60. Fraud Transaction Detection.ipynb

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          "text": [
            "Downloading fraud-transaction-detection.zip to /content\n",
            "\n",
            "Archive:  fraud-transaction-detection.zip\n",
            "  inflating: Final Transactions.csv  \n"
          ]
        },
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            "\r  0%|          | 0.00/34.0M [00:00<?, ?B/s]\r 15%|█▍        | 5.00M/34.0M [00:00<00:01, 20.3MB/s]\r 74%|███████▎  | 25.0M/34.0M [00:00<00:00, 67.5MB/s]\r100%|██████████| 34.0M/34.0M [00:00<00:00, 74.2MB/s]\n"
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      "source": [
        "%%bash\n",
        "\n",
        "pip install -q kaggle\n",
        "mkdir -p ~/.kaggle\n",
        "cp drive/MyDrive/kaggle.json ~/.kaggle/\n",
        "chmod 600 /root/.kaggle/kaggle.json\n",
        "kaggle datasets download -d sanskar457/fraud-transaction-detection\n",
        "unzip fraud-transaction-detection.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fraud Transaction Detection"
      ],
      "metadata": {
        "id": "RIM8wqZH8Avz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing required modules"
      ],
      "metadata": {
        "id": "pT3_lihx9kSC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "6G1OBLKx9nbj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting the dataset"
      ],
      "metadata": {
        "id": "YvjB4FG99zC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Final Transactions.csv\")\n",
        "df.head()"
      ],
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          "height": 374
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        "id": "bsan1_Fm9122",
        "outputId": "134dfeec-81be-4282-9f37-4901bcca547c"
      },
      "execution_count": 9,
      "outputs": [
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              "   Unnamed: 0  TRANSACTION_ID  ... TX_FRAUD  TX_FRAUD_SCENARIO\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cleaning dataset"
      ],
      "metadata": {
        "id": "L4t-qo2o-BCP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
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        "colab": {
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        "id": "erpL6MJ4-M2B",
        "outputId": "3971b23b-64cb-48ef-f931-82ae5040a2ce"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0           0\n",
              "TRANSACTION_ID       0\n",
              "TX_DATETIME          0\n",
              "CUSTOMER_ID          0\n",
              "TERMINAL_ID          0\n",
              "TX_AMOUNT            0\n",
              "TX_TIME_SECONDS      0\n",
              "TX_TIME_DAYS         0\n",
              "TX_FRAUD             0\n",
              "TX_FRAUD_SCENARIO    0\n",
              "dtype: int64"
            ]
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          "execution_count": 10
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      "source": [
        "df.drop([\"Unnamed: 0\", \"TRANSACTION_ID\", \"TX_DATETIME\", \"CUSTOMER_ID\", \"TERMINAL_ID\"], axis=1, inplace=True)\n",
        "df.head()"
      ],
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      "outputs": [
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              "   TX_AMOUNT  TX_TIME_SECONDS  TX_TIME_DAYS  TX_FRAUD  TX_FRAUD_SCENARIO\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pre-Processing Dataset"
      ],
      "metadata": {
        "id": "gRSp5GPE-d5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop(\"TX_FRAUD\", axis=1)\n",
        "y = df[\"TX_FRAUD\"]"
      ],
      "metadata": {
        "id": "oFYBXYxb-guj"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.values\n",
        "y = y.values"
      ],
      "metadata": {
        "id": "0WX7fcqd-wtI"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "FuJhQqLp_F0K"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Model"
      ],
      "metadata": {
        "id": "qEUCZkQZ-4Qz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgbclf = XGBClassifier()\n",
        "\n",
        "xgbclf.fit(X_train, y_train)\n",
        "xgbclf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaLR5vzd-9_H",
        "outputId": "6251d221-fdee-4661-bda5-982f6772abf5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rclf = RandomForestClassifier()\n",
        "\n",
        "rclf.fit(X_train, y_train)\n",
        "rclf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vndJGtoW_NLA",
        "outputId": "9c26c1d3-3f3e-4b27-d2fe-8d742ed3d114"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making Prediction"
      ],
      "metadata": {
        "id": "o5-cyOTA_tNR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgbclf.predict([[542.1, 32, 0, 1]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y72GVNsH_uoh",
        "outputId": "9c849cbc-14ef-4e1b-c920-8cb7ce84dbd5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3WnzM-VaAC4U"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}