Fraud_detection_in_financial_payment_services.ipynb

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  "metadata": {
    "colab": {
      "name": "Fraud detection in financial payment services.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/roshank1605A04/Fraud-Detection-in-Online-Transactions/blob/master/Fraud_detection_in_financial_payment_services.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "ITZ2dCCoGRgi",
        "colab_type": "code",
        "outputId": "7211c5d3-f27b-4c46-874b-ce50dd4dbe95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9SW6U0SvYChM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TZME7Dc-YT3m",
        "colab_type": "code",
        "outputId": "78207368-89c7-410c-d39f-1312b67eed70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# importing the dataset using dask\n",
        "\n",
        "%time data = pd.read_csv('drive/My Drive/Projects/Fraud/Fraud.csv')\n",
        "\n",
        "print(data.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 13.4 s, sys: 1.66 s, total: 15 s\n",
            "Wall time: 21 s\n",
            "(6362620, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ciAPU4JYdJG",
        "colab_type": "code",
        "outputId": "912a372d-2f74-45a0-e471-b110575856d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "cell_type": "code",
      "source": [
        "data.sample(15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6244711</th>\n",
              "      <td>596</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7097.27</td>\n",
              "      <td>C1838303722</td>\n",
              "      <td>76691.45</td>\n",
              "      <td>69594.18</td>\n",
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              "      <th>3937981</th>\n",
              "      <td>286</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>308456.47</td>\n",
              "      <td>C244565253</td>\n",
              "      <td>75927.78</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C886049859</td>\n",
              "      <td>1250895.07</td>\n",
              "      <td>1559351.54</td>\n",
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              "      <th>795121</th>\n",
              "      <td>40</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>66493.18</td>\n",
              "      <td>C1406636269</td>\n",
              "      <td>36400.00</td>\n",
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              "      <td>56844.65</td>\n",
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              "      <td>75950.15</td>\n",
              "      <td>C630433411</td>\n",
              "      <td>4091402.99</td>\n",
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              "    <tr>\n",
              "      <th>5012662</th>\n",
              "      <td>353</td>\n",
              "      <td>CASH_IN</td>\n",
              "      <td>17136.09</td>\n",
              "      <td>C457734124</td>\n",
              "      <td>820357.55</td>\n",
              "      <td>837493.63</td>\n",
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              "      <td>30175.49</td>\n",
              "      <td>8821.40</td>\n",
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              "      <td>PAYMENT</td>\n",
              "      <td>9289.60</td>\n",
              "      <td>C17866062</td>\n",
              "      <td>99353.00</td>\n",
              "      <td>90063.40</td>\n",
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              "      <td>18144.01</td>\n",
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              "      <td>281</td>\n",
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              "      <td>3455.74</td>\n",
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              "    <tr>\n",
              "      <th>4086765</th>\n",
              "      <td>301</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>21317.24</td>\n",
              "      <td>C1078889359</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C1329611434</td>\n",
              "      <td>811632.81</td>\n",
              "      <td>832950.05</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>976425</th>\n",
              "      <td>44</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>196524.19</td>\n",
              "      <td>C1824342098</td>\n",
              "      <td>1146.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C682469334</td>\n",
              "      <td>811556.55</td>\n",
              "      <td>1008080.74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4304511</th>\n",
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              "      <td>CASH_OUT</td>\n",
              "      <td>305638.12</td>\n",
              "      <td>C4081677</td>\n",
              "      <td>3298.00</td>\n",
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            ],
            "text/plain": [
              "         step      type     amount     nameOrig  oldbalanceOrg  \\\n",
              "6244711   596   PAYMENT    7097.27  C1838303722       76691.45   \n",
              "3937981   286  CASH_OUT  308456.47   C244565253       75927.78   \n",
              "795121     40  CASH_OUT   66493.18  C1406636269       36400.00   \n",
              "5902475   403  CASH_OUT   56844.65   C224919429           0.00   \n",
              "5029619   354   CASH_IN   75950.15   C630433411     4091402.99   \n",
              "5012662   353   CASH_IN   17136.09   C457734124      820357.55   \n",
              "3413044   256  CASH_OUT   20134.64   C328974881           0.00   \n",
              "616152     34   PAYMENT   21354.10   C428154933       30175.49   \n",
              "1380906   138   PAYMENT    9289.60    C17866062       99353.00   \n",
              "493945     20   PAYMENT   18144.01   C452022858       12912.00   \n",
              "3806635   281   PAYMENT    3455.74   C772668488        1650.00   \n",
              "3937737   286   PAYMENT    5165.42   C698560956           0.00   \n",
              "4086765   301  CASH_OUT   21317.24  C1078889359           0.00   \n",
              "976425     44  CASH_OUT  196524.19  C1824342098        1146.00   \n",
              "4304511   307  CASH_OUT  305638.12     C4081677        3298.00   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "6244711        69594.18   M884719309            0.00            0.00        0   \n",
              "3937981            0.00   C886049859      1250895.07      1559351.54        0   \n",
              "795121             0.00  C1173347036            0.00        66493.18        0   \n",
              "5902475            0.00  C1589672310      8134504.42      8191349.06        0   \n",
              "5029619      4167353.14  C1705828374      1490222.22      1414272.07        0   \n",
              "5012662       837493.63  C1891906866       970062.21       952926.12        0   \n",
              "3413044            0.00  C1440876390      1154267.78      1174402.43        0   \n",
              "616152          8821.40  M1182183706            0.00            0.00        0   \n",
              "1380906        90063.40  M1575421544            0.00            0.00        0   \n",
              "493945             0.00  M1864915088            0.00            0.00        0   \n",
              "3806635            0.00  M1403875164            0.00            0.00        0   \n",
              "3937737            0.00  M1911986584            0.00            0.00        0   \n",
              "4086765            0.00  C1329611434       811632.81       832950.05        0   \n",
              "976425             0.00   C682469334       811556.55      1008080.74        0   \n",
              "4304511            0.00   C614190962       140975.61       446613.72        0   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "6244711               0  \n",
              "3937981               0  \n",
              "795121                0  \n",
              "5902475               0  \n",
              "5029619               0  \n",
              "5012662               0  \n",
              "3413044               0  \n",
              "616152                0  \n",
              "1380906               0  \n",
              "493945                0  \n",
              "3806635               0  \n",
              "3937737               0  \n",
              "4086765               0  \n",
              "976425                0  \n",
              "4304511               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "5NfdLQ2zY7V2",
        "colab_type": "code",
        "outputId": "437260b9-aaef-4792-cc66-bd27010466f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "# getting the information related to data\n",
        "\n",
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            "step              int64\n",
            "type              object\n",
            "amount            float64\n",
            "nameOrig          object\n",
            "oldbalanceOrg     float64\n",
            "newbalanceOrig    float64\n",
            "nameDest          object\n",
            "oldbalanceDest    float64\n",
            "newbalanceDest    float64\n",
            "isFraud           int64\n",
            "isFlaggedFraud    int64\n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GYdQB-qUY9iR",
        "colab_type": "code",
        "outputId": "2c81494a-5b95-44a7-f07a-1bccbbc651d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "#describing the data\n",
        "\n",
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.433972e+02</td>\n",
              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.100702e+06</td>\n",
              "      <td>1.224996e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>6.038582e+05</td>\n",
