MedicalExpenseAnalyzer

This project aims to analyze and predict health insurance costs based on various variables. It involves exploring the relationships between factors such as BMI, smoking habits, region, and gender with insurance charges. Through data visualization, preprocessing, model selection, and hyperparameter optimization, the project builds predictive models to estimate the approximate cost of a person's health insurance. Finally, the performance of the models is evaluated using regression metrics like Mean Squared Error and Mean Absolute Error.