From e0407dc031e6a4d495199bf32d56f7e001f440e9 Mon Sep 17 00:00:00 2001
From: Padmalatha Kasireddy <padmalatha3633@gmail.com>
Date: Sat, 1 Jun 2024 22:27:04 +0530
Subject: [PATCH 1/2] Add new folder 'Credit card fraud detection'

---
 Credit card fraud detection/.gitkeep | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Credit card fraud detection/.gitkeep

diff --git a/Credit card fraud detection/.gitkeep b/Credit card fraud detection/.gitkeep
new file mode 100644
index 000000000..8d1c8b69c
--- /dev/null
+++ b/Credit card fraud detection/.gitkeep	
@@ -0,0 +1 @@
+ 

From d5f7bd8e4854bfd20f1948c75f5cacbf6c13fe9e Mon Sep 17 00:00:00 2001
From: Padmalatha Kasireddy <padmalatha3633@gmail.com>
Date: Sat, 1 Jun 2024 22:32:49 +0530
Subject: [PATCH 2/2] Add Jupyter Notebook

---
 .../credit_card_fraud_detection.ipynb         | 1616 +++++++++++++++++
 1 file changed, 1616 insertions(+)
 create mode 100644 Credit card fraud detection/credit_card_fraud_detection.ipynb

diff --git a/Credit card fraud detection/credit_card_fraud_detection.ipynb b/Credit card fraud detection/credit_card_fraud_detection.ipynb
new file mode 100644
index 000000000..a6c4367ad
--- /dev/null
+++ b/Credit card fraud detection/credit_card_fraud_detection.ipynb	
@@ -0,0 +1,1616 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.13",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "kaggle": {
+      "accelerator": "none",
+      "dataSources": [
+        {
+          "sourceId": 23498,
+          "sourceType": "datasetVersion",
+          "datasetId": 310
+        }
+      ],
+      "dockerImageVersionId": 30715,
+      "isInternetEnabled": false,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": false
+    },
+    "colab": {
+      "name": "notebookcb0c5ec34a",
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "source": [
+        "\n",
+        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
+        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
+        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
+        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
+        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
+        "# NOTEBOOK.\n",
+        "\n",
+        "import os\n",
+        "import sys\n",
+        "from tempfile import NamedTemporaryFile\n",
+        "from urllib.request import urlopen\n",
+        "from urllib.parse import unquote, urlparse\n",
+        "from urllib.error import HTTPError\n",
+        "from zipfile import ZipFile\n",
+        "import tarfile\n",
+        "import shutil\n",
+        "\n",
+        "CHUNK_SIZE = 40960\n",
+        "DATA_SOURCE_MAPPING = 'creditcardfraud:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F310%2F23498%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240601%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240601T160526Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
+        "\n",
+        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
+        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
+        "KAGGLE_SYMLINK='kaggle'\n",
+        "\n",
+        "!umount /kaggle/input/ 2> /dev/null\n",
+        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
+        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
+        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
+        "\n",
+        "try:\n",
+        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
+        "except FileExistsError:\n",
+        "  pass\n",
+        "try:\n",
+        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
+        "except FileExistsError:\n",
+        "  pass\n",
+        "\n",
+        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
+        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
+        "    download_url = unquote(download_url_encoded)\n",
+        "    filename = urlparse(download_url).path\n",
+        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
+        "    try:\n",
+        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
+        "            total_length = fileres.headers['content-length']\n",
+        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
+        "            dl = 0\n",
+        "            data = fileres.read(CHUNK_SIZE)\n",
+        "            while len(data) > 0:\n",
+        "                dl += len(data)\n",
+        "                tfile.write(data)\n",
+        "                done = int(50 * dl / int(total_length))\n",
+        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
+        "                sys.stdout.flush()\n",
+        "                data = fileres.read(CHUNK_SIZE)\n",
+        "            if filename.endswith('.zip'):\n",
