Data Adventures
Data analytics and data science made practical
17 articles and counting
How Bad Data Hides: A Guide to Data Quality
Bad data doesn’t always announce itself. Silent failures like label noise and distribution drift can degrade models long after deployment. This guide covers systematic auditing, principled cleaning, and production validation to catch data quality issues before they cost you.
How Does Model Selection Work as a Constraint Satisfaction Problem?
Model selection isn’t about finding the most powerful algorithm—it’s about satisfying your actual constraints. Learn a repeatable framework that starts with context, not a model zoo.
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.