text: from __future__ import print_function
  
  import numpy as np
  
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.utils import check_random_state
  
  
  # For reproducibility
  np.random.seed(1000)
  
  
  if __name__ == '__main__':
      # Load the dataset
      boston = load_boston()
      X = boston.data
      Y = boston.target
  
      print(X.shape)
      print(Y.shape)
  
      # Create train and test sets
      X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=1000)
  
      # Use a random state
      rs = check_random_state(1000)
      X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=rs)