Dash Predictive Maintenance
Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Examples | 60 | 5 months ago | 1 | mit | Jupyter Notebook | |||||
| 📝 Examples of how to use Neptune for different use cases and with various MLOps tools | ||||||||||
| Ds Ml Public | 49 | a year ago | 1 | Jupyter Notebook | ||||||
| Python Scripts and Jupyter Notebooks | ||||||||||
| Dash Predictive Maintenance | 10 | 2 years ago | Python | |||||||
| Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate. | ||||||||||
| Explainingtitanic | 5 | 3 years ago | mit | Python | ||||||
| A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc | ||||||||||
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