Credit Card Fraud Detection

Abstract

1. Introduction

Fraud Detection model based on anonymized credit card transactions

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

2. Objective

Fraud Detection model based on anonymized credit card transactions

This project demonstrates how Jupyter Notebook, 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/sagnikghoshcr7/Credit-Card-Fraud-Detection.git
cd Credit-Card-Fraud-Detection

1. get the code from the repository
2. download the dataset that will be used to train a transaction classifier. Unzip it and put the content (creditcard.csv) under main folder (Credit-Card-Fraud-Detection)
3. install required python packages if previously not installed
4. Finally run on Jupyter Notebook and enjoy

git clone https://github.com/sagnikghoshcr7/Credit-Card-Fraud-Detection.git

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 sagnikghoshcr7 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.