This project is build using machine learning algorithms named as:-
- Isolation Forest Algorithm
- Local Outlier Factor
Earlier, People used to detect fraudulents in credit card transactions using basic classifier models like decision tree, random forest, SVM, k-nearest neighbors , logisitic regression, etc. But these are some newly discovered techniques which works well in terms of accuracy_score, and overall classification_report.
Please check out the .ipnb file attached in this repo for more information.
Hope you find it useful.
✒📌NOTE: Dateset is taken from Kaggle click on this link
📝Any changes regarding the code in current repo is most welcome. I would like to get some good techniques for better model performance in this project.