KEYWORDS: Machine learning, Data modeling, Decision trees, Education and training, Random forests, Performance modeling, Deep learning, Matrices, Binary data, Support vector machines
Credit card fraud has been increasing with the rise of cashless payments, making it difficult to identify fraudulent transactions among thousands of normal ones. Machine learning algorithms can help address this issue by classifying transactions as either fraud or non-fraud in a dataset. For this project, we utilized a highly imbalanced dataset from Kaggle containing European cardholder data. While the dataset is mostly clean, we needed to balance it for training and testing purposes through undersampling. We developed six classification models to differentiate between fraudulent and non-fraudulent transactions and evaluated their performance based on accuracy and F1 score. The KNN model outperforms the other models for the dataset we used in our experiment.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.