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random-forest-classifier

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This repository contains code and resources for detecting diabetes using artificial intelligence (AI) techniques. The project leverages machine learning algorithms to predict the likelihood of diabetes based on various medical and demographic factors. The primary goal is to provide a reliable and accurate tool for early detection of diabetes.

  • Updated Feb 9, 2026
  • TypeScript

IoT-powered​‍​‌‍​‍‌​‍​‌‍​‍‌ smart factory simulation that combines Kafka, MongoDB, and Flask with machine learning-driven insights for Energy Optimization, Predictive Maintenance, and Operational Safety Monitoring.

  • Updated Jan 21, 2026
  • TypeScript

🌟 Signez - Interactive ASL learning with real-time hand sign recognition! 🖐️ Learn and practice ASL alphabet with percentage match, predicted words, scores, and a progress dashboard. Powered by advanced ML and full-stack integration 🚀✨

  • Updated Jul 19, 2025
  • TypeScript

AI Pregnancy Twin is a web-based maternal health prediction system that creates a digital health twin using clinical data to assess pregnancy risk and provide personalized care insights. Built with React (Vite), Supabase, , the system uses Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting for risk prediction.

  • Updated Dec 26, 2025
  • TypeScript

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