This project performs data analysis on the London bike sharing dataset obtained from Kaggle.com. The data comes from Transport for London's public data portal and contains information on the Santander Cycles bike sharing system.
The analysis focuses on examining bike usage patterns in London using historical trip data provided in the dataset. Key data fields analyzed include bike id, start and end stations, start and end times, and duration for each trip. Python libraries including Pandas and Matplotlib are used to import, clean, explore and visualize the data.
Insights uncovered through analysis of this dataset include:
Identification of peak rental hours during weekdays vs weekends Ranking of most and least used bike stations Average trip duration by day and time Trends in daily and monthly bike usage over the time period examined The project demonstrates thorough exploration and analysis of the bike sharing dataset using Python. Both high-level trends and granular insights are extracted through effective data manipulation and visualization using pandas and matplotlib. The findings provide an in-depth understanding of bike rental usage and patterns across London.