Machine Failure Prediction Model is a solution that leverages machine learning to predict potential failures in machines. Utilising the power of scikit-learn and data analytics, this model can assist industries and organisations in anticipating machinery breakdowns and taking preventive measures.
- Train a predictive model on historical machine data.
- Evaluate model performance with various metrics.
- Utilise various
scikit-learnmachine learning algorithms for optimal results. - Fine-tune and optimise model parameters.
- Predict potential failures before they occur.
- Python 3.7 or higher.
scikit-learnlibrary.- Basic understanding of machine learning concepts.
- Clone this repository:
git clone https://github.com/amidstdebug/Machine-Failure-Prediction-Model.git
- Navigate to project directory:
cd "Machine Failure Prediction Model"
-
Prepare your dataset by following the dataset format as mentioned in the
*.csvfile. -
Open the IPython notebook to view the implementation:
Jupyter Notebook "Machine Failure Prediction Model.ipynb"
- Fork the project.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -m 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a pull request.
For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under an Open Source but Non-Commercial Use Licence - see the LICENCE file for details.
- scikit-learn for providing the tools to develop the machine learning model.
- Machine Learning Mastery for tutorials and best practices.
- And all contributors who have aided in refining and enhancing this project!
If you encounter any issues or have questions, please file an issue or contact the maintainers. We welcome feedback and contributions!