Predictive Analytics Modeling

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In this presentation you will learn:
- Methods and models that support the effectiveness of predictive analytics applications
- Demonstration of today's predictive analytics tools and solutions
- How to translate respective data sets into predictive models and apply to business needs

Published in: Data & Analytics, Technology

Predictive Analytics Modeling

  1. 1. Predictive Analytics: Modeling Concepts April 15th, 2014 Presented by: Andrew Pulvermacher Director | Predictive Analytics in/drewpulvermacher  
  2. 2. 2   “We’re  forge+ng  how  to  fly.”   -­‐Rory  Kay,  United  captain  and  co-­‐chairman  of   a  Federal  AviaAon  AdministraAon  commiCee   “among  the  accidents  and  certain  categories   of  incidents  that  were  examined,  roughly   two-­‐thirds  of  the  pilots  either  had  difficulty   manually  flying  planes  or  made  mistakes   using  flight  computers.”         This  reliance  on  computer-­‐heavy  flight  decks   and  the  “problems  that  result  when  crews   fail  to  properly  keep  up  with  changes  in   levels  of  automaAon”  now  pose  “the  biggest   threats”  to  airliner  safety  in  the  world.  
  3. 3. 3  
  4. 4. 4   Predic@ve  Analy@cs  
  5. 5. 5      Forward-­‐Looking  Decision  Making      ObjecAve  |  Variables  |  Constraints   TODAY:  Modeling  Concepts   Predictive Analytics Series 1.  ExecuAve  IntroducAon   2.  Data  Modeling   3.  SimulaAon   4.  OpAmizaAon   5.  Data-­‐Driven  Leadership  
  6. 6. in/drewpulvermacher  
  7. 7. in/drewpulvermacher   Predictive Analytics Modeling Concepts WHAT is flying the plane…
  8. 8. 8   OBJECTIVE:   We  need  to  RETAIN   our  top  employees   and  RECRUIT  More   EffecAvely   Starts  with  Data   Easy  3  Step  Process   1)  Data   2)  What  Column   3)  Select   The  TECHNOLOGY  does  it  for  me   Recommenda@on:  Give   everyone  a  PosiAve   Performance  Review  (?)  
  9. 9. in/drewpulvermacher   Predictive Analytics Modeling Concepts 1. ClassificaAon   2. A/B  TesAng   3. Decision  Trees   4. Regression  
  10. 10. 10  10   Classifica@on   1)  Hidden  RelaAonships   2)  Data  Familiarity   3)  SegmentaAon  
  11. 11. 11  11   A/B  Tes@ng   In  markeAng,  A/B  tes@ng  is  a  simple  randomized   experiment  with  two  variants,  A  and  B,  which  are  the   control  and  treatment  in  the  controlled  experiment.       It  is  a  form  of  sta@s@cal  hypothesis  tes@ng.    
  12. 12. 12  12   A/B  Tes@ng   Customer  Response  Rate   Failure  Rate   Pricing   Loan  Default  
  13. 13. 13   Summary Statistics Probability  of  Breast  Cancer:  0.8%   •  Woman  >  40   MAMMOGRAM   Unknown:  Breast  Cancer   YES   NO   Test  Posi@ve   90%   7%   Flesh  &  Blood  Example  
  14. 14. 14   BAYES  THEOREM   1   70  
  15. 15. 15   Decision  Trees   Reason  for  Being:   Contingent Probabilities Noun:  the  probability  that  an  event   will  occur  given  that  one  or  more   other  events  have  occurred  
  16. 16. 16   Decision  Trees:  Scenario   PRODUCT  A   Prob($)  =  60%   PRODUCT  B   Prob($)  =  20%   Prob($)  =  40%  
  17. 17. 17   Let’s  Plant  a  (Decision)  Tree   3  Probabili@es  of  Success  for  Product  B   Results  are  NOT  Independent.   p(B)  =  p(B|A)  x  p  (A)   +  p(B  |  notA)  x  p(not  A)  
  18. 18. 18   One  More?   Regression  
  19. 19. 19   “As  our  machines  get  faster  and  ingest  more  data,  we  allow   ourselves  to  be  dumber.     Instead  of  wrestling  with  our  problems  in  earnest,  we  can  just   plug  in  billions  of  examples  of  them.       Which  is  a  bit  like  using  a  graphing  calculator  to  do  your  high-­‐ school  calculus  homework  –  it  works  great  un@l  you  need  to   actually  understand  calculus.”        -­‐  James  Somers,  “The  Man  Who  Would  Teach  Machines  to  Think,”          The  AtlanAc,  November  2013  
  20. 20. 20   Predic@ve  Analy@cs  
  21. 21. in/drewpulvermacher   Predictive Analytics Modeling Concepts 1. ClassificaAon   2. A/B  TesAng   3. Decision  Trees   4. Regression  
  22. 22. 22  Drew@PerformanceG2.com Q&A
  23. 23. Thank you for attending our webinar 23  Drew@PerformanceG2.com "  Call us: 877.742.4276 "    Email us: info@performanceg2.com or drew@performanceg2.com "    Visit our web site: performanceg2.com "    Read our Analytics blog: performanceg2.com/blog "    Follow us: "  (Twitter) @performanceg2 "  (Facebook) /performanceg2 "  (YouTube) /performanceg2 "  (LinkedIn) /performanceg2-inc
  24. 24. Predictive Analytics Series 1.  ExecuAve  IntroducAon   2.  Data  Modeling   3.  SimulaAon  –  May  15,  2014   4.  OpAmizaAon   5.  Data-­‐Driven  Leadership   Visit  hCp://www.performanceg2.com/ events  to  register  for  the  upcoming  series.  

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