Fraud_Detection_by_Data_Merge.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aivic-techie/Data-Analysis/blob/main/Fraud_Detection_by_Data_Merge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4UXxg-PCuo2a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from lightgbm import LGBMClassifier, Booster\n",
        "import pickle\n",
        "pd.set_option('display.max_columns', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWn_wCub7-gv",
        "outputId": "f9843306-5778-4d4f-d6b1-adb460186470"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "M2lYJuHxug3m",
        "outputId": "eefe2c70-584d-459b-be21-47bfb587d1d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction ID  User ID  Transaction Amount Transaction Date and Time  \\\n",
              "0        51595306     9822              163.08       2023-01-02 07:47:54   \n",
              "1        85052974     4698              430.74       2021-09-12 15:15:41   \n",
              "2        23954324     8666              415.74       2023-01-12 17:25:58   \n",
              "3        44108303     9012              565.89       2021-02-27 11:31:00   \n",
              "4        66622683     5185              955.49       2022-09-24 04:06:38   \n",
              "\n",
              "   Merchant ID Payment Method Country Code Transaction Type  \\\n",
              "0         4044   ACH Transfer          KOR          Charity   \n",
              "1         4576      2Checkout          VNM         Cashback   \n",
              "2         4629  Google Wallet          MEX           Reward   \n",
              "3         3322          Check          SGP         Purchase   \n",
              "4         7609       Worldpay          HKG      Acquisition   \n",
              "\n",
              "                   Device Type      IP Address Browser Type Operating System  \\\n",
              "0                   GPS Device   42.23.223.120        Links            Tizen   \n",
              "1               Medical Device   39.52.212.120       Beaker   Windows Server   \n",
              "2  Vehicle Infotainment System  243.180.236.29        Opera          Android   \n",
              "3                        Kiosk  212.186.227.14    Konqueror           CentOS   \n",
              "4                 Smart Mirror  166.113.10.199     Basilisk           Ubuntu   \n",
              "\n",
              "         Merchant Category  User Age User Occupation  User Income  \\\n",
              "0  Industrial & Scientific        68          Doctor     66826.21   \n",
              "1       Beauty & Cosmetics        22         Chemist     89356.71   \n",
              "2              Real Estate        71           Nurse     58438.63   \n",
              "3               Appliances        78           Nurse      3426.92   \n",
              "4                  Jewelry        31       Physicist     53080.12   \n",
              "\n",
              "         User Gender User Account Status Transaction Status  \\\n",
              "0  Prefer Not to Say                 Pro         In Transit   \n",
              "1        Genderqueer    Pending Approval           Resolved   \n",
              "2               Male                 Pro             Posted   \n",
              "3            Agender             Premium             Closed   \n",
              "4               Male                Free           Refunded   \n",
              "\n",
              "   Location Distance  Time Taken for Transaction Transaction Time of Day  \\\n",
              "0               9.34                       24.22                 Daytime   \n",
              "1              65.28                       55.11                 Daytime   \n",
              "2              44.05                       53.84                 Daytime   \n",
              "3              21.70                       21.62                 Daytime   \n",
              "4              56.63                       53.71                 Daytime   \n",
              "\n",
              "   User's Transaction History  Merchant's Reputation Score  \\\n",
              "0                          26                         2.71   \n",
              "1                          60                         3.95   \n",
              "2                          81                         3.81   \n",
              "3                          18                         2.67   \n",
              "4                          98                         3.19   \n",
              "\n",
              "  User's Device Location Transaction Currency    Transaction Purpose  \\\n",
              "0         United Kingdom                  NOK       Consultation Fee   \n",
              "1                 Mexico                  EGP        Cashback Reward   \n",
              "2                  Qatar                  MXN            Acquisition   \n",
              "3                  Spain                  CLP         Loan Repayment   \n",
              "4                 Israel                  RUB  Dividend Reinvestment   \n",
              "\n",
              "   User's Credit Score User's Email Domain  Merchant's Business Age  \\\n",
              "0                  343           cox.co.uk                        3   \n",
              "1                  688           gmail.com                       13   \n",
              "2                  371      rocketmail.com                        7   \n",
              "3                  687    roadrunner.co.uk                       15   \n",
              "4                  605    protonmail.co.uk                       17   \n",
              "\n",
              "  Transaction Authentication Method  Fraudulent Flag  \n",
              "0          Bluetooth Authentication                0  \n",
              "1                           NFC Tag                1  \n",
              "2                             Token                1  \n",
              "3                    Time-Based OTP                1  \n",
              "4                          Password                1  "
            ],
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              "      <th></th>\n",
              "      <th>Transaction ID</th>\n",
              "      <th>User ID</th>\n",
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              "      <th>User Occupation</th>\n",
              "      <th>User Income</th>\n",
              "      <th>User Gender</th>\n",
              "      <th>User Account Status</th>\n",
              "      <th>Transaction Status</th>\n",
              "      <th>Location Distance</th>\n",
              "      <th>Time Taken for Transaction</th>\n",
              "      <th>Transaction Time of Day</th>\n",
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              "      <th>Merchant's Reputation Score</th>\n",
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              "      <th>User's Email Domain</th>\n",
              "      <th>Merchant's Business Age</th>\n",
              "      <th>Transaction Authentication Method</th>\n",
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              "      <td>51595306</td>\n",
              "      <td>9822</td>\n",
              "      <td>163.08</td>\n",
              "      <td>2023-01-02 07:47:54</td>\n",
              "      <td>4044</td>\n",
              "      <td>ACH Transfer</td>\n",
              "      <td>KOR</td>\n",
              "      <td>Charity</td>\n",
              "      <td>GPS Device</td>\n",
              "      <td>42.23.223.120</td>\n",
              "      <td>Links</td>\n",
              "      <td>Tizen</td>\n",
              "      <td>Industrial &amp; Scientific</td>\n",
              "      <td>68</td>\n",
              "      <td>Doctor</td>\n",
              "      <td>66826.21</td>\n",
              "      <td>Prefer Not to Say</td>\n",
              "      <td>Pro</td>\n",
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              "      <td>24.22</td>\n",
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              "      <td>2.71</td>\n",
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              "      <td>NOK</td>\n",
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              "      <td>343</td>\n",
              "      <td>cox.co.uk</td>\n",
              "      <td>3</td>\n",
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              "      <td>85052974</td>\n",
              "      <td>4698</td>\n",
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              "      <td>2021-09-12 15:15:41</td>\n",
              "      <td>4576</td>\n",
              "      <td>2Checkout</td>\n",
              "      <td>VNM</td>\n",
              "      <td>Cashback</td>\n",
              "      <td>Medical Device</td>\n",
              "      <td>39.52.212.120</td>\n",
              "      <td>Beaker</td>\n",
              "      <td>Windows Server</td>\n",
              "      <td>Beauty &amp; Cosmetics</td>\n",
