The ride sharing bonanza continues! Seeing the success of notable players like Uber and Lyft, I was tasked to join a fledgling ride sharing company of my own. In this assignment, I was expected to offer data-backed guidance on new opportunities for market differentiation.

I was given access to the company's complete recordset of rides. This contains information about every active driver and historic ride, including details like city, driver count, individual fares, and city type.

The objective was to build a Bubble Plot that showcases the relationship between four key variables:

Average Fare ($) Per City

Total Number of Rides Per City

Total Number of Drivers Per City

City Type (Urban, Suburban, Rural)

In addition, the following three pie charts:

% of Total Fares by City Type

% of Total Rides by City Type

% of Total Drivers by City Type

Average Fare and Number of Rides

The chart above depicts three types of city types(Urban, Suburban, Rural), the number of rides per city and the average fare. One of the trends one can see immediately is that the Pyber app is more popular in Urban areas. The lower fare charges could be a result of people traveling shorter distances. There could be a strong case for the distances traveled given the city type.

Total Fares by City Type

The pie chart above shows us that the core userbase of the Pyber app favors Urban riders and is the majority source of revenue.

Total Rides by City Type

Again we see that a large majority of rides tend to be in the Urban city type, then suburban, followed by rural.

Total Drivers by City Type

In the last pie chart, the vast majority of the drivers drive in the urban city type followed by suburban.