A STUDY OF MACHINE LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION
Abstract
Therising concernof creditcardfraudnecessitates the development of effective predictive models to safeguard financial transactions. This paper presents a comprehensive comparison of machine learning models designed to predict credit card fraud. Through an exploratory analysis of a dataset containing transaction details, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Decision Tree models are employed. Hyperparameter tuning is performed to optimize themodels,andtheirperformanceisevaluatedonatestset.The results demonstrate the efficacy of the models in identifying fraudulent transactions, with a particular focus on the tuned SVC model achieving notable accuracy.
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Published
2024-06-09
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How to Cite
A STUDY OF MACHINE LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION. (2024). JOURNAL OF BASIC SCIENCE AND ENGINEERING, 21(1), 1047-1056. https://yigkx.org.cn/index.php/jbse/article/view/169