Authors : Dr. N. Dhivya; M. Subalakshmi

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3nhe9ztv

Scribd : https://tinyurl.com/884mz5fc

DOI : https://doi.org/10.38124/ijisrt/26apr1803

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The insurance industry increasingly relies on data-driven technologies to enhance pricing strategies and risk assessment. This study presents a machine learning-based approach to predict insurance charges using customer demographic and health-related attributes. The dataset consists of features such as age, gender, body mass index (BMI), number of children, smoking status, and region. Data preprocessing techniques, including data cleaning, categorical encoding, and normalization, were applied to improve model performance. A Linear Regression algorithm was implemented to develop the prediction model. The model was evaluated using performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) score. The proposed model achieved an R² score of 0.85 (85% accuracy), indicating a strong relationship between predicted and actual insurance charges. The results demonstrate that machine learning techniques can effectively model complex relationships in insurance data and provide reliable predictions. This system can assist insurance companies in making accurate, data-driven pricing decisions.

Keywords : Machine Learning, Insurance Prediction, Linear Regression, Data Science, Data Preprocessing, Predictive Analytics.

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