input: Write a function to create a Linear Regression model using the `sklearn` library that can predict housing prices based on the given living area and number of rooms. The function should fit the model on the provided data and return the predicted price for a given 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]) == 63750.0
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1800, 2.5]) == 50500.0
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [3000, 4]) == 85000.0
output: 
  from sklearn.linear_model import LinearRegression
  
  def predict_housing_price(X, y, new_data):
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_data])[0]
  