In this project, I have created a Song Recommender System using Turicreate Library and created two models i.e a model that uses popularity of songs to recommend songs and another model that recommends songs based on personalization. Then I have compared the two models to find out which of the 2 models recommends better and found out that the one which is personalized performs better.
To get started download the SFrame used by this project from here: https://d3c33hcgiwev3.cloudfront.net/dIBuXeIiEemm5A4ynZyB2A_749ef60351624a8d83beedd5ef23367e_song_data.sframe.zip?Expires=1648425600&Signature=c7jcu4wW66kjWAkoyE7K63ePECC9HQepa-EIqGN5ouitBgv0f6NfSatfABMBojDQWlxq1K2UJ6cDai80hMm08EU88RCU3PFB-R0hh482epXQFMfeH0d8ELKj4UvlXJrRWJo4YapS3jM~2sWsLylkSTnW8kebtCvJdrl1OSbyOas_&Key-Pair-Id=APKAJLTNE6QMUY6HBC5A.
Unzip the folder obtained from the link and locate the file named m_cccc16853452d1ed.0000.Then move this file to the folder people_wiki.sframe that is present in the same folder as this README.md file.
Next follow the instructions in the jupyter notebook and carry on.
Referred from Machine Learning Foundations: A Case Study Approach from Coursera.