn_7",
        "outputId": "41cd5a3e-1c46-4fb9-d89f-0604af0ac4c8"
      },
      "source": [
        "history = autoencoder.fit(normal_train_data, normal_train_data,\n",
        "                    epochs=nb_epoch,\n",
        "                    batch_size=batch_size,\n",
        "                    shuffle=True,\n",
        "                    validation_data=(test_data, test_data),\n",
        "                    verbose=1,\n",
        "                    callbacks=[cp, early_stop]\n",
        "                    ).history"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1777/1777 [==============================] - 5s 2ms/step - loss: 0.0158 - val_loss: 0.0017\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.00170, saving model to best_model.h5\n",
            "Epoch 2/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0016 - val_loss: 0.0015\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.00170 to 0.00145, saving model to best_model.h5\n",
            "Epoch 3/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0014 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.00145 to 0.00131, saving model to best_model.h5\n",
            "Epoch 4/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.00131 to 0.00129, saving model to best_model.h5\n",
            "Epoch 5/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.00129 to 0.00129, saving model to best_model.h5\n",
            "Epoch 6/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00129 to 0.00128, saving model to best_model.h5\n",
            "Epoch 7/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.00128 to 0.00126, saving model to best_model.h5\n",
            "Epoch 8/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0012 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00126 to 0.00117, saving model to best_model.h5\n",
            "Epoch 9/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0012 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.00117\n",
            "Epoch 10/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0012 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.00117\n",
            "Epoch 11/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.00117\n",
            "Epoch 12/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0014\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.00117\n",
            "Epoch 13/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0015\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.00117\n",
            "Epoch 14/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0016\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.00117\n",
            "Epoch 15/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0017\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.00117\n",
            "Epoch 16/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.00117\n",
            "Epoch 17/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.00117\n",
            "Epoch 18/50\n",
            "1777/1777 [==============================] - 4s 2ms/step - loss: 0.0011 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.00117\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00018: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8obHSa9nmk4"
      },
      "source": [
        "from tensorflow import keras\n",
        "autoencoder = keras.models.load_model(\"best_model.h5\",custom_objects={'leaky_relu': tf.keras.layers.LeakyReLU()})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "xMVzF2JfqWNp",
        "outputId": "e3261796-2d00-4836-f0d9-c48acc300d3e"
      },
      "source": [
        "plt.plot(history['loss'], linewidth=2, label='Train')\n",
        "plt.plot(history['val_loss'], linewidth=2, label='Test')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "#plt.ylim(ymin=0.70,ymax=1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCS2mxX_epDK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "b2d0e5b2-c3ce-4f7f-cd9d-1fe40a48e348"
      },
      "source": [
        "reconstructions = autoencoder.predict(normal_train_data)\n",
        "train_loss = tf.keras.losses.mse(reconstructions, normal_train_data)\n",
        "\n",
        "plt.hist(train_loss[None,:], bins=50)\n",
        "plt.xlabel(\"Train loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voaJkyZi001T"
      },
      "source": [
        "##Choosing a threshold "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5NDTxoJ06DY"
      },
      "source": [
        "Choose a threshold value that is one standard deviations above the mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnoeNb-EfGrU",
        "outputId": "09e2de02-48e4-45b6-af1f-3d42bb895fae"
      },
      "source": [
        "threshold = np.mean(train_loss) + np.std(train_loss)\n",
        "print(\"Threshold: \", threshold)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Threshold:  0.0030097882733085652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlb28pzE1AQt"
      },
      "source": [
        "##Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "WKF9zsKW3jcf",
        "outputId": "a95c7993-7609-4464-87ed-112fc783d8d8"
      },
      "source": [
        "reconstructions = autoencoder.predict(anomalous_test_data)\n",
        "test_loss = tf.keras.losses.mse(reconstructions, anomalous_test_data)\n",
        "\n",
        "plt.hist(test_loss[None, :], bins=50)\n",
        "plt.xlabel(\"Test loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHGwdegeqa6e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "def predict(model, data, threshold):\n",
        "  reconstructions = model(data)\n",
        "  loss = tf.keras.losses.mse(reconstructions, data)\n",
        "  return tf.math.logical_not (tf.math.less(loss, threshold))\n",
        "\n",
        "def print_stats(predictions, labels):\n",
        "  conf_matrix = confusion_matrix(labels, predictions)\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  LABELS = [\"Normal\",\"Anomalous\"]\n",
        "  sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel('True class')\n",
        "  plt.xlabel('Predicted class')\n",
        "  plt.show()\n",
        "  print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n",
        "  print(\"Precision = {}\".format(precision_score(labels, predictions)))\n",
        "  print(\"Recall = {}\".format(recall_score(labels, predictions)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "V8FuJhrh3NUQ",
        "outputId": "b06ecbe4-adff-4548-929b-a4370db7bcae"
      },
      "source": [
        "preds = predict(autoencoder, test_data, threshold)\n",
        "print_stats(preds, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 0.937660194515642\n",
            "Precision = 0.020487264673311186\n",
            "Recall = 0.8505747126436781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jaNH5di6oCp"
      },
      "source": [
        "reconstructions = autoencoder.predict(test_data)\n",
        "mse = tf.keras.losses.mse(reconstructions, test_data)\n",
        "error_df = pd.DataFrame({'Reconstruction_error': mse,\n",
        "                        'True_class': test_labels})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "RIGIVzDK6zG4",
        "outputId": "0c2d26bf-0133-447f-8a68-b2ce628ecb00"
      },
      "source": [
        "threshold_fixed = threshold\n",
        "groups = error_df.groupby('True_class')\n",
        "fig, ax = plt.subplots()\n",
        "for name, group in groups:\n",
        "    ax.plot(group.index, group.Reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
        "            label= \"Anomalous\" if name == 1 else \"Normal\")\n",
        "ax.hlines(threshold_fixed, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
        "ax.legend()\n",
        "plt.title(\"Reconstruction error for normal and anomalous data\")\n",
        "plt.ylabel(\"Reconstruction error\")\n",
        "plt.xlabel(\"Data point index\")\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}