• Sanjana Sain
• Preksha Jain

To determine the cost of health insurance, the insurance companies assess your lifestyle, medical history, pre-existing illnesses, and other similar factors. It’s a quite challenging task to analyse a sample from those potentialities. We work on a regression model to recognize and study a complex sample that allows us to predict and read the premium plans for medical insurance. We took a dataset from Kaggle that consists of 6 potentialities and 1339 instances. We will take the help of different regression models and read which model has an overall excessive performance in predicting health insurance.

artificial intelligence; gradient boosted regression trees; health insurance; machine learning; neural networks; forecast

"Medical Insurance Cost Prediction using PyCaret and Streamlit", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.a273-a276, May-2023, Available :http://www.jetir.org/papers/JETIR2305039.pdf

"Medical Insurance Cost Prediction using PyCaret and Streamlit", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppa273-a276, May-2023, Available at : http://www.jetir.org/papers/JETIR2305039.pdf