Fraud Detection

Abstract

1. Introduction

[1] Kaggle. (2017, Jan. 12). Credit Card Fraud Detection [Online]. Available: https://www.kaggle.com/dalpozz/creditcardfraud

Credit card fraud is a growing issue with many challenges including temporal drift and heavy class imbalance. This project attempts to tackle class imbalance using state-of-the-art techniques including Adaptive Synethtic Sampling Approach (ADASYN) and Synethetic Minority Oversampling Technique (SMOTE). Over 280k real transactions made in Europe in September 2013 [1] are used as the training dataset. Three types of machine learning models are compared: Random Forest, Support Vector Machine, and Multi-Layer Perceptron. Results show that the optimal sampling method for an imbalanced dataset is dependent on the dataset and the model being used.

b) Jupyter Notebook walking through machine learning tests conducted. You can run view and run them yourself. Included are also comments, reasoning, and figures. For your convenience I have included a copy of the original dataset [1] in this git repo, however please refer to the original source for the most up-to-date version.

2. Objective

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

This project demonstrates how Jupyter Notebook, scikit-learn, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/yazanobeidi/fraud-detection.git
cd fraud-detection

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by yazanobeidi and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.