+        "              with ZipFile(tfile) as zfile:\n",
+        "                zfile.extractall(destination_path)\n",
+        "            else:\n",
+        "              with tarfile.open(tfile.name) as tarfile:\n",
+        "                tarfile.extractall(destination_path)\n",
+        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
+        "    except HTTPError as e:\n",
+        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
+        "        continue\n",
+        "    except OSError as e:\n",
+        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
+        "        continue\n",
+        "\n",
+        "print('Data source import complete.')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LiTF5KAKjGrB",
+        "outputId": "5a62f429-ed1d-47be-b0ce-fce321bbc090"
+      },
+      "cell_type": "code",
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading creditcardfraud, 69155672 bytes compressed\n",
+            "[==================================================] 69155672 bytes downloaded\n",
+            "Downloaded and uncompressed: creditcardfraud\n",
+            "Data source import complete.\n"
+          ]
+        }
+      ],
+      "execution_count": 1
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import sklearn"
+      ],
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "trusted": true,
+        "id": "0K2N5ZPQjGrE"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(\"/kaggle/input/creditcardfraud/creditcard.csv\")\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "Kr0X2kNzjGrE",
+        "outputId": "a7761b51-f21d-41ea-e66a-b862359e64a0"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+              "\n",
+              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
+              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
+              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
+              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
+              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
+              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
+              "\n",
+              "        V26       V27       V28  Amount  Class  \n",
+              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
+              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
+              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
+              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
+              "4  0.502292  0.219422  0.215153   69.99      0  \n",
+              "\n",
+              "[5 rows x 31 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8da9d69f-4ee5-4925-8f30-675e1b677473\" 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>0</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.359807</td>\n",
+              "      <td>-0.072781</td>\n",
+              "      <td>2.536347</td>\n",
+              "      <td>1.378155</td>\n",
+              "      <td>-0.338321</td>\n",
+              "      <td>0.462388</td>\n",
+              "      <td>0.239599</td>\n",
+              "      <td>0.098698</td>\n",
+              "      <td>0.363787</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.018307</td>\n",
+              "      <td>0.277838</td>\n",
+              "      <td>-0.110474</td>\n",
+              "      <td>0.066928</td>\n",
+              "      <td>0.128539</td>\n",
+              "      <td>-0.189115</td>\n",
+              "      <td>0.133558</td>\n",
+              "      <td>-0.021053</td>\n",
+              "      <td>149.62</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.191857</td>\n",
+              "      <td>0.266151</td>\n",
+              "      <td>0.166480</td>\n",
+              "      <td>0.448154</td>\n",
+              "      <td>0.060018</td>\n",
+              "      <td>-0.082361</td>\n",
+              "      <td>-0.078803</td>\n",
+              "      <td>0.085102</td>\n",
+              "      <td>-0.255425</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.225775</td>\n",
+              "      <td>-0.638672</td>\n",
+              "      <td>0.101288</td>\n",
+              "      <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",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 31 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8da9d69f-4ee5-4925-8f30-675e1b677473')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8da9d69f-4ee5-4925-8f30-675e1b677473 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8da9d69f-4ee5-4925-8f30-675e1b677473');\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-53ec6431-f21a-4be3-8530-a1bc39815c39\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53ec6431-f21a-4be3-8530-a1bc39815c39')\"\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-53ec6431-f21a-4be3-8530-a1bc39815c39 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": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:14:05.729792Z",
