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 model should be trained on the given dataset and should return the predicted price when given a new set of attributes.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==prediction
output: def predict_housing_price(X, y, new_attributes):
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      prediction = model.predict([new_attributes])
      return prediction[0]