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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition
By Kattamuri S. Sarma, Ph.D.,
Anticipated publication date: Third quarter 2013
Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition, shows how to develop predictive models using SAS Enterprise Miner 12.1. Kattamuri Sarma, Ph.D., provides an in-depth explanation of the methodology and the theory behind each tool used in developing the predictive models, and then shows how the software performs the tasks. Topics discussed in detail include data collection and data cleaning; data exploration; decision trees and regression trees; logistic regression models; neural networks; variable selection, dimension reduction, and variable transformation; and analysis of textual data.
The 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 TextMining, as well as coverage of the nodes demonstrated in the previous edition.
Graduate students interested in predictive modeling, experts in data mining who are not familiar with SAS Enterprise Miner, and business analysts who need an introduction to predictive modeling using SAS Enterprise Miner will find this book helpful. Readers should be familiar with elements of statistical inference and probability, simple algebra, ordinary least squares, logistic regression, and Base SAS software.
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