Credit_Card_Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b262bcc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</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",
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       "</table>\n",
       "<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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import gridspec\n",
    "data = pd.read_csv(\"/home/harsh/Downloads/kashipara.com_code-database-zip260275251/creditcard1.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f943311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n",
      "                Time            V1            V2            V3            V4  \\\n",
      "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
      "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
      "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
      "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
      "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
      "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
      "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
      "\n",
      "                 V5            V6            V7            V8            V9  \\\n",
      "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
      "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
      "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
      "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
      "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
      "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
      "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
      "\n",
      "       ...           V21           V22           V23           V24  \\\n",
      "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
      "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-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   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-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]\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04dd601a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017304750013189597\n",
      "Fraud Cases: 492\n",
      "Valid Transactions: 284315\n"
     ]
    }
   ],
   "source": [
    "# Determine number of fraud cases in dataset\n",
    "fraud = data[data['Class'] == 1]\n",
    "valid = data[data['Class'] == 0]\n",
    "outlierFraction = len(fraud)/float(len(valid))\n",
    "print(outlierFraction)\n",
    "print('Fraud Cases: {}'.format(len(data[data['Class'] == 1])))\n",
    "print('Valid Transactions: {}'.format(len(data[data['Class'] == 0])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29c27fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "corrmat = data.corr()\n",
    "fig = plt.figure(figsize = (12, 9))\n",
    "sns.heatmap(corrmat, vmax = .8, square = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee981398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n",
      "(284807,)\n"
     ]
    }
   ],
   "source": [
    "# dividing the X and the Y from the dataset\n",
    "X = data.drop(['Class'], axis = 1)\n",
    "Y = data[\"Class\"]\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "# getting just the values for the sake of processing\n",
    "# (its a numpy array with no columns)\n",
    "xData = X.values\n",
    "yData = Y.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52ffd754",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Scikit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "xTrain, xTest, yTrain, yTest = train_test_split(\n",
    "\t\txData, yData, test_size = 0.2, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e617c4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the Random Forest Classifier (RANDOM FOREST)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# random forest model creation\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(xTrain, yTrain)\n",
    "# predictions\n",
    "yPred = rfc.predict(xTest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f68279f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model used is Random Forest classifier\n",
      "The accuracy is 0.9995786664794073\n",
      "The precision is 0.9743589743589743\n",
      "The recall is 0.7755102040816326\n",
      "The F1-Score is 0.8636363636363635\n",
      "The Matthews correlation coefficient is0.8690748763736589\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the classifier\n",
    "# printing every score of the classifier\n",
    "# scoring in anything\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score, matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "n_outliers = len(fraud)\n",
    "n_errors = (yPred != yTest).sum()\n",
    "print(\"The model used is Random Forest classifier\")\n",
    "\n",
    "acc = accuracy_score(yTest, yPred)\n",
    "print(\"The accuracy is {}\".format(acc))\n",
    "\n",
    "prec = precision_score(yTest, yPred)\n",
    "print(\"The precision is {}\".format(prec))\n",
    "\n",
    "rec = recall_score(yTest, yPred)\n",
    "print(\"The recall is {}\".format(rec))\n",
    "\n",
    "f1 = f1_score(yTest, yPred)\n",
    "print(\"The F1-Score is {}\".format(f1))\n",
    "\n",
    "MCC = matthews_corrcoef(yTest, yPred)\n",
    "print(\"The Matthews correlation coefficient is{}\".format(MCC))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa0a1e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# printing the confusion matrix\n",
    "LABELS = ['Normal', 'Fraud']\n",
    "conf_matrix = confusion_matrix(yTest, yPred)\n",
    "plt.figure(figsize =(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels = LABELS,\n",
    "\t\t\tyticklabels = LABELS, annot = True, fmt =\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "693465e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}