CreditCardFraudDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "04f52923-1f3d-449d-bd71-3bd7833fd122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\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>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import itertools\n",
    "\n",
    "\n",
    "dataframe = pd.read_csv(\"datasets/creditcard.csv\")\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdddbaa6-5801-407b-b063-1cef0788c9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c274907-319c-4a07-8ef6-a1c9cf519896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284807.000000\n",
       "mean         88.349619\n",
       "std         250.120109\n",
       "min           0.000000\n",
       "25%           5.600000\n",
       "50%          22.000000\n",
       "75%          77.165000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"Amount\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d85478e5-d7c2-414a-90bb-dad550adebcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genuine transactions:  284315\n",
      "Number of Fraud transactions:  492\n",
      "Percentage of Fraud transactions: 0.1727\n"
     ]
    }
   ],
   "source": [
    "non_fraud = len(dataframe[dataframe.Class == 0])\n",
    "fraud = len(dataframe[dataframe.Class == 1])\n",
    "fraud_percent = (fraud / (fraud + non_fraud)) * 100\n",
    "print(\"Number of Genuine transactions: \", non_fraud)\n",
    "print(\"Number of Fraud transactions: \", fraud)\n",
    "print(\"Percentage of Fraud transactions: {:.4f}\".format(fraud_percent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb868c09-4473-4447-8ade-96e5b1b65c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labels = [\"Genuine\", \"Fraud\"]\n",
    "count_classes = dataframe.value_counts(dataframe['Class'], sort= True)\n",
    "count_classes.plot(kind = \"bar\", rot = 0)\n",
    "plt.title(\"Visualization of Labels\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(range(2), labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0f62b57-6ebc-4038-9908-48d351c441ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "scaler = StandardScaler()\n",
    "dataframe[\"NormalizedAmount\"] = scaler.fit_transform(dataframe[\"Amount\"].values.reshape(-1, 1))\n",
    "dataframe.drop([\"Amount\", \"Time\"], inplace= True, axis= 1)\n",
    "Y = dataframe[\"Class\"]\n",
    "X = dataframe.drop([\"Class\"], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58cb3335-7f92-4364-bc06-14d8c988242e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train_X:  (213605, 29)\n",
      "Shape of test_X:  (71202, 29)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "(train_X, test_X, train_Y, test_Y) = train_test_split(X, Y, test_size= 0.25, random_state= 40)\n",
    "print(\"Shape of train_X: \", train_X.shape)\n",
    "print(\"Shape of test_X: \", test_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7124f499-f9cd-4410-ade2-367f1d60c005",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "random_forest = RandomForestClassifier(n_estimators= 110)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e86733db-a43e-4321-955c-e6f6b30f28a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree.fit(train_X, train_Y)\n",
    "predictions_dt = decision_tree.predict(test_X)\n",
    "decision_tree_score = decision_tree.score(test_X, test_Y) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34e1afce-7ca5-40d2-ae3a-a6a4d22bfbaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest.fit(train_X, train_Y)\n",
    "predictions_rf = random_forest.predict(test_X)\n",
    "random_forest_score = random_forest.score(test_X, test_Y) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4eaea7c-e7f1-465f-8226-5555be1dc4d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Score:  99.97050644644814\n",
      "Decision Tree Score:  99.90028369989608\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest Score: \", random_forest_score)\n",
    "print(\"Decision Tree Score: \", decision_tree_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "653b58d8-8396-4904-8066-ebbb68f55561",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def metrics(actuals, predictions):\n",
    "    print(\"Accuracy: {:.5f}\".format(accuracy_score(actuals, predictions)))\n",
    "    print(\"Precision: {:.5f}\".format(precision_score(actuals, predictions)))\n",
    "    print(\"Recall: {:.5f}\".format(recall_score(actuals, predictions)))\n",
    "    print(\"F1-score: {:.5f}\".format(f1_score(actuals, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4344968-ff31-42c1-8408-cc992ac0cdb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Random Forest\n",
      "[[71078     3]\n",
      " [   18   103]]\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix_rf = confusion_matrix(test_Y, predictions_rf.round())\n",
    "print(\"Confusion Matrix - Random Forest\")\n",
    "print(confusion_matrix_rf)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c73b1dbc-3fff-4733-96a9-4cfdb7b0958e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation of Random Forest Model\n",
      "\n",
      "Accuracy: 0.99971\n",
      "Precision: 0.97170\n",
      "Recall: 0.85124\n",
      "F1-score: 0.90749\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation of Random Forest Model\")\n",
    "print()\n",
    "metrics(test_Y, predictions_rf.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a93a077d-0864-4c26-9536-7bf311fd4402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Decision Tree\n",
      "[[71035    46]\n",
      " [   25    96]]\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix_dt = confusion_matrix(test_Y, predictions_dt.round())\n",
    "print(\"Confusion Matrix - Decision Tree\")\n",
    "print(confusion_matrix_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11676610-a06f-40ac-a62d-4488f76fd2dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation of Decision Tree Model\n",
      "\n",
      "Accuracy: 0.99900\n",
      "Precision: 0.67606\n",
      "Recall: 0.79339\n",
      "F1-score: 0.73004\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation of Decision Tree Model\")\n",
    "print()\n",
    "metrics(test_Y, predictions_dt.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81e37c9e-e87e-4b9c-9266-46a307422bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled shape of X:  (568630, 29)\n",
      "Resampled shape of Y:  (568630,)\n",
      "Counter({0: 284315, 1: 284315})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, Y_resampled = SMOTE().fit_resample(X, Y)\n",
    "print(\"Resampled shape of X: \", X_resampled.shape)\n",
    "print(\"Resampled shape of Y: \", Y_resampled.shape)\n",
    "value_counts = Counter(Y_resampled)\n",
    "print(value_counts)\n",
    "(train_X, test_X, train_Y, test_Y) = train_test_split(X_resampled, Y_resampled, test_size= 0.25, random_state= 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0784236-f0ba-4943-b601-8b7a3e31eee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in c:\\users\\apurv\\anaconda3\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\apurv\\anaconda3\\lib\\site-packages (from imbalanced-learn) (2.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\apurv\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.9.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\apurv\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.0.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\apurv\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\apurv\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.21.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db3bb1fd-3b08-46d1-9554-670a42fb98d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_resampled = RandomForestClassifier(n_estimators = 100)\n",
    "rf_resampled.fit(train_X, train_Y)\n",
    "\n",
    "predictions_resampled = rf_resampled.predict(test_X)\n",
    "random_forest_score_resampled = rf_resampled.score(test_X, test_Y) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff4405fb-4e51-4ec0-b063-e54716c1a878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix after Oversampling - Random Forest\n",
      "[[71335    15]\n",
      " [    0 70808]]\n"
     ]
    }
   ],
   "source": [
    "cm_resampled = confusion_matrix(test_Y, predictions_resampled)\n",
    "print(\"Confusion Matrix after Oversampling - Random Forest\")\n",
    "print(cm_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75304ba6-8bc4-4af6-8af8-3e9f7e38c944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation of Random Forest Model\n",
      "\n",
      "Accuracy: 0.99989\n",
      "Precision: 0.99979\n",
      "Recall: 1.00000\n",
      "F1-score: 0.99989\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation of Random Forest Model\")\n",
    "print()\n",
    "metrics(test_Y, predictions_resampled.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f56aecb-f722-4d8e-baf1-6284f2c9c8cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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