input: def predict_house_price(features: np.ndarray) -> float:
      """Given an array of features, this function predicts the price of a house using a neural network model.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      0.0
      """
output:     model = load_model('house_price_model.h5')
      prediction = model.predict(features)
      return prediction[0][0]