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This paper discusses an enhanced credit card fraud detection system utilizing four machine learning models: Logistic Regression, Decision Tree, Gradient Boosting, and XGBoost, optimized through Particle Swarm Optimization (PSO) and addressing class imbalance with SMOTE. The models were evaluated on the Kaggle Credit Card Fraud Dataset, with XGBoost achieving the highest accuracy of 99.98%. The study emphasizes the effectiveness of machine learning and optimization techniques in improving fraud detection cap

Author

Kishore

Language

EN