This document describes analyzing a donor dataset to predict whether a prospect will donate or not donate. It discusses the project goals, tools used, data preprocessing steps, and various predictive models tested including CHAID, forward regression, backward regression, and stepwise regression. The models are compared on their ability to capture the top donors based on cumulative lift charts. Forward regression performed best at capturing the top 20% of donors, while backward regression captured the top 30% best. Adding an additional correlated variable to forward regression did not significantly improve performance.