{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shayaf84/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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsPQAdhlisSg",
        "outputId": "1b022646-7fc1-4edc-e13f-69ba28487287"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from imblearn.over_sampling import BorderlineSMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive/', force_remount=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tY6Vxi8X-Fwf",
        "outputId": "74156519-99a6-49c4-f9bd-1a491609f9c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creditcard.csv\n"
          ]
        }
      ],
      "source": [
        "!ls \"/content/drive/MyDrive/Credit_Card\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wir7KI3um4pQ"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Credit_Card/creditcard.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5Ok114Xosaf",
        "outputId": "247f6c50-fc2b-4592-820e-71a473c0459e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
            "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
            "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
            "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
            "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
            "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
            "\n",
            "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
            "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
            "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
            "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
            "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
            "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
            "\n",
            "        V26       V27       V28  Amount  Class  \n",
            "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
            "1  0.125895 -0.008983  0.014724    2.69      0  \n",
            "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
            "3 -0.221929  0.062723  0.061458  123.50      0  \n",
            "4  0.502292  0.219422  0.215153   69.99      0  \n",
            "\n",
            "[5 rows x 31 columns] \n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "print(data.head(),\"\\n\"*3)\n",
        "\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjA8km6Towa4",
        "outputId": "eedb2338-bcf1-4d88-ff20-a2b0e15d24d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "3LdxVG-Noyfb",
        "outputId": "40fee0e0-74d3-40a4-e9da-6f9020b06411"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(x=data['Class'])\n",
        "data[\"Class\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "HE27u1Looyky",
        "outputId": "64d05722-6540-4ade-d1f1-fdfb4abbf9e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7434011-6486-460b-ae47-896f3dd452f4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</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>284807.000000</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>...</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>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\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>...</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>0.000000</td>\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>...</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>54201.500000</td>\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>...</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>84692.000000</td>\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>...</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>139320.500000</td>\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>...</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>172792.000000</td>\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>...</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",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7434011-6486-460b-ae47-896f3dd452f4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7434011-6486-460b-ae47-896f3dd452f4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7434011-6486-460b-ae47-896f3dd452f4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CQCPvxDo5dA",
        "outputId": "47874a73-0ce8-4745-eb4f-e0e26e1dcf3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LRAH6L95o-je"
      },
      "outputs": [],
      "source": [
        "fraud = data[data[\"Class\"] == 1]\n",
        "normal = data[data[\"Class\"] == 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "hUHu1D0TpEbx",
        "outputId": "55e3405f-10fd-4c1d-cce9-1e98a11449c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time          V1          V2          V3          V4  \\\n",
              "count     492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean    80746.806911   -4.771948    3.623778   -7.033281    4.542029   \n",
              "std     47835.365138    6.783687    4.291216    7.110937    2.873318   \n",
