FraudDetection_ModelPickle.ipynb

{
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      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q2Wy8Jb--Toy",
        "outputId": "b3664421-cee7-466a-c7c1-f15dc7e7efd9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sklearn in c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in c:\\users\\rramb\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sklearn) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages (from scikit-learn->sklearn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages (from scikit-learn->sklearn) (1.8.1)\n",
            "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages (from scikit-learn->sklearn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages (from scikit-learn->sklearn) (3.1.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n",
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n",
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n",
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n",
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n",
            "WARNING: Ignoring invalid distribution -illow (c:\\users\\rramb\\appdata\\roaming\\python\\python38\\site-packages)\n"
          ]
        }
      ],
      "source": [
        "#installing the libraries\n",
        "!pip3 install featurewiz\n",
        "!pip install streamlit\n",
        "!pip install -U scikit-learn scipy matplotlib\n",
        "!pip install sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hRxmIAxO8Pla"
      },
      "outputs": [],
      "source": [
        "#importing the libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#from featurewiz import featurewiz\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "\n",
        "import sklearn\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from tabulate import tabulate\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "RoqsLXs28eF_",
        "outputId": "c4d2f0ff-cc8a-4c14-acd4-a6a560460461"
      },
      "outputs": [
        {
          "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>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.00</td>\n",
              "      <td>160296.36</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.00</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\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>0.00</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>181.00</td>\n",
              "      <td>0.00</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>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.00</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7817.71</td>\n",
              "      <td>C90045638</td>\n",
              "      <td>53860.00</td>\n",
              "      <td>46042.29</td>\n",
              "      <td>M573487274</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7107.77</td>\n",
              "      <td>C154988899</td>\n",
              "      <td>183195.00</td>\n",
              "      <td>176087.23</td>\n",
              "      <td>M408069119</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7861.64</td>\n",
              "      <td>C1912850431</td>\n",
              "      <td>176087.23</td>\n",
              "      <td>168225.59</td>\n",
              "      <td>M633326333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>4024.36</td>\n",
              "      <td>C1265012928</td>\n",
              "      <td>2671.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>M1176932104</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>DEBIT</td>\n",
              "      <td>5337.77</td>\n",
              "      <td>C712410124</td>\n",
              "      <td>41720.00</td>\n",
              "      <td>36382.23</td>\n",
              "      <td>C195600860</td>\n",
              "      <td>41898.0</td>\n",
              "      <td>40348.79</td>\n",
              "      <td>0</td>\n",
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            ],
            "text/plain": [
              "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
              "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
              "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
              "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
              "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
              "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
              "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
              "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
              "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
              "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  M1979787155             0.0            0.00        0               0  \n",
              "1  M2044282225             0.0            0.00        0               0  \n",
              "2   C553264065             0.0            0.00        1               0  \n",
              "3    C38997010         21182.0            0.00        1               0  \n",
              "4  M1230701703             0.0            0.00        0               0  \n",
              "5   M573487274             0.0            0.00        0               0  \n",
              "6   M408069119             0.0            0.00        0               0  \n",
              "7   M633326333             0.0            0.00        0               0  \n",
              "8  M1176932104             0.0            0.00        0               0  \n",
