A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: models/General_model.php

Line Number: 92

Backtrace:

File: /home/takeoff99/public_html/application/models/General_model.php
Line: 92
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 193
Function: get_deptid_str

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: models/Project_model.php

Line Number: 674

Backtrace:

File: /home/takeoff99/public_html/application/models/Project_model.php
Line: 674
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 201
Function: generate_breadcrumb

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: models/Project_model.php

Line Number: 674

Backtrace:

File: /home/takeoff99/public_html/application/models/Project_model.php
Line: 674
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 201
Function: generate_breadcrumb

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: models/Project_model.php

Line Number: 674

Backtrace:

File: /home/takeoff99/public_html/application/models/Project_model.php
Line: 674
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 201
Function: generate_breadcrumb

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: models/Project_model.php

Line Number: 674

Backtrace:

File: /home/takeoff99/public_html/application/models/Project_model.php
Line: 674
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 201
Function: generate_breadcrumb

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

TAKEOFF - View Abstract
     Certificate Verification        Student Ambassador          Quick Pay        Request For Enquiry
Sell Your Project      Apply for franchise          
  • 0877-2261612       
  • +91-9030 333 433
  • +91-9966 062 884

An Experimental Study With Imbalanced Classification Approaches For Credit Card Fraud Detection

AN EXPERIMENTAL STUDY WITH IMBALANCED CLASSIFICATION APPROACHES FOR CREDIT CARD FRAUD DETECTION

  • Project Code :
  • TMMAAI22
  • .

A PHP Error was encountered

Severity: Notice

Message: Trying to get property of non-object

Filename: projects/view_abstract.php

Line Number: 278

Backtrace:

File: /home/takeoff99/public_html/application/views/static/projects/view_abstract.php
Line: 278
Function: _error_handler

File: /home/takeoff99/public_html/application/controllers/Projects.php
Line: 202
Function: view

File: /home/takeoff99/public_html/index.php
Line: 315
Function: require_once

Download Project Document / Synopsis

AN EXPERIMENTAL STUDY WITH IMBALANCED CLASSIFICATION APPROACHES FOR CREDIT CARD FRAUD DETECTION

Credit card fraud is a criminal offense. It causes severe damage to _nancial institutions and individuals. Therefore, the detection and prevention of fraudulent activities are critically important to nancial institutions. Fraud detection and prevention are costly, time-consuming, and labor-intensive tasks. A number of significant research works have been dedicated to developing innovative solutions to detect different types of fraud. However, these solutions have been proved ineffective. According to Cifa, 33 305 cases of credit card identity fraud were reported between January and June in 2018.1 Various weaknesses of existing solutions have been reported in the literature. Among them all, the imbalance classification is the most critical and well-known problem. Imbalance classification consists of having a small number of observations of the minority class compared with the majority in the data set. In this problem, the ratio fraud: legitimate is very small, which makes it extremely difficult for the classification algorithm to detect fraud cases. In this paper, we will conduct a rigorous experimental study with the solutions that tackle the imbalance classification problem. We explored these solutions along with the machine learning algorithms used for fraud detection. We identified their weaknesses and summarized the results that we obtained using a credit card fraud labeled dataset. According to this paper, imbalanced classification approaches are ineffective, especially when the data are highly imbalanced. This paper reveals that the existing approaches result in a large number of false alarms, which are costly to _nancial institutions. This may lead to inaccurate detection as well as increasing the occurrence of fraud cases.

innovative
innovative Request Video

Package Includes

  • 24/7 Support
  • Voice Conference
  • Video On Demand
  • Remote Connectivity
  • Customization
  • Live Chat Support

Features

  • Complete Source Code
  • Complete Documentation
  • Complete Presentation Slides
  • Flow Diagram
  • Database File
  • Screenshots
  • Execution Procedure
  • Readme File
  • Addons
  • Video Tutorials

Leave Your Comment!

Your email address will not be published. Required fields are marked *

Call us : (+91) 9030333433 / 08772261612
Mail us : takeoffstudentprojects@gmail.com
Mail us : info@takeoffprojects.com