Credit_card_fraud_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "SmmnAuklCJWJ",
        "2cQk0KuPRw9-",
        "MFgMt6chTrWO",
        "j_Q8VvRmT_tG"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Credit Card Fraud Detection using Machine learning \n",
        "\n",
        "> As this is a binary classification problem we will be using Logistic Regression model for model training \n",
        "### Workflow of model \n",
        "\n",
        "* Collection of data\n",
        "* Data Preprocessing\n",
        "* Splitting test and training data\n",
        "* Model Training\n",
        "* Model Evaluation\n",
        "* Prediction System\n"
      ],
      "metadata": {
        "id": "qMqWOzNg4QFZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing dependencies"
      ],
      "metadata": {
        "id": "SmmnAuklCJWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "FyHgC-XLCXcb"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading dataset into data frame"
      ],
      "metadata": {
        "id": "MdX7tvDvB_dL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "w04GAIaJ4GgQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "af7d5f3a-c4ac-4c9c-bd70-d92c7162625c"
      },
      "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",
              "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
              "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
              "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
              "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
              "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
              "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
              "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
              "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
              "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
              "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
              "5  0.105915  0.253844  0.081080    3.67      0  \n",
              "6 -0.257237  0.034507  0.005168    4.99      0  \n",
              "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
              "8 -0.384157  0.011747  0.142404   93.20      0  \n",
              "9  0.094199  0.246219  0.083076    3.68      0  \n",
              "\n",
              "[10 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4db7ddd4-ff24-4f24-8b2c-ac7410e5dcae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.425966</td>\n",
              "      <td>0.960523</td>\n",
              "      <td>1.141109</td>\n",
              "      <td>-0.168252</td>\n",
              "      <td>0.420987</td>\n",
              "      <td>-0.029728</td>\n",
              "      <td>0.476201</td>\n",
              "      <td>0.260314</td>\n",
              "      <td>-0.568671</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208254</td>\n",
              "      <td>-0.559825</td>\n",
              "      <td>-0.026398</td>\n",
              "      <td>-0.371427</td>\n",
              "      <td>-0.232794</td>\n",
              "      <td>0.105915</td>\n",
              "      <td>0.253844</td>\n",
              "      <td>0.081080</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.229658</td>\n",
              "      <td>0.141004</td>\n",
              "      <td>0.045371</td>\n",
              "      <td>1.202613</td>\n",
              "      <td>0.191881</td>\n",
              "      <td>0.272708</td>\n",
              "      <td>-0.005159</td>\n",
              "      <td>0.081213</td>\n",
              "      <td>0.464960</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.167716</td>\n",
              "      <td>-0.270710</td>\n",
              "      <td>-0.154104</td>\n",
              "      <td>-0.780055</td>\n",
              "      <td>0.750137</td>\n",
              "      <td>-0.257237</td>\n",
              "      <td>0.034507</td>\n",
              "      <td>0.005168</td>\n",
              "      <td>4.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.644269</td>\n",
              "      <td>1.417964</td>\n",
              "      <td>1.074380</td>\n",
              "      <td>-0.492199</td>\n",
              "      <td>0.948934</td>\n",
              "      <td>0.428118</td>\n",
              "      <td>1.120631</td>\n",
              "      <td>-3.807864</td>\n",
              "      <td>0.615375</td>\n",
              "      <td>...</td>\n",
              "      <td>1.943465</td>\n",
              "      <td>-1.015455</td>\n",
              "      <td>0.057504</td>\n",
              "      <td>-0.649709</td>\n",
              "      <td>-0.415267</td>\n",
              "      <td>-0.051634</td>\n",
              "      <td>-1.206921</td>\n",
              "      <td>-1.085339</td>\n",
              "      <td>40.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.894286</td>\n",
              "      <td>0.286157</td>\n",
              "      <td>-0.113192</td>\n",
              "      <td>-0.271526</td>\n",
              "      <td>2.669599</td>\n",
              "      <td>3.721818</td>\n",
              "      <td>0.370145</td>\n",
              "      <td>0.851084</td>\n",
              "      <td>-0.392048</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.073425</td>\n",
              "      <td>-0.268092</td>\n",
              "      <td>-0.204233</td>\n",
              "      <td>1.011592</td>\n",
              "      <td>0.373205</td>\n",
              "      <td>-0.384157</td>\n",
              "      <td>0.011747</td>\n",
              "      <td>0.142404</td>\n",
              "      <td>93.20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.338262</td>\n",
              "      <td>1.119593</td>\n",
              "      <td>1.044367</td>\n",
              "      <td>-0.222187</td>\n",
              "      <td>0.499361</td>\n",
              "      <td>-0.246761</td>\n",
              "      <td>0.651583</td>\n",
              "      <td>0.069539</td>\n",
              "      <td>-0.736727</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.246914</td>\n",
              "      <td>-0.633753</td>\n",
              "      <td>-0.120794</td>\n",
              "      <td>-0.385050</td>\n",
              "      <td>-0.069733</td>\n",
              "      <td>0.094199</td>\n",