              "9   C195600860         41898.0        40348.79        0               0  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#loading the dataset\n",
        "fraud_data = pd.read_csv('Fraud_Detection_Data.csv')\n",
        "fraud_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcPByAHnA8KN"
      },
      "outputs": [],
      "source": [
        "#Descriptive Analysis & EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kwz-9t2l-Gu0",
        "outputId": "9f6a7c75-486b-4925-dc22-9f2841774af0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of Rows and Columns: (6362620, 11)\n"
          ]
        }
      ],
      "source": [
        "#displaying number of rows and columns\n",
        "print(\"Total number of Rows and Columns:\",fraud_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1sgYROH-nLX",
        "outputId": "e974e0a5-ed91-4bc0-a4d3-b77deeac8c5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Column Names:\n",
            " Index(['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
            "       'nameDest', 'oldbalanceDest', 'newbalanceDest', 'isFraud',\n",
            "       'isFlaggedFraud'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "#displaying the data field values\n",
        "print(\"\\nColumn Names:\\n\",fraud_data.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXO4frIB-nUt",
        "outputId": "08c593e0-b565-496b-efb2-59d617e49745"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Data types:\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",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "#displaying the data types\n",
        "print(\"\\nData types:\\n\", fraud_data.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYVezLp5-nZC",
        "outputId": "79f88fa8-f186-47c1-aac2-8aba5958ecae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
          ]
        }
      ],
      "source": [
        "#information about the dataframe\n",
        "fraud_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GC7WB2bm-nb-",
        "outputId": "f0b17452-39ba-42c3-dbee-b8b67b1f49b1"
      },
      "outputs": [
        {
          "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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6362620.00</td>\n",
              "      <td>6362620.00</td>\n",
              "      <td>6362620.00</td>\n",
              "      <td>6362620.00</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6362620.00</td>\n",
              "      <td>6362620.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243.40</td>\n",
              "      <td>179861.90</td>\n",
              "      <td>833883.10</td>\n",
              "      <td>855113.67</td>\n",
              "      <td>1.100702e+06</td>\n",
              "      <td>1.224996e+06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>142.33</td>\n",
              "      <td>603858.23</td>\n",
              "      <td>2888242.67</td>\n",
              "      <td>2924048.50</td>\n",
              "      <td>3.399180e+06</td>\n",
              "      <td>3.674129e+06</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>156.00</td>\n",
              "      <td>13389.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>239.00</td>\n",
              "      <td>74871.94</td>\n",
              "      <td>14208.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.327057e+05</td>\n",
              "      <td>2.146614e+05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>335.00</td>\n",
              "      <td>208721.48</td>\n",
              "      <td>107315.18</td>\n",
              "      <td>144258.41</td>\n",
              "      <td>9.430367e+05</td>\n",
              "      <td>1.111909e+06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>743.00</td>\n",
              "      <td>92445516.64</td>\n",
              "      <td>59585040.37</td>\n",
              "      <td>49585040.37</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7903b476-8205-48ea-b132-76240d82a842')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7903b476-8205-48ea-b132-76240d82a842 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7903b476-8205-48ea-b132-76240d82a842');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             step       amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "count  6362620.00   6362620.00     6362620.00      6362620.00    6.362620e+06   \n",
              "mean       243.40    179861.90      833883.10       855113.67    1.100702e+06   \n",
              "std        142.33    603858.23     2888242.67      2924048.50    3.399180e+06   \n",
              "min          1.00         0.00           0.00            0.00    0.000000e+00   \n",
              "25%        156.00     13389.57           0.00            0.00    0.000000e+00   \n",
              "50%        239.00     74871.94       14208.00            0.00    1.327057e+05   \n",
              "75%        335.00    208721.48      107315.18       144258.41    9.430367e+05   \n",
              "max        743.00  92445516.64    59585040.37     49585040.37    3.560159e+08   \n",
              "\n",
              "       newbalanceDest     isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06  6362620.00       6362620.0  \n",
              "mean     1.224996e+06        0.00             0.0  \n",
              "std      3.674129e+06        0.04             0.0  \n",
              "min      0.000000e+00        0.00             0.0  \n",
              "25%      0.000000e+00        0.00             0.0  \n",
              "50%      2.146614e+05        0.00             0.0  \n",
