A Presentation on Techniques Used for Analyzing and Detecting Fraud Patterns (DBSCAN Algorithm)
Objective
It defines the professional fraudster, formalizes the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries.
Abstract
This project categorizes, compares, and summarizes from almost all
published technical and review articles in automated fraud detection. It
defines the professional fraudster, formalizes the main types and subtypes of
known fraud, and presents the nature of data evidence collected within affected
industries. Within the business context of mining the data to achieve higher
cost savings, this research presents methods and techniques together with their
problems. Compared to all related reviews on fraud detection, this survey
covers much more technical articles and is the only one, to the best of our
knowledge, which proposes alternative data and solutions from related domains
and identifies fraud patterns using DbScan.
Key words: Data mining applications, automated fraud detection, DbScan
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
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
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: Jupyter Notebook
- Libraries Used: Pandas, Numpy.
Learning Outcomes
- About Python.
- About Pandas.
- About Numpy.
- About Machine Learning.
- About Artificial Intelligent.
- About how to use the libraries.
- Virtualization.
- About how to generate the predictions with python code.
- 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.
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
