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Predictive Maintenance

Companion code for blog post Intro to Predictive Maintenance on NASA Turbofan Engine Dataset using Machine Learning.

Analysis and prediction of turbofan degradation using popular regression models, including XGBoost, CatBoost and Random Forest.


In your venv:

pip install xgboost

pip install scikit-learn

pip install (other dependencies e.g. numpy, pandas, matplotlib)

After downloading and opening the .ipynb file, ensure you modify the pd.read_csv() function so it points towards the location where you have stored the downloaded data.


NASA turbofan simulation data was used to develop the model. The data was collected by sensors in an effort to characterise fault evolution.

Link to dataset