Instructions to use mariamismael/signal-quality-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use mariamismael/signal-quality-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("mariamismael/signal-quality-models", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Signal Quality Prediction Models
Predicts RSRP and RSRQ cellular signal metrics from GPS coordinates.
Models
| File | Type | Target |
|---|---|---|
| rf_rsrp.pkl | Random Forest | RSRP |
| rf_rsrq.pkl | Random Forest | RSRQ |
| gpr_rsrp.pkl | Gaussian Process | RSRP |
| gpr_rsrq.pkl | Gaussian Process | RSRQ |
Each file is a full sklearn Pipeline (scaler + model).
Usage
import joblib
import pandas as pd
model = joblib.load("rf_rsrp.pkl")
new_point = pd.DataFrame({
'latitude': [30.0000],
'longitude': [31.0000]
})
prediction = model.predict(new_point)
print(f"RSRP: {prediction[0]:.2f}")
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