Credit Card Fraud Detection

Abstract

1. Introduction

Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal.

An end-to-end Machine Learning Project carried out by Group 3 Zummit Africa AI/ML Team to detect fraudulent credit card transactions. Built with FastAPI, Streamlit and Docker.

The machine learning model used for this web application was deployed as an API using the FastAPI framework and then accessed through a frontend interface with Streamlit.

2. Objective

Credit Card Fraud Detection App built with Streamlit, FastAPI and Docker.

This project demonstrates how Jupyter Notebook, Docker, FastAPI 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/Nneji123/Credit-Card-Fraud-Detection.git
cd Credit-Card-Fraud-Detection

1. Clone the repository to your Google Colab Instance.
2. Install the requirements by running the following codes:
3. Change the working directory:
4. Install Ngrok to your Google Colab Instance:
5. Copy the contents of the app.py file to an empty cell and then run the cell.
6. Instantiate ColabCode and run the FastAPI app by running the following code in a new cell:
7. Copy the contents of "streamlit_app.py" to an empty cell and at the top of cell write the following code and run the cell.
8. Install the requirements by running the following codes in different cells:

!git clone  https://github.com/Nneji123/Credit-Card-Fraud-Detection.git

%%writefile requirements.txt
colabcode
fastapi
uvicorn
pyngrok

!pip install -r requirements.txt

!cd /content/Credit-Card-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 Nneji123 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.