+          "iopub.execute_input": "2024-06-01T11:14:05.730217Z",
+          "iopub.status.idle": "2024-06-01T11:14:05.780548Z",
+          "shell.execute_reply.started": "2024-06-01T11:14:05.730182Z",
+          "shell.execute_reply": "2024-06-01T11:14:05.779226Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dTsC5CfwjGrF",
+        "outputId": "e078b794-295e-4f4d-c2d3-112383836d0a"
+      },
+      "execution_count": 4,
+      "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": [
+        "df.duplicated().sum()\n",
+        "df.drop_duplicates(inplace=True)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:15:02.231127Z",
+          "iopub.execute_input": "2024-06-01T11:15:02.23164Z",
+          "iopub.status.idle": "2024-06-01T11:15:04.634033Z",
+          "shell.execute_reply.started": "2024-06-01T11:15:02.231589Z",
+          "shell.execute_reply": "2024-06-01T11:15:04.632778Z"
+        },
+        "trusted": true,
+        "id": "u62ybWiSjGrG"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:15:22.040064Z",
+          "iopub.execute_input": "2024-06-01T11:15:22.040509Z",
+          "iopub.status.idle": "2024-06-01T11:15:22.641778Z",
+          "shell.execute_reply.started": "2024-06-01T11:15:22.040475Z",
+          "shell.execute_reply": "2024-06-01T11:15:22.639973Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 349
+        },
+        "id": "h499fzX5jGrG",
+        "outputId": "03ee526a-8bf6-42a1-add0-2f8dd088c922"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                Time             V1             V2             V3  \\\n",
+              "count  283726.000000  283726.000000  283726.000000  283726.000000   \n",
+              "mean    94811.077600       0.005917      -0.004135       0.001613   \n",
+              "std     47481.047891       1.948026       1.646703       1.508682   \n",
+              "min         0.000000     -56.407510     -72.715728     -48.325589   \n",
+              "25%     54204.750000      -0.915951      -0.600321      -0.889682   \n",
+              "50%     84692.500000       0.020384       0.063949       0.179963   \n",
+              "75%    139298.000000       1.316068       0.800283       1.026960   \n",
+              "max    172792.000000       2.454930      22.057729       9.382558   \n",
+              "\n",
+              "                  V4             V5             V6             V7  \\\n",
+              "count  283726.000000  283726.000000  283726.000000  283726.000000   \n",
+              "mean       -0.002966       0.001828      -0.001139       0.001801   \n",
+              "std         1.414184       1.377008       1.331931       1.227664   \n",
+              "min        -5.683171    -113.743307     -26.160506     -43.557242   \n",
+              "25%        -0.850134      -0.689830      -0.769031      -0.552509   \n",
+              "50%        -0.022248      -0.053468      -0.275168       0.040859   \n",
+              "75%         0.739647       0.612218       0.396792       0.570474   \n",
+              "max        16.875344      34.801666      73.301626     120.589494   \n",
+              "\n",
+              "                  V8             V9  ...            V21            V22  \\\n",
+              "count  283726.000000  283726.000000  ...  283726.000000  283726.000000   \n",
+              "mean       -0.000854      -0.001596  ...      -0.000371      -0.000015   \n",
+              "std         1.179054       1.095492  ...       0.723909       0.724550   \n",
+              "min       -73.216718     -13.434066  ...     -34.830382     -10.933144   \n",
+              "25%        -0.208828      -0.644221  ...      -0.228305      -0.542700   \n",
+              "50%         0.021898      -0.052596  ...      -0.029441       0.006675   \n",
+              "75%         0.325704       0.595977  ...       0.186194       0.528245   \n",
+              "max        20.007208      15.594995  ...      27.202839      10.503090   \n",
+              "\n",
+              "                 V23            V24            V25            V26  \\\n",
+              "count  283726.000000  283726.000000  283726.000000  283726.000000   \n",
+              "mean        0.000198       0.000214      -0.000232       0.000149   \n",
+              "std         0.623702       0.605627       0.521220       0.482053   \n",
+              "min       -44.807735      -2.836627     -10.295397      -2.604551   \n",
