SAS Enterprise Miner 7.1

Transcription

1 SAS Enterprise Miner 7.1 Data Mining using SAS IASRI Satyajit Dwivedi Transforming the World

2 DATA MINING SEMMA Process Sample Explore Modify Model Assess Utility 2

3 SEMMA Process - Creating Library Select File New Library 3

4 SEMMA PROCESS Use existing library 1. If you are using existing library then define the libname 1. Click on the Project Name at the top left hand side 2. Then click on the project startup code. 3. A Popup SAS code window would appear 4. Insert the libname statement of the pre-defined library as shown in the screenshot. Ensure that a semicolon is put at the end of the libname statement 5. Click on the run now and check the log for successful definition of library message. 4

5 SEMMA PROCESS - Create the data source 1. Right Click on Data source & click Create the Data Source 1. Select the Library 2. Select the Dataset to be analyzed 3. Select the roles of the variables 4. Select the sample during the process of selection or take the entire data 5. Select the table role (raw / Train / Validate / Test / Score / Transaction 6. The dataset should be visible under data source folder indicated d in the left section of the EMiner 5

6 SEMMA PROCESS - Create Diagram 1. Right Click on Diagrams 2. Name the Diagram 3. You will see a blank white canvas. 4. Drag the earlier defined dataset under data source 5. Build the data mining process using nodes from the SEMMA process windows 6

7 SEMMA PROCESS Import Diagram 1. Ensure that the import is done after the definition of the data source libraries and the selection of the dataset. 2. Right Click on Diagrams 3. Click on the import diagram from XML 4. Select the predefined XML file of the diagram using the browse button 5. Click Ok 6. The messaging importing diagram would be followed by the diagram 7. Test the diagram by updating the first node and subsequent nodes 7

8 SEMMA in ACTION Repeatable Process 8

9 Model Deployment Optimized score code generation (including data transformations) ti SAS code deployment through a scheduled job C or Java code deployment into applications (e.g. Experian, Blaze) PMML based scoring Scoring code and batch job built and executed in SAS Data Integration Model Scoring task in SAS Enterprise Guide Scoring code can be scheduled for execution in parallel (grid computing) 9

10 Modify and Model Binning Scaling Imputation Replacement/recoding Modeling Policies Prediction functions Classification functions Modeling Methods 10

11 Assess Compare training performance Champion / Challenger Training and monitoring Ensure Generalization Prevent over fitting Estimate deployment performance Acquire target t measures Select final model 11

12 JMP Pro 9 64 bit in-memory discovery & modeling desktop environment Exploratory visualization Descriptive and Predictive modeling Integrated with SAS Excel R 12

13 How JMP Pro can be used with SAS Enterprise Miner 7.1? Interactive discovery, visual pattern detection and modeling Preliminary variable selection Challenger model development Model assessment 13

14 Process Integration of JMP Pro & EMiner 14

15 What s new in SAS Enterprise Miner 7.1 Web Infrastructure Platform (WIP) mid-tier integration Standardized Web Infrastructure Platform (WIB) Midtier services for DI, BI, SAS Solutions and Analytics) New business applications Survival Data Mining Rate Making New data mining algorithms Time Series Data Mining Support Vector Machines Existing nodes enhancements New interactive tree extensions Enhancements of the LARs node User interface enhancements Improved Integration Mining Results Web Service Performance enhancements Improved speed for Large data sets. Procedures optimized for utility file usage. SAS code optimized for efficient file usage 15

16 Value of SAS Enterprise Miner 7.1 Powerful complete data mining workbench for departmental to enterprise wide business analytics Visual programming Large data volume processing Process flow driven data mining projects Collaborative environment Easy champion-challenger predictive model building Batch environment for model training and scoring Full integration into SAS Business Analytics Framework Complete data mining process documentation 16

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