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

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!) <h1>Machine Learning & Behavioral Psychology in Tech</h1> <img alt="" src="https://miro.medium.com/v2/resize:fit:700/0*7vLTGTsdjM71gxvT.png" style="height:700px; width:700px" /> Image by Author (Midjourney) In tech, research projects like mine are very common because a lot of the work revolves around delivering the best personalized experience for users/customers. <a href="https://towardsdatascience.com/feature-importance-analysis-with-shap-i-learned-at-spotify-aacd769831b4">Visit Now</a>