Fraud Detection in Credit Card.ipynb

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  "metadata": {
    "colab": {
      "name": "Fraud Detection in Credit Card.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOTUKsPwcGd+EjelwPrKYwM",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fitrieratna/DataScience-Portfolio/blob/master/Fraud%20Detection%20in%20Credit%20Card.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkDoPDztCrap"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENQNadb6Ct9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        },
        "outputId": "bee297ef-e98d-4507-b672-c471e83a22b4"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler,PowerTransformer\n",
        "from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold, cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline,make_pipeline\n",
        "from sklearn.metrics import mean_squared_error,classification_report,make_scorer,accuracy_score,plot_roc_curve,auc,roc_curve, confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report,precision_score, recall_score\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.model_selection import KFold\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import itertools\n",
        "from sklearn.metrics import f1_score\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "import cufflinks as cf\n",
        "import plotly.offline\n",
        "cf.go_offline()\n",
        "cf.set_config_file(offline=False, world_readable=True)\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2XYxA8RHJsn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54eab30a-088a-4843-8f6f-4b7110571797"
      },
      "source": [
        "# To connect Google Drive (GDrive) with Colab\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lU3o1JwPDH3C"
      },
      "source": [
        "data = pd.read_csv('/content/gdrive/My Drive/Colab Notebooks/IBM - Keras Deep Learning Course/creditcard.csv', encoding='unicode_escape' )"
      ],
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLyby4plS_kV"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "cB4Dl-nUJZf5",
        "outputId": "87866cea-42f7-4af8-abd1-10a3910f0ab4"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "      <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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "    <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",
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            ],
            "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": 9
        }
      ]
    },
    {
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      "metadata": {
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        },
        "id": "P0Htp7UOWa00",
        "outputId": "bb735880-1f0c-4789-dc10-53dab8992b1f"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AK5LYNCcJdT8",
        "outputId": "211e36a2-1913-4125-9072-fe486ebaa94a"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9A4OsFSTLbJ",
        "outputId": "15208bcc-6b9e-4ebc-e2ed-056b8330a2d1"
      },
      "source": [
        "missing_data = data.isnull()\n",
        "\n",
        "for column in missing_data.columns.values.tolist():\n",
        " print (column)\n",
        " print (missing_data[column].value_counts())\n",
        " print(\"\")  "
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
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            "Time\n",
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            "\n",
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            "\n",
            "V19\n",
            "False    284807\n",
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            "\n",
            "V20\n",
            "False    284807\n",
            "Name: V20, dtype: int64\n",
            "\n",
            "V21\n",
            "False    284807\n",
            "Name: V21, dtype: int64\n",
            "\n",
            "V22\n",
            "False    284807\n",
            "Name: V22, dtype: int64\n",
            "\n",
            "V23\n",
            "False    284807\n",
            "Name: V23, dtype: int64\n",
            "\n",
            "V24\n",
            "False    284807\n",
            "Name: V24, dtype: int64\n",
            "\n",
            "V25\n",
            "False    284807\n",
            "Name: V25, dtype: int64\n",
            "\n",
            "V26\n",
            "False    284807\n",
            "Name: V26, dtype: int64\n",
            "\n",
            "V27\n",
            "False    284807\n",
            "Name: V27, dtype: int64\n",
            "\n",
            "V28\n",
            "False    284807\n",
            "Name: V28, dtype: int64\n",
            "\n",
            "Amount\n",
            "False    284807\n",
            "Name: Amount, dtype: int64\n",
            "\n",
            "Class\n",
            "False    284807\n",
            "Name: Class, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAsGvkbhJo-t",
        "outputId": "8e5d3cb5-21cf-4065-f70b-6835ec373791"
      },
      "source": [
        "data.Class.value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cf6MUpt6Jj-H",
        "outputId": "0d4e57a3-28ce-47fa-d695-8c4fbf007a44"
      },
      "source": [
        "data.Class.value_counts(normalize=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.998273\n",
              "1    0.001727\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "va9E4jKOTS5k"
      },
      "source": [
        "No missing values found. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VKQgMAV8TLd7",
        "outputId": "f6179de7-b200-4990-8494-78cb2d676786"
      },
      "source": [
        "count_classes = pd.value_counts(data['Class'], sort = True)\n",
        "count_classes.plot(kind = 'bar', rot=0)\n",
        "\n",
        "plt.xticks(range(len(data['Class'].unique())), data.Class.unique())\n",
        "plt.title(\"Frequency by observation number\")\n",
