This project uses logistic regression to predict customer subscription behavior based on age and gender. By quantifying how these demographics drive conversion, this analysis identifies high-value customer segments. The resulting evidence-based insights will guide managerial decisions on marketing investment and targeting strategy.

This project utilizes event-based regression modeling to quantify how specific calendar events, such as paydays and holiday closures, drive daily customer traffic patterns at a bank branch.