Paper PublishingFraud Detection in Credit Card Data using Unsupervised Machine Learning Based Scheme
Objective
This paper proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Our method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering.
Abstract
Development of communication technologies and eCommerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. In this paper, researchers are also trying the novel techniques to detect and prevent such frauds. However, there is always a need of some techniques that should precisely and efficiently detect these frauds.
This project proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Proposed method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering.
Keywords: Unsupervised Learning, Anomaly Detection, Fraud Detection, Auto-Encoder, Credit Card.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy, os, sklearn, Pillow, TensorFlow.
Learning Outcomes
- Scope of Real Time Application Scenarios.
- What is a search engine and how browser can work.
- What type of technology versions.
- About datasets.
- Working of Support vector machine.
- About Machine learning.
- Need of Pycharm-IDE to develop a web application.
- Working of computer vision and role of open cv2.
- Features of Open CV.
- Working Procedure.
- Testing Techniques.
- Error Correction mechanisms.
- How to run and deploy the applications.
- Introduction to basic technologies.
- How project works.
- Input and Output modules.
- How test the project based on user inputs and observe the output.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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