input: 
  def predict_house_price(features: np.ndarray) -> float:
      """ Predict the price of a house given a set of features using a neural network.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      array([[0.12345678]])
      """
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense
  
  features = np.array([[0.8, 0.4, 0.7]])
  
  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)
  
output:     
      predicted_price = model.predict(features)
      return predicted_price[0][0]
  