Feature Importance Analysis with SHAP I Learned at Spotify (with the Help of the Avengers)

This article is one of a two-part piece documenting my learnings from my Machine Learning Thesis at Spotify. Be sure to also check out the second article on how I succeeded in significantly optimizing my model for this research. <h2><a href="https://towardsdatascience.com/boosting-model-accuracy-techniques-i-learned-during-my-machine-learning-thesis-at-spotify-code-8027f9c11e57?source=post_page-----aacd769831b4--------------------------------" rel="noopener follow" target="_blank">Boosting Model Accuracy: Techniques I Learned During My Machine Learning Thesis at Spotify (+Code…</a></h2> <h3><a href="https://towardsdatascience.com/boosting-model-accuracy-techniques-i-learned-during-my-machine-learning-thesis-at-spotify-code-8027f9c11e57?source=post_page-----aacd769831b4--------------------------------" rel="noopener follow" target="_blank">A tech data scientist’s stack to improve stubborn ML models</a></h3> <a href="https://towardsdatascience.com/boosting-model-accuracy-techniques-i-learned-during-my-machine-learning-thesis-at-spotify-code-8027f9c11e57?source=post_page-----aacd769831b4--------------------------------" rel="noopener follow" target="_blank">towardsdatascience.com</a> Two years ago, I conducted a fascinating research project at Spotify as part of my Master’s Thesis. I learned several useful ML techniques, which I believe any Data Scientist should have in their toolkit. And today, I’m here to walk you through one of them. During that time, I spent 6 months trying to build a prediction model and then deciphering its inner workings. My goal was to understand what made users satisfied with their music experience. It wasn’t so much about predicting whether a user was happy (or not), but rather understanding the underlying factors that contributed to their happiness (or lack thereof). Sounds exciting, right? It was! I loved every bit of it because I learned so much about how ML can be applied in the context of music and user behavior. (If you’re interested in the applications of ML in the music industry, then I highly recommend checking out this interesting <a href="https://research.atspotify.com/2018/07/understanding-and-evaluating-user-satisfaction-with-music-discovery/" rel="noopener ugc nofollow" target="_blank">research</a> led by Spotify’s top experts. It’s a must-read!) <a href="https://towardsdatascience.com/feature-importance-analysis-with-shap-i-learned-at-spotify-aacd769831b4">Read More</a>