Data

The model uses the 'healthcare-dataset-stroke-data.csv' file, which includes the following features:

• id
• gender
• age
• hypertension
• heart_disease
• ever_married
• work_type
• Residence_type
• avg_glucose_level
• bmi
• smoking_status
• stroke

Methodology

1. Data Preprocessing:

• Removal of unnecessary columns
• Handling missing data
• Standardization of numerical features

2. Clustering:

• K-means clustering is applied to identify patterns in the data
• The optimal number of clusters is determined using the elbow method

3. Analysis:

• Visualization of clusters
• Interpretation of results in the context of stroke prediction

Results

[Briefly describe the key findings and insights from your model. Include any relevant visualizations or metrics.]

Contributing

Contributions to this project are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License.