This project explores drug classification using machine learning by analyzing patient health data such as age, blood pressure, and electrolyte levels. Multiple models were tested, with the Random Forest algorithm achieving 92.5% accuracy.

The project demonstrates how AI can support personalized medicine and improve clinical decision-making.