國立高雄大學圖資館 |

Language: English

Back

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Demand prediction in retailby 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.
Contained By:	Springer Nature eBook
Subject:	Business logisticsStatistical methods.
Online resource:	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 /
LDR:02363nmm a2200337 a 4500 001 618809
003 DE-He213
005 20211221192635.0
006 m d
007 cr nn 008maaau
008 221103s2022 sz s 0 eng d
020 $a 9783030858551$q(electronic bk.)
020 $a 9783030858544$q(paper)
024 7 $a 10.1007/978-3-030-85855-1 $2 doi
035 $a 978-3-030-85855-1
040 $a GP $c GP
041 0 $a eng
050 4 $a HD38.5
072 7 $a KJS $2 bicssc
072 7 $a BUS058000 $2 bisacsh
072 7 $a KJS $2 thema
082 0 4 $a 658.500727 $2 23
090 $a HD38.5 $b .D371 2022
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 265414
650 0 $a Business logistics $x Management. $3 233497
650 0 $a Demand (Economic theory) $3 209902
650 1 4 $a Sales/Distribution. $3 737137
650 2 4 $a Supply Chain Management. $3 737140
650 2 4 $a Operations Management. $3 731073
650 2 4 $a Statistics, general. $3 275684
650 2 4 $a Trade. $3 674506
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Cohen, Maxime C. $3 918767
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer series in supply chain management ; $v v.1. $3 715434
856 4 0 $u https://doi.org/10.1007/978-3-030-85855-1
950 $a Business and Management (SpringerNature-41169)