input: Write a function to create a Linear Regression model in Python that can predict the housing prices given the living area and number of rooms. The function should fit the model on the provided data and make a prediction based on new input data.
  assert predict_house_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==71080.0
  assert predict_house_price([[1800, 3], [1500, 2.5], [2300, 3.5], [1300, 2], [2900, 4]], [70000, 40000, 56000, 30000, 80000], [2100, 3])==66600.0
  assert predict_house_price([], [], [2000, 3])==None
  assert predict_house_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1000, 1])==13050.0
output: 
  def predict_house_price(X, y, new_data):
      if not X or not y:
          return None
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_data])[0]
  