• Mr. Suryawanshi Vijay
• Mr. Pareek Peeyush

Abstract—Credit card fraud poses significant risks to both consumers and financial institutions. This paper presents a robust framework that leverages various machine learning methodologies to enhance fraud detection accuracy. The approach encompasses comprehensive preprocessing, normalization, class imbalance handling using synthetic oversampling, and rigorous hyperparameter tuning via grid search combined with cross-validation. Experimental results indicate that ensemble-based algorithms, particularly those utilizing gradient boos ng, offer the most promising performance.

Index Terms—Credit Card Fraud, Machine Learning, SMOTE, Ensemble Models, Feature Normalization, Hyperparameter Tuning.

"Intelligent Detection of Credit Card Fraud Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.j12-j17, June-2025, Available :http://www.jetir.org/papers/JETIR2506903.pdf

"Intelligent Detection of Credit Card Fraud Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppj12-j17, June-2025, Available at : http://www.jetir.org/papers/JETIR2506903.pdf