Analyzing Determinants of Happiness Score: A Comparison Based on Machine Learning Approaches

DOI

10.2991/978-94-6463-300-9_32How to use a DOI?

Keywords

Happiness Score; Machine Learning; Ensemble model

Abstract

In this research, the determinants of happiness scores across countries are explored using a data-driven, machine learning-based approach. The study employs a dataset comprising variables such as GDP per capita, social support, healthy life expectancy, freedom to make life choices, etc. to predict the Happiness Index Score for the years 2018 and 2019. Three distinct machine learning models - K-Nearest Neighbors (KNN), Random Forest (RF), and Linear Regression (LR) - are implemented individually and as an ensemble to ascertain the most accurate predictor. Model performance is evaluated via three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Findings indicate that while each individual model offers valuable insights, the ensemble model outperforms them with an MAE, MSE, and RMSE respectively. Feature importance, derived from the RF model, revealed ‘Social support’, ‘GDP per capita’, and ‘Healthy life expectancy’ as the most influential parameters. This study underscores the utility of machine learning techniques and ensemble modeling in exploring the multifaceted nature of societal well-being.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuxuan Xiong
PY  - 2023
DA  - 2023/11/27
TI  - Analyzing Determinants of Happiness Score: A Comparison Based on Machine Learning Approaches
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 314
EP  - 321
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_32
DO  - 10.2991/978-94-6463-300-9_32
ID  - Xiong2023
ER  -