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 mean squared error as the loss function and use the Adam optimizer. Then, it should train the model on the provided features for 200 epochs with a batch size of 3. If the input features are empty, return None.
  assert predict_house_price([0.8, 0.4, 0.7])==None
  assert predict_house_price([0.5, 0.3, 0.9])==None
  assert predict_house_price([])==None
output: def predict_house_price(features):
      if not features:
          return None
      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='mean_squared_error', optimizer='adam')
      model.fit(np.array([features]), np.array([0]), epochs=200, batch_size=3)
      return model.predict(np.array([features]))[0][0]