Databases/Web Dev
Spotify Billboard Analytics
A project pairing Spotify audio features with Billboard Hot 100 chart data, backed by PostgreSQL 16 on AWS RDS with a Node/Express API, a React + Recharts frontend, and a Python/pandas cleaning pipeline.
Audio twin finder
Pick a song. The demo ranks its nearest neighbours by straight-line distance across five
audio features, which is the same idea the real query runs across 447,000 tracks using a
GiST-indexed cube. Twenty tracks here, and the feature values are
approximations, so treat the percentages as illustrative.
Audio fingerprint
Closest twins k-NN · 5-D
A music analytics application built as a database systems course project, joining Spotify audio features with Billboard Hot 100 chart history to explore how a song’s characteristics relate to its chart performance.
The backend runs PostgreSQL 16 on AWS RDS with a Node/Express API, and a Python and pandas pipeline cleans and loads the raw data. The schema normalizes four core tables to 3NF and uses GiST/GIN indexes, pg_trgm for fuzzy text search, materialized views for heavy aggregates, and a k-NN similarity query for finding songs with similar audio profiles.
The frontend is built in React with Recharts. I owned the Trends and Analytics visualizations, turning the query results into interactive charts.
As a graded course assignment, the source code cannot be shared publicly under course policy.