Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst 1st Edition

This groundbreaking contribution to the field of predictive analytics delivers a unique gift: A how-to that is accessible, yet quite comprehensive, taking the reader through much of the established teachings of one of the industry's preeminent hands-on instructors. The author, Dean Abbott, is renowned as both a leading "rock star" hands-on consultant in predictive analytics, as well as a fantastic, 5-star-rated conference speaker and an acclaimed training workshop instructor. You get the best of all worlds with this particular expert: deep analytical insights, stellar execution, clear communication, and contagious enthusiasm. And he has translated these assets nicely into a book.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 WorldAuthor, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

I’ve read dozens of books on data mining. I’m also lead author on a data book that specifically uses IBM SPSS Modeler. Full disclosure: the author of this book and I coauthored the book about Modeler.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.

This is a very useful exploration of the practical aspects of machine learning -- especially considerations for deploying models for use in production operations. I went with 4 starts instead of 5 because of the content on text mining and NLP. That part of the book felt like it was partially written and perhaps rushed to publish. I don't think that's a huge deal, though, because there are plenty of books dedicated to text mining and NLP. As long as you are okay with the light treatment of unstructured data, you will find this book very useful and worth your time.

I bought this book for a student of mine starting his career in data science. I read the book cover to cover in a few days. I have a master in data science. This book covered all the important lessons I wanted to pass on to my student. Sure makes my job easier and we can move on more quickly to applied projects.

This book does an excellent job of explaining the principles of of Predictive Analytics and practical example of how to apply these principals using analytic software in a general way. It definitely helped my overall understanding of the different modeling techniques in general and the nuances of applying them.