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CLASSIFICATION AND DETECTION OF BANK NOTES USING ML
The detection and classification of banknotes are crucial tasks for preventing financial fraud, especially counterfeit currency circulation. This project proposes an efficient machine learning (ML)-based system to classify and detect the authenticity of banknotes using their statistical features. By training models such as Support Vector Machines (SVM), Random Forest, and KNearest Neighbors (KNN) on attributes like variance, skewness, kurtosis, and entropy extracted from images of banknotes, the system achieves high accuracy in distinguishing genuine and forged notes. The solution aims to assist financial institutions and ATM systems in automating the validation process with real-time results and minimal human intervention
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