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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition Paperback – 12 December 2013

3.8 out of 5 stars 24 ratings

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Product details

  • Publisher : SAS Institute; 2 edition (12 December 2013)
  • Language : English
  • Paperback : 500 pages
  • ISBN-10 : 1607647672
  • ISBN-13 : 978-1607647676
  • Dimensions : 21.59 x 2.87 x 27.94 cm
  • Customer Reviews:
    3.8 out of 5 stars 24 ratings

Product description


"Once again, Sarma has written a great book. His step-by-step directions make the content very clear, and the new screen shots are beautiful. Both make it very easy to follow the text and replicate the steps with your own data in your own work." --Andrea Wainwright-Zimmerman, SAS Programmer Analyst, SESUG 2013 Academic Conference Chair

"Predictive Modeling with SAS Enterprise Miner is a comprehensive, soup-to-nuts, how-to manual for SAS Enterprise Miner that showcases (with great detail) the power of the application, from data handling and scrubbing through model development. With the second edition of this book, Kattamuri Sarma extends the use of SAS Enterprise Miner 12.1 to new areas of modeling, such as text mining. As before, the book provides a helpful companion for modelers using SAS Enterprise Miner with a variety of goals and objectives in mind. The book draws upon the author's considerable industry experience, with realistic examples provided from finance and insurance, marketing analytics, and other areas of application. There is, in other words, something here for modelers of every stripe." --Lee Medoff, Statistician

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.

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