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linear-regression-tetuan-power — Linear Regression (Phase 1)sklearn.linear_model.LinearRegression (ordinary least squares), trained with
scikit-learn 1.6.1. No other regression algorithm is used in
this model.random_state=42.TemperatureHumidityWind Speedgeneral diffuse flowsdiffuse flowsZone 1 Power Consumption (float, same units as the
training target).| Metric | Value |
|---|---|
| MAE | 5119.2703 |
| MSE | 39281693.2639 |
| RMSE | 6267.5109 |
| MAPE | 0.1658 |
| R² | 0.207988 |
| Adjusted R² | 0.207543 |
1from huggingface_hub import hf_hub_download
2
3model_path = hf_hub_download(
4 repo_id="YOUR_USERNAME/linear-regression-tetuan-power",
5 filename="linear_regression.joblib",
6)1import joblib
2
3model = joblib.load(model_path)1import pandas as pd
2
3sample = pd.DataFrame([{
4 'Temperature': 0.0,
5 'Humidity': 0.0,
6 'Wind Speed': 0.0,
7 'general diffuse flows': 0.0,
8 'diffuse flows': 0.0,
9}])
10
11prediction = model.predict(sample)
12print(prediction)test_downloaded_model.ipynb in this repository for a full
download -> load -> predict -> evaluate example on a different CSV dataset.