Random forest regression with hyper parameter tuning for medical insurance premium prediction
Keywords:
random forest regression, hyper parameter, machine learning, predictionAbstract
The proposed effort has the purpose of predicting an individuals 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.
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