Innovative Poverty Estimation through Machine Learning Approaches

Abstract

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges.