Bike-Sharing Optimization & Anomaly Detection
Used SQL to reverse-engineer a bike-sharing system's operations — predicting peak demand, spotting anomalies in rider counts and revenue, and finding the operational sweet spots where efficiency meets profitability. Built interactive Power BI dashboards so operations teams can actually see when and where to deploy bikes instead of guessing.
SQL
Power BI
Anomaly Detection
Predictive Modeling
Data Analytics
This project aims to optimize rider efficiency and maximize profitability in a bike-sharing system through advanced data-driven insights. By leveraging SQL for data analysis and Power BI for dynamic visualization, the project identifies peak operational hours, predicts rider demand, and detects anomalies in rider counts and revenue.