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This project aims to identify and visualise potential defect patterns based on wafer coordinate data.\n\n## Project Structure\n\n- **`data/`**: Contains the dataset used for analysis and prediction.\n- **`notebooks/`**: Jupyter notebooks for data analysis, feature engineering, and model building.\n- **`README.md`**: Project overview and usage instructions.\n\n---\n\n## Features\n\n- Load silicon wafer coordinate data from CSV files\n- Apply DBSCAN clustering to detect potential defect patterns\n- Automatically save clustering visualisations as images\n- Adjustable DBSCAN parameters (`eps` and `min_samples`) for fine-tuning results\n\n## Tools \u0026 Libraries\n\n- **Python 3.10+**\n- **Pandas** - for data handling\n- **NumPy** - for numerical operations\n- **Scikit-learn** - for the DBSCAN clustering\n- **Matplotlib** - for visualisation\n\n---\n\n## How to Use\n\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/nurulashraf/dbscan-silicon-defect-detection.git\n   cd dbscan-silicon-defect-detection\n   ```\n\n2. **Install the required libraries:**\n   ```bash\n   pip install -r requirements.txt\n   ``` \n\n3. **Prepare your data**\n\n    Place your silicon wafer coordinate data in the `data/` folder as `silicon_defect_data.csv`. The file should have two columns: `x` and `y`. \n\n5. **Run the defect detection:**\n    ```bash\n    python dbscan_silicon_defect_detection.ipynb\n    ```\n\n6. Run the cells and explore the analysis.\n\n---\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fdbscan-silicon-defect-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnurulashraf%2Fdbscan-silicon-defect-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fdbscan-silicon-defect-detection/lists"}