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Demand prediction in retail : a practical guide to leverage data and predictive analytics / Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang.
Cohen, Maxime C. author.; Gras, Paul-Emile, author.; Pentecoste, Arthur, author.; Zhang, Renyu, author.
2022
HD38.5
Available Online
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Title
Demand prediction in retail : a practical guide to leverage data and predictive analytics / Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang.
Author
Cohen, Maxime C. author.
ISBN
9783030858551 (electronic bk.)
3030858553 (electronic bk.)
3030858545
9783030858544
DOI
https://doi.org/10.1007/978-3-030-85855-1
Publication Details
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-85855-1 doi
Call Number
HD38.5
Dewey Decimal Classification
658.50072/7
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 10, 2022).
Added Author
Gras, Paul-Emile, author.
Pentecoste, Arthur, author.
Zhang, Renyu, author.
Series
Springer series in supply chain management ; v. 14. 2365-6409
Available in Other Form
Print version: 9783030858544
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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.

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