bajiang/Electricity_Price_Predictor_Random_Forest_Regression

1import joblib2import numpy as np3 4# Load the trained model5model = joblib.load("random_forest_model.pkl")6 7def predict(features):8    """features: list of 24 numeric values"""9    features = np.array(features).reshape(1, -1)10    return model.predict(features)[0]11