Harper Cole's guide takes beginners to pros through oversampling, undersampling, SMOTE, ensembles like RUSBoost and XGBoost, cost-sensitive learning, and hybrids. With hands-on Python projects using scikit-learn and imbalanced-learn, explore real cases in fraud detection and diagnostics. Set up your environment, evaluate with precision-recall, and deploy via Streamlit.
Ideal for data scientists and analysts tackling churn, anomalies, or rare events, this book boosts your models' accuracy and impact. Transform imbalanced data into reliable insights
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