Predict whether a song is 'hot' or not, through analysis of the Million Song Dataset.

Project for CMU 15-780 Graduate Artificial Intelligence

Example code for processing the Million Song Dataset and other big music datasets

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

A command line tool to load the lyrics subset of the Million Song Dataset into an H2 SQL database

Classification of audio features using different ML algorithms on the MSD. The project was done for Machine Learning module at Coventry University.

Processing the Million Song Dataset with Apache Spark

Language clustering of the musicXmatch dictionary in the Million Song Dataset

Mining Million Song Dataset

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

Data Mining course project - Million Songs Dataset exploration

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

Music Recommendation Service

Trend analysis of pop music and prediction of release year.

Recommendation system on Million Song Dataset

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.

Analyze music history using Apache Cassandra.

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

This repository is inspired from Million Song Dataset Challenge from Kaggle. We aim to predict the year of song release by using timbre features' average and covariance.