Machine_Learning/CreditCardFraudDetection/Fraud_Detection_Machine_Learning_Project.ipynb

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        "<a href=\"https://colab.research.google.com/github/SimranShaikh20/Hackathon_Project/blob/main/Fraud_Detection_Machine_Learning_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "### **Fraud Detection Project**"
      ],
      "metadata": {
        "id": "CJcZvXn4e3fY"
      }
    },
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        "id": "cgEbe5wPeMmg",
        "outputId": "2b46b3b9-ae71-4d6d-9078-2e81d43795e0"
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      "outputs": [
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          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
          ]
        }
      ],
      "source": [
        "pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NBZssp0fJsk",
        "outputId": "13ffd02d-e433-413f-b045-275b75478325"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: missing destination file operand after 'kaggle.json~/.kaggle/'\n",
            "Try 'cp --help' for more information.\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# API to fetch  kaggle dataset\n",
        "!kaggle datasets download -d mlg-ulb/creditcardfraud"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6OnTj1ygtta",
        "outputId": "58a95a59-4fc9-414a-f9c6-f7babf5c49a1"
      },
      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n",
            "License(s): DbCL-1.0\n",
            "Downloading creditcardfraud.zip to /content\n",
            " 89% 59.0M/66.0M [00:00<00:00, 219MB/s]\n",
            "100% 66.0M/66.0M [00:00<00:00, 211MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extracting zip file\n",
        "from zipfile import ZipFile\n",
        "file_name = \"creditcardfraud.zip\"\n",
        "\n",
        "with ZipFile(file_name,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print(\"Done\")"
      ],
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        },
        "id": "smmqhDZwg9fE",
        "outputId": "a421a974-d09c-4390-c630-633d187ef960"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "u8XAie13g9kI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing data\n",
        "data=pd.read_csv(\"/content/creditcard.csv\")"
      ],
      "metadata": {
        "id": "Zu7kS1mKg9sK"
      },
      "execution_count": 6,
      "outputs": []
    },
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        "data.head()"
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              "    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-032610b6-86cd-4183-9042-0d4fb4a3e0d2 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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAo_1IP4iQ_6",
        "outputId": "08e049b6-d21f-4cbd-eef3-583b24877824"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUA5pxcDiRCk",
        "outputId": "8cd73b7e-7d45-4e4a-df1a-4d795ed4ba2d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "BaC0Ct1Rg9wL",
        "outputId": "b9a90be8-1ae3-44ae-bffd-c8b337560622"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0    284315\n",
              "1       492\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>284315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>492</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# statastics\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Z9i_dZ0gijg3",
        "outputId": "99b6a981-2229-4054-cf00-873ae6d38e98"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12b133d1-0a66-4ac3-b9c3-742f434c7f61\" 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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      padding: 0 0 0 0;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    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",
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              "  }\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-21cff1f8-fe98-4fdc-8af2-14bde127da33 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "legit=data[data.Class==0]\n",
        "fraud=data[data['Class']==1]"
      ],
      "metadata": {
        "id": "TZTQSHysijk-"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "legit\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "kSv5ZTvqijpf",
        "outputId": "1ce7e992-a241-4eb9-c29d-f460c91cb3b6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time         V1         V2        V3        V4        V5  \\\n",
              "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
              "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
              "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
              "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
              "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
              "...          ...        ...        ...       ...       ...       ...   \n",
              "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
              "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
              "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
              "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
              "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
              "\n",
              "              V6        V7        V8        V9  ...       V21       V22  \\\n",
              "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
              "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
              "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
              "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
              "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
              "...          ...       ...       ...       ...  ...       ...       ...   \n",
              "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
              "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
              "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
              "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
              "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
              "\n",
              "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
              "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
              "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
              "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
              "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
              "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
              "...          ...       ...       ...       ...       ...       ...     ...   \n",
              "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
              "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
              "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
              "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
              "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
              "\n",
