The ride sharing bonanza continues! Seeing the success of notable players like Uber and Lyft, some startups are joining in the action and creating fledgling ride sharing companies.
Offer data-backed guidance on new opportunities for market differentiation.
Use 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.
Data id analyzed with Pandas, Numpy and Matplotlib libraries
The project focuses on creating graphs that will help analyze the data quickly. It determines where is Pyber most used and what are the average fare prices per type of city. It also includes records on the percentage of fares, rides and drivers per city type.
Based on the analyzed data we see that Pyber services are more popular in urban than in suburban or rural cities. This trend could mostly be explain by larger population concentration in urban cities and therefore a bigger need for transportation services.
Due to the popularity of Pyber we also see a large amount of drivers in the urban cities. What we can't tell from this data is if the demand is meet with the current amount of drivers.
Based on the Ride Sharing Data graph we see on average 25 rides on urban cities with fares between $20-$30. There is a significant difference in price and number of rides when comparing to suburban and rural cities. This can be explain by a longer distances and a lower supply of drivers for those areas.
