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| Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management | 
 enlarge | Author: Olivia Parr Rud
Publisher: Wiley
Category: Book
List Price: $80.00
Buy New: $43.67
You Save: $36.33 (45%)
New (19) Used (9) from $43.09
Avg. Customer Rating:  14 reviews
Media: Paperback
Edition: 1
Number Of Items: 1
Pages: 367
Shipping Weight (lbs): 1.3
Dimensions (in): 9.1 x 7.4 x 0.9
ISBN: 0471385646
Dewey Decimal Number: 006.3
EAN: 9780471385646
Publication Date: November 3, 2000
Availability: Usually ships in 1-2 business days
Condition: Brand New, Perfect Condition, Please allow 4-14 business days for delivery. 100% Money Back Guarantee, Over 1,000,000 customers served.
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Product Description
Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions
In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.
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| Customer Reviews: Read 9 more reviews...
 Good, easy-to-read book, but lacks some best practice features November 5, 2007
1 out of 1 found this review helpful
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 use it in my work.
However, I was a bit disappointed that the data preparation seemed very coding intensive. The author could have shown readers how to merge lookup tables of risk values onto customer datasets, rather than hard-coding each of the rules and values; or to use the SAS procedure for creating indicator variables, instead of writing the rules for each category.
Overall, I'm glad that I purchased the book - it lives up to its claims - but it misses some of the better practices, and time saving devices, in data preparation
nice book April 17, 2007
Very nice book with a lot of SAS code. It is very helpful for the statistician who wants to enter the business area.
 Practical and Powerful January 19, 2006
3 out of 4 found this review helpful
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. This book covers the business envrionment from high level and go down to the working data level and then again relate how the results from mining the data can solve busines problem. It is a treasure for data mining analyst and modelers.
Just as the author point out, although there are many new model building techniques emerge every year, logistic regression still remains a very powerful data mining and model building tool. And it is well demonstrated in her detailed examples.
The True Data Mining Cookbook August 29, 2005
0 out of 3 found this review helpful
In the Data Mining field, this book is the most clear and concise and well-organized book for many years. This book truly deserves to be called the Data Mining Cookbook because it appeals to everyone interested in the subject. In other words, her writing style appeals to both the non-statistician and the statistician. The theory is well explained for the general public. She gives the kind of details that allows anyone with a college education and who is determined to be able to do some of this analysis on their own or at least supervise someone who is doing it for them.
Predictive Modeling Methodology For The Non-Statistical! March 24, 2004
6 out of 7 found this review helpful
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.
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