As machine learning models become increasingly accurate and complex, explainability has become essential to ensure trust, transparency, and informed decision-making. SHapley Additive exPlanations (SHAP) provide a rigorous and intuitive approach for interpreting model predictions, delivering consistent and theoretically grounded feature attributions. This article demonstrates the application of SHAP across two representative model types: boosted decision trees and neural networks.