Credit Card Fraud Detection  .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING PACKAGES\n",
    "\n",
    "import pandas as pd # data processing\n",
    "import numpy as np # working with arrays\n",
    "import matplotlib.pyplot as plt # visualization\n",
    "\n",
    "import itertools # advanced tools\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler # data normalization\n",
    "from sklearn.model_selection import train_test_split # data split\n",
    "from sklearn.tree import DecisionTreeClassifier # Decision tree algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier # KNN algorithm\n",
    "from sklearn.linear_model import LogisticRegression # Logistic regression algorithm\n",
    "from sklearn.svm import SVC # SVM algorithm\n",
    "from sklearn.ensemble import RandomForestClassifier # Random forest tree algorithm\n",
    "\n",
    "from sklearn.metrics import confusion_matrix # evaluation metric\n",
    "from sklearn.metrics import accuracy_score # evaluation metric\n",
    "from sklearn.metrics import f1_score # evaluation metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         V1        V2        V3        V4        V5        V6        V7  \\\n",
      "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
      "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
      "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
      "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
      "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
      "\n",
      "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
      "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
      "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
      "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
      "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
      "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
      "\n",
      "        V25       V26       V27       V28  Amount  Class  \n",
      "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
      "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
      "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
      "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
      "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
      "\n",
      "[5 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "# IMPORTING DATA\n",
    "\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.drop('Time', axis = 1, inplace = True)\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CASE COUNT\n",
      "--------------------------------------------\n",
      "Total number of cases are 284807\n",
      "Number of Non-fraud cases are 284315\n",
      "Number of Non-fraud cases are 492\n",
      "Percentage of fraud cases is 0.17\n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "cases = len(df)\n",
    "nonfraud_count = len(df[df.Class == 0])\n",
    "fraud_count = len(df[df.Class == 1])\n",
    "fraud_percentage = round(fraud_count/nonfraud_count*100, 2)\n",
    "\n",
    "print(('CASE COUNT'))\n",
    "print(('--------------------------------------------'))\n",
    "print(('Total number of cases are {}'.format(cases)))\n",
    "print(('Number of Non-fraud cases are {}'.format(nonfraud_count)))\n",
    "print(('Number of Non-fraud cases are {}'.format(fraud_count)))\n",
    "print(('Percentage of fraud cases is {}'.format(fraud_percentage)))\n",
    "print(('--------------------------------------------'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CASE AMOUNT STATISTICS\n",
      "--------------------------------------------\n",
      "NON-FRAUD CASE AMOUNT STATS\n",
      "count    284315.000000\n",
      "mean         88.291022\n",
      "std         250.105092\n",
      "min           0.000000\n",
      "25%           5.650000\n",
      "50%          22.000000\n",
      "75%          77.050000\n",
      "max       25691.160000\n",
      "Name: Amount, dtype: float64\n",
      "--------------------------------------------\n",
      "FRAUD CASE AMOUNT STATS\n",
      "count     492.000000\n",
      "mean      122.211321\n",
      "std       256.683288\n",
      "min         0.000000\n",
      "25%         1.000000\n",
      "50%         9.250000\n",
      "75%       105.890000\n",
      "max      2125.870000\n",
      "Name: Amount, dtype: float64\n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "nonfraud_cases = df[df.Class == 0]\n",
    "fraud_cases = df[df.Class == 1]\n",
    "\n",
    "print(('CASE AMOUNT STATISTICS'))\n",
    "print(('--------------------------------------------'))\n",
    "print(('NON-FRAUD CASE AMOUNT STATS'))\n",
    "print(nonfraud_cases.Amount.describe())\n",
