Development Of a Poverty Prediction Model Using Geospatial Data in The Oshikoto Region, Namibia

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ID: 310743
2025
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Abstract
This study addresses the challenge of eradicating poverty in developing nations by exploring machine learning (ML) as a tool for efficient poverty prediction. Traditional poverty assessments rely on decennial household surveys, which are resource-intensive and infrequent, especially in African countries. The research leveraged census data from Namibia's Oshikoto Region, training three ML models - Logistic Regression, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) to classify households as poor or non-poor. The models were trained with 10-fold cross-validation using two feature selection methods: Filter and SHAP.
Reference Key
imported_1768943811_696ff0c36e192 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nalina Suresh
Journal International Journal of Science and Social Science Research
Year 2025
DOI
10.5281/zenodo.17116597
URL
https://www.ijsssr.com/j/article/view/422
Keywords Keywords not found

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