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Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management 1st Edition
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Top Customer Reviews
As the author gives a very brief introduction to data mining, make sure before you even start reading this book that you have a grasp of statistical modelling and data mining in a CRM context, otherwise you will find the material presented in this book too much to take in at once, and worst, you may probably end up being put off building your own data mining applications.
The author clearly has a solid statistical (read SAS) background, making this book a strong contender as one of the best books on data mining around, providing the reader with a number of useful recipes, practical examples and pragmatic data mining approaches which should be studied and understood in detail. Being a cookbook, the author's (or should I say the chef's) particular style may not suite your palate. In other words, you may not like the author's bias towards using logistic regression as the main data mining technique. As a result, you will not learn how to cook exotic dishes using ingredients such as neural networks. However, the choice to use logistic regression as the main statistical techniques pays off, as this allows the reader to start learning to cook robust/reliable meals (models), before cooking with the more exotic ingredients (techniques).
The topics and interventions provided by the well-experienced contributors are in context with the author's material, strengthening the practical context in which data mining applications are presented.Read more ›
however, some parts of the book were pretty crude. It contains some mistakes. for example, in one chapter the author tries to compare a few repricing scenerios. she compared the account after rate increase with the account before rate increase. and before rate increase, the attrition rate is zero. and it is just not the right way to evaluate a strategy. normally, you would have to compare an account which got a rate raise with the same account as if it didn't receive the increase. and even without the rate increase, the attrition rate down the road can't be zero. normally, you have to use test and control group on this kind of situation. besides, the author made some calculation mistakes in the comparison table. the numbers simply don't add up.
Anyway, overall the book is still a nice one if you can absorb all the nice information in it.
The author lays out clear, concise methodologies to build robust predictive models using SAS. The nice thing is this book lays out the process step by step with SAS code examples. You do not have to be a statistics major to understand how to use the built in SAS functionality.
The modeling methods are unbelievably detailed including topics like defining the objective function, testing variables for predictability using chi squared, fitting continuous variables using the most linear variable transformation format (squared, cubed, cubed root, log, exponent, tangent, sine, cosine, etc... 19 total formats), changing categorical variables to continuous indicator variables for logistic regression use, using stepwise, backward, and score regression methods to further eliminate less predictive variables, defining deciles, and model testing methods like bootstrapping, jackknifing and gains tables to validate the model.
I do not fully understand the mathematical concepts involved throughout the entire process nor do I want to. The book provides a consistent repeatable programming methodology to follow that is broken down into very quantifiable steps.
I would recommend this book for anyone with limited statistical knowledge and a need to understand predictive modeling programming methodologies. Knowledge of the SAS programming language is essential to make full use of this material. The book uses real life examples from the banking, insurance, and marketing industries and contains additional valuable information related to these fields.
Most Recent Customer Reviews
Good book for learning about the data mining techniques of logistic and linear regression. It helped highlight some good uses, and fortunately, I've recently had the opportunity to... Read morePublished on November 5, 2007 by D. Grant
Very nice book with a lot of SAS code. It is very helpful for the statistician who wants to enter the business area.Published on April 16, 2007 by Xinhui Wen
This book is really a useful step by step guidance to build a model using logistic regression. It is very practical and to the point. Read morePublished on January 18, 2006 by mary
In the Data Mining field, this book is the most clear and concise and well-organized book for many years. Read morePublished on August 28, 2005 by Mary B. Gallo
I have been doing SAS for 11 years. So the SAS code does not bother me.
The best value of this book is that it is very practical; as the title suggests, it is a cookbook. Read more
I found this book every bit as comprehensive as suggested by the glowing forward by Michael Berry. I was pleased to see thorough and comprehensive treatment of the full modeling... Read morePublished on June 10, 2001 by Raymond W. Falk, Ph.D.