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 return the predicted price for a given input of living area and number of rooms.
  
  
  import numpy as np
  from sklearn import linear_model
  
  def predict_housing_price(data, y, new_data):
      X = np.array(data)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict(np.array([new_data]))
      return prediction[0]
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 64000.0
  assert predict_housing_price([[1800, 3], [1500, 2], [2100, 4], [1300, 1.5], [2800, 3]], [65000, 40000, 70000, 30000, 80000], [1900, 2.5]) == 55000.0
  assert predict_housing_price([[1700, 2.5], [1400, 2], [2200, 3], [1200, 1.5], [2700, 3.5]], [60000, 35000, 65000, 25000, 75000], [2000, 3]) == 62000.0
  assert predict_housing_price([], [], [2000, 3]) is None
  
output: def predict_housing_price(data, y, new_data):
      if not data or not y:
          return None
      X = np.array(data)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict(np.array([new_data]))
      return prediction[0]