+              "25%        -0.161703      -0.354453      -0.317485      -0.326763   \n",
+              "50%        -0.011159       0.041016       0.016278      -0.052172   \n",
+              "75%         0.147748       0.439738       0.350667       0.240261   \n",
+              "max        22.528412       4.584549       7.519589       3.517346   \n",
+              "\n",
+              "                 V27            V28         Amount          Class  \n",
+              "count  283726.000000  283726.000000  283726.000000  283726.000000  \n",
+              "mean        0.001763       0.000547      88.472687       0.001667  \n",
+              "std         0.395744       0.328027     250.399437       0.040796  \n",
+              "min       -22.565679     -15.430084       0.000000       0.000000  \n",
+              "25%        -0.070641      -0.052818       5.600000       0.000000  \n",
+              "50%         0.001479       0.011288      22.000000       0.000000  \n",
+              "75%         0.091208       0.078276      77.510000       0.000000  \n",
+              "max        31.612198      33.847808   25691.160000       1.000000  \n",
+              "\n",
+              "[8 rows x 31 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cb6fb285-ff6e-4060-a934-f70b52c38382\" 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>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "      <td>283726.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>94811.077600</td>\n",
+              "      <td>0.005917</td>\n",
+              "      <td>-0.004135</td>\n",
+              "      <td>0.001613</td>\n",
+              "      <td>-0.002966</td>\n",
+              "      <td>0.001828</td>\n",
+              "      <td>-0.001139</td>\n",
+              "      <td>0.001801</td>\n",
+              "      <td>-0.000854</td>\n",
+              "      <td>-0.001596</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.000371</td>\n",
+              "      <td>-0.000015</td>\n",
+              "      <td>0.000198</td>\n",
+              "      <td>0.000214</td>\n",
+              "      <td>-0.000232</td>\n",
+              "      <td>0.000149</td>\n",
+              "      <td>0.001763</td>\n",
+              "      <td>0.000547</td>\n",
+              "      <td>88.472687</td>\n",
+              "      <td>0.001667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>47481.047891</td>\n",
+              "      <td>1.948026</td>\n",
+              "      <td>1.646703</td>\n",
+              "      <td>1.508682</td>\n",
+              "      <td>1.414184</td>\n",
+              "      <td>1.377008</td>\n",
+              "      <td>1.331931</td>\n",
+              "      <td>1.227664</td>\n",
+              "      <td>1.179054</td>\n",
+              "      <td>1.095492</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.723909</td>\n",
+              "      <td>0.724550</td>\n",
+              "      <td>0.623702</td>\n",
+              "      <td>0.605627</td>\n",
+              "      <td>0.521220</td>\n",
+              "      <td>0.482053</td>\n",
+              "      <td>0.395744</td>\n",
+              "      <td>0.328027</td>\n",
+              "      <td>250.399437</td>\n",
+              "      <td>0.040796</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-56.407510</td>\n",
+              "      <td>-72.715728</td>\n",
+              "      <td>-48.325589</td>\n",
+              "      <td>-5.683171</td>\n",
+              "      <td>-113.743307</td>\n",
+              "      <td>-26.160506</td>\n",
+              "      <td>-43.557242</td>\n",
+              "      <td>-73.216718</td>\n",
+              "      <td>-13.434066</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-34.830382</td>\n",
+              "      <td>-10.933144</td>\n",
+              "      <td>-44.807735</td>\n",
+              "      <td>-2.836627</td>\n",
+              "      <td>-10.295397</td>\n",
+              "      <td>-2.604551</td>\n",
+              "      <td>-22.565679</td>\n",
+              "      <td>-15.430084</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>54204.750000</td>\n",
+              "      <td>-0.915951</td>\n",
+              "      <td>-0.600321</td>\n",
+              "      <td>-0.889682</td>\n",
+              "      <td>-0.850134</td>\n",
+              "      <td>-0.689830</td>\n",
+              "      <td>-0.769031</td>\n",
+              "      <td>-0.552509</td>\n",
+              "      <td>-0.208828</td>\n",
+              "      <td>-0.644221</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.228305</td>\n",
+              "      <td>-0.542700</td>\n",
+              "      <td>-0.161703</td>\n",