              "      <td>0.246219</td>\n",
              "      <td>0.083076</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4db7ddd4-ff24-4f24-8b2c-ac7410e5dcae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4db7ddd4-ff24-4f24-8b2c-ac7410e5dcae 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-4db7ddd4-ff24-4f24-8b2c-ac7410e5dcae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# importing data\n",
        "\n",
        "transaction_dataset= pd.read_csv(\"/content/drive/MyDrive/google_collab/creditcard.csv\")\n",
        "transaction_dataset.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "s5LISpi34O5j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# last 5 rows\n",
        "\n",
        "transaction_dataset.tail()"
      ],
      "metadata": {
        "id": "jAx7NQhjI85x",
        "outputId": "75c945c8-955f-4655-8872-df5d4b6cb7cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time         V1         V2        V3        V4        V5  \\\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",
              "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",
              "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",
              "284802      0  \n",
              "284803      0  \n",
              "284804      0  \n",
              "284805      0  \n",
              "284806      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6696e59f-c3ce-4f6f-8d4d-90876b27e6b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>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>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6696e59f-c3ce-4f6f-8d4d-90876b27e6b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6696e59f-c3ce-4f6f-8d4d-90876b27e6b9 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-6696e59f-c3ce-4f6f-8d4d-90876b27e6b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Analysis"
      ],
      "metadata": {
        "id": "y6JDCNaMVC4x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check shape of dataset\n",
        "\n",
        "transaction_dataset.shape\n",
        "\n",
        "# dataset has 73377 rows and 31 columns"
      ],
      "metadata": {
        "id": "5ibtKr5JKUoz",
        "outputId": "880833be-3801-4a99-9f60-1868b4af04d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset information \n",
        "\n",
        "transaction_dataset.info()"
      ],
      "metadata": {
        "id": "T_E7RSXTI82U",
        "outputId": "693f2846-7d5b-4ca0-b3a6-dff49cb6676c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "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": [
        "# dataset datatype\n",
        "\n",
        "transaction_dataset.dtypes"
      ],
      "metadata": {
        "id": "lDYPvWtFI80A",
        "outputId": "742bc702-634b-481f-f6ed-5a1ffeed1653",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      float64\n",
              "V1        float64\n",
              "V2        float64\n",
              "V3        float64\n",
              "V4        float64\n",
              "V5        float64\n",
              "V6        float64\n",
              "V7        float64\n",
              "V8        float64\n",
              "V9        float64\n",
              "V10       float64\n",
              "V11       float64\n",
              "V12       float64\n",
              "V13       float64\n",
              "V14       float64\n",
              "V15       float64\n",
              "V16       float64\n",
              "V17       float64\n",
              "V18       float64\n",
              "V19       float64\n",
              "V20       float64\n",
              "V21       float64\n",
              "V22       float64\n",
              "V23       float64\n",
              "V24       float64\n",
              "V25       float64\n",
              "V26       float64\n",
              "V27       float64\n",
              "V28       float64\n",
              "Amount    float64\n",
              "Class       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mathematical values of dataset\n",
        "\n",
        "transaction_dataset.describe()"
      ],
      "metadata": {
        "id": "3IdsQutAI8xg",
        "outputId": "8f2b6370-f450-410a-a7dc-f64fe3af4fc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "execution_count": 15,
      "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-31f64425-8ac2-499e-8f7a-77fd768e77a8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31f64425-8ac2-499e-8f7a-77fd768e77a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-31f64425-8ac2-499e-8f7a-77fd768e77a8 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-31f64425-8ac2-499e-8f7a-77fd768e77a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finding null values in dataset\n",
        "\n",
        "transaction_dataset.isnull().sum()"
      ],
      "metadata": {
        "id": "QCz0ff1RI8vP",
        "outputId": "c207b327-b271-41b5-b0fa-98248640ad54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# total null values in all rows and columns\n",
        "\n",
        "transaction_dataset.isnull().sum().sum()\n",
        "\n",
        "# hence there are total 28 null values"
      ],
      "metadata": {
        "id": "mnZwN5RCI8s3",
        "outputId": "253c1633-ce1a-4184-d1ba-b89fd0c7a9ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of legit transaction and fraudulent transaction\n",
        "\n",
        "transaction_dataset['Class'].value_counts()\n",
        "\n",
        "# data is highly unbalanced "
      ],
      "metadata": {
        "id": "wh7fnn4gI8qi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1a6a948-8072-4be2-aee7-579dc206e1e0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Seperating data for analysis\n",
        "  - 0 : Normal transaction\n",
        "  - 1 : Fraudulent transaction"
      ],
      "metadata": {
        "id": "mLSu0UqUNdtd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "legit = transaction_dataset[transaction_dataset.Class == 0]\n",
        "fraud = transaction_dataset[transaction_dataset.Class == 1]"
      ],
      "metadata": {
        "id": "9DOtWprJI8oG"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of legit : \", legit.shape)\n",
        "print(\"Shape of fraud : \", fraud.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhPqOPloN0hy",
