Yellowbrick (for ML visualizations) is one of my favorite Python libraries. You can also try Silhouette Visualizer, Validation Curve, Learning Curve, and Feature Importances visualizations. The Silhouette Visualizer is very useful for finding the optimal number of clusters in K-Menas clustering. The validation curve is used to plot the influence of a single hyperparameter. The learning curve is used to see how much your model benefits from adding more training data. The Feature Importances visualizer plots the most important features of a model. I've written separate articles for each of them and organized them into a list. If you are interested, I can share the link with you.