              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.399180e+06</td>\n",
              "      <td>3.674129e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.327057e+05</td>\n",
              "      <td>2.146614e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>9.430367e+05</td>\n",
              "      <td>1.111909e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
              "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
              "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
              "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
              "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
              "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
              "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
              "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
              "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
              "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "NKho3e1daDaf",
        "colab_type": "code",
        "outputId": "2598563c-77fe-494f-dc47-269dccae4887",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "#checking how many frauds are there\n",
        "\n",
        "data['isFraud'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6354407\n",
              "1       8213\n",
              "Name: isFraud, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "dJmgFe13btOE",
        "colab_type": "code",
        "outputId": "e96ed4e3-5375-4207-cac4-dcf7d216a595",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "cell_type": "code",
      "source": [
        "labels = ['Not a Fraud', 'Fraud']\n",
        "colors = ['pink', 'black']\n",
        "size = [6354407, 8213]\n",
        "explode = [0.1, 0.21]\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (10, 10)\n",
        "plt.pie(size, labels = labels, colors = colors, explode = explode, shadow = True)\n",
        "plt.axis('off')\n",
        "plt.title('A pie chart representing share of frauds amongst the customers',fontsize = 20)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XrwoKhUKYO3euQd3jxo0TFAqFcOvWrWKf36xZs/SOMa2oqCjB29tb6N+/v25ZUa+RMcbW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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FwR5JXB2aNHi",
        "colab_type": "code",
        "outputId": "f5555d0a-e2b7-48ea-f9d9-5d8cf34fb69a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "data['isFlaggedFraud'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6362604\n",
              "1         16\n",
              "Name: isFlaggedFraud, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "ONJTZw-zctFB",
        "colab_type": "code",
        "outputId": "45dd4a7f-44ed-41d1-ce8d-1908e8667320",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "cell_type": "code",
      "source": [
        "labels = ['Not a Flagged Fraud', 'Flagged Fraud']\n",
        "colors = ['lightblue', 'black']\n",
        "size = [6354407, 8213]\n",
        "explode = [0.1, 0.21]\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (10, 10)\n",
        "plt.pie(size, labels = labels, colors = colors, explode = explode, shadow = True)\n",
        "plt.axis('off')\n",
        "plt.title('A pie chart representing share of flagged frauds amongst the customers',fontsize = 20)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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faUG1a9dGQEAAtm7dCicnJ+zbt89o+bi4OEgkEoNuL8+rTm7SpAkEQdCdx0/S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\nIDADAAAAOfx/s1L9ZTKCj0kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "b9T9R8Claour",
        "colab_type": "code",
        "outputId": "be1f549d-7095-498f-c40b-bf17c612df10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "data['type'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CASH_OUT    2237500\n",
              "PAYMENT     2151495\n",
              "CASH_IN     1399284\n",
              "TRANSFER     532909\n",
              "DEBIT         41432\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "u1OHjHgndHod",
        "colab_type": "code",
        "outputId": "9871959e-b1c9-49df-9abd-2de0d4475232",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "cell_type": "code",
      "source": [
        "labels = ['Cash-out', 'Payment', 'Cash-in', 'Transfer', 'Debit']\n",
        "size = [2237500, 2151495, 1399284, 532909, 41432]\n",
        "colors = ['pink', 'lightgreen', 'lightblue', 'orange', 'yellow']\n",
        "explode = [0.1, 0.1, 0.1, 0.1, 0.1]\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (10, 10)\n",
        "plt.pie(size, colors = colors, explode = explode, labels = labels, shadow = True)\n",
        "plt.title('A pie chart representing different types of money transactions', fontsize = 20)\n",
        "plt.axis('off')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bd/Xq1YpGo1GWLVtmMe7Vq1cVjUaj/Oc//1EURVF0Op153S5ZssTqdGfOnGke1qtXL6Vj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nQuIIllsqrhYlsfPjV4NMO2R17E2nsfh8ViDZrEVct3YrfrddRIYWgHuPOfAF6Y/vMQAJRYRUx2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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "F9SzfpVtblOq",
        "colab_type": "code",
        "outputId": "dac58ac9-ce25-466b-a1a3-59af9a03f8b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different types of steps involved in transactions\n",
        "\n",
        "x = data['step'].value_counts().nunique()\n",
        "\n",
        "# printing the result\n",
        "print(\"The no. of different steps involved in transactions are :\", x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The no. of different steps involved in transactions are : 428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2er8JIaysTho",
        "colab_type": "code",
        "outputId": "f759a623-3349-4c65-86e4-76afa6f13740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "cell_type": "code",
      "source": [
        "# setting thesze of the figure\n",
        "plt.rcParams['figure.figsize'] =(14, 7)\n",
        "\n",
        "# importing norm\n",
        "from scipy.stats import norm\n",
        "\n",
        "sns.distplot(data.step, fit = norm, kde = False)\n",
        "plt.title('Distribution Plot for steps', fontsize = 20)\n",
        "plt.xlabel('Step', fontsize = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  alternative=\"'density'\", removal=\"3.1\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//npAhw79qsOHD+r06VOyWq0Jz7vkyKkSz5ZTXs9CyudZWO75CipXnnxyy5VH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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rOuIfJ1CuLi-",
        "colab_type": "code",
        "outputId": "becafc49-752c-4bdc-b43d-b7548fa264cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different values of amounts transationed\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (10, 7)\n",
        "\n",
        "data['amount'].value_counts().head(15).plot.bar()\n",
        "plt.title('15 Most Common Transaction amounts', fontsize = 20)\n",
        "plt.xlabel('Amounts')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cAQAAGIRyBgAAYBDKGQAAgEEoZwAAAAahnAEAABiEcgYAAGAQyhkAAIBBKGcA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\nEEI4QpMzQgghhBCO0OSMEEIIIYQj/we/241/W4EAtwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MatC3P5PIIfT",
        "colab_type": "code",
        "outputId": "13a5c11b-9e65-4c25-dd50-cef1f9d18750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the different values of amounts transationed\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (19, 7)\n",
        "\n",
        "data['amount'].value_counts().tail(50).plot.bar()\n",
        "plt.title('50 Least Common Transaction amounts', fontsize = 20)\n",
        "plt.xlabel('Amounts')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v/fr1mD9/PiZMmIBp06bBxMQEFy9exPz587F27VpmOz09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\nwhhjjDHGGGOMMSbBfwHU8XIxPC44wAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1368x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TMUDNZDPvKCS",
        "colab_type": "code",
        "outputId": "b1882694-8d1b-489e-fdc2-7076b1202d1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        }
      },
      "cell_type": "code",
      "source": [
        "# setting thesze of the figure\n",
        "plt.rcParams['figure.figsize'] =(14, 7)\n",
        "\n",
        "# setting the style in seaborn\n",
        "sns.set(style = 'dark', palette = 'colorblind')\n",
        "\n",
        "#plotting a dist plot\n",
        "ax = sns.distplot(data.amount)\n",
        "ax.set_title(label = 'Distribution Plot for amount', fontsize = 20)\n",
        "ax.set_xlabel(xlabel = 'Amount', fontsize = 10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  alternative=\"'density'\", removal=\"3.1\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zuofFoi7Naymosa+2n0jVj6lZOn8lR0dSMnUkJdP4nAAA9zEgoKHm9bu/QrZV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\nYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y_IRTieSbRBL",
        "colab_type": "code",
        "outputId": "77919ac2-9fe5-43e0-d73b-7e8df87cd10c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# finding  out the types of fraudulent transaction types out of all the transaction modes.\n",
        "\n",
        "print('The Types of fraudulent transactions are {}'.format(list(data.loc[data.isFraud == 1].type.drop_duplicates().values)) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Types of fraudulent transactions are ['TRANSFER', 'CASH_OUT']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aXc-odaRaznS",
        "colab_type": "code",
        "outputId": "47c4bbf7-f179-439b-f047-d89ce1083187",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# figuring out the fraud transfer and fraud cash outs\n",
        "dfFraudTransfer = data.loc[(data.isFraud == 1) & (data.type == 'TRANSFER')]\n",
