Music Recommendation Service
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Updated
Apr 24, 2019 - Python
Music Recommendation Service
Using machine learning to predict if a given song will be popular or not
Mining Million Song Dataset
Project for CMU 15-780 Graduate Artificial Intelligence
Processing the Million Song Dataset with Apache Spark
Predict whether a song is 'hot' or not, through analysis of the Million Song Dataset.
Notebooks and data to accompany Python instruction for Data in Social Context Fall 2018
Discovery Recommender System
Recommendation system on Million Song Dataset
Data Mining course project - Million Songs Dataset exploration
Classification of audio features using different ML algorithms on the MSD. The project was done for Machine Learning module at Coventry University.
Tools to run text-based experiments for large scale cover detection.
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.
Design and implementation from scratch of different models for a musical recommendation system
Trend analysis of pop music and prediction of release year.
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
A command line tool to load the lyrics subset of the Million Song Dataset into an H2 SQL database
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