ISBN:
9781607648185
,
1607648180
Language:
English
Pages:
1 online resource (1 v.)
,
ill.
Edition:
2nd ed.
Keywords:
Enterprise miner
;
SAS (Computer file)
;
Business ; Data processing
;
Data mining
;
Regression analysis ; Computer programs
;
Statistics ; Data processing
;
Electronic books
;
Electronic books ; local
Abstract:
Learn the theory behind and methods for predictive modeling using SAS Enterprise Miner. Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition. If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book. Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner. This book is part of the SAS Press program.
Note:
Previous ed.: 2007. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed June 24, 2014)
URL:
https://learning.oreilly.com/library/view/-/9781607648185/?ar