        "dfFraudCashout = data.loc[(data.isFraud == 1) & (data.type == 'CASH_OUT')]\n",
        "\n",
        "\n",
        "#printing the number of fraudulent transfers ans cash outs\n",
        "print ('The number of fraudulent TRANSFERs = {}'.format(len(dfFraudTransfer)))\n",
        "print ('The number of fraudulent CASH_OUTs = {}'.format(len(dfFraudCashout)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The number of fraudulent TRANSFERs = 4097\n",
            "The number of fraudulent CASH_OUTs = 4116\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pIgCRMa5cpLo",
        "colab_type": "code",
        "outputId": "3cd2bb1f-3651-4195-8138-54f601f486b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "# finding out the minimum and maximum transaction from Transfer and cash out transaction mode when it is set to be Fraud\n",
        "\n",
        "print(\"Transfer Transaction\")\n",
        "print(\"Minimum Transaction :\", data.loc[(data.isFraud == 1) & (data.type == 'TRANSFER')].amount.min())\n",
        "print(\"Maximum Transaction :\", data.loc[(data.isFraud == 1) & (data.type == 'TRANSFER')].amount.max())\n",
        "\n",
        "print(\"\\nCASH_OUT Transaction\")\n",
        "print(\"Minimum Transaction :\", data.loc[(data.isFraud == 1) & (data.type == 'CASH_OUT')].amount.min())\n",
        "print(\"Maximum Transaction :\", data.loc[(data.isFraud == 1) & (data.type == 'CASH_OUT')].amount.max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transfer Transaction\n",
            "Minimum Transaction : 63.8\n",
            "Maximum Transaction : 10000000.0\n",
            "\n",
            "CASH_OUT Transaction\n",
            "Minimum Transaction : 0.0\n",
            "Maximum Transaction : 10000000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sCyHO7HsYd5g",
        "colab_type": "code",
        "outputId": "56c11dac-ca58-4824-b19f-88589563bad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "#finding out the transactions which are flagged to be fraud\n",
        "\n",
        "print(\"The Type of Transaction in which IsFlagged is set :\", data.loc[data.isFlaggedFraud == 1].type.drop_duplicates().values)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Type of Transaction in which IsFlagged is set : ['TRANSFER']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e4fxRvUNa20S",
        "colab_type": "code",
        "outputId": "d0c06e76-269d-41a8-dac3-4d22323b4e08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# figuringing out the no. of flagged fraudulent transactions\n",
        "FlaggedFraud = data.loc[(data.isFlaggedFraud == 1) & (data.type == 'TRANSFER')]\n",
        "print(\"The no. of Flagged Fraudulent Transactions :\", len(FlaggedFraud))\n",
        "\n",
        "# printing the minimum and maximum transactions done when FlaggedFraud is set\n",
        "# the Transaction mode being Transfer\n",
        "print(\"Minimum Transaction :\", data.loc[data.isFlaggedFraud == 1].amount.min())\n",
        "print(\"Maximum Transaction :\", data.loc[data.isFlaggedFraud == 1].amount.max())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The no. of Flagged Fraudulent Transactions : 16\n",
            "Minimum Transaction : 353874.22\n",
            "Maximum Transaction : 10000000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QqD5X_vtcJBp",
        "colab_type": "code",
        "outputId": "45c02e59-2ebd-4f70-e0bb-335f461fd6dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3267
        }
      },
      "cell_type": "code",
      "source": [
        "# Figuring out the transactions where old balance dest. and new balance dest. are 0 being FlaggedFraud\n",
        "\n",
        "x = data.loc[(data.isFlaggedFraud == 0) & (data.oldbalanceDest == 0) & (data.newbalanceDest == 0)]\n",
        "print(\"Figuring out the no. of transactions where old balance dest. and new balance dest. are 0 being FlaggedFraud :\", x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figuring out the no. of transactions where old balance dest. and new balance dest. are 0 being FlaggedFraud :          step      type       amount     nameOrig  oldbalanceOrg  \\\n",
            "0           1   PAYMENT      9839.64  C1231006815      170136.00   \n",
            "1           1   PAYMENT      1864.28  C1666544295       21249.00   \n",
            "2           1  TRANSFER       181.00  C1305486145         181.00   \n",
            "4           1   PAYMENT     11668.14  C2048537720       41554.00   \n",
            "5           1   PAYMENT      7817.71    C90045638       53860.00   \n",
            "6           1   PAYMENT      7107.77   C154988899      183195.00   \n",
            "7           1   PAYMENT      7861.64  C1912850431      176087.23   \n",
            "8           1   PAYMENT      4024.36  C1265012928        2671.00   \n",
            "11          1   PAYMENT      3099.97   C249177573       20771.00   \n",
            "12          1   PAYMENT      2560.74  C1648232591        5070.00   \n",
            "13          1   PAYMENT     11633.76  C1716932897       10127.00   \n",
            "14          1   PAYMENT      4098.78  C1026483832      503264.00   \n",
            "16          1   PAYMENT      1563.82   C761750706         450.00   \n",
            "17          1   PAYMENT      1157.86  C1237762639       21156.00   \n",
            "18          1   PAYMENT       671.64  C2033524545       15123.00   \n",
            "20          1   PAYMENT      1373.43    C20804602       13854.00   \n",
            "23          1   PAYMENT      3876.41   C504336483       67852.00   \n",
            "25          1   PAYMENT      6061.13  C1043358826         443.00   \n",
            "26          1   PAYMENT      9478.39  C1671590089      116494.00   \n",
            "27          1   PAYMENT      8009.09  C1053967012       10968.00   \n",
            "28          1   PAYMENT      8901.99  C1632497828        2958.91   \n",
            "29          1   PAYMENT      9920.52   C764826684           0.00   \n",
            "30          1   PAYMENT      3448.92  C2103763750           0.00   \n",
            "31          1   PAYMENT      4206.84   C215078753           0.00   \n",
            "32          1   PAYMENT      5885.56   C840514538           0.00   \n",
            "33          1   PAYMENT      5307.88  C1768242710           0.00   \n",
            "34          1   PAYMENT      5031.22   C247113419           0.00   \n",
            "35          1   PAYMENT     24213.67  C1238616099           0.00   \n",
            "36          1   PAYMENT      8603.42  C1608633989         253.00   \n",
            "37          1   PAYMENT      2791.42   C923341586      300481.00   \n",
            "...       ...       ...          ...          ...            ...   \n",
            "6362558   738  TRANSFER    114490.39   C983453370      114490.39   \n",
            "6362560   739  TRANSFER    176549.59   C591532921      176549.59   \n",
            "6362562   739  TRANSFER   7758036.72   C365237849     7758036.72   \n",
            "6362564   739  TRANSFER    156691.56   C671228809      156691.56   \n",
            "6362566   739  TRANSFER      8116.53  C2072430566        8116.53   \n",
            "6362568   739  TRANSFER    194521.28  C1696012302      194521.28   \n",
            "6362570   740  TRANSFER    748139.48  C1183186026      748139.48   \n",
            "6362572   740  TRANSFER   1312694.50  C1681615431     1312694.50   \n",
            "6362574   740  TRANSFER   1755647.81   C507031767     1755647.81   \n",
            "6362576   741  TRANSFER  10000000.00   C780743034    45674547.89   \n",
            "6362578   741  TRANSFER  10000000.00  C1111699947    35674547.89   \n",
            "6362580   741  TRANSFER  10000000.00    C88849251    25674547.89   \n",
            "6362582   741  TRANSFER  10000000.00  C1945606464    15674547.89   \n",
            "6362586   741  TRANSFER     62297.18  C1565612524       62297.18   \n",
            "6362588   741  TRANSFER    114149.44  C1157446475      114149.44   \n",
            "6362590   741  TRANSFER    323726.61   C509686434      323726.61   \n",
            "6362592   741  TRANSFER    383661.10   C457476945      383661.10   \n",
            "6362594   741  TRANSFER    144945.34    C31496887      144945.34   \n",
            "6362596   741  TRANSFER     48442.88  C1112979339       48442.88   \n",
            "6362598   742  TRANSFER   4009058.39  C1044665079     4009058.39   \n",
            "6362600   742  TRANSFER    652993.91    C40604503      652993.91   \n",
            "6362602   742  TRANSFER   1819543.69  C2089752665     1819543.69   \n",
            "6362604   742  TRANSFER     54652.46  C1674778854       54652.46   \n",
            "6362606   742  TRANSFER    303846.74   C959102961      303846.74   \n",
            "6362608   742  TRANSFER    258355.42  C1226129332      258355.42   \n",
            "6362610   742  TRANSFER     63416.99   C778071008       63416.99   \n",
            "6362612   743  TRANSFER   1258818.82  C1531301470     1258818.82   \n",
            "6362614   743  TRANSFER    339682.13  C2013999242      339682.13   \n",
            "6362616   743  TRANSFER   6311409.28  C1529008245     6311409.28   \n",
            "6362618   743  TRANSFER    850002.52  C1685995037      850002.52   \n",
            "\n",
            "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
            "0             160296.36  M1979787155             0.0             0.0        0   \n",
            "1              19384.72  M2044282225             0.0             0.0        0   \n",
            "2                  0.00   C553264065             0.0             0.0        1   \n",
            "4              29885.86  M1230701703             0.0             0.0        0   \n",
