Authors: Shravani Mali, Yukta Koli, Mayuri Mohite, Nilam Patil
Abstract: This study presents an AI-driven allergy checker designed explicitly for skincare. It reviews the user's allergies, skin type, and skin conditions and suggests a product and skincare routine based on those factors. The random forest model is used to classify skin type while the Light GBM model evaluates the skincare routine recommendations. Then a K-Nearest Neighbors (KNN) algorithm uses the allergy information the user provides to make the recommendations. A YOLOv8 model also analyzes the image the user provides and determines if there are skin conditions visible to the naked eye. In review, the system developed is able to provide appropriate personalized data-driven recommendations for skincare products and routines with a lower likelihood of allergic body complications, while also allowing for informed selections of skincare according to allergenic history.
