Abstract
Credit card fraud presents a major challenge to financial institutions, resulting in significant economic losses each year. This paper investigates the use of machine learning techniques to detect fraudulent credit card transactions. By analyzing a publicly available dataset, we evaluate the performance of various algorithms, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. The objective is to identify the most effective model for accurately detecting fraudulent activities while minimizing false positives. Additionally, the paper addresses the challenges of dealing with imbalanced datasets and the strategies employed to overcome these issues.
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