Applying Machine Learning-Based Regression Models in the Prediction of Health Insurance Premium

It is no doubt that the insurance industry is no stranger to data driven decision making. The field of health insurance has seen profound transformation in recent times driven by technological advancement, data proliferation and evolved healthcare dynamics. Traditional methods for predicting health insurances premiums face several different challenges which can result in inaccurate pricing, adverse selection and suboptimal risk assessment. Some of these limitations including but not restricted to limited data utilization, static models and inefficiency in underwriting. This thesis project seeks to investigate comprehensively how machine learning based regression models and techniques, including linear regression, polynomial regression and XGBoost regression can be used in insurance to make predictions on health insurance premiums. Using a diverse historic US health insurance dataset gotten from Kaggle containing client insurance charges, demography information, lifestyle factors, these models meticulously tuned, trained, and evaluated. The study does in-depth examination of the methodologies, including exploratory data analysis, feature selection and engineering, hyperparameter optimization, and model evaluation, to determine the predictive accuracy of each model.