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Demand prediction in retail = a prac...

Cohen, Maxime C.

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Demand prediction in retail = a practical guide to leverage data and predictive analytics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Demand prediction in retail/ by Maxime C. Cohen ... [et al.].
Reminder of title:	a practical guide to leverage data and predictive analytics /
other author:	Cohen, Maxime C.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xvii, 155 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Business logistics - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-030-85855-1
ISBN:	9783030858551

Demand prediction in retail = a 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. 14..

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

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

1076866
Business logistics
--Statistical methods.

LC Class. No.: HD38.5

Dewey Class. No.: 658.500727

Demand prediction in retail = a practical guide to leverage data and predictive analytics /
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245 0 0 $a Demand prediction in retail $h [electronic resource] : $b a practical guide to leverage data and predictive analytics / $c by Maxime C. Cohen ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xvii, 155 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in supply chain management, $x 2365-6409 ; $v v. 14
505 0 $a 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.
520 $a 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.
650 0 $a Business logistics $x Statistical methods. $3 1076866
650 0 $a Business logistics $x Management. $3 756850
650 0 $a Demand (Economic theory) $3 656550
650 1 4 $a Sales/Distribution. $3 2179369
650 2 4 $a Supply Chain Management. $2 swd $3 1283588
650 2 4 $a Operations Management. $2 swd $3 1283589
650 2 4 $a Statistics, general. $3 896933
650 2 4 $a Trade. $3 2054344
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Cohen, Maxime C. $3 3591680
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Springer series in supply chain management ; $v v. 14. $3 3591681
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