nigelrowser/PyBer_Analysis

Using Python (Pandas, Matplotlib), data was analyzed to determine the total weekly taxi fares for each city type. Analyzed data was used to present new business recommendations.

PyBer_Analysis

The purpose of this analysis is to determine the total weekly fares for each city type.

In this analysis 'city_data' and 'ride_data' were used to build a visual model which offers a simiplified way for stakeholders to view data. Specifically, data from rural, suburban, and urban areas where used so recommendations could made to improve efficiency.

Results

The data shows that urban areas spend much more on fare compared to rural and suburban areas. There are many factors that affect this conclusion. Typically more people tend to live in urban areas. The data shows that there are 2,405 drivers in urban areas, 490 drivers in suburban areas, and only 78 drivers in rural areas. Urban areas also had the most total rides which was at 1,625 rides.

Summary

Business Recommendations

1. Business marketing efforts should be focused on urban areas due to the percieved increase in competition because of the amount of riders and the need for service.

2. The business should continue to recruit and hire urban drivers so the company is able to keep up with demand.

3. The company should weigh the cost of doing business versus the reward in regards on deciding to continue to serve rural areas because there are few riders.