v. isulize has given me to analyze ride share data. Out of that I create Summary Data Frame. Out of that we got total riders per city type, total drivers per city type, total fare per city type. Out of that we got total average fare per ride for each city type and total average fare per driver for each city type. Out of all that date we create PyBer_summary_df.
as per result we got total rides per city, total drivers, total fare, average fare, average fare per driver. In rural city type ride was less compare to urban city type ride but average fare per driver is higher than urban city type. Also, you can see in multi-line graph you can visualize better way to see. Urban city type fare always higher then other two city type. Also, I resample data frame by weekly sum of fare we can see urban city type weekly fare higher then rural and suburban.
Based on the result I have three propositions to CEO to addressing to improve among city type are,
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I would say first if they give incentive to driver in rural city type and urban city type would like weekly bonus, extra if they finish certain rides in will help them to increase driver in particular city type.
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In the other way if they give out some discount on ride in those cities where rides are low and certain time and city type then there possibility to increase more rides and once they got more rides and give in out incentive to drivers more driver will come out.
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I would suggest that company don’t need to think only higher profit to there end but instead keep in their end, I would say they need to pass sometime of partnership to their drivers. In this situation drivers will love to work for Pyber and even think to go some other place and will increase their business.