A case study in temporal data leakage: deliberately builds a 99 %-accurate but useless leaky model, then rebuilds it as a leakage-safe, time-aware ML pipeline with honest cross-validation and a Streamlit deployment.
python data-science machine-learning time-series scikit-learn tabular-data cross-validation pandas xgboost feature-engineering reproducibility case-study binary-classification model-evaluation mlops data-leakage production-ml streamlit time-aware-validation temporal-leakage
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Updated
May 2, 2026 - Jupyter Notebook