input: 
  from sklearn import linear_model
  import numpy as np
  
  def predict_housing_price(data):
      """ Given a list of lists where each sublist contains the living area and number of rooms of a house,
      train a Linear Regression model to predict the housing prices. Then return the predicted price for a new house with given features.
      
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]])
      63131.05882352941
      """
      X = np.array([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]])
      y = np.array([75000, 43000, 59000, 32000, 85000])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([[2200, 3]])
      return prediction[0]
  
output:     X = np.array(data)
      y = np.array([75000, 43000, 59000, 32000, 85000])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([[2200, 3]])
      return prediction[0]