K means algorithm implementation
It's actually about trial and error. There is no straight way to say which columns are going to help you the most until you try and figure it by yourself.
but you can use dimensionality reduction algorithms like PCA to project data to a lower dimension without much data loss. It's a common approach and also helps with the speed of your clustering algorithm.
What columns do I have to consider while implementing K Means? I have 91 columns after pre processing. And also to how many columns do I have to apply K Means clustering ? Is it all of them or only a few to be considered ?