        "outputId": "fe277f14-eeb1-4136-95ec-c221b5944b44"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of legit :  (284315, 31)\n",
            "Shape of fraud :  (492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# measures\n",
        "\n",
        "legit.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhMG3jZ8N7nj",
        "outputId": "cd53e30a-bdf3-4550-8fdd-6f1aa2038762"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    284315.000000\n",
              "mean         88.291022\n",
              "std         250.105092\n",
              "min           0.000000\n",
              "25%           5.650000\n",
              "50%          22.000000\n",
              "75%          77.050000\n",
              "max       25691.160000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud.Amount.describe()\n",
        "# fraud transaction description"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsEPrrK-N_wU",
        "outputId": "73c279b2-ec3b-4faa-947f-8158f918e492"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     492.000000\n",
              "mean      122.211321\n",
              "std       256.683288\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         9.250000\n",
              "75%       105.890000\n",
              "max      2125.870000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# comparing the values for both transaction \n",
        "\n",
        "transaction_dataset.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "fjIUZi_-OKMt",
        "outputId": "3e1d3d07-72c2-4b3d-d008-a96c43c36350"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3        V4        V5  \\\n",
              "Class                                                                   \n",
              "0      94838.202258  0.008258 -0.006271  0.012171 -0.007860  0.005453   \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      0.002419  0.009637 -0.000987  0.004467  ... -0.000644 -0.001235   \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     -0.000024  0.000070  0.000182 -0.000072 -0.000089 -0.000295 -0.000131   \n",
              "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
              "\n",
              "           Amount  \n",
              "Class              \n",
              "0       88.291022  \n",
              "1      122.211321  \n",
              "\n",
              "[2 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84b05686-34c4-4ff5-a191-28c25e21fece\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>V20</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>94838.202258</td>\n",
              "      <td>0.008258</td>\n",
              "      <td>-0.006271</td>\n",
              "      <td>0.012171</td>\n",
              "      <td>-0.007860</td>\n",
              "      <td>0.005453</td>\n",
              "      <td>0.002419</td>\n",
              "      <td>0.009637</td>\n",
              "      <td>-0.000987</td>\n",
              "      <td>0.004467</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000644</td>\n",
              "      <td>-0.001235</td>\n",
              "      <td>-0.000024</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>-0.000072</td>\n",
              "      <td>-0.000089</td>\n",
              "      <td>-0.000295</td>\n",
              "      <td>-0.000131</td>\n",
              "      <td>88.291022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80746.806911</td>\n",
              "      <td>-4.771948</td>\n",
              "      <td>3.623778</td>\n",
              "      <td>-7.033281</td>\n",
              "      <td>4.542029</td>\n",
              "      <td>-3.151225</td>\n",
              "      <td>-1.397737</td>\n",
              "      <td>-5.568731</td>\n",
              "      <td>0.570636</td>\n",
              "      <td>-2.581123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.372319</td>\n",
              "      <td>0.713588</td>\n",
              "      <td>0.014049</td>\n",
              "      <td>-0.040308</td>\n",
              "      <td>-0.105130</td>\n",
              "      <td>0.041449</td>\n",
              "      <td>0.051648</td>\n",
              "      <td>0.170575</td>\n",
              "      <td>0.075667</td>\n",
              "      <td>122.211321</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84b05686-34c4-4ff5-a191-28c25e21fece')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84b05686-34c4-4ff5-a191-28c25e21fece 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-84b05686-34c4-4ff5-a191-28c25e21fece');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Under-Sampling \n",
        "\n",
        "- build a sample dataset having similar distribution of normal and fraudulent transactions.\n",
        "- number of fraudulent transaction is = 492"
      ],
      "metadata": {
        "id": "SM0LaQFEOqhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ligit_sample = transaction_dataset.sample(n = 492)"
      ],
      "metadata": {
        "id": "QMLdLUueOpqJ"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Concatinating two samplings"
      ],
      "metadata": {
        "id": "e8TsNIg_PJIP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_transaction_dataset2 = pd.concat([ligit_sample, fraud], axis = 0)"
      ],
      "metadata": {
        "id": "6bb0u9nvOXHk"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_transaction_dataset2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "WXSLyXm8PZGg",
        "outputId": "47244f9c-3b44-48a9-d5ac-73aad71beeff"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "155292  105051.0 -0.499793  0.673704  2.197506 -0.324329  0.284650  0.206579   \n",
              "135003   81068.0  1.201997  0.320203  0.211550  1.247285 -0.104591 -0.533112   \n",
              "42692    41202.0 -0.342865  0.740781  0.985139  0.673561  0.727307  1.508112   \n",
              "97090    66069.0  1.389132 -1.145339  0.777612 -1.444203 -1.630253 -0.248654   \n",
              "247571  153621.0  1.942827 -0.020259 -2.000533  0.010153  1.362394  1.207958   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "155292  0.326897 -0.020506  1.495998  ... -0.305002 -0.521419 -0.359940   \n",
              "135003  0.208950 -0.087875 -0.205523  ...  0.014899  0.174079 -0.157315   \n",
              "42692   0.386136  0.501699 -0.783163  ...  0.328143  1.095287  0.077953   \n",
