Data Adventures
Data analytics and data science made practical
13 articles and counting
Feature Engineering: Building Better Models with Data
Feature engineering is often the primary lever for production model gains. This post gives practical techniques, validation workflows, and cost-aware guidance to build efficient, deployable features.
The Data Science Pipeline: Where Assumptions Hide
Every data scientist has trained a model that works perfectly in the notebook, then fails in production. The problem rarely lies in the algorithm itself—it’s hidden in upstream decisions about data collection, preprocessing, and validation. This guide reveals where those assumptions compound and how to catch them.