Credit Card Fraud Detection using Bagging and Boosting Algorithm
Kanishka R. Deogade, Dhanashree B. Thorat, Snehal V. Kale, Seema Rajput, Harjeet Kaur · 2022
In the era of continuously evolving technology and increasing number of credit card holders it has become necessary to find techniques to detect fraud transactions. This problem can be tackled with data science and machine learning. This project aims to build a model that can efficiently classify fraud and non-fraud transactions. After training the sample data set by the model, this model can be further used to detect whether new transactions are fraud or not. Our objective here is to detect fraud transactions with maximum accuracy and to reduce the incorrect classification of transactions. Fraud detection is a classification problem in machine learning. We have focused on analyzing and preprocessing the data set. Further the data is trained using classification algorithms like Random Forest, which is a Bagging based algorithm and Adaboost and XGBoost which are Boosting based algorithms.