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Demand prediction in retaila practical guide to leverage data and predictive analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Demand prediction in retailby Maxime C. Cohen ... [et al.].
其他題名:	a practical guide to leverage data and predictive analytics /
其他作者:	Cohen, Maxime C.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvii, 155 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Business logisticsStatistical methods.
電子資源:	https://doi.org/10.1007/978-3-030-85855-1
ISBN:	9783030858551$q(electronic bk.)

Demand prediction in retaila practical guide to leverage data and predictive analytics /
Demand prediction in retaila practical guide to leverage data and predictive analytics /[electronic resource] :by Maxime C. Cohen ... [et al.]. - Cham :Springer International Publishing :2022. - xvii, 155 p. :ill., digital ;24 cm. - Springer series in supply chain management,v. 142365-6409 ;. - Springer series in supply chain management ;v.1..

1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics.

From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

ISBN: 9783030858551$q(electronic bk.)

Standard No.: 10.1007/978-3-030-85855-1doiSubjects--Topical Terms:

265414
Business logistics
--Statistical methods.

LC Class. No.: HD38.5

Dewey Class. No.: 658.500727

Demand prediction in retaila practical guide to leverage data and predictive analytics /
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