            "5              46042.29   M573487274             0.0             0.0        0   \n",
            "6             176087.23   M408069119             0.0             0.0        0   \n",
            "7             168225.59   M633326333             0.0             0.0        0   \n",
            "8                  0.00  M1176932104             0.0             0.0        0   \n",
            "11             17671.03  M2096539129             0.0             0.0        0   \n",
            "12              2509.26   M972865270             0.0             0.0        0   \n",
            "13                 0.00   M801569151             0.0             0.0        0   \n",
            "14            499165.22  M1635378213             0.0             0.0        0   \n",
            "16                 0.00  M1731217984             0.0             0.0        0   \n",
            "17             19998.14  M1877062907             0.0             0.0        0   \n",
            "18             14451.36   M473053293             0.0             0.0        0   \n",
            "20             12480.57  M1344519051             0.0             0.0        0   \n",
            "23             63975.59  M1404932042             0.0             0.0        0   \n",
            "25                 0.00  M1558079303             0.0             0.0        0   \n",
            "26            107015.61    M58488213             0.0             0.0        0   \n",
            "27              2958.91   M295304806             0.0             0.0        0   \n",
            "28                 0.00    M33419717             0.0             0.0        0   \n",
            "29                 0.00  M1940055334             0.0             0.0        0   \n",
            "30                 0.00   M335107734             0.0             0.0        0   \n",
            "31                 0.00  M1757317128             0.0             0.0        0   \n",
            "32                 0.00  M1804441305             0.0             0.0        0   \n",
            "33                 0.00  M1971783162             0.0             0.0        0   \n",
            "34                 0.00   M151442075             0.0             0.0        0   \n",
            "35                 0.00    M70695990             0.0             0.0        0   \n",
            "36                 0.00  M1615617512             0.0             0.0        0   \n",
            "37            297689.58   M107994825             0.0             0.0        0   \n",
            "...                 ...          ...             ...             ...      ...   \n",
            "6362558            0.00  C1060799549             0.0             0.0        1   \n",
            "6362560            0.00  C1612941843             0.0             0.0        1   \n",
            "6362562            0.00   C332427023             0.0             0.0        1   \n",
            "6362564            0.00  C1102996671             0.0             0.0        1   \n",
            "6362566            0.00   C765215468             0.0             0.0        1   \n",
            "6362568            0.00   C411128068             0.0             0.0        1   \n",
            "6362570            0.00  C1620658775             0.0             0.0        1   \n",
            "6362572            0.00   C492082350             0.0             0.0        1   \n",
            "6362574            0.00   C692082136             0.0             0.0        1   \n",
            "6362576     35674547.89   C491519946             0.0             0.0        1   \n",
            "6362578     25674547.89   C949015157             0.0             0.0        1   \n",
            "6362580     15674547.89  C1939028448             0.0             0.0        1   \n",
            "6362582      5674547.89   C625944676             0.0             0.0        1   \n",
            "6362586            0.00   C435734396             0.0             0.0        1   \n",
            "6362588            0.00  C1367473638             0.0             0.0        1   \n",
            "6362590            0.00  C1310143791             0.0             0.0        1   \n",
            "6362592            0.00  C1688658215             0.0             0.0        1   \n",
            "6362594            0.00  C1251365829             0.0             0.0        1   \n",
            "6362596            0.00  C2114078084             0.0             0.0        1   \n",
            "6362598            0.00   C750074708             0.0             0.0        1   \n",
            "6362600            0.00  C1166857907             0.0             0.0        1   \n",
            "6362602            0.00   C112833674             0.0             0.0        1   \n",
            "6362604            0.00  C1930074465             0.0             0.0        1   \n",
            "6362606            0.00   C114421319             0.0             0.0        1   \n",
            "6362608            0.00  C1744173808             0.0             0.0        1   \n",
            "6362610            0.00  C1812552860             0.0             0.0        1   \n",
            "6362612            0.00  C1470998563             0.0             0.0        1   \n",
            "6362614            0.00  C1850423904             0.0             0.0        1   \n",
            "6362616            0.00  C1881841831             0.0             0.0        1   \n",
            "6362618            0.00  C2080388513             0.0             0.0        1   \n",
            "\n",
            "         isFlaggedFraud  \n",
            "0                     0  \n",
            "1                     0  \n",
            "2                     0  \n",
            "4                     0  \n",
            "5                     0  \n",
            "6                     0  \n",
            "7                     0  \n",
            "8                     0  \n",
            "11                    0  \n",
            "12                    0  \n",
            "13                    0  \n",
            "14                    0  \n",
            "16                    0  \n",
            "17                    0  \n",
            "18                    0  \n",
            "20                    0  \n",
            "23                    0  \n",
            "25                    0  \n",
            "26                    0  \n",
            "27                    0  \n",
            "28                    0  \n",
            "29                    0  \n",
            "30                    0  \n",
            "31                    0  \n",
            "32                    0  \n",
            "33                    0  \n",
            "34                    0  \n",
            "35                    0  \n",
            "36                    0  \n",
            "37                    0  \n",
            "...                 ...  \n",
            "6362558               0  \n",
            "6362560               0  \n",
            "6362562               0  \n",
            "6362564               0  \n",
            "6362566               0  \n",
            "6362568               0  \n",
            "6362570               0  \n",
            "6362572               0  \n",
            "6362574               0  \n",
            "6362576               0  \n",
            "6362578               0  \n",
            "6362580               0  \n",
            "6362582               0  \n",
            "6362586               0  \n",
            "6362588               0  \n",
            "6362590               0  \n",
            "6362592               0  \n",
            "6362594               0  \n",
            "6362596               0  \n",
            "6362598               0  \n",
            "6362600               0  \n",
            "6362602               0  \n",
            "6362604               0  \n",
            "6362606               0  \n",
            "6362608               0  \n",
            "6362610               0  \n",
            "6362612               0  \n",
            "6362614               0  \n",
            "6362616               0  \n",
            "6362618               0  \n",
            "\n",
            "[2317266 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UTIOzYmXOvpA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Dataset where only Transfer Transactions are mentioned**"
      ]
    },
    {
      "metadata": {
        "id": "eW2pR3M2LMla",
        "colab_type": "code",
        "outputId": "319228a5-0168-440b-fe25-dc12694b6dfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "# defining a dataset containing only transfer type of transactions\n",
        "\n",
        "dataTransfer = data.loc[data['type'] == 'TRANSFER']\n",
        "\n",
        "dataTransfer = pd.DataFrame(dataTransfer)\n",
        "\n",
        "dataTransfer.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>215310.30</td>\n",
              "      <td>C1670993182</td>\n",
              "      <td>705.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1100439041</td>\n",
              "      <td>22425.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>311685.89</td>\n",
              "      <td>C1984094095</td>\n",
              "      <td>10835.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C932583850</td>\n",
              "      <td>6267.00</td>\n",
              "      <td>2719172.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>62610.80</td>\n",
              "      <td>C1976401987</td>\n",
              "      <td>79114.00</td>\n",
              "      <td>16503.2</td>\n",
              "      <td>C1937962514</td>\n",
              "      <td>517.00</td>\n",
              "      <td>8383.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>42712.39</td>\n",
