Grant Program Result Prediction: a Real-Time Management System to Estimate Results for a Given Budget

Abstract

The integration of ARTeMiS by AUCA’s Center for Civic Engagement (CCE) as a management system for their Student Initiative Development Program (SIDP) grant program landmarks a steep increase in the SIDP Committee Members’ ability to make data-driven judgement calls. However, the system can be further improved by providing forecasts of the potential impact each project will have, wherefrom arises the need for a grant result program prediction. This paper will use regression, data mining, and dynamic programming to estimate the optimal allocation of budget to yield maximal impact.