Modelo predictivo basado en IA para la afluencia de visitantes en la región ecoturística de la Sierra Nororiental de Puebla

This article presents the development and application of an artificial intelligence (AI)-based predictive model to forecast visitor flow in the ecotourism complexes of the Northeastern Sierra of Puebla, Mexico. The primary objective is to enhance resource planning and management in this region through more accurate predictions, thereby optimizing the visitor experience and ensuring the sustainability of tourism destinations. For data analysis, the CRISP-DM methodology was applied to structure and organize the data mining process, ensuring prediction accuracy. Additionally, a Machine Learning approach was employed to develop supervised models that forecast visitor demand. The results indicate that the model effectively anticipates tourism peak periods with high accuracy, which is crucial for managing the capacity of ecotourism complexes and efficiently distributing resources. This, in turn, helps minimize environmental impact while maximizing tourist satisfaction and benefiting local communities.