{"id":31698722,"url":"https://github.com/nurulashraf/predictive-maintenance-analysis-for-machine-failure-prevention","last_synced_at":"2026-04-16T02:32:40.310Z","repository":{"id":318611042,"uuid":"1069806517","full_name":"nurulashraf/predictive-maintenance-analysis-for-machine-failure-prevention","owner":"nurulashraf","description":"Predictive maintenance analysis for machine failure prevention using sensor data and ML. 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This project builds a Random Forest model and a Gradio dashboard to identify high-risk machines for proactive maintenance.\n\n---\n\n## Project Structure\n\n- `data/` — contains datasets used in the analysis (raw, cleaned, processed)  \n- `notebooks/` — Jupyter notebooks for data exploration, model building, and dashboard prototyping  \n- `requirements.txt` — list of Python dependencies  \n- `LICENSE` — the licensing file  \n- `README.md` — this documentation  \n\n---\n\n## Features\n\n- Exploratory data analysis and visualization of sensor data  \n- Data cleaning, feature engineering, outlier handling  \n- Training of a **Random Forest** classifier to detect machines at risk of failure  \n- Model evaluation metrics: accuracy, precision, recall, F1 score, ROC curve  \n- Gradio-based dashboard for interactive inference / risk scoring  \n- Ability to flag high-risk machines to inform maintenance decisions  \n\n---\n\n## Tools \u0026 Libraries\n\n- `pandas`, `numpy` - Data handling\n- `matplotlib`, `seaborn` - Machine learning \n- `scikit-learn` - Machine learning \n- `gradio` - Interactive dashboard \n- `jupyter` - Notebook environment \n\n---\n\n## How to Use\n\n### Setup\n\n1. Clone the repository:  \n   ```bash\n   git clone https://github.com/nurulashraf/predictive-maintenance-analysis-for-machine-failure-prevention.git\n   cd predictive-maintenance-analysis-for-machine-failure-prevention\n   ````\n\n2. Create a virtual environment (recommended):\n\n   ```bash\n   python3 -m venv venv\n   source venv/bin/activate    # On Windows: venv\\Scripts\\activate\n   ```\n\n3. Install dependencies:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Open notebook**\n\n   Launch Jupyter:\n\n   ```bash\n   jupyter notebook\n   ```\n\n   or\n\n   ```bash\n   jupyter lab\n   ```\n\n   Then open the notebook in the `notebooks/` folder.\n\nThe dashboard allows users to input sensor features and get a risk prediction (e.g. “high risk” or probability) for machine failure.\n\n---\n\n## License\n\nThis project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for full details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fpredictive-maintenance-analysis-for-machine-failure-prevention","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnurulashraf%2Fpredictive-maintenance-analysis-for-machine-failure-prevention","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fpredictive-maintenance-analysis-for-machine-failure-prevention/lists"}