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Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management (Datawarehousing) by [Rud, Olivia Parr]
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Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management (Datawarehousing) 1st , Kindle Edition

5.0 out of 5 stars 1 customer review

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the descriptions are clear, concise, unambiguous she has clearly succeeded (The Institute of Direct Marketing –theidm.com

??the descriptions are clear, concise, unambiguous?she has clearly succeeded?? (The Institute of Direct Marketing –theidm.com

From the Inside Flap

CD–ROM InstructionsInsert the CD–ROM and launch the readme.htm file in a web browser, or navigate using Windows® Explorer to browse the contents of the CD. The model programs and output are in text format that can be opened in any editing software (including SAS) that reads ASCII files. Spreadsheets are in Microsoft® Excel 97/2000 or 5.0/95. Launch the application (SAS 6.12 or higher) and open the file directly from the CD–ROM. If you wish to make changes, you can rename the files and save them to your local hard drive.Customer Note: Please read the following before launching the CD–ROMThis software contains files to help you utilize the models and code described in the accompanying book, sold separately. By opening the package, you are agreeing to be bound by the following agreement:This software product is protected by copyright and all rights are reserved by the author, John Wiley & Sons, Inc., or their licensors. You are licensed to use this software as described in the software and the accompanying book. Copying the software for any other purpose may be a violation of the U.S. Copyright Law.This software product is sold as is without warranty of any kind, either express or implied, including but not limited to the implied warranty of merchantability and fitness for a particular purpose. Neither Wiley nor its dealers or distributors assumes any liability for any alleged or actual damages arising from the use of or the inability to use this software. (Some states do not allow the exclusion of implied warranties, so the exclusion may not apply to you.)©2000 John Wiley & Sons, Inc.

Product details

  • Format: Kindle Edition
  • File Size: 11723 KB
  • Print Length: 367 pages
  • Publisher: Wiley; 1 edition (5 May 2008)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B003UD7QG0
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: 5.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #1,297,665 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Format: Paperback
By reading this book you should learn how to cook data mining applications...but if you have very little or no appreciation of data mining and customer relationship management (CRM), and you have never used SAS software, you'll probably end up burning your first few cakes or even worst your fingers !! 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 ›
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Most Helpful Customer Reviews on Amazon.com (beta) (May include reviews from Early Reviewer Rewards Program)

Amazon.com: 4.2 out of 5 stars 14 reviews
23 of 28 people found the following review helpful
2.0 out of 5 stars SAS Tutorial or Data Mining Book? 24 Aug. 2001
By HBW - Published on Amazon.com
Format: Paperback Verified Purchase
I was disappointed after reading 3 chapters of this book. Leafing forward, the book is saturated with SAS examples that I not only cannot understand but do not care about. It seems the whole book was written just to promote the (sold separately)...CD ROM with source SAS code. If you are going to write a SAS book, label it as such.
18 of 18 people found the following review helpful
5.0 out of 5 stars Wholesome - no frills - data mining cooking !! 19 Dec. 2001
By Oliver Femminella - Published on Amazon.com
Format: Paperback
By reading this book you should learn how to cook data mining applications...but if you have very little or no appreciation of data mining and customer relationship management (CRM), and you have never used SAS software, you'll probably end up burning your first few cakes or even worst your fingers !!
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. On a few occasions, I found that the author does not discuss figures and tabulated outputs in a straightforward way, inevitably affecting the readability of the book. Notwithstanding, the methodology and material presented has a considerable amount of depth and rigour, and the general themes are well structured and maintained throughout.
Many figures and tabulated results are presented in the graphical output provided by the SAS system, which may be less appealing to you if you are not going to be using SAS. Also, many data mining software tools now available have significantly better graphical data presentation capabilities than those presented in this book, inevitably giving it a slightly dated look. Unsurprisingly, being the first version of the cookbook, there are a few typos (and one incorrect figure at the beginning of the first chapter).
In summary, this book is not for the novice, but will be a book that you will want read more than once.
14 of 14 people found the following review helpful
5.0 out of 5 stars A foundation book for middle-advanced analysts 26 Mar. 2002
By Michael Wexler - Published on Amazon.com
Format: Paperback
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.
7 of 8 people found the following review helpful
5.0 out of 5 stars Predictive Modeling Methodology For The Non-Statistical! 23 Mar. 2004
By Jake Drew - Published on Amazon.com
Format: Paperback
Logistic Regression From A - Z! This book has it all.
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.
10 of 11 people found the following review helpful
4.0 out of 5 stars a good book, but has mistakes 19 Dec. 2001
By M. Qiu - Published on Amazon.com
Format: Paperback
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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