Blog
Self-published2023Principal Component Analysis, built from first principles in Python and NumPy and wrapped in a scikit-learn-style class. We work through the covariance matrix, eigendecomposition, and component selection on a toy dataset.
Self-published2023We implement K Nearest Neighbors from first principles in Python and NumPy as a reusable class object. Distance computation, neighbor selection, and majority voting are each built up and tested on a toy dataset.
Self-published2021This post adapts test-time augmentation, a computer vision staple, to tabular data. We benchmark strategies for numeric and categorical features and measure how much accuracy they actually buy at inference.
Self-published2021Spreadsheets stop scaling once an experiment log runs to hundreds of rows. We move tracking onto Neptune.ai across a competition-style workflow, logging metrics, parameters, and artifacts, then comparing runs to decide what to keep.