DOC Fixes common pitfalls (scikit-learn#18617)

Author: thomasjpfan

doc/common_pitfalls.rst

@@ -30,9 +30,8 @@ and the model will not be able to perform effectively.
 For the following example, let's create a synthetic dataset with a
 single feature::
 
-    
-    
-    from sklearn.datasets import make_regression
+
+    >>> from sklearn.datasets import make_regression
     >>> from sklearn.model_selection import train_test_split
     ...
     >>> random_state = 42
@@ -225,4 +224,4 @@ Below are some tips on avoiding data leakage:
 * The scikit-learn :ref:`pipeline <pipeline>` is a great way to prevent data
   leakage as it ensures that the appropriate method is performed on the
   correct data subset. The pipeline is ideal for use in cross-validation
-  and hyper-parameter tuning functions.
+  and hyper-parameter tuning functions.