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

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

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
Imprint Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource
Other Standard Identifiers 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.
Access Note 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
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export