Building SkinSense AI — a multi-stage intelligent skincare system.
From ML → Deep Learning → Image Analysis → Gen AI.
// about me
I'm an ECE student and aspiring AI/ML engineer with a clear, long-term mission: to build intelligent systems that simplify complex, everyday decisions for real people.
My flagship project, SkinSense AI, started as a machine learning model and is evolving into a full-stack AI product — integrating deep learning, computer vision, and generative AI across multiple versions.
I'm especially drawn to applied AI — not just building models, but turning them into products that people can actually use and trust.
// tech stack
// projects
An ML-powered skincare analysis system. Users input 7 skin parameters — pigmentation, sebum level, redness, moisture, acne, pore size, and texture — and the Random Forest model classifies their skin type, assesses severity, and delivers personalized product recommendations based on budget, active ingredients needed, and day/night routines.
Evolving V1 into a neural network-powered system using TensorFlow/PyTorch. The deep learning model will recognize complex, non-linear relationships between skin parameters — improving recommendation accuracy with personalization based on user profiles and input history.
// certifications
// let's connect
Open to internships, research collaborations, and interesting AI/ML projects. Always happy to talk shop.