CODESOFT-

#CREDIT CARD FRAUD DETECTION import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix from imblearn.over_sampling import SMOTE

Load the dataset

data = pd.read_csv('creditcard.csv')

Check for missing values

print(data.isnull().sum())

Data preprocessing

X = data.drop(['Time', 'Class'], axis=1) y = data['Class']

Normalize the feature data

scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

Handle class imbalance using SMOTE

sm = SMOTE(random_state=42) X_res, y_res = sm.fit_resample(X_scaled, y)

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=42)

Train a logistic regression model

lr = LogisticRegression() lr.fit(X_train, y_train)

Train a random forest classifier

rf = RandomForestClassifier() rf.fit(X_train, y_train)

Make predictions

lr_pred = lr.predict(X_test) rf_pred = rf.predict(X_test)

Evaluate the models

def evaluate_model(y_test, y_pred): precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) cm = confusion_matrix(y_test, y_pred) return precision, recall, f1, cm

lr_precision, lr_recall, lr_f1, lr_cm = evaluate_model(y_test, lr_pred) rf_precision, rf_recall, rf_f1, rf_cm = evaluate_model(y_test, rf_pred)

print("Logistic Regression:") print(f"Precision: {lr_precision}") print(f"Recall: {lr_recall}") print(f"F1-Score: {lr_f1}") print(f"Confusion Matrix:\n{lr_cm}")

print("\nRandom Forest Classifier:") print(f"Precision: {rf_precision}") print(f"Recall: {rf_recall}") print(f"F1-Score: {rf_f1}") print(f"Confusion Matrix:\n{rf_cm}")