Getting Started with SAS Enterprise Miner 14

1 Getting Started with SAS Enterprise Miner DocumentationThe correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2015. Getting Started with SAS Enterprise Miner Cary, NC: SAS Institute Started with SAS Enterprise Miner 2015, SAS Institute Inc., Cary, NC, USAAll rights reserved. Produced in the United States of a hard-copy book: 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 a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law.

2 Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others' rights is Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR , DFAR (a), DFAR (a) and DFAR and, to the extent required under federal law, the minimum restricted rights as set out in FAR (DEC 2007).

3 If FAR is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government's rights in Software and documentation shall be only those set forth in this Institute Inc., SAS Campus Drive, Cary, North Carolina 2015 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 brand and product names are trademarks of their respective This Book .. vChapter 1 Introduction to SAS Enterprise Miner .. 1 What Is SAS Enterprise Miner ?.

4 1 How Does SAS Enterprise Miner Work? .. 2 Benefits of Using SAS Enterprise Miner .. 3 Accessibility Features of SAS Enterprise Miner .. 3 Getting to Know the Graphical User Interface .. 4 Chapter 2 Learning by Example: Building and Running a Process Flow .. 7 About the Scenario in This Book .. 7 Prerequisites for This Example .. 8 Chapter 3 Set Up the Project .. 9 About the Tasks That You Will Perform .. 9 Create a New Project .. 9 Create a Library .. 10 Create a Data Source .. 11 Create a Diagram and Add the Input Data Node .. 13 Chapter 4 Explore the Data and Replace Input Values .. 15 About the Tasks That You Will Perform .. 15 Generate Descriptive Statistics.

5 15 Partition the Data .. 18 Replace Missing Values .. 19 Chapter 5 Build Decision Trees .. 21 About the Tasks That You Will Perform .. 21 Automatically Train and Prune a Decision Tree .. 21 Interactively Train a Decision Tree .. 24 Create a Gradient Boosting Model of the Data .. 27 Chapter 6 Impute and Transform, Build Neural Networks, and Build a Regression Model . 29 About the Tasks That You Will Perform .. 29 Impute Missing Values .. 29 Transform Variables .. 31 Analyze with a Logistic Regression Model .. 33 Analyze with a Neural Network Model .. 36 Chapter 7 Compare Models and Score New Data .. 41 About the Tasks That You Will Perform .. 41 Compare Models.

6 41 Score New Data .. 43 Create a Sorted List of Potential Donors .. 44 Appendix 1 SAS Enterprise Miner Node Reference .. 47 About Nodes .. 47 Usage Rules for Nodes .. 55 Appendix 2 Sample Data Reference .. 57 Recommended Reading .. 61 Glossary .. 63 Index .. 69ivContentsAbout This BookAudienceThis book is intended primarily for users who are new to SAS Enterprise Miner . The documentation assumes familiarity with graphical user interface (GUI) based software applications and basic, but not advanced, knowledge of data mining and statistical modeling principles. Although this knowledge is assumed, users who do not have this knowledge will still be able to complete the example that is described in this book end-to-end.

7 In addition, SAS code is displayed in some result windows that are produced during the course of the example. However, SAS programming knowledge is not necessary to perform any task outlined in this This BookChapter 1 Introduction to SAS Enterprise Miner Is SAS Enterprise Miner ? .. 1 How Does SAS Enterprise Miner Work? .. 2 Benefits of Using SAS Enterprise Miner .. 3 Accessibility Features of SAS Enterprise Miner .. 3 Overview of Accessibility Features .. 3 Exceptions to Standard Keyboard Controls .. 4 Other Exceptions to Accessibility Standards .. 4 Getting to Know the Graphical User Interface .. 4 What Is SAS Enterprise Miner ?SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across an Enterprise .

8 Data mining is applicable in a variety of industries and provides methodologies for such diverse business problems as fraud detection, householding, customer retention and attrition, database marketing, market segmentation, risk analysis, affinity analysis, customer satisfaction, bankruptcy prediction, and portfolio SAS Enterprise Miner , the data mining process has the following (SEMMA) steps: Sample the data by creating one or more data sets. The sample should be large enough to contain significant information, yet small enough to process. This step includes the use of data preparation tools for data import, merge, append, and filter, as well as statistical sampling techniques.

9 Explore the data by searching for relationships, trends, and anomalies in order to gain understanding and ideas. This step includes the use of tools for statistical reporting and graphical exploration, variable selection methods, and variable clustering. Modify the data by creating, selecting, and transforming the variables to focus the model selection process. This step includes the use of tools for defining transformations, missing value handling, value recoding, and interactive binning. Model the data by using the analytical tools to train a statistical or machine learning model to reliably predict a desired outcome. This step includes the use of techniques such as linear and logistic regression, decision trees, neural networks, partial least 1squares, LARS and LASSO, nearest neighbor, and importing models defined by other users or even outside SAS Enterprise Miner .

10 Assess the data by evaluating the usefulness and reliability of the findings from the data mining process. This step includes the use of tools for comparing models and computing new fit statistics, cutoff analysis, decision support, report generation, and score code might or might not include all of the SEMMA steps in an analysis, and it might be necessary to repeat one or more of the steps several times before you are satisfied with the you have completed the SEMMA steps, you can apply a scoring formula from one or more champion models to new data that might or might not contain the target variable. Scoring new data that is not available at the time of model training is the goal of most data mining , advanced visualization tools enable you to quickly and easily examine large amounts of data in multidimensional histograms and to graphically compare modeling new data that is not available at the time of model training is the goal of most data mining exercises.