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
