In this study, we examine individual
insurance amounts using health data. The performance
of these algorithms has been compared using the three
regression models employed in this study: multiple linear
regression, decision tree regression, and decision tree
regression. The dataset is used to train the models, and
the training then assists in producing more predictions.
Later, the model will be tested and verified by
comparing the anticipated quantity with the actual data.
These models' accuracy levels will then be compared.
The decision tree and linear regression are outperformed
by the random forest regression algorithm, according to
the analysis. It enables a person to understand the
required amount based on their health situation. They
might examine any health insurance company, their
plans, and the benefits while keeping in mind the
anticipated amount from the project. Later, the
predicted amount will be compared with the real
amount. This can also be quite beneficial to someone
who wants to concentrate more on the useful aspects of
insurance than the health-related ones. In addition, most
people are susceptible to being duped regarding the cost
of insurance and may unnecessarily purchase expensive
medical coverage. This project does not provide the
precise sum needed by any health insurance provider,
but it does provide a general sense of the sum needed by
an individual for their personal health insurance.
Prediction is inaccurate and does not apply to any
organization; therefore, it should not be the only factor
considered when choosing a health insurance plan. First,
estimating the cost of health insurance is extremely
beneficial and helps in better examining the amount
required so that a person can be confident that the
amount he or she is going to justify It can also provide
you with a wonderful idea for maximizing your health
insurance profits.
Keywords :
Health Insurance Premium Prediction, Linear Regression, Decision Tree Regression, Multiple Regression Algorithm, Machine Learning, Python, Deep Learning, Insurance Amount Prediction, Random Forest Regression Algorithm.