First journal: Forecasting daily customer flow in restaurants

Forecasting daily customer flow in restaurants: a multifactor machine learning approach

First journal article produced by the project was written by Shah, Myller, and Islas Sedano (2025) exploring how to accurately forecast daily customer flow in a Flavoria restaurant using machine learning. By combining sensor data with external factors like weather, holidays, and menu details, the researchers found that the XGBoost model delivered the most accurate predictions. Their findings highlight how data-driven forecasting can support smarter planning and sustainability in food service operations.

The journal is open access and you can read it following this link: https://www.aimspress.com/article/doi/10.3934/aci.2025011