              "min       406.000000  -30.552380   -8.402154  -31.103685   -1.313275   \n",
              "25%     41241.500000   -6.036063    1.188226   -8.643489    2.373050   \n",
              "50%     75568.500000   -2.342497    2.717869   -5.075257    4.177147   \n",
              "75%    128483.000000   -0.419200    4.971257   -2.276185    6.348729   \n",
              "max    170348.000000    2.132386   22.057729    2.250210   12.114672   \n",
              "\n",
              "               V5          V6          V7          V8          V9  ...  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  ...   \n",
              "mean    -3.151225   -1.397737   -5.568731    0.570636   -2.581123  ...   \n",
              "std      5.372468    1.858124    7.206773    6.797831    2.500896  ...   \n",
              "min    -22.105532   -6.406267  -43.557242  -41.044261  -13.434066  ...   \n",
              "25%     -4.792835   -2.501511   -7.965295   -0.195336   -3.872383  ...   \n",
              "50%     -1.522962   -1.424616   -3.034402    0.621508   -2.208768  ...   \n",
              "75%      0.214562   -0.413216   -0.945954    1.764879   -0.787850  ...   \n",
              "max     11.095089    6.474115    5.802537   20.007208    3.353525  ...   \n",
              "\n",
              "              V21         V22         V23         V24         V25         V26  \\\n",
              "count  492.000000  492.000000  492.000000  492.000000  492.000000  492.000000   \n",
              "mean     0.713588    0.014049   -0.040308   -0.105130    0.041449    0.051648   \n",
              "std      3.869304    1.494602    1.579642    0.515577    0.797205    0.471679   \n",
              "min    -22.797604   -8.887017  -19.254328   -2.028024   -4.781606   -1.152671   \n",
              "25%      0.041787   -0.533764   -0.342175   -0.436809   -0.314348   -0.259416   \n",
              "50%      0.592146    0.048434   -0.073135   -0.060795    0.088371    0.004321   \n",
              "75%      1.244611    0.617474    0.308378    0.285328    0.456515    0.396733   \n",
              "max     27.202839    8.361985    5.466230    1.091435    2.208209    2.745261   \n",
              "\n",
              "              V27         V28       Amount  Class  \n",
              "count  492.000000  492.000000   492.000000  492.0  \n",
              "mean     0.170575    0.075667   122.211321    1.0  \n",
              "std      1.376766    0.547291   256.683288    0.0  \n",
              "min     -7.263482   -1.869290     0.000000    1.0  \n",
              "25%     -0.020025   -0.108868     1.000000    1.0  \n",
              "50%      0.394926    0.146344     9.250000    1.0  \n",
              "75%      0.826029    0.381152   105.890000    1.0  \n",
              "max      3.052358    1.779364  2125.870000    1.0  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d18f003c-c0b1-487d-b28d-57574c7e3ec3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</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>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>492.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>80746.806911</td>\n",
              "      <td>-4.771948</td>\n",
              "      <td>3.623778</td>\n",
              "      <td>-7.033281</td>\n",
              "      <td>4.542029</td>\n",
              "      <td>-3.151225</td>\n",
              "      <td>-1.397737</td>\n",
              "      <td>-5.568731</td>\n",
              "      <td>0.570636</td>\n",
              "      <td>-2.581123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.713588</td>\n",
              "      <td>0.014049</td>\n",
              "      <td>-0.040308</td>\n",
              "      <td>-0.105130</td>\n",
              "      <td>0.041449</td>\n",
              "      <td>0.051648</td>\n",
              "      <td>0.170575</td>\n",
              "      <td>0.075667</td>\n",
              "      <td>122.211321</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47835.365138</td>\n",
              "      <td>6.783687</td>\n",
              "      <td>4.291216</td>\n",
              "      <td>7.110937</td>\n",
              "      <td>2.873318</td>\n",
              "      <td>5.372468</td>\n",
              "      <td>1.858124</td>\n",
              "      <td>7.206773</td>\n",
              "      <td>6.797831</td>\n",
              "      <td>2.500896</td>\n",
              "      <td>...</td>\n",
              "      <td>3.869304</td>\n",
              "      <td>1.494602</td>\n",
              "      <td>1.579642</td>\n",
              "      <td>0.515577</td>\n",
              "      <td>0.797205</td>\n",
              "      <td>0.471679</td>\n",
              "      <td>1.376766</td>\n",
              "      <td>0.547291</td>\n",
              "      <td>256.683288</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>406.000000</td>\n",
              "      <td>-30.552380</td>\n",
              "      <td>-8.402154</td>\n",
              "      <td>-31.103685</td>\n",
