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INTRODUCTION TO SAS ENTERPRISE MINER MELODIE RUSHCUSTOMER LOYALTY SYSTEMS ENGINEERC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .Copyright 2011, SAS Institute Inc. All rights reserved.
TODAY’S AGENDA What is Data Mining & SAS Enterprise Miner SAS Enterprise Miner Demonstration C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .Benefits of using SAS Enterprise MinerEnterprise Miner GUI OverviewCreating a Data Mining FlowModeling & Model ComparisonScoring in Enterprise MinerRapid Predictive Modeler (RPM)Q&A
WHAT IS DATAMINING?Turning increasing amounts ofraw data into useful informationC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
TWO PASSAGES INTOTHE DATA MINEPredictive6C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .Descriptive
DESCRIPTIVEMODELS INVOLVEClustering(Segmentation)grouping together similarpeople, things, events Transactions that are likely tobe fraudulent, Customersthat are likely to have similarbehaviors.C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .Associationsaffinity, or how frequentlythings occur together, andsometimes in what order Customers who purchaseproduct A also purchaseproduct B
PREDICTIVEMODELSClassification modelspredict class membership 0 or 1: 1 if person responded; 0otherwise Low, Medium, High: acustomer’s likeliness to respondC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .Regression modelspredict a number 217.56 – Total profit, expense,cost for a customer 37 – The number of monthsbefore a customer churns
SASENTERPRISEMINER DEVELOPED TO Enhance Productivity for a Wider Target Audience Focus on analytic skills rather than coding – quicker time to analyticaleffectiveness Team working and sharing of best practice, with self documenting and audittrail Provide Additional Data Mining Algorithms and Techniques Which are not available outside EM Less reliance on coding skills - encourages more exploration/use ofdifferent techniques Generate Complete Scoring Formula Capture the scoring code for all stages of model development Reduce potential errors that can arise from manual conversions Deploy models quickly and ‘automatically’ Options: SAS, C, Java, PMML, “In-Database” Provide Integrated Model Assessment Enables results from numerous models and modeling techniques to becompared.C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
EM ANALYTICALFUNCTIONALITYINCLUDES:Neural NetworksKohonen NetworksSelf-Organising MapsK-Means ClusteringPrincipal Component AnalysisRules BuilderInteractive GroupingDecision Trees (CHAID, CART,C4.5, Hybrid)Bagging, Boosting, EnsembleRegression (Linear & Logistic,Stepwise, forward & backwardselection)Variable ClusteringImputationSupport Vector MachinesC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . Associations and Sequence AnalysisRule InductionTwo Stage ModellingMemory Based ReasoningTime Series AnalysisText MiningSampling (Random, Stratified, Weighted,Cluster ,Systematic)Filtering (outliers and rules)Variable Selection / GroupingData partition, merge, appendAssessment & Model Comparison (Lift charts,ROC curves, profit/cost matrix, Confusion matrix)Credit scoring functionalityAutomatic score code generation for the wholeprocess
UNIQUE EMANALYTICALFUNCTIONALITY Neural NetworksKohonen NetworksSelf-Organising Maps(Automatic) K-Means ClusteringPrincipal Component AnalysisRules BuilderInteractive GroupingDecision Trees (CHAID, CART,C4.5, Hybrid)Bagging, Boosting, EnsembleRegression (Linear & Logistic,Stepwise, forward & backwardselection)Variable ClusteringImputationSupport Vector Machines Associations and Sequence AnalysisRule InductionTwo Stage ModellingMemory Based ReasoningTime Series AnalysisText Mining(Out-of-the-box) Sampling (Random, Stratified,Weighted,Cluster ,Systematic)Filtering (outliers and rules)Variable Selection / Grouping(Out-of-the-box) Data partition, merge, append(Automatic) Assessment & Model Comparison(Lift charts, ROC curves, profit/cost matrix,Confusion matrix)Credit scoring functionalityAutomatic score code generation for the wholeprocess(I.E. NOT IN SAS/STAT)C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS ENTERPRISE MINERMODEL DEVELOPMENT PROCESSSampleUtilityC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .ExploreModifyModelAssess
Tool Bar Shortcut elp PanelC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .DiagramWorkspaceDiagram Navigation ToolbarSAS EnterpriseMiner GUI
SAS ENTERPRISE MINER DEMOC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RAPID PREDICTIVE MODELERC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RAPIDPREDICTIVEMODELER KEY DRIVERS (BUSINESS USERS) Need to generate numerous models to solve a variety ofbusiness problems in a credible mannerModels need to be developed in a quick time-frame usinga self-service approachDoes not want to always rely on analytic professionals(e.g. statistician or modeler or data miner)C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RAPIDPREDICTIVEMODELER KEY DRIVERS (ANALYTIC PROFESSIONALS) Solving more complex issues on hand to gain incrementalvalueFurther customize or refine models for better resultsC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RAPIDPREDICTIVEMODELER PACKAGING Included in the SAS Enterprise Miner bundle at no additional charge Delivered as a customized task in SAS Enterprise Guide and the SAS Add-infor MS Office (MS Excel only)C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RPM KEY CAPABILITIES Chose from prebuilt Enterprise Miner models that use a broadrange of classical and modern modeling techniques.Analytic experts can further customize and improve SAS RPMdeveloped models using SAS Enterprise Miner.RPM Basic ModelC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RPM KEY CAPABILITIES Analytic results are presented as a simple to understand reportsincluding scorecard, lift charts, and listing of key variables.C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS RPM KEY CAPABILITIES Models can be registered in SAS metadata for directuse in other products such as SAS Enterprise Guide,SAS Data Integration Studio, and SAS ModelManager.SASModelManagerSAS MetadataSAS EMModelImportNodeC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .SAS EGModelScoringTaskSAS DIMiningResultsTransformSASScoringAccelerator
DEMONSTRATE RAPID PREDICTIVE MODELERTASKC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
ENTERPRISE MINERRESOURCES SAS Rapid Predictive Modeler External Website E-Learning Class for Rapid Predictive Modeler (RPM) Product brief, Press release, Brief product demo, etc.Rapid Predictive Modeling for Business AnalystsSAS Enterprise Miner External Web Site SAS Enterprise Miner Technical Support Web Site SAS Enterprise Miner Technical Forum (Join Today!) SAS Enterprise Miner Training Getting Started with SAS Enterprise Miner (scroll down to your version ofEM)C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
FURTHER READING Identifying and Overcoming Common Data Mining Mistakes by DougWielenga, SAS Institute Inc., Cary, NC Best Practices for Managing Predictive Models in a Production Environmentby Robert Chu, David Duling, Wayne Thompson , SAS Institute Cary,NC From Soup to Nuts: Practices in Data Management for AnalyticalPerformance by David Duling, Howard Plemmons, Nancy Rausch,SAS Institute Cary, NC (All available on support.sas.com )C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
THANK YOU FOR USING SAS!C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .www.SAS.com
ENTERPRISE MINER RESOURCES SAS Rapid Predictive Modeler External Website Product brief, Press release, Brief product demo, etc. E-Learning Class for Rapid Predictive Modeler (RPM) Rapid Predictive Modeling for Business Analysts SAS Enterprise Miner External Web Site
