This analysis is based on a simulated dataset from Cyclistic, a bike-sharing program. The goal of the analysis is to gain insights into the behavior and preferences of annual members and casual riders. The analysis covers various aspects such as user demographics, bike type usage, popular ride days, seasonal ride frequency, ride length analysis, and popular bike stations. The analysis reveals that annual members are more prevalent than casual riders, indicating the potential for increased membership conversion. Casual riders show a preference for electric bikes, while annual members use classic and electric bikes almost equally. Saturday emerges as the most popular ride day, and summer sees the highest ride frequency. Additionally, the analysis highlights a significant difference in ride lengths between casual riders and members, with casual riders exhibiting longer rides on average. Furthermore, the analysis identifies the top-used and least-used bike stations, providing insights for optimizing bike availability and potential station expansion. The results also emphasize the importance of personalized marketing strategies, enhancing the digital media presence, and focusing on user experience to encourage casual riders to become annual members. It is important to note that this analysis is part of the Google Data Analytics Capstone project and uses simulated data. The findings and recommendations should be interpreted in an educational context and are not associated with any real company or organization.