        "plt.xlabel(\"Class\")\n",
        "plt.ylabel(\"Number of Observations\");"
      ],
      "execution_count": 87,
      "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": "TGiefdAMTrK3"
      },
      "source": [
        "From bar chart above we know that the dataset is imbalanced, with only 0.1% classified in Fraud transaction. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqvzMk8SWda5"
      },
      "source": [
        "Now take a closer look on what amount transaction are more probable to fraud? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9dTvo9CIWV1R",
        "outputId": "0ac45bcc-d297-4634-aab3-1dafc86564ab"
      },
      "source": [
        "# Save the normal and fradulent transactions in separate dataframe\n",
        "normal_dataset = data[data.Class == 0] \n",
        "fraud_dataset = data[data.Class == 1]\n",
        "\n",
        "#Visualize transaction amounts for normal and fraudulent transactions\n",
        "bins = np.linspace(200, 2500, 100)\n",
        "plt.hist(normal_dataset.Amount, bins=bins, alpha=1, density=True, label='Normal')\n",
        "plt.hist(fraud_dataset.Amount, bins=bins, alpha=0.5, density=True, label='Fraud')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title(\"Transaction amount vs Percentage of transactions\")\n",
        "plt.xlabel(\"Transaction amount (USD)\")\n",
        "plt.ylabel(\"Percentage of transactions\");\n",
        "plt.show()"
      ],
      "execution_count": 95,
      "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": "Jk_uElb6XBkh"
      },
      "source": [
        "Fraudulent transcation is more probable when transaction is below USD 1000, especially around USD 250 and USD 750."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPm9mptePm3A"
      },
      "source": [
        "Before we run any machine learning or deep learning model, we should understand what aim we targeted, is it high recall or high prediction? Or both? \n",
        "\n",
        "Fraud is financial crime in any banking insitution which preferably focus on how we reduce false negative (predicted non-fraud, actually fraud), so that we can mitigate any alleged or suspected fraud. \n",
        "\n",
        "But if banking insitution wanted to do both, mitigate early of alleged fraud and paralelly protect loyality from most of customer by not to block if there's alleged fraud, then we should care about F1-Score evaluation matrix, as it is evaluation metrix of balance between precision and recall."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVuCf7tGAUY8"
      },
      "source": [
        "# Machine Learning & Deep Learning Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMJA-qRKLPZg"
      },
      "source": [
        "### 1. Dummy Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQuLH9OkMGHi"
      },
      "source": [
        "def classification_report_with_validation(y_true, y_pred):\n",
        "    real_values.extend(y_true)\n",
        "    predicted_values.extend(y_pred)\n",
        "    return accuracy_score(y_true, y_pred)"
      ],
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15-A3Sss8dj4"
      },
      "source": [
        "def print_stats(labels, predictions):\n",
        "  print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n",
        "  print(\"Precision = {}\".format(precision_score(labels, predictions)))\n",
        "  print(\"Recall = {}\".format(recall_score(labels, predictions)))\n",
        "  print('F1 Score = {}'.format(f1_score(labels, predictions)))\n",
        "  print('AUC_1 Score = {}'.format(auc(fpr1,tpr1)))"
      ],
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSDz_JKsNLnC"
      },
      "source": [
        "X = data.drop('Class', axis=1)\n",
        "y = data['Class']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42, stratify=y)"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41U0lI9XKB44",
        "outputId": "c293acd4-269c-435d-ff6a-1ea4ca0d7ff7"
      },
      "source": [
        "dummy = DummyClassifier(strategy='most_frequent')\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
        "\n",
        "dummy.fit(X_train, y_train)\n",
        "\n",
        "result = cross_val_score(dummy, X_train, y_train,  scoring='accuracy',cv=cv)\n",
        "\n",
        "print(f'{round(np.mean(result),6)}')"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.998275\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTbH_SQPLMrl"
      },
      "source": [
        "With simple dummy classifier, model can predict with accuracy 99.8%. But the question is, does the model could predict greatly about suspected fraud? Let us check to confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fbxJ3cwMgfm",
        "outputId": "fe03b25a-cc52-4aa7-9ab8-4f2f02eb8089"
      },
      "source": [
        "yhat_dummy = dummy.predict(X_test)\n",
        "\n",
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, yhat_dummy, labels=[0,1])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "print (classification_report(y_test, yhat_dummy))\n",
        "\n",
        "#print(score_df)\n"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     85295\n",
            "           1       0.00      0.00      0.00       148\n",
            "\n",
            "    accuracy                           1.00     85443\n",
            "   macro avg       0.50      0.50      0.50     85443\n",
            "weighted avg       1.00      1.00      1.00     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRwZmwwoAD_h"
      },
      "source": [
        "From the confusion matrix above we know that the precision and recall generate by dummy classifier is poor, both 50%. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "XflXtQZv8A-s",
        "outputId": "1808c507-4744-4604-8f88-e7e8e1b712d6"
      },
      "source": [
