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Predictive Analytics: Advanced techniques in data mining

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SAS Asia Pacific
SAS Asia Pacific

Presented at SAS Business Analytics 2011 event in Singapore.

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Predictive Analytics Advanced Techniques in Data Mining Sara Venturina Copyright © 2011, SAS Institute Inc. All rights reserved.
Agenda • What is predictive analytics? • Predictive Analytics Process • Data Preparation techniques • Modeling Techniques • Model Monitoring techniques 2 Copyright © 2011, SAS Institute Inc. All rights reserved.
What is Predictive Analytics? Different levels of analytics Forecasting Predictive modeling Optimization Statistical analysis Query drilldown Alerts (or OLAP) Ad hoc reports Standard reports 3 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 4 Copyright © 2011, SAS Institute Inc. All rights reserved.
Predictive Analytics Process Defining Objectives Model Data Monitoring Preparation Predictive Analytics Process Deployment Modeling 5 Copyright © 2011, SAS Institute Inc. All rights reserved.
Data Preparation Techniques • Possible data sources • Data transformation techniques • Deriving “behavioral” information • Data quality check before modeling 6 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 7 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 8 Copyright © 2011, SAS Institute Inc. All rights reserved.
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.
Data Preparation Techniques Data quality check before modeling • Generation of summary statistics of derived variables • Random checking • Correct imputation of missing values 10 Copyright © 2011, SAS Institute Inc. All rights reserved.
Modeling Techniques • Use of SAS Enterprise Miner • Ensemble modeling outside of SAS • Base SAS modeling i.e. for categorical target, survival analysis, etc 11 Copyright © 2011, SAS Institute Inc. All rights reserved.
Modeling Techniques Use of SAS Enterprise Miner For initial /basic modeling, use Decision Tree, Regression. Neural networks can be used to provide diagnostic insights 12 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 13 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 14 Copyright © 2011, SAS Institute Inc. All rights reserved.
Model Monitoring Techniques • Comparing actual vs predicted • Scored base analysis: • Variable distribution analysis • Predicted Score distribution 15 Copyright © 2011, SAS Institute Inc. All rights reserved.
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 16 Copyright © 2011, SAS Institute Inc. All rights reserved.
Model Monitoring (cont’d) Scored base analysis i.e. • Variable distribution analysis 17 Copyright © 2011, SAS Institute Inc. All rights reserved.
Model Monitoring (cont’d) Scored base analysis i.e. • Predicted Score distribution 18 Copyright © 2011, SAS Institute Inc. All rights reserved.
Predictive Analytics as an Iterative Process Defining Objectives Model Data Monitoring Preparation Predictive Analytics Process Deployment Modeling 19 Copyright © 2011, SAS Institute Inc. All rights reserved.
Questions? 20 20 Copyright © 2011, SAS Institute Inc. All rights reserved.
21 21 Copyright © 2011, SAS Institute Inc. All rights reserved.
Copyright © 2011, SAS Institute Inc. All rights reserved.

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Predictive Analytics: Advanced techniques in data mining

  • 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 2 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 3 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 4 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 5. Predictive Analytics Process Defining Objectives Model Data Monitoring Preparation Predictive Analytics Process Deployment Modeling 5 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 6 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 7 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 8 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 10 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 11 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 12 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 13 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 14 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 15 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 16 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 17. Model Monitoring (cont’d) Scored base analysis i.e. • Variable distribution analysis 17 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 18. Model Monitoring (cont’d) Scored base analysis i.e. • Predicted Score distribution 18 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 19 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 20. Questions? 20 20 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 21. 21 21 Copyright © 2011, SAS Institute Inc. All rights reserved.
  • 22. Copyright © 2011, SAS Institute Inc. All rights reserved.