This repository focuses on credit card fraud detection using machine learning models, addressing class imbalance with SMOTE & undersampling, and optimizing performance via Grid Search & RandomizedSearchCV. It explores Logistic Regression, Random Forest, Voting Classifier, and XGBoost. balancing precision-recall trade-offs for fraud detection.
random-forest credit-card-fraud xgboost ensemble-learning logistic-regression hyperparameter-tuning imbalanced-data smote fraud-detection voting-classifier undersampling grid-search-hyperparameters precision-recall randomizedsearchcv classic-machine-learning threshold-tuning
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Updated
Feb 2, 2025 - Jupyter Notebook