Congratulations! We created an end-to-end workflow to examine the geographic patterns of usage in a bike sharing system and retrained our clustering model on new data.

You can always compare your results with a completed version of the project in the Gallery.

With the aim of better understanding public mobility in a congested metropolitan area, we:

  • Utilized many common data preparation recipes such as Download, Prepare, Join and Pivot

  • Leveraged visualization tools like charts and interactive maps to guide our analysis

  • Demonstrated features like adjusting sample sizes, previewing jobs and write-protecting datasets

  • Built (and re-built) a clustering model to identify similar stations in DC

Thank you for your time working through this use case. Next, you might try downloading more recent data from Capital Bikeshare and exploring how the clusters further evolve!