東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Demand prediction in retail = a prac...

Cohen, Maxime C.

FindBook

Google Book

Amazon

博客來

Demand prediction in retail = a 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 retail/ by 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.
內容註:	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
標題:	Business logistics - Statistical methods. -
電子資源:	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 /
LDR:02363nmm a2200337 a 4500 001 2296764
003 DE-He213
005 20211221192635.0
006 m d
007 cr nn 008maaau
008 230324s2022 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 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
773 0 $t Springer Nature eBook
830 0 $a Springer series in supply chain management ; $v v. 14. $3 3591681
856 4 0 $u https://doi.org/10.1007/978-3-030-85855-1
950 $a Business and Management (SpringerNature-41169)