Credit card fraud detection using machine learning techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/15784 |
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10361-15784 |
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10361-157842022-01-26T10:10:31Z Credit card fraud detection using machine learning techniques Kabir, Tasmia Nishat, Tahnin Tory, Saria Bulbul Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-43). The extensive use of the internet is perpetually drifting businesses to incorporate their administrations in the online environment. As a result of the development of e-commerce websites, people and monetary corporations count on online administrations to carry out their transactions. The ever-expanding utilization of internet banking associated with vast variety of online transactions has led to an exponential increase in credit card frauds. The fraudsters can likewise utilize anything to in uence the systematic operation of the current fraud detection system (FDS). Therefore, we have taken up the challenge to upgrade the existing FDS with the most potential exactness. This research intends to develop an e cient FDS using machine learning (ML) techniques that are adaptive to consumer behavior changes and tends to diminish fraud manipulation, by distinguishing and ltering fraud in real-time. The ML techniques include Logistic Regression, Support Vector Machine, na ve Bayes, K-nearest neighbor, Random Forest, and Decision tree. According to this study, the Decision Tree classi er has emerged as the most useful algorithm among the wide range of various strategies. Tasmia Kabir Tahnin Nishat Saria Bulbul Tory B. Computer Science 2021-12-29T04:39:40Z 2021-12-29T04:39:40Z 2021 2021-09 Thesis ID 17301015 ID 17301231 ID 17301039 http://hdl.handle.net/10361/15784 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud |
| spellingShingle |
Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud Kabir, Tasmia Nishat, Tahnin Tory, Saria Bulbul Credit card fraud detection using machine learning techniques |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Ashraf, Faisal Bin |
| author_facet |
Ashraf, Faisal Bin Kabir, Tasmia Nishat, Tahnin Tory, Saria Bulbul |
| format |
Thesis |
| author |
Kabir, Tasmia Nishat, Tahnin Tory, Saria Bulbul |
| author_sort |
Kabir, Tasmia |
| title |
Credit card fraud detection using machine learning techniques |
| title_short |
Credit card fraud detection using machine learning techniques |
| title_full |
Credit card fraud detection using machine learning techniques |
| title_fullStr |
Credit card fraud detection using machine learning techniques |
| title_full_unstemmed |
Credit card fraud detection using machine learning techniques |
| title_sort |
credit card fraud detection using machine learning techniques |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15784 |
| work_keys_str_mv |
AT kabirtasmia creditcardfrauddetectionusingmachinelearningtechniques AT nishattahnin creditcardfrauddetectionusingmachinelearningtechniques AT torysariabulbul creditcardfrauddetectionusingmachinelearningtechniques |
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