10 Things that Made us Smarter(We Think): A selection of Top Shelf Tips from SAS. Authors: Harsha & Matt

Transcription

1 10 Things that Made us Smarter(We Think): A selection of Top Shelf Tips from SAS Authors: Harsha & Matt

2 Paper 1 : Eposter : Debt Collection through SAS Analytics Lens

3 Paper 1 : Eposter : Debt Collection through SAS Analytics Lens

4 Paper 1 : Eposter : Debt Collection through SAS Analytics Lens

5 Paper 1 : Eposter : Debt Collection through SAS Analytics Lens

6 Paper 2 : Quick Tips: Running Projects for the Average Joe

7 Paper 2 : Quick Tips: Running Projects for the Average Joe

8 Paper 2 : Quick Tips: Running Projects for the Average Joe

9 Paper 2 : Quick Tips: Running Projects for the Average Joe

10 Paper 2 : Quick Tips: Running Projects for the Average Joe

11 Paper 2 : Quick Tips: Running Projects for the Average Joe

12 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

13 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

14 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

15 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

16 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

17 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

18 Paper 3 : HOW: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide

19 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

20 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

21 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

22 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

23 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

24 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

25 Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics

26 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

27 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

28 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

29 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

30 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

31 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

32 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

33 Paper 5 : Breakout: Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio

34 Matt s Top 5 Papers

35 Paper 6 : Reducing Credit Union Member Attrition with Predictive Analytics Retention and engagement are the key to business success A step-by-step overview of an approach to reducing member attrition Segment members into quadrants of lifetime value/attrition risk Want to pay attention to the aggressively retain group

36 Paper 6 : Reducing Credit Union Member Attrition with Predictive Analytics Use raw data to build a modeling (upper) data set and scoring (lower) data set Upper set is used to build a statistical model, lower set is predictive Use logistic regression to build and assess the models

37 Paper 6 : Reducing Credit Union Member Attrition with Predictive Analytics How was this chart created? Modified SAS Macro %makecharts( INDATA=validationForecasts, RESPONSE=attrition, P=p_1, EVENT=1, GROUPS=10, PLOT=gain, PATH=&outroot, FILENAME=Gain Chart ); Also provides macros for lift charts, K-S Charts & ROC charts

38 Paper 7 : Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age Proliferation of data and data sources in organizations has created an analytical bottleneck Solution is often purchase a black-box tool, or hire more data scientists/analysts/modelers Is there a better way?

39 Paper 7 : Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age Automated Predictive Modeling (APM) Solution positioned as a marketing campaign targeting tool Uses regression to predict likelihood of an event or financial outcome of a campaign Designed to match outcomes delivered by experienced modelers in a very short timeframe using data with a large variety of variables Can be used be technical and non-technical users alike Leverages SAS and Excel to address common and sophisticated tool audiences

40 Paper 7 : Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age

41 Paper 7 : Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age

42 Paper 7 : Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age

43 Paper 8 : More Hidden Base SAS Features to Impress Your Colleagues A dozen hidden features of SAS that many people aren t aware exist

44 Paper 8 : More Hidden Base SAS Features to Impress Your Colleagues Informat $ANYFDT is useful when trying to determine the structure of undocumented data with respect to dates and times Returns which informat would be most appropriate for each cell

45 Paper 8 : More Hidden Base SAS Features to Impress Your Colleagues Ensures text after a single embedded blank will be included in the string passed to the informat

46 Paper 8 : More Hidden Base SAS Features to Impress Your Colleagues

47 Paper 9 : Building Interactive Microsoft Excel Worksheets Using SAS Office Analytics Leveraging Visual Basic for Applications (VBA) to manipulate an Excel workbook and dynamically refresh data

48 Paper 9 : Building Interactive Microsoft Excel Worksheets Using SAS Office Analytics Leveraging Visual Basic for Applications (VBA) to manipulate an Excel workbook and dynamically refresh data

49 Paper 9 : Building Interactive Microsoft Excel Worksheets Using SAS Office Analytics

50 Paper 10 : College Football: Can the Public Predict Games Correctly? Examines an online game provided by ESPN, College Pick em Each week 10 close matches are offered competitors attempt to pick winners and rank games based on confidence level (1 lowest, 10 highest) % of participants who picked the team are shown as is national confidence average

51 Paper 10 : College Football: Can the Public Predict Games Correctly?

52 Paper 10 : College Football: Can the Public Predict Games Correctly? Picked is only significant variable is this the only useful piece of information? Information about betting lines neutralizes effect of public perception on who they believe will win Teams are LESS LIKELY to win when playing at home When including betting line variable, it loses significance and Home and Line are more statistically significant

53 References Paper1 - Eposter Debt Collection through SAS Analytics Lens Paper2 - Quick Tip Running Projects for the Average Joe Jennifer Davies, Department of Education Paper3 - HOW Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide Marje Fecht, Prowerk Consulting Paper 4 : Breakout: Making Better Decisions About Risk Classification Using Decision Trees in SAS Visual Analytics Stephen Overton & Ben Murphy, Zencos Consulting Paper5 - Breakout Analysis of IMDB Reviews For Movies And Television Series using SAS Enterprise Miner and SAS Sentiment Analysis Studio Ameya Jadhavar, Prithvi Raj Sirolikar, Dr. Goutam Chakraborty, Oklahoma State University

54 References Paper6 Reducing Credit Union Member Attrition with Predictive Analytics Nate Derby, Stakana Analytics Paper7 Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age Mariam Seirafi, Cornerstone Group of Companies Paper8 More Hidden Base SAS Features to Impress Your Colleagues Peter Crawford, Crawford Consultancy Limited Paper9 Building Interactive Microsoft Excel Worksheets with SAS Office Analytics Tim Reese, SAS Institute Paper10 College Football: Can the Public Predict Games Correctly? (Poster) Matthew B. Collins & Taylor K. Larkin, University of Alabama