diff --git a/TreeConcept.py b/TreeConcept.py
index 2335209..b53512e 100644
--- a/TreeConcept.py
+++ b/TreeConcept.py
@@ -1,17 +1,20 @@
 from sklearn import tree
+import graphviz
+import os
+os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
 
 # Define the training dataset with 8 attributes and corresponding labels
 training_data = [
-    [1, 0, 0, 1, 0, 1, 1, 'A'],
-    [1, 0, 0, 0, 1, 1, 1, 'A'],
-    [0, 1, 0, 1, 0, 1, 1, 'B'],
-    [0, 0, 0, 1, 0, 0, 1, 'B'],
-    [0, 1, 1, 0, 1, 0, 0, 'B'],
-    [1, 0, 0, 0, 1, 0, 1, 'A'],
-    [0, 0, 0, 1, 0, 0, 0, 'B'],
-    [1, 1, 0, 1, 1, 1, 0, 'A'],
-    [0, 0, 0, 0, 0, 0, 1, 'B'],
-    [0, 0, 1, 0, 0, 1, 0, 'B']
+    [0, 0, 0, 1, 0, 1, 1, 0, 'A'],
+    [1, 0, 0, 0, 1, 1, 1, 1, 'A'],
+    [0, 1, 0, 1, 0, 1, 1, 1, 'B'],
+    [1, 0, 0, 1, 1, 0, 1, 0, 'B'],
+    [1, 1, 1, 0, 1, 0, 0, 1, 'B'],
+    [0, 0, 0, 0, 1, 1, 1, 0, 'A'],
+    [0, 0, 0, 1, 0, 0, 0, 0, 'B'],
+    [1, 1, 0, 1, 1, 1, 0, 1, 'A'],
+    [0, 0, 0, 0, 0, 0, 1, 1, 'B'],
+    [1, 0, 1, 0, 0, 1, 0, 0, 'B']
 ]
 
 # Separate the attributes and labels
@@ -24,7 +27,12 @@ clf = tree.DecisionTreeClassifier(criterion='entropy')
 # Train the decision tree on the training data
 clf.fit(X_train, y_train)
 
+# Visualize the trained decision tree
+dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Attr1', 'Attr2', 'Attr3', 'Attr4', 'Attr5', 'Attr6', 'Attr7', 'Attr8'], class_names=['A', 'B'], filled=True)
+graph = graphviz.Source(dot_data)
+graph.render("decision_tree")  # Save the visualization as a PDF file
+
 # Test the decision tree with a new example
-new_example = [1, 0, 0, 1, 1, 0, 0]  # Example with 8 attributes
+new_example = [1, 0, 0, 1, 1, 0, 0, 1]  # Example with 8 attributes
 predicted_label = clf.predict([new_example])
 print("Predicted Label:", predicted_label[0])
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