Credit_Card_Fraud_Detection.ipynb

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  "metadata": {
    "colab": {
      "name": "Credit Card Fraud Detection",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShriyanshiSrivastava/Credit-Card-Fraudlent-Detection/blob/main/Credit_Card_Fraud_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDnoEQTWStTz"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlNfrSC1PGfZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0CTsNioTKnJ"
      },
      "source": [
        "# loading the dataset to a Pandas DataFrame\n",
        "credit_card_data = pd.read_csv('/content/creditcard.csv')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_iMfyHsTa6s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "abb2dd0b-6133-4666-afbe-072c21d1291b"
      },
      "source": [
        "# first 5 rows of the dataset\n",
        "credit_card_data.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
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              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
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              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
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              "      <td>-0.617801</td>\n",
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              "      <td>1.468177</td>\n",
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              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
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              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
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              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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",
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              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>-0.185226</td>\n",
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              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
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              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
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              "      <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",
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              "    <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>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
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              "      <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",
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            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWCP6YJjThIM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "0f9ea865-2811-45f1-e6c8-94255d51b8e4"
      },
      "source": [
        "credit_card_data.tail()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.104533</td>\n",
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              "      <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>-0.915427</td>\n",
              "      <td>-1.040458</td>\n",
              "      <td>-0.031513</td>\n",
              "      <td>-0.188093</td>\n",
              "      <td>-0.084316</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>-0.302620</td>\n",
              "      <td>-0.660377</td>\n",
              "      <td>0.167430</td>\n",
              "      <td>-0.256117</td>\n",
              "      <td>0.382948</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",
              "</div>"
            ],
            "text/plain": [
              "            Time         V1         V2  ...       V28  Amount  Class\n",
              "284802  172786.0 -11.881118  10.071785  ...  0.823731    0.77      0\n",
              "284803  172787.0  -0.732789  -0.055080  ... -0.053527   24.79      0\n",
              "284804  172788.0   1.919565  -0.301254  ... -0.026561   67.88      0\n",
              "284805  172788.0  -0.240440   0.530483  ...  0.104533   10.00      0\n",
              "284806  172792.0  -0.533413  -0.189733  ...  0.013649  217.00      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcCZquvEUD3A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6e11694-5b81-46db-cfc6-ba0ed0de372a"
      },
      "source": [
        "# dataset informations\n",
        "credit_card_data.info()"
      ],
      "execution_count": 41,
      "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",
      "metadata": {
        "id": "XxwasmfKUQiA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f96e073f-b8a2-4d89-e14d-5b82df473645"
      },
      "source": [
        "# checking the number of missing values in each column\n",
        "credit_card_data.isnull().sum()"
      ],
      "execution_count": 42,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIpoKfp5Ugri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "803a1c3f-7984-43ff-9fb6-f6334f719906"
      },
      "source": [
        "# distribution of legit transactions & fraudulent transactions\n",
        "credit_card_data['Class'].value_counts()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flz_InK7VGri"
      },
      "source": [
        "This Dataset is highly unblanced"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OlMhkHzVKMv"
