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

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.