K-means clustering using Spotify song features

Automation of playlist creation using high dimensional data clustering. The spotify API allows us to create a simple server-side application that accesses user related data from the Spotify app.

The spotify API allows us to create a simple server-side application that accesses user related data from the Spotify app. It also gives you access to information that is not available on the app, such as artist popularity, song metrics, album cover images, etc. It allows you to create, delete and modify existing playlists in a user’s account.

The _**_goal of this project**_ is to use a clustering algorithm to break down a large playlist into smaller ones. For this, song metrics such as ‘danceability’, ‘valence’, ‘tempo’, ‘liveness’, ‘speechiness’ are used._

In this article you will find:

• Data collection using the Spotify API
• Finding the ideal number of clusters
• Elbow method and silhouette analysis
• Feature selection
• Clustering algorithm
• Playlist creation and performance analysis

Data Collection

import spotipy
from spotipy.oauth2 import SpotifyOAuth
from spotipy.oauth2 import SpotifyClientCredentials

Connecting to the spotify API was pretty straightforward and the content is pretty well documented. This link will give you all the information you need for connecting and this will give you python sample code.

Go to https://developer.spotify.com/dashboard/ and click Create a Client ID or Create an App to get your “_Client ID_” and “_Client Secret_”. After that, Redirect URI must be changed to any page you decide on in the settings of your Spotify application.

programming artificial-intelligence data-science technology machine-learning data analysis

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.

Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.

Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.