Abstract

Lastly, evaluating machine learning models in the context of credit card fraud detection and categorization can yield important insights into their performance across diverse settings. After looking at F-score, recall, accuracy, and precision metrics, it's evident that Random Forest consistently outperforms other models, showing how well it handles class imbalances. Random Forest can continue to perform well even in balanced datasets by utilizing oversampling strategies to achieve class balance. This makes it an even more effective model. Because of its adaptability and reliability, the model is thus ideal for application in actual fraud detection systems. The consistent performance of ensemble, Logistic Regression, and Gradient Boosting approaches in fraud detection tasks demonstrates the necessity of utilizing a variety of machine learning algorithms and oversampling tactics to increase classification performance. The effectiveness of Random Forest in minimizing class differences and the significance of a balanced training dataset are both highlighted by these results. In sum, this study's results will aid in the development of more reliable machine learning models for fraud detection, which in turn will have practical applications in domains such as finance. Future research could look into other optimization tactics and ensemble approaches to see whether they help the model perform better in real-world scenarios.