Treffer: Implementation and Deployment of a Machine Learning-Based Credit Card Fraud Detection System.

This project presents the development and implementation of a machine learning-based system for detecting fraudulent credit card transactions. Utilizing a highly imbalanced realworld dataset, the study explores various classification algorithms--K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT)--to evaluate their effectiveness in fraud detection. Data preprocessing steps included class transformation for categorical visualization, normalization, and handling of imbalanced data. The system was developed using Python and Jupyter Notebook, employing libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib for modeling, data analysis, and visualization. Among the models, KNN and Decision Trees demonstrated exceptional performance, each achieving 100% accuracy in detecting fraud cases. The final model was deployed as a user-interactive web application that enables real-time monitoring of transactions for potential fraud. This application aims to provide financial institutions with a robust, scalable, and efficient tool for enhancing transactional security and minimizing fraudulent activities. [ABSTRACT FROM AUTHOR]

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