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Medical Insurance Cost Prediction Using Machine Learning

 

 

1G. MANOJ KUMAR, 2PEKETI LAHARI

1Assistant Professor, Department of MCA, 22MCA Final Semester

Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

 

 

ABSTRACT:

The increasing cost of healthcare services has made medical insurance a crucial financial tool. Predicting insurance costs accurately helps insurance providers assess risk and allows customers to plan better. This project focuses on building a machine learning model to predict individual medical insurance charges using key features such as age, sex, BMI, number of children, smoking status, and region. The model uses linear regression as a base method to map the relationship between these variables and insurance cost. The data is pre-processed and visualized using exploratory data analysis (EDA), followed by model training and evaluation using metrics like R² score. This project demonstrates the application of data science in real-world financial estimation, providing an efficient and scalable solution for insurance cost prediction.

Index Terms: Medical Insurance, Machine Learning, Linear Regression, Cost Prediction, EDA, Streamlit, Health Analytics, Insurance Premium Estimation.

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