This Drug Classification project utilizes a Decision Tree Classifier to accurately predict prescribed medications based on patient health profiles. The implementation features a modular scikit-learn pipeline with a ColumnTransformer to handle various data types.

A Decision Tree model from scikitlearn was employed to determine, with 98% accuracy, the most suitable drug for a patient from a historical dataset. The categorical values in the dataset were label-encoded and the model was trained to minimize entropy, with max depth = 4.