              "      <td>22</td>\n",
              "      <td>Chemist</td>\n",
              "      <td>89356.71</td>\n",
              "      <td>Genderqueer</td>\n",
              "      <td>Pending Approval</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>65.28</td>\n",
              "      <td>55.11</td>\n",
              "      <td>Daytime</td>\n",
              "      <td>60</td>\n",
              "      <td>3.95</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>EGP</td>\n",
              "      <td>Cashback Reward</td>\n",
              "      <td>688</td>\n",
              "      <td>gmail.com</td>\n",
              "      <td>13</td>\n",
              "      <td>NFC Tag</td>\n",
              "      <td>1</td>\n",
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              "      <td>8666</td>\n",
              "      <td>415.74</td>\n",
              "      <td>2023-01-12 17:25:58</td>\n",
              "      <td>4629</td>\n",
              "      <td>Google Wallet</td>\n",
              "      <td>MEX</td>\n",
              "      <td>Reward</td>\n",
              "      <td>Vehicle Infotainment System</td>\n",
              "      <td>243.180.236.29</td>\n",
              "      <td>Opera</td>\n",
              "      <td>Android</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>71</td>\n",
              "      <td>Nurse</td>\n",
              "      <td>58438.63</td>\n",
              "      <td>Male</td>\n",
              "      <td>Pro</td>\n",
              "      <td>Posted</td>\n",
              "      <td>44.05</td>\n",
              "      <td>53.84</td>\n",
              "      <td>Daytime</td>\n",
              "      <td>81</td>\n",
              "      <td>3.81</td>\n",
              "      <td>Qatar</td>\n",
              "      <td>MXN</td>\n",
              "      <td>Acquisition</td>\n",
              "      <td>371</td>\n",
              "      <td>rocketmail.com</td>\n",
              "      <td>7</td>\n",
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              "      <td>78</td>\n",
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              "      <td>Agender</td>\n",
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              "      <td>21.62</td>\n",
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              "      <td>31</td>\n",
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              "      <td>53.71</td>\n",
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              "      <td>3.19</td>\n",
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              "      <td>17</td>\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a8239b86-2ebc-4385-a93d-dfe53682c519 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "path = \"/content/drive/MyDrive/DatafestAfrica2023 Data Science Datathon/FraudDetectionDataset.csv\"\n",
        "data = pd.read_csv(path)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzFSlKupzmYv",
        "outputId": "5561c890-469d-40c6-9b75-95885fc50937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6000000 entries, 0 to 5999999\n",
            "Data columns (total 32 columns):\n",
            " #   Column                             Dtype  \n",
            "---  ------                             -----  \n",
            " 0   Transaction ID                     int64  \n",
            " 1   User ID                            int64  \n",
            " 2   Transaction Amount                 float64\n",
            " 3   Transaction Date and Time          object \n",
            " 4   Merchant ID                        int64  \n",
            " 5   Payment Method                     object \n",
            " 6   Country Code                       object \n",
            " 7   Transaction Type                   object \n",
            " 8   Device Type                        object \n",
            " 9   IP Address                         object \n",
            " 10  Browser Type                       object \n",
            " 11  Operating System                   object \n",
            " 12  Merchant Category                  object \n",
            " 13  User Age                           int64  \n",
            " 14  User Occupation                    object \n",
            " 15  User Income                        float64\n",
            " 16  User Gender                        object \n",
            " 17  User Account Status                object \n",
            " 18  Transaction Status                 object \n",
            " 19  Location Distance                  float64\n",
            " 20  Time Taken for Transaction         float64\n",
            " 21  Transaction Time of Day            object \n",
            " 22  User's Transaction History         int64  \n",
            " 23  Merchant's Reputation Score        float64\n",
            " 24  User's Device Location             object \n",
            " 25  Transaction Currency               object \n",
            " 26  Transaction Purpose                object \n",
            " 27  User's Credit Score                int64  \n",
            " 28  User's Email Domain                object \n",
            " 29  Merchant's Business Age            int64  \n",
            " 30  Transaction Authentication Method  object \n",
            " 31  Fraudulent Flag                    int64  \n",
            "dtypes: float64(5), int64(8), object(19)\n",
            "memory usage: 1.4+ GB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLdmapAr610_",
        "outputId": "6bd3d478-80cf-4a1e-96ce-48e4909dc307"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Transaction ID                       0\n",
              "User ID                              0\n",
              "Transaction Amount                   0\n",
              "Transaction Date and Time            0\n",
              "Merchant ID                          0\n",
              "Payment Method                       0\n",
              "Country Code                         0\n",
              "Transaction Type                     0\n",
              "Device Type                          0\n",
              "IP Address                           0\n",
              "Browser Type                         0\n",
              "Operating System                     0\n",
              "Merchant Category                    0\n",
              "User Age                             0\n",
              "User Occupation                      0\n",
              "User Income                          0\n",
              "User Gender                          0\n",
              "User Account Status                  0\n",
              "Transaction Status                   0\n",
              "Location Distance                    0\n",
              "Time Taken for Transaction           0\n",
              "Transaction Time of Day              0\n",
              "User's Transaction History           0\n",
              "Merchant's Reputation Score          0\n",
              "User's Device Location               0\n",
              "Transaction Currency                 0\n",
              "Transaction Purpose                  0\n",
              "User's Credit Score                  0\n",
              "User's Email Domain                  0\n",
              "Merchant's Business Age              0\n",
              "Transaction Authentication Method    0\n",
              "Fraudulent Flag                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "gWYA8S8AF7Fe",
        "outputId": "ddd89cb4-bb34-4f70-bb06-8377835b1259"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Transaction ID       User ID  Transaction Amount   Merchant ID  \\\n",
              "count    6.000000e+06  6.000000e+06        6.000000e+06  6.000000e+06   \n",
              "mean     5.501906e+07  5.499571e+03        5.005515e+02  5.500817e+03   \n",
              "std      2.598067e+07  2.598744e+03        2.884166e+02  2.598350e+03   \n",
              "min      1.000004e+07  1.000000e+03        1.000000e+00  1.000000e+03   \n",
              "25%      3.251666e+07  3.248000e+03        2.507900e+02  3.251000e+03   \n",
              "50%      5.502987e+07  5.499000e+03        5.005100e+02  5.501000e+03   \n",
              "75%      7.752487e+07  7.751000e+03        7.504400e+02  7.752000e+03   \n",
              "max      9.999988e+07  9.999000e+03        1.000000e+03  9.999000e+03   \n",
              "\n",
              "           User Age   User Income  Location Distance  \\\n",
              "count  6.000000e+06  6.000000e+06       6.000000e+06   \n",
              "mean   4.900159e+01  5.049328e+04       5.050600e+01   \n",
              "std    1.818538e+01  2.858223e+04       2.857562e+01   \n",
              "min    1.800000e+01  1.000020e+03       1.000000e+00   \n",
              "25%    3.300000e+01  2.573446e+04       2.576000e+01   \n",
              "50%    4.900000e+01  5.050294e+04       5.051000e+01   \n",
              "75%    6.500000e+01  7.523454e+04       7.525000e+01   \n",
