A Performance Analysis of Supervised Machine Learning Techniques for COVID-19 and Happiness Report Dataset

Farooq, Syed Abu; Shanmugam, Selvanayaki Kolandapalayam

doi:10.1007/978-981-16-5157-1_46

Cited by 2 publications

(1 citation statement)

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“…While the work [18] studied the relationship between global latitude and QoL measurements of the HDI and WHI using regression analysis and correlation. The authors of [19], combined two datasets, namely the happiness report dataset and the COVID-19 dataset, which comprises confirmed cases in multiple countries, to employ multiple machine learning algorithms, including linear regression, KNN, NB, SVM, and logistics regression. And in [20], authors conducted a study on WHR data to identify predictors of the WHI's happiness score using its other dimensions and used a variety of algorithms, which are: MLP, RF, One Rule, and XGBoost.…”

Section: Related Workmentioning

confidence: 99%

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

Jannani¹,

Sael²,

Benabbou³

2023

Math. Model. Comput.

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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.

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Section: Related Workmentioning

confidence: 99%