What are possible approaches to deal with unseparable data even after PCA? Greetings data scientists,

I am dealing with a complex classification/prediction problem and I am finding very difficult to separate the classes at all.
Even after PCA, my data (over the two PCs), look like this:



Given this dataset, what approach could be best to accurately predict the classes?

Background: I am trying to classifying modelled problems via the best approach/algorithm to solve them.

Many thanks,
Is your problem unsupervised? If not, the data does not need to be "visually" separable in two dimensions, you can use classifiers in multiple dimensions to create separations for you (linear or nonlinear, just depends on the classifier).

If your problem is unsupervised try other methods for clustering data such a K-Means or tree-based clustering (i.e. hierarchical).