              "max    8.000000e+01  1.000000e+05       1.000000e+02   \n",
              "\n",
              "       Time Taken for Transaction  User's Transaction History  \\\n",
              "count                6.000000e+06                6.000000e+06   \n",
              "mean                 3.049492e+01                5.048393e+01   \n",
              "std                  1.703567e+01                2.886705e+01   \n",
              "min                  1.000000e+00                1.000000e+00   \n",
              "25%                  1.574000e+01                2.500000e+01   \n",
              "50%                  3.050000e+01                5.000000e+01   \n",
              "75%                  4.526000e+01                7.500000e+01   \n",
              "max                  6.000000e+01                1.000000e+02   \n",
              "\n",
              "       Merchant's Reputation Score  User's Credit Score  \\\n",
              "count                 6.000000e+06         6.000000e+06   \n",
              "mean                  2.999382e+00         5.749533e+02   \n",
              "std                   1.154479e+00         1.590197e+02   \n",
              "min                   1.000000e+00         3.000000e+02   \n",
              "25%                   2.000000e+00         4.370000e+02   \n",
              "50%                   3.000000e+00         5.750000e+02   \n",
              "75%                   4.000000e+00         7.130000e+02   \n",
              "max                   5.000000e+00         8.500000e+02   \n",
              "\n",
              "       Merchant's Business Age  Fraudulent Flag  \n",
              "count             6.000000e+06     6.000000e+06  \n",
              "mean              1.049685e+01     4.999843e-01  \n",
              "std               5.764884e+00     5.000000e-01  \n",
              "min               1.000000e+00     0.000000e+00  \n",
              "25%               5.000000e+00     0.000000e+00  \n",
              "50%               1.000000e+01     0.000000e+00  \n",
              "75%               1.500000e+01     1.000000e+00  \n",
              "max               2.000000e+01     1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5229383-ab4a-4c4f-980c-58e38bf293c3\" 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>Transaction ID</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Transaction Amount</th>\n",
              "      <th>Merchant ID</th>\n",
              "      <th>User Age</th>\n",
              "      <th>User Income</th>\n",
              "      <th>Location Distance</th>\n",
              "      <th>Time Taken for Transaction</th>\n",
              "      <th>User's Transaction History</th>\n",
              "      <th>Merchant's Reputation Score</th>\n",
              "      <th>User's Credit Score</th>\n",
              "      <th>Merchant's Business Age</th>\n",
              "      <th>Fraudulent Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.501906e+07</td>\n",
              "      <td>5.499571e+03</td>\n",
              "      <td>5.005515e+02</td>\n",
              "      <td>5.500817e+03</td>\n",
              "      <td>4.900159e+01</td>\n",
              "      <td>5.049328e+04</td>\n",
              "      <td>5.050600e+01</td>\n",
              "      <td>3.049492e+01</td>\n",
              "      <td>5.048393e+01</td>\n",
              "      <td>2.999382e+00</td>\n",
              "      <td>5.749533e+02</td>\n",
              "      <td>1.049685e+01</td>\n",
              "      <td>4.999843e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.598067e+07</td>\n",
              "      <td>2.598744e+03</td>\n",
              "      <td>2.884166e+02</td>\n",
              "      <td>2.598350e+03</td>\n",
              "      <td>1.818538e+01</td>\n",
              "      <td>2.858223e+04</td>\n",
              "      <td>2.857562e+01</td>\n",
              "      <td>1.703567e+01</td>\n",
              "      <td>2.886705e+01</td>\n",
              "      <td>1.154479e+00</td>\n",
              "      <td>1.590197e+02</td>\n",
              "      <td>5.764884e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000004e+07</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.800000e+01</td>\n",
              "      <td>1.000020e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.000000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.251666e+07</td>\n",
              "      <td>3.248000e+03</td>\n",
              "      <td>2.507900e+02</td>\n",
              "      <td>3.251000e+03</td>\n",
              "      <td>3.300000e+01</td>\n",
              "      <td>2.573446e+04</td>\n",
              "      <td>2.576000e+01</td>\n",
              "      <td>1.574000e+01</td>\n",
              "      <td>2.500000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.370000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.502987e+07</td>\n",
              "      <td>5.499000e+03</td>\n",
              "      <td>5.005100e+02</td>\n",
              "      <td>5.501000e+03</td>\n",
              "      <td>4.900000e+01</td>\n",
              "      <td>5.050294e+04</td>\n",
              "      <td>5.051000e+01</td>\n",
              "      <td>3.050000e+01</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>5.750000e+02</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.752487e+07</td>\n",
              "      <td>7.751000e+03</td>\n",
              "      <td>7.504400e+02</td>\n",
              "      <td>7.752000e+03</td>\n",
              "      <td>6.500000e+01</td>\n",
              "      <td>7.523454e+04</td>\n",
              "      <td>7.525000e+01</td>\n",
              "      <td>4.526000e+01</td>\n",
              "      <td>7.500000e+01</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>7.130000e+02</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.999988e+07</td>\n",
              "      <td>9.999000e+03</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>9.999000e+03</td>\n",
              "      <td>8.000000e+01</td>\n",
              "      <td>1.000000e+05</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>6.000000e+01</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>8.500000e+02</td>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a5229383-ab4a-4c4f-980c-58e38bf293c3 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-a5229383-ab4a-4c4f-980c-58e38bf293c3');\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",
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              "\n",
              "\n",
              "<div id=\"df-57b5fa1d-3a15-42d9-80df-2d7702308f23\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57b5fa1d-3a15-42d9-80df-2d7702308f23')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57b5fa1d-3a15-42d9-80df-2d7702308f23 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZuNb3M5GO01",
        "outputId": "43a21de0-b515-4f54-b13f-1f57001f0ce3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000000, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "shape_b4 = data.shape\n",
        "shape_b4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ac9NR_ykGAle",
        "outputId": "1b4e5e6e-b727-44ad-9bfd-ecc10bf320de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# check for duplicate transactions\n",
        "duplicate_values = data['Transaction ID'].duplicated()\n",
        "print(duplicate_values.any())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQnPBJawGTQc",
        "outputId": "6f591bf4-27c9-4115-bbbf-41d8c35e1408"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape after removing duplicate transactions: (5805013, 32)\n",
            "number of duplicate entries removed: 194987\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# drop duplicate entries\n",
        "data = data.drop_duplicates(subset=['Transaction ID'], keep='first')\n",
        "shape_af = data.shape\n",
        "print('shape after removing duplicate transactions:', data.shape)\n",
        "print(f'number of duplicate entries removed: {shape_b4[0] - shape_af[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "coydJjZ7LM-D"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "WFtA-dSR7imH"
      },
      "outputs": [],
      "source": [
        "# data['transaction_date_and_time'] = data['transaction_date_and_time'].astype('datetime64[ns]')\n",
        "# data['fraudulent_flag'] = data['fraudulent_flag'].astype('int')\n",
        "# data['fraudulent_flag'].dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJlxcW4nBjX8",
        "outputId": "686feb22-3502-47c6-88f5-c97a07e2774f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                             Transaction ID   User ID  Transaction Amount  \\\n",
            "Transaction ID                     1.000000 -0.000218           -0.000288   \n",
            "User ID                           -0.000218  1.000000           -0.000404   \n",
            "Transaction Amount                -0.000288 -0.000404            1.000000   \n",
