Predicting_Fraudulent_Transactions.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/jadhavtejashri/Predicting_Fraudulent_Transactions./blob/main/Predicting_Fraudulent_Transactions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "33153bfb",
      "metadata": {
        "id": "33153bfb"
      },
      "source": [
        "#  Predicting Fraudulent Transactions "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "4b5e9b88",
      "metadata": {
        "id": "4b5e9b88"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "98488501",
      "metadata": {
        "id": "98488501"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5aef4f5e",
      "metadata": {
        "id": "5aef4f5e"
      },
      "source": [
        "# Pre-processing\n",
        "# Wrangling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "6cba8bff",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# upload data in your drive and then run this\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the train_dataset, train_salaries, test_dataset(pass your file path from drive)\n",
        "df= pd.read_csv('/content/drive/My Drive/project/Fraud.csv')"
      ],
      "metadata": {
        "id": "fDlkOQZ9_cZQ"
      },
      "id": "fDlkOQZ9_cZQ",
      "execution_count": 13,
      "outputs": []
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              "   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  "
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          "metadata": {},
          "execution_count": 14
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      "source": [
        "df.head(10)"
      ]
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    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "7de43c8f",
      "metadata": {
        "colab": {
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        },
        "id": "7de43c8f",
        "outputId": "2dfd139f-fe71-4276-d399-8fa194bd1c96"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6362620, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# shape the data\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19f1f980",
      "metadata": {
        "id": "19f1f980"
      },
      "source": [
        "# Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "4fdb281e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fdb281e",
        "outputId": "6fe57b26-ed7f-44ce-ef4d-3d2d68b60c6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Check for null values\n",
        "df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "79ad0f11",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79ad0f11",
        "outputId": "93948ef5-3f3a-4adc-91b7-fd87aa5a0b68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": [
        "# Getting information about data\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "339d732e",
      "metadata": {
        "id": "339d732e"
      },
      "outputs": [],
      "source": [
        "legit= len(df[df.isFraud==0])\n",
        "fraud= len(df[df.isFraud==1])\n",
        "legit_percent=(legit/(fraud+legit))*100\n",
        "fraud_percent=(fraud/(fraud+legit))*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "a2f951ce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2f951ce",
        "outputId": "3d5c3f88-93c8-4bf1-b4a8-35d70a51023c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of legit Transactions: 6354407\n",
            "Number of fraud Transactions: 8213\n",
            "Percentage of legit Transactions:99.8709%\n",
            "Percentage of fraud Transactions:0.1291%\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of legit Transactions:\",legit)\n",
        "print(\"Number of fraud Transactions:\",fraud)\n",
        "print(\"Percentage of legit Transactions:{:.4f}%\".format(legit_percent))\n",
        "print(\"Percentage of fraud Transactions:{:.4f}%\".format(fraud_percent))"
      ]
    },
    {
      "cell_type": "raw",
      "id": "2bdaabb6",
      "metadata": {
        "id": "2bdaabb6"
      },
      "source": [
        "These results prove that this is a highly unbalanced data as percentage of Legit transaction =99.87% and Percentade of fraud transaction =0.13% so Decision trees and Random Forests are Good methods for Imbalanced data."
      ]
    },
    {
      "cell_type": "raw",
      "id": "47d7902f",
      "metadata": {
        "id": "47d7902f"
      },
      "source": [
        "# Merchants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "5a9f2b28",
      "metadata": {
        "id": "5a9f2b28"
      },
      "outputs": [],
      "source": [
        "x= df[df['nameDest'].str.contains('M')]"
      ]
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      "id": "65d3b908",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step     type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1  PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1  PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
              "4     1  PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
              "5     1  PAYMENT   7817.71    C90045638        53860.0        46042.29   \n",
              "6     1  PAYMENT   7107.77   C154988899       183195.0       176087.23   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  M1979787155             0.0             0.0        0               0  \n",
              "1  M2044282225             0.0             0.0        0               0  \n",
              "4  M1230701703             0.0             0.0        0               0  \n",
              "5   M573487274             0.0             0.0        0               0  \n",
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              "      <td>1864.28</td>\n",
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              "      <td>29885.86</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>PAYMENT</td>\n",
              "      <td>7817.71</td>\n",
              "      <td>C90045638</td>\n",
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              "      <td>7107.77</td>\n",
              "      <td>C154988899</td>\n",
              "      <td>183195.0</td>\n",
              "      <td>176087.23</td>\n",
              "      <td>M408069119</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b2517a3-a1fd-447b-bbf4-2e8cbd2e1b24')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b2517a3-a1fd-447b-bbf4-2e8cbd2e1b24 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-1b2517a3-a1fd-447b-bbf4-2e8cbd2e1b24');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "x.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9304c363",
