This project analyses bike rental data from a bike-sharing system with the aim to:
- Analyse daily bike rental patterns to identify peak and low usage days, and understand how rentals vary over time and by day type
- Examine the impact of weather conditions on bike rental behavior and determine which factors most strongly influence demand
- Explore customer behavior differences between casual and registered users, including their usage patterns, preferred days, and sensitivity to weather changes
The analysis is presented through a Jupyter Notebook. View the Jupyter Notebook
This dataset contains detailed records of bike sharing activity collected from a bike sharing system from 2011 to 2012. Each row represents a single day and includes information about the type of day, weather conditions, rider type, and the total number of bikes rented.
- Jupyter Notebook (for data cleaning, wrangling and dashboard creation)
