Glycemic Control ML Classifier
89% accuracyA Random Forest pipeline that classifies diabetes risk conditions, with feature selection tuned so predictions return in under 100ms — fast enough to sit behind a live product, not just a notebook.
software engineer · bengaluru, in
I build full-stack apps
and applied ML models that works in production.
CS graduate · ex-intern at HostUp Cloud Technologies · Java, Spring Boot, React, Next.js, Node.js, Docker, cloud-native and a habit of measuring everything I ship.
zainab@bengaluru:~$ whoami
zainab@bengaluru:~$ ▍
01 · about
I'm a Computer Science graduate (CGPA 8.5/10) from Atria Institute of Technology, with hands-on experience shipping cloud-native web applications during my internship at HostUp Cloud Technologies. I'm equally comfortable configuring VPCs and containers as I am training a classification model — my projects span full-stack platforms, DevOps tooling, and applied machine learning, usually all three at once.
02 · experience
HostUp Cloud Technologies Pvt. Ltd. · Bengaluru
03 · projects
A Random Forest pipeline that classifies diabetes risk conditions, with feature selection tuned so predictions return in under 100ms — fast enough to sit behind a live product, not just a notebook.
A full-stack blog engine with Next.js and MongoDB (via Prisma), covering CRUD operations at 90–95% functional accuracy, built for fast expansion as features get added.
An AI-powered university timetable generator combining genetic algorithms with OR-Tools, serving optimized schedules in ~200–500ms per request with real-time updates and JWT-secured access.
04 · skills
05 · certificates & achievements
06 · contact