Utilizing a New Approach in a Multiple Linear Regression Model to Predict Insurance Charges

Insurance industries primarily rely on premiums as a source of income. Consequently, developing an accurate algorithm for predicting insurance charges is essential for these industries. The development of artificial intelligence and machine learning techniques has given rise to generating algorithms to predict insurance charges. This research presents a methodology to enhance the performance of the regression model, providing better outcomes than previous examinations. The performance of the regression model is analogous to the Random Forest and XG-boost algorithms. Although the regression method may not have better results in a mean absolute error, it performed better in the median absolute error value. Despite the simplicity of the regression models, they have demonstrated exemplary performance compared to previous studies and other models.