+              "      <td>-0.354453</td>\n",
+              "      <td>-0.317485</td>\n",
+              "      <td>-0.326763</td>\n",
+              "      <td>-0.070641</td>\n",
+              "      <td>-0.052818</td>\n",
+              "      <td>5.600000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>84692.500000</td>\n",
+              "      <td>0.020384</td>\n",
+              "      <td>0.063949</td>\n",
+              "      <td>0.179963</td>\n",
+              "      <td>-0.022248</td>\n",
+              "      <td>-0.053468</td>\n",
+              "      <td>-0.275168</td>\n",
+              "      <td>0.040859</td>\n",
+              "      <td>0.021898</td>\n",
+              "      <td>-0.052596</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.029441</td>\n",
+              "      <td>0.006675</td>\n",
+              "      <td>-0.011159</td>\n",
+              "      <td>0.041016</td>\n",
+              "      <td>0.016278</td>\n",
+              "      <td>-0.052172</td>\n",
+              "      <td>0.001479</td>\n",
+              "      <td>0.011288</td>\n",
+              "      <td>22.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>139298.000000</td>\n",
+              "      <td>1.316068</td>\n",
+              "      <td>0.800283</td>\n",
+              "      <td>1.026960</td>\n",
+              "      <td>0.739647</td>\n",
+              "      <td>0.612218</td>\n",
+              "      <td>0.396792</td>\n",
+              "      <td>0.570474</td>\n",
+              "      <td>0.325704</td>\n",
+              "      <td>0.595977</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.186194</td>\n",
+              "      <td>0.528245</td>\n",
+              "      <td>0.147748</td>\n",
+              "      <td>0.439738</td>\n",
+              "      <td>0.350667</td>\n",
+              "      <td>0.240261</td>\n",
+              "      <td>0.091208</td>\n",
+              "      <td>0.078276</td>\n",
+              "      <td>77.510000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>172792.000000</td>\n",
+              "      <td>2.454930</td>\n",
+              "      <td>22.057729</td>\n",
+              "      <td>9.382558</td>\n",
+              "      <td>16.875344</td>\n",
+              "      <td>34.801666</td>\n",
+              "      <td>73.301626</td>\n",
+              "      <td>120.589494</td>\n",
+              "      <td>20.007208</td>\n",
+              "      <td>15.594995</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.202839</td>\n",
+              "      <td>10.503090</td>\n",
+              "      <td>22.528412</td>\n",
+              "      <td>4.584549</td>\n",
+              "      <td>7.519589</td>\n",
+              "      <td>3.517346</td>\n",
+              "      <td>31.612198</td>\n",
+              "      <td>33.847808</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",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb6fb285-ff6e-4060-a934-f70b52c38382')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cb6fb285-ff6e-4060-a934-f70b52c38382 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cb6fb285-ff6e-4060-a934-f70b52c38382');\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-b0d4661c-d0aa-44bb-a7ea-40dc52131299\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0d4661c-d0aa-44bb-a7ea-40dc52131299')\"\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-b0d4661c-d0aa-44bb-a7ea-40dc52131299 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": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.pie(df['Class'].value_counts(), labels=['Legitimate', 'Fraudulent'], autopct='%1.1f%%', startangle=140)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:24:06.6351Z",
+          "iopub.execute_input": "2024-06-01T11:24:06.635564Z",
+          "iopub.status.idle": "2024-06-01T11:24:06.797708Z",
+          "shell.execute_reply.started": "2024-06-01T11:24:06.635523Z",
+          "shell.execute_reply": "2024-06-01T11:24:06.795671Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 510
+        },
+        "id": "m3Q3k3mEjGrH",
+        "outputId": "baed1f75-fdb1-4b31-b446-6d15709541f6"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "([<matplotlib.patches.Wedge at 0x7de9761e4ac0>,\n",
+              "  <matplotlib.patches.Wedge at 0x7de9761e4a60>],\n",
+              " [Text(0.838934233642034, -0.711469852926639, 'Legitimate'),\n",
+              "  Text(-0.8389342763157392, 0.7114698026076631, 'Fraudulent')],\n",
+              " [Text(0.457600491077473, -0.3880744652327121, '99.8%'),\n",