              "75%      1.111909e+06        0.00             0.0  \n",
              "max      3.561793e+08        1.00             1.0  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Statistical Analysis\n",
        "\n",
        "#describing the dataset\n",
        "round(fraud_data.describe(),2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cqBabwXb-ne7"
      },
      "outputs": [],
      "source": [
        "#Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALRPvq2X-nhw",
        "outputId": "113484cf-6e94-40e9-9179-9c5c2b2fa26c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
              "       'nameDest', 'oldbalanceDest', 'newbalanceDest', 'isFraud',\n",
              "       'isFlaggedFraud'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#listing field names\n",
        "column_names = fraud_data.columns\n",
        "column_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xH0yycnb-nkW",
        "outputId": "86a24bb5-0815-4617-d068-e299726435ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['STEP', 'TYPE', 'AMOUNT', 'NAMEORIG', 'OLDBALANCEORG', 'NEWBALANCEORIG',\n",
              "       'NAMEDEST', 'OLDBALANCEDEST', 'NEWBALANCEDEST', 'ISFRAUD',\n",
              "       'ISFLAGGEDFRAUD'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#upper casing the field values\n",
        "fraud_data.columns = fraud_data.columns.str.upper()\n",
        "fraud_data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NV3n5xEI-nn6",
        "outputId": "fd02fa29-765f-42ab-e234-39742c486329"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "STEP                                                   0\n",
            "TYPE                                                   0\n",
            "AMOUNT                                                 0\n",
            "NAMEORIG                                               0\n",
            "OLDBALANCEORG                                          0\n",
            "NEWBALANCEORIG                                         0\n",
            "NAMEDEST                                               0\n",
            "OLDBALANCEDEST                                         0\n",
            "NEWBALANCEDEST                                         0\n",
            "ISFRAUD                                                0\n",
            "ISFLAGGEDFRAUD                                         0\n"
          ]
        }
      ],
      "source": [
        "#checking for null values in the each column of the dataset\n",
        "for x in range(11):\n",
        "    print(\"%-45s %10d\" % (fraud_data.columns.values[x], fraud_data.iloc[:,x].isna().sum()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VPKEyXlWAwbT"
      },
      "outputs": [],
      "source": [
        "#Data Visualization - Tableau"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DzSpnT8pBBOT"
      },
      "outputs": [],
      "source": [
        "#Correlation Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "QWN9gHG6BMjk",
        "outputId": "abcf8562-efd7-47ce-f08b-3b47368b7e3d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (18,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(fraud_data.corr(),annot=True, fmt='.1f', ax=ax, cmap=\"YlGnBu\")\n",
        "ax.set_title('Correlation Plot'); "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exWGJAhSBrwF",
        "outputId": "44bd17ac-7262-43d1-bcbe-e75b2e09888b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique count of Transaction Types\n",
            " CASH_OUT    2237500\n",
            "PAYMENT     2151495\n",
            "CASH_IN     1399284\n",
            "TRANSFER     532909\n",
            "DEBIT         41432\n",
            "Name: TYPE, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#exploring data columns\n",
        "\n",
        "print(\"Unique count of Transaction Types\\n\",fraud_data['TYPE'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "sWt6ETfXH9e2",
        "outputId": "e86fcd17-6ac1-4add-e866-c1f5e7e983a7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plotting the count of unique values for transaction types\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.title(\"Frequency Count of each Category\")\n",
        "plt.ylabel(\"Count\")\n",
        "colors = ['skyblue']\n",
        "plot = sns.countplot(x=\"TYPE\", data=fraud_data, palette=colors)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQyGyMLRJc5z"
      },
      "outputs": [],
      "source": [
        "#Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cSYKCMluJfzu",
        "outputId": "e9d29579-06bc-445c-ba26-a1aedc173399"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "############################################################################################\n",
            "############       F A S T   F E A T U R E  E N G G    A N D    S E L E C T I O N ! ########\n",
            "# Be judicious with featurewiz. Don't use it to create too many un-interpretable features! #\n",
            "############################################################################################\n",
            "Skipping feature engineering since no feature_engg input...\n",
            "Skipping category encoding since no category encoders specified in input...\n",
            "**INFO: featurewiz can now read feather formatted files. Loading train data...\n",
            "    Shape of your Data Set loaded: (6362620, 11)\n",
            "    Caution: We will try to reduce the memory usage of dataframe from 533.97 MB\n",
