input: 
  from typing import List
  
  def predict_housing_price(area: float, rooms: float, data: List[List[float]]) -> float:
      """ Predict the housing price based on living area and number of rooms using a Linear Regression model.
      The data parameter should contain a list of lists, where each inner list contains the living area and number of rooms, followed by the corresponding house price.
      >>> predict_housing_price(2200, 3, [[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]])
      66800.0
      """
      import numpy as np
      from sklearn import linear_model
  
      X = np.array([entry[:2] for entry in data])
      y = np.array([entry[2] for entry in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([[area, rooms]])
      return prediction[0]
  
output:     import numpy as np
      from sklearn import linear_model
  
      X = np.array([entry[:2] for entry in data])
      y = np.array([entry[2] for entry in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([[area, rooms]])
      return prediction[0]