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 theProfessional Data Analyst shows tech-savvy business managers anddata analysts how to use predictive analytics to solve practicalbusiness problems. It teaches readers the methods, principles, andtechniques for conducting predictive analytics projects, from startto finish. Internationally recognized data mining and predictiveanalytics expert Dean Abbott provides a practical and authoritativeguide to best practices for successful predictive modeling,including expert tips and tricks to avoid common pitfalls.

This book explains the theory behind the principles ofpredictive analytics in plain English; readers don’t need anextensive background in math and statistics, which makes it idealfor most tech-savvy business and data analysts. Each of thechapters describes one or more specific techniques and how theyrelate to the overall process model for predictive analytics. Thedepth of the description of a technique will match the complexityof the approach, with the intent to describe the techniques inenough depth for a practitioner to understand the effect of themajor parameters needed to effectively use the technique andinterpret the results.

Each of the techniques is illustrated by examples, either uniqueto the task or as part of predictive modeling competitions. Thecompanion website will provide all of the data sets used togenerate these examples, along with links to open source andcommercial software, so that readers can recreate and explore theexamples.

With detailed descriptions of techniques that get results,Applied Predictive Analytics shows you how to:

  • Choose the proper analytics technique for variousscenarios
  • Avoid common mistakes and identify the weaknesses of varioustechniques
  • Mitigate outliers and fill in missing data whennecessary
  • Interpret predictive models often considered “blackboxes,” including model ensembles
  • Learn how to assess model performance so the best model isselected
  • Apply the appropriate sampling techniques for building andupdating models
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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


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    • 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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