input: Write a function to create a Linear Regression model that predicts housing prices based on the living area and number of rooms. The function should return the prediction for a given input of living area and number of rooms. If the input data is empty, the function should return None.
  
  def predict_housing_price(data, living_area, num_rooms):
      if not data:
          return None
      X = np.array([d[:2] for d in data])
      y = np.array([d[2] for d in data])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model.predict([[living_area, num_rooms]])
  
  # Test cases
  import numpy as np
  from sklearn import linear_model
  
  data = [[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]
  assert predict_housing_price(data, 2200, 3) == [61000.0]
  assert predict_housing_price([], 2200, 3) == None
output: 
  def predict_housing_price(data, living_area, num_rooms):
      if not data:
          return None
      X = np.array([d[:2] for d in data])
      y = np.array([d[2] for d in data])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model.predict([[living_area, num_rooms]])
  