              "      <td>C283039401</td>\n",
              "      <td>10363.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1330106945</td>\n",
              "      <td>57901.66</td>\n",
              "      <td>24044.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>77957.68</td>\n",
              "      <td>C207471778</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1761291320</td>\n",
              "      <td>94900.00</td>\n",
              "      <td>22233.65</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>17231.46</td>\n",
              "      <td>C1243171897</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C783286238</td>\n",
              "      <td>24672.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>78766.03</td>\n",
              "      <td>C1376151044</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1749186397</td>\n",
              "      <td>103772.00</td>\n",
              "      <td>277515.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>224606.64</td>\n",
              "      <td>C873175411</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C766572210</td>\n",
              "      <td>354678.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>125872.53</td>\n",
              "      <td>C1443967876</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C392292416</td>\n",
              "      <td>348512.00</td>\n",
              "      <td>3420103.09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "2      1  TRANSFER     181.00  C1305486145         181.00             0.0   \n",
              "19     1  TRANSFER  215310.30  C1670993182         705.00             0.0   \n",
              "24     1  TRANSFER  311685.89  C1984094095       10835.00             0.0   \n",
              "58     1  TRANSFER   62610.80  C1976401987       79114.00         16503.2   \n",
              "78     1  TRANSFER   42712.39   C283039401       10363.39             0.0   \n",
              "79     1  TRANSFER   77957.68   C207471778           0.00             0.0   \n",
              "80     1  TRANSFER   17231.46  C1243171897           0.00             0.0   \n",
              "81     1  TRANSFER   78766.03  C1376151044           0.00             0.0   \n",
              "82     1  TRANSFER  224606.64   C873175411           0.00             0.0   \n",
              "83     1  TRANSFER  125872.53  C1443967876           0.00             0.0   \n",
              "\n",
              "       nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "2    C553264065            0.00            0.00        1               0  \n",
              "19  C1100439041        22425.00            0.00        0               0  \n",
              "24   C932583850         6267.00      2719172.89        0               0  \n",
              "58  C1937962514          517.00         8383.29        0               0  \n",
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          "execution_count": 24
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    },
    {
      "metadata": {
        "id": "lID5QBU_OpHh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Minimum Original old Balance for Flagged Frauds**"
      ]
    },
    {
      "metadata": {
        "id": "OCtZlKY0L1CC",
        "colab_type": "code",
        "outputId": "48639ffa-b0de-4780-843f-c4b5376a5a2e",
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      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "# frauds having same old and new balance\n",
        "# sorted by old orignal balance to check original old balance for flagged frauds\n",
        "\n",
        "# this table shows flagged frauds with minimum original old balance\n",
        "\n",
        "\n",
        "dataTransfer.loc[(dataTransfer.isFlaggedFraud == 1) & (dataTransfer.oldbalanceOrg == dataTransfer.newbalanceOrig)].sort_values(by = 'oldbalanceOrg').head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
              "6205439   586  TRANSFER   353874.22  C1684585475      353874.22   \n",
              "3760288   279  TRANSFER   536624.41  C1035541766      536624.41   \n",
              "3247297   250  TRANSFER  1343002.08  C1100582606     1343002.08   \n",
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              "6362584   741  TRANSFER  5674547.89   C992223106     5674547.89   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "6205439       353874.22  C1770418982             0.0             0.0        1   \n",
              "3760288       536624.41  C1100697970             0.0             0.0        1   \n",
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              "6296014      3441041.46  C1082139865             0.0             0.0        1   \n",
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              "6362584      5674547.89  C1366804249             0.0             0.0        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
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      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "**Maximum Original Balance for Flagged Frauds**"
      ]
    },
    {
      "metadata": {
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        "colab_type": "code",
        "outputId": "6229c9d1-17b1-4a9b-96ef-a478ea4a76de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "# frauds having same old and new balance\n",
        "# sorted by old orignal balance to check original old balance for flagged frauds\n",
        "\n",
        "# this table shows flagged frauds with maximum original old balance\n",
        "\n",
        "\n",
        "dataTransfer.loc[(dataTransfer.isFlaggedFraud == 1) & (dataTransfer.oldbalanceOrg == dataTransfer.newbalanceOrig)].sort_values(by = 'oldbalanceOrg').tail(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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            "text/plain": [
              "         step      type       amount     nameOrig  oldbalanceOrg  \\\n",
              "6168499   554  TRANSFER   3576297.10   C193696150     3576297.10   \n",
              "5563713   387  TRANSFER   4892193.09   C908544136     4892193.09   \n",
              "2736446   212  TRANSFER   4953893.08   C728984460     4953893.08   \n",
              "6362584   741  TRANSFER   5674547.89   C992223106     5674547.89   \n",
              "6281482   646  TRANSFER  10000000.00    C19004745    10399045.08   \n",
              "6281484   646  TRANSFER    399045.08   C724693370    10399045.08   \n",
              "6362460   730  TRANSFER  10000000.00  C2140038573    17316255.05   \n",
              "6362462   730  TRANSFER   7316255.05  C1869569059    17316255.05   \n",
              "5996407   425  TRANSFER  10000000.00   C689608084    19585040.37   \n",
              "5996409   425  TRANSFER   9585040.37   C452586515    19585040.37   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "6168499      3576297.10   C484597480             0.0             0.0        1   \n",
              "5563713      4892193.09   C891140444             0.0             0.0        1   \n",
              "2736446      4953893.08   C639921569             0.0             0.0        1   \n",
              "6362584      5674547.89  C1366804249             0.0             0.0        1   \n",
              "6281482     10399045.08  C1806199534             0.0             0.0        1   \n",
              "6281484     10399045.08  C1909486199             0.0             0.0        1   \n",
              "6362460     17316255.05  C1395467927             0.0             0.0        1   \n",
              "6362462     17316255.05  C1861208726             0.0             0.0        1   \n",
              "5996407     19585040.37  C1392803603             0.0             0.0        1   \n",
              "5996409     19585040.37  C1109166882             0.0             0.0        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "6168499               1  \n",
              "5563713               1  \n",
              "2736446               1  \n",
              "6362584               1  \n",
              "6281482               1  \n",
              "6281484               1  \n",
              "6362460               1  \n",
              "6362462               1  \n",
              "5996407               1  \n",
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          "metadata": {
            "tags": []
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          "execution_count": 26
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      ]
    },
    {
      "metadata": {
        "id": "18rSjotPO4Oi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Minimum Original Old Balace for non-Flagged Frauds**"
      ]
    },
    {
      "metadata": {
        "id": "6NBUwEYTPAdQ",
        "colab_type": "code",
        "outputId": "6d4cccdc-2fc9-489a-bdc5-d4c54ed5bb59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "dataTransfer.loc[(dataTransfer['isFlaggedFraud'] == 0) & (dataTransfer.oldbalanceDest == dataTransfer.newbalanceDest)].sort_values(by = 'oldbalanceOrg').head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>C2126290948</td>\n",
              "      <td>22.00</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>119.00</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>119.65</td>\n",
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              "      <td>119.65</td>\n",
              "      <td>0.0</td>\n",
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              "         step      type     amount     nameOrig  oldbalanceOrg  \\\n",
              "160646     12  TRANSFER  342317.15  C2070529657           0.00   \n",
              "957368     44  TRANSFER  133711.48   C256147273           0.00   \n",
              "1248        1  TRANSFER  271161.74  C1658487789           0.00   \n",
