Presented at SAS Business Analytics 2011 event in Singapore.

1. Predictive Analytics
Advanced Techniques in Data Mining
Sara Venturina
Copyright © 2011, SAS Institute Inc. All rights reserved.

2. Agenda
• What is predictive analytics?
• Predictive Analytics Process
• Data Preparation techniques
• Modeling Techniques
• Model Monitoring techniques
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Copyright © 2011, SAS Institute Inc. All rights reserved.

3. What is Predictive Analytics?
Different levels of analytics
Forecasting Predictive
modeling Optimization
Statistical
analysis
Query drilldown Alerts
(or OLAP)
Ad hoc
reports
Standard
reports
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Copyright © 2011, SAS Institute Inc. All rights reserved.

4. What is Predictive Analytics?
Unfortunately, there is no “magic” involved!
• Use of data from different source tables
• Utilizing various data transformation techniques
• Employing statistical theories as foundation
• Will need software to manage this
Focus on business/commercial (as opposed to
research) analytics is trickier as you need to
balance the theories with realistic application
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Copyright © 2011, SAS Institute Inc. All rights reserved.

5. Predictive Analytics Process
Defining
Objectives
Model Data
Monitoring Preparation
Predictive
Analytics
Process
Deployment Modeling
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Copyright © 2011, SAS Institute Inc. All rights reserved.

6. Data Preparation Techniques
• Possible data sources
• Data transformation techniques
• Deriving “behavioral” information
• Data quality check before modeling
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Copyright © 2011, SAS Institute Inc. All rights reserved.

7. Data Preparation Techniques
Possible data sources
• Data warehouse/ data marts
• Operational systems i.e. transaction systems, billing,
call center data, etc
• External data i.e. survey data, campaign, data from
external agencies, etc
For external data make sure information is consistently available
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Copyright © 2011, SAS Institute Inc. All rights reserved.

8. Data Preparation Techniques
Data transformation techniques
• Entity-level information
• Indicator variables
• Are values skewed towards 1 level?
• Categorization/grouping of values
• Is there too many levels of values?
• Are there values that rarely occur?
• Binning of continuous variables
• Benchmarking information, i.e. industry benchmarking
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Copyright © 2011, SAS Institute Inc. All rights reserved.

9. Data Preparation Techniques
Deriving “behavioral” information using several time
periods
• Average behavior over the last X time periods
• Measures of variation
• Standard deviation
• Coefficient of Variation
• Deviation from the Mean
• Measures of trend information
• Ratio of 1 vs 3, 3 vs 6 time periods
• Proportion of Current vs Average of last X time periods
• Slope of regression line 9
Copyright © 2011, SAS Institute Inc. All rights reserved.

10. Data Preparation Techniques
Data quality check before modeling
• Generation of summary statistics of derived variables
• Random checking
• Correct imputation of missing values
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Copyright © 2011, SAS Institute Inc. All rights reserved.

11. Modeling Techniques
• Use of SAS Enterprise Miner
• Ensemble modeling outside of SAS
• Base SAS modeling i.e. for categorical target, survival
analysis, etc
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Copyright © 2011, SAS Institute Inc. All rights reserved.

12. Modeling Techniques
Use of SAS Enterprise Miner
For initial /basic modeling, use Decision Tree, Regression.
Neural networks can be used to provide diagnostic insights
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Copyright © 2011, SAS Institute Inc. All rights reserved.

13. Modeling Techniques
Ensemble modeling in and out of SAS EM
Ensemble Models based on the
Weightage
following models
Model 1 Decision 0.4
Model 2 Regression 0.6
Model 3 Regression 0.4
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Copyright © 2011, SAS Institute Inc. All rights reserved.

14. Modeling Techniques
Base SAS modeling
• Categorical data modeling i.e.
• PROC CATMOD/GENMOD
• PROC SURVEYLOGISTIC
• Survival analysis:
• PROC LIFEREG
• PROC LIFETEST
• PROC PHREG
Base SAS modeling requires more familiarity with underlying statistical
concepts
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Copyright © 2011, SAS Institute Inc. All rights reserved.

15. Model Monitoring Techniques
• Comparing actual vs predicted
• Scored base analysis:
• Variable distribution analysis
• Predicted Score distribution
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Copyright © 2011, SAS Institute Inc. All rights reserved.

16. Model Monitoring
Monitoring of model assessment charts i.e.
measures what percentage of all churners
Compares the effectiveness of running a are in the scoring list (i.e. top 10% scores
model versus selecting randomly captured 40% of actual churners)
Other model assessment statistics can be computed such as hit rate,
Gini coefficient, etc
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Copyright © 2011, SAS Institute Inc. All rights reserved.

17. Model Monitoring (cont’d)
Scored base analysis i.e.
• Variable distribution analysis
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Copyright © 2011, SAS Institute Inc. All rights reserved.

18. Model Monitoring (cont’d)
Scored base analysis i.e.
• Predicted Score distribution
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Copyright © 2011, SAS Institute Inc. All rights reserved.

19. Predictive Analytics as an Iterative Process
Defining
Objectives
Model Data
Monitoring Preparation
Predictive
Analytics
Process
Deployment Modeling
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Copyright © 2011, SAS Institute Inc. All rights reserved.

20. Questions?
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Copyright © 2011, SAS Institute Inc. All rights reserved.

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