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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Veröffentlicht in:Amfiteatru economic Jg. 23; H. SI 15; S. 1031 - 1045
Hauptverfasser: Chakraborty, Aditya, Tsokos, Chris P
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bucharest EDITURA ASE 2021
ASE Publishing House
The Bucharest University of Economic Studies
Bucharest Academy of Economic Studies, Faculty of Commerce
Editura ASE
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:1582-9146
2247-9104
2247-9104
DOI:10.24818/EA/2021/S15/1031