UMAP: https://umap-learn.readthedocs.io/en/latest/

scikit-learn also has options: https://scikit-learn.org/stable/auto_examples/manifold/plot_...

My instinct would be a principal component analysis (which someone has demonstrated here: https://www.youtube.com/watch?app=desktop&v=brt88wwoZtI). Not sure it would tell you much though, but it looks nice.

There's attempts but you can only do so much in hundreds/thousands of dimensions. Most of the time the visualization doesn't really provide anything meaningful.

Assuming you have a dimension-reduction or manifold learning tool of choice (UMAP,PacMAP,t-SNE,PyMDE,etc.) then DataMapPlot (https://datamapplot.readthedocs.io/en/latest/) is a library specifically designed to make visualizations of the outputs of your dimension reduction.

If you need them visualized, you're already on the wrong track.