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Anomaly_model
dhruvchourey/Anomaly_model
Anomaly_model is a machine learning model from dhruvchourey. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Gradio UI to hand-test the trained flood / sensor-anomaly model.
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Public
Updated Sep 6, 2026
Repo size
2.9 MB
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Repo makeup
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.joblib2.9 MB · 99%
At a glance
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026
- SHA
- 1e1b7dfc
Model card
From the Hugging Face model README
Anomaly-Detection Model — manual tester (v3)
Gradio UI to hand-test the trained flood / sensor-anomaly model.
Enter one set of sensor readings + a node, get the calibrated anomaly %.
The reading is appended to a 25-hour calm baseline so the model's rolling / rate
features have context, then scored through the real serving pipeline
(anomaly_service.AnomalyScorer).
Scope: good for flood / out-of-range checks. A stuck sensor cannot be reproduced from a single reading. On this dataset the anomaly % behaves as a 2-band indicator (~13 % normal, ~96 % anomalous).
Files
| file | role |
|---|---|
app.py | Gradio interface |
anomaly_service.py | serving wrapper (AnomalyScorer) |
anomaly_features.py | feature engineering + rule layer |
anomaly_model_v3.joblib | trained bundle (HGB + calibrator + rule spec) |
baseline_history.csv | 25 calm hours per node, for feature context |
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026