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jskswamy/
predictive-maintenance-model

Name: predictive-maintenance-model
Availability: InStock

sklearn

joblib

predictive-maintenance

tabular-classification

adaboost

mit

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Predictive Maintenance Model

AdaBoost classifier for predicting engine maintenance needs based on sensor readings.

Model Description

This model predicts whether a diesel truck engine requires maintenance (1) or is operating normally (0) based on 6 sensor inputs. It uses an AdaBoost ensemble with Decision Tree base estimators, optimized for maximum recall to minimize missed failures.

Architecture

Algorithm: AdaBoost Classifier
Base Estimator: Decision Tree (max_depth=3)
Ensemble Size: 383 estimators
Learning Rate: 0.261

Performance

Metric	Value
Recall	99.78%
Precision	63.2%
F2 Score	0.917
ROC-AUC	0.70

Threshold: 0.316 (optimized for maximum recall)

Usage

python

import joblib
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="jskswamy/predictive-maintenance-model",
    filename="best_model.joblib"
)
model = joblib.load(model_path)
prediction = model.predict_proba(features)[:, 1]
needs_maintenance = prediction > 0.316

Limitations

Trained on Class 8 diesel truck data
Requires all 6 sensor inputs
Static prediction (no temporal patterns)

License

MIT License