input: Write a function to create a Linear Regression model in Python that can predict housing prices based on the living area and number of rooms. The function should take the input data and return the predicted price for a given living area and number of rooms.
  
  def predict_housing_price(data, new_data):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array([entry[:2] for entry in data])
      y = np.array([entry[2] for entry in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      predicted_price = model.predict(new_data)
      return predicted_price[0]
  
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]], [[2200, 3]]) == round(52000, -3)
output: def predict_housing_price(data, new_data):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array([entry[:2] for entry in data])
      y = np.array([entry[2] for entry in data])
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      predicted_price = model.predict(new_data)
      return predicted_price[0]