Closed sujanrupu closed 1 year ago
I would like to work on this issue but can you elaborate more on what I need to do. I know that XGBoost algorithm to train for this kind of dataset works excellent with 97 percent accuracy and hope it will remove that oversampling problem.
@theyashwanthsai @khusheekapoor as a GSSOC '23 contributor, I want to work on this project..
@tanujbordikar since we are following the first-come-first-serve policy, we will not be able to assign you this issue. However, you can create another issue and use the same algorithm on a different dataset.
@sujanrupu Please specify the ml model which you are going to train
@theyashwanthsai I will use random forest classifier on the dataset present in kaggle. Dataset link: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
@theyashwanthsai may I start working on the issue?
@sujanrupu you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_dataset.ipynb
and link it in the readme of the labeled directory as algorithm - dataset
.
💥 Proposal (GSSOC 23)
A machine learning algorithm to recognize fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.
Main challenges involved in credit card fraud detection are: