card_fraud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card **Fraud Detection**\n",
    "\n",
    "* **Name:** Arsalan Ali\n",
    "* **Email:** arslanchaos@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Introduction**\n",
    "\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions<br> so that customers are not charged for items that they did not purchase. Feature names are hidden due to privacy.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Credit Card Dataset**\n",
    "\n",
    "The dataset contains transactions made by credit cards in September 2013 by European cardholders.<br>\n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.<br>\n",
    " The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Preparation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of Rows: 284807 \n",
      "Num of Columns: 31 \n",
      "The size (rows x columns) is: 8829017\n",
      "The Dimensions are: 2\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Data shape\n",
    "\n",
    "rows, columns = df.shape\n",
    "print(f\"Num of Rows: {rows} \") # instances\n",
    "print(f\"Num of Columns: {columns} \") # series\n",
    "print(f\"The size (rows x columns) is: {df.size}\") # size\n",
    "print(f\"The Dimensions are: {df.ndim}\") # dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "### Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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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>Amount</th>\n",
       "      <th>Class</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
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       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-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  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column Names\n",
    "V1 to V28 are confidential features due to privacy<br>\n",
    "Time, Amount and Class are the features that could come in handy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique Labels in Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Class\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:\n",
    "* 0 = Non Fraud\n",
    "* 1 = Fraud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Value Count of Each Label in Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Fraud Cases: 99.83 % in dataset\n",
      "Fraud Cases: 0.17 % in dataset\n"
     ]
    }
   ],
   "source": [
    "not_fraud = df[\"Class\"].value_counts()[0]/len(df[\"Class\"])*100\n",
    "fraud = df[\"Class\"].value_counts()[1]/len(df[\"Class\"])*100\n",
    "\n",
    "print(f\"Non Fraud Cases: {not_fraud:.2f} % in dataset\")\n",
    "print(f\"Fraud Cases: {fraud:.2f} % in dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**<br>\n",
    "As we can see that the ratio of Not Fraud and Fraud cases vary a lot. <br>\n",
    "This variation causes **Imbalanced data** and it'll deliver inaccurate ML results<br>\n",
    "We need to balance it out in order to proceed further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distributions \\n (0: Non Fraud || 1: Fraud)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Class', data=df)\n",
    "plt.title('Class Distributions \\n (0: Non Fraud || 1: Fraud)', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**<br>\n",
    "We can clearly see the one-sided data here. Skewness is huge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing other Features (Amount and Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 172792.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(18,4))\n",
    "\n",
    "amount_val = df['Amount'].values\n",
    "time_val = df['Time'].values\n",
    "\n",
    "sns.histplot(amount_val, ax=ax[0], color='r')\n",
    "ax[0].set_title(\"Distribution of Transaction Amount\", fontsize=14)\n",
    "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
    "\n",
    "sns.histplot(time_val, ax=ax[1], color='b')\n",
    "ax[1].set_title(\"Distribution of Transaction Time\", fontsize=14)\n",
    "ax[1].set_xlim([min(time_val), max(time_val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **What to do now?**\n",
    "Since we've a highly imbalanced data. We need to do the following:<br>\n",
    "* **Feature Scaling (Normalization and Standardization)** of Amount and Time\n",
    "* **Split** the dataset into training and testing\n",
    "* **Sub-sampling** of Dataset to create a balanced 50/50 dataset of Fraud and Non Fraud\n",
    "* Sub-sampling will resolve issues such as Overfitting and Wrong Correlations\n",
    "* After training the model on sub-sample we will then test it on the Split that we did on original dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "# RobustScaler is less prone to outliers. Thus we're using it \n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)\n",
    "\n",
    "# Time and Amount are scaled but are at the back of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-ordering Amount and Time to bring them to the front of dataset\n",
    "\n",
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Original Dataset into Training and Testing\n",
    "So we can use the Testing part of it to evaluate the model that's going to be<br>\n",
