Analysis of Regular Machine Learning and Ensemble Learning Approaches for Term Insurance Prediction in Banking Data

The pursuit of trend prediction in diverse datasets has captivated the interest of researchers over the years. The significance of predictive modeling spans various domains, including business models, scientific research, banking, medical applications, and industrial applications. To address these multifaceted challenges, a plethora of predictive mechanisms have been developed and explored. This paper undertakes a comprehensive comparison and analysis of predictive algorithms employed across various applications. Utilizing a dataset from the banking domain, our focus centers on forecasting whether a client will subscribe to term insurance, drawing insights from a multitude of contributing factors. The primary emphasis of this research lies in highlighting the efficacy and elegance of ensemble learning algorithms in addressing predictive tasks.