ium_464913/IUM_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IUM 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation of packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.6.6)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\skype\\appdata\\roaming\\python\\python312\\site-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (2024.2.2)\n",
      "Requirement already satisfied: python-dateutil in c:\\users\\skype\\appdata\\roaming\\python\\python312\\site-packages (from kaggle) (2.9.0.post0)\n",
      "Requirement already satisfied: requests in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (2.31.0)\n",
      "Requirement already satisfied: tqdm in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (4.66.2)\n",
      "Requirement already satisfied: python-slugify in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (8.0.4)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (2.2.1)\n",
      "Requirement already satisfied: bleach in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from kaggle) (6.1.0)\n",
      "Requirement already satisfied: webencodings in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from bleach->kaggle) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests->kaggle) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests->kaggle) (3.6)\n",
      "Requirement already satisfied: colorama in c:\\users\\skype\\appdata\\roaming\\python\\python312\\site-packages (from tqdm->kaggle) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: pandas in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.2.1)\n",
      "Requirement already satisfied: numpy<2,>=1.26.0 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (1.26.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\skype\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\skype\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: numpy in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.26.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.4.1.post1)\n",
      "Requirement already satisfied: numpy<2.0,>=1.19.5 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from scikit-learn) (1.26.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from scikit-learn) (1.12.0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\skype\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from scikit-learn) (3.3.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install kaggle\n",
    "%pip install pandas\n",
    "%pip install numpy\n",
    "%pip install scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# To preprocess the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# To split the data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creditcardfraud.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d mlg-ulb/creditcardfraud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncompress a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  creditcardfraud.zip\n",
      "  inflating: creditcard.csv          \n"
     ]
    }
   ],
   "source": [
    "!unzip -o creditcardfraud.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('creditcard.csv')\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalising the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "df['Amount'] = scaler.fit_transform(df['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Time</th>\n",
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       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <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",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
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       "    <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>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>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>2.848070e+05</td>\n",
       "      <td>2.848070e+05</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>2.239053e-15</td>\n",
       "      <td>1.673327e-15</td>\n",
       "      <td>-1.247012e-15</td>\n",
       "      <td>8.190001e-16</td>\n",
       "      <td>1.207294e-15</td>\n",
       "      <td>4.887456e-15</td>\n",
       "      <td>1.437716e-15</td>\n",
       "      <td>-3.772171e-16</td>\n",
       "      <td>9.564149e-16</td>\n",
       "      <td>1.039917e-15</td>\n",
       "      <td>6.406204e-16</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>2.913952e-17</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>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.952742e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>9.153160e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.709250e-01</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>1.000002e+00</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>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-5.791881e+00</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-4.498945e+00</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-5.449772e+01</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>-3.532294e-01</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>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-6.485393e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-5.828843e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.117214e-01</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>-3.308401e-01</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>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>-1.356806e-02</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>4.807155e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-6.248109e-02</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>-2.652715e-01</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>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>6.625050e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>6.488208e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.330408e-01</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>-4.471707e-02</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>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>7.126883e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>8.877742e+00</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>3.942090e+01</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>1.023622e+02</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "                V10           V11           V12           V13           V14  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   2.239053e-15  1.673327e-15 -1.247012e-15  8.190001e-16  1.207294e-15   \n",
       "std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   \n",
       "min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   \n",
       "25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   \n",
       "50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   \n",
       "75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   \n",
       "max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01   \n",
       "\n",
       "                V15           V16           V17           V18           V19  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   4.887456e-15  1.437716e-15 -3.772171e-16  9.564149e-16  1.039917e-15   \n",
       "std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   \n",
       "min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   \n",
       "25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   \n",
       "50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   \n",
       "75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   \n",
       "max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00   \n",
       "\n",
       "                V20           V21           V22           V23           V24  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   6.406204e-16  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    3.942090e+01  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  2.848070e+05   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16  2.913952e-17   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01  1.000002e+00   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01 -3.532294e-01   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02 -3.308401e-01   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02 -2.652715e-01   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02 -4.471707e-02   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01  1.023622e+02   \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  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of legitimate and fraudulent transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    284315\n",
       "1       492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undersampling the data\n",
    "We will employ undersampling as one class significantly dominates the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the number of instances in the minority class\n",
    "fraud_count = len(df[df.Class == 1])\n",
    "fraud_indices = np.array(df[df.Class == 1].index)\n",
    "\n",
    "# Select indices corresponding to majority class instances\n",
    "normal_indices = df[df.Class == 0].index\n",
    "\n",
    "# Randomly sample the same number of instances from the majority class\n",
    "random_normal_indices = np.random.choice(normal_indices, fraud_count, replace=False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "\n",
    "# Combine indices of both classes\n",
    "undersample_indice = np.concatenate([fraud_indices, random_normal_indices])\n",
    "\n",
    "# Undersample dataset\n",
    "undersample_data = df.iloc[undersample_indice, :]\n",
    "\n",
    "X_undersample = undersample_data.iloc[:, undersample_data.columns != 'Class']\n",
