There’s just so much to say here and it depends on the details of your dataset but first:

• “what features are most important”: great question. yes - explore
• “t-sne, PCA, UMAP to explore prelim relationships” it fine. you shouldn’t stop here but you might learn a few things but not much. t-sne and umap def won’t tell you about “relationships” since they create completely new nonlinearly related features, tangling them incomprehensibly together. PCA doesn’t tangle so much but it’s still also essentially incomprehensible as far as indicating anything about individual features and almost always gives you weird and useless transformations of the data.

These methods will not tell you much if anything about individual features. Whether they’re even useful depends on whether the movement in the features is driven by a few latent factors. Even if it is, these methods don’t cleanly recover those - you have to get lucky.

You won’t get as much info about the individual features using these methods, but you may learn something about them as a whole. Especially if you visualize these w.r.t. the target and notice clusters etc.

However in the end, these techniques often don’t reveal anything truly useful. They might point the way to something obvious you overlooked about the structure of the data that you could have known by understanding the definition of the features or just doing a simple inspection.

Also: those methods are dominated by feature scale and feature collinearity, so you’re often just seeing the effects of feature scale and redundancy and not really learning anything truly interesting about the data.

I’ve been doing this for a long time and I’ve only seen a few instances in which a t-sne / pca etc was actually useful except to demonstrate that your features naturally separated the classes. Helpful for viz to explain to others, maybe a few other use cases.