SAS Enterprise Miner 7 - IASRI Mining using SAS_Final... · SAS Enterprise Miner 7.1 Data Mining...

SAS Enterprise Miner 7.1 Data Mining using SAS

IASRISatyajit Dwivedi

Copyright © 2011, SAS Institute Inc. All rights reserved.

Transforming the World™

DATA MINING – SEMMA Process

Sample Explore Modify Model Assess

Utility


2Utility

SEMMA Process - Creating LibrarySelect File New Library


3

SEMMA PROCESS – Use existing library

1. If you are using existing library then define the libnamedefine the libname

1. Click on the Project Name at the top left hand side

2. Then click on the project startup codecode.

3. A Popup SAS code window would appear

4. Insert the libname statement of the pre defined library as shown in thepre-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 the5. Click on the run now and check the log for successful definition of library message.


4

SEMMA PROCESS - Create the data source

1. Right Click on Data source & click Create the Data Source1. Select the Library2. Select the Dataset to be analyzed3. Select the roles of the variables4. Select the sample during the process of selection

k h i dor take the entire data5. Select the table role (raw / Train / Validate / Test /

Score / Transaction6. The dataset should be visible under data source

f ld i di t d i th l ft ti f th EMifolder indicated in the left section of the EMiner


5

SEMMA PROCESS - Create Diagram

1. Right Click on Diagrams2. Name the Diagram3. You will see a blank white canvas. 4. Drag the earlier defined dataset under data sourceg5. Build the data mining process using nodes from the

SEMMA process windows


6

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 Diagrams3. Click on the import diagram from XML4. Select the predefined XML file of the diagram using the

b bbrowse button5. Click Ok6. The messaging importing diagram would be followed by

the diagram7 T t th di b d ti th fi t d d7. Test the diagram by updating the first node and

subsequent nodes


7

SEMMA in ACTION – Repeatable Process


8

Model Deployment

Optimized score code generation (including data t f ti )transformations) • SAS code deployment through a scheduled job• C or Java code deployment into applications (e.g.

Experian, Blaze)PMML based scoring• PMML based scoring

• Scoring code and batch job built and executed in SAS Data IntegrationData Integration

• Model Scoring task in SAS Enterprise Guide• Scoring code can be scheduled for execution inScoring code can be scheduled for execution in

parallel (grid computing)


9

Modify and Model

BinningBinning Scaling Imputationp Replacement/recoding Modeling Policies

• Prediction functions• Classification functions

M d li M th d Modeling Methods


10

Assess Compare training

performance Champion / Challenger

• Training and monitoring

Ensure Generalization• Prevent over fitting

Estimate deployment performance

A i t t• Acquire target measures

Select final model


11

JMP Pro 9 64 bit in-memory discovery &

modeling desktop environment

Exploratory visualization

Descriptive and Predictive modeling

Integrated with • SAS• Excel• R…


12

How JMP Pro can be used with SAS Enterprise Miner 7 1?Enterprise Miner 7.1?

Interactive discovery, visual pattern detection and modeling

Preliminary variable selection

Challenger model development

Model assessment


13

Process Integration of JMP Pro & EMiner


14

What’s new in SAS Enterprise Miner 7.1Web 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 g

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

• SAS code optimized for efficient file usage

Value of SAS Enterprise Miner 7.1

Powerful complete data mining workbench for departmental to enterprise wide business analyticsdepartmental 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 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

SAS Enterprise Miner 7 - IASRI Mining using SAS_Final... · SAS Enterprise Miner 7.1 Data Mining...

Documents

Transcript of SAS Enterprise Miner 7 - IASRI Mining using SAS_Final... · SAS Enterprise Miner 7.1 Data Mining...