Winner of the 2014 Technometrics Ziegel Prize for Outstanding Book

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning.  The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems.  Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.

The text illustrates all parts of the modeling process through many hands-on, real-life examples.  And every chapter contains extensive R code for each step of the process.  The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.

This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses.  To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package.

Readers and students interested in implementing the methods should have some basic knowledge of R.  And a handful of the more advanced topics require some mathematical knowledge.

Review

"'Applied Predictive Modeling' aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book...Highly recommended." (Bojan Tunguz, tunguzreview.com, June, 2015)

"There are a wide variety of books available on predictive analytics and data modeling around the web...we've carefully selected the following 10 books...1.  Applied Predictive Modeling."  (Timothy King, Business Intelligence Solutions Review, solutions-review.com, June, 2015)

"I used this as a supplement in teaching a data science course that I use a range of different resources...The next time I teach this course, I will use only this book because it covers all of these aspects of the field."  (Louis Luangkesorn, lugerpitt.blogspot.com, June, 2015)

"This is such a good book...Well thought out examples with the R packages and example code. Take your time and work through this book."  (Mary Anne, Cats and Dogs with Data, maryannedata.com, February, 2015)

"This monograph presents a very friendly practical course on prediction techniques for regression and classification models...The authors are recognized experts in modeling and forecasting...It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques."  (Stan Lipovetsky, Technometrics, Vol. 56 (3), August, 2014)

"There are hundreds of books that have something worthwhile to say about predictive modeling...in my judgment, Applied Predictive Modeling...ought to be at the very top of the reading list...a remarkable text..."  (Joseph Rickert, blog.revolutionanalytics.com, June, 2014)

Review

"This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution." (Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die)