        "fpr1, tpr1, thresholds = roc_curve(y_test, yhat_dummy, pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve Undersampling+MLPC (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v84y0ieN8f51",
        "outputId": "e358cec1-f758-47a0-f1c2-41eaa257d37c"
      },
      "source": [
        "eval_met1 = print_stats(y_test, yhat_dummy)\n",
        "eval_met1"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9982678510820079\n",
            "Precision = 0.0\n",
            "Recall = 0.0\n",
            "F1 Score = 0.0\n",
            "AUC_1 Score = 0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7y5C5JXaPlCi"
      },
      "source": [
        "### 2. Vanilla Logistic Regression "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTakhEBy7Hbp",
        "outputId": "9acd7c82-62a9-43a1-f868-6e0bc5ce9218"
      },
      "source": [
        "def classification_report_with_validation(y_true, y_pred):\n",
        "    real_values.extend(y_true)\n",
        "    predicted_values.extend(y_pred)\n",
        "    return accuracy_score(y_true, y_pred)\n",
        "\n",
        "s = MinMaxScaler()\n",
        "lr = LogisticRegression(solver='liblinear')\n",
        "\n",
        "pipeline = Pipeline([(\"scaler\", s),\n",
        "                      (\"LogRegression\", lr)])\n",
        "\n",
        "\n",
        "print(classification_report(real_values, predicted_values))"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00    199020\n",
            "           1       0.88      0.54      0.67       344\n",
            "\n",
            "    accuracy                           1.00    199364\n",
            "   macro avg       0.94      0.77      0.83    199364\n",
            "weighted avg       1.00      1.00      1.00    199364\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9dPKY_H-KMV",
        "outputId": "63097cdc-3498-49d4-fde3-e0ccea0c1ab3"
      },
      "source": [
        "confusion_matrix(real_values, predicted_values)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[198995,     25],\n",
              "       [   159,    185]])"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zANSLxqM8uc8",
        "outputId": "a2ef182e-d83e-4404-8417-40658f064cb5"
      },
      "source": [
        "pipeline.fit(X_train, y_train)\n"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))),\n",
              "                ('LogRegression',\n",
              "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                    fit_intercept=True, intercept_scaling=1,\n",
              "                                    l1_ratio=None, max_iter=100,\n",
              "                                    multi_class='auto', n_jobs=None,\n",
              "                                    penalty='l2', random_state=None,\n",
              "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
              "                                    warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CvTM8d885dW"
      },
      "source": [
        "probs = pipeline.predict_proba(X_test)"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "8blBJddx5nEY",
        "outputId": "5a48ba4a-2464-4230-d21f-09c72adf166d"
      },
      "source": [
        "fpr1, tpr1, thresholds = roc_curve(y_test, probs[:, 1], pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve Log Reg (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mURvg5mlBoXY",
        "outputId": "5b487355-c770-48b0-fefc-4d9055fd3698"
      },
      "source": [
        "eval_met2 = print_stats(real_values, predicted_values)\n",
        "eval_met2"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9990770650669127\n",
            "Precision = 0.8809523809523809\n",
            "Recall = 0.5377906976744186\n",
            "F1 Score = 0.6678700361010831\n",
            "AUC_1 Score = 0.9614445414404381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OiA0pPf1Lp3M"
      },
      "source": [
        "### 3. Logistic Regression with Ridge Regularization (LR2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0D5Lafs5UXQ"
      },
      "source": [
        "s = MinMaxScaler()\n",
        "LR_L2 = LogisticRegression(penalty='l2', max_iter=100, solver='liblinear')\n"
      ],
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfuhsVYP_Pqb",
        "outputId": "8d8219ef-d987-42d7-c10a-337864a2305f"
      },
      "source": [
        "estimator = Pipeline([(\"scaler\", s),\n",
        "                      (\"LogRegression\", LR_L2)])\n",
        "\n",
        "estimator.fit(X_train, y_train)\n",
        "y_predLR = estimator.predict(X_test)\n",
        "print(classification_report(y_predLR, y_test))\n"
      ],
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.96      0.98     96688\n",
            "           1       0.96      0.99      0.97     90960\n",
            "\n",
            "    accuracy                           0.97    187648\n",
            "   macro avg       0.97      0.98      0.97    187648\n",
            "weighted avg       0.98      0.97      0.97    187648\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "oFvNjxXUCqeW",
        "outputId": "1f002e4a-1722-4be2-e92c-d9701eabaa99"
      },
      "source": [
        "y_prob = estimator.predict_proba(X_test)\n",
        "fpr1, tpr1, thresholds = roc_curve(y_test, y_prob[:, 1], pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve Log Reg+Ridge (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5BXagsX_uwO",
        "outputId": "487f7a6e-d0d2-4176-fe53-6c5decc584a1"
      },
      "source": [
        "eval_met3 = print_stats(y_test, y_predLR)\n",
        "eval_met3"
      ],
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9748092172578445\n",
            "Precision = 0.9900285839929639\n",
            "Recall = 0.9593067229128717\n",
            "F1 Score = 0.9744255625348287\n",
            "AUC_1 Score = 0.996035812271742\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JrFeDZoAfAa"
      },
      "source": [