    "trained on a sub-sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sub-Sampling the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253065</th>\n",
       "      <td>-0.286593</td>\n",
       "      <td>0.838661</td>\n",
       "      <td>1.942085</td>\n",
       "      <td>0.568184</td>\n",
       "      <td>-0.797381</td>\n",
       "      <td>3.659970</td>\n",
       "      <td>0.641435</td>\n",
       "      <td>0.181236</td>\n",
       "      <td>0.180971</td>\n",
       "      <td>0.013821</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.335606</td>\n",
       "      <td>-0.294402</td>\n",
       "      <td>-0.906116</td>\n",
       "      <td>0.397432</td>\n",
       "      <td>0.495407</td>\n",
       "      <td>-0.264414</td>\n",
       "      <td>-0.362524</td>\n",
       "      <td>-0.043622</td>\n",
       "      <td>-0.040240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6734</th>\n",
       "      <td>-0.293440</td>\n",
       "      <td>-0.895699</td>\n",
       "      <td>0.314597</td>\n",
       "      <td>2.660670</td>\n",
       "      <td>-5.920037</td>\n",
       "      <td>4.522500</td>\n",
       "      <td>-2.315027</td>\n",
       "      <td>-2.278352</td>\n",
       "      <td>-4.684054</td>\n",
       "      <td>1.202270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.562706</td>\n",
       "      <td>0.743314</td>\n",
       "      <td>0.064038</td>\n",
       "      <td>0.677842</td>\n",
       "      <td>0.083008</td>\n",
       "      <td>-1.911034</td>\n",
       "      <td>0.322188</td>\n",
       "      <td>0.620867</td>\n",
       "      <td>0.185030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71512</th>\n",
       "      <td>-0.167819</td>\n",
       "      <td>-0.356759</td>\n",
       "      <td>1.196534</td>\n",
       "      <td>0.204051</td>\n",
       "      <td>0.246937</td>\n",
       "      <td>0.660416</td>\n",
       "      <td>-0.286366</td>\n",
       "      <td>-0.624916</td>\n",
       "      <td>-0.051158</td>\n",
       "      <td>-0.007889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162854</td>\n",
       "      <td>-0.267050</td>\n",
       "      <td>-0.805588</td>\n",
       "      <td>0.164187</td>\n",
       "      <td>0.005688</td>\n",
       "      <td>0.104861</td>\n",
       "      <td>0.126752</td>\n",
       "      <td>-0.014981</td>\n",
       "      <td>0.026591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151462</th>\n",
       "      <td>-0.284636</td>\n",
       "      <td>0.127668</td>\n",
       "      <td>-16.308650</td>\n",
       "      <td>11.614801</td>\n",
       "      <td>-19.739386</td>\n",
       "      <td>10.463866</td>\n",
       "      <td>-12.599146</td>\n",
       "      <td>-1.202393</td>\n",
       "      <td>-23.380508</td>\n",
       "      <td>-5.781133</td>\n",
       "      <td>...</td>\n",
       "      <td>2.106034</td>\n",
       "      <td>-4.884983</td>\n",
       "      <td>1.140910</td>\n",
       "      <td>1.392953</td>\n",
       "      <td>0.348997</td>\n",
       "      <td>-2.167510</td>\n",
       "      <td>-0.798754</td>\n",
       "      <td>-2.942775</td>\n",
       "      <td>-0.462680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50211</th>\n",
       "      <td>-0.293440</td>\n",
       "      <td>-0.473443</td>\n",
       "      <td>-4.617461</td>\n",
       "      <td>3.663395</td>\n",
       "      <td>-5.297446</td>\n",
       "      <td>3.880960</td>\n",
       "      <td>-3.263551</td>\n",
       "      <td>-0.918547</td>\n",
       "      <td>-5.715262</td>\n",
       "      <td>0.831040</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.346456</td>\n",
       "      <td>2.698175</td>\n",
       "      <td>-0.027081</td>\n",
       "      <td>0.366775</td>\n",
       "      <td>-0.123011</td>\n",
       "      <td>-0.300457</td>\n",
       "      <td>-0.239996</td>\n",
       "      <td>-0.183463</td>\n",
       "      <td>-0.073360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time         V1         V2         V3  \\\n",
       "253065      -0.286593     0.838661   1.942085   0.568184  -0.797381   \n",
       "6734        -0.293440    -0.895699   0.314597   2.660670  -5.920037   \n",
       "71512       -0.167819    -0.356759   1.196534   0.204051   0.246937   \n",
       "151462      -0.284636     0.127668 -16.308650  11.614801 -19.739386   \n",
       "50211       -0.293440    -0.473443  -4.617461   3.663395  -5.297446   \n",
       "\n",
       "               V4         V5        V6         V7        V8  ...       V20  \\\n",
       "253065   3.659970   0.641435  0.181236   0.180971  0.013821  ... -0.335606   \n",
       "6734     4.522500  -2.315027 -2.278352  -4.684054  1.202270  ...  0.562706   \n",
       "71512    0.660416  -0.286366 -0.624916  -0.051158 -0.007889  ... -0.162854   \n",
       "151462  10.463866 -12.599146 -1.202393 -23.380508 -5.781133  ...  2.106034   \n",
       "50211    3.880960  -3.263551 -0.918547  -5.715262  0.831040  ... -0.346456   \n",
       "\n",
       "             V21       V22       V23       V24       V25       V26       V27  \\\n",
       "253065 -0.294402 -0.906116  0.397432  0.495407 -0.264414 -0.362524 -0.043622   \n",
