Mastering Credit Card Fraud Detection with XGBoost: A Comprehensive Tutorial with AMH

Описание: Project Summary:

Dataset: We utilized a dataset that encompasses a range of credit card transactions, encompassing both legitimate and fraudulent transactions.
Data Processing: A series of data preprocessing procedures were executed, including feature normalization, to ensure data quality.
Addressing Class Imbalance: We tackled the issue of class imbalance by implementing the Synthetic Minority Over-sampling Technique (SMOTE).
Model Training: Our XGBoost classifier underwent training via RandomizedSearchCV for hyperparameter optimization.
Performance Assessment: The model's performance was rigorously assessed on the test dataset, including the presentation of classification metrics, ROC-AUC scores, and more.
For the complete code and comprehensive explanations, be sure to explore our GitHub repository. We encourage you to examine and adapt it for your own projects and learning endeavors.