            "        memory usage after optimization is: 462.98 MB\n",
            "        decreased by 13.3%\n",
            "    Loaded train data. Shape = (6362620, 11)\n",
            " setting a hard limit of 900K samples for train since some it is huge and breaks pandas...\n",
            "No test data filename given...\n",
            "Classifying features using a random sample of 10000 rows from dataset...\n",
            "#### Single_Label Binary_Classification problem ####\n",
            "    loading a random sample of 10000 rows into pandas for EDA\n",
            "#######################################################################################\n",
            "######################## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "#######################################################################################\n",
            "Classifying variables in data set...\n",
            "    10 Predictors classified...\n",
            "        1 variable(s) to be removed since ID or low-information variables\n",
            "    \tvariables removed = ['ISFLAGGEDFRAUD']\n",
            "train data shape before dropping 1 columns = (900000, 11)\n",
            "\ttrain data shape after dropping columns = (900000, 10)\n",
            "    Converted pandas dataframe into a Dask dataframe ...\n",
            "GPU active on this device\n",
            "    Tuning XGBoost using GPU hyper-parameters. This will take time...\n",
            "    After removing redundant variables from further processing, features left = 9\n",
            "No interactions created for categorical vars since feature engg does not specify it\n",
            "#### Single_Label Binary_Classification problem ####\n",
            "#######################################################################################\n",
            "#####  Searching for Uncorrelated List Of Variables (SULOV) in 6 features ############\n",
            "#######################################################################################\n",
            "    there are no null values in dataset...\n",
            "    Removing (2) highly correlated variables:\n",
            "    ['NEWBALANCEORIG', 'NEWBALANCEDEST']\n",
            "    Following (4) vars selected: ['STEP', 'AMOUNT', 'OLDBALANCEORG', 'OLDBALANCEDEST']\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time taken for SULOV method = 61 seconds\n",
            "    Adding 3 categorical variables to reduced numeric variables  of 4\n",
            "Final list of selected vars after SULOV = 7\n",
            "Readying dataset for Recursive XGBoost by converting all features to numeric...\n",
            "    there were no mixed data types or object columns that errored. Data is all numeric...\n",
            "Shape of train data after pruning = (900000, 10)\n",
            "#######################################################################################\n",
            "#####    R E C U R S I V E   X G B O O S T : F E A T U R E   S E L E C T I O N  #######\n",
            "#######################################################################################\n",
            "    using regular XGBoost\n",
            "Train and Test loaded into Dask dataframes successfully after feature_engg completed\n",
            "Current number of predictors = 7 \n",
            "    XGBoost version: 1.6.1\n",
            "Number of booster rounds = 20\n",
            "        using 7 variables...\n",
            "            Time taken for regular XGBoost feature selection = 28 seconds\n",
            "        using 5 variables...\n",
            "            Time taken for regular XGBoost feature selection = 47 seconds\n",
            "        using 3 variables...\n",
            "            Time taken for regular XGBoost feature selection = 59 seconds\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            Total time taken for XGBoost feature selection = 60 seconds\n",
            "#######################################################################################\n",
            "#####          F E A T U R E   S E L E C T I O N   C O M P L E T E D            #######\n",
            "#######################################################################################\n",
            "Selected 7 important features:\n",
            "['OLDBALANCEDEST', 'OLDBALANCEORG', 'TYPE', 'AMOUNT', 'STEP', 'NAMEDEST', 'NAMEORIG']\n",
            "\n",
            "    Time taken for feature selection = 170 seconds\n",
            "Returning 2 dataframes: dataname and test_data with 7 important features.\n"
          ]
        }
      ],
      "source": [
        "#automatic feature selection\n",
        "target = 'ISFRAUD'\n",
        "\n",
        "features, train = featurewiz(fraud_data, target, corr_limit=0.7, verbose=2, sep=\",\",\n",
        "header=0,test_data=\"\", feature_engg=\"\", category_encoders=\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYKglLVpMIsI",
        "outputId": "9564df43-5484-455d-ecb3-629162018c67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Important Features Selected are: ['OLDBALANCEDEST', 'OLDBALANCEORG', 'TYPE', 'AMOUNT', 'STEP', 'NAMEDEST', 'NAMEORIG']\n"
          ]
        }
      ],
      "source": [
        "print(\"Important Features Selected are:\",features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "GRpdfJmr-s5X"
      },
      "outputs": [],
      "source": [
        "#label encoding\n",
        "\n",
        "labelencoder = LabelEncoder()\n",
