input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the given living area and number of rooms. The function should take the training data as input and return the trained model. Additionally, it should predict the housing price for an input house with a living area of 2200 square feet and 3 rooms.
  assert create_house_price_model([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]) == 66999.99999999999
output: def create_house_price_model(training_data):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      X = np.array([[data[0], data[1]] for data in training_data])
      y = np.array([data[2] for data in training_data])
      
      model = LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array([[2200, 3]]))
      
      return prediction[0]