Data Science
Use machine learning to categorize music genres based on song attributes
An unsupervised ML menternship by Needl.ai! Discover the world of Spotify music and discover new genres with the power of Machine Learning 
About this Menternship
Opportunity:
needl.ai is a visionary tech company that makes it possible for its clients to process their data from diverse apps like Audible, Spotify and Redditt on a single platform.

Problem:
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!

Deliverable:
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 
Why take up this Menternship?
On completing this Menternship, you will learn about
You will explore: 
Python 
Unsupervised Learning
K-means clustering

You will apply:
Data wrangling 
Data cleaning
Data Visualisation
Unsupervised Learning Algorithms (K-Means Clustering)

You will create:
A model to cluster songs into custom genres based on song attributes provided in the Spotify Data
Expected Output
  1. Video of Self
  2. Research report on applications of ML for content streaming
  3. Report on Spotify song attributes
  4. Python notebook - Spotify Dataset - Extracted from the API
  5. Complete Codes for all previous components - Python notebook
  6. Screenshots of Data Visualisation
  7. Final Report on Findings of the process
Use machine learning to categorize music genres based on song attributes
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