              "175828     12  TRANSFER   18931.59   C257351690           0.00   \n",
              "282796     15  TRANSFER  421255.41  C1734485985           0.00   \n",
              "381356     17  TRANSFER  630315.29  C2126290948          22.00   \n",
              "4965641   351  TRANSFER      63.80  C1293504491          63.80   \n",
              "277265     15  TRANSFER     119.00  C1995557473         119.00   \n",
              "6281815   653  TRANSFER     119.65  C1497766467         119.65   \n",
              "1796322   162  TRANSFER     151.00  C1172437299         151.00   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "160646              0.0   C621118463             0.0             0.0        0   \n",
              "957368              0.0  C1582233295             0.0             0.0        0   \n",
              "1248                0.0  C1219161283             0.0             0.0        0   \n",
              "175828              0.0  C1108098323             0.0             0.0        0   \n",
              "282796              0.0   C981307359             0.0             0.0        0   \n",
              "381356              0.0  C1203344902             0.0             0.0        0   \n",
              "4965641             0.0  C1368130863             0.0             0.0        1   \n",
              "277265              0.0  C1480876722             0.0             0.0        1   \n",
              "6281815             0.0   C543477940             0.0             0.0        1   \n",
              "1796322             0.0   C315826176             0.0             0.0        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "160646                0  \n",
              "957368                0  \n",
              "1248                  0  \n",
              "175828                0  \n",
              "282796                0  \n",
              "381356                0  \n",
              "4965641               0  \n",
              "277265                0  \n",
              "6281815               0  \n",
              "1796322               0  "
            ]
          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "metadata": {
        "id": "Q2LMnDOGPBUB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Maximum Original Old Balance for Non-Flagged Frauds**"
      ]
    },
    {
      "metadata": {
        "id": "ASt9TNqMPJ26",
        "colab_type": "code",
        "outputId": "2e03e38d-2f37-4e05-f19f-21e092374283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "dataTransfer.loc[(dataTransfer['isFlaggedFraud'] == 0) & (dataTransfer.oldbalanceDest == dataTransfer.newbalanceDest)].sort_values(by = 'oldbalanceOrg').tail(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>10000000.0</td>\n",
              "      <td>C507645439</td>\n",
              "      <td>47316255.05</td>\n",
              "      <td>37316255.05</td>\n",
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              "    <tr>\n",
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              "      <td>10000000.0</td>\n",
              "      <td>C1551381510</td>\n",
              "      <td>49585040.37</td>\n",
              "      <td>39585040.37</td>\n",
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              "      <td>40399045.08</td>\n",
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              "      <td>730</td>\n",
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              "      <td>10000000.0</td>\n",
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              "      <td>47316255.05</td>\n",
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              "    <tr>\n",
              "      <th>5996399</th>\n",
              "      <td>425</td>\n",
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              "      <td>10000000.0</td>\n",
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              "      <td>59585040.37</td>\n",
              "      <td>49585040.37</td>\n",
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            ],
            "text/plain": [
              "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
              "5996403   425  TRANSFER  10000000.0  C1619838170    39585040.37   \n",
              "6281476   646  TRANSFER  10000000.0   C130070267    40399045.08   \n",
              "6266405   617  TRANSFER  10000000.0   C794290057    42542664.27   \n",
              "5563705   387  TRANSFER  10000000.0   C576718894    44892193.09   \n",
              "6362576   741  TRANSFER  10000000.0   C780743034    45674547.89   \n",
              "6362454   730  TRANSFER  10000000.0   C507645439    47316255.05   \n",
              "5996401   425  TRANSFER  10000000.0  C1551381510    49585040.37   \n",
              "6281474   646  TRANSFER  10000000.0   C590657619    50399045.08   \n",
              "6362452   730  TRANSFER  10000000.0   C726730575    57316255.05   \n",
              "5996399   425  TRANSFER  10000000.0    C40489106    59585040.37   \n",
              "\n",
              "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
              "5996403     29585040.37  C1934167334             0.0             0.0        1   \n",
              "6281476     30399045.08   C970791522             0.0             0.0        1   \n",
              "6266405     32542664.27   C262998076             0.0             0.0        1   \n",
              "5563705     34892193.09   C673002421             0.0             0.0        1   \n",
              "6362576     35674547.89   C491519946             0.0             0.0        1   \n",
              "6362454     37316255.05   C270374999             0.0             0.0        1   \n",
              "5996401     39585040.37  C1042012237             0.0             0.0        1   \n",
              "6281474     40399045.08  C1971187430             0.0             0.0        1   \n",
              "6362452     47316255.05  C1364745638             0.0             0.0        1   \n",
              "5996399     49585040.37   C650095152             0.0             0.0        1   \n",
              "\n",
              "         isFlaggedFraud  \n",
              "5996403               0  \n",
              "6281476               0  \n",
              "6266405               0  \n",
              "5563705               0  \n",
              "6362576               0  \n",
              "6362454               0  \n",
              "5996401               0  \n",
              "6281474               0  \n",
              "6362452               0  \n",
              "5996399               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "NtnPQ0OFhakz",
        "colab_type": "code",
        "outputId": "44bd8ddd-8ae6-4cd5-eb15-f86f6fbad4bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# finding out the minimum and maximum amount for oldbalance original where oldbalance and newbalance for dest is same.\n",
        "\n",
        "dataFlagged = data.loc[data.isFlaggedFraud == 1]\n",
        "\n",
        "print('Minimum Balance of oldBalanceOrig for FlaggedFraud and Transfer mode :', dataFlagged.oldbalanceOrg.min())\n",
        "print('Maximum Balance of oldbalanceOrig for FlaggedFraud and Transfer mode :', dataFlagged.oldbalanceOrg.max())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Minimum Balance of oldBalanceOrig for FlaggedFraud and Transfer mode : 353874.22\n",
            "Maximum Balance of oldbalanceOrig for FlaggedFraud and Transfer mode : 19585040.37\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qhaDYqkOLCV6",
        "colab_type": "code",
        "outputId": "71e0855a-632d-4da7-cb9f-ee43864a3a26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Finding the maximum and minimum balance in original balance when newbalance original and oldbalance original are equal\n",
        "\n",
        "print('Minimum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode:',\n",
        "      data[(data.type == 'TRANSFER') & (data.isFlaggedFraud == 0) & (data.oldbalanceOrg == data.newbalanceOrig)].oldbalanceOrg.min())\n",
        "print('Maximum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode:',\n",
        "      data[(data.type == 'TRANSFER') & (data.isFlaggedFraud == 0) & (data.oldbalanceOrg == data.newbalanceOrig)].oldbalanceOrg.max())\n",
        "\n",
        "# Can isFlaggedFraud be set based on seeing a customer transacting more than once? Note that duplicate customer names don't exist within \n",
        "# transactions where isFlaggedFraud is set, but duplicate customer names exist within transactions where isFlaggedFraud is not set. It turns \n",
        "# out that originators of transactions that have isFlaggedFraud set have transacted only once. Very few destination accounts of transactions \n",
        "# that have isFlaggedFraud set have transacted more than once."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Minimum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode: 0.0\n",
            "Maximum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode: 575667.54\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9vxmr19ZUMp3",
        "colab_type": "code",
        "outputId": "67e9c525-96f2-4bd1-e7fb-140edd9979d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "print('Minimum Balance of oldbalanceOrig for  FlaggedFraud and Transfer mode:',\n",
        "      data[(data.type == 'TRANSFER') & (data.isFlaggedFraud == 1) & (data.oldbalanceOrg == data.newbalanceOrig)].oldbalanceOrg.min())\n",
        "print('Maximum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode:',\n",
        "      data[(data.type == 'TRANSFER') & (data.isFlaggedFraud == 1) & (data.oldbalanceOrg == data.newbalanceOrig)].oldbalanceOrg.max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Minimum Balance of oldbalanceOrig for  FlaggedFraud and Transfer mode: 353874.22\n",
