Prediction of coffee consumption using Graph Neural Networks and Explainable AI

Accurate forecasting regional sales in heterogeneous locations presents a complex challenge that extends beyond the capabilities of traditional predictive models. In this study, we focus on predicting coffee sales for one of the local coffee companies in Italy by integrating machine learning techniques with graph-based deep learning models. We begin by establishing a baseline using a Multi-Layer Perceptron (MLP) and subsequently apply six Graph Neural Network (GNN) architectures: GCN, GAT, GraphSAGE, GIN, ChebNet, and GraphConv to capture spatial dependencies among distribution points. To enhance model interpretability and guide feature selection, we incorporate Integrated Gradients from the Explainable AI (XAI) framework. Experimental results demonstrate that GNNs consistently outperform the MLP baseline, particularly in capturing location-driven relational patterns. In particular, the results show how GraphSAGE and ChebNet outperformed the other architectures. The integration of graph-based modeling with interpretable learning provides valuable insights for optimizing sales strategies in geographically distributed markets.