input: from typing import List
  
  def predict_house_price(features: List[float]) -> float:
      """ Predict the price of a house given a set of features using a neural network.
      >>> predict_house_price([0.8, 0.4, 0.7])
      0.0
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Example neural network model
      model = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=1000)
      model.fit(np.random.rand(100, 3), np.random.rand(100))
  
      # Predict the house price
      prediction = model.predict([features])
      return prediction[0]