Music Recommendation Service

Using machine learning to predict if a given song will be popular or not

Trend analysis of pop music and prediction of release year.

Project for CMU 15-780 Graduate Artificial Intelligence

An R project that investigates whether different genres of songs have significantly different durations through the use of a one-way ANOVA test and post hoc significance tests conducted over an excerpt of a dataset consisting of 1 million popular songs compiled by The Echo Nest and a lab at Columbia University.

Mining Million Song Dataset

Data Mining course project - Million Songs Dataset exploration

Recommendation system on Million Song Dataset

Notebooks and data to accompany Python instruction for Data in Social Context Fall 2018

For this project, we plan to build a basic music recommendation system using the MLlib libraries that are part of the Spark installation. Our dataset will be the Million Song Dataset, which is a collection of audio features and metadata for one million contemporary popular music tracks.

An online song recommender based on a K-means model using the Spotify API and the MillionSongSubset

This repository implements pre-processing operations of the MELON PLAYLIST DATASET released by Ferraro et al.

Discovery Recommender System

Who would you listen to?: A Frequent Itemset Mining and Recommender System approach on the Million Song Dataset

Classying music genre based on audio features and lyrics (using the Million Song Dataset).

Language clustering of the musicXmatch dictionary in the Million Song Dataset

Tools to run text-based experiments for large scale cover detection.

Predicting Year in Million Song Dataset with Linear Regression using Pyspark