Machine learning approach to predict the depression in job sectors in Bangladesh

Abstract

Depression is a significant and growing issue that substantially affects an individual's way of life, interrupting typical functioning and blocking viewpoints. At the same time, they may be unaware they are suffering such a problem. This research focuses on depression prediction and determines which sex is sadder and more satisfied with their employment. The writers gathered data from both men and females to get accurate statistics. We used factor analysis, Random Forest Classifier, Random Forest Regression, Naive Bayes, and K Neighbors Classifier algorithms to determine which sources of stress predict stress-related symptoms in people exploring job satisfaction as predicted and job depression by age, monthly income, gender, occupation, children, city, previous job, marital status, and current job satisfaction level.