              "97090  -1.287493  0.130540 -1.924039  ... -0.279466 -0.500653  0.223721   \n",
              "247571 -0.133240  0.340120  0.192131  ... -0.262181 -0.555047  0.339253   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "155292 -0.633552  0.251746 -0.713259 -0.093065 -0.163703    2.12      0  \n",
              "135003  0.357910  0.810643 -0.278249  0.005817  0.003237    1.00      0  \n",
              "42692  -0.796567 -0.524963 -0.243130  0.033730  0.006651   32.68      0  \n",
              "97090   0.161893 -0.021786 -0.409347  0.050689  0.018159   19.60      0  \n",
              "247571 -1.026343 -0.415139  0.278351 -0.014410 -0.055815    1.79      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f62f6aa9-75aa-47d0-8523-10ef5b691a3d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>155292</th>\n",
              "      <td>105051.0</td>\n",
              "      <td>-0.499793</td>\n",
              "      <td>0.673704</td>\n",
              "      <td>2.197506</td>\n",
              "      <td>-0.324329</td>\n",
              "      <td>0.284650</td>\n",
              "      <td>0.206579</td>\n",
              "      <td>0.326897</td>\n",
              "      <td>-0.020506</td>\n",
              "      <td>1.495998</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.305002</td>\n",
              "      <td>-0.521419</td>\n",
              "      <td>-0.359940</td>\n",
              "      <td>-0.633552</td>\n",
              "      <td>0.251746</td>\n",
              "      <td>-0.713259</td>\n",
              "      <td>-0.093065</td>\n",
              "      <td>-0.163703</td>\n",
              "      <td>2.12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135003</th>\n",
              "      <td>81068.0</td>\n",
              "      <td>1.201997</td>\n",
              "      <td>0.320203</td>\n",
              "      <td>0.211550</td>\n",
              "      <td>1.247285</td>\n",
              "      <td>-0.104591</td>\n",
              "      <td>-0.533112</td>\n",
              "      <td>0.208950</td>\n",
              "      <td>-0.087875</td>\n",
              "      <td>-0.205523</td>\n",
              "      <td>...</td>\n",
              "      <td>0.014899</td>\n",
              "      <td>0.174079</td>\n",
              "      <td>-0.157315</td>\n",
              "      <td>0.357910</td>\n",
              "      <td>0.810643</td>\n",
              "      <td>-0.278249</td>\n",
              "      <td>0.005817</td>\n",
              "      <td>0.003237</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42692</th>\n",
              "      <td>41202.0</td>\n",
              "      <td>-0.342865</td>\n",
              "      <td>0.740781</td>\n",
              "      <td>0.985139</td>\n",
              "      <td>0.673561</td>\n",
              "      <td>0.727307</td>\n",
              "      <td>1.508112</td>\n",
              "      <td>0.386136</td>\n",
              "      <td>0.501699</td>\n",
              "      <td>-0.783163</td>\n",
              "      <td>...</td>\n",
              "      <td>0.328143</td>\n",
              "      <td>1.095287</td>\n",
              "      <td>0.077953</td>\n",
              "      <td>-0.796567</td>\n",
              "      <td>-0.524963</td>\n",
              "      <td>-0.243130</td>\n",
              "      <td>0.033730</td>\n",
              "      <td>0.006651</td>\n",
              "      <td>32.68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97090</th>\n",
              "      <td>66069.0</td>\n",
              "      <td>1.389132</td>\n",
              "      <td>-1.145339</td>\n",
              "      <td>0.777612</td>\n",
              "      <td>-1.444203</td>\n",
              "      <td>-1.630253</td>\n",
              "      <td>-0.248654</td>\n",
              "      <td>-1.287493</td>\n",
              "      <td>0.130540</td>\n",
              "      <td>-1.924039</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.279466</td>\n",
              "      <td>-0.500653</td>\n",
              "      <td>0.223721</td>\n",
              "      <td>0.161893</td>\n",
              "      <td>-0.021786</td>\n",
              "      <td>-0.409347</td>\n",
              "      <td>0.050689</td>\n",
              "      <td>0.018159</td>\n",
              "      <td>19.60</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247571</th>\n",
              "      <td>153621.0</td>\n",
              "      <td>1.942827</td>\n",
              "      <td>-0.020259</td>\n",
              "      <td>-2.000533</td>\n",
              "      <td>0.010153</td>\n",
              "      <td>1.362394</td>\n",
              "      <td>1.207958</td>\n",
              "      <td>-0.133240</td>\n",
              "      <td>0.340120</td>\n",
              "      <td>0.192131</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.262181</td>\n",
              "      <td>-0.555047</td>\n",
              "      <td>0.339253</td>\n",
              "      <td>-1.026343</td>\n",
              "      <td>-0.415139</td>\n",
              "      <td>0.278351</td>\n",
              "      <td>-0.014410</td>\n",
              "      <td>-0.055815</td>\n",
              "      <td>1.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f62f6aa9-75aa-47d0-8523-10ef5b691a3d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f62f6aa9-75aa-47d0-8523-10ef5b691a3d 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-f62f6aa9-75aa-47d0-8523-10ef5b691a3d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_transaction_dataset2.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "u47pBC8RPaKW",
        "outputId": "27818b8c-ea09-40ee-b069-d20d25a867e2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\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",
              "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",
              "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",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0cba2138-ef84-477e-ab73-5b169df0a8b0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>279863</th>\n",
              "      <td>169142.0</td>\n",
              "      <td>-1.927883</td>\n",
              "      <td>1.125653</td>\n",
              "      <td>-4.518331</td>\n",
              "      <td>1.749293</td>\n",
              "      <td>-1.566487</td>\n",
