Update app.py

app.py

@@ -15,7 +15,8 @@ model_card = f"""
 
 The **DecisionTreeClassifier** employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as **ccp_alpha**.
 By increasing the value of **ccp_alpha**, a greater number of nodes can be pruned.
-In this demo, a DecisionTreeClassifier will be trained on the Breast Cancer dataset. Then, the effect of **ccp_alpha** in many terms of the tree-based model like the impurity of leaves, depth, number of nodes, and accuracy on train and test data are shown in many figures.
+In this demo, a DecisionTreeClassifier will be trained on the Breast Cancer dataset. 
+Then, the effect of **ccp_alpha** in many terms of the tree-based model like the impurity of leaves, depth, number of nodes, and accuracy on train and test data are shown in many figures.
 Based on this information, the best number of **ccp_alpha** is chosen. This demo also shows the results of the best **ccp_alpha** with accuracy on train and test datasets.
 You can play around with different ``test size`` and  ``random state``