Data Adventures
Data analytics and data science made practical
17 articles and counting
How Can Feature Engineering Build Better Predictive Models?
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.
How Do You Manage Assumptions in the Data Science Pipeline?
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.