input: def predict_house_price(features: np.ndarray) -> float:
      """Design and implement a neural network in Python to predict the price of a house given a set of features.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      0.0
      """
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
  
      model.fit(features, batch_size=3,epochs=200)
      return model.predict(features)[0][0]
output:     
      return model.predict(features)[0][0]
  