Risk analysis is a crucial part for classifying applicants in life insurance business. Since the traditional underwriting strategies are time-consuming, recent works have focused on machine learning based methods to make the steps of underwriting more effective and strengthening the supervisory. The aim of this study is to evaluate the linear and non-linear regression-based models to determine the degree of risk. Therefore, four linear and non-linear regression algorithms are trained and evaluated on a life insurance dataset. The parameters of algorithms are optimized using Grid Search approach. The experimental results show that the non-linear regression models achieve more accurate predictions than linear regression models and the LGBM algorithm has the best performance among the all regression models with the highest R2, lowest MAE and RMSE values.