Data Visualization | d3.js | Webscraping | Machine Learning
Thesis for Parsons MS Data Visualization
Guided by Sam Lavigne & Matias Pina
Live SiteGithub What smells “feminine” or “masculine” isn’t chemistry — it’s about culture, time, and storytelling. They change — just like our ideas of gender. My thesis, Fluidnotes, maps over 25,000 perfumes and 100 years of gender history to explore how fragrance culture reflects - and sometimes challenges - our evolving understanding of gender.
Using machine learning and computer vision tools, this project was able to analyze 250+ vintage advertisements, map fragrance notes into categories, and connect visuals to scent compositions.
While the dataset does not represent the entirety of the global perfume market, it covers a broad span to reflect the Western industry’s dominant trends.