A Real Data-Driven Clustering Approach for Countries Based on Happiness Score

In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a grou...

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Vydané v:	Amfiteatru economic Ročník 23; číslo SI 15; s. 1031 - 1045
Hlavní autori:	Chakraborty, Aditya, Tsokos, Chris P
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bucharest EDITURA ASE 2021 ASE Publishing House The Bucharest University of Economic Studies Bucharest Academy of Economic Studies, Faculty of Commerce Editura ASE
Predmet:	Accuracy Algorithms Business Economy / Management Classification Cluster analysis Clustering Clustering Algorithms Data Decision trees Economic Indicators GDP Gross Domestic Product Groups Happiness Industrialized nations Machine learning Machine Learning Classification Algorithms Productivity Research methodology Social indicators Socioeconomic factors Stability Measures Statistical methods Subjective Well Being (SWB) Clustering Algorithms Machine Learning Classification Algorithms Economic Indicators Stability Measures Subjective Well Being (SWB)
ISSN:	1582-9146, 2247-9104, 2247-9104
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Abstract	In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a group is minimized and the variation between the groups is maximized. It falls under the class of unsupervised learning techniques. It is primarily a tool to classify individuals on the basis of similarity and dissimilarity between them. Our present study utilizes the world happiness data of 156 countries collected by the Gallup World Poll. Our study proposes a useful clustering approach with a very high degree of accuracy to classify different countries of the world based on several economic and social indicators. The most appropriate clustering algorithm has been selected based on different statistical methods. We also proceed to rank the top ten countries in each of the three clusters according to their happiness score. The three leading countries in terms of happiness from cluster 1 (medium happiness), cluster 2 (high happiness), and cluster 3 (low happiness) are Oman, Denmark, and Guyana, respectively, followed by United Arab Emirates, Finland, and Pakistan. Finally, we use four popular machine learning classification algorithms to validate our cluster-based algorithm and obtained very consistent results with high accuracy.
AbstractList	In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a group is minimized and the variation between the groups is maximized. It falls under the class of unsupervised learning techniques. It is primarily a tool to classify individuals on the basis of similarity and dissimilarity between them. Our present study utilizes the world happiness data of 156 countries collected by the Gallup World Poll. Our study proposes a useful clustering approach with a very high degree of accuracy to classify different countries of the world based on several economic and social indicators. The most appropriate clustering algorithm has been selected based on different statistical methods. We also proceed to rank the top ten countries in each of the three clusters according to their happiness score. The three leading countries in terms of happiness from cluster 1 (medium happiness), cluster 2 (high happiness), and cluster 3 (low happiness) are Oman, Denmark, and Guyana, respectively, followed by United Arab Emirates, Finland, and Pakistan. Finally, we use four popular machine learning classification algorithms to validate our cluster-based algorithm and obtained very consistent results with high accuracy.
Author	Chakraborty, Aditya Tsokos, Chris P
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SubjectTerms	Accuracy Algorithms Business Economy / Management Classification Cluster analysis Clustering Clustering Algorithms Data Decision trees Economic Indicators GDP Gross Domestic Product Groups Happiness Industrialized nations Machine learning Machine Learning Classification Algorithms Productivity Research methodology Social indicators Socioeconomic factors Stability Measures Statistical methods Subjective Well Being (SWB)
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Title	A Real Data-Driven Clustering Approach for Countries Based on Happiness Score
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