input: Write a function to create a Linear Regression model that can predict housing prices based on the living area and number of rooms. The function should take a dataset containing the living area and number of rooms along with their corresponding housing prices, fit the model, and then return the model. The function should also have a method to predict the price for a new input using the trained model.
  
  
  assert create_housing_price_model([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]).predict([[2200, 3]]) == [63000]
  
output: def create_housing_price_model(dataset):
      from sklearn.linear_model import LinearRegression
      X = [[data[0], data[1]] for data in dataset]
      y = [data[2] for data in dataset]
      model = LinearRegression()
      model.fit(X, y)
      
      def predict(new_input):
          return [round(pred, 2) for pred in model.predict(new_input)]
      
      model.predict = predict
      return model