A machine learning approach to classify songs by mood.
-
Updated
Nov 2, 2016 - OpenEdge ABL
A machine learning approach to classify songs by mood.
Based on the idea of Spotify : a concrete example to understand how graph databases work with Neo4j. The challenge is to create a music recommendation algorithm using a very large database of songs (Million Song Challenge Dataset) with an API to interact with (Symfony).
Song lyrics generation using Recurrent Neural Networks (RNNs)
musical snobbery, with a touch of machine learning
This is a dataset consisting of all song lyric words found on all of Taylor Swift's studio albums (up to and including TTPD), as well as a selection of other songs written by her.
Recommending great songs to users based on their listening history!
Song Popularity Predictor
Data Modeling with Postgres
Final Project for STA 141C with Dr. Bo Yu-Chien Ning
Analysis of new songs website data using Postgres SQL Functions to extract insights, business improvement, and understanding the relations between features.
This app simulates a music tracker system on a client-server architecture
ETL Pipeline from AWS S3 to Redshift
Data Engineering Projects: SQL, NoSQL, Data Warehousing, Date Lake & Data Pipeline
Add a description, image, and links to the song-dataset topic page so that developers can more easily learn about it.
To associate your repository with the song-dataset topic, visit your repo's landing page and select "manage topics."