The Choose-Drug project leverages machine learning to recommend the most suitable medication for patients based on their characteristics. Using a Decision Tree Classifier, the model predicts the best drug by analyzing key patient features such as age, gender, and symptoms.
✅ Smart Drug Recommendation – Predicts the best medication based on patient data.
✅ Machine Learning Model – Utilizes a Decision Tree Classifier for accurate predictions.
✅ Data-Driven Insights – Based on the drug200 dataset, containing real-world pharmaceutical data.
Choose-Drug/
├── drug200.csv # Dataset used for training the model
├── Choose_Drug.ipynb # Jupyter Notebook with implementation and analysis
└── README.md # Project documentation
- Python – Core programming language.
- Libraries:
pandas– Data manipulation and preprocessing.scikit-learn– Machine learning model implementation.matplotlib&seaborn– Data visualization.
- Source: Kaggle - Drug200
- Contains patient attributes such as age, gender, and medical conditions, helping train the model for drug classification.
This project is open-source and licensed under the MIT License.