Author : Yu Tan 1
Date of Publication :20th May 2021
Abstract: Happiness is the current important research issues in psychology and social sciences, which is affecting people's daily lifestyle, work habits and thinking patterns, it also provides guidance for government policy making. However, in the current analysis of happiness, there are many challenges in variable selection and prediction. Due to the large personal differences and differences in determinants of the experience of happiness, this undoubtedly makes the modeling of happiness more difficult. Based on reviewing several academic literatures, this paper uses questionnaire data from 20 countries in World Values Survey Database, then uses machine learning methods to compare traditional regression approach with machine learning regression approach to predict happiness. Finally, some determinants variables were found. To a certain extent, the elastic net method applied in model was successfully used to predict happiness, social factors, economic factors and personal factors affect the modeling and prediction of happiness in varying degrees, which brought new opportunities for the development of related theories and practices at the study of happiness.
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