Applied Predictive Analytics
Principles and Techniques for the Professional Data Analyst
By Dean Abbott
Publisher: Wiley
Final Release Date: March 2014
Pages: 456


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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oreillyApplied Predictive Analytics

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(2 of 3 customers found this review helpful)


Solid intro book

By Shane

from Seattle, WA

Verified Buyer

Comments about oreilly Applied Predictive Analytics:

It's a survey of theoretical considerations. No code. Little math. If you're a practitioner skip it. If you're new to predictive analytics, it's a really good baseline.

(6 of 6 customers found this review helpful)


Excellent write-up of a hot topic

By Marc

from Europe

About Me Analyst, Designer, Educator

Verified Reviewer


  • Accurate
  • Concise
  • Helpful examples
  • Well-written


    Best Uses

    • Intermediate

    Comments about oreilly Applied Predictive Analytics:

    This book is written by one of the leading experts in applied predictive analytics, and it shows on every page.

    The examples are very hands-on, yet tool-agnostic, the guidance is concise and just deep enough to be useful (look elsewhere for greater detail), and the comparisons with related fields like statistics, data mining and BI are one of a kind.

    I would have ticked "easy to understand" very happily as well, but this book does require some degree of specialist expertise to be used to its full advantage. Then again, there's no other book I'd rather recommend to a technically and/or quantitatively inclined audience.

    All in all, someone had to write this book, and I'm very happy Dean took on the task.

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