I agree with @saifkhanengr. It would be useful to understand:
- how your feature dependencies look like
- how much data you have
- how much domain knowledge is available (which you can potentially encode in your features)
So what would be helpful is if you could provide e.g. a scatterhist plot for all your features and labels.
Something like this (only that you would have three labels instead of one):
Taking a look not only at the distribution of label classes but also at the distribution and dependency of your features in addition will help to understand the data better which is an essential step in the CRISP-DM which helps you to prepare better data to succeed in modelling.
So after analysing your data, probably you can judge then better:
-
data processing wise: how to improve your features and how important your features are, see also CRISP-DM:
-
modelling wise: if e.g. a Gaussian mixture model might be helpful and with which (selected or transformed) features you want to feed it, see: 2.1. Gaussian mixture models — scikit-learn 1.3.2 documentation.
Please let me know if this helps!
Best regards
Christian