The data consists of information regarding 1,83,412 records in the dataset with 16 features. The attributes included in the interested featues in the dataset such as duration, customer, subscriber, male, female and age. The dataset can be found in the repository here, with feature documentation available here.
In the exploration, I found that there was a strong relationship between the duration of trip and day of week, user types, member gender and bike share for all trip. The relationship is unimodel between trip duration and total number of trips when trip duration is transformed to be on a logarithmic scale. I found a somewhat surprising result initially when the trip count for the user type, member gender, and bike share for all trip variables indicated that higher count was associated with subscriber and male featires. When I isolated trip duration with user type, gender and bike share, there was a clear pattern showing that customer and female were preferring long duration trips where as all were reluctant while sharing the bike during the trip.
For the dataset given, there was an interesting interaction in the categorical variable features. The maximum long duration trips were taken by female customer during weekend as compared with male subscriber male.
For the presentation, I focus on just the influence of the duration, user type, member gender and day of week and leave out most of the intermediate derivations. I start by introducing the duration variable, followed by the pattern in trip duration distribution, then plot the trip count barplot.
Afterwards, I introduce each of the categorical variables one by one. To start, I use the box plots of duration across categoric variables. I'm only looking at various plot here since it's the clearest example of how the categorical variables affect trip duration. The combination of all categorical variables are covered afterwords using heatmap plots. The different colors to in heatmap clears the difference between trip duration.