input: from typing import List, Tuple
  
  def predict_housing_price(data: List[Tuple[int, float]],  prices: List[int]) -> float:
      """ Predict the price of a house given its living area and number of rooms using Linear Regression.
      Given a list of tuples where each tuple contains the living area (in square feet) and the number of rooms,
      and a list of corresponding prices, train a Linear Regression model and use it to predict the price
      of a house with specific features.
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000])
      55000.0
      """
output:     import numpy as np
      from sklearn.linear_model import LinearRegression
  
      X = np.array([[area, rooms] for area, rooms in data])
      y = np.array(prices)
  
      model = LinearRegression()
      model.fit(X, y)
  
      new_house = np.array([[2000, 3]])
      predicted_price = model.predict(new_house)
  
      return predicted_price[0]