        "### 4. SMOTE + Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Eq1N-tsAmNX"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 279,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY8Z7GcSApQv"
      },
      "source": [
        "sm = SMOTE(sampling_strategy='auto', random_state=42, k_neighbors=4)\n",
        "X_oversampled , y_oversampled = sm.fit_resample(X, y)"
      ],
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JD_2_7sRApTr",
        "outputId": "152d120b-e61a-4114-ff57-dcb455e04b86"
      },
      "source": [
        "s = MinMaxScaler()\n",
        "pipeline = Pipeline([('scaler',s),('model', LogisticRegression(solver='liblinear'))])\n",
        "\n",
        "pipeline.fit(X_oversampled, y_oversampled)"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))),\n",
              "                ('model',\n",
              "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                    fit_intercept=True, intercept_scaling=1,\n",
              "                                    l1_ratio=None, max_iter=100,\n",
              "                                    multi_class='auto', n_jobs=None,\n",
              "                                    penalty='l2', random_state=None,\n",
              "                                    solver='liblinear', tol=0.0001, verbose=0,\n",
              "                                    warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lE-_AgX6ApW-",
        "outputId": "9ceaae5d-e31a-4dc1-cc2d-2ab0814e28b7"
      },
      "source": [
        "unique_original, counts_original = np.unique(y, return_counts=True)\n",
        "unique_oversampled, counts_oversampled = np.unique(y_oversampled, return_counts=True)\n",
        "\n",
        "print('Original fraud class distribution:', dict(zip(unique_original, counts_original)))\n",
        "print('New transformed fraud class distribution:',dict(zip(unique_oversampled, counts_oversampled)))"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original fraud class distribution: {0: 284315, 1: 492}\n",
            "New transformed fraud class distribution: {0: 284315, 1: 284315}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBAlBvkuApZE"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_oversampled, y_oversampled, test_size=0.33, random_state=42)"
      ],
      "execution_count": 283,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6h8qnQ9IrWP",
        "outputId": "2d151d84-45f2-466b-f571-2c2f852b205d"
      },
      "source": [
        "model_lr_smt = LogisticRegression(solver='liblinear')\n",
        "model_lr_smt.fit(X_train, y_train)"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQCrJuWsJ--l",
        "outputId": "5dbcb017-d9b9-4368-b704-a1ea2e50608d"
      },
      "source": [
        "tst_lr_smt_pred = model_lr_smt.predict(X_test)\n",
        "tst_lr_smt_acc = accuracy_score(tst_lr_smt_pred, y_test)\n",
        "print(round(tst_lr_smt_acc*100, 2))"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "96.24\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7olC_8MmIrYp",
        "outputId": "1b7626a7-a7cc-4e84-a837-e23c67169d3a"
      },
      "source": [
        "print(classification_report(tst_lr_smt_pred,y_test))"
      ],
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.94      0.96     98141\n",
            "           1       0.94      0.98      0.96     89507\n",
            "\n",
            "    accuracy                           0.96    187648\n",
            "   macro avg       0.96      0.96      0.96    187648\n",
            "weighted avg       0.96      0.96      0.96    187648\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "kMXy4XN7K-1u",
        "outputId": "148800d0-c290-46d8-fed1-fc7bbbcfd73d"
      },
      "source": [
        "fpr1, tpr1, thresholds = roc_curve(y_test, tst_lr_smt_pred, pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve SMOTE+LogReg (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIIq6xqZAPhX",
        "outputId": "906df698-bd52-4b86-e5c3-9e0c5dc8320d"
      },
      "source": [
        "eval_met4 = print_stats(y_test, tst_lr_smt_pred)\n",
        "eval_met4"
      ],
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9623976807639836\n",
            "Precision = 0.9849732423162434\n",
            "Recall = 0.9391624854857095\n",
            "F1 Score = 0.9615225215399716\n",
            "AUC_1 Score = 0.9624098217884426\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "UJolXd0uRvUK",
        "outputId": "10b2a30c-b254-40d3-fd03-82833a0ae64a"
      },
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test,tst_lr_smt_pred, labels=[1,0])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['Normal Trx = 0','Fraud = 1'],normalize= False,  title='Confusion matrix')"
      ],
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[88162  5711]\n",
            " [ 1345 92430]]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "63B6AlBAApmS"
      },
      "source": [
        "### 5. Undersampling + Multi Layer Perceptron Classifier (MLP Classifier)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eozHJayKA0VE"
      },
      "source": [
        "number_records_fraud = len (data[data.Class==1])\n",
        "\n",
        "fraud_indices = np.array (data[data.Class==1].index)\n",
        "\n",
        "normal_indices = np.array (data[data.Class==0].index)"
      ],
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pIwm-0GwX_s",
        "outputId": "359887a6-f57a-4a99-f0d0-80bdbd27e2a9"
      },
      "source": [
        "normal_indices.shape"
      ],
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284315,)"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RLZTPbVwjUS",
        "outputId": "e510736f-b79d-40a3-cadc-e81d2c9f591c"
      },
      "source": [
        "number_records_fraud"
      ],
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "492"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIk5wGkAA4dP"
      },
