Analyzing Income and Welfare in South Korea

Abstract

How can machine learning classification models be utilized to correlate socioeconomic conditions with income? What kinds of insights could be generated from the unsupervised clustering of data? This paper aims to find clear and accurate correlations between socioeconomic factors and mean income, as well as cluster seemingly unrelated data together to find hidden trends or patterns in data. The machine-learning classification models provided some insights into the socioeconomic conditions of South Korea and other global countries, suggesting that several socioeconomic factors, most notably education level and number of family members, gave somewhat strong levels of correlation on whether an individual would meet or exceed the average income. This was further reinforced by unsupervised clustering performed on both datasets, where clear differences especially in education level and family members were perceived among cluster outputs, indicating its importance in the socioeconomic analysis of this paper. However, given some potential limitations and some low evaluation metrics, more research is certainly welcome to paint an even clearer picture