Health Insurance Cost predictor by using Machine Learning, Linear and Random Forest Regression Models
Insurance is a policy that eliminates or decreases loss costs occurred by various risks. Various factors influence the cost of insurance. These considerations contribute to the insurance policy formulation. Machine learning (ML) for the insurance industry sector can make the wording of insurance policies more efficient. This study demonstrates how different models of regression can forecast insurance costs. And we will compare the results of models, for example, Liner regression and Random Forest Regressor
To create the claim cost model predictor, we obtained the data set through the Kaggle site. The data set includes seven attributes see table 1; the data set is separated into two-part the first part called training data, and the second called test data; training data makes up about 80 percent of the total data used, and the rest for test data The training data set is applied to build a model as a predictor of medical insurance cost year and the test set will use to evaluate the regression model. the following table shows the Description of the Dataset.
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