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)
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.
Keywords:
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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