Project
DermAI: Personalized Skincare Recommender
Overview
Built a recommender system that analyzes skincare reviews and user profiles to suggest personalized skincare products.
Why It Matters
Personalized skincare recommendations can improve user satisfaction and outcomes, addressing the growing demand for customized beauty solutions.
Outcome
Developed a prototype that matches users to products based on skin concerns and preferences, with potential integration of computer vision for condition detection.
Resources
Data Sources:
Amazon Product Review Data: Scrape or filter for skincare categories like “face wash,” “moisturizer,” etc.
Skincare subreddit / forums (r/SkincareAddiction): Scrape posts for sentiment and user skin types.
DermNet NZ Image Library: Public dermatology images, categorized by condition (for computer vision training).
User-submitted reviews from sites like: Sephora, Ulta, or INCIdecoder (scraping needed).
Optional: Use spaCy/NLTK to extract skin concerns (e.g., “acne-prone,” “dry patches”).
Category:
Domain: Consumer HealthTech / Skincare Analytics
Industry: BeautyTech, Wellness, E-commerce Personalization
Technical Specialization: NLP, Recommender Systems, Computer Vision, User Profiling
