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 function should train the model using the provided data, then return the prediction for a new input. If the input data is empty, return False.
  assert housing_price_prediction([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==67000.0
  assert housing_price_prediction([], [], [2200, 3])==False
output: def housing_price_prediction(area_rooms, prices, new_input):
      if not area_rooms:
          return False
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(area_rooms, prices)
      return model.predict([new_input])[0]