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Predictive Modeling with SAS Enterprise Miner, 3rd Edition / Sarma, Kattamuri.
- Format:
- Book
- Author/Creator:
- Sarma, Kattamuri, author.
- Language:
- English
- Subjects (All):
- Enterprise miner.
- SAS (Computer file).
- Business--Data processing.
- Business.
- Data mining.
- Regression analysis--Computer programs.
- Regression analysis.
- Statistics--Data processing.
- Statistics.
- Physical Description:
- 1 online resource (574 pages)
- Edition:
- 3rd edition
- Place of Publication:
- SAS Institute, 2017.
- System Details:
- text file
- Summary:
- A step-by-step guide to predictive modeling! Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. 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!
- Notes:
- Previous edition published: 2013.
- Includes bibliographical references and index.
- Online resource; Title from title page (viewed July 20, 2017)
- OCLC:
- 1003227369
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