              "      <td>-2.010494</td>\n",
              "      <td>-0.882850</td>\n",
              "      <td>0.697211</td>\n",
              "      <td>-2.064945</td>\n",
              "      <td>...</td>\n",
              "      <td>0.778584</td>\n",
              "      <td>-0.319189</td>\n",
              "      <td>0.639419</td>\n",
              "      <td>-0.294885</td>\n",
              "      <td>0.537503</td>\n",
              "      <td>0.788395</td>\n",
              "      <td>0.292680</td>\n",
              "      <td>0.147968</td>\n",
              "      <td>390.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280143</th>\n",
              "      <td>169347.0</td>\n",
              "      <td>1.378559</td>\n",
              "      <td>1.289381</td>\n",
              "      <td>-5.004247</td>\n",
              "      <td>1.411850</td>\n",
              "      <td>0.442581</td>\n",
              "      <td>-1.326536</td>\n",
              "      <td>-1.413170</td>\n",
              "      <td>0.248525</td>\n",
              "      <td>-1.127396</td>\n",
              "      <td>...</td>\n",
              "      <td>0.370612</td>\n",
              "      <td>0.028234</td>\n",
              "      <td>-0.145640</td>\n",
              "      <td>-0.081049</td>\n",
              "      <td>0.521875</td>\n",
              "      <td>0.739467</td>\n",
              "      <td>0.389152</td>\n",
              "      <td>0.186637</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280149</th>\n",
              "      <td>169351.0</td>\n",
              "      <td>-0.676143</td>\n",
              "      <td>1.126366</td>\n",
              "      <td>-2.213700</td>\n",
              "      <td>0.468308</td>\n",
              "      <td>-1.120541</td>\n",
              "      <td>-0.003346</td>\n",
              "      <td>-2.234739</td>\n",
              "      <td>1.210158</td>\n",
              "      <td>-0.652250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.751826</td>\n",
              "      <td>0.834108</td>\n",
              "      <td>0.190944</td>\n",
              "      <td>0.032070</td>\n",
              "      <td>-0.739695</td>\n",
              "      <td>0.471111</td>\n",
              "      <td>0.385107</td>\n",
              "      <td>0.194361</td>\n",
              "      <td>77.89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281144</th>\n",
              "      <td>169966.0</td>\n",
              "      <td>-3.113832</td>\n",
              "      <td>0.585864</td>\n",
              "      <td>-5.399730</td>\n",
              "      <td>1.817092</td>\n",
              "      <td>-0.840618</td>\n",
              "      <td>-2.943548</td>\n",
              "      <td>-2.208002</td>\n",
              "      <td>1.058733</td>\n",
              "      <td>-1.632333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.583276</td>\n",
              "      <td>-0.269209</td>\n",
              "      <td>-0.456108</td>\n",
              "      <td>-0.183659</td>\n",
              "      <td>-0.328168</td>\n",
              "      <td>0.606116</td>\n",
              "      <td>0.884876</td>\n",
              "      <td>-0.253700</td>\n",
              "      <td>245.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281674</th>\n",
              "      <td>170348.0</td>\n",
              "      <td>1.991976</td>\n",
              "      <td>0.158476</td>\n",
              "      <td>-2.583441</td>\n",
              "      <td>0.408670</td>\n",
              "      <td>1.151147</td>\n",
              "      <td>-0.096695</td>\n",
              "      <td>0.223050</td>\n",
              "      <td>-0.068384</td>\n",
              "      <td>0.577829</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.164350</td>\n",
              "      <td>-0.295135</td>\n",
              "      <td>-0.072173</td>\n",
              "      <td>-0.450261</td>\n",
              "      <td>0.313267</td>\n",
              "      <td>-0.289617</td>\n",
              "      <td>0.002988</td>\n",
              "      <td>-0.015309</td>\n",
              "      <td>42.53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cba2138-ef84-477e-ab73-5b169df0a8b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0cba2138-ef84-477e-ab73-5b169df0a8b0 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-0cba2138-ef84-477e-ab73-5b169df0a8b0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_transaction_dataset2['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsudZtHJPeTu",
        "outputId": "6157f874-94dd-4cbf-d9fc-b30b1b720bfd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    494\n",
              "0    490\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_transaction_dataset2.groupby('Class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "7sm2LYEIPjXj",
        "outputId": "4b463be4-f8bc-4252-b559-aa34df2fa6a8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time        V1        V2        V3       V4        V5  \\\n",
              "Class                                                                  \n",
              "0      95990.902041  0.123493  0.084168  0.048935  0.03707  0.022880   \n",
              "1      80743.186235 -4.766861  3.614813 -7.022345  4.53823 -3.144182   \n",
              "\n",
              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
              "Class                                          ...                       \n",
              "0      0.076276  0.037980  0.056860  0.063291  ...  0.025979 -0.023721   \n",
              "1     -1.399171 -5.557948  0.568995 -2.577896  ...  0.374362  0.716279   \n",
              "\n",
              "            V22       V23       V24       V25       V26       V27       V28  \\\n",
              "Class                                                                         \n",
              "0     -0.026782 -0.019659  0.021465 -0.009236  0.027581  0.021006  0.002142   \n",
              "1      0.015498 -0.038176 -0.102763  0.039635  0.050905  0.169466  0.079408   \n",
              "\n",
              "           Amount  \n",
              "Class              \n",
              "0       85.838469  \n",
              "1      122.582085  \n",
              "\n",
