Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania

Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania

Over fifty percent of the population in Tanzania suffers from multidimensional poverty. Because of the high poverty rate and slow improvement, ending poverty by the year 2030 remains challenging and empirically testable proposition and part of a shared challenge. The main purpose of this study is to...

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Bibliographic Details
Main Authors: Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Multidimensional poverty
machine learning
zones
Online Access:https://ieeexplore.ieee.org/document/10869458/
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