Health Insurance Amount Prediction

Abstract

In this thesis, we analyse the personal health data to predict insurance amount for individuals. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. Dataset was used for training the models and that training helped to come up with some predictions. Then the predicted amount was compared with the actual data to test and verify the model. Later the accuracies of these models were compared. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression.