The dataset is about Ford-Go-Bike system that was launched in 2017 and still on work until this moment. I analyzed the first quarter of 2018 that consist of around 290k rows and 19 columns. Columns included duration per trip in minutes , Biker`s age , Biker's gender , User'type and more features found here. Dataset is provided here.
I was intersted on finding the features that related to high duration trip , Which gender is more capable to spend higher time per trip and other useful facts relevent to the data.
Some of my insights :
- the variables we discussed on this dataset seemed to be strongly independant , in the other hand the interaction between some features seemed to has more effect on each other.
- The majority of bike riders are between 25 to 45 years old.
- There are less bikers in Jan comparing with Mar & Feb. (Seasonality , climate and vacation could be the main reason - need more investigation).
- Males are more likley to bike (Males constitue the majority of the dataset by 74% and females constitue just 25%) despite the fact that females tend to spend more duration per a trip .
- Customer bikers spend more time riding a bike in comparison of Subscribe bikers.