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Pyber

Observed Trends:

  1. There is a correlation between the total number or rides/drivers and average fare cost. Urban cities have the highest number of rides / the highest number of drivers and lowest average fare cost. The farther away you get from the city, the less rides/drivers there are and the more expensive the average fare cost is.
  2. Urban cities account for 68.42% of total rides taken and 62.97% of total fares, making urban markets the best areas for ride sharing.
  3. Urban areas have the lowest fare cost and highest amount of rides which may indicate that there is a correlation between fare cost and total number of rides.

About

Built 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). Skills Needed: Python, Pandas Library, Jupyter Notebook, Matplotlib and Seaborn Libraries

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