- Tapa blanda: 304 páginas
- Editor: Morgan Kaufmann (19 de febrero de 2014)
- Colección: The Savvy Manager's Guides
- Idioma: Inglés
- ISBN-10: 0124166024
- ISBN-13: 978-0124166028
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
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Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects (The Savvy Manager's Guides) (Inglés) Tapa blanda – 19 feb 2014
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Descripción del producto
"...a mandatory volume for anyone who runs data mining projects, since all the steps and most important details that should not be forgotten are described here...I strongly recommend this book for anyone even slightly involved with data mining projects."--IEEE Communications Magazine, Commercial Data Mining
"I strongly disagree with Bellin’s statement that the book will not help practitioners, and one can only conclude that the reviewer is not familiar with what data mining practitioners need." -Computing Reviews, Oct 13, 2014
Reseña del editor
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.
Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.
- Illustrates cost-benefit evaluation of potential projects
- Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
- Approachable reference can be read from cover to cover by readers of all experience levels
- Includes practical examples and case studies as well as actionable business insights from author's own experience
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The book is also great in that there are many great bibliographical references to technical, but practical papers in the academic literature.