       "6734    0.743314  0.064038  0.677842  0.083008 -1.911034  0.322188  0.620867   \n",
       "71512  -0.267050 -0.805588  0.164187  0.005688  0.104861  0.126752 -0.014981   \n",
       "151462 -4.884983  1.140910  1.392953  0.348997 -2.167510 -0.798754 -2.942775   \n",
       "50211   2.698175 -0.027081  0.366775 -0.123011 -0.300457 -0.239996 -0.183463   \n",
       "\n",
       "             V28  Class  \n",
       "253065 -0.040240      0  \n",
       "6734    0.185030      1  \n",
       "71512   0.026591      0  \n",
       "151462 -0.462680      1  \n",
       "50211  -0.073360      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sample(frac=1)\n",
    "\n",
    "# amount of fraud classes 492 rows.\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "# Shuffle dataframe rows\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(new_df['Class'].value_counts()/len(new_df))\n",
    "\n",
    "sns.countplot(x='Class', data=new_df)\n",
    "plt.title('Equally Distributed Classes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see our Data is now **balanced**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation among the Sub-sample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sub-sample Correlation Matrix')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,16))\n",
    "\n",
    "sub_sample_corr = new_df.corr()\n",
    "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax)\n",
    "ax.set_title('Sub-sample Correlation Matrix', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V12 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Outlier/Anamoly Detection & Removal**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Visualize Distributions\n",
    "* Determine the Threshold\n",
    "* Conditional Dropping\n",
    "* Boxplot Representations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
    "\n",
    "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
    "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
    "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "\n",
    "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
    "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -9.692722964972386 | Quartile 75: -4.282820849486865\n",
      "iqr: 5.409902115485521\n",
      "Cut Off: 8.114853173228282\n",
      "V14 Lower: -17.807576138200666\n",
      "V14 Upper: 3.8320323237414167\n",
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V10 outliers:[-18.0499976898594, -19.2143254902614, -18.8220867423816, -18.4937733551053]\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.4311310279993, -18.0475965708216, -18.6837146333443, -18.5536970096458]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "Number of Instances after outliers removal: 976\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.92033495834214\n",
      "V10 outliers: [-16.3035376590131, -14.9246547735487, -15.1237521803455, -18.2711681738888, -15.1241628144947, -16.7460441053944, -16.2556117491401, -15.2399619587112, -15.2318333653018, -24.4031849699728, -17.1415136412892, -20.9491915543611, -24.5882624372475, -16.6011969664137, -14.9246547735487, -16.6496281595399, -18.9132433348732, -23.2282548357516, -15.5637913387301, -22.1870885620007, -22.1870885620007, -22.1870885620007, -15.2399619587112, -15.5637913387301, -15.3460988468775, -22.1870885620007, -19.836148851696]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "Number of Instances after outliers removal: 949\n"
     ]
    }
   ],
   "source": [
    "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\n",
    "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "v14_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v14_iqr))\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "print('Cut Off: {}'.format(v14_cut_off))\n",
    "print('V14 Lower: {}'.format(v14_lower))\n",
    "print('V14 Upper: {}'.format(v14_upper))\n",
    "\n",
    "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "print('V10 outliers:{}'.format(outliers))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n",
    "print('----' * 44)\n",
    "\n",
    "# -----> V12 removing outliers from fraud transactions\n",
    "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\n",
    "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
    "print('V12 outliers: {}'.format(outliers))\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
    "print('----' * 44)\n",
    "\n",
    "# Removing outliers V10 Feature\n",
    "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
    "v10_iqr = q75 - q25\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\n",
    "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
    "print('V10 outliers: {}'.format(outliers))\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "colors = ['#B3F9C5', '#f9c5b3']\n",
    "# Boxplots with outliers removed\n",
    "\n",
    "# # Feature V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax3.annotate('Fewer extreme \\n outliers', xy=(1,-15), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python ('tf')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "25a19fbe0a9132dfb9279d48d161753c6352f8f9478c2e74383d340069b907c3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}