This took way more NLP than I anticipated.

Recipes that have similar ingredients are closer together. The idea was the take 3700 different recipes for pie, but understand how much variation there is in terms of their ingredients. So the algorithm will cluster recipes that have very similar ingredients and give them one color, and place the cluster in such a manner that clusters with very different ingredients are far away from each other.

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Tools: NLTK, UMAP, HDBSCAN

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e.g. Sugar dimension represents-> ['brown sugar', 'light brown sugar', 'cinnamon sugar', 'white sugar', 'powdered sugar']

Most I can get from this is that its' probably not the amount of guns you allow your people to have but who you allow to have them, or what culture/training you have around guns.

But without the US being in this chart it feels intentionally misleading.

This is an example of what it looks like when you can not take any conclusions from the data

This is where the statement "Something is better than nothing" fails.

I also hire DSs and I do the same as you.

My assumption is that these types of interviews were designed by consultancy companies for companies that don't have experienced DSs of their own.