        "fraud_data['Transaction Type']  = labelencoder.fit_transform(fraud_data[\"TYPE\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hnMbXo5a_oLf",
        "outputId": "f3bde3f2-fa93-487f-9898-ed42770cdeae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b995febc-698d-4619-a1c6-c2163150b133\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>Transaction Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.00</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>M1979787155</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.00</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\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>0.00</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</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>181.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.00</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7817.71</td>\n",
              "      <td>C90045638</td>\n",
              "      <td>53860.00</td>\n",
              "      <td>46042.29</td>\n",
              "      <td>M573487274</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7107.77</td>\n",
              "      <td>C154988899</td>\n",
              "      <td>183195.00</td>\n",
              "      <td>176087.23</td>\n",
              "      <td>M408069119</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7861.64</td>\n",
              "      <td>C1912850431</td>\n",
              "      <td>176087.23</td>\n",
              "      <td>168225.59</td>\n",
              "      <td>M633326333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>4024.36</td>\n",
              "      <td>C1265012928</td>\n",
              "      <td>2671.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>M1176932104</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>DEBIT</td>\n",
              "      <td>5337.77</td>\n",
              "      <td>C712410124</td>\n",
              "      <td>41720.00</td>\n",
              "      <td>36382.23</td>\n",
              "      <td>C195600860</td>\n",
              "      <td>41898.0</td>\n",
              "      <td>40348.79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b995febc-698d-4619-a1c6-c2163150b133')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ],
            "text/plain": [
              "   STEP      TYPE    AMOUNT     NAMEORIG  OLDBALANCEORG  NEWBALANCEORIG  \\\n",
              "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
              "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
              "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
              "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
              "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
              "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
              "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
              "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
              "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
              "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
              "\n",
              "      NAMEDEST  OLDBALANCEDEST  NEWBALANCEDEST  ISFRAUD  ISFLAGGEDFRAUD  \\\n",
              "0  M1979787155             0.0            0.00        0               0   \n",
              "1  M2044282225             0.0            0.00        0               0   \n",
              "2   C553264065             0.0            0.00        1               0   \n",
              "3    C38997010         21182.0            0.00        1               0   \n",
              "4  M1230701703             0.0            0.00        0               0   \n",
              "5   M573487274             0.0            0.00        0               0   \n",
              "6   M408069119             0.0            0.00        0               0   \n",
              "7   M633326333             0.0            0.00        0               0   \n",
              "8  M1176932104             0.0            0.00        0               0   \n",
              "9   C195600860         41898.0        40348.79        0               0   \n",
              "\n",
              "   Transaction Type  \n",
              "0                 3  \n",
              "1                 3  \n",
              "2                 4  \n",
              "3                 1  \n",
              "4                 3  \n",
              "5                 3  \n",
              "6                 3  \n",
              "7                 3  \n",
              "8                 3  \n",
              "9                 2  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fraud_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bgvPKZW9MUQM"
      },
      "outputs": [],
      "source": [
        "#extracting the features and labels\n",
        "\n",
        "x = np.array(fraud_data[[\"Transaction Type\", \"AMOUNT\", \"OLDBALANCEORG\",\"OLDBALANCEDEST\",\"NEWBALANCEORIG\",\"NEWBALANCEDEST\"]])\n",
        "y = np.array(fraud_data[[\"ISFRAUD\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxsowtvrO_kZ",
        "outputId": "b33444db-701a-4072-9d12-9f0e8c701d89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5090096, 6)\n",
            "(1272524, 6)\n"
          ]
        }
      ],
      "source": [
        "#splitting data into training and testing set\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(x,y,random_state=42, test_size=0.2)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTRUkzos-A20",
