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@@ -33,8 +33,8 @@ <h3 id="title"> Build and test your own decision tree model</h3>
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     <div id="instructions-set" class='invisbox-small' style="display: none;">
       <h4>Classification Game</h4>
-      <p><b>Credit</b>: Dataset and original concept courtesy of <a href="https://www.linkedin.com/in/stephaniejyee/" target="_blank">Stephanie Yee</a> and <a href="https://www.linkedin.com/in/tonyhschu/" target="_blank">Tony Chu</a> @ <a href="https://r2d3.us" target="_blank">r2d3.us</a> where you can find further reading on machine learning that extends this exercise.</p>
       <p>Your challenge is to create a decision tree that you think can successfully classify properties as either being in New York or San Francisco. Your decision must be based upon a limited number of characteristics.</p>
+      <p><b>Credit</b>: Dataset and original concept courtesy of <a href="https://www.linkedin.com/in/stephaniejyee/" target="_blank">Stephanie Yee</a> and <a href="https://www.linkedin.com/in/tonyhschu/" target="_blank">Tony Chu</a> @ <a href="https://r2d3.us" target="_blank">r2d3.us</a> where you can find further reading on machine learning that extends this exercise.</p>
       <h4>Building your decision tree</h4>
       <p>In order to help build your decision tree, you have been provided a <a href="#" onClick="selectSet('training');" style="text-decoration: underline;">training set</a> with details of 10 properties in San Francisco and 10 in New York.  Use the data to decide on your factors and boundaries that you think will solve the challenge. You can also download the training set as CSV if you wish to do some analysis of the data in Excel. You can also drag and drop the cards around the screen.</p>
       <h4>Buttons</h4>