      "metadata": {
        "id": "9304c363"
      },
      "source": [
        "# Visualisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "65422cde",
      "metadata": {
        "id": "65422cde"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "376c190c",
      "metadata": {
        "id": "376c190c"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "f1e40c2c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "f1e40c2c",
        "outputId": "7a084e7f-5171-401b-f756-1df891b5fcd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0769a81550>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "corr=df.corr()\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.heatmap(corr,annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44d7d20d",
      "metadata": {
        "id": "44d7d20d"
      },
      "source": [
        "# Number of Legit and Fraud Transactions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "a470bddf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "a470bddf",
        "outputId": "22c22e4a-57e1-4f17-a06a-3a8442df5246"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(5,10))\n",
        "labels=[\"Legit\",\"Fraud\"]\n",
        "count_classes=df.value_counts(df['isFraud'],sort=True)\n",
        "count_classes.plot(kind=\"bar\",rot=0)\n",
        "plt.title(\"Visualization of Labels\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.xticks(range(2),labels)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4709bd89",
      "metadata": {
        "id": "4709bd89"
      },
      "source": [
        "# Problem Solving"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "5cddacc2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "5cddacc2",
        "outputId": "6dd63775-ae33-4c47-d37a-73e63181c9e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b12f1cc4-4dd7-4c6a-91aa-558760db327c\">\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",
              "    </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",
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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",
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              "      <td>C2048537720</td>\n",
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              "      <td>0</td>\n",
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              "      <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",
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              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <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",
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              "    <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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b12f1cc4-4dd7-4c6a-91aa-558760db327c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b12f1cc4-4dd7-4c6a-91aa-558760db327c 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-b12f1cc4-4dd7-4c6a-91aa-558760db327c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Creating a copy of original dataset to train and test models\n",
        "new_df=df.copy()\n",
        "new_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1efe59d",
      "metadata": {
        "id": "f1efe59d"
      },
      "source": [
        "# Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "e80fa257",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e80fa257",
        "outputId": "ce4e08c1-8f89-46cc-9656-94dcc7bb42f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['type', 'nameOrig', 'nameDest'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Checking how many attributes are dtype: object\n",
        "objlist= new_df.select_dtypes(include=\"object\").columns\n",
        "print(objlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "865fd166",
      "metadata": {
        "id": "865fd166"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "6c11d8a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c11d8a5",
        "outputId": "03b2fa4b-c354-4920-dd26-d62a91f696e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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            int64  \n",
            " 2   amount          float64\n",
            " 3   nameOrig        int64  \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        int64  \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(6)\n",
            "memory usage: 534.0 MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "le=LabelEncoder()\n",
        "for feat in objlist:\n",
        "    new_df[feat]=le.fit_transform(new_df[feat].astype(str))\n",
        "print(new_df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "3c3ff10c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3c3ff10c",
        "outputId": "7f9af2b0-2e7e-4c65-c174-1b388b4f4e69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step  type    amount  nameOrig  oldbalanceOrg  newbalanceOrig  nameDest  \\\n",
              "0     1     3   9839.64    757869       170136.0       160296.36   1662094   \n",
              "1     1     3   1864.28   2188998        21249.0        19384.72   1733924   \n",
              "2     1     4    181.00   1002156          181.0            0.00    439685   \n",
              "3     1     1    181.00   5828262          181.0            0.00    391696   \n",
              "4     1     3  11668.14   3445981        41554.0        29885.86    828919   \n",
              "\n",
              "   oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0             0.0             0.0        0               0  \n",
              "1             0.0             0.0        0               0  \n",
              "2             0.0             0.0        1               0  \n",
              "3         21182.0             0.0        1               0  \n",
              "4             0.0             0.0        0               0  "
            ],
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              "      <div>\n",
              "<style scoped>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22bbbb7b-06bf-4df5-b3ea-fbb6b8e1bae6')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22bbbb7b-06bf-4df5-b3ea-fbb6b8e1bae6 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-22bbbb7b-06bf-4df5-b3ea-fbb6b8e1bae6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "new_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9dcaa6e",
      "metadata": {
        "id": "b9dcaa6e"
      },
      "source": [
        "# Multicolinearity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "57e305cc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "57e305cc",
        "outputId": "a42567fb-eafa-4081-8a7b-34f63ef92609"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         variables         VIF\n",
              "0             step    2.791610\n",
              "1             type    4.467405\n",
              "2           amount    4.149312\n",
              "3         nameOrig    2.764234\n",
              "4    oldbalanceOrg  576.803777\n",
              "5   newbalanceOrig  582.709128\n",
              "6         nameDest    3.300975\n",
              "7   oldbalanceDest   73.349937\n",
              "8   newbalanceDest   85.005614\n",
              "9          isFraud    1.195305\n",
              "10  isFlaggedFraud    1.002587"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0510243f-cd29-438b-a5d8-ee375e63eeb3\">\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>variables</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>step</td>\n",
              "      <td>2.791610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>type</td>\n",
              "      <td>4.467405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>amount</td>\n",
              "      <td>4.149312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nameOrig</td>\n",
              "      <td>2.764234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>oldbalanceOrg</td>\n",
              "      <td>576.803777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>newbalanceOrig</td>\n",
              "      <td>582.709128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>nameDest</td>\n",
              "      <td>3.300975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>oldbalanceDest</td>\n",
              "      <td>73.349937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>newbalanceDest</td>\n",
              "      <td>85.005614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>isFraud</td>\n",
              "      <td>1.195305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>isFlaggedFraud</td>\n",
              "      <td>1.002587</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0510243f-cd29-438b-a5d8-ee375e63eeb3')\"\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",
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              "  </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-0510243f-cd29-438b-a5d8-ee375e63eeb3 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-0510243f-cd29-438b-a5d8-ee375e63eeb3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Import library for VIF (VARIANCE INFLATION FACTOR)\n",
        "\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "def calc_vif(df):\n",
        "    vif = pd.DataFrame()\n",
        "    vif[\"variables\"] = df.columns\n",
        "    vif[\"VIF\"] = [variance_inflation_factor(df.values, i) for i in range(df.shape[1])]\n",
        "\n",
        "    return(vif)\n",
        "calc_vif(new_df)\n"
      ]
    },
    {
      "cell_type": "raw",
      "id": "e003ce2e",
      "metadata": {
        "id": "e003ce2e"
      },
      "source": [
        "We can see that oldbalanceOrg and newbalanceOrig have too high VIF thus they are highly correlated. Similarly oldbalanceDest and newbalanceDest. Also nameDest is connected to nameOrig.Thus combine these pairs of collinear attributes and drop the individual ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "399e9ae7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "399e9ae7",
        "outputId": "086b50d9-99f1-4c5d-ab08-68d115f24e7b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            variables       VIF\n",
              "0                type  2.687803\n",
              "1              amount  3.818902\n",
              "2             isFraud  1.184479\n",
              "3      isFlaggedFraud  1.002546\n",
              "4  Actual_amount_orig  1.307910\n",
              "5  Actual_amount_dest  3.754335\n",
              "6     TransactionPath  2.677167"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5515dbe0-9da8-4894-b951-d2940d16d584\">\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>variables</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>type</td>\n",
              "      <td>2.687803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>amount</td>\n",
              "      <td>3.818902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>isFraud</td>\n",
              "      <td>1.184479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>isFlaggedFraud</td>\n",
              "      <td>1.002546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Actual_amount_orig</td>\n",
              "      <td>1.307910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Actual_amount_dest</td>\n",
              "      <td>3.754335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>TransactionPath</td>\n",
              "      <td>2.677167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5515dbe0-9da8-4894-b951-d2940d16d584')\"\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",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-5515dbe0-9da8-4894-b951-d2940d16d584 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-5515dbe0-9da8-4894-b951-d2940d16d584');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "new_df['Actual_amount_orig'] = new_df.apply(lambda x: x['oldbalanceOrg'] - x['newbalanceOrig'],axis=1)\n",
        "new_df['Actual_amount_dest'] = new_df.apply(lambda x: x['oldbalanceDest'] - x['newbalanceDest'],axis=1)\n",
        "new_df['TransactionPath'] = new_df.apply(lambda x: x['nameOrig'] + x['nameDest'],axis=1)\n",
        "\n",
        "#Dropping columns\n",
        "new_df = new_df.drop(['oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest','step','nameOrig','nameDest'],axis=1)\n",
        "\n",
        "calc_vif(new_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "21ba0d39",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "21ba0d39",
