Based on the analysis of the Seoul Bike Sharing Demand dataset, several factors were found to influence the number of rented bikes, including temperature, weather conditions, hour of the day, and season. Temperature and weather conditions were found to have a direct impact on bike rental demand, with higher temperatures and good weather conditions leading to more bike rentals. The hour of the day also played a role, with more rentals occurring during evening and nighttime hours. Additionally, the season was found to be a factor, with higher bike rentals occurring in the warmer months.

To predict the number of rented bikes, machine learning models were utilized, and the decision tree regressor achieved the highest accuracy of approximately 75% on the test set. Other models were also trained, and their results were compared, as shown in a plot.

Overall, the findings of this analysis can be used to inform policies and infrastructure improvements that aim to increase bike rental demand and usage. For example, by taking into account the factors that influence bike rental patterns, bike sharing companies and policymakers can strategically allocate resources and develop marketing campaigns to encourage more bike rentals during certain times of the day or year. Additionally, the predictive models developed in this analysis can be used to forecast bike rental demand and inform decisions related to bike sharing operations and planning.