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| # model_train_save.py | |
| import joblib | |
| import numpy as np | |
| from sklearn.ensemble import RandomForestRegressor | |
| # Sample dummy data (replace this with real values) | |
| X = np.array([ | |
| [1.0, 4500], | |
| [0.8, 4800], | |
| [1.2, 4200], | |
| [0.6, 5000], | |
| [1.1, 4600], | |
| [0.9, 4700] | |
| ]) # [sulfur, gcv] | |
| y = np.array([60, 65, 55, 68, 62, 66]) # combustion efficiency (%) | |
| # Train model | |
| model = RandomForestRegressor(n_estimators=100, random_state=42) | |
| model.fit(X, y) | |
| # Save model | |
| joblib.dump(model, "model.pkl") | |