I'm a final-year Computer Science student who works with data end-to-end — from writing SQL and building ETL pipelines, to training ML models, to shipping the FastAPI/React applications that put those models in front of users.
What I do
- Build full-stack, production-shaped systems — not just notebooks: deployed APIs, real dashboards, working demos
- Work across the stack: SQL/ETL → model training & evaluation → backend API → frontend/dashboard
- Focus on explainability and correctness — e.g. auditing an AI-generated SQL layer for real security bypasses, not just accuracy metrics
Currently
- 🔭 Building BrewCo CRM (full-stack CRM, adding an ML churn-prediction layer) and extending SentinelML (fraud detection & explainability)
- 🌱 Deepening Advanced SQL, Data Warehousing, Deep Learning & NLP
- 💬 Happy to talk about SQL, data pipelines, FastAPI, or applied ML
Languages
Data & Analytics
AI & Machine Learning
Backend & Frontend
Databases, Cloud & DevOps
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🤖 SentinelML
Fraud detection & explainability system on 284K+ transactions — tuned XGBoost (0.878 PR-AUC, 0.92 precision), PyTorch autoencoder, SHAP explanations, MLflow tracking, deployed as a live API + dashboard.
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⚽ Football Analytics Warehouse
ETL pipeline transforming 230K+ matches (2000–2025) into a Dockerized PostgreSQL star schema, powering a 12-view analytical layer and a 4-page Power BI dashboard.
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☕ BrewCo CRM
Full-stack CRM for a coffee brand — React + FastAPI + PostgreSQL, with AI-driven customer segmentation, campaign management, and a churn-prediction model. Found & patched 2 real PII-leakage bypasses in the AI-generated SQL layer.
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😊 Moodline (Emotion Prediction)
NLP emotion classifier using frozen MiniLM embeddings + BiGRU, reaching 86% test accuracy with confidence thresholding to flag uncertain predictions instead of forcing a label.
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Actively looking for opportunities in Data Analytics, Data Engineering, and AI/ML Engineering — open to discussing projects, internships, and collaborations.