      "source": [
        "random_normal_indices = np.random.choice (normal_indices, number_records_fraud, replace = False )\n",
        "\n",
        "under_sample_indices = np.concatenate ([fraud_indices, random_normal_indices])"
      ],
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hODqS48nA4f9"
      },
      "source": [
        "under_sample_data = data.iloc[under_sample_indices,:]\n",
        "\n",
        "X_undersample = under_sample_data.iloc [:, under_sample_data.columns != 'Class'];\n",
        "\n",
        "y_undersample = under_sample_data.iloc [:, under_sample_data.columns == 'Class'];"
      ],
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8iLm8_SwEJH",
        "outputId": "49c17a28-ea6e-45ee-ebde-3287ea7f8632"
      },
      "source": [
        "y_undersample.shape"
      ],
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(984, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ipjDlyfA4iX"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)\n",
        "\n",
        "X_train_under, X_test_under, y_train_under, y_test_under = train_test_split (X_undersample,y_undersample, test_size = 0.3, random_state = 42)"
      ],
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlM7LBDUPLGf"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier"
      ],
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8M0v1ouPMrG"
      },
      "source": [
        "MLPC = MLPClassifier(hidden_layer_sizes=(200,), max_iter=10000)\n",
        "\n",
        "s = MinMaxScaler()\n",
        "pipeline = Pipeline([('scaler',s),('mlpc', MLPC)])\n",
        "\n",
        "pipeline.fit(X_train_under, y_train_under)\n",
        "\n",
        "y_predMLPC = pipeline.predict(X_test_under)"
      ],
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANH4kqrtPLLL",
        "outputId": "9e6b2ac9-5ab3-4969-8bf9-7f47dc3720d4"
      },
      "source": [
        "print(classification_report(y_predMLPC,y_test_under))"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.90      0.93       158\n",
            "           1       0.89      0.97      0.93       138\n",
            "\n",
            "    accuracy                           0.93       296\n",
            "   macro avg       0.93      0.93      0.93       296\n",
            "weighted avg       0.94      0.93      0.93       296\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "7iHvEVRSPLNw",
        "outputId": "7379a864-5867-4536-a5d0-1c3ef4db61ce"
      },
      "source": [
        "fpr1, tpr1, thresholds = roc_curve(y_test_under, y_predMLPC, pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve Undersampling+MLPC (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCkON1gYEV-X",
        "outputId": "ea4cf660-8967-4f39-f89b-403f5f8b4e89"
      },
      "source": [
        "eval_met5 = print_stats(y_test_under, y_predMLPC)\n",
        "eval_met5"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9324324324324325\n",
            "Precision = 0.9710144927536232\n",
            "Recall = 0.8933333333333333\n",
            "F1 Score = 0.9305555555555556\n",
            "AUC_1 Score = 0.9329680365296803\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nbsT1dyRIfQ",
        "outputId": "f5006790-4670-498d-b3d5-78e3ed387bb0"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\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.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "print(confusion_matrix(y_test_under, y_predMLPC, labels=[1,0]))"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[134  16]\n",
            " [  4 142]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "Zcyg5Ty3Q8nL",
        "outputId": "4460bcc7-9473-4ac6-cb4e-7dbb27f01201"
      },
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test_under, y_predMLPC, labels=[1,0])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['Normal Trx = 0','Fraud = 1'],normalize= False,  title='Confusion matrix')"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[134  16]\n",
            " [  4 142]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VgvNafSq0iW+QmVnulTCWdkZEDGrrpGKyJ1nLfqrTydbMcq3KT5C9KalfRLyelQmmZftfBVYoOK/Np0JdRjCz3KvQDbKWXAvskb3eg8+e/LwW+Ek2KmEj4O2CckOL3LM1s9yrRM9W0mXAEFJ9dyowGvgDcIWkEcBLwE7Z6TcC25KeCP0A2Kut9p1szSzfVJlZvyJil1YObdHCuQEc0J72nWzNLNeEH9c1M6uBxplsphgnWzPLvUaZRrEYJ1szy7W8TB7uZGtmueeerZlZDeQg1zrZmlnOuYxgZlZ9onHWGSum1WQr6a9Aq5MuRMTBVYnIzKydcpBri/ZsJ9YsCjOzDuie5zJCRIwtfC+pZ0R8UP2QzMxKpwo9rlttbc76JWmwpCdIS4Uj6euSzqh6ZGZmJereTUW3RlDKFIunAFsDMwEi4hFgs2oGZWbWHlWcYrFiShqNEBGvNOumz6tOOGZm7SPSiIRGV0qyfUXSxkBIWhA4BHiyumGZmZVIjVMqKKaUMsJ+pHkb+wOvAevSznkczcyqqVOUESJiBrBbDWIxM2s3kY+hX6WMRviKpOskTZc0TdI1kr5Si+DMzEohqejWCEopI1wKXAH0A5YDrgQuq2ZQZmalaquEUGqulXSopMclPSbpMkk9JK0s6T5JUySNk7RQuXGWkmx7RsRFETE32y4GepR7QTOzSusuFd3aIqk/cDAwKCLWBroDOwN/BE6OiFWBt4AR5cbYarKV1FtSb+AmSUdKWknSlyX9krSypJlZQ6hQGWEBYBFJCwA9gdeBzYF/ZMfHAsPLjbHYDbIHSRPRNEW6b8GxAI4q96JmZpWi0oZ+9ZFUON/LmIgY0/QmIl6VdCLwMjAHuJWUA2dHxNzstKmkUVllKTY3wsrlNmpmVksldF5nRMSg1j+vJYFhwMrAbNK9qW0qFR+U+ASZpLWBtSio1UbEhZUMxMysXBUYcbAl8EJETM/a+xewCdBL0gJZ73Z54NVyL9BmspU0GhhCSrY3AkOBewAnWzOruwqNs30Z2EhST1IZYQvSNLN3AD8ALgf2AK4p9wKljEb4QXbhNyJiL+DrwBLlXtDMrNLUxtaWiLiPdCPsIeBRUm4cAxwBHCZpCrAUcG65MZZSRpgTEfMlzZW0ODANWKHcC5qZVZJUmSfIImI0MLrZ7ueBDTvcOKUl24mSegFnk+7OvQdMqMTFzcwqoVGeEiumlLkR9s9eniXpZmDxiJhc3bDMzEqXg1xbdMHHgcWORcRD1QnJzKx0JY6zrbtiPduTihwL0pMVVkXrrbYc429vXkKyallygwPrHUKX8dHTr1S0vVyXESLi27UMxMysHIKS5j+ot5IeajAza2Q5qCI42ZpZ/jnZmplVWaXG2VZbKSs1SNKPJR2bvV9RUkUG+ZqZVUIe1iAr5XHdM4DBwC7Z+3eB06sWkZlZOwjoJhXdGkEpZYRvRMRASQ8DRMRbHVkawsys0ro3Rj4tqpRk+4mk7qSxtUhaGphf1ajMzEqkBuq9FlNKsj0NuApYRtLvSLOAHVPVqMzM2qF7KQXROitlboRLJD1ImmZRwPCIeLLqkZmZlaCpZtvoSpk8fEXgA+C6wn0R8XI1AzMzK1UOcm1JZYQb+Gzhxx6kNXqeBr5axbjMzEqjTvK4bkSsU/g+mw1s/1ZONzOrqVRGqHcUbWt3WTmbWvEbVYjFzKws3bup6FYKSb0k/UPSU5KelDRYUm9Jt0l6Nvt3yXJjLKVme1jB227AQOC1ci9oZlZJFezZngrcHBE/yJ4l6AkcDdweEX+QdCRwJGldsnYrpWe7WMG2MKmGO6yci5mZVVwbj+qWUs6VtASwGdmCjhHxcUTMJuW6sdlpY4Hh5YZZtGebPcywWESMKvcCZmbVJGCBjndtVwamA+dL+jppvcVDgL4R8Xp2zhtA33Iv0GrPVtICETEP2KTcxs3MaqGEnm0fSRMLtpHNmliAVCI9MyLWA94nlQw+FRFB9iRtOYr1bO/PLj5J0rXAlVkATRf+V7kXNTOrFKFShn7NiIhBRY5PBaZGxH3Z+3+Qku2bkvpFxOuS+gHTyo2zlHG2PYCZpDXHmsbbBuBka2b1p47fIIuINyS9Imn1iHia9MTsE9m2B/CH7N9ryr1GsWS7TDYS4TE+S7KfxlbuBc3MKq1Cj+seBFySjUR4HtiLVGq9QtII4CVgp3IbL5ZsuwOL8vkk28TJ1swagqjMSg0RMQloqdSwRYcbp3iyfT0ijq/ERczMqikHT+sWTbY5CN/Mujp1grkRKtJ1NjOrtsZPtUWSbUTMqmUgZmbl6DTz2ZqZNbo8zPrlZGtmOSfknq2ZWXWJ/N8gMzPLhcZPtU62ZpZ3wmUEM7NqcxnBzKxGGj/VOtmaWSeQg46tk62Z5ZvLCGZmNSGUg0KCk62Z5Zp7tmZmtVDiCrr1VspS5mYlmzdvHhsNWo8dh21f71A6hbNG78ZLt/+eiVce/YVjh+y+OXMe/htL9foSADsPHcT9447igSuO5o4LDmOd1frXOty66ehS5rXgZGsV9bfTTmX1NdesdxidxkXX3cuwA07/wv7l+/Zii43W5OXXP5uc78XXZrLVPqewwU7/x+/PvpnTj9mllqHWTVMZodjWCJxsrWKmTp3KzTfdwF5771PvUDqN8Q89x6y3P/jC/j+N+j6/OvVq0urayb2PvMDsd+cAcP/kF+jft1fN4qw3tfF/JbcjdZf0sKTrs/crS7pP0hRJ47L1ycriZGsV84vDf87vfv8nunXzf1bVtP2QdXht2mwefebVVs/Zc/jG3DL+iRpGVV/dpKJbOxwCPFnw/o/AyRGxKvAWMKLsGMv9YKOQNE/SpIJtpSpc40VJfTrYxhqSJkj6SNKoSsXWKG684XqWWXoZBq6/fr1D6dQW6bEgv9x7a44/84ZWz9ls0AD2GD6YY04te9XtXEmThxffSmpHWh7YDjgney9gc+Af2SljgeHlxtkZRiPMiYh1WzqQfVmKiPk1jqkls4CD6cD/sxrZhP+N5/rrr+Xmm2/kow8/5J133mGvn/yY8y+8uN6hdSpfWX5pvtx/Ke4fdxQA/ZfpxYRLj2DT3f/MmzPfZe0By3Hmsbsy7MAzmfX2+3WOtlZKKhX0kTSx4P2YiBjT7JxTgF8Ci2XvlwJmR8Tc7P1UoOy7jrnv2TYnaSVJT0u6EHgMWEHSmZImSnpc0m8Kzv20xyppkKQ7s9dLSbo1O/8cKvDodURMi4gHgE862lYjOuF3v+e5F6fy9JQXufCSyxny7c2daKvg8Smv8eUtjmKN7UazxnajeXXabAbv+kfenPkuKyy7JJef+FNG/PpCprw8rd6h1k4bvdqsZzsjIgYVbJ9LtJK2B6ZFxIPVCrMz9GwXkTQpe/0CcCgwANgjIu4FkPSriJglqTtwu6SvRcTkIm2OBu6JiOMlbUcrdRpJ44DVWzj0l4i4sJwfRtJIYCTACiuuWE4T1omM/f2ebLr+APr0WpQpN5/ACWfdyNirJ7R47lEjh9K715c45agfATB33ny+udufahluXVRoDbJNgO9J2hboASwOnAr0krRA1rtdHmi9UN5WnIV3M/NI0nsRsWjB+5WAOyJi5YJ9+5ES2AJAP+CgiLhc0ovAoIiYIWkQcGJEDMmS944R8Xz2+VnAahExowLxHge8FxEntnXu+usPivH3TWzrNKuQJTc4sN4hdBkfPX0F8z+YVpExWWuus16cf/UdRc8ZvOqSD0bEoFLakzQEGBUR20u6Evhnli/OAiZHxBnlxNkZerYt+bRYJWllYBSwQUS8JekC0m8ugLl8VkrpQTtVo2drZu1XxbkRjgAul/Rb4GHg3HIb6qzJttDipOT7tqS+wFDgzuzYi8D6wE3A9ws+cxewK/BbSUOBJVtqOCJ+VJ2Qzaw9KvncQkTcSZYjsr9uN6xEu50+2UbEI5IeBp4CXgHGFxz+DXCupBP4LAE37b9M0uPA/4CXOxqHpGWBiaTkP1/Sz4G1IuKdjrZt1tU1yENiReU+2RbWa7P3LwJrN9u3ZyufvRtYrYX9M4GtKhZkavMNUoHdzCpIVLWMUDG5T7Zm1sW148GFenKyNbP8c7I1M6s2r9RgZlZ1TXMjNDonWzPLPydbM7Pqq8DjulXnZGtmudf4qdbJ1szyTuQi2zrZmlmuVWjWr6pzsjWz3Gv8VOtka2adgNyzNTOrvhzkWidbM8u/HORaJ1szyzfhMoKZWfXJZQQzs5rIQa7tfEuZm1lXI6TiW5stSCtIukPSE5Iel3RItr+3pNskPZv92+ISWaVwsjWz3JOKbyWYCxweEWsBGwEHSFoLOBK4PSIGALdn78viZGtmuZZukHUs2UbE6xHxUPb6XeBJoD8wDBibnTYWGF5unK7ZmlnulTB5eB9JEwvej4mIMS22Ja0ErAfcB/SNiNezQ28AfcuN0cnWzHKvhN7rjIgY1HY7WhT4J/DziHinsN4bESEpyo3RZQQzy7dswcdiW0nNSAuSEu0lEfGvbPebkvplx/sB08oN08nWzDoBtbG18enUhT0XeDIi/lJw6Fpgj+z1HsA15UboMoKZ5VqF1iDbBNgdeFTSpGzf0cAfgCskjQBeAnYq9wJOtmaWex19giwi7qH1LvAWHWs9cbI1s9zzUuZmZjXguRHMzKqsHU+J1ZWTrZnlnqdYNDOrgcZPtU62ZtYJ5KBj62RrZvkmlIulzP0EmZlZDbhna2a5l4eerZOtmeWbh36ZmVVfaVPN1J+TrZnlnsfZmpnVQA5yrZOtmeWfk62ZWQ3kYdYvRZS9pI5VmaTppAmL86YPMKPeQXQRef2uvxwRS1eiIUk3k76HYmZExDaVuF65nGyt4iRNLGVxPes4f9f54SfIzMxqwMnWzKwGnGytGsbUO4AuxN91Trhma2ZWA+7ZmpnVgJOtmVkNONmadTHKw0QCnZCfILOGJmlDYA3gAWBaRMysc0i5Jakv8FZEfCxJ4Rs2NeWerTUsSdsDlwNDgCOBP0r6al2DyilJw4Frgd0l9YiIcA+3tjwawRqWpD8AD0fEuCzJbgN8CzgyIp6ob3T5Iak/KdE+DbwAPAOMi4gP3cOtHfdsrZEtCGwEEBGPAxcD/wUOlNSrnoHlzCzgp8C+pGQ7ENhZ0peyHq7zQA34S7ZGdhIwWNL+ABHxJnA7sGS2WQkiYg7weES8C5wHTCYl3B9mp6xSr9i6Eidba0iSukXEa8ARwNaSDgCIiElAd1KysBJFxEdZyWA+cCHwCLCKpHHA/yS1NWuWdZBrttbQJC0EfIPUy70PeIX0J/F3IuLFOoaWS4U1Wkn/JP3SGhYRk+sbWefnZGsNI+vNzs9ed4+IeQXH+pKSrICrI+LROoWZC8VufGU12hWBe4DtIuKRmgbXRTnZWkNoSq5ZUp0XETMKjn2ahK1tBd9lb6BfdnOxpfN6R8SsGofXZblma3VXkByWB/4DrFd4h9yJtnQF3+UKwI1Az6wU8wVOtLXlZGt11SzRXgkcQxqedIWkhT3wvnTNvsvLgRNJS+aM9lC5+nOytbrJ6opNvbBxwJ+BScClwKUR8ZEH3JcmK7UU/tI6CXgQuAa4LyJm1zVAc83W6kvSUsAdwG+Ah0iJ4jcRcZ2fbmqfrN59DfAn4GHSd3lcRFxf18AMcLK1OpM0AOhN+nP3H8CvnRxK0/yXkaS1gWWB50jf5XERcV294rPPc7K1mmhKDM3/zY4tCPwOmBARV9U30sYnaV3giYj4uIVjC5Eea77IibaxONlaTUkaGBEPtbB/8Yh4x6WDtkm6BvgSaYzsRy30cHu5Rtt4fIPMakJJP+BUST2bT34SEe9k/zrRtkJSd4CIGJbtOkfSQi1Ml/h27aOztjjZWtU0SwALkpLAYsAiETHfw7rap+mJOkm7Ai8BWwG3NE+4/oXVmJxsrWoKarJDSEORtgKmARtIWtRJof0kbUkauXEUsBrwPnC9pAX9fTY212yt4iR9BZiR1WB/CgwC7iZN5fcz4B3gTeBJ0o2eU+oWbM5IGgTsFxH7FOx7ApgObBURH9UtOCvKa5BZRUnaDjgW2FLSMGAHYJ9sukQkvQwsAdwELENKutaG7LucThrWtZqkDSPi/uzwecCPSEPoXq9TiNYGJ1urGIqdfd8AAAkiSURBVElbA38Bvk/6b+sA4NWmRJt5jvQn8BkR8XTto8yHFobGrQL8AvgxqSRzlqRLSAl2I2CHiHCibWCu2VpFZIn2bODDbNf7wHXAqpK+X3DqHOADwPWrIgrrrxHxCXAOab6D80hPh/0MmEdKtgdHxNR6xGmlc83WOiy7AXYyaRKZhUjzzv4+Iu6WNBLYELih6YEFSf3cC2tb9ktqeETsnr1fFBhJWvjycM/pmy/u2VolzAf2jYgbgLuAfwFHStqE1CObQFpgcDiAE23LWhgKdzOwuqSzACLiPeDfQE/gt5J6eLHG/HDP1ioum87v+8COwG9Jy9nsDtzqRNuyZjXaDUgTqD8kqQdwC/AUcBDwA2AD4I8R8UbdArZ2c7K1smQLBL6d1RNbOt6LNBJhBOlP3vtqGV9eSRoFbA+8B0whzUk7HbiCVO/eEPhua6svWOPyaARrl+xP3X7AJcApkm6IiLnNz4uI2dkz/HOB15oft6RZj3ZH0kKWQySdCgwllWhOIv3iWgogIqbXK14rn3u2VhZJI4CdgNOAW1pKuNl5Xj+sFc0S7WrAosBbwNbAcOAQ0rLjLwJ/iogH6xSqVYB7tlaywmfvI+JcSfOAw7NjLSZcJ9qWZY/XfpK9/hEwCvgmaUjcusD/RcTTku4E+pISruWYk62VpFkvbPmImBoRF0j6mDYSrn2epO+Saq+/lrQDcBiwV9OjttlTdqdJugL4NrBzRMysW8BWEU62VpKCRHso8C1J7wAXRMSlkj4h/cm7kKTrnHBbJ+k7wAnAEdmumcCapJEbj2X7TgdmkxLyXhHxfK3jtMpzzdZKJmkv4Cek2bvGk+6Yn5Ml3J+QhiXtEhHv1zHMhiVpc+BaYGBEPCNpJeDrpCWB/g6cFhFjCs7/tNRg+ecB0daqphqtpG7ZxNVLkZLtAaQe2VXAKEl7RsSFwG5OtEXNABYBVsq+z0uB/hExnvSd7iPp4KaTnWg7F/dsrUXNarSLRsR7WfJdGjgvIrbPjt0L3Asc27TagrUue2DhVtK8BgdFxGUFx7YEfg0MI41h9v84OxHXbK1FBYl2X+C7km4C/klabWGprHb7Emlu2pOcaEsTEQ9I2oz0WHPTd9wtO/ZvSeMjYk49Y7TqcLK1zykcFyvpm8AupElmRpDKCJeQbu4cRpqPdt+IeKVO4eZSRDwqaSvg1mxxxrMKjjnRdlIuI9inmpUONgGWBXpGxEWS1gZ+TpqP9mrSs/pLeBXX8klaH3gAGBER59c7HqsuJ1sDvpBof0pa52oiaWLqDSLiJUlrAscBk0hPNM2rV7ydhaT1gA88kXrn52RrzRPtYNLsUkdExCuSRgPbkoZ0PS9pAPCeZ+8yax/XbLu4Zol2K2BPYGXga5JejYjfSJoP3Cxpq4h4to7hmuWWk20XV5Bomx5WOIG00sLGpNm6Ho6IE7LHcptPbm1mJXIZwZC0BbAPcHZE/EfS8qRFGWcDV0fEA3UN0KwT8BNkXVDBk2FNPdV1gFWBjbOhSFNJKywsDwyVtHB9IjXrPNyz7WKa1WhXB16PiHeyxQW/B1wJ/Dci3pW0LICXXzHrONdsu5iCRLs/sDfwrKTewHeBhUmzTy0k6VYnWbPKcRmhi5C0WMHrTUlLYv8A2IO01tU9wDhgMmkOVTOrICfbLkDSKqSJqjfIds0GJkTEi8AnEXEA8DxpIcFTgNHZstlmViFOtl3DEqSFA3eQtC5pesStJG1fMLPUa8CSABExqz5hmnVevkHWiWUjC2Znr78K7EyaT/VE0uiDq0grt3YHvk9afuWZOoVr1qm5Z9tJZXOj3i/p1Kx8MIu03Mp7pCVspgDfIfV4FyNN/O1Ea1Yl7tl2Ulm54F7gY+BoUoL9I7AGMJ00PeIpnh7RrDY89KuTiohJkgYC/yVN8L0VaZTB+qQa7rpAN0lHkG6S+beuWRW5Z9vJZSWEfwOHZEuPdyctMrgVcE1EPFnXAM26CCfbLqBg3atfRcQZ9Y7HrCtyGaELyNa92hJ4QNKHEXFevWMy62rcs+1CvCqAWf042ZqZ1YDH2ZqZ1YCTrZlZDTjZmpnVgJOtmVkNONmamdWAk63VhaR5kiZJekzSlZJ6dqCtCyT9IHt9jqS1ipw7RNLGZVzjRUl9St3f7Jx2zQ0s6ThJo9obozU2J1urlzkRsW5ErE2aLGe/woOSynrgJiL2iYgnipwyhLRMu1lNOdlaI7gbWDXrdd4t6VrgCUndJf1Z0gOSJkvaF9KilZL+JulpSf8mzWBGduxOSYOy19tIekjSI5Jul7QSKakfmvWqN5W0tKR/Ztd4QNIm2WeXknSrpMclnQOINki6WtKD2WdGNjt2crb/dklLZ/tWkXRz9pm7Ja1RiS/TGpMf17W6ynqwQ4Gbs10DgbUj4oUsYb0dERtky6mPl3QrsB6wOrAW0Bd4AjivWbtLA2cDm2Vt9Y6IWZLOAt6LiBOz8y4FTo6IeyStCNwCrAmMBu6JiOMlbQeMKOHH2Tu7xiKkR6P/GREzgS8BEyPiUEnHZm0fCIwB9ouIZyV9AzgD2LyMr9FywMnW6mURSZOy13cD55L+vL8/Il7I9m8FfK2pHkuaGnIAsBlwWUTMA16T9J8W2t8IuKuprSJL/WwJrCV92nFdXNKi2TV2zD57g6S3SviZDpa0Q/Z6hSzWmaQJ2sdl+y8G/pVdY2PgyoJrL1zCNSynnGytXuZExLqFO7Kk837hLuCgiLil2XnbVjCObsBGEfFhC7GUTNIQUuIeHBEfSLoT6NHK6ZFdd3bz78A6L9dsrZHdAvxM0oIAklaT9CXgLuBHWU23Hy0vvX4vsJmklbPP9s72v0taBqjJrcBBTW+yFS7IrrFrtm8o2WKYRSwBvJUl2jVIPesm3UjLxpO1eU9EvAO8IOmH2TUk6ettXMNyzMnWGtk5pHrsQ5IeA/5O+mvsKuDZ7NiFwITmH4yI6cBI0p/sj/DZn/HXkVYZniRpU+BgYFB2A+4JPhsV8RtSsn6cVE54uY1YbwYWkPQk8AdSsm/yPrBh9jNsDhyf7d8NGJHF9zgwrITvxHLKs36ZmdWAe7ZmZjXgZGtmVgNOtmZmNeBka2ZWA062ZmY14GRrZlYDTrZmZjXw/6ULbpUoS3oMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3bnWVsVA4wH"
      },
      "source": [
        "### 6. AutoEncoder "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2c4ZnRJZSFN"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zd6OcIo4S90O"
      },
      "source": [
        "sc=StandardScaler()\n",
        "data['Amount'] = sc.fit_transform(data['Amount'].values.reshape(-1, 1))"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyGK2im_S937"
      },
      "source": [
        "raw_data = data.values\n",
        "# The last element contains if the transaction is normal which is represented by a 0 and if fraud then 1\n",
        "labels = raw_data[:, -1]\n",
        "data = raw_data[:, 0:-1]\n",
        "train_data, test_data, train_labels, test_labels = train_test_split(\n",
        "    data, labels, test_size=0.3, random_state=42)"
      ],
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeq5ZgUeS96r"
      },
      "source": [
        "#same with MinMaxScaler for each train and test data separately\n",
        "min_val = tf.reduce_min(train_data)\n",
        "max_val = tf.reduce_max(train_data)\n",
        "\n",
        "train_data = (train_data - min_val) / (max_val - min_val)\n",
        "test_data = (test_data - min_val) / (max_val - min_val)\n",
        "train_data = tf.cast(train_data, tf.float32)\n",
        "test_data = tf.cast(test_data, tf.float32)"
      ],
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pWqxofvZZZg",
        "outputId": "ba080195-87ca-4197-ae51-41f7e215ec3a"
      },
      "source": [
        "train_labels = train_labels.astype(bool) \n",
        "test_labels = test_labels.astype(bool)\n",
        "\n",
        "#creating normal and fraud datasets\n",
        "normal_train_data = train_data[~train_labels]\n",
        "normal_test_data = test_data[~test_labels]\n",
        "\n",
        "fraud_train_data = train_data[train_labels]\n",
        "fraud_test_data = test_data[test_labels]\n",
        "\n",
        "print(\" No. of records in Fraud Train Data=\", len(fraud_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(fraud_test_data))\n",
        "print(\" No. of records in Normal Test data=\",len(normal_test_data))"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " No. of records in Fraud Train Data= 356\n",
            " No. of records in Normal Train data= 199008\n",
            " No. of records in Fraud Test Data= 136\n",
            " No. of records in Normal Test data= 85307\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqzKAaGiA7q3"
      },
      "source": [
        "nb_epoch = 10\n",
        "batch_size = 64\n",
        "input_dim = 30 #normal_train_data.shape[1] #num of columns, 30\n",
        "encoding_dim = 14\n",
        "hidden_dim_1 = 7\n",
        "learning_rate = 1e-7"
      ],
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "te8VVufES98y",
        "outputId": "acdc0def-1378-487b-a82c-7cd1013a46d8"
      },
      "source": [
        "#input Layer\n",
        "input_layer = tf.keras.layers.Input(shape=(input_dim, ))\n",
        "#Encoder\n",
        "encoder = tf.keras.layers.Dense(encoding_dim, activation=\"relu\",                               \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='sigmoid')(encoder)\n",
        "\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='sigmoid')(decoder)\n",
        "\n",
        "#Autoencoder\n",
        "autoencoder = tf.keras.Model(inputs=input_layer, outputs=decoder)\n",
        "autoencoder.summary()"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 30)]              0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 14)                434       \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 14)                0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 7)                 105       \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 7)                 56        \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 7)                 0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 14)                112       \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 30)                450       \n",
            "=================================================================\n",
            "Total params: 1,157\n",
            "Trainable params: 1,157\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLFr3L9DZ302"
      },
      "source": [
        "autoencoder.compile(loss='mean_squared_error',\n",
        "                    optimizer='adam')"
