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])
      0.5623
      """
      pass
output:     
      import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Sample training data
      X_train = np.array([[0.8, 0.4, 0.7], [0.5, 0.3, 0.6], [0.7, 0.2, 0.5]])
      y_train = np.array([0.5623, 0.4567, 0.3456])
  
      # Train a simple neural network
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict the house price
      return model.predict([features])[0]
  