input: Write a function to create a Linear Regression model in Python that can predict housing prices based on the living area and number of rooms. The function should return the predicted price for a given input. If the input is empty, return 0.
  
  assert housing_price_predictor([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==50800
  assert housing_price_predictor([[2500, 4], [2000, 3], [1500, 2]], [80000, 65000, 40000], [2300, 3.5])==75800
  assert housing_price_predictor([], [], [2200, 3])==0
output: def housing_price_predictor(X, y, input_data):
      if not X:
          return 0
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return round(model.predict([input_data])[0])