            "Merchant ID                       -0.000092 -0.000511            0.000104   \n",
            "User Age                           0.000372  0.000074            0.000143   \n",
            "User Income                        0.000191 -0.000700           -0.000451   \n",
            "Location Distance                  0.000200 -0.000401           -0.000409   \n",
            "Time Taken for Transaction         0.000528  0.000072           -0.000270   \n",
            "User's Transaction History        -0.000143 -0.000126           -0.001056   \n",
            "Merchant's Reputation Score        0.000054 -0.000761           -0.000617   \n",
            "User's Credit Score                0.000312  0.000671           -0.000005   \n",
            "Merchant's Business Age           -0.000080 -0.000374            0.000143   \n",
            "Fraudulent Flag                   -0.000662 -0.000468            0.000099   \n",
            "\n",
            "                             Merchant ID  User Age  User Income  \\\n",
            "Transaction ID                 -0.000092  0.000372     0.000191   \n",
            "User ID                        -0.000511  0.000074    -0.000700   \n",
            "Transaction Amount              0.000104  0.000143    -0.000451   \n",
            "Merchant ID                     1.000000 -0.000138    -0.000151   \n",
            "User Age                       -0.000138  1.000000    -0.000116   \n",
            "User Income                    -0.000151 -0.000116     1.000000   \n",
            "Location Distance               0.000222  0.000264     0.000651   \n",
            "Time Taken for Transaction     -0.000509 -0.000499     0.000126   \n",
            "User's Transaction History      0.000345  0.000207     0.000193   \n",
            "Merchant's Reputation Score    -0.000759 -0.000535     0.000111   \n",
            "User's Credit Score            -0.000348  0.000049    -0.000277   \n",
            "Merchant's Business Age         0.000842  0.000489     0.000949   \n",
            "Fraudulent Flag                -0.000213  0.000364     0.000534   \n",
            "\n",
            "                             Location Distance  Time Taken for Transaction  \\\n",
            "Transaction ID                        0.000200                    0.000528   \n",
            "User ID                              -0.000401                    0.000072   \n",
            "Transaction Amount                   -0.000409                   -0.000270   \n",
            "Merchant ID                           0.000222                   -0.000509   \n",
            "User Age                              0.000264                   -0.000499   \n",
            "User Income                           0.000651                    0.000126   \n",
            "Location Distance                     1.000000                   -0.000048   \n",
            "Time Taken for Transaction           -0.000048                    1.000000   \n",
            "User's Transaction History            0.000440                   -0.000344   \n",
            "Merchant's Reputation Score          -0.000407                    0.000268   \n",
            "User's Credit Score                   0.000204                   -0.000113   \n",
            "Merchant's Business Age               0.000640                    0.000212   \n",
            "Fraudulent Flag                       0.000301                   -0.000276   \n",
            "\n",
            "                             User's Transaction History  \\\n",
            "Transaction ID                                -0.000143   \n",
            "User ID                                       -0.000126   \n",
            "Transaction Amount                            -0.001056   \n",
            "Merchant ID                                    0.000345   \n",
            "User Age                                       0.000207   \n",
            "User Income                                    0.000193   \n",
            "Location Distance                              0.000440   \n",
            "Time Taken for Transaction                    -0.000344   \n",
            "User's Transaction History                     1.000000   \n",
            "Merchant's Reputation Score                   -0.000456   \n",
            "User's Credit Score                           -0.000212   \n",
            "Merchant's Business Age                        0.000526   \n",
            "Fraudulent Flag                                0.000693   \n",
            "\n",
            "                             Merchant's Reputation Score  User's Credit Score  \\\n",
            "Transaction ID                                  0.000054             0.000312   \n",
            "User ID                                        -0.000761             0.000671   \n",
            "Transaction Amount                             -0.000617            -0.000005   \n",
            "Merchant ID                                    -0.000759            -0.000348   \n",
            "User Age                                       -0.000535             0.000049   \n",
            "User Income                                     0.000111            -0.000277   \n",
            "Location Distance                              -0.000407             0.000204   \n",
            "Time Taken for Transaction                      0.000268            -0.000113   \n",
            "User's Transaction History                     -0.000456            -0.000212   \n",
            "Merchant's Reputation Score                     1.000000            -0.000386   \n",
            "User's Credit Score                            -0.000386             1.000000   \n",
            "Merchant's Business Age                         0.000418             0.000705   \n",
            "Fraudulent Flag                                 0.000439            -0.000179   \n",
            "\n",
            "                             Merchant's Business Age  Fraudulent Flag  \n",
            "Transaction ID                             -0.000080        -0.000662  \n",
            "User ID                                    -0.000374        -0.000468  \n",
            "Transaction Amount                          0.000143         0.000099  \n",
            "Merchant ID                                 0.000842        -0.000213  \n",
            "User Age                                    0.000489         0.000364  \n",
            "User Income                                 0.000949         0.000534  \n",
            "Location Distance                           0.000640         0.000301  \n",
            "Time Taken for Transaction                  0.000212        -0.000276  \n",
            "User's Transaction History                  0.000526         0.000693  \n",
            "Merchant's Reputation Score                 0.000418         0.000439  \n",
            "User's Credit Score                         0.000705        -0.000179  \n",
            "Merchant's Business Age                     1.000000         0.000203  \n",
            "Fraudulent Flag                             0.000203         1.000000  \n",
            "Transaction ID                -0.000288\n",
            "User ID                       -0.000404\n",
            "Transaction Amount             1.000000\n",
            "Merchant ID                    0.000104\n",
            "User Age                       0.000143\n",
            "User Income                   -0.000451\n",
            "Location Distance             -0.000409\n",
            "Time Taken for Transaction    -0.000270\n",
            "User's Transaction History    -0.001056\n",
            "Merchant's Reputation Score   -0.000617\n",
            "User's Credit Score           -0.000005\n",
            "Merchant's Business Age        0.000143\n",
            "Fraudulent Flag                0.000099\n",
            "Name: Transaction Amount, dtype: float64\n",
            "No correlation between these numerical variables\n"
          ]
        }
      ],
      "source": [
        "# Determining correlation metrics between numerical variables\n",
        "numeric_columns = data.select_dtypes(include=['number'])\n",
        "\n",
        "correlation_matrix = numeric_columns.corr()\n",
        "correlation_matrix\n",
        "\n",
        "# Check correlation with transaction amount\n",
        "correlation_with_transaction_amount = correlation_matrix['Transaction Amount']\n",
        "print(correlation_matrix)\n",
        "\n",
        "print(correlation_with_transaction_amount)\n",
        "print(\"No correlation between these numerical variables\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_RQ9agGujEf-",
        "outputId": "5f39d550-a046-4e8e-817d-210cfb61ea95"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most of the Numerical Columns are Normally Distributed\n"
          ]
        }
      ],
      "source": [
        "# checking distribution for user income, transaction income\n",
        "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(10, 10))\n",
        "\n",
        "# Create horizontal boxplots by setting vert to False\n",
        "ax[0, 0].boxplot(data['Transaction Amount'], vert=False)\n",
        "ax[0, 1].boxplot(data['User Age'], vert=False)\n",
        "ax[1, 0].boxplot(data['User Income'], vert=False)\n",
        "ax[1, 1].boxplot(data['Location Distance'], vert=False)\n",
        "\n",
        "# Set individual titles for each subplot\n",
        "ax[0, 0].set_title('Transaction Amount plot')\n",
        "ax[0, 1].set_title('User Age plot')\n",
        "ax[1, 0].set_title('User Income plot')\n",
        "ax[1, 1].set_title('Location Distance plot')\n",
        "\n",
        "# Remove y-axis labels for the middle plots\n",
        "ax[0, 1].set_yticks([])\n",
        "ax[0, 0].set_yticks([])\n",
        "ax[1, 0].set_yticks([])\n",
        "ax[1, 1].set_yticks([])\n",
        "\n",
        "# Adjust subplot spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "print('Most of the Numerical Columns are Normally Distributed')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "v_RVc1S8LOeU",
        "outputId": "874c16a7-50f8-407f-a539-ce95952c48b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User Income Group  Low income  Mid income  High income\n",
              "Fraudulent Flag                                       \n",
              "0                      967840      968568       966047\n",
              "1                      967039      967691       967828"
            ],
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              "      <th>User Income Group</th>\n",
              "      <th>Low income</th>\n",
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              "          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",
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              "\n",
              "\n",
              "<div id=\"df-ae9efc6b-a0c1-4f99-9307-120822cb8d69\">\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae9efc6b-a0c1-4f99-9307-120822cb8d69 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# grouping user income into different class\n",
        "num_of_bins = 3\n",
        "labels =  ['Low income', 'Mid income', 'High income']\n",
        "bins = pd.cut(data['User Income'], bins = num_of_bins,\n",
        "                                              retbins = True, include_lowest = True)[1]\n",
        "\n",
        "data['User Income Group'] = pd.cut(data['User Income'], bins = bins, labels = labels)\n",
        "data.groupby('Fraudulent Flag')['User Income Group'].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "ISwjTaXBLoIk",
        "outputId": "52ddac75-93f2-42f1-c98d-d3a1135fafd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User Age Group    Young  Mid Age  Old Age\n",
              "Fraudulent Flag                          \n",
              "0                967952   967057   967446\n",
              "1                967684   966557   968317"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>User Age Group</th>\n",
              "      <th>Young</th>\n",
              "      <th>Mid Age</th>\n",
              "      <th>Old Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fraudulent Flag</th>\n",
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              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>967952</td>\n",
              "      <td>967057</td>\n",
              "      <td>967446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>967684</td>\n",
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              "      <td>968317</td>\n",
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              "\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-d74bb305-cc9e-48a5-8fc6-bac31b654d5b 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-d74bb305-cc9e-48a5-8fc6-bac31b654d5b');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "            style=\"display:none;\">\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-beb81e82-e140-46fa-8200-ab9b582de52b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# grouping user age into categorical class\n",
        "\n",
        "number_of_bins = 3\n",
        "labels =  ['Young', 'Mid Age', 'Old Age']\n",
        "bins = pd.cut(data['User Age'], bins = num_of_bins,\n",
        "                                              retbins = True, include_lowest = True)[1]\n",
        "\n",
        "data['User Age Group'] = pd.cut(data['User Age'], bins = bins, labels = labels)\n",
        "data.groupby('Fraudulent Flag')['User Age Group'].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "800VgIZJR60k",
        "outputId": "2f84a4ad-95b0-4b0b-9de2-ba1d5cb74826"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "data['Time Taken for Transaction'].dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "_paLTgWTRqtB",
        "outputId": "3f3a3477-46da-4dcc-f108-19e7423c008c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time Group       Short time  Norm Time  Long Time\n",
              "Fraudulent Flag                                  \n",
              "0                    967829     966792     967834\n",
              "1                    968609     966910     967039"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th>Time Group</th>\n",
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              "      <th>Norm Time</th>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2932b9c2-64ee-43ad-9d3f-c114c7e5939c')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2932b9c2-64ee-43ad-9d3f-c114c7e5939c 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-2932b9c2-64ee-43ad-9d3f-c114c7e5939c');\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",
              "\n",
              "\n",
              "<div id=\"df-989f4888-6b21-4fbe-82e0-9f69741b3424\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-989f4888-6b21-4fbe-82e0-9f69741b3424')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-989f4888-6b21-4fbe-82e0-9f69741b3424 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# group time taken for transaction\n",
        "number_of_bins = 3\n",
        "labels =  ['Short time', 'Norm Time', 'Long Time']\n",
        "bins = pd.cut(data['Time Taken for Transaction'], bins = num_of_bins,\n",
        "                                              retbins = True, include_lowest = True)[1]\n",
        "\n",
        "data['Time Group'] = pd.cut(data['Time Taken for Transaction'], bins = bins, labels = labels)\n",
        "data.groupby('Fraudulent Flag')['Time Group'].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJ3xvANoPNXj",
        "outputId": "c76ff5dd-3573-4959-fb63-ca52b5337f90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.941     , 20.66666667, 40.33333333, 60.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "bins"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "_YpIqXKWSiSC"
      },
      "outputs": [],
      "source": [
        "# defining a function for categorizing numerical columns\n",
        "def group(df, old_col, new_col, num_bins=None, labels=None):\n",
        "    if labels is None:\n",
        "        labels = [f'Group {i + 1}' for i in range(num_bins)]\n",
        "\n",
        "    bins = pd.cut(df[old_col], bins=num_bins, retbins=True, include_lowest=True)[1]\n",
        "    df[new_col] = pd.cut(df[old_col], bins=bins, labels=labels).astype('object')\n",
        "\n",
        "    #return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "xTravXl3Wx-y",
        "outputId": "fefabb4c-b439-4c6a-e224-307deed88884"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location Distance Group  Long Distace  Short Distance\n",
              "Fraudulent Flag                                      \n",
              "0                             1451434         1451021\n",
              "1                             1451525         1451033"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5c89dcc-d3a1-4c6f-93c8-add3676ca18d\" 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>Location Distance Group</th>\n",
              "      <th>Long Distace</th>\n",
              "      <th>Short Distance</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fraudulent Flag</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1451434</td>\n",
              "      <td>1451021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1451525</td>\n",
              "      <td>1451033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5c89dcc-d3a1-4c6f-93c8-add3676ca18d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c5c89dcc-d3a1-4c6f-93c8-add3676ca18d 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-c5c89dcc-d3a1-4c6f-93c8-add3676ca18d');\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",
              "\n",
              "\n",
              "<div id=\"df-27bc3ff6-7f2a-4efd-b9a5-ccbfdebd7d96\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27bc3ff6-7f2a-4efd-b9a5-ccbfdebd7d96')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-27bc3ff6-7f2a-4efd-b9a5-ccbfdebd7d96 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# group location column\n",