+              "  Text(-0.4576005143540395, 0.38807443778599804, '0.2%')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "# Plot the distribution of the 'Amount' feature\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.histplot(df['Amount'], bins=50, kde=True, color='blue')\n",
+        "plt.title('Distribution of Transaction Amount')\n",
+        "plt.xlabel('Amount')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot the distribution of the 'Time' feature\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.histplot(df['Time'], bins=50, kde=True, color='green')\n",
+        "plt.title('Distribution of Transaction Time')\n",
+        "plt.xlabel('Time')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot the class distribution\n",
+        "plt.figure(figsize=(6, 4))\n",
+        "sns.countplot(df['Class'], palette='Set1')\n",
+        "plt.title('Class Distribution')\n",
+        "plt.xlabel('Class (0: Legitimate, 1: Fraudulent)')\n",
+        "plt.ylabel('Count')\n",
+        "plt.show()\n",
+        "\n",
+        "# Correlation heatmap of features\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "sns.heatmap(df.corr(), cmap='coolwarm', annot=False, fmt=\".2f\")\n",
+        "plt.title('Correlation Heatmap')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:32:20.286235Z",
+          "iopub.execute_input": "2024-06-01T11:32:20.286689Z",
+          "iopub.status.idle": "2024-06-01T11:32:26.177186Z",
+          "shell.execute_reply.started": "2024-06-01T11:32:20.286655Z",
+          "shell.execute_reply": "2024-06-01T11:32:26.174646Z"
+        },
+        "trusted": true,
+        "id": "CllJaHM-jGrH",
+        "outputId": "fd6bd22e-4b4f-4dd7-e4db-69a3f7087543"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 1000x600 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stderr",
+          "text": "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 1000x600 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 600x400 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 1200x800 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Separate features and target variable\n",
+        "X = df.drop('Class', axis=1)\n",
+        "y = df['Class']\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:35:55.080443Z",
+          "iopub.execute_input": "2024-06-01T11:35:55.081629Z",
+          "iopub.status.idle": "2024-06-01T11:35:55.387428Z",
+          "shell.execute_reply.started": "2024-06-01T11:35:55.081566Z",
+          "shell.execute_reply": "2024-06-01T11:35:55.386149Z"
+        },
+        "trusted": true,
+        "id": "IAsXX2KpjGrI"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Apply SMOTE for oversampling to overcome imbalance in dataset\n",
+        "from imblearn.over_sampling import SMOTE\n",
+        "smote = SMOTE(random_state=42)\n",
+        "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:37:34.462984Z",
+          "iopub.execute_input": "2024-06-01T11:37:34.463413Z",
+          "iopub.status.idle": "2024-06-01T11:37:35.575174Z",
+          "shell.execute_reply.started": "2024-06-01T11:37:34.463381Z",
+          "shell.execute_reply": "2024-06-01T11:37:35.573684Z"
+        },
+        "trusted": true,
+        "id": "ZoN9g7V4jGrI"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Standardize the features\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "X_train_resampled = scaler.fit_transform(X_train_resampled)\n",
+        "X_test = scaler.transform(X_test)\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:41:01.333076Z",
+          "iopub.execute_input": "2024-06-01T11:41:01.334882Z",
+          "iopub.status.idle": "2024-06-01T11:41:01.54427Z",
+          "shell.execute_reply.started": "2024-06-01T11:41:01.334826Z",
+          "shell.execute_reply": "2024-06-01T11:41:01.542852Z"
+        },
+        "trusted": true,
+        "id": "SRstUBA9jGrI"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "# Initialize different classifiers\n",
+        "classifiers = {\n",
+        "    'Logistic Regression': LogisticRegression(random_state=42),\n",
+        "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
+        "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n",
+        "    'Gradient Boosting': GradientBoostingClassifier(random_state=42)\n",
+        "}\n",
+        "\n",
+        "# Fit the data with each classifier and print classification reports\n",
+        "for name, classifier in classifiers.items():\n",
+        "    print(f\"Training {name}...\")\n",
+        "    classifier.fit(X_train_resampled, y_train_resampled)\n",
+        "    y_pred = classifier.predict(X_test)\n",
+        "    print(f\"Classification Report for {name}:\")\n",
+        "    print(classification_report(y_test, y_pred))\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T11:43:06.208803Z",