      },
      "source": [
        "0 --> Normal Transaction\n",
        "\n",
        "1 --> fraudulent transaction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNn77bdbU2Lf"
      },
      "source": [
        "# separating the data for analysis\n",
        "legit = credit_card_data[credit_card_data.Class == 0]\n",
        "fraud = credit_card_data[credit_card_data.Class == 1]"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsrMgOdoVnrV",
        "outputId": "a9fea1cd-e658-407a-d280-ed5f941433fd"
      },
      "source": [
        "print(legit.shape)\n",
        "print(fraud.shape)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(284315, 31)\n",
            "(492, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1s3KGshBVsTb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f15e9d1-4c15-4c05-9d51-ddc6a4e1c117"
      },
      "source": [
        "# statistical measures of the data\n",
        "legit.Amount.describe()"
      ],
      "execution_count": 47,
      "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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkU3Mzr5V7fR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad889185-f110-41ac-8c61-581418a03023"
      },
      "source": [
        "fraud.Amount.describe()"
      ],
      "execution_count": 48,
      "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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFUMMHwYWMvp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "4c19061f-aae6-47f1-963b-af373628c068"
      },
      "source": [
        "# compare the values for both transactions\n",
        "credit_card_data.groupby('Class').mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</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",
              "      <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>0.009824</td>\n",
              "      <td>-0.006576</td>\n",
              "      <td>0.010832</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.012064</td>\n",
              "      <td>0.000161</td>\n",
              "      <td>0.007164</td>\n",
              "      <td>0.011535</td>\n",
              "      <td>0.003887</td>\n",
              "      <td>-0.001178</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>-5.676883</td>\n",
              "      <td>3.800173</td>\n",
              "      <td>-6.259393</td>\n",
              "      <td>-0.109334</td>\n",
              "      <td>-6.971723</td>\n",
              "      <td>-0.092929</td>\n",
              "      <td>-4.139946</td>\n",
              "      <td>-6.665836</td>\n",
              "      <td>-2.246308</td>\n",
              "      <td>0.680659</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",
              "</div>"
            ],
            "text/plain": [
              "               Time        V1        V2  ...       V27       V28      Amount\n",
              "Class                                    ...                                \n",
              "0      94838.202258  0.008258 -0.006271  ... -0.000295 -0.000131   88.291022\n",
              "1      80746.806911 -4.771948  3.623778  ...  0.170575  0.075667  122.211321\n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybHNYPpEW0N6"
      },
      "source": [
        "Under-Sampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlXkIGEIW3KM"
      },
      "source": [
        "Build a sample dataset containing similar distribution of normal transactions and Fraudulent Transactions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCj3Dee7XB0F"
      },
      "source": [
        "Number of Fraudulent Transactions --> 492"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtWT13mKWjJ_"
      },
      "source": [
        "legit_sample = legit.sample(n=492)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNiYI_SmXeim"
      },
      "source": [
        "Concatenating two DataFrames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yiXrYiRXcnE"
      },
      "source": [
        "new_dataset = pd.concat([legit_sample, fraud], axis=0)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuLw43AXX0bq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "05ee6cbc-fbcd-47c1-9f1e-1f24656d5ebe"
      },
      "source": [
        "new_dataset.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
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              "      <th>V12</th>\n",
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              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
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              "      <th>V17</th>\n",
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              "      <td>-1.464438</td>\n",
              "      <td>-0.631840</td>\n",
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              "      <td>0.789841</td>\n",
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              "      <td>-0.103458</td>\n",
              "      <td>0.769994</td>\n",
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              "      <td>-0.030456</td>\n",
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              "      <td>-0.169678</td>\n",
              "      <td>1.063199</td>\n",
              "      <td>-0.840978</td>\n",
