UTILIZING LOGISTIC REGRESSION TO IDENTIFY HOUSEHOLD POVERTY STATUS IN BENUE STATE OF NIGERIA - Gaav, Terseer Andrew; Akumba, Beatrice O.; Otor, Samera U.; Agber, Selumun (FUDMA JOURNAL OF SCIENCES, 2024)
Despite numerous poverty alleviation programmes in Nigeria, household poverty continues to rise. This paper aimed to improve poverty detection by analyzing household socio-economic and environmental data using logistic regression models. Data was collected through Google forms and pre-processing and feature extractions were performed. Five logistic models were developed to represent different types of poverty. The models were evaluated for accuracy, precision, recall, and F1 score, achieving 80% for Absolute Poverty and 100% for Chronic/Structural, Conjectural/Transitory, and Locational/Spatial poverty. The analysis revealed that over half of the households were poor, with Spatial/Locational poverty being the most prevalent. Targeting this type of poverty is recommended for effective intervention.