        "outputId": "436b5e67-522b-4fe2-d01c-a38e070e341f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f06e062e160>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "corr=new_df.corr()\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.heatmap(corr,annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3e6657a",
      "metadata": {
        "id": "a3e6657a"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "1d5f1ec5",
      "metadata": {
        "id": "1d5f1ec5"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import itertools\n",
        "from collections import Counter\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "4129b50f",
      "metadata": {
        "id": "4129b50f"
      },
      "outputs": [],
      "source": [
        "# NORMALIZING (SCALING) AMOUNT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "48414eb4",
      "metadata": {
        "id": "48414eb4"
      },
      "outputs": [],
      "source": [
        "# Perform Scaling\n",
        "scaler = StandardScaler()\n",
        "new_df[\"NormalizedAmount\"] = scaler.fit_transform(new_df[\"amount\"].values.reshape(-1, 1))\n",
        "new_df.drop([\"amount\"], inplace= True, axis= 1)\n",
        "\n",
        "Y = new_df[\"isFraud\"]\n",
        "X = new_df.drop([\"isFraud\"], axis= 1)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "b0271d6d",
      "metadata": {
        "id": "b0271d6d"
      },
      "source": [
        "I did not normalize the complete dataset because it may lead to decrease in accuracy of model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d835fcd",
      "metadata": {
        "id": "8d835fcd"
      },
      "source": [
        "# TRAIN-TEST SPLIT¶"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "a49d6467",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a49d6467",
        "outputId": "557a674a-be65-4691-ae15-9c702b3a1248"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train:  (4453834, 6)\n",
            "Shape of X_test:  (1908786, 6)\n"
          ]
        }
      ],
      "source": [
        "# Split the data\n",
        "(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, test_size= 0.3, random_state= 42)\n",
        "print(\"Shape of X_train: \", X_train.shape)\n",
        "print(\"Shape of X_test: \", X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "494a3b28",
      "metadata": {
        "id": "494a3b28"
      },
      "source": [
        "# Model training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "82dedd96",
      "metadata": {
        "id": "82dedd96"
      },
      "outputs": [],
      "source": [
        "# DECISION TREE\n",
        "decision_tree = DecisionTreeClassifier()\n",
        "decision_tree.fit(X_train, Y_train)\n",
        "Y_pred_dt = decision_tree.predict(X_test)\n",
        "decision_tree_score = decision_tree.score(X_test, Y_test) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "0d2dbe11",
      "metadata": {
        "id": "0d2dbe11"
      },
      "outputs": [],
      "source": [
        "# Random Forest\n",
        "random_forest = RandomForestClassifier(n_estimators= 100)\n",
        "random_forest.fit(X_train, Y_train)\n",
        "Y_pred_rf = random_forest.predict(X_test)\n",
        "random_forest_score = random_forest.score(X_test, Y_test) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0e3e80d",
      "metadata": {
        "id": "d0e3e80d"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "02f0e850",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02f0e850",
        "outputId": "d60698b7-e30e-4623-c318-03a7b0f5c247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Score:  99.9226733641173\n",
            "Random Forest Score:  99.95866482675376\n"
          ]
        }
      ],
      "source": [
        "# Print scores of our classifiers\n",
        "print(\"Decision Tree Score: \", decision_tree_score)\n",
        "print(\"Random Forest Score: \", random_forest_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "fccd686a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fccd686a",
        "outputId": "ef6f0899-2f83-4b43-a834-f878a13682d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TP,FP,TN,FN - Decision Tree\n",
            "True Positives: 1714\n",
            "False Positives: 755\n",
            "True Negatives: 1905596\n",
            "False Negatives: 721\n",
            "----------------------------------------------------------------------------------------\n",
            "TP,FP,TN,FN - Random Forest\n",
            "True Positives: 1709\n",
            "False Positives: 63\n",
            "True Negatives: 1906288\n",
            "False Negatives: 726\n"
          ]
        }
      ],
      "source": [
        "# key terms of Confusion Matrix - DT\n",
        "\n",
        "print(\"TP,FP,TN,FN - Decision Tree\")\n",
        "tn, fp, fn, tp = confusion_matrix(Y_test, Y_pred_dt).ravel()\n",
        "print(f'True Positives: {tp}')\n",
        "print(f'False Positives: {fp}')\n",
        "print(f'True Negatives: {tn}')\n",
        "print(f'False Negatives: {fn}')\n",
        "\n",
        "print(\"----------------------------------------------------------------------------------------\")\n",
        "\n",
        "# key terms of Confusion Matrix - RF\n",
        "\n",
        "print(\"TP,FP,TN,FN - Random Forest\")\n",
        "tn, fp, fn, tp = confusion_matrix(Y_test, Y_pred_rf).ravel()\n",
        "print(f'True Positives: {tp}')\n",
        "print(f'False Positives: {fp}')\n",
        "print(f'True Negatives: {tn}')\n",
        "print(f'False Negatives: {fn}')"
      ]
    },
    {
      "cell_type": "raw",
      "id": "258d26fc",
      "metadata": {
        "id": "258d26fc"
      },
      "source": [
        "TP(Decision Tree) ~ TP(Random Forest) so no competetion here.\n",
        "FP(Decision Tree) >> FP(Random Forest) - Random Forest has an edge\n",
        "TN(Decision Tree) < TN(Random Forest) - Random Forest is better here too\n",
        "FN(Decision Tree) ~ FN(Random Forest)\n",
        "Here Random Forest looks good."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "c6152e09",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6152e09",
        "outputId": "62121502-b89c-4312-d9b8-4f5c40598cbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix - Decision Tree\n",
            "[[1905596     755]\n",
            " [    721    1714]]\n",
            "----------------------------------------------------------------------------------------\n",