            "Maximum Balance of oldbalanceOrig for non FlaggedFraud and Transfer mode: 19585040.37\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2b0CljHsVFJC",
        "colab_type": "code",
        "outputId": "a8cb71eb-b622-42b4-8ef1-18300920e79c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "data['nameDest'].value_counts().head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1286084959    113\n",
              "C985934102     109\n",
              "C665576141     105\n",
              "C2083562754    102\n",
              "C1590550415    101\n",
              "C248609774     101\n",
              "C1789550256     99\n",
              "C451111351      99\n",
              "C1360767589     98\n",
              "C1023714065     97\n",
              "C97730845       96\n",
              "C977993101      96\n",
              "C392292416      95\n",
              "C1899073220     92\n",
              "C306206744      91\n",
              "C1782113663     90\n",
              "C1234776885     90\n",
              "C716083600      89\n",
              "C998351292      89\n",
              "C909295153      88\n",
              "Name: nameDest, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "Swyn0RcoYb0P",
        "colab_type": "code",
        "outputId": "b59b1fa0-e566-494e-a603-360d3cafaa3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# making dependent and independent sets\n",
        "\n",
        "X = data.loc[(data.type == 'TRANSFER') | (data.type == 'CASH_OUT')]\n",
        "\n",
        "# shape of x\n",
        "X.shape\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2770409, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "metadata": {
        "id": "1KB6Ul7JfzO6",
        "colab_type": "code",
        "outputId": "d92ddb43-9472-46fe-f01a-230cda12069c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C840083671</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>229133.94</td>\n",
              "      <td>C905080434</td>\n",
              "      <td>15325.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C476402209</td>\n",
              "      <td>5083.0</td>\n",
              "      <td>51513.44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>215310.30</td>\n",
              "      <td>C1670993182</td>\n",
              "      <td>705.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1100439041</td>\n",
              "      <td>22425.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>311685.89</td>\n",
              "      <td>C1984094095</td>\n",
              "      <td>10835.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C932583850</td>\n",
              "      <td>6267.0</td>\n",
              "      <td>2719172.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "2      1  TRANSFER     181.00  C1305486145          181.0             0.0   \n",
              "3      1  CASH_OUT     181.00   C840083671          181.0             0.0   \n",
              "15     1  CASH_OUT  229133.94   C905080434        15325.0             0.0   \n",
              "19     1  TRANSFER  215310.30  C1670993182          705.0             0.0   \n",
              "24     1  TRANSFER  311685.89  C1984094095        10835.0             0.0   \n",
              "\n",
              "       nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "2    C553264065             0.0            0.00        1               0  \n",
              "3     C38997010         21182.0            0.00        1               0  \n",
              "15   C476402209          5083.0        51513.44        0               0  \n",
              "19  C1100439041         22425.0            0.00        0               0  \n",
              "24   C932583850          6267.0      2719172.89        0               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "metadata": {
        "id": "sRTYxQjokzwV",
        "colab_type": "code",
        "outputId": "db27b405-3377-436e-a334-59584872cf71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "# feature engg.\n",
        "\n",
        "X['errorBalanceOrig'] = X.newbalanceOrig + X.amount - X.oldbalanceOrg\n",
        "X['errorBalanceDest'] = X.oldbalanceDest + X.amount - X.newbalanceDest"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "34OwpsU-bd7g",
        "colab_type": "code",
        "outputId": "5c29e18b-203a-4dd3-bae7-8b2831103436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# removing unwanted columns\n",
        "\n",
        "X = X.drop(['nameDest','nameOrig'], axis = 1)\n",
        "\n",
        "# checking the new shape of data\n",
        "X.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2770409, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "metadata": {
        "id": "DytlE02Ranrg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# label encoding for the transaction type\n",
        "\n",
        "X['type'].replace('TRANSFER', 0, inplace = True)\n",
        "X['type'].replace('CASH_OUT', 1, inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ULp8vvi0akTE",
        "colab_type": "code",
        "outputId": "09389b29-e297-47ac-bb00-bf461211dcef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "# looking at the head of the data\n",
        "\n",
        "X.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "      <th>errorBalanceOrig</th>\n",
              "      <th>errorBalanceDest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>181.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>21363.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>229133.94</td>\n",
              "      <td>15325.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5083.0</td>\n",
              "      <td>51513.44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>213808.94</td>\n",
              "      <td>182703.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>215310.30</td>\n",
              "      <td>705.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22425.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>214605.30</td>\n",
              "      <td>237735.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>311685.89</td>\n",
              "      <td>10835.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6267.0</td>\n",
              "      <td>2719172.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>300850.89</td>\n",
              "      <td>-2401220.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>110414.71</td>\n",
              "      <td>26845.41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>288800.0</td>\n",
              "      <td>2415.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>83569.30</td>\n",
              "      <td>396799.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>56953.90</td>\n",
              "      <td>1942.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70253.0</td>\n",
              "      <td>64106.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55011.88</td>\n",
              "      <td>63100.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5346.89</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>652637.0</td>\n",
              "      <td>6453430.91</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5346.89</td>\n",
              "      <td>-5795447.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>23261.30</td>\n",
              "      <td>20411.53</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25742.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2849.77</td>\n",
              "      <td>49003.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>62610.80</td>\n",
              "      <td>79114.00</td>\n",
              "      <td>16503.2</td>\n",
              "      <td>517.0</td>\n",
              "      <td>8383.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>54744.51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    step  type     amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "2      1     0     181.00         181.00             0.0             0.0   \n",
              "3      1     1     181.00         181.00             0.0         21182.0   \n",
              "15     1     1  229133.94       15325.00             0.0          5083.0   \n",
              "19     1     0  215310.30         705.00             0.0         22425.0   \n",
              "24     1     0  311685.89       10835.00             0.0          6267.0   \n",
              "42     1     1  110414.71       26845.41             0.0        288800.0   \n",
              "47     1     1   56953.90        1942.02             0.0         70253.0   \n",
              "48     1     1    5346.89           0.00             0.0        652637.0   \n",
              "51     1     1   23261.30       20411.53             0.0         25742.0   \n",
              "58     1     0   62610.80       79114.00         16503.2           517.0   \n",
              "\n",
              "    newbalanceDest  isFraud  isFlaggedFraud  errorBalanceOrig  \\\n",
              "2             0.00        1               0              0.00   \n",
              "3             0.00        1               0              0.00   \n",
              "15        51513.44        0               0         213808.94   \n",
              "19            0.00        0               0         214605.30   \n",
              "24      2719172.89        0               0         300850.89   \n",
              "42         2415.16        0               0          83569.30   \n",
              "47        64106.18        0               0          55011.88   \n",
              "48      6453430.91        0               0           5346.89   \n",
              "51            0.00        0               0           2849.77   \n",
              "58         8383.29        0               0              0.00   \n",
              "\n",
