Project Overview Processed and analyzed over

Project Overview Processed and analyzed over 3.48M bike-share trip records using Python (Pandas) to evaluate behavioral differences between annual members and casual riders. Built an interactive Tableau dashboard to uncover usage patterns across days, ride durations, and seasonal trends. ​Key Insights & Dashboard Features ​Rider Behavior Comparison: Discovered that casual riders cycle 179% longer on weekends compared to annual members. ​Temporal & Seasonal Trends: Mapped trip volume distributions throughout the week and across seasons to identify peak usage windows. ​Strategic Conversion Recommendations: Formulated 3 data-driven marketing strategies to convert casual riders into annual subscribers. ​Tools & Technologies ​Data Processing & Feature Engineering: Python (Pandas) ​Data Visualization & Dashboarding: Tableau