Data Cleaning Strategies for Messy Real-World Datasets
Every data scientist eventually learns the same lesson the hard way. The dataset in the tutorial is clean, well-labeled, and ready to model. The dataset at work is none of those things. It has missing values scattered like confetti, duplicate rows hiding in plain sight, inconsistent formatting, and outliers that may be genuine anomalies or simply typos. Cleaning that mess is not glamorous,...
