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

Abstract

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

Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model

“…Recently, machine learning techniques have gained attention for data prediction and analysis [12][13][14][15]. Cross-validation is also commonly performed in machine learning research, and the optimization of models using hyperparameter tuning techniques has been used to optimize the available options and parameters [16][17][18]. Recent studies have either compared the prediction performance (error) of geostatistics and machine learning techniques in interpolation [19,20] or combined both techniques for interpolation [21][22][23][24][25].…”