              "      <td>-1.313275</td>\n",
              "      <td>-22.105532</td>\n",
              "      <td>-6.406267</td>\n",
              "      <td>-43.557242</td>\n",
              "      <td>-41.044261</td>\n",
              "      <td>-13.434066</td>\n",
              "      <td>...</td>\n",
              "      <td>-22.797604</td>\n",
              "      <td>-8.887017</td>\n",
              "      <td>-19.254328</td>\n",
              "      <td>-2.028024</td>\n",
              "      <td>-4.781606</td>\n",
              "      <td>-1.152671</td>\n",
              "      <td>-7.263482</td>\n",
              "      <td>-1.869290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41241.500000</td>\n",
              "      <td>-6.036063</td>\n",
              "      <td>1.188226</td>\n",
              "      <td>-8.643489</td>\n",
              "      <td>2.373050</td>\n",
              "      <td>-4.792835</td>\n",
              "      <td>-2.501511</td>\n",
              "      <td>-7.965295</td>\n",
              "      <td>-0.195336</td>\n",
              "      <td>-3.872383</td>\n",
              "      <td>...</td>\n",
              "      <td>0.041787</td>\n",
              "      <td>-0.533764</td>\n",
              "      <td>-0.342175</td>\n",
              "      <td>-0.436809</td>\n",
              "      <td>-0.314348</td>\n",
              "      <td>-0.259416</td>\n",
              "      <td>-0.020025</td>\n",
              "      <td>-0.108868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75568.500000</td>\n",
              "      <td>-2.342497</td>\n",
              "      <td>2.717869</td>\n",
              "      <td>-5.075257</td>\n",
              "      <td>4.177147</td>\n",
              "      <td>-1.522962</td>\n",
              "      <td>-1.424616</td>\n",
              "      <td>-3.034402</td>\n",
              "      <td>0.621508</td>\n",
              "      <td>-2.208768</td>\n",
              "      <td>...</td>\n",
              "      <td>0.592146</td>\n",
              "      <td>0.048434</td>\n",
              "      <td>-0.073135</td>\n",
              "      <td>-0.060795</td>\n",
              "      <td>0.088371</td>\n",
              "      <td>0.004321</td>\n",
              "      <td>0.394926</td>\n",
              "      <td>0.146344</td>\n",
              "      <td>9.250000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>128483.000000</td>\n",
              "      <td>-0.419200</td>\n",
              "      <td>4.971257</td>\n",
              "      <td>-2.276185</td>\n",
              "      <td>6.348729</td>\n",
              "      <td>0.214562</td>\n",
              "      <td>-0.413216</td>\n",
              "      <td>-0.945954</td>\n",
              "      <td>1.764879</td>\n",
              "      <td>-0.787850</td>\n",
              "      <td>...</td>\n",
              "      <td>1.244611</td>\n",
              "      <td>0.617474</td>\n",
              "      <td>0.308378</td>\n",
              "      <td>0.285328</td>\n",
              "      <td>0.456515</td>\n",
              "      <td>0.396733</td>\n",
              "      <td>0.826029</td>\n",
              "      <td>0.381152</td>\n",
              "      <td>105.890000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>170348.000000</td>\n",
              "      <td>2.132386</td>\n",
              "      <td>22.057729</td>\n",
              "      <td>2.250210</td>\n",
              "      <td>12.114672</td>\n",
              "      <td>11.095089</td>\n",
              "      <td>6.474115</td>\n",
              "      <td>5.802537</td>\n",
              "      <td>20.007208</td>\n",
              "      <td>3.353525</td>\n",
              "      <td>...</td>\n",
              "      <td>27.202839</td>\n",
              "      <td>8.361985</td>\n",
              "      <td>5.466230</td>\n",
              "      <td>1.091435</td>\n",
              "      <td>2.208209</td>\n",
              "      <td>2.745261</td>\n",
              "      <td>3.052358</td>\n",
              "      <td>1.779364</td>\n",
              "      <td>2125.870000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d18f003c-c0b1-487d-b28d-57574c7e3ec3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d18f003c-c0b1-487d-b28d-57574c7e3ec3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d18f003c-c0b1-487d-b28d-57574c7e3ec3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "fraud.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "TrZFcF2spHN_",
        "outputId": "4f512d8f-763e-4384-d0fd-e3504e4db0a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time             V1             V2             V3  \\\n",
              "count  284315.000000  284315.000000  284315.000000  284315.000000   \n",
              "mean    94838.202258       0.008258      -0.006271       0.012171   \n",
              "std     47484.015786       1.929814       1.636146       1.459429   \n",
              "min         0.000000     -56.407510     -72.715728     -48.325589   \n",
              "25%     54230.000000      -0.917544      -0.599473      -0.884541   \n",
              "50%     84711.000000       0.020023       0.064070       0.182158   \n",
              "75%    139333.000000       1.316218       0.800446       1.028372   \n",
              "max    172792.000000       2.454930      18.902453       9.382558   \n",
              "\n",
              "                  V4             V5             V6             V7  \\\n",
              "count  284315.000000  284315.000000  284315.000000  284315.000000   \n",
              "mean       -0.007860       0.005453       0.002419       0.009637   \n",
              "std         1.399333       1.356952       1.329913       1.178812   \n",
              "min        -5.683171    -113.743307     -26.160506     -31.764946   \n",
              "25%        -0.850077      -0.689398      -0.766847      -0.551442   \n",
              "50%        -0.022405      -0.053457      -0.273123       0.041138   \n",
              "75%         0.737624       0.612181       0.399619       0.571019   \n",
              "max        16.875344      34.801666      73.301626     120.589494   \n",
              "\n",
              "                  V8             V9  ...            V21            V22  \\\n",
              "count  284315.000000  284315.000000  ...  284315.000000  284315.000000   \n",
              "mean       -0.000987       0.004467  ...      -0.001235      -0.000024   \n",
              "std         1.161283       1.089372  ...       0.716743       0.723668   \n",
              "min       -73.216718      -6.290730  ...     -34.830382     -10.933144   \n",
              "25%        -0.208633      -0.640412  ...      -0.228509      -0.542403   \n",
              "50%         0.022041      -0.049964  ...      -0.029821       0.006736   \n",
              "75%         0.326200       0.598230  ...       0.185626       0.528407   \n",
              "max        18.709255      15.594995  ...      22.614889      10.503090   \n",
              "\n",
              "                 V23            V24            V25            V26  \\\n",
              "count  284315.000000  284315.000000  284315.000000  284315.000000   \n",
              "mean        0.000070       0.000182      -0.000072      -0.000089   \n",
              "std         0.621541       0.605776       0.520673       0.482241   \n",
              "min       -44.807735      -2.836627     -10.295397      -2.604551   \n",
              "25%        -0.161702      -0.354425      -0.317145      -0.327074   \n",
              "50%        -0.011147       0.041082       0.016417      -0.052227   \n",
              "75%         0.147522       0.439869       0.350594       0.240671   \n",
              "max        22.528412       4.584549       7.519589       3.517346   \n",
              "\n",
              "                 V27            V28         Amount     Class  \n",
              "count  284315.000000  284315.000000  284315.000000  284315.0  \n",
              "mean       -0.000295      -0.000131      88.291022       0.0  \n",
              "std         0.399847       0.329570     250.105092       0.0  \n",
              "min       -22.565679     -15.430084       0.000000       0.0  \n",
              "25%        -0.070852      -0.052950       5.650000       0.0  \n",
              "50%         0.001230       0.011199      22.000000       0.0  \n",
              "75%         0.090573       0.077962      77.050000       0.0  \n",
              "max        31.612198      33.847808   25691.160000       0.0  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59e66047-b38a-4898-93c1-dceee4c7ec9c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</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>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.000000</td>\n",
              "      <td>284315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94838.202258</td>\n",
              "      <td>0.008258</td>\n",
              "      <td>-0.006271</td>\n",
              "      <td>0.012171</td>\n",
              "      <td>-0.007860</td>\n",
              "      <td>0.005453</td>\n",
              "      <td>0.002419</td>\n",
              "      <td>0.009637</td>\n",
              "      <td>-0.000987</td>\n",
              "      <td>0.004467</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001235</td>\n",
              "      <td>-0.000024</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>-0.000072</td>\n",
              "      <td>-0.000089</td>\n",
              "      <td>-0.000295</td>\n",
              "      <td>-0.000131</td>\n",
              "      <td>88.291022</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47484.015786</td>\n",
              "      <td>1.929814</td>\n",
              "      <td>1.636146</td>\n",
              "      <td>1.459429</td>\n",
              "      <td>1.399333</td>\n",
              "      <td>1.356952</td>\n",
              "      <td>1.329913</td>\n",
