input: Write a function to create a Linear Regression model using the scikit-learn library in Python that can predict housing prices based on the given living area and number of rooms. The function should return the predicted price for a given input. If the input data is empty, the function should return 0.
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==79000.0
  assert predict_housing_price([[2000, 3], [1600, 2.5]], [75000, 43000], [1800, 2.5])==70200.0
  assert predict_housing_price([], [], [2200, 3])==0
  assert predict_housing_price([[2000, 3]], [75000], [2000, 3])==75000.0
output: def predict_housing_price(X, y, input_data):
      if not X:
          return 0
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([input_data])[0]