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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R (FT Press Analytics) [Kindle Edition]

Thomas W. Miller
5.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

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Product Description

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.

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.

 


Product details

  • Format: Kindle Edition
  • File Size: 64481 KB
  • Print Length: 348 pages
  • Simultaneous Device Usage: Up to 5 simultaneous devices, per publisher limits
  • Publisher: Pearson FT Press; 1 edition (23 Aug. 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00EQ8D30Q
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #492,857 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
0 of 1 people found the following review helpful
5.0 out of 5 stars Five Stars 10 April 2015
Format:Hardcover|Verified Purchase
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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 3.8 out of 5 stars  16 reviews
52 of 54 people found the following review helpful
3.0 out of 5 stars 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.
15 of 15 people found the following review helpful
4.0 out of 5 stars 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.
7 of 8 people found the following review helpful
4.0 out of 5 stars Data/Code is available 1 Oct. 2014
By Shawn Mehan - Published on Amazon.com
Format:Hardcover
Why people are whinging that they can't find the downloadable programs and data sets is beyond me. http://www.ftpress.com/promotions/modeling-techniques-in-predictive-analytics-139480
7 of 9 people found the following review helpful
5.0 out of 5 stars 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.
5 of 6 people found the following review helpful
5.0 out of 5 stars Great introduction/intermediate instruction in PA 16 Oct. 2013
By Noah - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
The writing style is great, and the techniques are illustrated in solid real-world examples. As a relative beginner to PA with a background in statistics, I find the examples to be just the right level of complicated/challenging. Also, a great resource for improving skills in R as all code is provided on the publisher's site.
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