K means visualisation after reducing dimensionality with PCA
K-means minimizes sum of squared errors, and PCA finds the projection with maximum sum of squares. So they are a quite natural fit.

Just run k-means and project it to 2d for visualization with PCA. They you are largely seeing the data the same way as k-means (if you only use the rotation, not the scaling!)

What I'd be more concerned about is the input data, as it is not particularly well suited for neither k-means nor PCA. So I wouldn't be surprised if the results are barely interpretable. Both methods make most sense if the input variables are continuous and of the same scale.