Predicting Customer Loyalty Using Machine Learning for Hotel Industry



Machine learning, Classification, CRISP-DM, Confusion matrix


The popularity of machine learning is growing and the demand for it is increasing in various fields including tourism and hospitality industry specifically hotels industry. The purpose of this research is to apply machine learning classification techniques to predict customers’ loyalty in hotel company so that hotel company can use the result to create possible solutions for customer relationship management. The experiment will be performed by implementing CRISP-DM methodology and three proposed algorithms such as decision tree, random forest and logistic regression and the result will be compared with each other to obtain the best algorithm among them by using confusion matrix. The dataset that will be used is obtained from Findbulous technology company. From the analysis result, logistic regression, decision tree and random forest algorithms generate 57.83%, 71.44% and 69.91% accuracy score respectively. For further improvement, this research approach can be used with other dataset or implement a new algorithm to identify each algorithm strengths and limitations.




How to Cite

Hamdan, I. Z. P. ., & Othman, M. . (2022). Predicting Customer Loyalty Using Machine Learning for Hotel Industry. Journal of Soft Computing and Data Mining, 3(2), 31–42. Retrieved from