Machine learning,
shipped like software.

A bitemporal financial data platform built around one question: how do you know the number is right? Point-in-time price storage, a dataset-level quality gate, and an AI analyst whose every numeric claim is checked against the tool output its prompt was built from. On its first run against a live scheduler, the quality gate caught a fabricated 9751% price move — two currencies silently sharing one database row — introduced by my own bug fix, root-caused and fixed the same day.

Upload a table and a LangGraph agent cleans it, picks a target, trains and ranks models, and measures what drove the predictions — then refuses any conclusion the data can't support. Dual leakage detection caught Titanic's leaked alive column (MI 1.000) and abandoned a bike-share target that was just arithmetic (R² 1.000). LLM for judgement only; every metric is deterministic.

Can public environmental data predict road-accident risk in Indian cities? ~596K records from 453 CPCB stations + MoRTH data → PostgreSQL warehouse → 3×3 model ablation → per-prediction SHAP → FastAPI + Streamlit dashboard. Dockerized, CI, live-hosted. Its anomaly detector's first real catch was a faulty gauge reporting 20× actual rainfall — excluded and documented.

5 years of my own chess (4,635 games · 328,258 Stockfish-analyzed moves) treated as an ML problem. Five models compared with 5-fold CV, McNemar's test, ROC analysis — and a data-leakage fix that traded a flattering 78.21% for an honest 72.49%. The insights took my rating from 597 → 1,423.

Ensemble (XGBoost + LogReg + RF) over 18 seasons of ball-by-ball data, 49 engineered features — Elo, phase stats, venue history. Walk-forward validation, 2,000-run Monte Carlo season simulation, P10–P90 player projections. Gave RCB the top title probability (25.4%) — and RCB won.

Published, peer-reviewed and awarded: real-time accident detection via sensor fusion + ML classification, neural network trained on 10,000+ samples, MQTT emergency-alert pipeline on Arduino. Best Paper at IC-CCDS 2025 (MULTICON-W), Mumbai.

Full-stack platform: adaptive psychometric assessments, Gemini-powered personalized roadmaps, automated PDF reports, Razorpay payments. RESTful API with 12+ endpoints at <200ms, scaled to 200+ concurrent users on Railway. 50+ profiles processed, 89% satisfaction.

Final-year project: a digital twin for industrial motors. Multi-layer fault-detection pipeline on synthetic MATLAB data, deployed as an ESP32 IoT system streaming over MQTT to a real-time dashboard with remaining-useful-life estimation. End-to-end latency under 500ms.