Five Common Data Leakage Traps That Make ML Models Look Better Than They Are

Data leakage is one of the most frequent reasons machine learning results appear stronger than they actually are, yet few practitioners check for it systematically. The five main culprits include duplicate or near-duplicate rows shared across training and test sets, temporal look-ahead leakage from random splits on time-series data, and features that inadvertently encode the target variable. A real-world demonstration using the EEG Eye State dataset showed a model's AUC score collapsing from 0.971 to 0.546 simply by switching from a random split to a time-respecting one. To address this, the author developed two open-source Python tools — LeakHound, which detects all five leak types and reports honest versus inflated scores, and safesplit, which prevents leakage by enabling group-aware, time-aware, and sequence-aware data splitting.