input: Write a function to create a linear regression model using sklearn that predicts housing prices based on the living area and number of rooms, given the input data. The function should take the input data as a list of lists, where each sublist contains the living area and number of rooms, and the corresponding housing prices as a separate list. The function should return a callable model that can be used to predict housing prices for new data points.
  
  def predict_housing_price(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 predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000]).predict([[2200, 3]]) == np.array([65000.])
output: def predict_housing_price(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