              "      <td>0.257042</td>\n",
              "      <td>-0.413040</td>\n",
              "      <td>0.128657</td>\n",
              "      <td>-0.105073</td>\n",
              "      <td>-0.253598</td>\n",
              "      <td>0.011452</td>\n",
              "      <td>0.031574</td>\n",
              "      <td>-0.120574</td>\n",
              "      <td>0.711618</td>\n",
              "      <td>-0.032720</td>\n",
              "      <td>-0.173510</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>162216</th>\n",
              "      <td>114938.0</td>\n",
              "      <td>0.338518</td>\n",
              "      <td>0.864410</td>\n",
              "      <td>0.506710</td>\n",
              "      <td>0.971164</td>\n",
              "      <td>0.489854</td>\n",
              "      <td>-0.512320</td>\n",
              "      <td>0.964169</td>\n",
              "      <td>-0.486845</td>\n",
              "      <td>-0.512023</td>\n",
              "      <td>0.241556</td>\n",
              "      <td>-0.198243</td>\n",
              "      <td>-0.071429</td>\n",
              "      <td>0.440241</td>\n",
              "      <td>0.337105</td>\n",
              "      <td>1.951616</td>\n",
              "      <td>-1.513341</td>\n",
              "      <td>0.450861</td>\n",
              "      <td>0.301844</td>\n",
              "      <td>2.361266</td>\n",
              "      <td>0.333062</td>\n",
              "      <td>0.346631</td>\n",
              "      <td>1.357021</td>\n",
              "      <td>-0.207703</td>\n",
              "      <td>1.226899</td>\n",
              "      <td>-0.486290</td>\n",
              "      <td>0.169571</td>\n",
              "      <td>0.071281</td>\n",
              "      <td>-0.009638</td>\n",
              "      <td>15.62</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>15492</th>\n",
              "      <td>26877.0</td>\n",
              "      <td>-1.659383</td>\n",
              "      <td>0.971326</td>\n",
              "      <td>1.775307</td>\n",
              "      <td>-2.058113</td>\n",
              "      <td>-0.828743</td>\n",
              "      <td>-0.226136</td>\n",
              "      <td>-0.394307</td>\n",
              "      <td>-0.571741</td>\n",
              "      <td>-0.398407</td>\n",
              "      <td>0.995735</td>\n",
              "      <td>-0.285913</td>\n",
              "      <td>-1.009069</td>\n",
              "      <td>-0.353581</td>\n",
              "      <td>-0.595859</td>\n",
              "      <td>0.749278</td>\n",
              "      <td>1.169108</td>\n",
              "      <td>0.240186</td>\n",
              "      <td>-1.381066</td>\n",
              "      <td>-0.664089</td>\n",
              "      <td>0.370660</td>\n",
              "      <td>0.989799</td>\n",
              "      <td>0.588880</td>\n",
              "      <td>-0.209231</td>\n",
              "      <td>0.070003</td>\n",
              "      <td>0.612091</td>\n",
              "      <td>-0.120108</td>\n",
              "      <td>0.648986</td>\n",
              "      <td>0.327110</td>\n",
              "      <td>8.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130885</th>\n",
              "      <td>79453.0</td>\n",
              "      <td>-9.889053</td>\n",
              "      <td>7.659856</td>\n",
              "      <td>-7.653713</td>\n",
              "      <td>-2.872547</td>\n",
              "      <td>-3.402951</td>\n",
              "      <td>2.020564</td>\n",
              "      <td>-5.617204</td>\n",
              "      <td>7.584182</td>\n",
              "      <td>1.258301</td>\n",
              "      <td>2.203123</td>\n",
              "      <td>-1.997330</td>\n",
              "      <td>2.408520</td>\n",
              "      <td>0.865714</td>\n",
              "      <td>3.398593</td>\n",
              "      <td>0.418472</td>\n",
              "      <td>2.390013</td>\n",
              "      <td>1.159480</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>-1.070221</td>\n",
              "      <td>1.064509</td>\n",
              "      <td>-0.121531</td>\n",
              "      <td>-1.179877</td>\n",
              "      <td>0.944512</td>\n",
              "      <td>0.941292</td>\n",
              "      <td>1.284707</td>\n",
              "      <td>0.862838</td>\n",
              "      <td>0.404363</td>\n",
              "      <td>0.317317</td>\n",
              "      <td>7.68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80786</th>\n",
              "      <td>58651.0</td>\n",
              "      <td>-0.359248</td>\n",
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              "      <td>1.126988</td>\n",
              "      <td>1.047447</td>\n",
              "      <td>0.339984</td>\n",
              "      <td>0.333054</td>\n",
              "      <td>0.453986</td>\n",
              "      <td>0.254021</td>\n",
              "      <td>-0.266435</td>\n",
              "      <td>0.157432</td>\n",
              "      <td>-0.139487</td>\n",
              "      <td>-0.305984</td>\n",
              "      <td>-1.571442</td>\n",
              "      <td>0.433230</td>\n",
              "      <td>-0.511193</td>\n",
              "      <td>-0.422160</td>\n",
