input: 
  def predict_housing_price(data, living_area, num_rooms):
      """ Predict the housing price using Linear Regression given the living area and number of rooms.
      >>> data = [[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]
      >>> predict_housing_price(data, 2200, 3)
      58800.0
      >>> predict_housing_price(data, 1800, 2)
      37100.0
      """
      import numpy as np
      from sklearn import linear_model
      
      X = np.array([row[:2] for row in data])
      y = np.array([row[2] for row in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([[living_area, num_rooms]])
      return prediction[0]
  
output:     import numpy as np
      from sklearn import linear_model
      
      X = np.array([row[:2] for row in data])
      y = np.array([row[2] for row in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([[living_area, num_rooms]])
      return prediction[0]