Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition

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9781607647676: Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition
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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.

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From the Inside Flap:

What Does This Book Cover? The book shows how to rapidly develop and test predictive models using SAS Enterprise Miner. Topics include Logistic Regression, Regression, Decision Trees, Neural Networks, Variable Clustering, Observation-Clustering, Data Imputation, Binning, Data Exploration, Variable Selection, Variable Transformation, Modeling Binary and continuous targets, Analysis of textual data, Eigenvalues, Eigenvectors and principal components, Gradient Boosting, Ensemble, Time Series Data Preparation, Time Series Dimension Reduction, Time Series Similarity and importing external data into SAS Enterprise Miner. The book demonstrates various methods using simple examples and shows how to apply them to real-world business data using SAS Enterprise Miner. It integrates theoretical explanations with the computations done by various SAS nodes. The examples include manual computations with simple examples as well computations done using SAS code with real data sets from different businesses. Support Vector Machines and Association rules are not covered in this book. Is This Book for You? If you are a business analyst, a student trying to learn predictive modeling using SAS Enterprise Miner, a data scientist who wants process data efficiently and build predictive models, this book is for you. If you want to learn how to select key variables, test a variety of models quickly and develop robust predictive models in a short period of time using SAS Enterprise Miner, this book gives you step-by-step guidance with simple explanation of the procedures and the underlying theory.

About the Author:

Kattamuri S. Sarma, PhD, is an economist and statistician with 30 years of experience in American business, including stints with IBM and AT&T. He is the founder and president of Ecostat Research Corp., a consulting firm specializing in predictive modeling and forecasting. Over the years, Dr. Sarma has developed predictive models for the banking, insurance, telecommunication, and technology industries. He has been a SAS user since 1992, and he has extensive experience with multivariate statistical methods, econometrics, decision trees, and data mining with neural networks. The author of numerous professional papers and publications, Dr. Sarma is a SAS Certified Professional and a SAS Alliance Partner. He received his bachelor's degree in mathematics and his master's degree in economic statistics from universities in India. Dr. Sarma received his PhD in economics from the University of Pennsylvania, where he worked under the supervision of Nobel Laureate Lawrence R. Klein.

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