diff --git a/supervised/algorithms/knn.py b/supervised/algorithms/knn.py
index 3978e562..c7a879e9 100644
--- a/supervised/algorithms/knn.py
+++ b/supervised/algorithms/knn.py
@@ -55,6 +55,14 @@ def fit(
         else:
             self.model.fit(X, y)
 
+    @property
+    def _classes(self):
+        # Returns the unique classes based on the fitted model
+        if hasattr(self.model, "classes_"):
+            return self.model.classes_
+        else:
+            return None
+
 
 class KNeighborsAlgorithm(KNNFit, RegressorMixin):
     algorithm_name = "k-Nearest Neighbors"
diff --git a/tests/tests_algorithms/test_knn.py b/tests/tests_algorithms/test_knn.py
index 1572a549..3e4119be 100644
--- a/tests/tests_algorithms/test_knn.py
+++ b/tests/tests_algorithms/test_knn.py
@@ -17,7 +17,11 @@ class KNeighborsRegressorAlgorithmTest(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls.X, cls.y = datasets.make_regression(
-            n_samples=100, n_features=5, n_informative=4, shuffle=False, random_state=0
+            n_samples=100,
+            n_features=5, 
+            n_informative=4, 
+            shuffle=False, 
+            random_state=0
         )
 
     def test_reproduce_fit(self):
@@ -77,3 +81,15 @@ def test_is_fitted(self):
         self.assertFalse(model.is_fitted())
         model.fit(self.X, self.y)
         self.assertTrue(model.is_fitted())
+
+    def test_classes_attribute(self):
+        params = {"ml_task": "binary_classification"}
+        model = KNeighborsAlgorithm(params)
+        model.fit(self.X,self.y)
+
+        try:
+            classes = model._classes  
+        except AttributeError:
+            classes = None
+
+        self.assertTrue(np.array_equal(np.unique(self.y), classes))