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An introductory analysis to optimize prices of car sharing services, with an interest in geographic effects.

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PyBer_Analysis

Purpose:

We at Pyber want to expand access to our riding services, to all members of our community. To achieve expanded access we need affordable options accross all regions, whether rural, suburban, or urban. In the new analysis below we have add a line graph for the sum of all rides and fares for the past 4 months, to account for a possible monthly variation.

Analysis:

Below we have Total Fare by City type, there is little variation between the months Jan to April in 2019, however a large difference between fares collected depnding upon city type,

Urban, collecting the most 62.7%, followed by suburban 30.5%, and lastly rural 6.8%.

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Of note, please observe that the average fare price is highest in the rural cities, and least in the urban cities.

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Summary:

As shown above, there appears to be a correlation between average fare price, and the amount of rides in an area. If we can increase the amount of drivers which in turn may decrease the fare price. Our aim can be achieved in providing more access to our community despite city type.

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An introductory analysis to optimize prices of car sharing services, with an interest in geographic effects.

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