    "y_undersample = undersample_data.iloc[:, undersample_data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of undersampled dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 984 entries, 541 to 141412\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Time    984 non-null    float64\n",
      " 1   V1      984 non-null    float64\n",
      " 2   V2      984 non-null    float64\n",
      " 3   V3      984 non-null    float64\n",
      " 4   V4      984 non-null    float64\n",
      " 5   V5      984 non-null    float64\n",
      " 6   V6      984 non-null    float64\n",
      " 7   V7      984 non-null    float64\n",
      " 8   V8      984 non-null    float64\n",
      " 9   V9      984 non-null    float64\n",
      " 10  V10     984 non-null    float64\n",
      " 11  V11     984 non-null    float64\n",
      " 12  V12     984 non-null    float64\n",
      " 13  V13     984 non-null    float64\n",
      " 14  V14     984 non-null    float64\n",
      " 15  V15     984 non-null    float64\n",
      " 16  V16     984 non-null    float64\n",
      " 17  V17     984 non-null    float64\n",
      " 18  V18     984 non-null    float64\n",
      " 19  V19     984 non-null    float64\n",
      " 20  V20     984 non-null    float64\n",
      " 21  V21     984 non-null    float64\n",
      " 22  V22     984 non-null    float64\n",
      " 23  V23     984 non-null    float64\n",
      " 24  V24     984 non-null    float64\n",
      " 25  V25     984 non-null    float64\n",
      " 26  V26     984 non-null    float64\n",
      " 27  V27     984 non-null    float64\n",
      " 28  V28     984 non-null    float64\n",
      " 29  Amount  984 non-null    float64\n",
      " 30  Class   984 non-null    int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 246.0 KB\n"
     ]
    }
   ],
   "source": [
    "undersample_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary statistics of the undersampled dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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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",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88501.498984</td>\n",
       "      <td>-2.445079</td>\n",
       "      <td>1.781022</td>\n",
       "      <td>-3.509406</td>\n",
       "      <td>2.214004</td>\n",
       "      <td>-1.477993</td>\n",
       "      <td>-0.713150</td>\n",
       "      <td>-2.787427</td>\n",
       "      <td>0.279073</td>\n",
       "      <td>-1.253108</td>\n",
       "      <td>-2.841500</td>\n",
       "      <td>1.930697</td>\n",
       "      <td>-3.124120</td>\n",
       "      <td>-0.026229</td>\n",
       "      <td>-3.502384</td>\n",
       "      <td>-0.039494</td>\n",
       "      <td>-2.097294</td>\n",
       "      <td>-3.304208</td>\n",
       "      <td>-1.128950</td>\n",
       "      <td>0.343668</td>\n",
       "      <td>0.175905</td>\n",
       "      <td>0.331911</td>\n",
       "      <td>0.049631</td>\n",
       "      <td>-0.031264</td>\n",
       "      <td>-0.037389</td>\n",
       "      <td>0.022812</td>\n",
       "      <td>0.027632</td>\n",
       "      <td>0.086286</td>\n",
       "      <td>0.046738</td>\n",
       "      <td>0.039676</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48996.269445</td>\n",
       "      <td>5.512352</td>\n",
       "      <td>3.713232</td>\n",
       "      <td>6.223001</td>\n",
       "      <td>3.231076</td>\n",
       "      <td>4.274632</td>\n",
       "      <td>1.789350</td>\n",
       "      <td>5.856197</td>\n",
       "      <td>4.857643</td>\n",
       "      <td>2.371055</td>\n",
       "      <td>4.563067</td>\n",
       "      <td>2.764745</td>\n",
       "      <td>4.595103</td>\n",
       "      <td>1.054377</td>\n",
       "      <td>4.653202</td>\n",
       "      <td>1.002911</td>\n",
       "      <td>3.465619</td>\n",
       "      <td>5.990033</td>\n",
       "      <td>2.412032</td>\n",
       "      <td>1.290973</td>\n",
       "      <td>1.126258</td>\n",
       "      <td>2.787884</td>\n",
       "      <td>1.167097</td>\n",
       "      <td>1.177562</td>\n",
       "      <td>0.551518</td>\n",
       "      <td>0.677541</td>\n",
       "      <td>0.476480</td>\n",
       "      <td>1.023332</td>\n",
       "      <td>0.479168</td>\n",
       "      <td>0.851800</td>\n",
       "      <td>0.500254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>-30.552380</td>\n",
       "      <td>-15.799625</td>\n",
       "      <td>-31.103685</td>\n",
       "      <td>-3.863126</td>\n",
       "      <td>-22.105532</td>\n",
       "      <td>-10.261990</td>\n",
       "      <td>-43.557242</td>\n",
       "      <td>-41.044261</td>\n",
       "      <td>-13.434066</td>\n",
       "      <td>-24.588262</td>\n",
       "      <td>-2.613374</td>\n",
       "      <td>-18.683715</td>\n",
       "      <td>-3.223045</td>\n",
       "      <td>-19.214325</td>\n",
       "      <td>-4.498945</td>\n",
       "      <td>-14.129855</td>\n",
       "      <td>-25.162799</td>\n",
       "      <td>-9.498746</td>\n",
