input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the living area and number of rooms. The function should take input data as a list of lists, where each sublist contains the living area and number of rooms, and a corresponding list of housing prices. The function should train the model and return the prediction for a new input consisting of living area and number of rooms.
  
  def predict_housing_price(data, prices, new_input):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array(new_input).reshape(1, -1))
      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])==[prediction value]
output: def predict_housing_price(data, prices, new_input):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array(new_input).reshape(1, -1))
      return prediction[0]