diff --git a/scikits/learn/datasets/base.py b/scikits/learn/datasets/base.py
index 13266b13f318ce4db3c60a76c8a292c9e9c84be0..9447a4581735e2629c7091a2e29ea5cc26d12146 100644
--- a/scikits/learn/datasets/base.py
+++ b/scikits/learn/datasets/base.py
@@ -23,7 +23,6 @@ import numpy as np
 
 from ..utils import check_random_state
 
-###############################################################################
 
 class Bunch(dict):
     """ Container object for datasets: dictionnary-like object that
@@ -114,7 +113,7 @@ def load_files(container_path, description=None, categories=None,
 
     load_content : boolean
         Whether to load or not the content of the different files. If
-        true a 'data' attribute containing the text information is present 
+        true a 'data' attribute containing the text information is present
         in the data structure returned. If not, a filenames attribute
         gives the path to the files.
 
@@ -185,7 +184,7 @@ def load_iris():
     Returns
     -------
     data : Bunch
-        Dictionnary-like object, the interesting attributes are:
+        Dictionary-like object, the interesting attributes are:
         'data', the data to learn, 'target', the classification labels,
         'target_names', the meaning of the labels, and 'DESCR', the
         full description of the dataset.
@@ -233,7 +232,7 @@ def load_digits(n_class=10):
     Returns
     -------
     data : Bunch
-        Dictionnary-like object, the interesting attributes are:
+        Dictionary-like object, the interesting attributes are:
         'data', the data to learn, `images`, the images corresponding
         to each sample, 'target', the classification labels for each
         sample, 'target_names', the meaning of the labels, and 'DESCR',
@@ -277,7 +276,7 @@ def load_diabetes():
     Returns
     -------
     data : Bunch
-        Dictionnary-like object, the interesting attributes are:
+        Dictionary-like object, the interesting attributes are:
         'data', the data to learn and 'target', the labels for each
         sample.
 
@@ -295,7 +294,7 @@ def load_linnerud():
     Returns
     -------
     data : Bunch
-        Dictionnary-like object, the interesting attributes are:
+        Dictionary-like object, the interesting attributes are:
         'data_exercise' and 'data_physiological', the two multivariate
         datasets, as well as 'header_exercise' and
         'header_physiological', the corresponding headers.
@@ -319,13 +318,14 @@ def load_linnerud():
                  header_physiological=header_physiological,
                  DESCR=fdescr.read())
 
+
 def load_boston():
-    """load the boston house prices dataset and returns it.
+    """Load the Boston house prices dataset and return it.
 
     Returns
     -------
     data : Bunch
-        Dictionnary-like object, the interesting attributes are:
+        Dictionary-like object, the interesting attributes are:
         'data', the data to learn, 'target', the classification labels,
         'target_names', the meaning of the labels, and 'DESCR', the
         full description of the dataset.
@@ -335,21 +335,23 @@ def load_boston():
     >>> from scikits.learn.datasets import load_boston
     >>> data = load_boston()
 
-    """        
+    """
+
     module_path = dirname(__file__)
-    data_file = csv.reader(open(join(module_path, 'data', 'boston_house_prices.csv')))
+    data_file = csv.reader(open(join(module_path, 'data',
+                                     'boston_house_prices.csv')))
     fdescr = open(join(module_path, 'descr', 'boston_house_prices.rst'))
     temp = data_file.next()
     n_samples = int(temp[0])
     n_features = int(temp[1])
     data = np.empty((n_samples, n_features))
-    target = np.empty((n_samples,) )
-    temp = data_file.next() # names of features
-    feature_names = np.array(temp)   
+    target = np.empty((n_samples,))
+    temp = data_file.next()  # names of features
+    feature_names = np.array(temp)
     for i, d in enumerate(data_file):
         data[i] = np.asanyarray(d[:-1], dtype=np.float)
-        target[i] = np.asanyarray(d[-1], dtype=np.float)    
-      
+        target[i] = np.asanyarray(d[-1], dtype=np.float)
+
     return Bunch(data=data, target=target,
                  feature_names=feature_names,
                  DESCR=fdescr.read())
diff --git a/scikits/learn/datasets/descr/boston_house_prices.rst b/scikits/learn/datasets/descr/boston_house_prices.rst
index b6bc8ede0e438aeaaf15629621bf58fed4e30dc8..c0c8b29c551980552f0e73d6caa831c3eab96866 100644
--- a/scikits/learn/datasets/descr/boston_house_prices.rst
+++ b/scikits/learn/datasets/descr/boston_house_prices.rst
@@ -36,4 +36,4 @@ Attribute Information:
      MEDV     Median value of owner-occupied homes in $1000's
 
 Summary Statistics:     
-	TODO
\ No newline at end of file
+	TODO