A music recommendation REST API which makes a machine learning algorithm work with the Django REST Framework

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How it works

The REST API has a simple operational flow which goes like so :

  1. User signs up at the /sign-up endpoint

  2. User logs in using Django REST Framework's basic authentication via the /login endpoint

  3. After successful authentication user can then navigate to the /recommend endpoint

Deeper Intuition

The code I have designed is connected directly to a packaged machine learning model, a user signs up by providing data such as gender, age, name etc. Once a sign up is successful then the user can login to use the /recommend endpoint to get recommendations on music that best fits the user's age/gender. Music Albums are stored in an SQLite database and are picked then displayed to user at the /recommend endpoint. Now the question is what's the process look like in simple steps? :

  1. User signs up and provides info like gender, age, name, etc..

  2. Once user logs in and navigates to /recommend endpoint, the back end will send that authenticated user's age & gender to the packaged ML model for evaluation / to get a prediction on what genre of music would be best for the user's age/gender type.

  3. once a genre is predicted by ML model the result is sent to a queryset for filtering thus returning music from the database the REST API is connected to which has the genre that was predicted in the first place.


  1. run the command pip3 install -r requirements.txt to install required libraries

  2. setup migrations by running command python3 makemigrations accounts and python3 makemigrations api

  3. finally apply migrations by running command python3 migrate

  4. create a super user for accessing /admin by running command python3 createsuperuser

  5. after that just fill the database with some albums of different genres from the admin panel

  6. and you are Done!

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