        "labels = ['Short Distance', 'Long Distace']\n",
        "#num_bins = 2\n",
        "group(data, 'Location Distance', 'Location Distance Group', 2, labels)\n",
        "data.groupby('Fraudulent Flag')['Location Distance Group'].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "90-l_ustXyN4",
        "outputId": "74256134-60ef-4de2-968f-cdde52f07c5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time Take for Transaction Group  Long Time  Short Time\n",
              "Fraudulent Flag                                       \n",
              "0                                  1451530     1450925\n",
              "1                                  1449974     1452584"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>Time Take for Transaction Group</th>\n",
              "      <th>Long Time</th>\n",
              "      <th>Short Time</th>\n",
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              "    <tr>\n",
              "      <th>Fraudulent Flag</th>\n",
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              "      <th>1</th>\n",
              "      <td>1449974</td>\n",
              "      <td>1452584</td>\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c2c39c69-b0b2-4223-bb31-e54e606f09f5 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-c2c39c69-b0b2-4223-bb31-e54e606f09f5');\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",
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              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b9870199-ebc7-41d2-acc3-9a8bed884517 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Time taken for transaction\n",
        "labels = ['Short Time', 'Long Time']\n",
        "group(data, 'Time Taken for Transaction', 'Time Take for Transaction Group', 2, labels)\n",
        "data.groupby('Fraudulent Flag')['Time Take for Transaction Group'].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "Rtky3eDIYIGy",
        "outputId": "29424847-8e2f-4f4d-d97c-22632b9e7d98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User's Credit Score Group  High Score  Low Score  Normal Score\n",
              "Fraudulent Flag                                               \n",
              "0                              968493     968458        965504\n",
              "1                              969574     968888        964096"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1251656a-c896-4754-8e8d-3d50b1eaa05f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>User's Credit Score Group</th>\n",
              "      <th>High Score</th>\n",
              "      <th>Low Score</th>\n",
              "      <th>Normal Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fraudulent Flag</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>0</th>\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-1251656a-c896-4754-8e8d-3d50b1eaa05f button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1251656a-c896-4754-8e8d-3d50b1eaa05f');\n",
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              "          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",
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              "\n",
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              "    <g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6aaa2fe9-f10c-474e-8ceb-e42b04f4bdab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Group user credit score\n",
        "labels = ['Low Score', 'Normal Score', 'High Score']\n",
        "group(data, \"User's Credit Score\", \"User's Credit Score Group\", 3, labels)\n",
        "data.groupby('Fraudulent Flag')[\"User's Credit Score Group\"].value_counts().unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "Omh9L2rsZVLc",
        "outputId": "b9d67478-1216-4f65-beca-f381c3e43a14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# understanding the top 5 transaction type that aided fraudulent activity\n",
        "transaction_type = data.groupby('Fraudulent Flag')['Transaction Type'].value_counts().unstack()\n",
        "transaction_type\n",
        "fraudulent_flag_1 = transaction_type.loc[1.0]\n",
        "\n",
        "sorted_data = fraudulent_flag_1.sort_values(ascending=False)\n",
        "\n",
        "# Select the top 5 values\n",
        "top_5_data = sorted_data.head(5)\n",
        "\n",
        "# Plot the top 5 values in a bar chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "top_5_data.plot(kind='bar', color='skyblue')\n",
        "plt.xlabel('Transaction Type')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Top 5 Transaction Types for Fraudulent Flag True (1.0)', pad= 10)\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()# accessing all transaction type that aided fraudulent acts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "83K_vRHBdKMw",
        "outputId": "65df9f07-ec2b-40a4-fca1-cede9178e80f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# understanding the top 5 Device type that aided fraudulent activity\n",
        "device_type = data.groupby('Fraudulent Flag')['Device Type'].value_counts().unstack()\n",
        "device_type\n",
        "fraudulent_flag_1 = device_type.loc[1.0]\n",
        "\n",
        "sorted_data = fraudulent_flag_1.sort_values(ascending=False)\n",
        "\n",
        "# Select the top 5 values\n",
        "top_5_data = sorted_data.head(5)\n",
        "\n",
        "# Plot the top 5 values in a bar chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "top_5_data.plot(kind='bar', color='skyblue')\n",
        "plt.xlabel('Device Type')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Top 5 Device Type for Fraudulent Flag True (1.0)', pad= 10)\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "4YlOcS4li2k3",
        "outputId": "660e1b4d-4fad-4f6d-ed2a-8956bfef1058"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# understanding the top 5 authentication type that aided fraudulent activity\n",
        "authentication_type = data.groupby('Fraudulent Flag')['Transaction Authentication Method'].value_counts().unstack()\n",
        "authentication_type\n",
        "fraudulent_flag_1 = authentication_type.loc[1.0]\n",
        "\n",
        "sorted_data = fraudulent_flag_1.sort_values(ascending=False)\n",
        "\n",
        "# Select the top 5 values\n",
        "top_5_data = sorted_data.head(5)\n",
        "\n",
        "# Plot the top 5 values in a bar chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "top_5_data.plot(kind='bar', color='skyblue')\n",
        "plt.xlabel('Authentication Type')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Top 5 Authentication Method that Supported Fraudulent Flag (True (1.0))', pad= 10)\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "R9e1d7ps8s1Z"
      },
      "outputs": [],
      "source": [
        "for col in ['User Income Group', 'User Age Group', 'Time Group']:\n",
        "    data[col] = data[col].astype('object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wExtqhLxjUy",
        "outputId": "4097526a-49b6-4ce9-a3ae-e43f0242b31c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Transaction ID                         int64\n",
              "User ID                                int64\n",
              "Transaction Amount                   float64\n",
              "Transaction Date and Time             object\n",
              "Merchant ID                            int64\n",
              "Payment Method                        object\n",
              "Country Code                          object\n",
              "Transaction Type                      object\n",
              "Device Type                           object\n",
              "IP Address                            object\n",
              "Browser Type                          object\n",
              "Operating System                      object\n",
              "Merchant Category                     object\n",
              "User Age                               int64\n",
              "User Occupation                       object\n",
              "User Income                          float64\n",
              "User Gender                           object\n",
              "User Account Status                   object\n",
              "Transaction Status                    object\n",
              "Location Distance                    float64\n",
              "Time Taken for Transaction           float64\n",
              "Transaction Time of Day               object\n",
              "User's Transaction History             int64\n",
              "Merchant's Reputation Score          float64\n",
              "User's Device Location                object\n",
              "Transaction Currency                  object\n",
              "Transaction Purpose                   object\n",
              "User's Credit Score                    int64\n",
              "User's Email Domain                   object\n",
