Machine Learning Insights into Personalized Insurance Pricing

The use of machine learning techniques to forecast the cost of medical insurance is thoroughly examined in this study. We experienced numerous health problems during the COVID-19 pandemic. The creation and assessment of predictive models for the purpose of calculating medical costs are the subjects of our investigation. This study has utilized the 4968-row USA medical insurance dataset from Kaggle. The dataset's features allow a summary of an individual's personal and medical circumstances. Regression analysis and gradient boosting are the two machine learning methods utilized to build predictive models. The analysis reflected the link between features and costs. After training the model, a 94% accuracy rate has been obtained.