Credit Card Fraud Detection Comparing Multiple Supervised Learning Algorithms for Optimal Accuracy

Supervised machine learning algorithms are widely used for classification problems across various domains. However, selecting the best model requires a thorough evaluation of accuracy, robustness, and generalization ability. This research compares multiple supervised learning techniques using real- world datasets, focusing on evaluation metrics such as accuracy, sensitivity, specificity, and AUC-ROC. The study also considers the risk of overfitting, using cross- validation techniques to strengthen the conclusions. Results indicate that AdaBoost achieves near-perfect accuracy while Stochastic Gradient Descent (SGD) provides a balanced performance and generalisation, making their hybrid or combination a preferable choice for fraud detection.