              "    errorBalanceDest  \n",
              "2             181.00  \n",
              "3           21363.00  \n",
              "15         182703.50  \n",
              "19         237735.30  \n",
              "24       -2401220.00  \n",
              "42         396799.55  \n",
              "47          63100.72  \n",
              "48       -5795447.02  \n",
              "51          49003.30  \n",
              "58          54744.51  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "metadata": {
        "id": "blsBnEPIbnno",
        "colab_type": "code",
        "outputId": "73f83b39-6916-4348-81eb-fc0741219788",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# checking if the dataset contains any NULL values\n",
        "\n",
        "X.isnull().any().any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "UStokQz1mDrQ",
        "colab_type": "code",
        "outputId": "bca5d819-68a7-46f9-df16-ec673758f386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "cell_type": "code",
      "source": [
        "# visualizing the distribution of errorbalance\n",
        "\n",
        "# setting the size of the plot\n",
        "plt.rcParams['figure.figsize'] = (21, 7)\n",
        "\n",
        "plt.subplot(121)\n",
        "sns.distplot(X['errorBalanceOrig'], color = 'red')\n",
        "plt.title('Error Balance of Origin', fontsize = 20)\n",
        "\n",
        "plt.subplot(122)\n",
        "sns.distplot(X['errorBalanceDest'], color = 'red')\n",
        "plt.title('Error Balance of Destination', fontsize = 20)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  alternative=\"'density'\", removal=\"3.1\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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uvfZamjVrRrNmzcocA3DPPfcwevRoFi5cSKNG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\nCVgJCb6TFBT4zl9YgGV3YEVH+1Y4cDgxPG4oLMQoKvL9IHU6sJxRWA4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IiIhITVBwJSIi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\n",
            "text/plain": [
              "<Figure size 1512x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "clj18Au6cNDB",
        "colab_type": "code",
        "outputId": "5ee322e9-f1fb-4dc4-ef43-e01118a7e1ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "cell_type": "code",
      "source": [
        "# checking the no. of frauds in the dataset\n",
        "\n",
        "X['isFraud'].Fvalue_counts()\n",
        "\n",
        "# plotting a pie chart to represent the fraction of the frauds \n",
        "\n",
        "size = [2762196, 8213]\n",
        "labels = ['NON-FRAUD', 'FRAUD']\n",
        "colors = ['lightblue', 'yellow']\n",
        "explode = [0, 0.2]\n",
        "\n",
        "plt.pie(size, labels = labels, colors = colors, explode = explode, shadow  = True, autopct = '%.2f%%')\n",
        "plt.title('Frauds v/s Non-Frauds', fontsize = 20)\n",
        "plt.axis('off')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bfm5XGzlyJL7++mu8/PLLePTRR2G1WvHf//4XN910EwIDA5GcnIwVK1Zg9OjRSEhIwK23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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1512x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2QI2zZm-fpTI",
        "colab_type": "code",
        "outputId": "63fb2f7f-d809-4cbd-ff05-fccb9eddbb9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "X['isFraud'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2762196\n",
              "1       8213\n",
              "Name: isFraud, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "6Vpus-psqJ6o",
        "colab_type": "code",
        "outputId": "544f4c00-2422-4c89-d3fe-344c103c4fb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# making our independent set\n",
        "\n",
        "Y = X['isFraud']\n",
        "\n",
        "# removing the dependent set\n",
        "X = X.drop(['isFraud'], axis = 1)\n",
        "\n",
        "# getting the shapes of x and y\n",
        "print(\"Shape of x: \", X.shape)\n",
        "print(\"Shape of y: \", Y.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of x:  (2770409, 10)\n",
            "Shape of y:  (2770409,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g_Cy6QDirCNl",
        "colab_type": "code",
        "outputId": "46c92ba0-9868-41d3-c14c-2764b596d59d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "      <th>errorBalanceOrig</th>\n",
              "      <th>errorBalanceDest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>181.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>181.00</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>21363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>229133.94</td>\n",
              "      <td>15325.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5083.0</td>\n",
              "      <td>51513.44</td>\n",
              "      <td>0</td>\n",
              "      <td>213808.94</td>\n",
              "      <td>182703.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>215310.30</td>\n",
              "      <td>705.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22425.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>214605.30</td>\n",
              "      <td>237735.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>311685.89</td>\n",
              "      <td>10835.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6267.0</td>\n",
              "      <td>2719172.89</td>\n",
              "      <td>0</td>\n",
              "      <td>300850.89</td>\n",
              "      <td>-2401220.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    step  type     amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "2      1     0     181.00          181.0             0.0             0.0   \n",
              "3      1     1     181.00          181.0             0.0         21182.0   \n",
              "15     1     1  229133.94        15325.0             0.0          5083.0   \n",
              "19     1     0  215310.30          705.0             0.0         22425.0   \n",
              "24     1     0  311685.89        10835.0             0.0          6267.0   \n",
              "\n",
              "    newbalanceDest  isFlaggedFraud  errorBalanceOrig  errorBalanceDest  \n",
              "2             0.00               0              0.00             181.0  \n",
              "3             0.00               0              0.00           21363.0  \n",
              "15        51513.44               0         213808.94          182703.5  \n",
              "19            0.00               0         214605.30          237735.3  \n",
              "24      2719172.89               0         300850.89        -2401220.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "metadata": {
        "id": "7Ez0xcQQpbGf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**OVER-SAMPLING**"
      ]
    },
    {
      "metadata": {
        "id": "sCEGR_C5peLj",
        "colab_type": "code",
        "outputId": "35d2baaf-d3c1-4b29-8b2d-c80b4fa77a30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "x_resample, y_resample = SMOTE().fit_sample(X, Y.values.ravel())\n",
        "\n",
        "# getting the shapes of x and y after resampling\n",
        "print(\"Shape of x: \", x_resample.shape)\n",
        "print(\"Shape of y:\", y_resample.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of x:  (5524392, 10)\n",
            "Shape of y: (5524392,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "up74lZw7q9O_",
        "colab_type": "code",
        "outputId": "4a72cae0-81ca-44c0-91a5-b3498fe2952c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# splitting the dataset into train and tests\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_resample, y_resample, test_size = 0.2, random_state = 0)\n",
        "\n",
        "# checking the new shapes\n",
        "print(\"Shape of x_train: \", x_train.shape)\n",
        "print(\"Shape of x_test: \", x_test.shape)\n",
        "print(\"Shape of y_train: \", y_train.shape)\n",
        "print(\"Shape of y_test: \", y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of x_train:  (4419513, 10)\n",
            "Shape of x_test:  (1104879, 10)\n",
            "Shape of y_train:  (4419513,)\n",
            "Shape of y_test:  (1104879,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fHAgbVICsQA3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# performing standard scaling on the data for better fit\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FqMGTIUIsk4Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Modelling using Xg-Boost Model**"
      ]
    },
    {
      "metadata": {
        "id": "iNJvDr7EskBB",
        "colab_type": "code",
        "outputId": "4a42e9a5-a912-4435-fe46-9699d5eef564",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Using XGBOOST\n",
        "\n",
        "\n",
        "import xgboost as xgb\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "from sklearn.metrics import average_precision_score\n",
        "\n",
        "model = XGBClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# score of the model\n",
        "auprc = average_precision_score(y_test, y_pred)\n",
        "print(\"The Area under Precision Recall Curve Score is\", auprc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Area under Precision Recall Curve Score is 0.9974199699027203\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sHsOfexItVO7",
        "colab_type": "code",
        "outputId": "d6b135ee-a2aa-4284-d3e5-d0178a7320d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# looking at the confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(cm)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[552579    541]\n",
            " [  1764 549995]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3tGSbarit_x4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}