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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Flask-Based Credit card Fraud Detection System with Machine Learning |
| Authors Name: | V. Rohini , M. Pavan Teja , M. Balaji , M. Bhavani Shankar , N. V. Sathvick |
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IJNRD_192137
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| Published Paper Id: | IJNRD2304498 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Both the use of credit cards for ordinary purchases and online purchases is skyrocketing, as is the amount of credit card fraud. Every day, a sizable amount of transactions are fraudulent. a number of contemporary methods, such as artificial neural networks. In order to identify these fraudulent transactions, various machine learning methods, such as Logistic Regression, Decision Trees, Random Forest, Artificial Neural Networks, Logistic Regression, K-Nearest Neighbors, and K- means clustering, among others, are compared. In order to identify the best answer to the problem and subtly produce the outcome of the fraudulent transaction, this paper employs evolutionary algorithms and neural networks. The key goals are to identify the fraudulent transaction and create a strategy for producing test data. This algorithm uses a heuristic method to solve problems of great complexity. In this project, we suggest a system for detecting credit card fraud that makes use of machine learning to spot fraudulent transactions. In order to accurately identify fraudulent transactions, our system incorporates a range of machine learning methods, such as decision trees, logistic regression, and boosting methods. The Flask web framework is used to create the system, which is intended to be easily deploy-able and flexible in a range of financial situations. |
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| Cite Article: | "Flask-Based Credit card Fraud Detection System with Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e732-e735, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304498.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304498 Registration ID: 192137 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: e732-e735 Country: Nellore, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304498 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304498 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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