              "      <td>-0.112934</td>\n",
              "      <td>0.715717</td>\n",
              "      <td>1.693778</td>\n",
              "      <td>0.104438</td>\n",
              "      <td>-0.082877</td>\n",
              "      <td>-0.005807</td>\n",
              "      <td>-0.283351</td>\n",
              "      <td>-0.525708</td>\n",
              "      <td>0.011182</td>\n",
              "      <td>-0.213814</td>\n",
              "      <td>0.399174</td>\n",
              "      <td>0.185946</td>\n",
              "      <td>6.12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Time        V1        V2  ...       V28  Amount  Class\n",
              "54567    46508.0 -1.398597  1.438774  ... -0.173510    0.77      0\n",
              "162216  114938.0  0.338518  0.864410  ... -0.009638   15.62      0\n",
              "15492    26877.0 -1.659383  0.971326  ...  0.327110    8.40      0\n",
              "130885   79453.0 -9.889053  7.659856  ...  0.317317    7.68      0\n",
              "80786    58651.0 -0.359248  0.912884  ...  0.185946    6.12      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_Rjt1qDX3AQ",
        "colab": {
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          "height": 226
        },
        "outputId": "ae1af4b6-49f4-419a-ec7f-a4befafef5a5"
      },
      "source": [
        "new_dataset.tail()"
      ],
      "execution_count": 54,
      "outputs": [
        {
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              "      <th>279863</th>\n",
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              "      <td>-3.096915</td>\n",
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              "      <td>-0.688505</td>\n",
              "      <td>0.737657</td>\n",
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              "      <td>1.794969</td>\n",
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              "      <td>-4.057162</td>\n",
              "      <td>-0.712616</td>\n",
              "      <td>-1.603015</td>\n",
              "      <td>-5.035326</td>\n",
              "      <td>-0.507000</td>\n",
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              "      <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>-0.888722</td>\n",
              "      <td>0.491140</td>\n",
              "      <td>0.728903</td>\n",
              "      <td>0.380428</td>\n",
              "      <td>-1.948883</td>\n",
              "      <td>-0.832498</td>\n",
              "      <td>0.519436</td>\n",
              "      <td>0.903562</td>\n",
              "      <td>1.197315</td>\n",
              "      <td>0.593509</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",
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              "      <td>0.002988</td>\n",
              "      <td>-0.015309</td>\n",
              "      <td>42.53</td>\n",
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            "text/plain": [
              "            Time        V1        V2  ...       V28  Amount  Class\n",
              "279863  169142.0 -1.927883  1.125653  ...  0.147968  390.00      1\n",
              "280143  169347.0  1.378559  1.289381  ...  0.186637    0.76      1\n",
              "280149  169351.0 -0.676143  1.126366  ...  0.194361   77.89      1\n",
              "281144  169966.0 -3.113832  0.585864  ... -0.253700  245.00      1\n",
              "281674  170348.0  1.991976  0.158476  ... -0.015309   42.53      1\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFsRcj0gX-3M",
        "colab": {
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        },
        "outputId": "bfff4593-18bf-4388-e12e-d661a2fb2032"
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      "source": [
        "new_dataset['Class'].value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    492\n",
              "0    492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jan1xMeWYLrM",
        "colab": {
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          "height": 163
        },
        "outputId": "07b21b08-9d87-4a37-ce33-922acbc3fccf"
      },
      "source": [
        "new_dataset.groupby('Class').mean()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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>95566.973577</td>\n",
              "      <td>-0.008469</td>\n",
              "      <td>-0.158629</td>\n",
              "      <td>-0.022818</td>\n",
              "      <td>0.020102</td>\n",
              "      <td>0.050988</td>\n",
              "      <td>-0.024505</td>\n",
              "      <td>-0.000454</td>\n",
              "      <td>-0.068359</td>\n",
              "      <td>0.029802</td>\n",
              "      <td>0.020014</td>\n",
              "      <td>0.017413</td>\n",
              "      <td>-0.001745</td>\n",
              "      <td>0.038947</td>\n",
              "      <td>0.030066</td>\n",
              "      <td>-0.040579</td>\n",
              "      <td>-0.045948</td>\n",
              "      <td>0.033107</td>\n",
              "      <td>0.015243</td>\n",
              "      <td>0.061972</td>\n",
              "      <td>0.021546</td>\n",
              "      <td>-0.020757</td>\n",
              "      <td>-0.059273</td>\n",
