To view the recording of this webinar please use the below URL: http://wso2.com/library/webinars/2016/02/predicting-winners-of-the-big-game-with-wso2-machine-learner/ Online commerce, mobile interactions, and the Internet of Things (IoT) are driving the need for enterprises not only to react in real-time, but also to predict future scenarios in order to make meaningful interactions. This has popularized the use of machine learning to make predictions based on vast streams of data handled by organizations every day. Machine learning is still relatively new to many enterprises, so the team at WSO2 found a way to bring it to life; through American football. The sport would allow us to explore algorithms and test the WSO2 Machine Learner product while having some fun along the way. The WSO2 BigDataGame correctly predicted the outcomes of 7 out of 11 playoff games. In this session, Nirmal Fernando, the team lead of WSO2 Machine Learner and an associate technical lead at WSO2, will explore how we Gathered the data Picked the machine learning algorithm Built the machine learning model Deployed the model and obtained the results Faced the challenges it brought about

1. PREDICTING WINNERS OF
THE BIG GAME
WITH
WSO2 MACHINE LEARNER
Nirmal Fernando
Team Lead of WSO2 Machine Learner

3. What is the Big Game?
http://news.yahoo.com/american-football-broncos-reach-
super-bowl-edging-patriots-010504321.html

4. Why were we interested in it?
“Here's a crazy idea...a
really crazy idea in
fact…”

5. What data do we have? - offensive strength
By Ben Stanfield (user name acaben) of flickr - http://www.flickr.com/photos/acaben/1307980635/in/set-
72157601826753840/, CC BY-SA 2.0, https://commons.wikimedia.org/w/index.php?curid=2689957

6. What data do we have? - defensive strength
http://bigeasybeliever.com/2015/03/31/saints-may-target-all-american-linebacker-with-31st-pick/

7. What type of a model do we build?
Winning probability

8. How we gathered the data

9. How we picked the Machine Learning algorithm
Response variable: Winning
probability (numerical value)
Dataset: Supervised
Algorithm Type:
Numerical Prediction

10. Algorithms supported by WSO2 Machine Learner
What we picked

11. The model we built

12. AWS
WSO2 ML
Predictive API
How we predicted game results
Big Game
REST API
Team A, Team B
Team A, Team B
Team A = 0.67
Team B = 0.33
Team A = 67%
Team B = 33%

13. The final results
http://wso2.com/landing/big-data-game/

14. Challenges we faced
● Domain knowledge
● Lack of data
● Impact from injuries
● Game day condition
etc..

15. Interested in getting started with your own
predictions?
● Product download http://wso2.com/products/machine-learner/
● Product documentation https://docs.wso2.
com/display/ML110/WSO2+Machine+Learner+Documentation
● Upcoming webinars http://wso2.com/events/webinars/

16. Thank You!