input: Write a function named predict_housing_price that takes a list of tuples where each tuple represents the living area and number of rooms of a house, and returns a list of predicted prices using Linear Regression from the sklearn library.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]])==[75000, 43000, 59000, 32000, 85000]
  assert predict_housing_price([[2200, 3]])==[50000]
  assert predict_housing_price([])==[]
output: 
  def predict_housing_price(houses):
      if not houses:
          return []
      from sklearn.linear_model import LinearRegression
      X = [[h[0], h[1]] for h in houses]
      y = [75000, 43000, 59000, 32000, 85000]
      model = LinearRegression()
      model.fit(X[:len(y)], y)
      return [int(round(p)) for p in model.predict(X)]
  