🧠 Stroke Prediction Models

This project utilizes Kaggle's stroke prediction dataset to develop and compare three decision tree-based machine learning models:

1. XGBoost
2. Random Forest
3. Decision Tree (scikit-learn)

📂 Project Structure

• 📜 model.ipynb – Jupyter Notebook containing the implementation and comparison of the three models.
• 📜 README.md – Project documentation.
• 📜 requirements.txt – List of dependencies required to run the project.

⚙️ Installation

Ensure you have Python installed, then install the required dependencies using:

pip install -r requirements.txt

🚀 Usage

1. Open model.ipynb in Jupyter Notebook or JupyterLab.
2. Run the notebook cells to train, evaluate, and compare the models.
3. Analyze the results and accuracy metrics.

📊 Conclusion

Based on the accuracy results:

✅ XGBoost provides the highest accuracy.
✅ Random Forest performs well but is slightly less accurate than XGBoost.
⚠️ Decision Tree has the lowest accuracy among the three models.

📁 Dataset

The dataset used in this project is the Stroke Prediction Dataset from Kaggle. You can access it here.

📜 License

This project is licensed under the MIT License.