              "Merchant's Business Age                int64\n",
              "Transaction Authentication Method     object\n",
              "Fraudulent Flag                        int64\n",
              "User Income Group                     object\n",
              "User Age Group                        object\n",
              "Time Group                            object\n",
              "Location Distance Group               object\n",
              "Time Take for Transaction Group       object\n",
              "User's Credit Score Group             object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "_oTUNXkt8EGJ"
      },
      "outputs": [],
      "source": [
        "X = data.copy()\n",
        "y = X.pop(\"Fraudulent Flag\")\n",
        "\n",
        "# Label encoding for categoricals\n",
        "for colname in X.select_dtypes(\"object\"):\n",
        "    X[colname], _ = X[colname].factorize()\n",
        "\n",
        "# All discrete features should now have integer dtypes\n",
        "discrete_features = X.dtypes == int"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "rNZJdPZQ8OnN",
        "outputId": "89882200-f9b7-47a9-c23e-cb8587a9f9f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Transaction ID  User ID  Transaction Amount  \\\n",
              "991341         95289283     6079              109.14   \n",
              "1373169        66060189     1512              951.07   \n",
              "5022000        45305236     8839              382.19   \n",
              "4215047        93770101     2623              936.26   \n",
              "302525         44047965     1502              962.22   \n",
              "\n",
              "         Transaction Date and Time  Merchant ID  Payment Method  Country Code  \\\n",
              "991341                      980089         7767              28            31   \n",
              "1373169                    1351588         7224              39            26   \n",
              "5022000                    4740770         1451               3            20   \n",
              "4215047                    4015316         1775              23            25   \n",
              "302525                      201315         7240               9            15   \n",
              "\n",
              "         Transaction Type  Device Type  IP Address  Browser Type  \\\n",
              "991341                 10           17      985783            17   \n",
              "1373169                32           35     1362511            38   \n",
              "5022000                35           10     4882096            11   \n",
              "4215047                 3            8     4116036            35   \n",
              "302525                 21           37      302030            31   \n",
              "\n",
              "         Operating System  Merchant Category  User Age  User Occupation  \\\n",
              "991341                 32                 29        74               15   \n",
              "1373169                10                 18        62                3   \n",
              "5022000                22                 18        21               20   \n",
              "4215047                16                 11        59               14   \n",
              "302525                 27                 29        20               13   \n",
              "\n",
              "         User Income  User Gender  User Account Status  Transaction Status  \\\n",
              "991341      47680.49            1                    0                  32   \n",
              "1373169     32998.18            4                   11                   5   \n",
              "5022000     62491.43            0                   16                  34   \n",
              "4215047     74913.87            1                   15                  15   \n",
              "302525      78839.41            2                    4                  38   \n",
              "\n",
              "         Location Distance  Time Taken for Transaction  \\\n",
              "991341                3.91                       27.70   \n",
              "1373169              92.62                        8.95   \n",
              "5022000              65.19                       21.48   \n",
              "4215047              87.91                       36.89   \n",
              "302525               13.57                       45.21   \n",
              "\n",
              "         Transaction Time of Day  User's Transaction History  \\\n",
              "991341                         1                          93   \n",
              "1373169                        2                          62   \n",
              "5022000                        2                          80   \n",
              "4215047                        1                          18   \n",
              "302525                         0                          90   \n",
              "\n",
              "         Merchant's Reputation Score  User's Device Location  \\\n",
              "991341                          4.98                      17   \n",
              "1373169                         3.80                      14   \n",
              "5022000                         1.44                      23   \n",
              "4215047                         2.31                       3   \n",
              "302525                          2.44                      20   \n",
              "\n",
              "         Transaction Currency  Transaction Purpose  User's Credit Score  \\\n",
              "991341                     19                   29                  388   \n",
              "1373169                    24                   13                  763   \n",
              "5022000                    30                   11                  838   \n",
              "4215047                    13                   22                  513   \n",
              "302525                     24                   13                  500   \n",
              "\n",
              "         User's Email Domain  Merchant's Business Age  \\\n",
              "991341                    25                       16   \n",
              "1373169                    9                       16   \n",
              "5022000                   14                       17   \n",
              "4215047                   13                       10   \n",
              "302525                    21                       19   \n",
              "\n",
              "         Transaction Authentication Method  User Income Group  User Age Group  \\\n",
              "991341                                  28                  0               0   \n",
              "1373169                                 37                  2               0   \n",
              "5022000                                  8                  0               1   \n",
              "4215047                                 24                  1               2   \n",
              "302525                                  30                  1               1   \n",
              "\n",
              "         Time Group  Location Distance Group  Time Take for Transaction Group  \\\n",
              "991341            0                        0                                0   \n",
              "1373169           2                        1                                0   \n",
              "5022000           0                        1                                0   \n",
              "4215047           0                        1                                1   \n",
              "302525            1                        0                                1   \n",
              "\n",
              "         User's Credit Score Group  \n",
              "991341                           0  \n",
              "1373169                          1  \n",
              "5022000                          1  \n",
              "4215047                          2  \n",
              "302525                           2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83ef4bf1-a569-45cb-a45e-32a21532c1e2\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction ID</th>\n",
              "      <th>User ID</th>\n",
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              "      <th>User's Device Location</th>\n",
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              "      <th>Transaction Purpose</th>\n",
              "      <th>User's Credit Score</th>\n",
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              "      <th>Time Group</th>\n",
              "      <th>Location Distance Group</th>\n",
              "      <th>Time Take for Transaction Group</th>\n",
              "      <th>User's Credit Score Group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>991341</th>\n",
              "      <td>95289283</td>\n",
