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  3. Volume 10, Number 2, 2023
  4. Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators

Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators

MMC.
2023;
Volume 10, Number 2
: pp. 534–546
https://doi.org/10.23939/mmc2023.02.534
Received: January 31, 2023
Accepted: April 22, 2023

Mathematical Modeling and Computing, Vol. 10, No. 2, pp. 534–546 (2023)

Authors:
  1. A. Jannani
  2. N. Sael
  3. F. Benabbou
1
Laboratory of Information Technology and Modeling, Faculty of sciences Ben M'Sik, Hassan II University of Casablanca
2
Laboratory of Information Technology and Modeling, Faculty of sciences Ben M'Sik, Hassan II University of Casablanca
3
Laboratory of Information Technology and Modeling, Faculty of sciences Ben M'Sik, Hassan II University of Casablanca

Machine learning algorithms play an important role in analyzing complex data in research across various fields.  In this paper, we employ multiple regression algorithms and statistical techniques to investigate the relationship between objective and subjective quality of life indicators and reveal the key factors affecting happiness at the international level based on data from the Human Development Index and the World Happiness Index covering the period from 2015 to 2021.  The Pearson correlation analysis showed that happiness is related to the HDI score and GNI per capita.  The best-performing model for forecasting happiness was the random forest regression, with a R2 score of 0.93667, a mean squared error of 0.0033048, and a root mean squared error of 0.05748, followed by the XGBoost regression and the Decision Tree regression, respectively.  These models indicated that GNI per capita is the most significant feature in predicting happiness.

machine learning
quality of life indicators
Random Forest Regression
XGBoost Regression
Decision Tree Regression
statistical analysis
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