input: Write a function to create a Linear Regression model in Python that predicts housing prices given the living area and number of rooms. The function should train the model using the provided data and return the predicted price for a new input of living area and number of rooms. If the input data is empty, return None.
  assert housing_price_predictor([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 66333.33
  assert housing_price_predictor([[2000, 3]], [75000], [2200, 3]) == 82500.0
  assert housing_price_predictor([], [], [2200, 3]) == None
output: def housing_price_predictor(X, y, new_input):
      if not X:
          return None
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_input])[0]