input: from typing import List, Tuple
  
  def predict_housing_price(data: List[Tuple[int, float]], living_area: int, rooms: float) -> float:
      """
      Create a Linear Regression model using the given data to predict housing prices. The data
      consists of lists where each list contains the living area and the number of rooms of a house,
      followed by the corresponding house price. The function will train the model on this data and
      then return the predicted price for a house with the given 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)
      64500.0
      """
      import numpy as np
      from sklearn import linear_model
      
      X = np.array([[d[0], d[1]] for d in data])
      y = np.array([d[2] for d in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([[living_area, rooms]])
      return prediction[0]
output:     import numpy as np
      from sklearn import linear_model
      
      X = np.array([[d[0], d[1]] for d in data])
      y = np.array([d[2] for d in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([[living_area, rooms]])
      return prediction[0]