Analysis of Filipino Family Households Income Classification and Expenditure Patterns Using Machine Learning

Abstract

This study employs machine learning algorithms to analyze income and expenditure patterns in Filipino family households, aiming to support socioeconomic development and effective policy-making. Utilizing a comprehensive dataset from across the Philippines, which includes demographic details, monthly income, and expenditure data, the study applies Naïve Bayes, IBk, and decision trees to categorize household income levels and identify spending trends. Among the 11 algorithms tested, Naïve Bayes proves most effective, achieving the highest accuracy rate (70%), F-measure (0.695), and kappa statistics (0.5579). By leveraging these machine learning techniques, the research provides nuanced insights into the financial behaviors of Filipino families, enhancing the precision of socioeconomic planning and resource allocation. This analysis not only facilitates a deeper understanding of economic stability at the household level but also aids in tailoring governmental support to those in need, particularly during challenging economic times.