input: Write a function to design and implement a neural network in Python to predict the price of a house given a set of features. The function should return the trained model.
  
  def predict_house_price():
      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)
      return model
  
  assert type(predict_house_price()) == Sequential
output: def predict_house_price():
      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)
      return model