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008			230308s2023 si | o |||| 0|eng d
020			_a9789811993695
024	7		_a10.1007/978-981-19-9369-5 _2doi
040			_aES-VaUE _bspa _cES-VaUE _dES-VaUE
050		4	_aG154.9 _b2024 EB
245	0	0	_aTourism Analytics Before and After COVID-19 : _bCase Studies from Asia and Europe _cedited by Yok Yen Nguwi
250			_a1st ed 2023
264		1	_aSingapore _c2023 _bSpringer International Publishing
300	0	0	_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aImpacts 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.
520			_aThis 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.
988			_aSpringer_Business_2023
650		7	_2embne _9286792 _aTurismo
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-9369-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
998			_b05/2024 _db _eb _zSI