              "        Class  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  \n",
              "...       ...  \n",
              "284802      0  \n",
              "284803      0  \n",
              "284804      0  \n",
              "284805      0  \n",
              "284806      0  \n",
              "\n",
              "[284315 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <td>-0.072781</td>\n",
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              "      <td>-0.338321</td>\n",
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              "      <td>-0.018307</td>\n",
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              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
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              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
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              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>...</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>...</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>...</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>...</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284315 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "      .colab-df-generate:hover {\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('legit');\n",
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              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud"
      ],
      "metadata": {
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          "height": 444
        },
        "id": "NWPiqg01ijtY",
        "outputId": "ac867454-ad73-4ddd-9bb2-c75cd9244a64"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "541        406.0 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
              "623        472.0 -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823   \n",
              "4920      4462.0 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788   \n",
              "6108      6986.0 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536   \n",
              "6329      7519.0  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
              "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
              "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
              "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
              "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "541    -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211   \n",
              "623     0.325574 -0.067794 -0.270953  ...  0.661696  0.435477  1.375966   \n",
              "4920    0.562320 -0.399147 -0.238253  ... -0.294166 -0.932391  0.172726   \n",
              "6108   -3.496197 -0.248778 -0.247768  ...  0.573574  0.176968 -0.436207   \n",
              "6329    1.713445 -0.496358 -1.282858  ... -0.379068 -0.704181 -0.656805   \n",
              "...          ...       ...       ...  ...       ...       ...       ...   \n",
              "279863 -0.882850  0.697211 -2.064945  ...  0.778584 -0.319189  0.639419   \n",
              "280143 -1.413170  0.248525 -1.127396  ...  0.370612  0.028234 -0.145640   \n",
              "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
              "281144 -2.208002  1.058733 -1.632333  ...  0.583276 -0.269209 -0.456108   \n",
              "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "541     0.320198  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
              "623    -0.293803  0.279798 -0.145362 -0.252773  0.035764  529.00      1  \n",
              "4920   -0.087330 -0.156114 -0.542628  0.039566 -0.153029  239.93      1  \n",
              "6108   -0.053502  0.252405 -0.657488 -0.827136  0.849573   59.00      1  \n",
              "6329   -1.632653  1.488901  0.566797 -0.010016  0.146793    1.00      1  \n",
              "...          ...       ...       ...       ...       ...     ...    ...  \n",
              "279863 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00      1  \n",
              "280143 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76      1  \n",
              "280149  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89      1  \n",
              "281144 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00      1  \n",
              "281674 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53      1  \n",
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              "      <th>541</th>\n",
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              "      <th>4920</th>\n",
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              "      <td>2.330243</td>\n",
              "      <td>-0.821628</td>\n",
              "      <td>-0.075788</td>\n",
              "      <td>0.562320</td>\n",
              "      <td>-0.399147</td>\n",
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              "      <td>-0.294166</td>\n",
              "      <td>-0.932391</td>\n",
              "      <td>0.172726</td>\n",
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              "      <td>0.039566</td>\n",
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              "      <th>6108</th>\n",
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              "      <td>1.358367</td>\n",
              "      <td>-2.592844</td>\n",
              "      <td>2.679787</td>\n",
              "      <td>-1.128131</td>\n",
              "      <td>-1.706536</td>\n",
              "      <td>-3.496197</td>\n",
              "      <td>-0.248778</td>\n",
              "      <td>-0.247768</td>\n",
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              "      <td>0.573574</td>\n",
              "      <td>0.176968</td>\n",
              "      <td>-0.436207</td>\n",
              "      <td>-0.053502</td>\n",
              "      <td>0.252405</td>\n",
              "      <td>-0.657488</td>\n",
              "      <td>-0.827136</td>\n",
              "      <td>0.849573</td>\n",
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              "      <th>6329</th>\n",
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              "      <td>0.147968</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "type": "dataframe",
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      "source": [
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      "outputs": [
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          "data": {
            "text/plain": [
              "(284315, 31)"
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          "metadata": {},
          "execution_count": 15
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        "fraud.shape"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(492, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
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      "source": [
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        "data=pd.concat([legit_s,fraud],axis=0)\n"
