UC Berkeley team's submission for RecSys Challenge 2018

Implementation of music genre classification, audio-to-vec, song recommender, and music search in mxnet

In this project, I create a recommendation algorithm to give song recommendations on Spotify.

Web app for custom Spotify playlists based on track audio properties such as danceability, energy, tempo & your personal Spotify listening analysis

This is Content based music recommendation system. I have used both audio features and lyrics (text based) features for recommending most similar songs to a given query song.

My first end-to-end project, using Web Scraping with BeautifulSoup and Spotify API. Clustering songs based on its audio features.

A Prolog song recommender

A simple song recommendation engine using K-Nearest Neighbors with the Spotify Music Dataset

Song Recommendation using K-NN.

SpotifySongRecommender is a C++ project for analyzing and recommending songs. It includes features like recommending songs and artists based on musical genres, and generating popularity rankings by artist or genre.

Spotify Playlist Generator

This is Content based music recommendation system. I have used both audio features and lyrics (text based) features for recommending most similar songs to a given query song.

A website which suggest songs on the basis of the recognized facial emotion.

Sangeet.com made use of Spotify API to get songs recommendations and provide link so that you can enjoy more fresh songs. Made using html, css, node, express with help of Postman

Machine Learning Algorithms using GraphLab

I analysed a dataset obtained from last.fm to recommend the next songs a user is likely to hear. Used NearestNeighbor algorithm for predictive analysis.

A mac-based song analyzer application built to classify and play songs based on your mood.

Web Portal for the [RUMusic](https://github.com/vraj152/RUMusic)

Song recommender built end-to-end combining BeautifulSoap4 and Spotify API