All machine learning algorithms require your data to be represented as vectors (usually they’re high dimensional).
Many times, visualizing those vectors in order to get insights, even before you run them through a machine learning process, is something which can tell you if you’re heading toward the right solution – or at least let you know if you don’t.
This python notebook contains a small script that can take a set of any n-dimensional vectors and “project” them onto a 2D/3D plain using Tensorboard.
After visualizing your vectors, you can explore and cluster them using PCA / TSNE