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Titlebook: Applied Data Science in Tourism; Interdisciplinary Ap Roman Egger Textbook 2022 The Editor(s) (if applicable) and The Author(s), under excl

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發(fā)表于 2025-3-21 19:17:28 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Applied Data Science in Tourism
期刊簡稱Interdisciplinary Ap
影響因子2023Roman Egger
視頻videohttp://file.papertrans.cn/160/159768/159768.mp4
發(fā)行地址Presents the latest approaches like machine learning, text analysis, network analysis, agent based modeling.Includes useful “how-to" and “fact-sheet” sections with each chapter.Examines possible appli
學(xué)科分類Tourism on the Verge
圖書封面Titlebook: Applied Data Science in Tourism; Interdisciplinary Ap Roman Egger Textbook 2022 The Editor(s) (if applicable) and The Author(s), under excl
影響因子Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field.?.The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important
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發(fā)表于 2025-3-22 00:00:23 | 只看該作者
Switching in International English corresponding subfields of AI research: problem-solving, intelligent agents, natural language processing (NLP), speech recognition, computer vision, robotics, knowledge representation, and machine learning. Despite its ups and downs, the use of AI technologies and systems has become so widespread t
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https://doi.org/10.1007/978-3-030-88597-7s, decision trees and ensemble variants thereof, support vector machines, and finally, artificial neural networks. All of the concepts and methods will then be applied to a specific use case in an accompanying Jupyter notebook, demonstrating the practical implementation of these concepts and methods
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English for Academic Correspondencenput variables and, in turn, to add an interpretation to the model prediction. In this regard, this chapter will describe how to use interpretability tools with ML models and will also provide intuition on the usefulness and limitations of these tools.
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Clusteringlization, . processes for .-means, ., and . will be shown, and the clustering results will be discussed. Lastly, a tourism case applying .-means and . to identify points of interest based on uploaded photo data extracted from the platform Flickr will conclude the chapter.
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