              "      <td>1.178812</td>\n",
              "      <td>1.161283</td>\n",
              "      <td>1.089372</td>\n",
              "      <td>...</td>\n",
              "      <td>0.716743</td>\n",
              "      <td>0.723668</td>\n",
              "      <td>0.621541</td>\n",
              "      <td>0.605776</td>\n",
              "      <td>0.520673</td>\n",
              "      <td>0.482241</td>\n",
              "      <td>0.399847</td>\n",
              "      <td>0.329570</td>\n",
              "      <td>250.105092</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-56.407510</td>\n",
              "      <td>-72.715728</td>\n",
              "      <td>-48.325589</td>\n",
              "      <td>-5.683171</td>\n",
              "      <td>-113.743307</td>\n",
              "      <td>-26.160506</td>\n",
              "      <td>-31.764946</td>\n",
              "      <td>-73.216718</td>\n",
              "      <td>-6.290730</td>\n",
              "      <td>...</td>\n",
              "      <td>-34.830382</td>\n",
              "      <td>-10.933144</td>\n",
              "      <td>-44.807735</td>\n",
              "      <td>-2.836627</td>\n",
              "      <td>-10.295397</td>\n",
              "      <td>-2.604551</td>\n",
              "      <td>-22.565679</td>\n",
              "      <td>-15.430084</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54230.000000</td>\n",
              "      <td>-0.917544</td>\n",
              "      <td>-0.599473</td>\n",
              "      <td>-0.884541</td>\n",
              "      <td>-0.850077</td>\n",
              "      <td>-0.689398</td>\n",
              "      <td>-0.766847</td>\n",
              "      <td>-0.551442</td>\n",
              "      <td>-0.208633</td>\n",
              "      <td>-0.640412</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.228509</td>\n",
              "      <td>-0.542403</td>\n",
              "      <td>-0.161702</td>\n",
              "      <td>-0.354425</td>\n",
              "      <td>-0.317145</td>\n",
              "      <td>-0.327074</td>\n",
              "      <td>-0.070852</td>\n",
              "      <td>-0.052950</td>\n",
              "      <td>5.650000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84711.000000</td>\n",
              "      <td>0.020023</td>\n",
              "      <td>0.064070</td>\n",
              "      <td>0.182158</td>\n",
              "      <td>-0.022405</td>\n",
              "      <td>-0.053457</td>\n",
              "      <td>-0.273123</td>\n",
              "      <td>0.041138</td>\n",
              "      <td>0.022041</td>\n",
              "      <td>-0.049964</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.029821</td>\n",
              "      <td>0.006736</td>\n",
              "      <td>-0.011147</td>\n",
              "      <td>0.041082</td>\n",
              "      <td>0.016417</td>\n",
              "      <td>-0.052227</td>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139333.000000</td>\n",
              "      <td>1.316218</td>\n",
              "      <td>0.800446</td>\n",
              "      <td>1.028372</td>\n",
              "      <td>0.737624</td>\n",
              "      <td>0.612181</td>\n",
              "      <td>0.399619</td>\n",
              "      <td>0.571019</td>\n",
              "      <td>0.326200</td>\n",
              "      <td>0.598230</td>\n",
              "      <td>...</td>\n",
              "      <td>0.185626</td>\n",
              "      <td>0.528407</td>\n",
              "      <td>0.147522</td>\n",
              "      <td>0.439869</td>\n",
              "      <td>0.350594</td>\n",
              "      <td>0.240671</td>\n",
              "      <td>0.090573</td>\n",
              "      <td>0.077962</td>\n",
              "      <td>77.050000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930</td>\n",
              "      <td>18.902453</td>\n",
              "      <td>9.382558</td>\n",
              "      <td>16.875344</td>\n",
              "      <td>34.801666</td>\n",
              "      <td>73.301626</td>\n",
              "      <td>120.589494</td>\n",
              "      <td>18.709255</td>\n",
              "      <td>15.594995</td>\n",
              "      <td>...</td>\n",
              "      <td>22.614889</td>\n",
              "      <td>10.503090</td>\n",
              "      <td>22.528412</td>\n",
              "      <td>4.584549</td>\n",
              "      <td>7.519589</td>\n",
              "      <td>3.517346</td>\n",
              "      <td>31.612198</td>\n",
              "      <td>33.847808</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59e66047-b38a-4898-93c1-dceee4c7ec9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-59e66047-b38a-4898-93c1-dceee4c7ec9c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-59e66047-b38a-4898-93c1-dceee4c7ec9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "normal.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "49naUOY2pnBH",
