Adds Python syntax highlighting to README.md (#76)

Author: Mottl

README.md

@@ -172,34 +172,36 @@ __verbose__ : int, default=0
 
 ## Examples ##
 
-    import pandas as pd
-    from sklearn.ensemble import RandomForestClassifier
-    from boruta import BorutaPy
-    
-    # load X and y
-    # NOTE BorutaPy accepts numpy arrays only, hence the .values attribute
-    X = pd.read_csv('examples/test_X.csv', index_col=0).values
-    y = pd.read_csv('examples/test_y.csv', header=None, index_col=0).values
-    y = y.ravel()
-    
-    # define random forest classifier, with utilising all cores and
-    # sampling in proportion to y labels
-    rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5)
-    
-    # define Boruta feature selection method
-    feat_selector = BorutaPy(rf, n_estimators='auto', verbose=2, random_state=1)
-    
-    # find all relevant features - 5 features should be selected
-    feat_selector.fit(X, y)
-    
-    # check selected features - first 5 features are selected
-    feat_selector.support_
-    
-    # check ranking of features
-    feat_selector.ranking_
-    
-    # call transform() on X to filter it down to selected features
-    X_filtered = feat_selector.transform(X)
+```python
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from boruta import BorutaPy
+
+# load X and y
+# NOTE BorutaPy accepts numpy arrays only, hence the .values attribute
+X = pd.read_csv('examples/test_X.csv', index_col=0).values
+y = pd.read_csv('examples/test_y.csv', header=None, index_col=0).values
+y = y.ravel()
+
+# define random forest classifier, with utilising all cores and
+# sampling in proportion to y labels
+rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5)
+
+# define Boruta feature selection method
+feat_selector = BorutaPy(rf, n_estimators='auto', verbose=2, random_state=1)
+
+# find all relevant features - 5 features should be selected
+feat_selector.fit(X, y)
+
+# check selected features - first 5 features are selected
+feat_selector.support_
+
+# check ranking of features
+feat_selector.ranking_
+
+# call transform() on X to filter it down to selected features
+X_filtered = feat_selector.transform(X)
+```
 
 ## References ##