Time-aware lead scoring pipeline with leakage-safe event features and CatBoost.
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Updated
Jul 20, 2026 - Jupyter Notebook
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time-aware-validation
Here are 3 public repositories matching this topic...
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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May 2, 2026 - Jupyter Notebook
End-to-end churn prediction project using the KKBox dataset
data-science machine-learning xgboost classification lightgbm feature-engineering churn-prediction customer-churn leakage-detection time-aware-validation
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Jun 4, 2026 - Python
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