      ],
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HldVDT0Z33g",
        "outputId": "61ed0083-ed9f-4245-d3a3-0038fc20974b"
      },
      "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='auto').history"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "3110/3110 [==============================] - 7s 2ms/step - loss: 0.0095 - val_loss: 2.6822e-04\n",
            "Epoch 2/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 2.2706e-04 - val_loss: 3.6571e-04\n",
            "Epoch 3/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 1.5518e-04 - val_loss: 4.3644e-04\n",
            "Epoch 4/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 1.2053e-04 - val_loss: 3.9662e-04\n",
            "Epoch 5/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 9.9638e-05 - val_loss: 5.1097e-04\n",
            "Epoch 6/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 8.6279e-05 - val_loss: 6.4745e-04\n",
            "Epoch 7/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 7.3564e-05 - val_loss: 8.6000e-04\n",
            "Epoch 8/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 5.0765e-05 - val_loss: 0.0015\n",
            "Epoch 9/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 4.3776e-05 - val_loss: 0.0017\n",
            "Epoch 10/10\n",
            "3110/3110 [==============================] - 6s 2ms/step - loss: 3.9055e-05 - val_loss: 0.0019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "AekvQc_Vaoe8",
        "outputId": "d43590d5-3087-4a90-895d-4afc8575c2fc"
      },
      "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": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "po5HMOIBdQrU",
        "outputId": "58e7ebca-cc1c-40bb-d515-d528ef81c936"
      },
      "source": [
        "reconstructions = autoencoder.predict(normal_train_data)\n",
        "train_loss = tf.keras.losses.mae(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": 244,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42YL8MwxdX66",
        "outputId": "af04f783-276f-4526-ea16-31f719cd083b"
      },
      "source": [
        "threshold = np.mean(train_loss) + np.std(train_loss)\n",
        "print(\"Threshold: \", threshold)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Threshold:  0.010921288\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "bIuPs0cEdfj4",
        "outputId": "8abb2f2f-e77d-4a61-99a7-5e0f90b2b953"
      },
      "source": [
        "anomalous_train_data = train_data[~train_labels]\n",
        "anomalous_test_data = test_data[~test_labels]\n",
        "\n",
        "reconstructions = autoencoder.predict(anomalous_test_data)\n",
        "test_loss = tf.keras.losses.mae(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": 246,
      "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": "ecGK3qCZdp_D"
      },
      "source": [
        "def predict(model, data, threshold):\n",
        "  reconstructions = model(data)\n",
        "  loss = tf.keras.losses.mae(reconstructions, data)\n",
        "  return tf.math.less(loss, threshold)\n"
      ],
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNpUeUqYRtqr",
        "outputId": "7fdc29f6-d3dd-4d94-a07a-aebc3f395051"
      },
      "source": [
        "print(classification_report(test_labels, preds))"
      ],
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       1.00      0.10      0.18     85307\n",
            "        True       0.00      0.90      0.00       136\n",
            "\n",
            "    accuracy                           0.10     85443\n",
            "   macro avg       0.50      0.50      0.09     85443\n",
            "weighted avg       1.00      0.10      0.18     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "sXqiXksl7z8w",
        "outputId": "5472825a-292e-4573-f111-833c45e23267"
      },
      "source": [
        "fpr1, tpr1, thresholds = roc_curve(test_labels, preds, pos_label=1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "fig, ax = plt.subplots(figsize=(7.5, 7.5))\n",
        " \n",
        "plt.plot(fpr1, tpr1, label='ROC Curve AutoEncoder (AUC = %0.2f)' % (roc_auc1))\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red', label='Random Classifier')   \n",
        "plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='green', label='Perfect Classifier')\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXxLEz1bd7Za",
        "outputId": "15fd9802-ba97-452e-b10a-4edcb9277459"
      },
      "source": [
        "preds = predict(autoencoder, test_data, threshold)\n",
        "eval_met6 = print_stats(test_labels, preds)\n",
        "eval_met6"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.27106960195686014\n",
            "Precision = 0.001875601154216095\n",
            "Recall = 0.8602941176470589\n",
            "F1 Score = 0.003743041781303986\n",
            "AUC_1 Score = 0.500343784283615\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocY73oepFHOV"
      },
      "source": [
        "# Evaluation Matrix Comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Br3WM42TLHve"
      },
      "source": [
        "Here are the evaluation matrix result from six models examined: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MB9sR5zQLBBQ"
      },
      "source": [
        "| Algorithm            | Accuracy| Precision|Recall |F1 Score  |AUC Score |\n",
        "|----------------------|---------|----------|-------|----------|--------- |\n",
        "| Dummy Classifier     | 0.99    | 0.00     | 0.00  | 0.00     | 0.5 \n",
        "| Vanilla Log Reg      | 0.99    | 0.88     | 0.53  | 0.66     | 0.96\n",
        "| Log Reg + Ridge      | 0.97    | 0.99     | 0.95  | 0.97     | 0.99\n",
        "| SMOTE + Log Reg      | 0.96    | 0.98     | 0.93  | 0.96     | 0.96\n",
        "| Undersampling + MLPC | 0.93    | 0.97     | 0.89  | 0.93     | 0.93\n",
        "| Auto Encoder         | 0.27    | 0.002    | 0.8   | 0.003    | 0.5"
      ]
    }
  ]
}