- Paperback: 456 pages
- Publisher: Wiley; 1 edition (April 14, 2014)
- Language: English
- ISBN-10: 1118727967
- ISBN-13: 978-1118727966
- Product Dimensions: 7.4 x 0.9 x 9.3 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 4.0 out of 5 stars See all reviews (25 customer reviews)
- Amazon Best Sellers Rank: #254,012 in Books (See Top 100 in Books)
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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst 1st Edition
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This book provides an excellent background to predictive analytics (BCS, December 2014)
From the Back Cover
APPLY THE RIGHT ANALYTIC TECHNIQUE
Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst shows tech-savvy business managers and data analysts how to use predictive analytics to solve practical business problems. It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish. Internationally recognized data mining and predictive analytics expert Dean Abbott provides a practical and authoritative guide to best practices for successful predictive modeling, including expert tips and tricks to avoid common pitfalls.
This book explains the theory behind the principles of predictive analytics in plain English; readers don’t need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the chapters describes one or more specific techniques and how they relate to the overall process model for predictive analytics. The depth of the description of a technique will match the complexity of the approach, with the intent to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.
Each of the techniques is illustrated by examples, either unique to the task or as part of predictive modeling competitions. The companion website will provide all of the data sets used to generate these examples, along with links to open source and commercial software, so that readers can recreate and explore the examples.
With detailed descriptions of techniques that get results, Applied Predictive Analytics shows you how to:
- Choose the proper analytics technique for various scenarios
- Avoid common mistakes and identify the weaknesses of various techniques
- Mitigate outliers and fill in missing data when necessary
- Interpret predictive models often considered “black boxes,” including model ensembles
- Learn how to assess model performance so the best model is selected
- Apply the appropriate sampling techniques for building and updating models
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Top Customer Reviews
This book takes a unique, and badly needed, approach to the subject. It is a “how-to” without being a software book. Too many software instruction books focus so much on features and functions that you lose sight of the big picture. Also, too many data mining books focus solely on algorithms – often one chapter per algorithm. While many of those books are good, and necessary, there are plenty of them already.
This book invests approximately equal coverage to the six phases of the Cross Industry Standard Process for Data Mining (CRISP-DM). The evidence that the author is an expert is easy to find. Rather than merely providing the usual boilerplate on statistical significance, he reminds the reader that data miners interpret the ability of their model to generalize differently and with different tools. Rather than writing a section on regression right out of a introductory statistics book, he shows how he sometimes uses regression for classification, an approach that is technically against the rules. Rather than just a laundry list of algorithms he dedicates an entire chapter to ensembles, describing it not as another algorithm, but as a way of thinking about problems. His descriptions of boosting and bagging are clear and succinct. The essence of the book is in someways captured by the fact that one brief section is entitled “Models Ensembles and Occam’s Razor,” a section that praises ensembles even though they seem to threaten parsimony.
Perhaps, most importantly, he gives lots of advice. A book like this, on a topic like this, can be overwhelming in its factual detail. Knowledge of how the technique works does not imply action in and of itself. You need to know what you should do with this information. Applied Predictive Analytics is a coaching and mentoring session with someone that has been doing it for more than 20 years.
Abbott's stated mission with this book (as mentioned in its "Introduction" at the end of the book) is to provide very practical guidance for executing on predictive analytics, as if chatting to someone peering over his shoulder as he works through a project. This mission is accomplished, and in doing so it accomplishes something even more significant: The book takes much of Abbott's well-honed training agenda (do attend his in-person sessions if you can!), along with the accessibility of his casual speaking style, and translates them onto the page. As a result, this book reads in a much more conducive and engaging manner than, say, a more formally structured textbook.
The book is extremely practical. It is mostly organized around project execution steps, rather than around analytical methods, application areas, or industry verticals.
"Applied Predictive Analytics" focuses on the issues and tasks that consume the vast majority of any hands-on predictive analytics project. Some reviewers of this book - as well as others in the industry in general - appear to believe you must understand the theory behind the analytical modeling methods in order to be an effective hands-on practitioner of the art. There's a religious debate to be had over this. But, either way, this book covers necessary knowledge; no one book covers all this as well as all the in-depth math behind analytical modeling methods. In the end, executing on predictive analytics in a commercial context is an empirical exercise more than an exercise in applying theory. For example, pragmatic choices in the data preparation often makes a much bigger difference than the choice of predictive modeling method. Also, regardless of the modeling method employed and its theoretically sound capabilities, the proof is always in the pudding: The results of modeling must be empirically validated over unseen test data. It's a kind of experimental science.
I do feel this book can serve as a great follow-on for "digging in" after reading my book, "Predictive Analytics," which, unlike Abbott's book, is not a how-to, but rather introduces the concepts and provides an industry overview.
Eric Siegel, Ph.D.
Founder, Predictive Analytics World
Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Most Recent Customer Reviews
Dr. Abbott does a great job covering a all the core concepts and most utilized algorithms in data mining...Read more
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