Enhancing Explainable AI: Leveraging SHAP for Transparent Decision-Making in Machine Learning

In recent years, the swift progress in Artificial Intelligence (AI) has resulted in to impressive performance across various domains, but the opacity of complex models, often referred to as “black-box” models, has raised concerns regarding trust and interpretability. This paper addresses these challenges through Explainable Artificial Intelligence (XAI), focusing on Shapley Additive Explanations (SHAP) to interpret a predictive model built on an online retail dataset. The research employs Gradient Boosting Machines (GBM) to predict customer repeat purchases and uses SHAP to provide clear insights into the model’s decision-making process. SHAP effectively identifies key features such as TotalPrice, UnitPrice, and Quantity as the most significant factors driving the model’s predictions. TotalPrice was found to be the most influential feature, demonstrating its strong association with repeat purchases. The analysis reveals that the total price of items purchased is the most significant predictor of repeat purchases, with a SHAP value of 0.45, indicating a strong positive correlation. Additionally, unit price and quantity also play important roles, contributing SHAP values of 0.32 and 0.20, respectively The model achieved an accuracy of 85%, with a precision of 0.78 and a recall of 0.80. These results indicate a robust performance in predicting repeat purchases. Furthermore, the research highlights that consumers from certain countries demonstrate distinct purchasing patterns, influencing overall sales performance. These insights empower retail managers to identify critical variables and adapt marketing strategies accordingly. The analysis not only enhances model transparency but also ensures that the predictions align with business expectations, allowing stakeholders to trust and act on the model’s insights.