Predicting Yulu Bike Demand
Understanding the impact of weather and season on bike-sharing demand using ANOVA (Python).
Introduction
This project aims to uncover the factors influencing the demand for Yulu bike rentals in India. By analyzing variables such as weather, season, temperature, humidity, and holidays using statistical methods like ANOVA, we identified the significant drivers of demand. These insights help optimize operations and enhance market strategies for bike-sharing systems.
Key Variables & Methods
- Weather & Season: Analyzed their effects on ride counts using ANOVA.
- Temperature, Humidity, Wind Speed: Converted continuous vars into categories.
- Holiday & Working Days: Used t-tests to evaluate demand differences.


Analysis Workflow
- Data Preprocessing
- ANOVA for categorical variables like weather & season
- t-Tests for holidays vs. working days
- Post-hoc tests for specific category differences
Conclusion
The analysis revealed that weather and season significantly influence Yulu bike demand, offering actionable recommendations for operational adjustments and marketing strategies to enhance market success.
