input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the living area and number of rooms. The function should take in the data as an input, fit the model, and then return the prediction for a given house with specified living area and number of rooms. If the data is empty, the function should return None.
  import numpy as np
  from sklearn import linear_model
  
  def predict_housing_price(data):
      if not data:
          return None
      X = np.array([d[:2] for d in data])
      y = np.array([d[-1] for d in data])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict([[2200, 3]])
      return prediction
  
  # Test cases
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]])==np.ndarray([79300.0])
  assert predict_housing_price([])==None
output: import numpy as np
  from sklearn import linear_model
  
  def predict_housing_price(data):
      if not data:
          return None
      X = np.array([d[:2] for d in data])
      y = np.array([d[-1] for d in data])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict([[2200, 3]])
      return prediction