Omar and I were given two data sets from PyBer to clean and merge to provide analysis to V. Isualize. We utilized Pandas and Matplotlib to review the data, determine our data counts by type, city, driver counts, etc. and based the analysis off "city type" to provide V. Isualize detailed information that they can use to make strategic business decisions moving forward. The two data sets we were provided were city data and ride data from January 2019 through April 2019.
From the data range we were provided we determined that the "Urban" city types had the most amount of total rides, drivers, and totla fares. The highest Average Fare Per Ride and Average Fare Per Driver were the highest in "Rural" city types.
- Total Rides
- Rural: 125
- Suburban: 625
- Urban: 1625
- Total Drivers
- Rural: 78
- Suburban: 490
- Urban: 2405
- Total Fares
- Rural: $4, 327.93
- Suburban: $19,356.33
- Urban: $39,854.38
- Avg Fare Per Ride
- Rural: $34.62
- Suburban: $30.97
- Urban: $24.63
- Avg Fare Per Driver
- Rural: $55.49
- Suburban: $39.50
- Urban: $16.57
Among the analysis we completed on the data provided Omar and I have three recommendations that we would like to provide to the PyBer decision makers regarding what we found during our analysis.
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Continue to keep or hire more drivers that work in the "Urban" city types. 62.7% of the total amount of fares were in the "Rural" city type and 68.4% of all rides came from the "Rural" area. If we lose drivers in these areas we may lose business to competitors.
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By city, "Urban" cities account for our 46 largest cities by driver count. How do we want to determine our driver count per city? If we had population size within our data set we could determine if we have enough drivers staffed to accommodate the total capita of the city or also determine if we are overstaffed in certain areas and have some drivers move to a "Suburban" or "Rural" city where they are able to make more money per ride.
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If we had more information we could determine if the average fares rose due to time it took to complete the ride or the distance traveled and analyze this to make even more determinations to drive business decisions. This would be able to tell if we should put more focus on the "Rural" city types to assist in increasing revenue for the company. The average fare per ride and driver are over three times more than the "Urban" and "Suburban" city types. This could be more likely due to the distance needed to travel to complete one ride. But without this data our analysis is inconclusive.