Michael CP

Summary:  Analyzed ride sharing data to understand the relationships between city types, average fares, drivers, and total number of rides.

 

Imported and merged data from multiple sources.  Created a dataframe on average fares, total rides, and total drivers by city.  Produced a dynamic bubble plot (sizing based on total number of drives).  This graph allowed us to understand the relationship of all four variables in one snapshot:  average fares(y axis); total number of rides(x axis); city type(color/legend); drivers per city(dynamic sizing of bubbles).  Also created pie charts on Total Fares, Drivers, and Riders by City Type.

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