input: 
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense
  
  
  def predict_house_price(features):
      """
      Create a neural network model to predict the price of a house based on given features.
      
      >>> features = np.array([[0.8, 0.4, 0.7]])
      >>> predict_house_price(features)
      (None, array([0.123,], dtype=float32))
      """
      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'])
  
      return model.fit(features, batch_size=3, epochs=200)
  
output:     model.fit(features, np.zeros((features.shape[0], 1)), batch_size=3, epochs=200)
      predictions = model.predict(features)
      return None, predictions