A supervised ML classification project that predicts sleep disorders- Insomnia, Sleep Apnea, or None from health and lifestyle features including age, BMI, stress level, sleep duration, and blood pressure, using Logistic Regression, Random Forest, and SVM.

Predicting sleep disorders using machine learning on patient lifestyle and health data

AI Framework for Sleep Disorder Identification using Ensemble ML models with Django

Full-stack Machine Learning web application for sleep disorder prediction using Flask, Random Forest, SQLite, and Scikit-learn.

A web-based machine learning app that predicts potential sleep disorders based on health and lifestyle data. Built with Next.js and FastAPI, featuring an interactive UI, real-time predictions, and dark/light mode support.

A Streamlit-based web app for predicting sleep disorders using machine learning.

Sleep tracking PWA for Non-24-Hour Sleep-Wake Disorder. Actogram visualization, cycle-based logging, offline-first. Built with React + Vite + Supabase.