input: Write a function to create a linear regression model using the scikit-learn library that can predict housing prices based on given living area and number of rooms. The function should take a list of tuples as input, where each tuple contains the living area and number of rooms, and their corresponding housing prices. It should return a trained model and a prediction for a specified input.
  
  def predict_housing_price(data, new_data):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array([d[:-1] for d in data])
      y = np.array([d[-1] for d in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array(new_data).reshape(1, -1))
      return model, prediction
  
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]], [[2200, 3]])[1].tolist() == [62000.0]
output: def predict_housing_price(data, new_data):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array([d[:-1] for d in data])
      y = np.array([d[-1] for d in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array(new_data).reshape(1, -1))
      return model, prediction