input: Write a function to design and implement a neural network in Python using Keras to predict the price of a house given a set of features. The function should include the model architecture with two hidden layers, the compilation of the model, and the fitting of the model with provided input features.
  
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense
  
  def predict_house_price(features):
      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)
  
  assert predict_house_price(np.array([[0.8, 0.4, 0.7]])) == None
output: def predict_house_price(features):
      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)