input: 
  def predict_housing_price(data: List[List[float]], prediction_data: List[float]) -> float:
      """ Given a dataset of housing attributes and the corresponding prices, 
      create a Linear Regression model that can predict the price of a house 
      given the living area and number of rooms.
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [[2200, 3]])
      67000.0
      """
  
output:     from sklearn.linear_model import LinearRegression
      import numpy as np
  
      X = np.array([d[:-1] for d in data])
      y = np.array([d[-1] for d in data])
  
      model = LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array(prediction_data))
      return prediction[0]