input: 
  def predict_housing_price(living_area: int, rooms: float, data: List[List[float]]) -> int:
      """ Use Linear Regression to predict the housing price given the living area and number of rooms.
      The data contains a list of lists, where each sublist contains two elements: the living area and number of rooms.
      The output is the predicted housing price based on the model trained with the provided data.
      
      >>> data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      >>> predict_housing_price(2200, 3, data)
      67000
      """
      X = np.array([[d[0], d[1]] for d in data])
      y = np.array([75000, 43000, 59000, 32000, 85000])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([[living_area, rooms]])
      return int(prediction[0])
  
output:     import numpy as np
      from sklearn import linear_model