Are you interested to see how the top placing teams came about their solutions to claim their top ranking place in the 2017 Data Science Bowl? Check out their code via the links below.
|Leaderboard Ranking||Team Name||Link to open sourced version|
|2||Julian de Wit & Daniel Hammack||https://github.com/dhammack/DSB2017 ; https://github.com/juliandewit/kaggle_ndsb2017|
|5||Pierre Fillard (Therapixel)||https://github.com/pfillard/tpx-kaggle-dsb2017|
|8||Alex |Andre |Gilberto |Shize||https://github.com/astoc/kaggle_dsb2017|
Do you have an idea for the 2018 Data Science Bowl? We’d like to hear your ideas. Ideas must meet the following criteria:
It’s a compelling problem with the potential to change the world and obvious social good.
There is potential for changing the world. Seriously, we’re looking for a big, thorny challenge to solve, the kind that impacts many (millions and worldwide we hope).
Benefit to the social good is obvious. Our aim is altruistic; this competition is a chance to use the power of data science to help a federal or non-profit entity.
A data source is identified. Bottom line, a data set must exist to address the challenge. It needs to be:
- Large data set
- Releasable to all the competitors
- And have a variable of interest that is not publicly available
Submit your ideas at https://datasciencebowl.com/contact/