      ],
      "metadata": {
        "id": "s0lx5ll0j4Tn"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UkxKUtyYj4aY",
        "outputId": "a340cd62-6857-4564-b6c0-28cd51f186ee"
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      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(984, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
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    {
      "cell_type": "code",
      "source": [
        "data['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "BJ5HcAJCj4eG",
        "outputId": "89c6aa44-053a-44b9-9a3b-999866e4468e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "0    492\n",
              "1    492\n",
              "Name: count, dtype: int64"
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      "source": [
        "data.groupby('Class').mean()"
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      "execution_count": 20,
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            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0      98342.237805  0.092158  0.037565 -0.094989 -0.081415  0.003273   \n",
              "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0     -0.022033  0.093430 -0.017537 -0.011504  ...  0.036294 -0.012277   \n",
              "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
              "\n",
              "            V22       V23      V24       V25       V26       V27       V28  \\\n",
              "Class                                                                        \n",
              "0      0.013503  0.028525  0.02086 -0.011723  0.008126  0.000801  0.013371   \n",
              "1      0.014049 -0.040308 -0.10513  0.041449  0.051648  0.170575  0.075667   \n",
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              "           Amount  \n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-751f0469-d908-490d-a684-e096ca1d558c');\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-2b295bac-d108-456e-9bf8-5b595302416e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2b295bac-d108-456e-9bf8-5b595302416e')\"\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-2b295bac-d108-456e-9bf8-5b595302416e 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"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop('Class',axis=1)\n",
        "y=data['Class']"
      ],
      "metadata": {
        "id": "oS9EL437k-RD"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "173lxqaYk-T0",
        "outputId": "000cd7b5-25f7-4d18-89fa-134879cbb01e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(984, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLd3clgFk-Xh",
        "outputId": "a50f6f0e-2060-438a-d027-35b948dabde7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(984,)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,stratify=y,random_state=2)"
      ],
      "metadata": {
        "id": "-ns-kvj0lfsF"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression()\n",
        "model.fit(x_train,y_train)\n",
        "ypred=model.predict(x_test)\n",
        "accuracy_score(y_test,ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wn2fxiwplfuv",
        "outputId": "14d7e37e-9050-4bb4-90c3-3b9290a62aee"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9593908629441624"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualization"
      ],
      "metadata": {
        "id": "FRnVqs60vUyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Set the style\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Plot the distribution of the 'Class' variable (fraudulent vs. non-fraudulent transactions)\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.countplot(x='Class', data=data)\n",
        "plt.title('Distribution of Fraudulent and Non-Fraudulent Transactions')\n",
        "plt.xlabel('Class (0: Non-Fraud, 1: Fraud)')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "xp0SxKoBvUjW",
        "outputId": "196608b3-cb75-4f7f-eecf-13659a737d89"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the correlation matrix\n",
        "corr = data.corr()\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "plt.figure(figsize=(12, 10))\n",
        "\n",
        "# Draw the heatmap\n",
        "sns.heatmap(corr, annot=False, fmt=\".1f\", cmap='coolwarm', square=True, cbar_kws={\"shrink\": .8})\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 876
        },
        "id": "twpVUupdvUf2",
        "outputId": "55b34816-dff8-4c46-c13d-6c6a62bb0316"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the distribution of transaction amounts\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.histplot(data['Amount'], bins=50, kde=True)\n",
        "plt.title('Distribution of Transaction Amounts')\n",
        "plt.xlabel('Transaction Amount')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "zEbh2B1fvUbX",
        "outputId": "ae27ef49-9160-4dee-955c-5eb3658f0948"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the proportion of fraudulent transactions\n",
        "fraud_count = len(data[data['Class'] == 1])\n",
        "non_fraud_count = len(data[data['Class'] == 0])\n",
        "\n",
        "# Create a pie chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "labels = ['Non-Fraudulent', 'Fraudulent']\n",
        "sizes = [non_fraud_count, fraud_count]\n",
        "colors = ['#ff9999','#66b3ff']\n",
        "explode = (0.1, 0)  # explode 1st slice\n",
        "\n",
        "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
        "        autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "\n",
        "plt.title('Proportion of Fraudulent Transactions')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "MACWRE3DvUXw",
        "outputId": "24d09a53-b35a-4def-e3e9-187fee425780"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot to visualize the relationship between V1 and V2\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.scatterplot(x='V1', y='V2', hue='Class', data=data)\n",
        "plt.title('Scatter Plot: V1 vs V2')\n",
        "plt.xlabel('V1')\n",
        "plt.ylabel('V2')\n",
        "plt.legend(title='Class')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "a5e74QkFvUU5",
        "outputId": "23d27be3-4222-4d4f-e992-c46930b16093"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a line chart to visualize the average transaction amount over time\n",
        "data['Time'] = pd.to_datetime(data['Time'], unit='s')\n",
        "data.set_index('Time', inplace=True)\n",
        "\n",
        "avg_amount = data['Amount'].resample('D').mean()\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "avg_amount.plot()\n",
        "plt.title('Average Transaction Amount over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Average Transaction Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "oVU_jmVEvw86",
        "outputId": "2a76a76c-1389-4ae9-b543-8570a067a247"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Thank You :)**"
      ],
      "metadata": {
        "id": "l8NYTWmhuGFK"
      }
    }
  ]
}