Determinants of Tourism Demand Using Machine Learning Techniques

The purpose of the current study was to determine factors affecting tourism demand using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RMSE and R2 . The random forest model outperformed the linear regression model on both the training and test sets. Climate, consumer price index, political stability, distance, promotion expenditure, and region of residence are all important factors in explaining total arrivals. The findings of the current research, therefore, provide additional evidence for the effectiveness of the AI based models to improve tourism demand forecasting compared to linear regression models.

# Hospitality industry. Hotels, clubs, restaurants, etc. Food service # Business # tourism demand # random forest # feature importance

Musonera Abdou, Edouard Musabanganji, Herman Musahara (2022). "Determinants of Tourism Demand Using Machine Learning Techniques". African Journal of Hospitality, Tourism and Leisure. https://doi.org/10.46222/ajhtl.19770720.256