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This book is compilation of different analytics and machine learning techniques focusing on the tourism industry, particularly in measuring the impact of COVID-19 as well as forging a path ahead toward recovery. It includes case studies on COVID-19's effects on tourism in Europe, Hong Kong, China, and Singapore with the objective of looking at the issues through a data analytical lens and uncovering potential solutions. It adopts descriptive analytics, predictive analytics, machine learning predictive models, and some simulation models to provide holistic understanding.
There are three ways in which readers will benefit from reading this work. Firstly, readers gain an insightful understanding of how tourism is impacted by different factors, its intermingled relationship with macro and business data, and how different analytics approaches can be used to visualize the issues, scenarios, and resolutions. Secondly, readers learn to pick up data analytics skills from the illustrated examples. Thirdly, readers learn the basics of Python programming to work with the different kinds of datasets that may be applicable to the tourism industry.
Sommario
Impacts on aviation and accommodation in Europe using deep learning machine learning.- Time series model tourism forecasting, the case for Hainan, China.- Impacts on Covid on Singapore's hotel industry and pricing strategy.- Inbound tourist analysis on arrival and length of stay distribution, the case for Indonesian tourists.- Modeling tourism in Hong Kong using Ridge Linear Regression, Support Vector Machine and XGBoost approach.- Analytics on the prediction of hotel booking cancellation, the case for Portugal hotels.
Info autore
Yok-Yen is Senior Lecturer of Data Analytics in College of Business (Nanyang Business School). She obtained her B.Eng.(Computer) from the University of Newcastle, Australia, before completing her Ph.D. in Computer Engineering at Nanyang Technological University. Apart from that, she also received ACCA accountancy qualification.
She accumulated her tertiary teaching experience since 2006. She has taught students at undergraduate and graduate levels as well as supervised honors and research students. In terms of research, she enjoys discovering the intelligence within data and shaping the right algorithm for data analysis. She has studied data of different forms and published work in the domains of intelligent transport system, cognitive-based emotion recognition, and health and psychology informatics. Her work has appeared in Expert System with Applications, Neural Computing and Applications, Journal of Technology and Behavioral Science as well as Connection Science.
