An Analytical View on World Happiness with Unsupervised Machine Learning

Abstract

This analysis covers the underlying behavior and meaning of the data provided by the World Happiness Report (WHR). The data includes six parameters that the WHR uses to calculate world rankings. From the data analysis, several issues with the growth of the world as a whole and the lack of resources in the trailing countries can be inferred. From the trends in bar graphs and histograms, world growth can be categorized as a “spearhead” type of happiness growth, with developing countries that are behind developed countries and a few trailing countries that lag behind the rest of the world. A heatmap was generated to show the correlation between the 6 variables, showing that corruption and generosity have no correlation with any other variable including the happiness score. Using hierarchical clustering, an unsupervised machine learning model, 3 clusters of countries were found, which supports the results of the heatmap and shows that the poorest cluster, while ranking high in generosity, still rank much lower than the other two groups of countries.