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 take the training data as input, fit the model, and then predict the price for a given set of living area and number of rooms. If the input data is empty, the function should return None.
  
  def predict_housing_price(data):
      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)
      prediction = model.predict([[2200, 3]])
      return prediction[0]
  
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]) == 62000.0
  assert predict_housing_price([]) == None
output: def predict_housing_price(data):
      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)
      prediction = model.predict([[2200, 3]])
      return prediction[0]