online_fraud_detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/SaumyaSubham/fraud_check/blob/main/online_fraud_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "First of all, Welcome Everyone!\n",
        "\n",
        "This is a project of Machine Learning named as FraudCheck which is basically a online fraud detection system. The ML model is implemented by using DecisionTreeClassifier from sklearn.tree module.\n",
        "\n",
        "The data are preprocessed and distributed by pie chart with the help of plotly module.\n",
        "\n",
        "The steps are must be followed:\n",
        "1. Check if there are null value.\n",
        "2. Explore the Transactions type.\n",
        "3. Check Correlation.\n",
        "4. Split the data.\n",
        "5. Train the ML model.\n",
        "\n",
        "So, Let's Start!!"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.read_csv('onlinefraud.csv')"
      ]
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      "source": [
        "data.head()"
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              "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
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              "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
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      "source": [
        "data.isnull().sum()"
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              "step              0\n",
              "type              0\n",
              "amount            0\n",
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      "source": [
        "# Exploring transaction types\n",
        "data.type.value_counts()"
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              "CASH_OUT    1695434\n",
              "PAYMENT     1617824\n",
              "CASH_IN     1048698\n",
              "TRANSFER     396878\n",
              "DEBIT         29995\n",
              "Name: type, dtype: int64"
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        "type = data['type'].value_counts()\n",
        "transactions = type.index\n",
        "quantity = type.values\n",
        "\n",
        "import plotly.express as px\n",
        "figure = px.pie(data,\n",
        "                values = quantity,\n",
        "                names = transactions, hole = 0.5,\n",
        "                title = 'Distribution of Transaction type')\n",
        "figure.show()"
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "# Checking correlation\n",
        "correlation = data.corr()\n",
        "print(correlation[\"isFraud\"].sort_values(ascending = False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KvLQ_gEYQsE",
        "outputId": "598c7b2a-273a-4820-ae3f-e9de721193a6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-9d32d0f9c1a7>:2: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "isFraud           1.000000\n",
            "amount            0.050855\n",
            "isFlaggedFraud    0.027907\n",
            "oldbalanceOrg     0.005661\n",
            "newbalanceDest   -0.000371\n",
            "step             -0.004285\n",
            "oldbalanceDest   -0.005582\n",
            "newbalanceOrig   -0.007477\n",
            "Name: isFraud, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['type'] = data['type'].map({'CASH_OUT' : 1, 'PAYMENT' : 2,\n",
        "                                 'CASH_IN' : 3, 'TRANSFER' : 4,\n",
        "                                 'DEBIT' : 5})\n",
        "data[\"isFraud\"] = data[\"isFraud\"].map({0: \"No Fraud\", 1: \"Fraud\"})"
      ],
      "metadata": {
        "id": "klmDL46XZnoC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 206
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        "id": "JD5PbF1DcEEs",
        "outputId": "d0714a6b-a7c2-4cac-d233-9667ad03b57f"
      },
      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   step  type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1     2   9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1     2   1864.28  C1666544295        21249.0        19384.72   \n",
              "2     1     4    181.00  C1305486145          181.0            0.00   \n",
              "3     1     1    181.00   C840083671          181.0            0.00   \n",
              "4     1     2  11668.14  C2048537720        41554.0        29885.86   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest   isFraud  isFlaggedFraud  \n",
              "0  M1979787155             0.0             0.0  No Fraud             0.0  \n",
              "1  M2044282225             0.0             0.0  No Fraud             0.0  \n",
              "2   C553264065             0.0             0.0     Fraud             0.0  \n",
              "3    C38997010         21182.0             0.0     Fraud             0.0  \n",
              "4  M1230701703             0.0             0.0  No Fraud             0.0  "
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>type</th>\n",
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              "        const element = document.querySelector('#df-8ba4515d-18b0-482a-b925-2c1458d2806a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-41c63f7b-556e-4882-af88-275c6797f339\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41c63f7b-556e-4882-af88-275c6797f339')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41c63f7b-556e-4882-af88-275c6797f339 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data\n",
        "from sklearn.model_selection import train_test_split\n",
        "data.dropna(inplace=True)\n",
        "x = np.array(data[[\"type\", \"amount\", \"oldbalanceOrg\", \"newbalanceOrig\"]])\n",
        "y = np.array(data[[\"isFraud\"]])\n"
      ],
      "metadata": {
        "id": "YlTN7y1AcKPx"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the ML model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.10, random_state=42)\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KL97tSBddvFt",
        "outputId": "fc0b7409-be7c-4cb2-9353-5007c26359c4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9996825947047608"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction\n",
        "# features = [type, amount, oldbalanceOrg, newbalanceOrig]\n",
        "features = np.array([[4, 9000.60, 9000.60, 0.0]])\n",
        "print(model.predict(features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTX1U_bLfvk0",
        "outputId": "0d7f4e7a-e319-4323-f35d-0a6373ad02ce"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Fraud']\n"
          ]
        }
      ]
    }
  ]
}