Statistical Analysis of Insurance Charges: A Predictive Modeling Study

This study presents a comprehensive statistical analysis of insurance charges using demographic and health-related predictors. We employed linear regression and Ridge regression techniques to identify key factors influencing insurance costs and develop predictive models. Our analysis reveals that smoking status is the most significant predictor of insurance charges, followed by the number of children, BMI, age, region, and sex. The final linear regression model achieved an R-squared value of 0.769, explaining approximately 77% of the variance in insurance charges.