Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects

By: David Nettleton

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Paperback | 19 February 2014 | Edition Number 1

At a Glance

Paperback
304 Pages

Dimensions(cm)
22.86 x 15.24 x 1.91

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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

Industry Reviews

"...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

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