/PyBer_Analysis

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PyBer Analysis

Overview of the PyBer Analysis

The purpose of this analysis is to present several charts to the PyBer CEO to help her understand how rideshare data behaves in the different city types.

Results

From the Bubble chart below we can say that there is a correlation between the number of rides, the number of drivers, and the price of the ride. The more rides there are, the more drivers there are, and less the fare ($)

In terms of money ($), the Urban cities represent more than 60% of the total fares.

The total number of Urban rides represents almost 70%

The great majority of the drivers are concentrated in the urban cities, with a wedge of more than 85%

The Urban cities fare surpass the Suburban and Rural cities for more than double and quadruple respectively, over the period between January and April of 2019.

Summary

  1. Since Urban cities represent a huge part of the business, I will recommend focusing our efforts on this type of city.
  2. I will recommend using the Suburban cities to experiment with different types of strategies or campaigns since those cities are more lucrative than Urban cities.
  3. I will also suggest doing a geographic analysis to understand how cities that are closer to each other behave.
  4. One more suggestion will be to analyze the data per hour and weekdays.