# 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")