input: Write a function named `predict_housing_price` that uses Linear Regression to predict housing prices based on the living area and number of rooms provided in the input data. The function should take a list of lists `data` as input, where each sublist contains the living area and number of rooms for a house, and a target list `prices` that contains the actual prices. The function should return the predicted price for a new house given its living area and number of rooms. The prediction should be made using a Linear Regression model from the `sklearn` library.
  
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 65000
  assert predict_housing_price([[1800, 3], [1500, 2], [2300, 4], [1200, 2]], [68000, 37000, 75000, 29000], [2000, 3]) == 60000
  assert predict_housing_price([], [], [2000, 3]) == None
  assert predict_housing_price([[2000, 3]], [75000], [2000, 3]) == 75000
  
output: def predict_housing_price(data, prices, new_house):
      if not data or not prices:
          return None
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(data, prices)
      return int(model.predict([new_house])[0])