              "[2 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba1f46fd-6896-41f2-8567-e666455fce91\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>V20</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>95990.902041</td>\n",
              "      <td>0.123493</td>\n",
              "      <td>0.084168</td>\n",
              "      <td>0.048935</td>\n",
              "      <td>0.03707</td>\n",
              "      <td>0.022880</td>\n",
              "      <td>0.076276</td>\n",
              "      <td>0.037980</td>\n",
              "      <td>0.056860</td>\n",
              "      <td>0.063291</td>\n",
              "      <td>...</td>\n",
              "      <td>0.025979</td>\n",
              "      <td>-0.023721</td>\n",
              "      <td>-0.026782</td>\n",
              "      <td>-0.019659</td>\n",
              "      <td>0.021465</td>\n",
              "      <td>-0.009236</td>\n",
              "      <td>0.027581</td>\n",
              "      <td>0.021006</td>\n",
              "      <td>0.002142</td>\n",
              "      <td>85.838469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80743.186235</td>\n",
              "      <td>-4.766861</td>\n",
              "      <td>3.614813</td>\n",
              "      <td>-7.022345</td>\n",
              "      <td>4.53823</td>\n",
              "      <td>-3.144182</td>\n",
              "      <td>-1.399171</td>\n",
              "      <td>-5.557948</td>\n",
              "      <td>0.568995</td>\n",
              "      <td>-2.577896</td>\n",
              "      <td>...</td>\n",
              "      <td>0.374362</td>\n",
              "      <td>0.716279</td>\n",
              "      <td>0.015498</td>\n",
              "      <td>-0.038176</td>\n",
              "      <td>-0.102763</td>\n",
              "      <td>0.039635</td>\n",
              "      <td>0.050905</td>\n",
              "      <td>0.169466</td>\n",
              "      <td>0.079408</td>\n",
              "      <td>122.582085</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba1f46fd-6896-41f2-8567-e666455fce91')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba1f46fd-6896-41f2-8567-e666455fce91 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-ba1f46fd-6896-41f2-8567-e666455fce91');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization"
      ],
      "metadata": {
        "id": "Cq5EM_Z4U9rx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20,11))\n",
        "# heatmap size in ration 16:9\n",
        "\n",
        "sns.heatmap(new_transaction_dataset2.corr(), annot = True, cmap = 'coolwarm')\n",
        "# heatmap parameters\n",
        "\n",
        "plt.title(\"Heatmap for correlation matrix for credit card data \", fontsize = 22)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "N8BEPLsMP8Ap",
        "outputId": "0291f054-9340-4d35-eb84-db10806275b4"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x792 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(new_transaction_dataset2.Class)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "BvEnx017Qax_",
        "outputId": "a0146af8-baca-4d49-efcd-0b9af3701fe5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Class', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting the data into features and Targets"
      ],
      "metadata": {
        "id": "k5ZisRhvRd-7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_transaction_dataset2.drop(columns = 'Class', axis = 1)\n",
        "Y = new_transaction_dataset2['Class']"
      ],
      "metadata": {
        "id": "LQzFbcaiQ59M"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "uACVlTxBRsHa",
        "outputId": "b949b383-fa48-4a40-9929-03ba8ad540de"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "155292  105051.0 -0.499793  0.673704  2.197506 -0.324329  0.284650  0.206579   \n",
              "135003   81068.0  1.201997  0.320203  0.211550  1.247285 -0.104591 -0.533112   \n",
              "42692    41202.0 -0.342865  0.740781  0.985139  0.673561  0.727307  1.508112   \n",
              "97090    66069.0  1.389132 -1.145339  0.777612 -1.444203 -1.630253 -0.248654   \n",
              "247571  153621.0  1.942827 -0.020259 -2.000533  0.010153  1.362394  1.207958   \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  ...       V20       V21       V22  \\\n",
              "155292  0.326897 -0.020506  1.495998  ... -0.001094 -0.305002 -0.521419   \n",
              "135003  0.208950 -0.087875 -0.205523  ... -0.160865  0.014899  0.174079   \n",
              "42692   0.386136  0.501699 -0.783163  ... -0.178210  0.328143  1.095287   \n",
              "97090  -1.287493  0.130540 -1.924039  ... -0.377421 -0.279466 -0.500653   \n",
              "247571 -0.133240  0.340120  0.192131  ... -0.192093 -0.262181 -0.555047   \n",
              "...          ...       ...       ...  ...       ...       ...       ...   \n",
              "279863 -0.882850  0.697211 -2.064945  ...  1.252967  0.778584 -0.319189   \n",
              "280143 -1.413170  0.248525 -1.127396  ...  0.226138  0.370612  0.028234   \n",
              "280149 -2.234739  1.210158 -0.652250  ...  0.247968  0.751826  0.834108   \n",
              "281144 -2.208002  1.058733 -1.632333  ...  0.306271  0.583276 -0.269209   \n",
              "281674  0.223050 -0.068384  0.577829  ... -0.017652 -0.164350 -0.295135   \n",
              "\n",
              "             V23       V24       V25       V26       V27       V28  Amount  \n",
              "155292 -0.359940 -0.633552  0.251746 -0.713259 -0.093065 -0.163703    2.12  \n",
              "135003 -0.157315  0.357910  0.810643 -0.278249  0.005817  0.003237    1.00  \n",
              "42692   0.077953 -0.796567 -0.524963 -0.243130  0.033730  0.006651   32.68  \n",
              "97090   0.223721  0.161893 -0.021786 -0.409347  0.050689  0.018159   19.60  \n",
              "247571  0.339253 -1.026343 -0.415139  0.278351 -0.014410 -0.055815    1.79  \n",
              "...          ...       ...       ...       ...       ...       ...     ...  \n",
              "279863  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00  \n",
              "280143 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76  \n",
              "280149  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89  \n",
              "281144 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00  \n",
              "281674 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53  \n",
