Predictive Analytics for Sustainable Tourism Development: A Data-Driven Approach
Keywords:
Predictive analytics, sustainable tourism, machine learning, data science, Malaysia.Abstract
Tourism plays a significant role in Malaysia's economic and social development, with efforts increasingly aligned to Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth). This project addresses the lack of predictive analytics for sustainable tourism by employing a structured methodology encompassing data collection and preparation, exploratory data analysis (EDA), descriptive and predictive analytics, and feature engineering to identify key factors influencing sustainable tourism in Malaysia. The results show trends and patterns in tourism that inform the development of robust machine learning models to forecast sustainable tourism outcomes, in which 92% and 100% accuracy were achieved with Gradient Boosting and Support Vector Machine respectively. These models aim to support data-driven decision-making and promote long-term sustainability in Malaysia's tourism industry.
References
Ministry of Tourism, Arts and Culture Malaysia (MOTAC), "National Tourism Policy 2020-2030," 2020. [Online]. Available: https://motac.gov.my/en/archives/2020/national-tourism-policy-2020-2030
Tourism Malaysia, "Tourism contributes RM86.14 billion to Malaysia economy with 26.1 million tourists in 2019," 2019. [Online]. Available:https://www.tourism.gov.my/media/view/tourism-contributes-rm86-14-billion-to-malaysia-economy-with-26-1-million-tourists-in-2019
A. Mai, K. Thi, T. Thi, and T. Le, "Factors influencing on tourism sustainable development in Vietnam," 2020. [Online]. Available: http://growingscience.com/beta/msl/3686-factors-influencing-on-tourism-sustainable-development-in-vietnam.html
N. F. Nasir, M. A. Nasir, M. N. F. Nasir, and M. F. Nasir, "Understanding of Domestic Tourism in Malaysia," International Research Journal of Modernization in Engineering Technology and Science, 2020. [Online]. Available:
https://www.irjmets.com/uploadedfiles/paper/volume2/issue_10_october_2020/4490/1628083177.pdf
R. Agrawal, V. A. Wankhede, A. Kumar, S. Luthra, and D. Huisingh, "Big data analytics and sustainable tourism: A comprehensive review and network-based analysis for potential future research," International Journal of Information Management Data Insights, vol. 2, no. 2, p. 100122, 2022. [Online]. Available:
https://doi.org/10.1016/j.jjimei.2022.100122
F. J. Hoffmann, F. Braesemann, and T. Teubner, "Measuring sustainable tourism with online platform data," EPJ Data Science, vol. 11, no. 1, 2022. [Online]. Available:
https://doi.org/10.1140/epjds/s13688-022-00354-6
A. Louati, H. Louati, M. Alharbi, E. Kariri, T. Khawaji, Y. Almubaddil, and S. Aldwsary, "Machine Learning and Artificial Intelligence for a sustainable tourism: A case study on Saudi Arabia," Information, vol. 15, no. 9, p. 516, 2024. [Online]. Available:
https://doi.org/10.3390/info15090516
E. S. Journal, "State grant for tourism product development to roll out by end-Jan - tourism Selangor," Selangor Journal, 2025. [Online]. Available: https://selangorjournal.my/2025/01/state-grant-for-tourism-product-development-to-roll-out-by-end-jan-tourism-selangor/
A. Sharon, "Malaysia: New Digital Portal Drives Tourism in Selangor," OpenGov Asia, 2024. [Online]. Available:
https://opengovasia.com/2024/12/25/malaysia-new-digital-portal-drives-tourism-in-selangor/
S. Online, "Selangor looking to sports tourism to boost figures," The Star, 2024. [Online]. Available: https://www.thestar.com.my/news/nation/2024/11/04/selangor-looking-to-sports-tourism-to-boost-figures
