input: 
  def predict_housing_price(data: List[List[float]], living_area: float, num_rooms: float) -> float:
      """Predicts the housing price using a Linear Regression model trained on the given data.
      The model takes into account the living area and the number of rooms.
  
      Example:
      data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      living_area = 2200
      num_rooms = 3
  
      >>> predict_housing_price(data, 2200, 3)
      79000.0
      """
      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)
  
      prediction = model.predict([[living_area, num_rooms]])
      return prediction[0]
  
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)
  
      prediction = model.predict([[living_area, num_rooms]])
      return prediction[0]