Paper Title
Flask-Based Credit card Fraud Detection System with Machine Learning
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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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How To Cite (APA)
V. Rohini, M. Pavan Teja, M. Balaji, M. Bhavani Shankar, & N. V. Sathvick (April-2023). Flask-Based Credit card Fraud Detection System with Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e732-e735. https://ijnrd.org/papers/IJNRD2304498.pdf
Issue
Volume 8 Issue 4, April-2023
Pages : e732-e735
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Paper Reg. ID: IJNRD_192137
Published Paper Id: IJNRD2304498
Downloads: 000122253
Research Area: Engineering
Author Type: Indian Author
Country: Nellore, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2304498.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304498
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