input: Write a function to create a Linear Regression model using the `sklearn` library that can predict housing prices based on the living area and number of rooms. The function should fit the model using provided training data and then predict the price for a new set of features. If the input data is empty, the function should return None.
  assert predict_housing_price([[2000, 3]], [75000])==37000.0
  assert predict_housing_price([[1600, 2.5], [2400, 3.5]], [43000, 59000])==51000.0
  assert predict_housing_price([], [])==None
  assert predict_housing_price([[1400, 2], [3000, 4]], [32000, 85000])==58500.0
  assert predict_housing_price([[2000, 3]], [75000])==37000.0
output: 
  def predict_housing_price(X, y):
      if not X:
          return None
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([X[-1]])[0]
  