Song Recommender with Unsupervised Machine Learning and Python: A Step-By-Step Coding Tutorial

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.

What type of algorithm does Spotify use for song recommendations?

• Genetic algorithm

• Reinforcement learning algorithm

• Unsupervised learning algorithm

• Supervised learning algorithm

Which platform is used in the tutorial to write and run Python code?

• Google Collab

• Visual Studio Code

• Jupyter Notebook

• PyCharm

What is the purpose of dropping NAN values during data preprocessing?

• To improve model accuracy

• To reduce computation time

• To increase the dataset size

• To add more features

Why is it important to normalize features in K-means clustering?

• To decrease the number of features

• To increase the number of clusters

• To ensure all features have the same scale

• To make the dataset larger

What method is used to determine the optimal number of clusters in K-means?

• Hierarchical clustering

• Elbow method

• Gap statistic

• Silhouette method

What does the song recommender function do?

• Recommends songs from different clusters

• Recommends songs based on user input

• Recommends songs from the same cluster

• Recommends songs randomly

How can you compare the accuracy of the recommender and randomizer functions?

• By checking the runtime of each function

• By comparing the number of songs recommended

• By evaluating the proportion of liked songs

• By analyzing the number of clusters