Scalable and Explainable Credit Card Fraud Detection Using ML

Our project primarily focuses on real-world credit card fraud detection. To begin, we will collect credit card datasets to serve as the training dataset. Subsequently, we will provide user credit card queries as the testing dataset. The classification process will be carried out using the Random Forest algorithm, which will analyse both the pre-existing dataset and the newly provided user data. The ultimate goal is to optimize the accuracy of the results. Additionally, we will process specific attributes to detect fraudulent activities and present the findings using graphical model visualization. The performance of the applied techniques will be evaluated based on key metrics, including accuracy, sensitivity, specificity, and precision. The results indicate that the optimal accuracy achieved by the Decision Tree algorithm is approximately 98.6%.