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

By: Contributor(s): Material type: TextTextSeries: Springer series in supply chain management ; 14Publication details: Springer 2022 SwitzerlandDescription: 155p. Includes references and indexISBN:
  • 9783030858551
Subject(s): DDC classification:
  • 658.500727 C6D3
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. https://link.springer.com/book/10.1007/978-3-030-85855-1
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 39-A / Slot 2191 (2nd Floor, East Wing) Non-fiction General Stacks 658.500727 C6D3-2 (Browse shelf(Opens below)) Available 206022
Books Vikram Sarabhai Library Rack 39-A / Slot 2191 (2nd Floor, East Wing) Non-fiction General Stacks 658.500727 C6D3 (Browse shelf(Opens below)) Available 204749

Table of contents
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.

https://link.springer.com/book/10.1007/978-3-030-85855-1

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