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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R Hardcover – 1 Oct 2014


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From the Back Cover

This uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will help you master crucial skills you don't yet have.

 

Unlike most books on predictive analytics, this guide illuminates the discipline through practical case studies, realistic vignettes, and intuitive data visualizations–not complex mathematics. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through every step: defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.

 

Each chapter focuses on one of today’s most important applications for predictive analytics, giving you the skills and knowledge to put models to work–and gain maximum value from them.

--This text refers to an alternate Hardcover edition.

About the Author

THOMAS W. MILLER is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.

 

Miller is co-founder and director of product development at ToutBay, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets, and has worked with predictive models for over 30 years. Miller’s books include Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.

 

Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin—Madison.

 

He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota, and an MBA and master’s degree in economics from the University of Oregon.

 


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Amazon.com: 15 reviews
42 of 43 people found the following review helpful
More like a collection of magazine/newspaper articles than a book 27 Dec 2013
By Prof Ed U. Cate - Published on Amazon.com
Format: Hardcover Verified Purchase
I purchased this book before I had a chance to read any sample chapter and was disappointed after I went through the book.

Every chapter is dedicated to an application of a particular model of predictive analytics, where a (more or less) real problem is described and discussed, name of a model to use is mentioned, chart outputs are shown and used for a conclusion. In very much the same format and content of an article that you would see in for example Bloomberg business magazine. There is no substantial discussion of any of the models, and without a good understanding of such models you cannot conduct predictive Analytics.

The content of this book could be used in the first 2-3 weeks of an introductory course in Analytics discussing what is Analytics and what are some example applications. I ended up keeping the book mostly due to hassle of a return, and partly for using it as a list of major models to read elsewhere and learn.
10 of 10 people found the following review helpful
Good book for end-users 27 Dec 2013
By JoeT - Published on Amazon.com
Format: Hardcover Verified Purchase
This is a good book on using R for predictive modeling.
The books website contains all the code that is used in the book.
I tried all of the downloadable R files and they all worked as advertised.
I admit not trying the text processing though (Chapter 7) only because I don't like R for text processing.
Rather use perl or Rapidminer.

Pros:
1. All the code works
2. A good sample space of topics, so you get a feel of predictive modeling in different situations.
3. You really don't need an extensive math background, since there is virtually no math described at all.

Cons:
1. If there was one thing I wish was better done is the analysis of the results. Some of the results, unless you are already familiar with the statistical technique used, might seem foreign and will require you to do some additional research.

Summary:
Overall a good book, minus the 1-Con above.
Hint: If you do download the R programs, go through each one a piece at a time, to see what's going on. I found it's better than just "running the code". You'll have a better understanding of what's going on.
4 of 4 people found the following review helpful
Disappointing quality and support 11 Aug 2014
By Ronald P. Reck - Published on Amazon.com
Format: Hardcover Verified Purchase
I wanted to like this book but this is what I found.

1. the publisher chose not to include the programs from the book, mention them or link to them from their website. At this day and age it seems like a simple thing to expect. Maybe it was my fault for not checking this first.
2. I contacted the publisher for help finding the programs from the book but received no reply (2 months),
3. I have not see any errata published although I have found sloppy errors like URLs that are missing characters (and are inconsistent because they are presented correctly elsewhere in the text). I thought this is exactly the type of thing tech editting is supposed to address.
4. a motivated individual is likely to remain frustrated because even those willing to type the examples in by hand will find that several of the chapters use some proprietary data file that might have made things easy for the author but in turn ruined the ability of people to get the data for themselves had public data been chosen. even given hand typed programs, not knowing what the input is makes it less than useful.
5. I am admit to be neither a statistician, mathematician nor programmer yet I found that programs might have been designed slightly less rigid to have it describe the technique behind the analysis. I encounter one situation where knowledge of the outcome of the program's output seemed to be the criteria for a cutoff where, to me, it seemed more reasonable to have made it like 10% (which equated to the same). The is purely subjective and I may be entirely incorrect in this statement.

I personally did not derive the value I had hoped from this book, and it was not from my lack of trying. I do not think it was because I expected something from the book it did not intended to deliver. I would maintain the book sought to deliver it, it just did so poorly.
5 of 6 people found the following review helpful
What is so useful about this? 30 July 2014
By Jaewoo Kim - Published on Amazon.com
Format: Hardcover
The book does not provide downloadable data nor can the reader download the R codes which are used to analyze the data.

So I searched online and the publisher (Pearson) does not provide them either.

What am I supposed to do? Type them in? Trust me, you wouldn't want to.

So the book annoyed me immediately.

As I was perusing through the examples, I realized that most of the text are words. If you are looking for a math-oriented book, then you would be disappointed. Math equations are hardly to be found. What are also missing are the explanation of the R analysis of the data (it does provide the R codes in text only).

Because of this, I thought the book was wonderful at explaining the high view of data analytics, but unfortunately also highly insufficient in explaining the output of those analytics. What this book does not teach you is how to properly analyze data through practice and learning from the mistakes, mainly because the answers and explanations of the analysis are hardly given.

The author also assumes the reader has high proficiency with regression (linear, multivariate, logistical, time series).

Pros:
1)Great at explaining how a data can be analyzed using various methods.
2)The author seems to have deep knowledge of R, and shares his insightful code (but not downloadable).

Cons:
1)No downloadable data or R codes.
2)No answers nor explanations of the analysis done by the R codes.
3)No practice problems nor any learning from trial.
6 of 8 people found the following review helpful
A must read book for the business exec who wants to get more meaning from data 5 Nov 2013
By Ram Mohan - Published on Amazon.com
Format: Hardcover Verified Purchase
I had been looking for an easy to read/understand book on data mining and predictive analytics in a business context using R. The author explains the problem, the approach in easy to understand terms, provides real world problems and the compelte solution in R which I was able to execute and test easily. Definitely takes me to my next level of interest in digging further to get a better understanding of the solution and R. Would strongly recommend the book to business folks who want to get in to R and learn more about data mining and predictive analysis.
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