code: def get_boston_data():
      """Get SKLearn's Boston House Prices regression dataset
  
      Returns
      -------
      df: `pandas.DataFrame`
          The Boston House Prices dataset of shape (506, 13 + 1)
  
      Notes
      -----
      The intended target column in this dataset is "MEDV"; however, the weighted distances
      column "DIS" can also be used as the target column
  
      Examples
      --------
      >>> pd.set_option("display.max_columns", 10000)  # Ensure all columns are printed below
      >>> pd.set_option("display.width", 110)  # Ensure all columns are printed below
      >>> get_boston_data().head()
            CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  PTRATIO       B  LSTAT  MEDV
      0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0     15.3  396.90   4.98  24.0
      1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0     17.8  396.90   9.14  21.6
      2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0     17.8  392.83   4.03  34.7
      3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0     18.7  394.63   2.94  33.4
      4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0     18.7  396.90   5.33  36.2
      """
      data = load_boston()
      df = pd.DataFrame(data=data.data, columns=data.feature_names)
      df["MEDV"] = data.target
      return df
  
file_path: hyperparameter_hunter/hyperparameter_hunter/utils/learning_utils.py
imports: import pandas
  from sklearn.datasets import load_boston, load_breast_cancer, load_diabetes, load_iris, make_classification
label_desc: Use image node_id 3 for calling a global function with example usage: get_boston_data() and returns: df