8000 GitHub - Leovita/IoTML: Predictive maintenance using machine learning to analyze sensor data from machines. The project involves data preprocessing, feature extraction, class balancing with SMOTE, and model training with BalancedRandomForest to predict machine health and prevent failures.
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Predictive maintenance using machine learning to analyze sensor data from machines. The project involves data preprocessing, feature extraction, class balancing with SMOTE, and model training with BalancedRandomForest to predict machine health and prevent failures.

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Leovita/IoTML

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Dataset utlizzato:

https://www.kaggle.com/code/shawkyelgendy/pump-sensor-data-timeseriesanalysis/input

  • in aggiunta -> AI generated BROKEN sensors dataset per trainare il modello per via della scarsita' dei dati "corrotti" (solo 8 nel df originale).

Per addestrare il modello: python script.py --train

Per fare previsioni su un file CSV senza etichette: python script.py --predict --input path/to/unlabeled_data.csv --output preds/predictions.csv

Per entrambe le operazioni: python script.py --train --predict --input path/to/unlabeled_data.csv

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Predictive maintenance using machine learning to analyze sensor data from machines. The project involves data preprocessing, feature extraction, class balancing with SMOTE, and model training with BalancedRandomForest to predict machine health and prevent failures.

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