- Do exploratory data analysis of Divvy Bikes data in Chicago
- Try to draw a network map between each bike station to see journey flow
- Check journey flow according to time periods - Morning, Afternoon, Evening
- This will give an insight as to when and where people are travelling, which could potentially be very interesting
- Try to make origin-destination network map of journeys to and from each bike station (DONE)
- For each OD pair, make weights based on the number of journeys that take place for that pair (DONE)
- Carry out exploratory analysis - user gender to trip times, routes with the highest number of journeys, subscriber status to number of trips etc
- Can unsupervised learning be used to cluster groups of bike users - i.e. hobby cyclist, daily commute etc.
- The following is a graph map I have made using the origin-destinations presented in the data
