Bike Sharing Analytics
Bike Sharing Analytics
About this project
The project describes Bike-sharing systems, which are a modern method of bike rentals. The aim of the project is to analyze the bike-sharing. The dataset contains the daily count of rental bikes between 2011-2012 with the corresponding weather and seasonal information.
Power BI was used to analyze the rate of bike rentals around the city, explain possible factors for maximizing bike sharing, and find possible solutions to the problems faced by the company.
Business Questions Used for the Analysis
What is the total number of bike rentals on weekends compared to weekdays?
What is the ratio of registered and non-registered bike users? What are the possible factors responsible for the registration status of customers?
How do the seasons affect bike rentals and registration for bike sharing?
How do holidays affect bike rentals?
What are the 3 topmost numbers of bike rentals recorded?
Key Insights
There are more bike rentals on weekdays when compared to weekends. This suggests that many of the users are workers who commute to and from work.
There are 81.17% more registered users of bikes than casual users. This is a ratio of about 4:1. i for every casual bike user, there are 4 registered users. If there is an 81.17% chance that a user will use the bike-sharing system, then the company should prioritize converting old and would-be clients to registered users. Doing this will increase the number of customers who patronize the bike-hailing system.
The highest bike sharing is observed in the summer months (June, August, and September), reflecting the effect of seasons on the bike-sharing business. The highest records in the month of August for the two years consecutively. A sharp drop in bike use (less than half the number recorded in summer) is observed in winter. The coldest winter months of December and January are the lowest observations. The company can efficiently manage resource allocation, staff recruitment, and other logistics to maximize services and sales during the summer.
Holidays have a profound effect on the number of bike users. 97.62% of people use bikes when there is no holiday while 2.38% use bikes when there is a holiday. This suggests that the workforce is a major customer segment, that should be considered when making value propositions and developing the business model canvas.
There was a significant rise in the number of rentals in 2012.