        "outputId": "09fe4842-9990-4195-c52f-a641b6903fd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f92cb653950>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Adjusting figuresize, and fontsize\n",
        "plt.rcParams[\"figure.figsize\"] = \"8,6\"\n",
        "font = {'size': 12}\n",
        "plt.rc('font', **font)\n",
        "\n",
        "# Adding titles to the plots and axes\n",
        "plt.title(\"Distribution of Amount over Both Classes\")\n",
        "plt.xlabel(\"Amount\")\n",
        "plt.ylabel(\"Class\")\n",
        "\n",
        "# Plotting the Amount column vs. Class Column\n",
        "plt.scatter(data[\"Amount\"],data[\"Class\"])\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fXMOorpRp8mo"
      },
      "outputs": [],
      "source": [
        "X = data.drop([\"Class\",\"Time\"],axis=1)\n",
        "y = data[\"Class\"]\n",
        "\n",
        "\n",
        "X_train_Before, X_test, y_train_Before, y_test = train_test_split(\n",
        "    X, y, test_size = 0.3, \n",
        "    random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tw1kdGZ9bJ7B"
      },
      "outputs": [],
      "source": [
        "#Data Normalization\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "for i in X_train_Before:\n",
        "    scaler = StandardScaler()\n",
        "    X_train_Before[i] = scaler.fit_transform(X_train_Before[i].values.reshape(-1,1))\n",
        "    X_test[i] = scaler.transform(X_test[i].values.reshape(-1,1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WUI5P98fp_f6"
      },
      "outputs": [],
      "source": [
        "#Balancing with Borderline_SMOTE\n",
        "borderlineSMOTE = BorderlineSMOTE(k_neighbors = 10, random_state = 42)\n",
        "X_train, y_train = borderlineSMOTE.fit_resample(X_train_Before,y_train_Before)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "g1jhOEbFqC41",
        "outputId": "8253552a-18d8-43ef-f1a5-d4b0521d45fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92cb626350>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Unbalanced training data distribution\n",
        "sns.countplot(x=y_train_Before)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "S2paolRcqHpr",
        "outputId": "ae21a0bb-2080-4acf-b518-013d8e0fa62c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92cb5c7d50>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Balanced training data distribution\n",
        "sns.countplot(x=y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LaePMvTLqJ8F"
      },
      "outputs": [],
      "source": [
        "#Random Forest Classifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators = 35,random_state=42)\n",
        "\n",
        "\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "7VvOwsuWxVxv",
        "outputId": "292e4d78-fac9-4ce0-80a2-0d40be3bf54b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92cb889450>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Random Forest Classifier Confusion Matrix\n",
        "cm = confusion_matrix(y_test,y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='g',yticklabels=[\"Real Negative\",\"Real Positive\"],xticklabels=[\"Predicted Negative\", \"Predicted Positive\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwkJQImjt9tL",
        "outputId": "417f59a8-099a-4753-8eb0-ed8703e32051"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  87.88 %\n",
            "Recall:  85.29 %\n",
            "F1:  86.57 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Precision: \", round(precision*100,2),\"%\")\n",
        "print(\"Recall: \", round(recall*100,2),\"%\")\n",
        "print(\"F1: \", round(f1*100,2),\"%\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sgkE-auW-CEL"
      },
      "outputs": [],
      "source": [
        "#Logistic Regression Classifier\n",
        "\n",
        "logReg = LogisticRegression()\n",
        "\n",
        "logReg.fit(X_train, y_train)\n",
        "\n",
        "log_y_pred = logReg.predict(X_test)\n",
        "\n",
        "log_precision = precision_score(y_test, log_y_pred)\n",
        "log_recall = recall_score(y_test, log_y_pred)\n",
        "log_f1 = f1_score(y_test, log_y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "L06gPPFk-iqA",
        "outputId": "00abd74a-0843-4069-97f5-bc744f564f36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  8.78 %\n",
            "Recall:  88.97 %\n",
            "F1:  15.98 %\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92cb498cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"Precision: \", round(log_precision*100,2),\"%\")\n",
        "print(\"Recall: \", round(log_recall*100,2),\"%\")\n",
        "print(\"F1: \", round(log_f1*100,2),\"%\")\n",
        "print(\"\\n\"*3)\n",
        "\n",
        "cm = confusion_matrix(y_test,log_y_pred)\n",
        "sns.heatmap(cm,annot=True,fmt='g',yticklabels=[\"Real Negative\",\"Real Positive\"],xticklabels=[\"Predicted Negative\", \"Predicted Positive\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XECYUlEjSCZn"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}