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 return the model compiled with Mean Squared Error (MSE) loss and the Adam optimizer. If the input array is empty, the function should return None.
  assert house_price_prediction([0.8, 0.4, 0.7]) is not None
  assert house_price_prediction([1.0, 0.5, 0.8]) is not None
  assert house_price_prediction([0.2, 0.1, 0.3]) is not None
  assert house_price_prediction([]) is None
output: def house_price_prediction(features):
      if not features:
          return None
      
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.optimizers import Adam
      
      model = Sequential()
      model.add(Dense(10, input_dim=len(features), activation='relu'))
      model.add(Dense(1, activation='linear'))
      
      model.compile(loss='mean_squared_error', optimizer=Adam())
      
      return model