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 compile the model with 'mse' loss and 'adam' optimizer, and fit the model with the given features. If the given features array is empty, return "No data provided".
  assert predict_house_price([0.8, 0.4, 0.7])==None
  assert predict_house_price([])=='No data provided'
output: def predict_house_price(features):
      if not features:
          return "No data provided"
      
      from keras.models import Sequential
      from keras.layers import Dense
      
      model = Sequential()
      model.add(Dense(64, input_dim=len(features), activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1))
      
      model.compile(loss='mse', optimizer='adam')
      model.fit(np.array([features]), np.array([0]), epochs=10, verbose=0)
      
      return None