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Title: Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management by Olivia Parr Rud ISBN: 0-471-38564-6 Publisher: John Wiley & Sons Pub. Date: 03 November, 2000 Format: Paperback Volumes: 1 List Price(USD): $60.00 |
Average Customer Rating: 4.11 (9 reviews)
Rating: 1
Summary: DM Cookbook CD ROM
Comment: Book is OK, but DON'T BUY THE CD-ROM! I dropped [a large amount] for what I thought would be a worth-while "self-learning" course on Data Mining programming in SAS. To my great disappointment, I found that while Olivia had included the code (which you can type in yourself), there was NO DATA PROVIDED, making the code all but useless (can't run the models with no data!). I e-mailed her asking for some kind of a sample data set. She agreed, but after months of begging she provided nothing whatsoever. Don't make the same mistake I did - STAY AWAY FROM THE CD-ROM!!!
Rating: 5
Summary: A foundation book for middle-advanced analysts
Comment: Yes, its not for beginners or executives. That's great, because so many other books are aimed at glossing over the details for novices. This book gets down and dirty with exactly how one goes about analyzing and modeling data in applied marketing. It assumes some knowledge of data and basic analysis, but it also reviews assumptions along the way and points out "gotchas" for the less experienced. Yes, there is an overemphasis on Logistic Regression and a paucity of info on other techniques, but the Logistic Regression work is well done. Yes, it is very SAS based, but the code is not hard to translate to other systems. She doesn't spend as much time as I would prefer on explaining all the output that she presents, though its an excellent start. But she does provide specific details on ALL the steps, from getting and transforming data to how to present your results and use your model, things that are ignored in many other books. Sure, there are easy quibbles and minor errors throughout, but what tech book today is error free? So, if you are looking for the basic guidebook on just how one goes about "modeling", then this is the one. Its got a permanent place on my bookshelf, and is one of the standards I recommend along with Kimball, Pyle, and other "ya gotta have" books.
Rating: 4
Summary: a good book, but has mistakes
Comment: Generally speaking, it is a good book. Contains a lot of real world examples. and it gets into a lot of details on modeling which you don't normally see in a data mining book.
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.
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