text: #!/usr/bin/env python
  
  ### In a script, we ran the following to find our load function
  
  # >>> import sklearn.datasets
  # >>> [func for func in dir(sklearn.datasets) if func.startswith("load")
  
  from sklearn.datasets import load_boston
  boston_data = load_boston()
  
  dir(boston_data)
  ###Now, I want to list the properties of the dataset, and what they each "mean"
  #TODO:
  #'DESCR': this is a dataset description
  # data: numpy array, in the shape rows x columns
  # feature_Names: feature labels for the dataset
  # target: numpy array, x rows, containing the thing we're trying to predict
  # target_names: name of the class we're predicting in the categorical case
  
  # TODO:
  # 1 first, visualize your data from loading it *this way*
  #  - this can actually include converting to pandas, and running your script from before
  # 2 train & apply either a KNN-classifier or KNN-regressor on your dataset :)
  # 3 print the output "score" or "performance" of the classifoer/regressor
  # (if you did 2&3 right, your performance will *not* be perfect :) 
  
   
  