Enhancing Management of Diverse Taiwanese Hotels Through Machine Learning and Data Mining
Document Type
Conference Proceeding
Publication Date
1-1-2023
Publication Title
Proceedings of the 3rd IEEE International Conference on Social Sciences and Intelligence Management, SSIM 2023
Abstract
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.
Department
Information Systems and Computer Science
First Page
149
Last Page
152
DOI
10.1109/SSIM59263.2023.10468732
Recommended Citation
Huang, Yu Hsiang John; Yao, Jenq Foung; and Yang, Cheng Ying, "Enhancing Management of Diverse Taiwanese Hotels Through Machine Learning and Data Mining" (2023). Faculty and Staff Works. 672.
https://kb.gcsu.edu/fac-staff/672