Tailored for final-year CSE (BTech/MTech) students, these IEEE-compliant machine learning projects align with 2024 academic and professional goals. Designed for capstone/thesis work, they merge advanced ML concepts with real-world applications, preparing graduates for emerging AI/ML industry challenges. (50 words
AnomaData uses ML algorithms to analyze real-time sensor data, detect equipment anomalies, predict failures, and optimize maintenance schedules, reducing downtime and costs in industrial systems. Ideal for predictive upkeep in manufacturing, energy, or IoT-driven sectors.
Find Default uses ML models (e.g., supervised learning, neural networks) to analyze transaction patterns, detect fraudulent credit card activities in real-time, minimize financial losses, and enhance transaction security for banking/e-commerce platforms.
Preprocess order/health data, train models, validate via RMSE/MAE, deploy on cloud for personalized e-commerce product suggestions. Integrates user behavior and health metrics for tailored recommendations
DocAssist uses AI/ML and NLP to analyze patient records, clinical data, and research, generating real-time diagnostic/treatment insights for healthcare providers, improving decision accuracy in hospitals/telemedicine.
MoodforMusic uses AI/ML and emotion recognition (voice/face analysis) to detect user mood, then recommends personalized playlists via collaborative filtering, enhancing engagement in music streaming apps like Spotify