In a world of skewed data—like rare fraud in transactions or diseases in medical records—standard machine learning models often flop. "Machine Learning with Imbalanced Data: Proven Techniques to Build Accurate Models from Skewed Datasets" arms you with the tools to fix that.
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
