msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '9cd5795573'
${ noResults }
ML-For-Beginners/Clustering
History
Jen Looper 1634092fe5
Challenge typography edit 		4 years ago
..
1-Visualize Challenge typography edit 4 years ago
2-K-Means Challenge typography edit 4 years ago
3-Centroid Challenge typography edit 4 years ago
4-API Challenge typography edit 4 years ago
data renumbering all segments 4 years ago
images renumbering all segments 4 years ago
translations renumbering all segments 4 years ago
README.md some edits to web app 4 years ago

README.md

Clustering Models for Machine Learning

Regional topic: Clustering models for a Nigerian audience's musical taste

Nigeria's diverse audience has diverse musical tastes. Using data scraped from Spotify (inspired by this article, let's look at some music popular in Nigeria. This dataset includes data about various songs' 'danceability' score, 'acousticness', loudness, 'speechiness', popularity and energy. It will be interesting to discover patterns in this data!

A turntable

Photo by Marcela Laskoski on Unsplash

In this series of lessons, you will discover new ways to analyze data using Clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then Classification techniques such as those you learned in previous lessons are more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.

Lessons

  1. Introduction to Clustering with Data Visualizations
  2. K-Means Clustering
  3. Centroid Clustering
  4. Build an API for Recommendations

Credits

"Introduction to Clustering" was written with ♥️ by Jen Looper

The Nigerian Songs dataset was sourced from Kaggle as scraped from Spotify.

The Flask API project was suggested by this GitHub repo