This repo's analysis is based on the London public bike share dataset, accessed through GCP's BigQuery.
Customers frequently complain about bike stations being empty. I analyzed real-time rider data to answer the following business questions through SQL and made suggestions about how to improve the service quality of bike sharing systems in London.
- Find traces of empty stations.
- How big is this problem?
- What are the most popular stations in the network?
- When does their usage peak?
- What are the most popular trips in the network?
- Are there differences in the types of rides that people take?
- Is there a pattern in the types of stations that are empty?
London Bike Share User Analysis: This document contains data-driven two insights about the complaints of empty bike stations. Finally, an actionable recommendation is given based on these insights.
London Bike Share Data Dashboard: This dashboard contains visualization of key findings from the user data analysis, including fill rate per station, popular trips, popular stations and usage patterns.
Queries for the Analysis: This file contains all the queries where the insights and recommendations are based on.