input: 
  from typing import List
  
  def house_price_prediction(features: List[float]) -> float:
      """ Design and implement a neural network in Python to predict the price of a house given a set of features.
      >>> house_price_prediction([0.8, 0.4, 0.7])
      """
  
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Example training data
      X_train = np.array([[0.7, 0.3, 0.6], [0.5, 0.2, 0.8], [0.9, 0.1, 0.5]])
      y_train = np.array([150000, 200000, 250000])
  
      # Train a neural network
      model = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=1000)
      model.fit(X_train, y_train)
  
      # Predict the house price
      prediction = model.predict([features])
      return prediction[0]