Applied Analytics Using SAS Enterprise Miner Course Notes Applied Analytics Using SAS Enterprise Miner Course Notes was developed by Peter Christie, Jim Georges, Jeff Thompson, and Chip Wells. Additional contributions were made by Tom Bohannon, Mike Hardin, Dan Kelly, Bob Lucas, and Sue Walsh. Editing and production support was provided by the Curriculum Development and Support Department. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Applied Analytics Using SAS Enterprise Miner Course Notes Copyright 2011 SAS Institute Inc. Cary, NC, USA. All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. Book code E2056, course code LWAAEM71/AAEM71, prepared date 18Oct2011. LWAAEM71_001 ISBN 978-1-61290-139-8 For Your Information iii Table of Contents Course Description . x Prerequisites . xi Chapter 1 Introduction . 1-1 1.1 Introduction to SAS Enterprise Miner . 1-3 1.2 Solutions . 1-24 Solutions to Student Activities (Polls/Quizzes) . 1-24 Chapter 2 Accessing and Assaying Prepared Data . 2-1 2.1 Introduction . 2-3 2.2 Creating a SAS Enterprise Miner Project, Library, and Diagram . 2-5 Demonstration: Creating a SAS Enterprise Miner Project . 2-6 Demonstration: Creating a SAS Library . 2-10 Demonstration: Creating a SAS Enterprise Miner Diagram . 2-12 Exercises . 2-13 2.3 Defining a Data Source . 2-14 Demonstration: Defining a Data Source . 2-18 Exercises . 2-33 2.4 Exploring a Data Source . 2-34 Demonstration: Exploring Source Data . 2-35 Demonstration: Changing the Explore Window Sampling Defaults . 2-59 Exercises . 2-61 Demonstration: Modifying and Correcting Source Data . 2-62 2.5 Chapter Summary . 2-74 2.6 Solutions . 2-75 iv For Your Information Solutions to Exercises . 2-75 Solutions to Student Activities (Polls/Quizzes) . 2-76 Chapter 3 Introduction to Predictive Modeling: Decision Trees . 3-1 3.1 Introduction . 3-3 Demonstration: Creating Training and Validation Data . 3-23 3.2 Cultivating Decision Trees . 3-28 Demonstration: Constructing a Decision Tree Predictive Model . 3-43 3.3 Optimizing the Complexity of Decision Trees . 3-61 Demonstration: Assessing a Decision Tree .