This tutorial explains how Spotify uses machine learning to recommend songs. It covers the implementation of the K-means algorithm using Python in Google Colab, including data preprocessing, clustering, and evaluating song recommendations. The tutorial also provides functions for comparing the accuracy of recommendations against random selections.

How does Spotify generate song recommendations for its users?

What is the impracticality of employing individuals to create personalized playlists for each user?

What is the K means algorithm and how does it work in the context of Spotify?

Why is it necessary to preprocess the data set before training the model?

What is the Elbow method and how is it used to determine the optimal number of clusters?

How does the song recommender function work in relation to the clusters?

What steps can be taken to compare the accuracy of the song recommender and the randomizer function?