Prediction of Happiness Score by Adopting Simple Linear Regression Model

Happiness is a topic that experts are studying more and more in today’s culture. Investigating pleasure from the standpoint of data mining is intriguing. The use of mining data, which operates from a fresh knowledge-driven perspective, is steadily becoming more ubiquitous and well-liked in the psychology field. Research on machine learning in particular, which is now being studied, brings fresh concepts and novel approaches to research to the field of psychiatry. This study examines the accuracy of learning-based happiness prediction methods using linear regression. We discuss the happiness variables that influence it and present the experimental findings of happiness prediction. This study examines the factors influencing happiness and examine the effects of various ensembles for pleasure prediction using the four stages of factor analysis, feature engineering, model establishment, and evaluation. According to the findings of the investigation, certain individual factors—such as mental health, socioeconomic level, and social rank—have a stronger impact on happiness than others. These factors include societal attitudes, income of the person and family capital. Furthermore, boosting performs the best in modelling fusion among the happiness prediction models created by these five characteristics.