ABSTRACT
With increasing population levels and poverty rate it has become a major problem for Non-profit organizations and agencies to ensure that the right kind of people receive alleviation. The world’s poorest typically cannot provide the necessary income and expense records to prove that they qualify for aid. In Latin America, one popular method to verify income qualification is by using an algorithm called the Proxy Means Test (or PMT). With PMT, agencies use a model that considers a family’s observable household attributes like the material of their walls and ceiling, or the assets found in the home to classify them and predict their level of need. In this paper we aim to examine and analyse how the individual and household characteristics attribute to poverty levels in Costa Rica and ensemble the results of various machine learning models to achieve better accuracy.
