Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate.
The purpose of this project is to develop, describe and validate a machine learning model that detects if a customer is involved in money laundering. We used many models{NN,Random Forest,Logestic regression,SVM} to finaly choose the best model with the best accuracy. The models were applied to a large data set related to money laundering that we imported from the link below:
https://www.kaggle.com/datasets/maryam1212/money-laundering-data
As a result we found out that random forest is the best model with an accuracy of 97%
The contibuters to this repo are: Hind MOUTALATTIF & Khaoula JERRARI & Hajar ZAINI
Link to the repository: https://github.com/khaoula789/money_laundering_detection