input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the living area and the number of rooms. The function should take a list of housing data where each element is a tuple containing the living area and the number of rooms, and another list containing the corresponding housing prices. The function should return a prediction for a given input of living area and number of rooms.
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==[63400.0]
output: 
  def predict_housing_price(housing_data, prices, input_data):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      X = np.array([[data[0], data[1]] for data in housing_data])
      y = np.array(prices)
      
      model = LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array([input_data]))
      return prediction.tolist()
  