Abstract
Credit Card Fraud Detection is an open-source AI & Machine Learning project. Credit Card Fraud Detection. This is a Kaggle Credit Card Fraud Detection : Anonymized credit card transactions labeled as fraudulent or genuine - Credit Card Fraud Detection. The objective of the project is to perform data visulalization techniques to understand the insight of the data. It is built using Jupyter Notebook, Machine Learning, Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
This is a Kaggle Credit Card Fraud Detection : Anonymized credit card transactions labeled as fraudulent or genuine - Credit Card Fraud Detection. The objective of the project is to perform data visulalization techniques to understand the insight of the data. Machine learning often required to getting the understanding of the data and its insights. This project aims apply various Python tools to get a visual understanding of the data and clean it to make it ready to apply machine learning opertation on it.
Author : Sanjoy Biswas Email : sanjoy.eee32@gmail.com Linkedin : Sanjoy Biswas
2. Objective
Credit Card Fraud Detection
This project demonstrates how Jupyter Notebook, Machine Learning, Deep 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/imsanjoykb/Credit-Card-Fraud-Detection.git
cd Credit-Card-Fraud-DetectionFull 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.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by imsanjoykb 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.
Want to build this as your internship project?
Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.
Apply for AI & Machine Learning Internship