Date of Publication

2023

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Statistics Major in Actuarial Science

Subject Categories

Statistics and Probability

College

College of Science

Department/Unit

Mathematics and Statistics Department

Thesis Advisor

Olivia P. Pagulayan

Defense Panel Chair

Maria Angeli S. Reyes

Defense Panel Member

Kevynn P. Delgado

Abstract/Summary

Poverty remains one of the most significant issues the Philippines faces today. Despite the country’s poverty rate slowly decreasing over the years, the COVID-19 pandemic caused the situation to worsen once again. This study aimed to propose an alternative classification for poverty by using machine learning and k-fold cross-validation among the decision tree algorithm, logistic regression, and Naïve Bayes classifier to get a better representation of the poverty-stricken households in the Philippines. The criteria used to determine the best classification algorithm will be accuracy, specificity, recall, and F1 score. This study found that the algorithm with the highest sensitivity was the Naïve Bayes classifier, while the algorithm with the highest specificity was the decision tree algorithm. However, the logistic regression algorithm was deemed the “best” among the three since it is able to determine both poverty and non-poverty households due to it having the most balanced results across all four criteria.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Poverty--Philippines; Algorithms

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Embargo Period

8-14-2023

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