Random forest regression with hyper parameter tuning for medical insurance premium prediction

The proposed effort has the purpose of predicting an individual’s insurance expenses also identifying people having medical plans and clinical data, irrespective their health concerns. A patient will require many types insurance. Regardless type coverage a person has, it is feasible to estimate expenditures depends on degree critical care they get. random forest Regression one regressors used in this investigation. When accuracies were compared, hyper parameter tuning was most effective all approaches, with 98 percent accuracy. Finally, prediction fit calculate expense user costs.