Journal of Current Research on Business and Economics (JoCReBE) - ISSN: 2547-9628

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Abstract


Investigation of Happiness of Countries with K-Means Clustering and Discriminant Analysis

The purpose of this study is to determine the effect degrees of happiness indicators on happiness and similar countries in terms of happiness. For this purpose, 156 countries included in the 2019 World Happiness Report were analyzed with K-Means clustering and discriminant analysis. Discriminant analysis was conducted to determine the functions that distinguish the 9 clusters obtained as a result of K-Means and the indicators that best represent the groups while creating these functions. K-Means cluster analysis results are visualized by combining them in two-dimensional space obtained from discriminant analysis. Thanks to the new visual obtained by using these two analysis results together, the K-Means clustering structures are better understood and the results are more easily interpreted. From this point of view, a new visualization approach based on discriminant analysis can be proposed in which the K-Means cluster analysis results can be interpreted. Discriminant analysis results obtained in the study were found to be compatible with the results of K-Means cluster analysis. In both methods, it was found that the "GDP Per Capita" indicator affected the happiness states of the countries the most. We believe that these results will provide important information by policy makers in increasing the happiness level of the countries.



Keywords
K-Means Clustering, Discriminant Analysis, Happiness



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