            "Confusion Matrix - Random Forest\n",
            "[[1906288      63]\n",
            " [    726    1709]]\n"
          ]
        }
      ],
      "source": [
        "# confusion matrix - DT\n",
        "\n",
        "confusion_matrix_dt = confusion_matrix(Y_test, Y_pred_dt.round())\n",
        "print(\"Confusion Matrix - Decision Tree\")\n",
        "print(confusion_matrix_dt,)\n",
        "\n",
        "print(\"----------------------------------------------------------------------------------------\")\n",
        "\n",
        "# confusion matrix - RF\n",
        "\n",
        "confusion_matrix_rf = confusion_matrix(Y_test, Y_pred_rf.round())\n",
        "print(\"Confusion Matrix - Random Forest\")\n",
        "print(confusion_matrix_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "dd02a487",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd02a487",
        "outputId": "e3442aff-538b-40b0-d5b4-2189a31f5e8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report - Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00   1906351\n",
            "           1       0.69      0.70      0.70      2435\n",
            "\n",
            "    accuracy                           1.00   1908786\n",
            "   macro avg       0.85      0.85      0.85   1908786\n",
            "weighted avg       1.00      1.00      1.00   1908786\n",
            "\n",
            "----------------------------------------------------------------------------------------\n",
            "Classification Report - Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00   1906351\n",
            "           1       0.96      0.70      0.81      2435\n",
            "\n",
            "    accuracy                           1.00   1908786\n",
            "   macro avg       0.98      0.85      0.91   1908786\n",
            "weighted avg       1.00      1.00      1.00   1908786\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# classification report - DT\n",
        "\n",
        "classification_report_dt = classification_report(Y_test, Y_pred_dt)\n",
        "print(\"Classification Report - Decision Tree\")\n",
        "print(classification_report_dt)\n",
        "\n",
        "print(\"----------------------------------------------------------------------------------------\")\n",
        "\n",
        "# classification report - RF\n",
        "\n",
        "classification_report_rf = classification_report(Y_test, Y_pred_rf)\n",
        "print(\"Classification Report - Random Forest\")\n",
        "print(classification_report_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "6f507ebf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "6f507ebf",
        "outputId": "c0b9f305-829e-425d-cac7-0118be0397e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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HNbu0bZoYWOtWvFfHurXJD/veWwazx/4r6Nu/kb0PXs6jfxrI0oXJ35BlS+p4Z26PYlWtt//YZTxw22AAHr1nIHt9YjkSfGzMct54qTer3xcN9fDcE/0YsdOa6nxhOTTm+KUbdGcHb7Fu/ecDj36PN17pDcCAwfV065b8Ld5qxBq22XYNb8/O7zW/plHadry1rGKq2cKbShsmBna0/zl9JM890Y/3FnfnlI/txhe/9Tb19ckP7pgvLWL2zF7877+PQMDInVfzzUuS1t7IndZw6rnz+e5J2xMBdd2DM380ly2HrStytsRRJy/ip2eP5MsH7kr/gfWcf1UyZaL/wAY++7V3OetTOyHBvoctY7/Dl5WozcrRa5MG9jl4OZed+8H0ktP+Yz7b776KCHhnbk8uT7ftuf8KvvSd5P9BY6O4/LxhLF/a4Z2jDtVZR2mVzOGrUuXJvW8/54OJgT8stv/ovXrHU5OHF9vFasyRW+/d0SFYBk/GgyyLxRvV9Bq0yxZx2PjPlbXvHQddNX1jbi2rtKr+mWrLxEAzq3212F0tR77b5WaWWYUfANqunPDMLDMnPDPLBc/DM7Nc6azz8JzwzCyTCKgv/wGgNcUJz8wyc5fWzHLB1/DMLFfCCc/M8sKDFmaWCxG+hmdmuSEaOukobeeM2sw6VITKWkqRNF7SAkkvtLL9FEnPSXpe0uOS9irY9kZa/kyzl323yi08M8ukwvfSXkfyVrIbWtn+OvDJiFgi6WiShwUXPjn90IhYWO7JnPDMLJtIruNVpKqIKZJGFdn+eMHq30geJNxm7tKaWWYZ3lo2RNK0gmXcRpz2NODegvUA7pc0vdx63cIzs0wi26DFwko8AFTSoSQJ7xMFxZ+IiHmStgAekPRyREwpVo9beGaWWUR5SyVI+ghwLXBcRCz6IIaYl/67ALiT5NWwRTnhmVlmlRqlLUXSCOAO4IsR8WpBeV9J/Zs+A2OBFkd6C7lLa2aZJK23yozSSpoAjCG51jcXuBDokZwnrgYuADYDrpQEUJ92kbcE7kzLugO3RMR9pc7nhGdmmVVqWkpEnFxi+1eBr7ZQ/hqw14ePKM4Jz8wyq+LLDqvKCc/MMglEYye9tcwJz8wy66QNPCc8M8uogoMW7c0Jz8yy66RNPCc8M8usy7XwJP2CInk8Is6uSkRmVtMCaGzsYgkPKOv5UmaWMwF0tRZeRFxfuC6pT0S8X/2QzKzWddZ5eCUn00g6QNIM4OV0fS9JV1Y9MjOrXVHmUmPKmT34c+BIYBFARDwLHFLNoMyslpX34IBaHNgoa5Q2IuakN+k2aahOOGbWKdRg660c5SS8OZIOBEJSD+DfgJeqG5aZ1ayA6KSjtOV0ab8OfAPYBngL2DtdN7PcUplLbSnZwkvfCHRKO8RiZp1FJ+3SljNKu52kuyW9m74/8o+StmuP4MysRnXhUdpbgN8DQ4GtgduACdUMysxqWNPE43KWGlNOwusTETdGRH263AT0rnZgZla72vMlPpVU7F7awenHeyWdB9xKkttPBCa1Q2xmVqs66ShtsUGL6SQJrukr+1rBtgC+W62gzKy2qQZbb+VotUsbEdtGxHbpv80XD1qY5VW5AxZlJEVJ49PB0BZfsajE5ZJmSXpO0j4F206VNDNdTi0n9LLutJC0B7AbBdfuIuKGco41s66mogMS1wG/BFrLJ0cDO6bLfsBVwH7pJbcLgdEkqXW6pIkRsaTYycqZlnIh8It0ORT4KXBsOV+JmXVRFWrhRcQUYHGRXY4DbojE34CBkoaS3N//QEQsTpPcA8BRpc5Xzijt54B/At6OiK+QvAtyQBnHmVlX1Vjmkrxge1rBMi7jmbYB5hSsz03LWisvqpwu7aqIaJRUL2lTYAEwvPx4zaxLyfYA0IURMbqK0WRSTgtvmqSBwK9JRm6fBp6oalRmVtMU5S0VMI8NG1jD0rLWyosqmfAi4oyIWBoRVwNHAKemXVszy6v2u7VsIvCldLR2f+C9iJgPTAbGShokaRAwNi0rqtjE432KbYuIp7PHbmb2AUkTgDEk1/rmkoy89gBIG1mTgE8Bs4D3ga+k2xZL+i9galrVRRFRbPADKH4N75Ii2wI4rFTlWb36XB+O3HrvSldrZhVWqYnHEXFyie1BK4+ji4jxwPgs5yv2Ep9Ds1RkZjkRdMlby8zMWtZJby1zwjOzzDrrvbROeGaWXSdNeOXcWiZJX5B0Qbo+QtK+1Q/NzGpWF37i8ZXAAUDTaMpy4IqqRWRmNa3cSce12O0tp0u7X0TsI+nvABGxRFLPKsdlZrWsC4/SrpNUR9pAlbQ5TbcFm1ku1WLrrRzldGkvB+4EtpD0Q+CvwI+qGpWZ1bZOeg2vnPfS3ixpOskjogQcHxEvVT0yM6tNNXp9rhwlE56kEST3sN1dWBYRs6sZmJnVsK6a8IA/8cHLfHoD2wKvALtXMS4zq2HqpFfxy+nS7lm4nj5F5YyqRWRmViWZ77SIiKcl7VeNYMysk+iqXVpJ5xSsdgP2Ad6qWkRmVtu68qAF0L/gcz3JNb3bqxOOmXUKXTHhpROO+0fEt9spHjPrDLpawpPUPSLqJR3UngGZWW0TXXOU9imS63XPSJoI3AasbNoYEXdUOTYzq0Vd/Bpeb2ARyTssmubjBeCEZ5ZXFUp4ko4CLgPqgGsj4sfNtv8MaHrdRB9gi4gYmG5rAJ5Pt82OiGNLna9YwtsiHaF9gQ8SXZNOmt/NrCIqkAHSMYIrSF7/OheYKmliRMxYf5qIbxbsfxbw0YIqVkVEprd+FUt4dUA/Nkx06+PIchIz61oq1KXdF5gVEa8BSLoVOA6Y0cr+J5O8xrHNiiW8+RFx0cZUbmZdVPkJb4ikaQXr10TENennbYA5BdvmAi3e1CBpJMltrQ8VFPdO664HfhwRd5UKpljC65xP+DOz6opMo7QLI2J0Bc56EvCHiGgoKBsZEfMkbQc8JOn5iPhHsUqKPQ/vnyoQpJl1RZV5Ht48YHjB+rC0rCUnARM2CCFiXvrva8DDbHh9r0WtJryIWFzqYDPLpwq902IqsKOkbdPXRpwETPzQuaRdgEHAEwVlgyT1Sj8PAQ6i9Wt/6/k1jWaWXQUGLdIbG84EJpMMko6PiBclXQRMi4im5HcScGtEFJ51V+BXkhpJGm4/LhzdbY0TnpllU8HHt0fEJGBSs7ILmq1/v4XjHgf2bF5eihOemWUiuvadFmZmG3DCM7P8cMIzs9xwwjOzXOjiT0sxM9uQE56Z5UVXfAComVmL3KU1s3yo4MTj9uaEZ2bZOeGZWR74TgszyxU1ds6M54RnZtn4Gp6Z5Ym7tGaWH054ZpYXbuGZWX444ZlZLmR7a1lNccIzs0w8D8/M8iU6Z8Yr9l5aM7MWVeg1jUg6StIrkmZJOq+F7V+W9K6kZ9LlqwXbTpU0M11OLSdut/A20rDtV3P+1W+uX99qxFpuvHgrNhu6jv2PWMa6tWL+mz255JsjWLmsjv6D6vnPa95gp71X8cDvB3HF94Z1YPT5cc6ls9nv8OUsXdidrx22