Making Data Analysis Easier: A Case Study on Credit Card Fraud Detection Based on PyCaret

DOI

10.2991/978-94-6463-256-9_122How to use a DOI?

Keywords

data analysis; machine learning; credit card fraud detection; PyCaret; random forest classifier

Abstract

As credit card usage surges globally, associated security challenges, particularly credit card fraud, come into sharp focus. The prevailing method of fraud detection entails employing machine learning algorithms—a skillset necessitating specialized programming and algorithmic training. This research endeavors to mitigate this complexity by harnessing PyCaret—a streamlined data analysis tool—for credit card fraud detection. The study constructed ten distinct machine learning classification models, leveraging Kaggle's credit card transaction dataset, to compare diverse models’ performance in fraud detection. Notably, the Random Forest Classifier exhibited superior performance metrics, with an accuracy of 0.9996, an AUC of 0.9439, a recall rate of 0.8022, a precision rate of 0.9423, an F1 score of 0.8654, and an AUPRC of 0.79, thereby indicating commendable performance amid severely imbalanced data. This research further highlights PyCaret's user-friendly programming environment and rich visualization capabilities, achievable with a mere twelve lines of code. This potential for simplicity has significant implications for reducing data analysis barriers for non-technical practitioners while offering preliminary data exploration tools for professional data analysts.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chang Huang
AU  - Pao-Min Tu
AU  - Chun-You Lin
PY  - 2023
DA  - 2023/10/09
TI  - Making Data Analysis Easier: A Case Study on Credit Card Fraud Detection Based on PyCaret
BT  - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
PB  - Atlantis Press
SP  - 1203
EP  - 1211
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-256-9_122
DO  - 10.2991/978-94-6463-256-9_122
ID  - Huang2023
ER  -