Anticipating Human Happiness: Exploring Machine Learning Strategies

Happiness, a fundamental aspect of human well-being, has garnered increasing attention in the context of rising psychological health concerns. Given the current surge in psychological health issues and a decline in happiness indicators, understanding and predicting happiness levels have become significant research area. This research paper explores the application of diverse machine learning techniques to assess the levels of happiness in individuals. In this study, we employed the Oxford Happiness Questionnaire as a means to predict happiness levels. We collected data from 601 individuals using Google Forms, based on their responses to the Oxford Happiness Questionnaire. Unlike traditional approaches that rely on questionnaires and surveys to measure happiness indices, this article represents a shift toward a machine learning approach. We implemented various machine learning methods, utilizing a range of artificial intelligence classification algorithms through the R programming language. The effectiveness of machine learning methods was assessed using multiple parameters, such as accuracy rate, precision, and the F1 measure. It offers an indication of the quantity of people who are “pleased” versus those who are “unpleased.” This paper provides insights into the potential of machine learning to forecast and understand happiness, contributing to the broader discourse on human emotional well-being. Predicting happiness is valuable because it not only provides insights into individuals’ emotional states but also has wide-ranging implications for public policy, health care, business, and personal growth. It helps in creating happier, healthier, and more prosperous societies.