USE OF FUZZY REGRESSION MODELS AND MACHINE LEARNING IN A PYTHON ENVIRONMENT TO PREDICT HOTEL OCCUPANCY
C. Guerrero Dávalos. Universidad Michoacana de San Nicolás de Hidalgo, Morelia, México. E-mail: cuauhtemoc.guerrero@umich.mx
F. Ávila Carreón. Instituto Tecnológico de Morelia, México. E-mail: fernando.ac@morelia.tecnm.mx
M. L. Jiménez López. Universidad Michoacana de San Nicolás de Hidalgo, Morelia, México. E-mail: maria.jimenez@umich.mx
- Fuzzy Economic Review: Volume 30, Number 2, 2025
- DOI: 10.25102/fer.2025.02.02
This study compares six approaches to predicting hotel occupancy based on six dimensions of perceived quality (value for money, location, comfort, room quality, cleanliness, and service) in a sample of 337 hotels. In general terms, machine learning (ML) algorithms outperform traditional models in predictive accuracy: Random Forest obtains R2 ≈…
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