Poverty prediction using machine learning models: Insights from HICES survey in Egypt

Abstract

This study focuses on the poverty problem in Egypt. Data from household expenditure and income surveys is used to determine the poverty status of Egyptian households. Nevertheless, conducting these kinds of surveys is challenging, costly, and time-consuming. This procedure might be revolutionized by machine learning. This work contributes to the field by utilizing machine learning techniques to evaluate and track the poverty levels of Egyptian households. This method brings poverty detection closer to real-time, and lower costs, and accuracy. A significant portion of this work involves managing unbalanced data and preparing data. Eleven machine-learning classification models are applied. The classification algorithms of the Gradient Boosting Machine and support vector machine have achieved the best performance. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes adoption across many domains in both the private sector and government.