Abstract

Credit card fraud has become a significant challenge in the financial sector. The use of machine learning techniques has shown promising results in detecting fraudulent transactions efficiently. This paper discusses the implementation of a credit card fraud detection system using various machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Neural Networks. We evaluate the performance of these models based on accuracy, precision, recall, and F1-score.