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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R
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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R [Format Kindle]

Thomas W. Miller

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Descriptions du produit

Présentation de l'éditeur

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).

Quatrième de couverture

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.

Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 58966 KB
  • Nombre de pages de l'édition imprimée : 348 pages
  • Utilisation simultanée de l'appareil : Jusqu'à  appareils simultanés, selon les limites de l'éditeur
  • Editeur : Pearson FT Press; Édition : 1 (23 août 2013)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B00EQ8D30Q
  • Synthèse vocale : Activée
  • X-Ray :
  • Word Wise: Non activé
  • Classement des meilleures ventes d'Amazon: n°148.041 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)
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Commentaires client les plus utiles sur Amazon.com (beta)
Amazon.com: 3.9 étoiles sur 5  15 commentaires
44 internautes sur 45 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 More like a collection of magazine/newspaper articles than a book 27 décembre 2013
Par Prof Ed U. Cate - Publié sur Amazon.com
Format:Relié|Achat vérifié
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.
11 internautes sur 11 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Good book for end-users 27 décembre 2013
Par JoeT - Publié sur Amazon.com
Format:Relié|Achat vérifié
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.

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.

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.

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.
6 internautes sur 8 ont trouvé ce commentaire utile 
2.0 étoiles sur 5 What is so useful about this? 31 juillet 2014
Par Jaewoo Kim - Publié sur Amazon.com
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).

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).

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 internautes sur 8 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 A must read book for the business exec who wants to get more meaning from data 5 novembre 2013
Par Ram Mohan - Publié sur Amazon.com
Format:Relié|Achat vérifié
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.
4 internautes sur 5 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Great introduction/intermediate instruction in PA 16 octobre 2013
Par Noah - Publié sur Amazon.com
Format:Format Kindle|Achat vérifié
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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