        "outputId": "7b4223a8-98f7-45f4-e96b-20439a97ce40"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\rramb\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "#Logistic Regression Model\n",
        "\n",
        "logisticregression_model = LogisticRegression()\n",
        "logisticregression_model = logisticregression_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "35wzPyfKADoi"
      },
      "outputs": [],
      "source": [
        "#prediction for test set\n",
        "predicted_LR = logisticregression_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBOghCOvARvs",
        "outputId": "c3aeb7ae-18de-46a0-9aef-d246d8659560"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Logistic Regression on training set: 1.00\n",
            "Accuracy of Logistic Regression on test set:     1.00\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#accuracy of the logistic regression model for training and testing set\n",
        "\n",
        "print('Accuracy of Logistic Regression on training set: {:.2f}'.format(logisticregression_model.score(X_train, y_train)))\n",
        "print('Accuracy of Logistic Regression on test set:     {:.2f}'.format(logisticregression_model.score(X_test, y_test)))\n",
        "\n",
        "testaccuracy_LR = logisticregression_model.score(X_test, y_test)\n",
        "accuracy_result_LR = round(testaccuracy_LR,2)\n",
        "accuracy_result_LR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "sEc9XJMbAZ6a",
        "outputId": "834cc027-bb84-4236-c2a9-fa00140c871e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#confusion matrix for LR Model\n",
        "\n",
        "confusion_matrix_LR = confusion_matrix(y_test, predicted_LR)\n",
        "\n",
        "\n",
        "fig = sns.heatmap(confusion_matrix_LR, annot=True,  annot_kws={\"size\": 18}, cmap = 'PuRd',fmt='d')\n",
        "fig.xaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.yaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.set_xlabel('Predicted Values')\n",
        "fig.set_ylabel('Actual Values ');\n",
        "fig.set_title('Confusion Matrix for LR Model')\n",
        "sns.set(font_scale=1.4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GcgdIZ6ArVH",
        "outputId": "19b8b25e-3acd-4e4d-cc53-d90eea25bd89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " Classification report LogisticRegression():\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00   1270904\n",
            "           1       0.33      0.80      0.47      1620\n",
            "\n",
            "    accuracy                           1.00   1272524\n",
            "   macro avg       0.67      0.90      0.73   1272524\n",
            "weighted avg       1.00      1.00      1.00   1272524\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#classification report for LR Model\n",
        "\n",
        "print(\"\\n Classification report %s:\\n%s\\n\" % (logisticregression_model, metrics.classification_report(y_test, predicted_LR)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMFw418oA_2q",
        "outputId": "7291b5a3-b982-4ce6-c6f9-7c25c78a4215"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\rramb\\AppData\\Local\\Temp/ipykernel_1536/4098600538.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  randomforest_model.fit(X_train, y_train)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Random Forest Classifier\n",
        "\n",
        "randomforest_model = RandomForestClassifier()\n",
        "randomforest_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "huqy7-QmBFrR"
      },
      "outputs": [],
      "source": [
        "#prediction for test set\n",
        "predicted_RFC = randomforest_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hq6mL_EBTv_",
        "outputId": "965726c2-4f73-4418-8631-ebdc0727b480"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Random Forest Classifier on training set: 1.00\n",
            "Accuracy of Random Forest Classifier on test set:     1.00\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.999704524236871"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#accuracy of the random forest classifier for training and testing set\n",
        "\n",
        "print('Accuracy of Random Forest Classifier on training set: {:.2f}'.format(randomforest_model.score(X_train, y_train)))\n",
        "print('Accuracy of Random Forest Classifier on test set:     {:.2f}'.format(randomforest_model.score(X_test, y_test)))\n",
        "\n",
        "accuracy_result_RFC = randomforest_model.score(X_test, y_test)\n",
        "accuracy_result_RFC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "0GGWrthfBXx-",
        "outputId": "ea734d96-d039-4ea3-8ee9-ba462afcba51"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#confusion matrix\n",
        "\n",
        "confusion_matrix_RFC = confusion_matrix(y_test, predicted_RFC)\n",
        "\n",
        "\n",
        "fig = sns.heatmap(confusion_matrix_RFC, annot=True,  annot_kws={\"size\": 18}, cmap = 'PuRd',fmt='d')\n",
        "fig.xaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.yaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.set_xlabel('Predicted Values')\n",
        "fig.set_ylabel('Actual Values ');\n",
        "fig.set_title('Confusion Matrix for Random Forest Classifier')\n",
        "sns.set(font_scale=1.4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1WhvI3QBdkZ",
        "outputId": "18e05e25-563c-444c-8948-d7a95f19e270"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " Classification report RandomForestClassifier():\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00   1270904\n",