              "\n",
              "[984 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68efd934-7a57-4b53-8136-e0db761faee1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>V20</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>155292</th>\n",
              "      <td>105051.0</td>\n",
              "      <td>-0.499793</td>\n",
              "      <td>0.673704</td>\n",
              "      <td>2.197506</td>\n",
              "      <td>-0.324329</td>\n",
              "      <td>0.284650</td>\n",
              "      <td>0.206579</td>\n",
              "      <td>0.326897</td>\n",
              "      <td>-0.020506</td>\n",
              "      <td>1.495998</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001094</td>\n",
              "      <td>-0.305002</td>\n",
              "      <td>-0.521419</td>\n",
              "      <td>-0.359940</td>\n",
              "      <td>-0.633552</td>\n",
              "      <td>0.251746</td>\n",
              "      <td>-0.713259</td>\n",
              "      <td>-0.093065</td>\n",
              "      <td>-0.163703</td>\n",
              "      <td>2.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135003</th>\n",
              "      <td>81068.0</td>\n",
              "      <td>1.201997</td>\n",
              "      <td>0.320203</td>\n",
              "      <td>0.211550</td>\n",
              "      <td>1.247285</td>\n",
              "      <td>-0.104591</td>\n",
              "      <td>-0.533112</td>\n",
              "      <td>0.208950</td>\n",
              "      <td>-0.087875</td>\n",
              "      <td>-0.205523</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.160865</td>\n",
              "      <td>0.014899</td>\n",
              "      <td>0.174079</td>\n",
              "      <td>-0.157315</td>\n",
              "      <td>0.357910</td>\n",
              "      <td>0.810643</td>\n",
              "      <td>-0.278249</td>\n",
              "      <td>0.005817</td>\n",
              "      <td>0.003237</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42692</th>\n",
              "      <td>41202.0</td>\n",
              "      <td>-0.342865</td>\n",
              "      <td>0.740781</td>\n",
              "      <td>0.985139</td>\n",
              "      <td>0.673561</td>\n",
              "      <td>0.727307</td>\n",
              "      <td>1.508112</td>\n",
              "      <td>0.386136</td>\n",
              "      <td>0.501699</td>\n",
              "      <td>-0.783163</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.178210</td>\n",
              "      <td>0.328143</td>\n",
              "      <td>1.095287</td>\n",
              "      <td>0.077953</td>\n",
              "      <td>-0.796567</td>\n",
              "      <td>-0.524963</td>\n",
              "      <td>-0.243130</td>\n",
              "      <td>0.033730</td>\n",
              "      <td>0.006651</td>\n",
              "      <td>32.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97090</th>\n",
              "      <td>66069.0</td>\n",
              "      <td>1.389132</td>\n",
              "      <td>-1.145339</td>\n",
              "      <td>0.777612</td>\n",
              "      <td>-1.444203</td>\n",
              "      <td>-1.630253</td>\n",
              "      <td>-0.248654</td>\n",
              "      <td>-1.287493</td>\n",
              "      <td>0.130540</td>\n",
              "      <td>-1.924039</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.377421</td>\n",
              "      <td>-0.279466</td>\n",
              "      <td>-0.500653</td>\n",
              "      <td>0.223721</td>\n",
              "      <td>0.161893</td>\n",
              "      <td>-0.021786</td>\n",
              "      <td>-0.409347</td>\n",
              "      <td>0.050689</td>\n",
              "      <td>0.018159</td>\n",
              "      <td>19.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247571</th>\n",
              "      <td>153621.0</td>\n",
              "      <td>1.942827</td>\n",
              "      <td>-0.020259</td>\n",
              "      <td>-2.000533</td>\n",
              "      <td>0.010153</td>\n",
              "      <td>1.362394</td>\n",
              "      <td>1.207958</td>\n",
              "      <td>-0.133240</td>\n",
              "      <td>0.340120</td>\n",
              "      <td>0.192131</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.192093</td>\n",
              "      <td>-0.262181</td>\n",
              "      <td>-0.555047</td>\n",
              "      <td>0.339253</td>\n",
              "      <td>-1.026343</td>\n",
              "      <td>-0.415139</td>\n",
              "      <td>0.278351</td>\n",
              "      <td>-0.014410</td>\n",
              "      <td>-0.055815</td>\n",
              "      <td>1.79</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>279863</th>\n",
              "      <td>169142.0</td>\n",
              "      <td>-1.927883</td>\n",
              "      <td>1.125653</td>\n",
              "      <td>-4.518331</td>\n",
              "      <td>1.749293</td>\n",
              "      <td>-1.566487</td>\n",
              "      <td>-2.010494</td>\n",
              "      <td>-0.882850</td>\n",
              "      <td>0.697211</td>\n",
              "      <td>-2.064945</td>\n",
              "      <td>...</td>\n",
              "      <td>1.252967</td>\n",
              "      <td>0.778584</td>\n",
              "      <td>-0.319189</td>\n",
              "      <td>0.639419</td>\n",
              "      <td>-0.294885</td>\n",
              "      <td>0.537503</td>\n",
              "      <td>0.788395</td>\n",
              "      <td>0.292680</td>\n",
              "      <td>0.147968</td>\n",
              "      <td>390.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280143</th>\n",
              "      <td>169347.0</td>\n",
              "      <td>1.378559</td>\n",
              "      <td>1.289381</td>\n",
              "      <td>-5.004247</td>\n",
              "      <td>1.411850</td>\n",
              "      <td>0.442581</td>\n",
              "      <td>-1.326536</td>\n",
              "      <td>-1.413170</td>\n",
              "      <td>0.248525</td>\n",
              "      <td>-1.127396</td>\n",
              "      <td>...</td>\n",
