I build ML systems that deploy to production, not just live in notebooks.
Currently working on early disease detection from clinical time-series data and building computer vision pipelines with end-to-end deployment on AWS.
- End-to-end ML — from raw data ingestion to production REST API
- Accuracy without scalability is just a prototype — I build for both
- B.Tech CSE (AI/ML) @ KIIT · Building systems that actually matter
| Project | What it does | Result |
|---|---|---|
| SolarVerse | Solar energy production forecasting — XGBoost ensemble on meteorological time-series, Flask REST API backend | 94% prediction accuracy |
| DualFusion | Multi-modal image-text fusion — cross-attention transformer architecture, shared embedding space for cross-domain retrieval | SOTA cross-domain performance |
| NEO SEPSIS | Early sepsis detection from clinical time-series — scikit-learn pipeline with SHAP explainability, deployed on AWS via Flask | 96% sensitivity, production-ready |
| Song Popularity Analysis | Predicts Spotify track popularity using XGBoost on 114K tracks — FastAPI backend + interactive web frontend | Binary classification with F1 evaluation |
| Map My Way | SIH trip tracker — GPS trajectory analysis, business/leisure classification via logistic regression, OCR expense tracking, Phi-3 Mini chatbot, Go/Echo REST API | Multi-modal AI integration |
| Jurify | AI legal document simplifier — BART model for summarization, sentence-transformers for Q&A chatbot, multi-format upload (PDF, DOCX, TXT), side-by-side comparison | Production-ready NLP pipeline |
Computer Vision · XGBoost · Scikit-Learn · OpenCV · Flask · REST APIs
Deep Learning · NLP · Transformers · AWS EC2 · Pandas · NumPy · Docker