input: from typing import List
  
  def predict_house_price(features: List[float]) -> float:
      """Design and implement a neural network in Python to predict the price of a house 
      given a set of features.
      >>> predict_house_price([0.8, 0.4, 0.7])
      0.598
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Define the neural network model
      model = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
  
      # Dummy training data
      X_train = np.array([[0.8, 0.4, 0.7], [0.5, 0.3, 0.6], [0.9, 0.2, 0.8]])
      y_train = np.array([0.598, 0.456, 0.789])
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict the house price
      prediction = model.predict([features])
  
      return prediction[0]