input: def predict_housing_price(data, predicted):
      """This function creates a model using Linear Regression that predicts 
      the housing prices based on the given living area and number of rooms.
      The input is a list of lists where each sublist contains the living area 
      and the number of rooms. The output is the predicted price for a given 
      combination of living area and number of rooms.
      
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [[2200, 3]])
      array([75400.])
      """
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array([75000, 43000, 59000, 32000, 85000])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      return model.predict(predicted)
output:     import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array([75000, 43000, 59000, 32000, 85000])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      return model.predict(predicted)