I'm a fourth-year Computer Science student at KIIT University, building full-stack applications and AI/ML systems — from RAG pipelines to production-style data platforms. I care about shipping things that actually work end-to-end, not just prototypes.
Currently prepping for placements and going deeper into data engineering, backend architecture, and applied ML.
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Languages
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Frontend
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Backend
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Data
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ML
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AI / RAG
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A retrieval-augmented system that lets you query a codebase in natural language and get accurate, context-aware answers.
| Metric | Result |
|---|---|
| Precision@10 | 100% |
| Avg. query latency | ~8.75s |
| Indexed | 106 chunks · 33 files · 20+ languages |
Next.js Flask FAISS pgvector HuggingFace Groq Tree-sitter
A full-stack platform for retaining data structures & algorithms knowledge using spaced repetition tuned for coding practice.
| Metric | Result |
|---|---|
| Algorithm | SM-2, adapted with DSA-specific weighting |
| Coverage | 18 DSA topics · 7 mistake types tracked |
| Extras | Judge0 execution · Redis caching · Chrome extension |
React Vite Node.js Express PostgreSQL (Neon) Prisma Redis Judge0 Groq
An agriculture platform combining crop recommendation, rainfall forecasting, and plant disease detection in one product.
| Metric | Result |
|---|---|
| Crop recommendation (Random Forest) | 99.3% accuracy, 22 classes |
| Rainfall forecasting (Stacked Ensemble) | R² = 0.81, MAE = 35.96mm |
| Disease detection | CNN, deployed Flask backend |
React Flask Scikit-learn Random Forest CNN