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Designing and deploying intelligent systems that bridge research and real-world impact — with measurable performance, clean pipelines, and production-grade code.
Engineering student building production-ready ML systems that solve real problems.
My work centres on the full lifecycle of ML — data wrangling, feature engineering, model training, rigorous evaluation, and clean documentation. I care deeply about the gap between a working notebook and a system that delivers genuine value in production environments.
End-to-end ML pipelines with clear, measurable metrics
EDA, feature engineering, model selection
Clean code, documentation & reproducibility
Ship reliable, production-grade AI solutions
End-to-end data analytics pipeline converting raw survey data into actionable business insights through preprocessing, EDA, and interactive visualizations.
ML pipeline predicting industrial equipment failure using IoT sensor telemetry with regression modelling and Pandas-based data engineering.
AI-based autonomous navigation leveraging A* path planning and simulation to demonstrate real-world agent decision-making and environment modelling.
Expanding into model deployment, experiment tracking, and scaling ML workflows beyond notebooks into production-ready systems.
Have a project in mind or want to discuss ML? I'm always open to interesting conversations and new opportunities.