about
Third-year Computer Science student focused on building deployable machine learning systems, computer vision applications, and intelligent backend architectures.
I have a builder mindset — I prefer shipping projects over reading theory. Most of my learning comes from breaking my own models and figuring out why they failed. That's where real engineering happens.
Currently pushing toward advanced ML, deep learning architectures, and real-world deployment pipelines.
work
End-to-end regression pipeline — data cleaning, outlier removal, feature engineering (location encoding, area scaling). Trained and compared Linear Regression, Decision Tree, and Random Forest with RMSE and cross-validation evaluation. Simulates a full real-world ML workflow from data to inference.
Real-time hand landmark detection and finger tracking with high accuracy. Built for extensibility — usable for gesture control, virtual mouse, and HCI systems. Optimised frame processing for smooth real-time performance.
Vision-based system to estimate body metrics and classify obesity levels. Uses image processing to extract body proportions and features, with planned ML integration for BMI estimation. Focus on production-ready health-assist deployment.
From-scratch implementations of core ML algorithms — Logistic Regression, KNN, Linear/Ridge/Lasso Regression — alongside full data preprocessing pipelines and model evaluation. Focused on deeply understanding model behaviour, not just using libraries.
skills
Languages
ML / AI
Backend & Frameworks
Cloud & Infra
Databases
⚡ fun fact
Most of my learning comes from breaking my own models and figuring out why they failed — that's where the real engineering happens.