From eb99791077dfddad384e21cacd6e2e80fb25bcb1 Mon Sep 17 00:00:00 2001
From: Trevor Grant <trevor.d.grant@gmail.com>
Date: Thu, 15 Feb 2024 14:08:29 -1000
Subject: [PATCH] [hotfix][website] test removing license to fix build

---
 .../classification/bankmarketing-example.md   | 22 ++-----------------
 1 file changed, 2 insertions(+), 20 deletions(-)

diff --git a/website/documentation/users/classification/bankmarketing-example.md b/website/documentation/users/classification/bankmarketing-example.md
index fa55673004..2037a7a420 100644
--- a/website/documentation/users/classification/bankmarketing-example.md
+++ b/website/documentation/users/classification/bankmarketing-example.md
@@ -1,24 +1,6 @@
-<!--
- Licensed to the Apache Software Foundation (ASF) under one or more
- contributor license agreements.  See the NOTICE file distributed with
- this work for additional information regarding copyright ownership.
- The ASF licenses this file to You under the Apache License, Version 2.0
- (the "License"); you may not use this file except in compliance with
- the License.  You may obtain a copy of the License at
-
-     http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
--->
 ---
 layout: default
-title:
-
-    
+title: Bank Marketing Example  
 ---
 
 Notice:    Licensed to the Apache Software Foundation (ASF) under one
@@ -66,4 +48,4 @@ This example consists of 3 classes:
 
 When you run the main method of BankMarketingClassificationMain it parses the dataset using the TelephoneCallParser and trains
 a logistic regression model with 20 runs and 20 passes. The TelephoneCallParser uses Mahout's feature vector encoder
-to encode the features in the dataset into a vector. Afterwards the model is tested and the learning rate and AUC is printed accuracy is printed to standard output.
\ No newline at end of file
+to encode the features in the dataset into a vector. Afterwards the model is tested and the learning rate and AUC is printed accuracy is printed to standard output.