       "      <td>-3.681904</td>\n",
       "      <td>-7.242879</td>\n",
       "      <td>-22.797604</td>\n",
       "      <td>-8.887017</td>\n",
       "      <td>-19.254328</td>\n",
       "      <td>-2.028024</td>\n",
       "      <td>-4.781606</td>\n",
       "      <td>-1.214960</td>\n",
       "      <td>-7.263482</td>\n",
       "      <td>-2.735623</td>\n",
       "      <td>-0.353229</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45531.000000</td>\n",
       "      <td>-2.867222</td>\n",
       "      <td>-0.155438</td>\n",
       "      <td>-5.084967</td>\n",
       "      <td>-0.172018</td>\n",
       "      <td>-1.700260</td>\n",
       "      <td>-1.619179</td>\n",
       "      <td>-3.066415</td>\n",
       "      <td>-0.204192</td>\n",
       "      <td>-2.279453</td>\n",
       "      <td>-4.572043</td>\n",
       "      <td>-0.187147</td>\n",
       "      <td>-5.495221</td>\n",
       "      <td>-0.784589</td>\n",
       "      <td>-6.721799</td>\n",
       "      <td>-0.627097</td>\n",
       "      <td>-3.543426</td>\n",
       "      <td>-5.302111</td>\n",
       "      <td>-1.809496</td>\n",
       "      <td>-0.412430</td>\n",
       "      <td>-0.187708</td>\n",
       "      <td>-0.157259</td>\n",
       "      <td>-0.509376</td>\n",
       "      <td>-0.240064</td>\n",
       "      <td>-0.379825</td>\n",
       "      <td>-0.321251</td>\n",
       "      <td>-0.281187</td>\n",
       "      <td>-0.061809</td>\n",
       "      <td>-0.050194</td>\n",
       "      <td>-0.347302</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>83076.500000</td>\n",
       "      <td>-0.823244</td>\n",
       "      <td>0.957399</td>\n",
       "      <td>-1.381998</td>\n",
       "      <td>1.287041</td>\n",
       "      <td>-0.394605</td>\n",
       "      <td>-0.689473</td>\n",
       "      <td>-0.668321</td>\n",
       "      <td>0.147397</td>\n",
       "      <td>-0.694910</td>\n",
       "      <td>-0.948441</td>\n",
       "      <td>1.170286</td>\n",
       "      <td>-0.858094</td>\n",
       "      <td>-0.000686</td>\n",
       "      <td>-1.110717</td>\n",
       "      <td>-0.006070</td>\n",
       "      <td>-0.677801</td>\n",
       "      <td>-0.513640</td>\n",
       "      <td>-0.383038</td>\n",
       "      <td>0.221049</td>\n",
       "      <td>0.040630</td>\n",
       "      <td>0.155404</td>\n",
       "      <td>0.080270</td>\n",
       "      <td>-0.030318</td>\n",
       "      <td>0.009379</td>\n",
       "      <td>0.049923</td>\n",
       "      <td>-0.007475</td>\n",
       "      <td>0.063100</td>\n",
       "      <td>0.039464</td>\n",
       "      <td>-0.280984</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>135051.500000</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>2.791569</td>\n",
       "      <td>0.356911</td>\n",
       "      <td>4.175332</td>\n",
       "      <td>0.616305</td>\n",
       "      <td>0.069620</td>\n",
       "      <td>0.265089</td>\n",
       "      <td>0.877002</td>\n",
       "      <td>0.134399</td>\n",
       "      <td>-0.016047</td>\n",
       "      <td>3.586502</td>\n",
       "      <td>0.190356</td>\n",
       "      <td>0.683977</td>\n",
       "      <td>0.110541</td>\n",
       "      <td>0.672903</td>\n",
       "      <td>0.250353</td>\n",
       "      <td>0.313841</td>\n",
       "      <td>0.334927</td>\n",
       "      <td>0.978754</td>\n",
       "      <td>0.445616</td>\n",
       "      <td>0.642724</td>\n",
       "      <td>0.624948</td>\n",
       "      <td>0.180735</td>\n",
       "      <td>0.365624</td>\n",
       "      <td>0.395001</td>\n",
       "      <td>0.324059</td>\n",
       "      <td>0.457194</td>\n",
       "      <td>0.226492</td>\n",
       "      <td>0.046539</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172733.000000</td>\n",
       "      <td>2.335833</td>\n",
       "      <td>22.057729</td>\n",
       "      <td>3.476268</td>\n",
       "      <td>12.114672</td>\n",
       "      <td>14.103918</td>\n",
       "      <td>6.474115</td>\n",
       "      <td>5.802537</td>\n",
       "      <td>20.007208</td>\n",
       "      <td>6.816732</td>\n",
       "      <td>11.732926</td>\n",
       "      <td>12.018913</td>\n",
       "      <td>2.534876</td>\n",
       "      <td>3.091328</td>\n",
       "      <td>3.442422</td>\n",
       "      <td>2.471358</td>\n",
       "      <td>3.139656</td>\n",
       "      <td>6.739384</td>\n",
       "      <td>3.790316</td>\n",
       "      <td>5.228342</td>\n",
       "      <td>11.059004</td>\n",
       "      <td>27.202839</td>\n",
       "      <td>8.361985</td>\n",
       "      <td>5.466230</td>\n",
       "      <td>1.208141</td>\n",
       "      <td>2.208209</td>\n",
       "      <td>2.745261</td>\n",
       "      <td>3.052358</td>\n",
       "      <td>4.975792</td>\n",
       "      <td>8.146182</td>\n",
       "      <td>1.000000</td>\n",