Some of the code I've put together to do weird/interesting/cool things (mostly at work) with machine learning models for classification problems. The core code is in ML_snippets.py and usage examples are shown in demos.ipynb. This is heavily reliant on the pandas and scikit-learn libraries, though it wouldn't be too difficult to rewrite and avoid the need for pandas.
explanations_demo.ipynb has a demonstration of two methods for generating human-interpretable explanations/justifications of the predictions of a scikit-learn random forest classifier. The code for one of those methods is found in tree_explainer.py.
price_clustering.ipynb has a demo of segmenting a set of products into natural price bins. That notebook is self-contained; it pulls a small data set from the web.