input: from typing import List
  
  def predict_housing_price(data: List[List[float]], features: List[float]) -> float:
      """ Create a model using Linear Regression that predicts the housing prices based on 
      the living area and number of rooms. The function should train the model on the given 
      data and then predict the price for the provided features.
      >>> 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([features]))
      return prediction[0]