Keshav Sundararaman

Amrita Vishwa Vidyapeetham × Sony India

Built automated network scripts to continuously ingest raw IoT sensor readings into structured tabular datasets. Cleaned and validated multi-sensor experimental logs in Pandas to eliminate sensor noise, drift, and missing entries. Analyzed food drying behavior across sensor runs and presented analytical findings and Matplotlib charts directly to Sony representatives and project leads.

Built a Chrome extension that scans all variants on an Amazon product page and finds the lowest price across every condition — New, Like New, Very Good, Good, and Acceptable. Designed the architecture first, then built it with Claude as a dev partner. Shipped v1.1 within 24 hours of launch adding support for size-and-color variant grids and bundle listings.

End-to-end ML pipeline predicting abnormal next-day price moves across 505 S&P 500 stocks (619K+ daily OHLCV rows spanning 5 years), engineering 12 no-lookahead features from price and volume history. Used strict time-based train/validation/test splits to prevent data leakage and tuned hyperparameters on validation F1 across four model families. Random Forest performed best (test AUC 0.72, F1 0.31 at a 2% move threshold); used feature importance and L1 sparsification to identify volatility and price-range features as the strongest signals.

F1 data engineering pipeline built on the OpenF1 public API. Ingests raw race data (lap times, pit stops, driver positions, tyre compounds, weather) into a bronze/silver/gold Delta Lake medallion architecture on Databricks. In progress — bronze ingestion layer complete.