+          "iopub.execute_input": "2024-06-01T11:43:06.209197Z",
+          "iopub.status.idle": "2024-06-01T12:07:20.724393Z",
+          "shell.execute_reply.started": "2024-06-01T11:43:06.209168Z",
+          "shell.execute_reply": "2024-06-01T12:07:20.723082Z"
+        },
+        "trusted": true,
+        "id": "IoiA8nd_jGrJ",
+        "outputId": "fdd3861b-c305-487b-af9d-2bc05c4f58ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Training Logistic Regression...\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease 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": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "Classification Report for Logistic Regression:\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00     56656\n           1       0.00      0.00      0.00        90\n\n    accuracy                           1.00     56746\n   macro avg       0.50      0.50      0.50     56746\nweighted avg       1.00      1.00      1.00     56746\n\nTraining Decision Tree...\nClassification Report for Decision Tree:\n              precision    recall  f1-score   support\n\n           0       1.00      0.67      0.80     56656\n           1       0.00      0.14      0.00        90\n\n    accuracy                           0.67     56746\n   macro avg       0.50      0.41      0.40     56746\nweighted avg       1.00      0.67      0.80     56746\n\nTraining Random Forest...\nClassification Report for Random Forest:\n              precision    recall  f1-score   support\n\n           0       1.00      0.92      0.96     56656\n           1       0.01      0.54      0.02        90\n\n    accuracy                           0.92     56746\n   macro avg       0.51      0.73      0.49     56746\nweighted avg       1.00      0.92      0.96     56746\n\nTraining Gradient Boosting...\nClassification Report for Gradient Boosting:\n              precision    recall  f1-score   support\n\n           0       1.00      0.39      0.56     56656\n           1       0.00      0.97      0.00        90\n\n    accuracy                           0.39     56746\n   macro avg       0.50      0.68      0.28     56746\nweighted avg       1.00      0.39      0.56     56746\n\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.ensemble import GradientBoostingClassifier\n",
+        "model = GradientBoostingClassifier(random_state=42)\n",
+        "model.fit(X_train_resampled, y_train_resampled)\n",
+        "\n"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-06-01T12:14:30.465834Z",
+          "iopub.execute_input": "2024-06-01T12:14:30.466318Z",
+          "iopub.status.idle": "2024-06-01T12:28:41.050567Z",
+          "shell.execute_reply.started": "2024-06-01T12:14:30.466284Z",
+          "shell.execute_reply": "2024-06-01T12:28:41.048779Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "6DQS6MdcjGrJ",
+        "outputId": "3ff4cbed-b625-41ed-9dea-5db00c28014c"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GradientBoostingClassifier(random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make predictions on the test set\n",
+        "y_prob = model.predict_proba(X_test)[:, 1]\n",
+        "\n",
+        "\n",
+        "from sklearn.metrics import accuracy_score, log_loss, roc_auc_score, f1_score, recall_score, roc_curve\n",
+        "# Calculate AUC-ROC score\n",
+        "auc_roc = roc_auc_score(y_test, y_prob)\n",
+        "\n",
+        "# Calculate F1-score\n",
+        "f1 = f1_score(y_test, model.predict(X_test))\n",
+        "\n",
+        "# Calculate recall score\n",
+        "recall = recall_score(y_test, model.predict(X_test))\n",
+        "\n",
+        "# Print the results\n",
+        "print(f\"AUC-ROC Score: {auc_roc:.4f}\")\n",
+        "print(f\"F1-score: {f1:.4f}\")\n",
+        "print(f\"Recall: {recall:.4f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZuA4lizGmsFt",
+        "outputId": "2877e839-b118-48a5-a851-ebf84052fdc5"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "AUC-ROC Score: 0.9670\n",
+            "F1-score: 0.2884\n",
+            "Recall: 0.8444\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(fpr, tpr, color='blue', lw=2, label='ROC Curve (AUC = %0.4f)' % auc_roc)\n",
+        "plt.plot([0, 1], [0, 1], color='red', lw=2, linestyle='--', label='Random Guess')\n",
+        "plt.xlim([0.0, 1.0])\n",
+        "plt.ylim([0.0, 1.05])\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "Hw1PmQfsjGrJ",
+        "outputId": "7bf04647-20c8-4cce-a23d-85f5eb33d07b"
+      },
+      "execution_count": 13,
+      "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": [],
+      "metadata": {
+        "id": "DnsWnJssm4K4"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file