              "      <td>0.226138</td>\n",
              "      <td>0.370612</td>\n",
              "      <td>0.028234</td>\n",
              "      <td>-0.145640</td>\n",
              "      <td>-0.081049</td>\n",
              "      <td>0.521875</td>\n",
              "      <td>0.739467</td>\n",
              "      <td>0.389152</td>\n",
              "      <td>0.186637</td>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280149</th>\n",
              "      <td>169351.0</td>\n",
              "      <td>-0.676143</td>\n",
              "      <td>1.126366</td>\n",
              "      <td>-2.213700</td>\n",
              "      <td>0.468308</td>\n",
              "      <td>-1.120541</td>\n",
              "      <td>-0.003346</td>\n",
              "      <td>-2.234739</td>\n",
              "      <td>1.210158</td>\n",
              "      <td>-0.652250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247968</td>\n",
              "      <td>0.751826</td>\n",
              "      <td>0.834108</td>\n",
              "      <td>0.190944</td>\n",
              "      <td>0.032070</td>\n",
              "      <td>-0.739695</td>\n",
              "      <td>0.471111</td>\n",
              "      <td>0.385107</td>\n",
              "      <td>0.194361</td>\n",
              "      <td>77.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281144</th>\n",
              "      <td>169966.0</td>\n",
              "      <td>-3.113832</td>\n",
              "      <td>0.585864</td>\n",
              "      <td>-5.399730</td>\n",
              "      <td>1.817092</td>\n",
              "      <td>-0.840618</td>\n",
              "      <td>-2.943548</td>\n",
              "      <td>-2.208002</td>\n",
              "      <td>1.058733</td>\n",
              "      <td>-1.632333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.306271</td>\n",
              "      <td>0.583276</td>\n",
              "      <td>-0.269209</td>\n",
              "      <td>-0.456108</td>\n",
              "      <td>-0.183659</td>\n",
              "      <td>-0.328168</td>\n",
              "      <td>0.606116</td>\n",
              "      <td>0.884876</td>\n",
              "      <td>-0.253700</td>\n",
              "      <td>245.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281674</th>\n",
              "      <td>170348.0</td>\n",
              "      <td>1.991976</td>\n",
              "      <td>0.158476</td>\n",
              "      <td>-2.583441</td>\n",
              "      <td>0.408670</td>\n",
              "      <td>1.151147</td>\n",
              "      <td>-0.096695</td>\n",
              "      <td>0.223050</td>\n",
              "      <td>-0.068384</td>\n",
              "      <td>0.577829</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.017652</td>\n",
              "      <td>-0.164350</td>\n",
              "      <td>-0.295135</td>\n",
              "      <td>-0.072173</td>\n",
              "      <td>-0.450261</td>\n",
              "      <td>0.313267</td>\n",
              "      <td>-0.289617</td>\n",
              "      <td>0.002988</td>\n",
              "      <td>-0.015309</td>\n",
              "      <td>42.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>984 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68efd934-7a57-4b53-8136-e0db761faee1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-68efd934-7a57-4b53-8136-e0db761faee1 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-68efd934-7a57-4b53-8136-e0db761faee1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxlPKgz4Rszt",
        "outputId": "dd0565b1-a980-44d9-9d41-d4d8aafc56f2"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "155292    0\n",
              "135003    0\n",
              "42692     0\n",
              "97090     0\n",
              "247571    0\n",
              "         ..\n",
              "279863    1\n",
              "280143    1\n",
              "280149    1\n",
              "281144    1\n",
              "281674    1\n",
              "Name: Class, Length: 984, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting the data into training and testing data\n"
      ],
      "metadata": {
        "id": "2cQk0KuPRw9-"
      }
    },
    {
      "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": "6ss2CLTdRtTY"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of X_train \", X_train.shape)\n",
        "print(\"Shape of X_test \", X_test.shape)\n",
        "print(\"Shape of Y_train \", Y_train.shape)\n",
        "print(\"Shape of Y_test \", Y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBYS3AdjSHDk",
        "outputId": "f310e532-319c-4f69-f859-c7de8931c376"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train  (787, 30)\n",
            "Shape of X_test  (197, 30)\n",
            "Shape of Y_train  (787,)\n",
            "Shape of Y_test  (197,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Training "
      ],
      "metadata": {
        "id": "MFgMt6chTrWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3RmZYruSXSd",
        "outputId": "0ca92366-b64a-4532-8693-3c72c56b6527"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation\n",
        "\n",
        "- Accuracy Score"
      ],
      "metadata": {
        "id": "j_Q8VvRmT_tG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on training data \n",
        "\n",
        "X_train_prediction = model.predict(X_train)\n",
        "traning_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "hmjlv8OySXPP"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on Training data \",traning_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W5pG6uh5SXM3",
        "outputId": "a883291d-e072-43e8-d0df-b959edf55e1c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data  0.9390088945362135\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on testing data \n",
        "\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "lxKAlcnmSXKW"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on Training data \",test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsmbqgraSXH_",
        "outputId": "52c0285a-4a7f-4e15-95db-6afc2fd173e6"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data  0.9441624365482234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7LVAg8IPUkzl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}