A Presentation on Techniques Used for Analyzing and Detecting Fraud Patterns (DBSCAN Algorithm)

Project Code :TCMAPY433

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

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: 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.

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