              "      <td>-0.042396</td>\n",
              "      <td>0.005776</td>\n",
              "      <td>0.026286</td>\n",
              "      <td>-0.003297</td>\n",
              "      <td>-0.039355</td>\n",
              "      <td>-0.007926</td>\n",
              "      <td>107.385711</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>-5.676883</td>\n",
              "      <td>3.800173</td>\n",
              "      <td>-6.259393</td>\n",
              "      <td>-0.109334</td>\n",
              "      <td>-6.971723</td>\n",
              "      <td>-0.092929</td>\n",
              "      <td>-4.139946</td>\n",
              "      <td>-6.665836</td>\n",
              "      <td>-2.246308</td>\n",
              "      <td>0.680659</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",
              "</div>"
            ],
            "text/plain": [
              "               Time        V1        V2  ...       V27       V28      Amount\n",
              "Class                                    ...                                \n",
              "0      95566.973577 -0.008469 -0.158629  ... -0.039355 -0.007926  107.385711\n",
              "1      80746.806911 -4.771948  3.623778  ...  0.170575  0.075667  122.211321\n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbe0g12oYhyK"
      },
      "source": [
        "Splitting the data into Features & Targets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiC0OOyUYUoD"
      },
      "source": [
        "X = new_dataset.drop(columns='Class', axis=1)\n",
        "Y = new_dataset['Class']"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u7wbWqzYyrI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa7a44bb-15b5-496a-c674-bdb1ddfe8d33"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Time        V1        V2  ...       V27       V28  Amount\n",
            "54567    46508.0 -1.398597  1.438774  ... -0.032720 -0.173510    0.77\n",
            "162216  114938.0  0.338518  0.864410  ...  0.071281 -0.009638   15.62\n",
            "15492    26877.0 -1.659383  0.971326  ...  0.648986  0.327110    8.40\n",
            "130885   79453.0 -9.889053  7.659856  ...  0.404363  0.317317    7.68\n",
            "80786    58651.0 -0.359248  0.912884  ...  0.399174  0.185946    6.12\n",
            "...          ...       ...       ...  ...       ...       ...     ...\n",
            "279863  169142.0 -1.927883  1.125653  ...  0.292680  0.147968  390.00\n",
            "280143  169347.0  1.378559  1.289381  ...  0.389152  0.186637    0.76\n",
            "280149  169351.0 -0.676143  1.126366  ...  0.385107  0.194361   77.89\n",
            "281144  169966.0 -3.113832  0.585864  ...  0.884876 -0.253700  245.00\n",
            "281674  170348.0  1.991976  0.158476  ...  0.002988 -0.015309   42.53\n",
            "\n",
            "[984 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHnRPzZvYz-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c11ccc53-17d8-490c-e89c-003c70dbc6a4"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54567     0\n",
            "162216    0\n",
            "15492     0\n",
            "130885    0\n",
            "80786     0\n",
            "         ..\n",
            "279863    1\n",
            "280143    1\n",
            "280149    1\n",
            "281144    1\n",
            "281674    1\n",
            "Name: Class, Length: 984, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78wEbV41Y6j3"
      },
      "source": [
        "Split the data into Training data & Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrsqqwT0Y3n5"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUmwXz99ZuvK",
        "outputId": "0a9d2989-c672-4a74-c252-77979c3777ef"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(984, 30) (787, 30) (197, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aowgjqbeZ7d1"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0NkjaxyZ96E"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0HHd1v8Z2Mq"
      },
      "source": [
        "model = LogisticRegression()\n"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rshSouvaGrv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "009fca7f-457f-4b92-ebb4-100e45e67a84"
      },
      "source": [
        "# training the Logistic Regression Model with Training Data\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9FYWyKjalvC"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6O6kbwYaoxH"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC2kCJhOaage"
      },
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ijq6gAevbDwm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f60aae1-cfa9-4437-86c0-f337e6156a5b"
      },
      "source": [
        "print('Accuracy on Training data : ', training_data_accuracy)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data :  0.9390088945362135\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tryJUs6mbJM7"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxEuN24ib3hS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59c8f93c-1be8-4797-ddfe-a019fa167558"
      },
      "source": [
        "print('Accuracy score on Test Data : ', test_data_accuracy)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score on Test Data :  0.9441624365482234\n"
          ]
        }
      ]
    }
  ]
}