Credit Card Fraud Detection
Summary
Built a predictive machine learning pipeline to identify fraudulent credit card transactions using imbalanced historical data. • Performed extensive data preprocessing and feature engineering with pandas and scikit-learn to improve model performance. • Trained and evaluated multiple classification algorithms including Logistic Regression, Random Forest, and XGBoost, achieving an accuracy of 95% and significantly improving fraud detection rates. • Applied SMOTE to handle class imbalance