Drug classification project

This project focuses on building and evaluating machine learning models to classify patients into different drug categories based on their medical attributes. The primary goal is to predict which of the five available drugs (DrugA, DrugB, DrugC, DrugX, and DrugY) would be most suitable for a patient.

a comprehensive approach to building a robust drug classification model. Several models achieve high accuracy, with some, like the Bagging Classifier and CatBoost, reaching a perfect 1.0 on the test set, indicating their strong predictive capability. The project effectively uses data analysis and a range of machine learning techniques to solve a real-world classification problem.