MwDnX/0Gw7ZfA0DfTRtYuayOM47Yuayf0feve52hI9aurytXKjTxWFIdcAVwBDAXmCppYguvW/xdRJzZ7NjBwIXA6DSa6emxS4qds2oJT9J44BhgQUTsUa3zdLS5/+jNGUck/+m7dQtufnoGj907gGE7rGH8j4bS2CBO+95bnHTWO/zmh1uzdrW4/uKtGLXzakbtsrqDo8+P+383mIm/HcJ3LpuzvuxHXx+1/vO4C95i5fKkw1PqZ3TQ0UtZvTLfnaMKDVrsC8yKiNcAJN0KHEcZL9QGjgQeiIjF6bEPAEcBE4odVM2f2nVpALmx98ErmP9mTxbM68nTj/SnsUEAvDS9L0OGrgNgzao6XnyqH2vX5PsXpr298GQ/li9p7e97cMixS/nLXYOA4j+j3n0a+OzX3uWWn29ZxWhrnxrLW4AhkqYVLOMKqtkGmFOwPjcta+7/SHpO0h8kDc947Aaq1sKLiCmSRlWr/lo05rglPJz+0hQ68uTFPPLHgR0QkZVjj/1WsuTd7rz1eq+S+5567tvcfvUWrFmV4z9YQZZBi4URMXojznY3MCEi1kj6GnA9cFhbK+vwn5qkcU3Zfx1rOjqcNuveo5H9xy5jyt0DNig/+ex3aKiHh+5wwqtVhx6/lIfvKv3z2W73VQwdtZbH7xtQct+urkKDFvOA4QXrw9Ky9SJiUUQ0JYZrgY+Ve2xLOjzhRcQ1ETE6Ikb3oPRf2Fr18cOWM+v5TVi6sMf6siP+ZTH7Hr6Mn5w5kmT2ktWabnXBQZ96j0cmlk54u31sJTt95H2uf3IGl9w1i222W8NP/zCrHaKsQVHmUtxUYEdJ20rqCZwETCzcQdLQgtVjgZfSz5OBsZIGSRoEjE3LivIobYWMOX7pBt3Z0WOWccIZC/jOZ3fId/enxu1z8HLmzOrFwvk9S+57zw1DuOeGIQBsOWwtF93wOud+bodqh1hzKjXxOCLqJZ1JkqjqgPER8aKki4BpETEROFvSsUA9sBj4cnrsYkn/RZI0AS5qGsAoxgmvAnpt0sA+By/nsnM/mL7wjR/Oo0ev4H9+9w8AXp7el8vPS7Zf/+QM+vZrpHvP4IAjl3H+ydsxe2bvDok9L8678k0+csAKBgyu56ZpM7jxki2ZPGEzPnlcy91Z/4yKiKjYA0AjYhIwqVnZBQWfvwt8t5VjxwPjs5xPUaUZ05ImAGOAIcA7wIUR8Ztix2yqwbGf/qkq8ZgZPBkPsiwWb9T1lf4Dh8VHD/m3svZ99O5zp2/koEVFVXOU9uRq1W1mHcv30ppZPgTgd1qYWW50znznhGdm2blLa2a54dc0mlk++DWNZpYXycTjzpnxnPDMLDu/08LM8sItPDPLB1/DM7P8qNy9tO3NCc/MsnOX1sxywS/iNrNccQvPzHKjc+Y7Jzwzy06NnbNP64RnZtkEnnhsZvkgotNOPPbbZcwsu4jylhIkHSXpFUmzJJ3XwvZzJM1IX8T9oKSRBdsaJD2TLhObH9sSt/DMLLsKtPAk1QFXAEcAc4GpkiZGxIyC3f4OjI6I9yWdDvwUODHdtioi9s5yTrfwzCybpmt45SzF7QvMiojXImItcCtw3AanivhLRLyfrv6N5IXbbeaEZ2aZqbGxrAUYImlawTKuoJptgDkF63PTstacBtxbsN47rfNvko4vJ253ac0so/Kuz6UWVuI1jZK+AIwGPllQPDIi5knaDnhI0vMR8Y9i9biFZ2bZBJUatJgHDC9YH5aWbUDS4cD3gGMjYs36MCLmpf++BjwMfLTUCZ3wzCy7ylzDmwrsKGlbST2Bk4ANRlslfRT4FUmyW1BQPkhSr/TzEOAgoHCwo0Xu0ppZZpWYhxcR9ZLOBCYDdcD4iHhR0kXAtIiYCFwM9ANukwQwOyKOBXYFfiWpkaTh9uNmo7stcsIzs+wqNPE4IiYBk5qVXVDw+fBWjnsc2DPr+ZzwzCybCGjonPeWOeGZWXad9NYyJzwzy84Jz8xyIQC/08LM8iEgfA3PzPIg8KCFmeWIr+GZWW444ZlZPmR6eEBNccIzs2wC8Et8zCw33MIzs3zwrWVmlhcB4Xl4ZpYbvtPCzHLD1/DMLBciPEprZjniFp6Z5UMQDQ0dHUSbOOGZWTZ+PJSZ5YqnpZhZHgQQbuGZWS6EHwBqZjnSWQctFDU0vCzpXeDNjo6jCoYACzs6CMukq/7MRkbE5htTgaT7SL4/5VgYEUdtzPkqqaYSXlclaVpEjO7oOKx8/pl1Td06OgAzs/bihGdmueGE1z6u6egALDP/zLogX8Mzs9xwC8/McsMJz8xywwmviiQdJekVSbMkndfR8VhpksZLWiDphY6OxSrPCa9KJNUBVwBHA7sBJ0varWOjsjJcB9TMRFmrLCe86tkXmBURr0XEWuBW4LgOjslKiIgpwOKOjsOqwwmverYB5hSsz03LzKyDOOGZWW444VXPPGB4wfqwtMzMOogTXvVMBXaUtK2knsBJwMQOjsks15zwqiQi6oEzgcnAS8DvI+LFjo3KSpE0AXgC2FnSXEmndXRMVjm+tczMcsMtPDPLDSc8M8sNJzwzyw0nPDPLDSc8MzxggB4AAAMGSURBVMsNJ7xORFKDpGckvSDpNkl9NqKu6yR9Lv18bbEHG0gaI+nANpzjDUkfertVa+XN9lmR8Vzfl/TtrDFavjjhdS6rImLviNgDWAt8vXCjpDa9ZzgivhoRM4rsMgbInPDMao0TXuf1KLBD2vp6VNJEYIakOkkXS5oq6TlJXwNQ4pfp8/n+DGzRVJGkhyWNTj8fJelpSc9KelDSKJLE+s20dXmwpM0l3Z6eY6qkg9JjN5N0v6QXJV0LqNQXIekuSdPTY8Y12/aztPxBSZunZdtLui895lFJu1Tim2n50KYWgXWstCV3NHBfWrQPsEdEvJ4mjfci4uOSegGPSbof+CiwM8mz+bYEZgDjm9W7OfBr4JC0rsERsVjS1cCKiPjfdL9bgJ9FxF8ljSC5m2RX4ELgrxFxkaR/Bsq5S+Ff03NsAkyVdHtELAL6AtMi4puSLkjrPpPk5Tpfj4iZkvYDrgQOa8O30XLICa9z2UTSM+nnR4HfkHQ1n4qI19PyscBHmq7PAQOAHYFDgAkR0QC8JemhFurfH5jSVFdEtPZcuMOB3aT1DbhNJfVLz/HZ9Ng/SVpSxtd0tqTPpJ+Hp7EuAhqB36XlNwF3pOc4ELit4Ny9yjiHGeCE19msioi9CwvSX/yVhUXAWRExudl+n6pgHN2A/SNidQuxlE3SGJLkeUBEvC/pYaB3K7tHet6lzb8HZuXyNbyuZzJwuqQeAJJ2ktQXmAKcmF7jGwoc2sKxfwMOkbRteuzgtHw50L9gv/uBs5pWJDUloCnA59Oyo4FBJWIdACxJk90uJC3MJt2Aplbq50m6ysuA1yWdkJ5DkvYqcQ6z9Zzwup5rSa7PPZ2+iOZXJC35O4GZ6bYbSJ4IsoGIeBcYR9J9fJYPupR3A59pGrQAzgZGp4MiM/hgtPgHJAnzRZKu7ewSsd4HdJf0EvBjkoTbZCWwb/o1HAZclJafApyWxvcifmy+ZeCnpZhZbriFZ2a54YRnZrnhhGdmueGEZ2a54YRnZrnhhGdmueGEZ2a58f8BsU15/F9+ffoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# visualising confusion matrix - DT\n",
        "\n",
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_dt)\n",
        "disp.plot()\n",
        "plt.title('Confusion Matrix - DT')\n",
        "plt.show()\n",
        "\n",
        "# visualising confusion matrix - RF\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_rf)\n",
        "disp.plot()\n",
        "plt.title('Confusion Matrix - RF')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "ae78dc80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ae78dc80",
        "outputId": "8e3f2e8c-f1a0-4a0d-d867-8fa22836d99a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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SeiCp8khy3rRpoUwO4IUXwm82jRvHG1MeMLOJ7t6tKl8b92S21IC1a8uutjZ5cvi/tG5d2N+4cViHeeDAZFJo316VR5Jnvv46rA71t78lm/idfHLcURUFJYo8k6g8Sk0Ks2aFe4sAdtghJIOrrkqOFtq1U+WR5DF3eOopuPLK0Bv+1luhe/lKe8kmJYoctmTJ5gvrpFYe/eAHIRmcemoyKeyxhyqPpMBccEG4H6JbN3j99eRlJ6k1ShQ5IFF5VD4ppFYetWkTksGFFyaTwm67xRezSFalNvE7/PAwgfbTn6qJX0x01mtZovKofFJYtizsr1MH9tkHjjiibOXRDjvEGrZI7Zk7N6w/e+654Tejiy+OO6Kip0SRRYnKo9RS1NTKo3r1QuVR6qWjDh1UwCFFauNGuO++sJBQ3bpw/vlxRyQRJYoasnZtxT2PEpVH224bRgYXXJBMCvvvr8ojESB0kbzoInjvPTjppNDEr2XLuKOSiBJFFaxcuflqa6mVR9tvH5LBlVcmk8Lee6vySGSL5s0LlRqPPw4DBqgiI8coUVRi6dLN5xPmzEnu3223kAz69k22zFblkUgGPvgg/MZ1ySVhFDF3LjRtGndUUgEliog7LFy4+WprCxcmj9lzz5AIEjeude4cSlRFZCusWQO//jXcc0/4req880J/JiWJnFWUiWLTpjDKLZ8Uvv467DcLPY969Sq72tqOO8Ybt0jeGzs2NPH79FO49FK4/XY18csDBZ8oSkuTPY8SiWHKFFi1KuxPVB716VO28qhJk3jjFik4CxfCsceGUcQbb4QeTZIXCipRlJRUXHlUUhL2N2oURgbnnZecT2jfHho0iDdukYI2dWpoNtayJTz3XLhJSN0n80reJoqVK8P3X2pSmDkzWXnUrFlIBFdckbxxTZVHIrVo6dKw3OHo0eGS0+GHw4knxh2VVEHeJYq5c8MP/E8+Sb62664hEfTpk0wKe+6pyiORWLjDE0/A1VeHLpa33QY9esQdlVRD3iWKFSvCyPX885NJQZVHIjnkvPPgscdCh9dHHgl3lkpeyzhRmNm27r4mm8FkokEDeOaZuKMQkTI2bQpDeLMwSd21axhR6FpvQahT2QFm1tPMZgIfRdsdzeyBrEcmIvlhzpywDOmf/xy2L744LDCkJFEwKk0UwD3A8cAyAHefCvTKZlAikgdKS+Guu8L6EJMnq3FZAcvo0pO7L7CyM8MbsxOOiOSF6dNDC/AJE0L/mgcegN13jzsqyZJMEsUCM+sJuJnVA64BZmU3LBHJafPnw+efh+qm/v1VYljgMkkUPwH+CLQAFgGvApdnMygRyUHvvRduXho8ONwPMXeuWhgUiUzmKPZx93PcfVd338XdzwX2y3ZgIpIjvvsOrrsu3Atxxx3JRVaUJIpGJonivgxfE5FC88YbofnZPffAT34SWiCo503R2eKlJzPrAfQEdjaz61J2bQeo7k2k0C1cCMcfD23awFtvhXbKUpTSzVHUB5pEx6Q2il8JnJ7NoEQkRpMnh7YHLVvC88+HHk2NGsUdlcTI3D39AWZ7uPvntRRPpRo16uZr106IOwyRwvPVV+Fu6iefTDbxk4JhZhPdvVtVvjaTqqc1ZnYnsD/w/Qoj7n5UVT5QRHKMe+jNdM01sHo1DB0KPXvGHZXkkEwmsx8jtO9oA9wGfAZ8kMWYRKQ2nX12aOS3zz5hVa9f/jKs6CUSyWRE0dzdHzGza9z9LeAtM1OiEMlnqU38jjsulL5ecYX6M0mFMhlRbIj+/NLMTjKzzoBWjxbJVx9/HDq8jhwZti+8UJ1eJa1MRhRDzawZcD3h/ontgJ9mNSoRqXmlpXD33XDrrdCwoSqZJGOVJgp3fyF6+i1wJICZHZLNoESkhn34IVx0EUycCD/+Mdx/v1b8koylu+GuLtCf0OPpZXefbmYnA78AGgGdaydEEam2hQthwQJ46ino109N/GSrpJujeAQYBDQH7jWzvwF3AXe4e0ZJwsx6m9lsM5tjZjdv4Zj+ZjbTzGaY2eNb+xcQkS3473/hoYfC80QTv9NPV5KQrZbu0lM3oIO7bzKzhsBiYC93X5bJG0cjkvuBY4GFwAdmNsbdZ6Yc0w74OXCIuy83s12q+hcRkcjq1aHE9b77YK+9wmR1gwbQuHHckUmeSjeiWO/umwDcvQSYm2mSiBwEzHH3ue6+HngC6FvumEuA+919efQ5S7bi/UWkvFdfhQMOCEniiivUxE9qRLoRxb5m9mH03IC9om0D3N07VPLeLYAFKdsLge7ljtkbwMzeITQa/I27v1z+jcxsMDAYoF69jpV8rEiRWrAATjopjCLGjYNDD407IikQ6RJFbaw5sQ3QDjgCaAmMM7MD3X1F6kHuPhwYDqHXUy3EJZI/Jk6Erl2hVSt48UU47LBQ/ipSQ7Z46cndP0/3yOC9FwGtUrZbRq+lWgiMcfcN7j4P+JiQOESkMosXwxlnQLduoQ04wLHHKklIjcvkzuyq+gBoZ2ZtzKw+MAAYU+6YZwmjCcxsJ8KlqLlZjEkk/7nDo49C+/ahDfhvf6smfpJVmdyZXSXuXmpmVwKvEOYfRrr7DDMbAkxw9zHRvuPMbCawEbhhKyfMRYrPgAGhFfghh8CIEbDvvnFHJAWu0vUoAMysEdDa3WdnP6T0tB6FFKXUJn6PPgqrVsHll0OdbF4UkEJSnfUoKv0uM7NTgCnAy9F2JzMrfwlJRLLlo4/CMqSPPBK2L7gArrxSSUJqTSbfab8h3BOxAsDdpxDWphCRbNqwIcw/dOwIM2dCkyZxRyRFKpM5ig3u/q2Vve1fJaoi2TRlSrijesqU0Hbjvvtgt93ijkqKVCaJYoaZnQ3UjVpuXA38N7thiRS5xYvD4x//gNNOizsaKXKZXHq6irBe9jrgcUK7ca1HIVLT3n4bHnggPO/dGz79VElCckKlVU9m1sXdJ9VSPJVS1ZMUnFWr4Oc/D2tEtGsH06apP5PUuKxWPQG/N7NZZvY/ZnZAVT5ERLbglVdCE78HHoBrrlETP8lJlSYKdz+SsLLdUmCYmU0zs1uyHplIoVuwAE4+GbbdNlx2+sMfVNkkOSmjQmx3X+zu9wI/IdxT8eusRiVSqNzh/ffD81at4KWXYPJkteCQnJbJDXf7mdlvzGwacB+h4qll1iMTKTRffhmWIe3ePdnE75hj1MRPcl4m5bEjgb8Dx7v7F1mOR6TwuMOoUXDddVBSArffHvo0ieSJShOFu/eojUBEClb//vD002GdiBEjYO+9445IZKtsMVGY2ZPu3j+65JRaQ5vpCncixWvjxtDAr04dOOUUOOoouPRS9WeSvJRuRHFN9OfJtRGISMGYNQsuvji04LjkEjj//LgjEqmWdCvcfRk9vbyC1e0ur53wRPLIhg0wdCh06gSzZ0OzZnFHJFIjMhkHH1vBayfUdCAieW3y5LAk6a9+BT/+cRhV9O8fd1QiNSLdHMVlhJHDD83sw5RdTYF3sh2YSF756iv4+mt49lno2zfuaERq1BZ7PZlZM2AH4HfAzSm7Vrn7N7UQW4XU60lyxrhxoS/TFVeE7bVroVGjeGMS2YJs9Xpyd/8MuAJYlfLAzHasyoeJFISVK8MypIcfDvfeC+vWhdeVJKRApat6epxQ8TSRUB6bunKRAz/MYlwiuenFF0OZ6xdfhBvohgxREz8peFtMFO5+cvSnlj0VgdDEr29f2GefcANd9+5xRyRSKzLp9XSImTWOnp9rZnebWevshyaSA9xh/PjwvFUrePXV0ApcSUKKSCblsQ8Ca8ysI3A98Cnw16xGJZILvvgCTj0VevRINvE78kioXz/euERqWSaJotRDaVRf4E/ufj+hRFakMLmHnkzt24cRxF13qYmfFLVMuseuMrOfA+cBh5lZHaBedsMSidHpp8Mzz4SqphEjoG3buCMSiVUmI4ozgXXARe6+mLAWxZ1ZjUqktm3cCJs2heenngoPPQRvvKEkIUKaG+7KHGS2K/CjaPN9d1+S1ajS0A13UuOmT4dBg0Ijv0suiTsakazI1g13iTfvD7wPnAH0B94zs9Or8mEiOWX9erjtNujSBT79FHbYIe6IRHJSJnMUvwR+lBhFmNnOwGvA09kMTCSrJk6EgQPDaOLss+EPf4Cdd447KpGclEmiqFPuUtMyMpvbEMldy5bBihXw/PNwspZcEUknk0Txspm9AoyOts8EXsxeSCJZ8uaboYnf1VfDccfBJ59Aw4ZxRyWS8yodGbj7DcAwoEP0GO7uN2U7MJEa8+23oT/TUUfBgw8mm/gpSYhkJN16FO2Au4C9gGnAz9x9UW0FJlIjnn8efvITWLwYfvazMHmtJn4iWyXdiGIk8ALQj9BB9r5aiUikpixYAP36QfPmoV/TnXfCttvGHZVI3kk3R9HU3R+Ons82s0m1EZBItbjDu+9Cz57JJn49e6o/k0g1pBtRNDSzzmbWxcy6AI3KbVfKzHqb2Wwzm2NmN6c5rp+ZuZlV6WYQEQAWLoQ+fUJfpkQTvyOOUJIQqaZ0I4ovgbtTthenbDtwVLo3NrO6wP3AscBC4AMzG+PuM8sd1xS4Bnhv60IXiWzaBA8/DDfcAKWlcPfdcOihcUclUjDSLVx0ZDXf+yBgjrvPBTCzJwgdaGeWO+5/gNuBG6r5eVKs+vWDZ58NVU0PPww/1OKLIjUpmzfOtQAWpGwvjF77XnQJq5W7/yvdG5nZYDObYGYTNm4srflIJf+Uliab+PXrFxLEa68pSYhkQWx3WEftyu8mLIaUlrsPd/du7t6tbt1M7hGUgvbhh2ExoYejWotzzw1N/czSf52IVEk2E8UioFXKdsvotYSmwAHAWDP7DDgYGKMJbdmidevg1luha1f4/HP1ZhKpJZl0j7VorexfR9utzeygDN77A6CdmbUxs/rAAGBMYqe7f+vuO7n7nu6+JzAe6OPu6iEum/vgg9DldcgQOOssmDULTjst7qhEikImI4oHgB7AWdH2KkI1U1ruXgpcCbwCzAKedPcZZjbEzPpUMV4pVsuXw+rV8OKL8Je/hJvoRKRWVLpwkZlNcvcuZjbZ3TtHr0119461EmE5WrioiLzxRmjid801YXvdOrXfEKmirC5cBGyI7onw6MN2BjZV5cNEMrJiRVhp7uijYdiwZBM/JQmRWGSSKO4F/gnsYmb/C7wN/DarUUnxeu45aN8eRo6EG28MCwwpQYjEqtJaU3d/zMwmAkcDBpzq7rOyHpkUn/nz4YwzYL/9YMwY6KYCOJFcUGmiMLPWwBrg+dTX3H1+NgOTIuEOb78Nhx0GrVuHm+YOPlj9mURySCZ3r/2LMD9hQEOgDTAb2D+LcUkxmD8/rBXx0kswdiwcfjj06hV3VCJSTiaXng5M3Y7ablyetYik8G3aBA89BDfdFEYU996rJn4iOWyr+2G4+yQz656NYKRInHZamLQ+9lgYPhz23DPuiEQkjUzmKK5L2awDdAG+yFpEUphKS6FOnfA480zo2xcGDlR/JpE8kEl5bNOURwPCnEXfbAYlBWbqVOjePYweILTguPBCJQmRPJF2RBHdaNfU3X9WS/FIISkpgaFD4fbbYccdYbfd4o5IRKpgi4nCzLZx91IzO6Q2A5IC8f77cMEF8NFH4c+77w7JQkTyTroRxfuE+YgpZjYGeAr4LrHT3Z/JcmySz1auhLVr4eWX4fjj445GRKohk6qnhsAywhrZifspHFCikLJefRVmzIBrr4VjjoHZs9V+Q6QApEsUu0QVT9NJJoiE9C1npbgsXw7XXQejRsH++8Pll4cEoSQhUhDSVT3VBZpEj6YpzxMPEXjmmdDE769/hZ//HCZMUIIQKTDpRhRfuvuQWotE8s/8+TBgABxwQFhQqHPnuCMSkSxIN6JQkbtszh3eeis8b906LC703ntKEiIFLF2iOLrWopD88PnncMIJcMQRyWRx6KFQr16sYYlIdm0xUbj7N7UZiOSwTZvgT38KE9Vvvw333RfagotIUdjqpoBShE49FZ5/PtwPMWwY7LFH3BGJSC1SopCKbdgAdeuGJn5nnQWnnw7nnaf+TCJFKJOmgFJsJk2Cgw4Ka0ZASBTnn68kIVKklCgkae3acC/EQQfB4sXQqlXcEWjOoY0AABJ0SURBVIlIDtClJwnGjw/N+z7+GC66CO66C3bYIe6oRCQHKFFI8N13YV7i3/8OfZpERCJKFMXs5ZdDE7/rr4ejjw4twevXjzsqEckxmqMoRsuWhctMJ5wAjz4K69eH15UkRKQCShTFxB2efjo08Xv8cbjlFvjgAyUIEUlLl56Kyfz5cPbZ0KFDWDuiY8e4IxKRPKARRaFzD437INxRPXZsqHBSkhCRDClRFLJ58+C448JEdaKJX8+esI0GkiKSOSWKQrRxI/zxj2GdiPfegwcfVBM/Eaky/WpZiPr2hX/9C048MbTh0B3WIlINShSFIrWJ33nnhf5MZ5+t/kwiUm1ZvfRkZr3NbLaZzTGzmyvYf52ZzTSzD83sdTNT/+qqmDABunULl5gAzjwTzjlHSUJEakTWEoWZ1QXuB04A2gNnmVn7codNBrq5ewfgaeCObMVTkNauhZtugu7dYelSrRMhIlmRzRHFQcAcd5/r7uuBJ4C+qQe4+5vuvibaHA+0zGI8heXdd0OJ6x13hCZ+M2fCySfHHZWIFKBszlG0ABakbC8Euqc5/mLgpYp2mNlgYDBAvXqq/wfCaGLTJnjttVD+KiKSJTkxmW1m5wLdgMMr2u/uw4HhAI0adfNaDC23vPhiaOJ3ww1w1FEwaxbUqxd3VCJS4LJ56WkRkFqX2TJ6rQwzOwb4JdDH3ddlMZ789fXXcO65cNJJ8NhjySZ+ShIiUguymSg+ANqZWRszqw8MAMakHmBmnYFhhCSxJIux5Cd3eOIJ2G8/ePJJuPVWeP99NfETkVqVtUtP7l5qZlcCrwB1gZHuPsPMhgAT3H0McCfQBHjKQinnfHfvk62Y8s78+aEdeMeO8MgjcOCBcUckIkXI3PPrkn+jRt187doJcYeRPe7w+uvJVebGj4cf/SjcTCciUkVmNtHdu1Xla9XrKZd8+mmoYDr22GQTv4MPVpIQkVgpUeSCjRvh7rvDpaWJE2HYMDXxE5GckRPlsUXvlFPgpZfCDXMPPggtdd+hiOQOJYq4rF8f1oWoUwcGDgyN/AYMUH8mEck5uvQUh/ffh65d4YEHwnb//qHbq5KEiOQgJYratGYNXH899OgBy5fDXnvFHZGISKV06am2vP12uCdi7ly49FK4/XZo1izuqEREKqVEUVsSCwu9+SYccUTc0YiIZEyJIpuefz407rvxRjjyyNAKfBudchHJL5qjyIalS8MypH36wOjRySZ+ShIikoeUKGqSOzz+eGji9/TTMGQIvPeemviJSF7Tr7g1af58uPBC6Nw5NPHbf/+4IxIRqTaNKKpr0yZ45ZXwfI894D//gXfeUZIQkYKhRFEdn3wSVprr3RvGjQuvHXSQmviJSEFRoqiK0lK4807o0AGmTAmXmdTET0QKlOYoquLkk8Plpr59QxuO3XePOyKRnLRhwwYWLlxISUlJ3KEUjYYNG9KyZUvq1eBSyVq4KFPr1oU1quvUCRVNmzbBGWeoP5NIGvPmzaNp06Y0b94c0/+VrHN3li1bxqpVq2jTpk2ZfVq4KNvGj4cuXeD++8P26aeHRn76xhdJq6SkREmiFpkZzZs3r/ERnBJFOt99B9deCz17wqpV0K5d3BGJ5B0lidqVjfOtOYot+c9/QhO/efPg8svhd7+D7baLOyoRkVqnEcWWlJaGOYm33gqXnJQkRPLWs88+i5nx0Ucfff/a2LFjOfnkk8scN3DgQJ5++mkgTMTffPPNtGvXji5dutCjRw9eeumlasfyu9/9jrZt27LPPvvwSuIerHJef/11unTpQqdOnTj00EOZM2cOAKNGjWLnnXemU6dOdOrUiREjRlQ7nkwoUaR69tkwcoDQxG/GDOjVK96YRKTaRo8ezaGHHsro0aMz/ppf/epXfPnll0yfPp1Jkybx7LPPsmrVqmrFMXPmTJ544glmzJjByy+/zOWXX87GjRs3O+6yyy7jscceY8qUKZx99tkMHTr0+31nnnkmU6ZMYcqUKQwaNKha8WRKl54AvvoKrroKnnoqTFpff33oz6QmfiI15qc/Dbcd1aROneAPf0h/zOrVq3n77bd58803OeWUU7jtttsqfd81a9bw8MMPM2/ePBo0aADArrvuSv/+/asV73PPPceAAQNo0KABbdq0oW3btrz//vv06NGjzHFmxsqVKwH49ttv2T3mEvzi/knoDn/7W/gOXr0a/vd/4YYbwiUnESkIzz33HL1792bvvfemefPmTJw4ka5du6b9mjlz5tC6dWu2y+CS87XXXsubb7652esDBgzg5ptvLvPaokWLOPjgg7/fbtmyJYsWLdrsa0eMGMGJJ55Io0aN2G677Rg/fvz3+/7xj38wbtw49t57b+655x5atWpVaYzVVdyJYv58GDQIunULd1fvu2/cEYkUrMp+88+W0aNHc8011wDhh/fo0aPp2rXrFquDtrZq6J577ql2jBW954svvkj37t258847ue666xgxYgSnnHIKZ511Fg0aNGDYsGFccMEFvPHGGzX++eUVX6JINPE74YTQxO+dd0K3V/VnEik433zzDW+88QbTpk3DzNi4cSNmxp133knz5s1Zvnz5ZsfvtNNOtG3blvnz57Ny5cpKRxVbM6Jo0aIFCxYs+H574cKFtGjRoswxS5cuZerUqXTv3h0IcxK9e/cGoHnz5t8fN2jQIG688cYMzkINcPe8ejRs2NWrbPZs98MOcwf3sWOr/j4ikpGZM2fG+vnDhg3zwYMHl3mtV69e/tZbb3lJSYnvueee38f42WefeevWrX3FihXu7n7DDTf4wIEDfd26de7uvmTJEn/yySerFc/06dO9Q4cOXlJS4nPnzvU2bdp4aWlpmWM2bNjgzZs399mzZ7u7+4gRI/y0005zd/cvvvji++OeeeYZ7969e4WfU9F5ByZ4FX/uFseIorQUfv97uPVWaNQI/vxnVTOJFIHRo0dz0003lXmtX79+jB49ml69evG3v/2NCy+8kJKSEurVq8eIESNo1qwZAEOHDuWWW26hffv2NGzYkMaNGzNkyJBqxbP//vvTv39/2rdvzzbbbMP9999P3ehqxoknnsiIESPYfffdefjhh+nXrx916tRhhx12YOTIkQDce++9jBkzhm222YYdd9yRUaNGVSueTBVHr6fjj4dXX4XTTgv3ROy2W3aCE5EyZs2axX777Rd3GEWnovNenV5PhTuiKCkJ1Ut168LgweHRr1/cUYmI5J3CvOHunXdCgXWiiV+/fkoSIiJVVFiJYvVquPrqsIhQSQloyCsSu3y7vJ3vsnG+CydRvPUWHHAA/OlPcOWVMH06HHts3FGJFLWGDRuybNkyJYta4tF6FA0bNqzR9y2sOYpttw1dXw85JO5IRIRw5/HChQtZunRp3KEUjcQKdzUpv6uennkGPvoIfvGLsL1xo26cExGpQM6ucGdmvc1stpnNMbObK9jfwMz+Hu1/z8z2zOiNFy8Oq8z16wf//CesXx9eV5IQEalxWUsUZlYXuB84AWgPnGVm7csddjGw3N3bAvcAt1f2vttvXBYmqV94IbQE/+9/Q6dXERHJimyOKA4C5rj7XHdfDzwB9C13TF/g0ej508DRVklHrt03fB4mradOhZtvVqdXEZEsy+ZkdgtgQcr2QqD7lo5x91Iz+xZoDnydepCZDQYGR5vr7O23p6vTKwA7Ue5cFTGdiySdiySdi6R9qvqFeVH15O7DgeEAZjahqhMyhUbnIknnIknnIknnIsnMtrL3UVI2Lz0tAlJX1GgZvVbhMWa2DdAMWJbFmEREZCtlM1F8ALQzszZmVh8YAIwpd8wY4ILo+enAG55v9boiIgUua5eeojmHK4FXgLrASHefYWZDCH3RxwCPAH81sznAN4RkUpnh2Yo5D+lcJOlcJOlcJOlcJFX5XOTdDXciIlK7CqfXk4iIZIUShYiIpJWziSJr7T/yUAbn4jozm2lmH5rZ62a2Rxxx1obKzkXKcf3MzM2sYEsjMzkXZtY/+t6YYWaP13aMtSWD/yOtzexNM5sc/T85MY44s83MRprZEjObvoX9Zmb3RufpQzPrktEbV3Wx7Ww+CJPfnwI/BOoDU4H25Y65HHgoej4A+Hvcccd4Lo4Eto2eX1bM5yI6rikwDhgPdIs77hi/L9oBk4Edou1d4o47xnMxHLgset4e+CzuuLN0LnoBXYDpW9h/IvASYMDBwHuZvG+ujiiy0v4jT1V6Ltz9TXdfE22OJ9yzUogy+b4A+B9C37CS2gyulmVyLi4B7nf35QDuvqSWY6wtmZwLB7aLnjcDvqjF+GqNu48jVJBuSV/gLx6MB7Y3sx9U9r65migqav/RYkvHuHspkGj/UWgyORepLib8xlCIKj0X0VC6lbv/qzYDi0Em3xd7A3ub2TtmNt7MetdadLUrk3PxG+BcM1sIvAhcVTuh5Zyt/XkC5EkLD8mMmZ0LdAMOjzuWOJhZHeBuYGDMoeSKbQiXn44gjDLHmdmB7r4i1qjicRYwyt1/b2Y9CPdvHeDum+IOLB/k6ohC7T+SMjkXmNkxwC+BPu6+rpZiq22VnYumwAHAWDP7jHANdkyBTmhn8n2xEBjj7hvcfR7wMSFxFJpMzsXFwJMA7v4u0JDQMLDYZPTzpLxcTRRq/5FU6bkws87AMEKSKNTr0FDJuXD3b919J3ff0933JMzX9HH3KjdDy2GZ/B95ljCawMx2IlyKmlubQdaSTM7FfOBoADPbj5AoinF91jHA+VH108HAt+7+ZWVflJOXnjx77T/yTobn4k6gCfBUNJ8/3937xBZ0lmR4LopChufiFeA4M5sJbARucPeCG3VneC6uBx42s2sJE9sDC/EXSzMbTfjlYKdoPuZWoB6Auz9EmJ85EZgDrAEuzOh9C/BciYhIDcrVS08iIpIjlChERCQtJQoREUlLiUJERNJSohARkbSUKCQnmdlGM5uS8tgzzbGra+DzRpnZvOizJkV3727te4wws/bR81+U2/ff6sYYvU/ivEw3s+fNbPtKju9UqJ1SpfaoPFZykpmtdvcmNX1smvcYBbzg7k+b2XHAXe7eoRrvV+2YKntfM3sU+Njd/zfN8QMJHXSvrOlYpHhoRCF5wcyaRGttTDKzaWa2WddYM/uBmY1L+Y37sOj148zs3ehrnzKzyn6AjwPaRl97XfRe083sp9Frjc3sX2Y2NXr9zOj1sWbWzcz+D2gUxfFYtG919OcTZnZSSsyjzOx0M6trZnea2QfROgGXZnBa3iVq6GZmB0V/x8lm9l8z2ye6S3kIcGYUy5lR7CPN7P3o2Iq674qUFXf/dD30qOhBuJN4SvT4J6GLwHbRvp0Id5YmRsSroz+vB34ZPa9L6P20E+EHf+Po9ZuAX1fweaOA06PnZwDvAV2BaUBjwp3vM4DOQD/g4ZSvbRb9OZZo/YtETCnHJGL8MfBo9Lw+oZNnI2AwcEv0egNgAtCmgjhXp/z9ngJ6R9vbAdtEz48B/hE9Hwj8KeXrfwucGz3fntD/qXHc/9565PYjJ1t4iABr3b1TYsPM6gG/NbNewCbCb9K7AotTvuYDYGR07LPuPsXMDicsVPNO1N6kPuE38YrcaWa3EHoAXUzoDfRPd/8uiuEZ4DDgZeD3ZnY74XLVf7bi7/US8EczawD0Bsa5+9roclcHMzs9Oq4ZoYHfvHJf38jMpkR//1nAv1OOf9TM2hFaVNTbwucfB/Qxs59F2w2B1tF7iVRIiULyxTnAzkBXd99goTtsw9QD3H1clEhOAkaZ2d3AcuDf7n5WBp9xg7s/ndgws6MrOsjdP7aw7sWJwFAze93dh2Tyl3D3EjMbCxwPnElYZAfCimNXufsrlbzFWnfvZGbbEnobXQHcS1is6U13/3E08T92C19vQD93n51JvCKgOQrJH82AJVGSOBLYbF1wC2uFf+XuDwMjCEtCjgcOMbPEnENjM9s7w8/8D3CqmW1rZo0Jl43+Y2a7A2vc/W+EhowVrTu8IRrZVOTvhGZsidEJhB/6lyW+xsz2jj6zQh5WNLwauN6SbfYT7aIHphy6inAJLuEV4CqLhlcWOg+LpKVEIfniMaCbmU0Dzgc+quCYI4CpZjaZ8Nv6H919KeEH52gz+5Bw2WnfTD7Q3ScR5i7eJ8xZjHD3ycCBwPvRJaBbgaEVfPlw4MPEZHY5rxIWl3rNw9KdEBLbTGCSmU0ntI1PO+KPYvmQsCjPHcDvor976te9CbRPTGYTRh71othmRNsiaak8VkRE0tKIQkRE0lKiEBGRtJQoREQkLSUKERFJS4lCRETSUqIQEZG0lChERCSt/wfHVW+QSFP4MQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# AUC ROC - DT\n",
        "# calculate the fpr and tpr for all thresholds of the classification\n",
        "\n",
        "fpr, tpr, threshold = metrics.roc_curve(Y_test, Y_pred_dt)\n",
        "roc_auc = metrics.auc(fpr, tpr)\n",
        "\n",
        "plt.title('ROC - DT')\n",
        "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()\n",
        "\n",
        "# AUC ROC - RF\n",
        "# calculate the fpr and tpr for all thresholds of the classification\n",
        "\n",
        "fpr, tpr, threshold = metrics.roc_curve(Y_test, Y_pred_rf)\n",
        "roc_auc = metrics.auc(fpr, tpr)\n",
        "\n",
        "plt.title('ROC - RF')\n",
        "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "9c4c28a5",
      "metadata": {
        "id": "9c4c28a5"
      },
      "source": [
        "THE AUC for both Decision Tree and Random Forest is equal, so both models are pretty good at what they do."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "96618ceb",
      "metadata": {
        "id": "96618ceb"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "92d249d7",
      "metadata": {
        "id": "92d249d7"
      },
      "source": [
        " \n",
        "We have seen that Accuracy of both Random Forest and Decision Tree is equal, although teh precision of Random Forest is more. In a fraud detection model, Precision is highly important because rather than predicting normal transactions correctly we want Fraud transactions to be predicted correctly and Legit to be left off.If either of the 2 reasons are not fulfiiled we may catch the innocent and leave the culprit.\n",
        "This is also one of the reason why Random Forest and Decision Tree are used unstead of other algorithms.\n",
        "\n",
        "\n",
        "Also the reason I have chosen this model is because of highly unbalanced dataset (Legit: Fraud :: 99.87:0.13). Random forest makes multiple decision trees which makes it easier (although time taking) for model to understand the data in a simpler way since Decision Tree makes decisions in a boolean way.\n",
        "\n",
        "\n",
        "Models like XGBoost, Bagging, ANN, and Logistic Regression may give good accuracy but they won't give good precision and recall values"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.2"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}