Data using https://www.kaggle.com/mlg-ulb/creditcardfraud
I compare many models for fraud detection of credit cards using Isolation Forest and its extended, Random Undersampler + (Logistic Regression, SVM, KNN, Decision Tree, Random Forest), PyCaret (Random Forest), XGBOOST, and Artificial Neural Network.
In this project, we must attention to high Precision because we couldn't harm others with the fault of detecting Fraud but actually, the customers weren't a fraud.
XGBOOST is the best classifier for this project with the highest precision and other performance too.