A machine learning approach to classify songs by mood.
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Updated
Nov 2, 2016 - OpenEdge ABL
A machine learning approach to classify songs by mood.
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.
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
Recommending great songs to users based on their listening history!
Data Modeling with Postgres
Song Popularity Predictor
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
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