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@article{171287,
        author = {K.Nikitha and G.Rohitha and Fazal Ur Rahman and K.Kushal},
        title = {Predicting Medical Insurance Costs},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {4032-4038},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171287},
        abstract = {In healthcare industry It is very important for both the insurance companies and the insured insurance to predict as accurately as possible the cost of medical cover for an individual by using machine learning. This project centres around applying a machine learning model to predict the medical premium of an individual, based on individual characteristics-demeanour, age, gender, Body Mass Index, smoking status, number of dependents, and region of residence. Considering a dataset with earlier mentioned variables, then a predictive model can be built, which will be highly accurate in estimating insurance costs. It is employed in this research to predict medical insurance costs using Machine learning techniques like Linear Regression, Support Vector Regression (SVR), and Random Forest Regressor. Numerous models will thus be built and evaluated on how they estimate insurance premiums. Linear Regression is the simplest way to ascertain the linear relationships, while SVR captures even more complex non-linear patterns, and Random Forest Regressor uses ensemble learning to improve accuracy in predictions. The achieved results will show the capability of these models in predicting costs. At the same time, they will tell how well the models perform with respect to each other in that and which area they were good with respect to their applicability.},
        keywords = {Medical Insurance, Python, Machine Learning, Predictive Modeling, Insurance Premiums, Healthcare.},
        month = {January},
        }