/PyBer_Analysis

Created more insight into this company by looking at variables that determine profit and provided suggestions to improve upon for months to come.

Primary LanguageJupyter Notebook

PyBer_Analysis

Overview of Analysis

The purpose of this anaysis was to gather more insight on per city type, which are rural, suburban, and urban types in terms of fares and driver count.

Results

I have contructed summary data frame in which we can look at the final results of our overall pupose of this project which was to analyze per city type and to see which one has the most fares, driver count, total rides, and averages.

Screen Shot 2020-08-16 at 12 59 06 PM

  1. Rural:
  • Had the least amount of Total Rides, Total Drivers, Total Fares
  • But had the highest Average Fare per Ride and Average Fare per Driver.
  1. Suburan:
  • Was in the middle of the two spectrums. Nothing significant about these numbers, it is given that a suburban city type is not an outlier rather lies in between.
  1. Urban:
  • Had the highest amount of total rides, total drivers, and total fares.
  • Had the smallest Average Fare per Ride and Average Fare per Driver.

Further Analysis

image

Here we can see visually of specifically the Total Fares by city type over the course of about four months in 2019. Can visualize that the Rural is at the bottom and Urban is at the top of the line chart. We see that fares decrease around the middle of February then get back up near the end of Februrary then all city types decrease in the start of the month of March. Overall we see that Urban has the most random peaks while Suburban and Rural stay relatively constant up until April.

Summary

Overall we can see that there are two key city types and those are Rural and Urban.

-My first recommendation is to improve your Rural city type statistics and offer more incentive to drivers in these areas thus more people in these areas will want to take a ride due to the decreasing fares ude to more supply of drivers. Also taking note here that Rural types had the highest Average Fare per Ride and Driver.

-My second recommendation is to somehow decrease or disincentivize the amount of urban drivers since it is vastly skewed. It is almost as if your are only operating in urban parts, which in retrospect makes sense but we need to be addressing other city types as well.

-Finally, I reccommend that you try to offer more promotions during months that have holidays, example being March where we saw a major decrease at the start for total fares. We would want customers to use PyBer when going out during for this month of March case being St. Patricks day, specifically suburban and rural city types. Our numbers should have spiked and capitalized on this holiday yet we saw a constant for suburban and rural city types. I do understand that most of these people that live in these cities might have gon out in the urban areas but there are still activites in rural and suburban areas.