Kmeans clustering measuring important features
For categorical values there is K-Modes and for mixed (categorical and continuous values) there is K-Prototype. That might be worth trying and potentially easier to evaluate. You wouldn't use one-hot encoding there though.
How do you measure with features of your dataframe are important for your Kmeans model?
I'm working with a dataframe that has 37 columns of which 33 columns are of categorical data.
These 33 data columns go through one-hot-encoding and now I have 400 columns.
I want to see which columns have an impact on my model and which don't.
Is there a method for this or do I loop this?