            "           1       0.96      0.79      0.87      1620\n",
            "\n",
            "    accuracy                           1.00   1272524\n",
            "   macro avg       0.98      0.90      0.93   1272524\n",
            "weighted avg       1.00      1.00      1.00   1272524\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#classification report for Random Forest Classifier Model\n",
        "\n",
        "print(\"\\n Classification report %s:\\n%s\\n\" % (randomforest_model, metrics.classification_report(y_test, predicted_RFC)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2k_E0dEdBr4D"
      },
      "outputs": [],
      "source": [
        "#XGBoost Classifier\n",
        "model_XGB = XGBClassifier()\n",
        "model_XGB.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dNLbB3xmBr7L"
      },
      "outputs": [],
      "source": [
        "#prediction for test set\n",
        "predicted_XGB = model_XGB.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sShwFEdkBr90"
      },
      "outputs": [],
      "source": [
        "#accuracy of the XGB model for training and testing set\n",
        "\n",
        "print('Accuracy of XGB on training set: {:.2f}'.format(model_XGB.score(X_train, y_train)))\n",
        "print('Accuracy of XGB on test set:     {:.2f}'.format(model_XGB.score(X_test, y_test)))\n",
        "\n",
        "accuracy_result_XGB = model_XGB.score(X_test, y_test)\n",
        "accuracy_result_XGB = round(accuracy_result_XGB,3)\n",
        "accuracy_result_XGB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KmWb0hSpBsAp"
      },
      "outputs": [],
      "source": [
        "#confusion matrix\n",
        "\n",
        "confusion_matrix_XGB = confusion_matrix(y_test, predicted_XGB)\n",
        "\n",
        "\n",
        "fig = sns.heatmap(confusion_matrix_XGB, annot=True,  annot_kws={\"size\": 18}, cmap = 'PuRd',fmt='g')\n",
        "fig.xaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.yaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.set_xlabel('Predicted Values')\n",
        "fig.set_ylabel('Actual Values ');\n",
        "fig.set_title('Confusion Matrix for XGBoost Classifier')\n",
        "sns.set(font_scale=1.4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t3ow5FkXBsDu"
      },
      "outputs": [],
      "source": [
        "#classification report for XGB Model\n",
        "\n",
        "print(\"\\n Classification report %s:\\n%s\\n\" % (model_XGB, metrics.classification_report(y_test, predicted_XGB)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "paf7Xx9MBsHA"
      },
      "outputs": [],
      "source": [
        "#Decision Tree Classifier\n",
        "\n",
        "model_decisiontree = DecisionTreeClassifier()\n",
        "model_decisiontree.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DOBWoqXUChr2"
      },
      "outputs": [],
      "source": [
        "#prediction for test set\n",
        "predicted_decisiontree = model_decisiontree.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BSc5MGCfChui"
      },
      "outputs": [],
      "source": [
        "#accuracy of the Decision Tree model for training and testing set\n",
        "\n",
        "print('Accuracy of Decision Tree Classifier on training set: {:.2f}'.format(model_decisiontree.score(X_train, y_train)))\n",
        "print('Accuracy of Decision Tree Classifier on test set:     {:.2f}'.format(model_decisiontree.score(X_test, y_test)))\n",
        "\n",
        "accuracy_result_decisiontree = model_decisiontree.score(X_test, y_test)\n",
        "accuracy_result_decisiontree = round(accuracy_result_decisiontree,3)\n",
        "accuracy_result_decisiontree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ge_gpoGNChxM"
      },
      "outputs": [],
      "source": [
        "#confusion matrix\n",
        "\n",
        "confusion_matrix_decisiontree = confusion_matrix(y_test, predicted_decisiontree)\n",
        "\n",
        "\n",
        "fig = sns.heatmap(confusion_matrix_decisiontree, annot=True,  annot_kws={\"size\": 18}, cmap = 'PuRd',fmt='g')\n",
        "fig.xaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.yaxis.set_ticklabels(['Fraud','Not Fraud'])\n",
        "fig.set_xlabel('Predicted Values')\n",
        "fig.set_ylabel('Actual Values ');\n",
        "fig.set_title('Confusion Matrix for Decision Tree Classifier')\n",
        "sns.set(font_scale=1.4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F8GYXy1ZCh0l"
      },
      "outputs": [],
      "source": [
        "#classification report for Decision Tree Model\n",
        "\n",
        "print(\"\\n Classification report %s:\\n%s\\n\" % (model_decisiontree, metrics.classification_report(y_test, predicted_decisiontree)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "cOni2axpH-ay"
      },
      "outputs": [],
      "source": [
        "#pickling the model\n",
        "\n",
        "pickle_out = open(\"FraudDataClassifier.pkl\", \"wb\")\n",
        "pickle.dump(randomforest_model, pickle_out)\n",
        "pickle_out.close()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "FraudDetection.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "96f6aca0992fa78294534ac66cdd908a3a093b71fbf49883661e2f8d6094dd12"
    },
    "kernelspec": {
      "display_name": "Python 3.8.8 64-bit",
      "language": "python",
      "name": "python3"
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    "language_info": {
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