Health Insurance Prediction Using Machine Leaning
Health care costs increases day by day. As there are a greater number of new viruses entering into people, there is a need to predict health charges. This type of prediction helps governments to make a decision regarding health issues. People also know the importance of health care costs. Machine Learning is a field that has an impact on every field. The health care system also uses machine learning models for several health-related applications. In this paper, we have done a predicate analysis on medical health insurance charges. We build a model to predict the medical insurance cost of a person based on gender. We collect the data set from Kaggle, which contains 1338 rows of data with the features age, gender, smoker, BMI, children, region, insurance charges. The data contains medical information and costs billed by health insurance companies. We applied various regression algorithms to this dataset to predict medical costs. For implementation, we used the Python programming language.
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