title: How to preprocess a column with too many NAN values?
body: <p>I have a training data of shape <code>(596000, 58)</code> and below image has some of the features vs number of "NAN"s in the features/rows using <code>train.isnull().sum()</code>.</p>
  
  <p>Considering each feature has equal importance and are categorical values <strong>Should I remove the column cat6 and cat8?</strong> as for <code>cat6</code> 411792/596000 are NaNs and for <code>cat8</code> 266928/596000 are NaNs.</p>
  
  <p>Is there a way to deal with this situation ? or I have to remove both the columns ?</p>
  
  <hr>
  
  <p>input [12]: <code>train.isnull().sum()</code></p>
  
  <p>output [12]:</p>
  
  <p><a href="https://i.stack.imgur.com/P7xnS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/P7xnS.png" alt="enter image description here"></a></p>