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Demand Prediction in Retail

- A Practical Guide to Leverage Data and Predictive Analytics

Demand Prediction in Retail

- A Practical Guide to Leverage Data and Predictive Analytics
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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.

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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.

Produktdetaljer
Sprog: Engelsk
Sider: 155
ISBN-13: 9783030858575
Indbinding: Paperback
Udgave:
ISBN-10: 303085857X
Kategori: Indkøb
Udg. Dato: 23 dec 2022
Længde: 13mm
Bredde: 154mm
Højde: 233mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 23 dec 2022
Forfatter(e) Paul-Emile Gras, Renyu Zhang, Maxime C. Cohen, Arthur Pentecoste


Kategori Indkøb


ISBN-13 9783030858575


Sprog Engelsk


Indbinding Paperback


Sider 155


Udgave


Længde 13mm


Bredde 154mm


Højde 233mm


Udg. Dato 23 dec 2022


Oplagsdato 23 dec 2022


Forlag Springer Nature Switzerland AG

Kategori sammenhænge