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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.