Real Time Credit Card Transaction Classification System Using High Dimension Feature Optimization

Real Time Credit Card Transaction Classification System Using High Dimension Feature Optimization

Deepika. S

Department Of ECE Panimalar Institute of Technology

Chennai, India deepikasankar76@gmail.com

Devdarshini. S

Department Of ECE Panimalar Institute of Technology

Chennai, India devdarshinis@gmail.com

Hema Akshaya. K

Department of ECE Panimalar Institute of Technology

Chennai, India hemaakshaya18074@gmail.com

Dr.S.Sathiya Priya ME.Phd.,

Department Of ECE Panimalar Institute of Technology

Chennai, India Sathiyapriyas.ece.pit@gmail.com

Dr.Jeyaramya  ME,Phd.,

Department Of ECE Panimalar Institute of Technology

Chennai, India jeyaramyavv@gmail.com

Gurupandi ME.,

Department Of ECE Panimalar Institute of Technology

Chennai, India Gurupandi85@gmail.com

Abstract-- Credit card fraud is a significant challenge in financial transactions. Traditional rule-based detection methods fail to adapt to evolving fraud techniques. This paper explores machine learning (ML) models, including logistic regression, decision trees, random forests, and deep learning, to improve fraud detection accuracy. The proposed system integrates anomaly detection and ensemble learning to reduce false positives and enhance predictive performance. The research highlights data preprocessing techniques, feature selection strategies, and model evaluation metrics to develop a robust fraud detection system. Experimental results demonstrate an increased fraud detection rate while minimizing legitimate transaction disruptions. Additionally, real- world dataset testing provides insights into the advantages and limitations of different ML techniques.

Keywords — Credit Card Fraud, Machine Learning, Anomaly Detection, Fraud Detection, Financial Security.