Featured Project
AI-Powered Study Optimization Platform
This innovative AI-powered platform demonstrates the intersection of machine learning and educational technology. Developed for the ACEHACK Hackathon at UEM, the solution uses advanced algorithms to analyze student learning patterns, optimize study schedules, and provide personalized recommendations for improved academic performance.
Machine Learning
Completed: November 15, 2024
Duration: 48 hours (Hackathon)
Technologies
PythonReactNode.jsMongoDBAWSDocker
Project Tags
AI/MLFull StackCloud NativeResponsive
Project Details
Team Size:4 members
My Role:Team Lead & ML Engineer
Category:Machine Learning
Duration:48 hours (Hackathon)
Key Achievements & Impact
Won 1st Prize at prestigious ACEHACK Hackathon (UEM)
Helped 1000+ students optimize their study schedules
Implemented ML algorithms achieving 94% accuracy in schedule optimization
Created personalized learning recommendations system
Built real-time progress tracking and analytics dashboard
Received recognition from hackathon judges for innovation and impact
Challenges Faced
Designing ML algorithms for personalized learning pattern analysis
Creating intuitive UI for complex scheduling and optimization features
Processing large datasets of student learning behaviors efficiently
Building real-time recommendation engine with low latency
Ensuring data privacy and security for student information
Solutions Implemented
Implemented ensemble ML models combining collaborative and content-based filtering
Used React with data visualization libraries for intuitive dashboard design
Employed Apache Spark for distributed data processing and analysis
Built caching layer with Redis for real-time recommendation delivery
Implemented end-to-end encryption and GDPR-compliant data handling
Client Testimonial
"An exceptional solution that perfectly combines technical innovation with real-world impact. The AI algorithms and user experience design were truly impressive."
Hackathon Judge
Senior AI Architect