Machine Learning Algorithms using GraphLab
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Updated
Nov 21, 2017 - Jupyter Notebook
Machine Learning Algorithms using GraphLab
UC Berkeley team's submission for RecSys Challenge 2018
A website which suggest songs on the basis of the recognized facial emotion.
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.
These individiual projects are part of the coursework for the Coursera University of Washington course. I've also tried some fun new experiments with the data. Have fun checking them out !!
Song Recommendation using K-NN.
Web Portal for the [RUMusic](https://github.com/vraj152/RUMusic)
My first end-to-end project, using Web Scraping with BeautifulSoup and Spotify API. Clustering songs based on its audio features.
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.
Song recommender built end-to-end combining BeautifulSoap4 and Spotify API
Recommending songs with personalization and based on simple popularity-based recommender.
In this project, I create a recommendation algorithm to give song recommendations on Spotify.
A mac-based song analyzer application built to classify and play songs based on your mood.
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.
Song recommender system using Turicreate library to recommend songs.
Spotify Playlist Generator
Implementation of music genre classification, audio-to-vec, song recommender, and music search in mxnet
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