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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 12, 2023.
Abstract: Poverty is a problem that various government agencies are attempting to address accurately and precisely. This solution relies on data and analysis of features affecting poverty. Machine Learning is a technique to analyze and focus on poverty features encompassing five livelihood capitals: human, physical, economic, natural, and social capital to understand the household context and environment. The dataset contains 1,598 poverty households from Kut Bak district, Sakon Nakhon, Thailand. K-prototype was used to group categorical and numerical dataset into four clusters and labelled as Destitute, Extreme poor, Moderate poor, and Vulnerable non-poor. The performances of the Decision tree classifier with feature selection algorithms, including MI, ReliefF, RFE, and SFS, are compared. The best performance is SFS with F-measure, precision, and recall at 74.6%, 74.8%, and 74.7%, respectively. The result is the decision tree rules to predict the poverty level of households, enabling the establishment of guidelines for resolving household issues, and addressing broader problems within the areas.
Sutisa Songleknok and Suthasinee Kuptabut, “Combining Unsupervised and Supervised Learning to Predict Poverty Households in Sakon Nakhon, Thailand” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141283
@article{Songleknok2023,
title = {Combining Unsupervised and Supervised Learning to Predict Poverty Households in Sakon Nakhon, Thailand},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141283},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141283},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {12},
author = {Sutisa Songleknok and Suthasinee Kuptabut}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.