UC Berkeley team's submission for RecSys Challenge 2018
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Updated
May 4, 2018 - Jupyter Notebook
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.
Machine Learning Algorithms using GraphLab
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.
Following is a song recommender NN model in google colab using turiCreate developed and used by apple for research. It aims to predict likeable songs based on their history and data of liked ones.
Web Portal for the [RUMusic](https://github.com/vraj152/RUMusic)
Song Recommendation using K-NN.
Song recommender built end-to-end combining BeautifulSoap4 and Spotify API
Recommending songs with personalization and based on simple popularity-based recommender.
My first end-to-end project, using Web Scraping with BeautifulSoup and Spotify API. Clustering songs based on its audio features.
The best trvia app for music lovers, boasting the ability to retrive songs realtime based on the users spotify taste profile! Allows multiple users to log in or even select songs based on your favourite artists.
Spotify Playlist Generator
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
Plays a random song from the Spotify catalog
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.
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.
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