The goal of this competition is to predict bike share use, given the hour, day, and information about the weather. Companies like Divvy try to predict how much demand there will be for bikes on any given day to allocate resources to redistribute bikes so that, ideally, very few bike stations are ever full (when you can’t park your bike) or empty (when you can’t pick up a bike if you want to).

The [data tab][1] provides detailed information on the data set and necessary downloads.

See the class google doc page on details about teams, grading, and updated tips.


  [1]: https://www.kaggle.com/c/csce5300-bikeshare/data