diff --git a/ML/aufgaben/comparison/compare_ml_algorihms.py b/ML/aufgaben/comparison/compare_ml_algorihms.py
index d11658c..7053189 100644
--- a/ML/aufgaben/comparison/compare_ml_algorihms.py
+++ b/ML/aufgaben/comparison/compare_ml_algorihms.py
@@ -20,6 +20,7 @@ from sklearn.metrics import accuracy_score, classification_report, adjusted_rand
 
 import numpy as np
 
+
 def kmeans_true_labels(X, y, n_classes):
     """
     Since k-means is unsupervised it comes up with its own classes, that
@@ -31,7 +32,7 @@ def kmeans_true_labels(X, y, n_classes):
     # train classifier and do a fit, set the rng seed to a fixed value
     kmeans = KMeans(n_clusters=n_classes, init="k-means++", n_init=10, random_state=42)
     kmeans.fit(X)
-    
+
     # creates an empty array the same shape as the cluster assignments
     labels = np.zeros_like(kmeans.labels_)
     # for each cluster i ...
@@ -61,12 +62,15 @@ def evaluate(name, dataset, target_names):
         dataset.data, dataset.target, test_size=0.3, random_state=42
     )
 
-    # test the supervised leanring algorithms
-    for classifier_name, classifier in [
+    # all the supervised leanring algorithms to test
+    algorithms = [
         ("Decision Tree", DecisionTreeClassifier(random_state=42)),
         ("Naive Bayes", GaussianNB()),
         ("Random Forest", RandomForestClassifier(n_estimators=100, random_state=42)),
-    ]:
+    ]
+
+    #  do the test on the supervised learning algorithms
+    for classifier_name, classifier in algorithms:
         classifier.fit(X_train, y_train)
         y_pred = classifier.predict(X_test)
         print(f"\n--- {classifier_name} ---")
@@ -74,15 +78,14 @@ def evaluate(name, dataset, target_names):
         print(f"Adj. Rand: {adjusted_rand_score(y_test, y_pred):.3f}")
         print(classification_report(y_test, y_pred, target_names=target_names))
 
-    # test the unsupervised learning algorithm (evaluated on full dataset)
+    # tdo the test on the unsupervised learning algorithm (evaluated on full dataset)
     n_classes = len(target_names)
     mapped_labels, kmeans = kmeans_true_labels(dataset.data, dataset.target, n_classes)
     print(f"\n--- K-Means (mapped) ---")
     print(f"Accuracy: {accuracy_score(dataset.target, mapped_labels):.3f}")
     print(f"Adj. Rand: {adjusted_rand_score(dataset.target, kmeans.labels_):.3f}")
-    print(
-        classification_report(dataset.target, mapped_labels, target_names=target_names)
-    )
+    print(classification_report(dataset.target, mapped_labels, target_names=target_names))
+
 
 # evaluate all ML algorithms on iris
 iris = datasets.load_iris()