Enhancing Management of Diverse Taiwanese Hotels Through Machine Learning and Data Mining

Using the capabilities of Machine Learning and Data Mining techniques, we formulated a Decision Tree model to aid hotel management in optimizing resource allocation for profit maximization. Additionally, we constructed an Artificial Neural Network (ANN) model to predict hotel occupancy rates based on various known predictors. To establish a baseline for correct prediction, we employed the ZeroR model that achieved an accuracy rate of 42.22%. In contrast, the developed Decision Tree model outperformed with a correct prediction rate of 73.33%, while the ANN model showed a rate of 70.56%. These results showed the effectiveness of both models in predicting outcomes for Taiwanese international tourist hotels.