Machine Learning based Credit Card Fraud Detection Model
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Abstract
Credit card fraud continues to be a pervasive and costly issue in the financial industry, necessitating robust and efficient solutions for detection and prevention. Machine learning has become an effective method for determining fraudulent transactions, offering the potential to save financial institutions and consumers billions of dollars annually. This research paper explores the application of Python , a comprehensive machine learning platform, to deploy a fraud detection system for credit cards. In this research, we initially review the existing literature on credit card fraud detection methods and highlight the challenges faced by conventional approaches. Then, we describe our approach, which entails feature engineering, data preparation, and the application of various machine learning techniques. To leverage the scalability, ease of deployment, and cost-efficiency of Python , we guide the reader through the model development and deployment process on the platform. Our findings indicate the machine learning model's efficacy in precisely identifying fraudulent credit card transactions. We provide a thorough analysis of the model's effectiveness, including measures like accuracy, precision, recall, F1-score. Furthermore, we discuss the advantages and considerations of using Python for using machine learning models in actual situations involving fraud detection. This research paper aims to provide financial institutions, data scientists, and researchers with valuable insights into leveraging Python detection of credit card fraud. Finally things conclude by emphasizing the significance of this approach in enhancing security and minimizing financial losses due to fraudulent activities.
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