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              "      <td>109.14</td>\n",
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              "      <td>15</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3.91</td>\n",
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              "      <td>388</td>\n",
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              "    <tr>\n",
              "      <th>1373169</th>\n",
              "      <td>66060189</td>\n",
              "      <td>1512</td>\n",
              "      <td>951.07</td>\n",
              "      <td>1351588</td>\n",
              "      <td>7224</td>\n",
              "      <td>39</td>\n",
              "      <td>26</td>\n",
              "      <td>32</td>\n",
              "      <td>35</td>\n",
              "      <td>1362511</td>\n",
              "      <td>38</td>\n",
              "      <td>10</td>\n",
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              "      <td>32998.18</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>92.62</td>\n",
              "      <td>8.95</td>\n",
              "      <td>2</td>\n",
              "      <td>62</td>\n",
              "      <td>3.80</td>\n",
              "      <td>14</td>\n",
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              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>5022000</th>\n",
              "      <td>45305236</td>\n",
              "      <td>8839</td>\n",
              "      <td>382.19</td>\n",
              "      <td>4740770</td>\n",
              "      <td>1451</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
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              "      <td>10</td>\n",
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              "      <td>11</td>\n",
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              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>62491.43</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>34</td>\n",
              "      <td>65.19</td>\n",
              "      <td>21.48</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>1.44</td>\n",
              "      <td>23</td>\n",
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              "      <th>4215047</th>\n",
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              "      <td>2</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<div id=\"df-f35c3f2c-8410-44a6-97d0-0c3d54317bd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f35c3f2c-8410-44a6-97d0-0c3d54317bd5')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f35c3f2c-8410-44a6-97d0-0c3d54317bd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "X.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "80QVBQkW8XPV"
      },
      "outputs": [],
      "source": [
        "# Droping ID columns\n",
        "X = X.drop(columns =[i for i in X.columns if 'ID' in i.split()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR9aoJsA8aZf",
        "outputId": "8f7b8c2b-d5b0-4d27-8825-57e7e6122a29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Transaction Amount', 'Payment Method', 'Country Code',\n",
              "       'Transaction Type', 'Device Type', 'IP Address', 'Browser Type',\n",
              "       'Operating System', 'Merchant Category', 'User Occupation',\n",
              "       'User Gender', 'User Account Status', 'Transaction Status',\n",
              "       'Transaction Time of Day', 'User's Transaction History',\n",
              "       'Merchant's Reputation Score', 'User's Device Location',\n",
              "       'Transaction Currency', 'Transaction Purpose', 'User's Credit Score',\n",
              "       'User's Email Domain', 'Merchant's Business Age',\n",
              "       'Transaction Authentication Method', 'User Income Group',\n",
              "       'User Age Group', 'Location Distance Group',\n",
              "       'Time Take for Transaction Group'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "already_extracted_cols = ['Transaction Date and Time','User Income', 'User Age', 'Time Group',\n",
        "          'Location Distance', 'Time Taken for Transaction', \"User's Credit Score Group\"]\n",
        "for i in already_extracted_cols:\n",
        "  X.pop(i)\n",
        "\n",
        "X.columns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsauS5cZ-K_B",
        "outputId": "2b2e3e2e-8d57-473e-faeb-b3203645f425"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27 38\n"
          ]
        }
      ],
      "source": [
        "print(len(X.columns), len(data.columns))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYgjc-PO_PBO",
        "outputId": "a366f5ea-6204-44af-91d6-fe32d3e4d08a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "selected_features = ['Transaction Amount', 'User Income Group', 'Time Take for Transaction Group',\n",
        "       \"User's Credit Score\", 'Location Distance Group',\n",
        "       \"Merchant's Reputation Score\", \"User's Device Location\",\n",
        "       \"User's Transaction History\", 'User Age Group', 'Country Code',\n",
        "       'Operating System', 'Transaction Status', 'Payment Method',\n",
        "       'Merchant Category', \"User's Email Domain\", 'Device Type']\n",
        "\n",
        "len(selected_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "ITQXixcr-Tx-"
      },
      "outputs": [],
      "source": [
        "# Train-set, Validation-set and Test-set\n",
        "\n",
        "train_X, test_X, train_y, test_y = train_test_split(X[selected_features], y, test_size=0.2, stratify=y, random_state = 33)\n",
        "\n",
        "train_X, val_X, train_y, val_y = train_test_split(test_X, test_y, test_size=0.25, random_state = 33)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Train size: {train_X.shape[0]}\")\n",
        "print(f\"Validation size: {val_X.shape[0]}\")\n",
        "print(f\"Test size: {test_X.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvpUcHmcdDF3",
        "outputId": "a99514bc-9b83-46e6-a8a9-ec6cc11f4372"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size: 870752\n",
            "Validation size: 290251\n",
            "Test size: 1161003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmV7rpStA7fD",
        "outputId": "4a33644b-37da-49f4-b0e1-f89b2aa6a4e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score: 0.9411\n",
            "Test-set accuracy score: 0.8310\n"
          ]
        }
      ],
      "source": [
        "# lightGBM model fit\n",
        "gbm = LGBMClassifier(deterministic=True,\n",
        "                     max_depth=10,\n",
        "                     metric='binary_logloss',\n",
        "                     n_estimators=1500,\n",
        "                     num_leaves=511,\n",
        "                     objective='binary',\n",
        "                     learning_rate=0.065,\n",
        "                     tree_learner='data',\n",
        "                     force_row_wise=True,\n",
        "                     min_data_in_leaf=17,\n",
        "                     class_weight='balanced',\n",
        "                     verbose=-1,\n",
        "                     save_binary=True)\n",
        "\n",
        "gbm.fit(train_X,\n",
        "        train_y,\n",
        "        eval_set=[(val_X, val_y)],\n",
        "        eval_metric='auc',\n",
        "        )\n",
        "train_pred = gbm.predict(train_X)\n",
        "\n",
        "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(train_pred, train_y)))\n",
        "\n",
        "pred = gbm.predict(test_X)\n",
        "print('Test-set accuracy score: {0:0.4f}'. format(accuracy_score(pred, test_y)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model\n",
        "gbm.fit(train_X,\n",
        "        train_y,\n",
        "        eval_set=[(val_X, val_y)],\n",
        "        eval_metric='auc',\n",
        "        )\n",
        "\n",
        "pred = gbm.predict(test_X)\n",
        "print('Test-set accuracy score: {0:0.4f}'. format(accuracy_score(pred, test_y)))"
      ],
      "metadata": {
        "id": "EtvD_95nX7YJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "q0yTBCnGHe_y"
      },
      "outputs": [],
      "source": [
        "# Save model\n",
        "\n",
        "# Create a folder in the root directory\n",
        "!mkdir -p \"/content/drive/My Drive/Fraud Detection Model\"\n",
        "\n",
        "pickle.dump(gbm, open('/content/drive/My Drive/Fraud Detection Model/model.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model\n",
        "\n",
        "pickled_model = pickle.load(open('/content/drive/My Drive/Fraud Detection Model/model.pkl', 'rb'))\n",
        "pickled_pred = pickled_model.predict(test_X)\n",
        "\n",
        "# Testing\n",
        "print('Test-set accuracy score: {0:0.4f}'. format(accuracy_score(pickled_pred, test_y)))"
      ],
      "metadata": {
        "id": "XIiKZGDVMjhJ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xhOLnobCZvot"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}