This project was designed to analyze to sets of data and create visual representations for PyBer, a ride-sharing comapny,
from Jnauary to early May of 2019. These data includes the total rides, total drivers, total fare, average fare per driver,
and total fare per city type.
Data Source: city_data.csv, ride_data_csv.
Software: Python 3.9.7.Anaconda Navigator 1.9.6, Jupyter Notebook 6.0.1.
Base on the city type, the data showed an overall direct correation between total rides, total drivers, and total fares, and
showed an inverse relationship for average fare per ride and average fare per driver.
Total Ride Total Drivers Total Fares Average Fare per Ride Average Fare per Driver
Rural 125 78 $4,327.93 34.62 55.49
Suburban 625 490 $19,356.33 30.97 39.50
Urban 1625 2405 $39,854.38 24.53 16.57
Based on our knowledge of ride-sharing services and basic economic principle lead us to expect an increase in price based on increase in demand, increased distance, and driver availability. In larger cities and urban areas, the number of drivers is about 30 times greater than in rural areas, which translates to an increased relative demand. In fact, there are so many drivers in the urban area that at any given time, many drivers are not contracted, as shown by the contrast between the total number of rides and the total number of drivers. Prices are likely also increased in rural areas based on the distance between destinations. At a glance, the line chart shows exactly what's expected: an overall higher volume in total fares for urban areas, lowest volume for rural areas, with suburban falling in between.
Based on the above results, I have the following recommendations:
1. Focus recruitment marketing resources in rural areas to increase the number of available drivers at a given time,
while decreasing recruitment marketing in urban areas.
2. Place a cap on fare to keep the service affordable in rural areas.
This would make it more accessible to those who may be living in underserved service areas while still allowing for profit.
3. Increase user-facing marketing efforts across the board, but particularly in urban areas,
focusing on the services affordability and short wait times based on driver availability.