Medical Cost

In the Medical Cost Prediction project, I was responsible for

predicting individual medical insurance costs using demographic and health data.

I performed data cleaning, categorical encoding, and exploratory analysis to understand feature impacts.

Linear regression and polynomial regression were implemented to capture both linear and non-linear patterns.

The project utilized Python, Pandas, NumPy, Scikit-learn, Seaborn, and Matplotlib.

Results showed that age, BMI, and smoking status are key cost drivers, with polynomial