    "print(('--------------------------------------------'))\n",
    "print(('FRAUD CASE AMOUNT STATS'))\n",
    "print(fraud_cases.Amount.describe())\n",
    "print(('--------------------------------------------'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.244964\n",
      "1   -0.342475\n",
      "2    1.160686\n",
      "3    0.140534\n",
      "4   -0.073403\n",
      "5   -0.338556\n",
      "6   -0.333279\n",
      "7   -0.190107\n",
      "8    0.019392\n",
      "9   -0.338516\n",
      "Name: Amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "amount = df['Amount'].values\n",
    "\n",
    "df['Amount'] = sc.fit_transform(amount.reshape(-1, 1))\n",
    "\n",
    "print((df['Amount'].head(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train samples :  [[-1.11504743  1.03558276  0.80071244 -1.06039825  0.03262117  0.85342216\n",
      "  -0.61424348 -3.23116112  1.53994798 -0.81690879 -1.30559201  0.1081772\n",
      "  -0.85960958 -0.07193421  0.90665563 -1.72092961  0.79785322 -0.0067594\n",
      "   1.95677806 -0.64489556  3.02038533 -0.53961798  0.03315649 -0.77494577\n",
      "   0.10586781 -0.43085348  0.22973694 -0.0705913  -0.30145418]]\n",
      "X_test samples :  [[-0.32333357  1.05745525 -0.04834115 -0.60720431  1.25982115 -0.09176072\n",
      "   1.1591015  -0.12433461 -0.17463954 -1.64440065 -1.11886302  0.20264731\n",
      "   1.14596495 -1.80235956 -0.24717793 -0.06094535  0.84660574  0.37945439\n",
      "   0.84726224  0.18640942 -0.20709827 -0.43389027 -0.26161328 -0.04665061\n",
      "   0.2115123   0.00829721  0.10849443  0.16113917 -0.19330595]]\n",
      "y_train samples :  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "y_test samples :  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# DATA SPLIT\n",
    "\n",
    "X = df.drop('Class', axis = 1).values\n",
    "y = df['Class'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(('X_train samples : '), X_train[:1])\n",
    "print(('X_test samples : '), X_test[0:1])\n",
    "print(('y_train samples : '), y_train[0:20])\n",
    "print(('y_test samples : '), y_test[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODELING\n",
    "\n",
    "# 1. Decision Tree\n",
    "\n",
    "tree_model = DecisionTreeClassifier(max_depth = 4, criterion = 'entropy')\n",
    "tree_model.fit(X_train, y_train)\n",
    "tree_yhat = tree_model.predict(X_test)\n",
    "\n",
    "# 2. K-Nearest Neighbors\n",
    "\n",
    "n = 5\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = n)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_yhat = knn.predict(X_test)\n",
    "\n",
    "# 3. Logistic Regression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "lr_yhat = lr.predict(X_test)\n",
    "\n",
    "# 4. SVM \n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "svm_yhat = svm.predict(X_test)\n",
    "\n",
    "# 5. Random Forest Tree\n",
    "\n",
    "rf = RandomForestClassifier(max_depth = 4)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_yhat = rf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY SCORE\n",
      "------------------------------------------------------------------------\n",
      "Accuracy score of the Decision Tree model is 0.9993679997191109\n",
      "------------------------------------------------------------------------\n",
      "Accuracy score of the KNN model is 0.9995259997893332\n",
      "------------------------------------------------------------------------\n",
      "Accuracy score of the Logistic Regression model is 0.9991924440855307\n",
      "------------------------------------------------------------------------\n",
      "Accuracy score of the SVM model is 0.9993153330290369\n",
      "------------------------------------------------------------------------\n",
      "Accuracy score of the Random Forest Tree model is 0.9992977774656788\n",
      "------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 1. Accuracy score\n",
    "\n",
    "print(('ACCURACY SCORE'))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('Accuracy score of the Decision Tree model is {}'.format(accuracy_score(y_test, tree_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('Accuracy score of the KNN model is {}'.format(accuracy_score(y_test, knn_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('Accuracy score of the Logistic Regression model is {}'.format(accuracy_score(y_test, lr_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('Accuracy score of the SVM model is {}'.format(accuracy_score(y_test, svm_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('Accuracy score of the Random Forest Tree model is {}'.format(accuracy_score(y_test, rf_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 SCORE\n",
