The global event saw crème de la crème students around the world compete for the title of Data Science Champion.
The final stage of the 2nd edition of the Data Science Game, an international platform for prestigious institutes to gather and encourage their scholars to demonstrate their skillsets in the field of Data Science, was held on 10th and 11th of September at Capgemini’s Les Fontaines campus. The gathering saw eighty students compete against one another to face a coding challenge and strive to build the best algorithm to win the hackathon that included finding the best solutions to a predictive problem pertaining to Insurance, by using machine learning models.
A tribute to deep learning
To celebrate the finals, participants from around the globe were welcomed at the headquarters of Microsoft France, in Paris, during a gathering of the Data Science international community. The event culminated in an unprecedented roundtable devoted to engineering and deep learning. The roundtable was chaired by Isabelle Guyon, President of ChaLearn, and involved senior analytics figures from the three main partners of the Data Science Game: Capgemini Consulting, Microsoft, and AXA.
After the conference, students and partners enjoyed a special cocktail and networking session.
A fierce hackathon
This year once again, Data Science Game was a resounding success thanks to its partners’ support, who are all key contributors in the field of Data Science. For the final phase, the students were welcomed in the Château Les Fontaines, the training and development campus of the Capgemini Group. Microsoft provided its support by giving free access to its Azure computing clusters while the final challenge was set by AXA. A mammoth 30-hour hackathon witnessed the creation of some of the most innovative and refined solutions to the problem.
The challenge dataset contained requests for automobile insurance quotes received by AXA from different brokers and comparison websites. The participants were asked to predict whether the person who requested a given quote bought the associated insurance policy.
In the end, teams were ranked according to their prediction score. The performance measure used was log-loss, a measure that strongly penalizes predictions that are both confident (probabilities are close to 0 or 1) and wrong.
A finalist points out:
“We felt helpless after training a neural net for hours, and then noticing the importance of feature engineering in this challenge”
|Prize Capgemini, for 1st place||Russian Data Mafia||Moscow Institute of Physics and Technology||Russia|
|Prize AXA, for 2nd place||Cantab||Cambridge University||United Kingdom|
|Prize Microsoft, for 3rd place||We just want our name to be the longest one||Skoltech University||Russia|
|Prize Milliman, for 4th place||Jonquille||Université Pierre et Marie Curie||France|
|Prize Numberly, for 5th place||Brosio2BeWild||University of Padova||Italy|
|Prize QuantCube Technology, for Jury’s innovation prize||ml_noobs||Moscow State University||Russia|
Shortlisted countries included: France, Netherlands, Russia, Germany, The UK, Singapore, USA, Japan, India, and Italy.