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 the input data consisting of living area and number of rooms, fit the model, and return the model object.
  
  def predict_housing_prices(data, prices):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      return model
  
  assert isinstance(predict_housing_prices([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000]), linear_model.LinearRegression)
  assert isinstance(predict_housing_prices([[1800, 3], [1500, 2], [2200, 3.5], [1300, 2], [2800, 4]], [70000, 40000, 55000, 30000, 80000]), linear_model.LinearRegression)
  assert isinstance(predict_housing_prices([[1900, 3], [1550, 2.5], [2300, 3.5], [1350, 2], [2900, 4]], [72000, 41000, 57000, 31000, 82000]), linear_model.LinearRegression)
  assert isinstance(predict_housing_prices([], []), linear_model.LinearRegression)
  assert isinstance(predict_housing_prices([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000]), linear_model.LinearRegression)
output: def predict_housing_prices(data, prices):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      return model