💎💿 Portfolio 💿💎
I love music + data. I build web apps, discovery tools, scrapers, and regularly dig into data to find stories worth telling.
Featured
Two flagship builds.
Vanzari
Explore and compare the sales history of music’s most iconic artists in one extensive database.
Critica
Compare album reviews from Pitchfork, Metacritic, and Fantano all in one streamlined app.
Labs
Smaller projects that sharpened my skills.
TikTok artist discovery tool
- Uses Python + Selenium to browse TikTok hashtag pages and collect profile links.
- Pulls key signals (followers, bio, captions, Spotify links) from each profile.
- Keeps likely unsigned pop artists (within follower range) and exports a CSV with pandas.
Reddit artist discovery tool
- Pulls new post titles from music subreddits using Reddit’s JSON feed.
- Scans titles for likely artist names and logs them to a master CSV.
- Ranks the top mentions, finds “risers,” and saves a chart PNG.
Major label Billboard presence
- Imports Billboard chart data from a CSV into a normalized SQLite database.
- Converts chart ranks into points and totals them by label bucket.
- Exports weekly label totals into a simple CSV report.
Wikipedia certification scraper
- Hits the Wikipedia API, grabs the discography HTML, and parses tables.
- Extracts album titles and U.S./RIAA certification text.
- Outputs a clean JSON list of albums you can reuse anywhere.
Sales % change calculator
- Calculates each artist’s sales % change from one release to the next.
- Prints a neat leaderboard (and shows N/A when prior sales are zero).
- Flags the biggest positive jump as the “Fastest riser.”
Lyric analyzer
- Reads two lyric files and cleans them (lowercase, remove punctuation).
- Counts how often each word shows up in each song.
- Prints unique/shared word stats plus each song’s top words.
Learning
Coursera: Five Course Python Track
Python fundamentals → data structures → web data → databases → capstone-style builds.
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