      "------------------------------------------------------------------------\n",
      "F1 score of the Decision Tree model is 0.8105263157894738\n",
      "------------------------------------------------------------------------\n",
      "F1 score of the KNN model is 0.8571428571428572\n",
      "------------------------------------------------------------------------\n",
      "F1 score of the Logistic Regression model is 0.7356321839080459\n",
      "------------------------------------------------------------------------\n",
      "F1 score of the SVM model is 0.7771428571428572\n",
      "------------------------------------------------------------------------\n",
      "F1 score of the Random Forest Tree model is 0.7727272727272727\n",
      "------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 2. F1 score\n",
    "\n",
    "print(('F1 SCORE'))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('F1 score of the Decision Tree model is {}'.format(f1_score(y_test, tree_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('F1 score of the KNN model is {}'.format(f1_score(y_test, knn_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('F1 score of the Logistic Regression model is {}'.format(f1_score(y_test, lr_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('F1 score of the SVM model is {}'.format(f1_score(y_test, svm_yhat))))\n",
    "print(('------------------------------------------------------------------------'))\n",
    "print(('F1 score of the Random Forest Tree model is {}'.format(f1_score(y_test, rf_yhat))))\n",
    "print(('------------------------------------------------------------------------'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Confusion Matrix\n",
    "\n",
    "# defining the plot function\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, title, normalize = False, cmap = plt.cm.Blues):\n",
    "    title = 'Confusion Matrix of {}'.format(title)\n",
    "    if normalize:\n",
    "        cm = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation = 'nearest', cmap = cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation = 45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment = 'center',\n",
    "                 color = 'white' if cm[i, j] > thresh else 'black')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix for the models\n",
    "\n",
    "tree_matrix = confusion_matrix(y_test, tree_yhat, labels = [0, 1]) # Decision Tree\n",
    "knn_matrix = confusion_matrix(y_test, knn_yhat, labels = [0, 1]) # K-Nearest Neighbors\n",
    "lr_matrix = confusion_matrix(y_test, lr_yhat, labels = [0, 1]) # Logistic Regression\n",
    "svm_matrix = confusion_matrix(y_test, svm_yhat, labels = [0, 1]) # Support Vector Machine\n",
    "rf_matrix = confusion_matrix(y_test, rf_yhat, labels = [0, 1]) # Random Forest Tree\n",
    "\n",
    "\n",
    "# Plot the confusion matrix\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (6, 6)\n",
    "\n",
    "# 1. Decision tree\n",
    "\n",
    "tree_cm_plot = plot_confusion_matrix(tree_matrix, \n",
    "                                classes = ['Non-fraud(0)','fraud(1)'], \n",
    "                                normalize = False, title = 'Decision Tree')\n",
    "plt.savefig('tree_cm_plot.png')\n",
    "plt.show()\n",
    "\n",
    "# 2. K-Nearest Neighbors\n",
    "\n",
    "knn_cm_plot = plot_confusion_matrix(knn_matrix, \n",
    "                                classes = ['Non-fraud(0)','fraud(1)'], \n",
    "                                normalize = False, title = 'KNN')\n",
    "plt.savefig('knn_cm_plot.png')\n",
    "plt.show()\n",
    "\n",
    "# 3. Logistic regression\n",
    "\n",
    "lr_cm_plot = plot_confusion_matrix(lr_matrix, \n",
    "                                classes = ['Non-fraud(0)','fraud(1)'], \n",
    "                                normalize = False, title = 'Logistic Regression')\n",
    "plt.savefig('lr_cm_plot.png')\n",
    "plt.show()\n",
    "\n",
    "# 4. Support Vector Machine\n",
    "\n",
    "svm_cm_plot = plot_confusion_matrix(svm_matrix, \n",
    "                                classes = ['Non-fraud(0)','fraud(1)'], \n",
    "                                normalize = False, title = 'SVM')\n",
    "plt.savefig('svm_cm_plot.png')\n",
    "plt.show()\n",
    "\n",
    "# 5. Random forest tree\n",
    "\n",
    "rf_cm_plot = plot_confusion_matrix(rf_matrix, \n",
    "                                classes = ['Non-fraud(0)','fraud(1)'], \n",
    "                                normalize = False, title = 'Random Forest Tree')\n",
    "plt.savefig('rf_cm_plot.png')\n",
    "plt.show()"
   ]
  }
 ],
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