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A production-oriented Machine Learning pipeline that predicts whether an active user session will result in a purchase.
Model: XGBoost Classifier optimized for class imbalance.
Performance: ROC AUC 0.936 | F1-score 0.71 (at 0.30 threshold).
Key Features: Reproducible environment (uv), modular CLI for training/inference, leakage-free preprocessing, and SHAP interpretability analysis.
Data: UCI Online Shoppers Purchasing Intention Dataset.
Tech Stack: Python, XGBoost, Scikit-learn, Pandas, SHAP.
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