Predicting Poverty in the Us Using Machine Learning on Demographic and Socioeconomic Data

Poverty is a pervasive issue worldwide, including in the US, where millions face financial hardship and limited access to essential resources. Poverty prediction is crucial for developing interventions that alleviate hardship and reduce cyclical inequality. We used supervised machine learning models to predict poverty in the US, offering a more efficient and accessible approach than traditional methods. Four binary-classification models were trained on data from the 2022 American Community Survey Public Use Microdata Sample. The Extreme Gradient Boosting (XGBoost) algorithm performed best, yielding an accuracy of 88.80% and a F1-score of 88.37%. Results identify employment, education, mobility status, relationship to householder, and age as the most indicative factors of poverty. This study demonstrates that classification algorithms are valuable tools for identifying and understanding poverty across America.