Insurance Pricing Forecast Using XGBoost Regressor
Developed an XGBoost Regressor model to predict healthcare charges, optimizing insurance premium strategies based on customer features like age, BMI, smoking status, and region. Conducted exploratory data analysis (EDA) and correlation tests to inform model development, improving prediction accuracy by replacing a baseline linear regression model with XGBoost. Enhanced model performance using Sklearn's Pipeline and BayesSearchCV for hyperparameter optimization, presenting results in an accessible format for non-technical stakeholders.