Data Science
Use machine learning to categorize music genres based on song attributes
If you don't like fitting your music into a box labelled pop or rock - this menternship is for you. Create your own music genres using some data from Spotify and a little bit of Python mojo.
Certified by
Role
Data Scientist
Industry
Venture Capital
No. of Subscribers
13
Level
Intermediate
Time Commitment
Submit First Draft in 30 days
Duration
60 days
Tools you’ll learn
Here’s What You Work On
About the Company
The Atal Incubation Center within CIE has been set up exclusively for startups that deploy tech innovation for social impact to help India achieve the Sustainable Development Goals (SDGs). As Indian researchers and innovators begin to expand the boundaries of science, Atal Incubation Center provides a platform where these ideas can be turned into sustainable business solutions.
Explore
the following work techniques
Python
Unsupervised Learning
K-means clustering
Bridging the gap
Do you like music? When someone asks you - what kind of music do you like, are you always able to name the exact genre? If you are like most listeners (and song makers!) on Spotify, you like a large variety of music. Depending on your day and mood, you sift through playlists to identify a special kind of song! This machine learning menternship challenges you to identify these ‘undefined’ music genres based on song attributes in the Spotify database. You will discover music that exists beyond the conventional genres of pop, rock, and classical!
Apply
the following skills
Data Analysis
Data preparation
Hypothesis testing
Expected output
In this menternship, you will develop a machine learning model to classify songs into custom genres based on song attributes provided in the Spotify music library
Create
the following deliverables
Data preparation of the given dataset
A model to cluster songs into custom genres based on song attributes provided in the Spotify Data
What you’ll need before starting
Python, K-means clustering
