The document proposes demand prediction and dynamic pricing models for a bike sharing business utilizing machine learning. It first analyzes factors influencing demand like location, day of week, and weather. A random forest regression model is then created to predict demand based on these factors, achieving good accuracy. Finally, a dynamic pricing model is proposed to set prices based on predicted demand fluctuations rather than static pricing, aiming to better match supply and demand.