This project is designed to provide personalized music recommendations for relaxation and meditation. Leveraging ML and data analysis, the system suggests tracks based on user preferences such as tempo, energy, and genre. Join us in enhancing music discovery through advanced algorithms and community-driven contributions.

The `MKGCN` class, coupled with the Spotify API, orchestrates a multi-modal knowledge graph convolutional network to enhance music recommendation systems by integrating user interaction data and diverse music modalities.

This project summarizes the basic steps required to implement a basic recommendation engines that suggests new bands to users. Data are fetched from the open dataset of ListenBrainz in Bigquery. The recommendation engine is built by hacking the keras embedding layers to perform matrix factorization.

Implementing a music recommender with decision tree.

This is a simple song recommendation system which uses concepts of Machine Learning to provide personalized experience based on the past history of the user and user's choice preferences.

Client-side application for Vibify, a JavaScript-based Spotify API interaction tool. Features include fetching user's top tracks, creating playlists, getting audio features for a playlist, and a recommendation system.

Code for project "Music recommendation system"

The goal is to build a music recommendation system that can provide custom playlists for individual users based on collaborative and metadata filtering.

A Music Recommondation System using python and flet framework.

Natural Language Processing Chat Bot that can recommend the user songs depending on the user mood and favourite genres.

Recommendation System website

Client side Youtube music personalization

Explore various recommendation systems for music artist recommendation based on the Last.fm dataset.

This project creates Music Recommendation Engine in Python using Spotify API and Machine Learning.

a Node.js API that interacts with the Spotify API to fetch user data, currently playing track, top tracks, and other Spotify functionalities.

Design and implementation from scratch of different models for a musical recommendation system

The laboratory from Algorithmic Machine Learning Course at EURECOM

Music player and recommender for Discord servers