This GitHub project is a comprehensive implementation of a banknote authentication system using machine learning. It leverages a dataset of genuine and counterfeit banknote images to train a model that can accurately distinguish between authentic and fake banknotes. The core of this project lies in its utilization of machine learning algorithms, specifically supervised learning techniques, to create a robust and reliable banknote authentication tool.
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Data Preprocessing: A detailed data preprocessing pipeline that includes image normalization, feature extraction, and dataset splitting to ensure model readiness.
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Machine Learning Models: Implementations of various machine learning models, such as Random Forest, Support Vector Machine (SVM), and Neural Networks, for banknote classification.
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Model Evaluation: Robust evaluation metrics and techniques, including accuracy, precision, recall, F1-score, and ROC curves, to assess the model's performance.
Counterfeit banknotes can have severe economic consequences, making the need for reliable authentication systems critical. This project empowers financial institutions, businesses, and individuals to validate banknotes with confidence, thereby enhancing security and trust in financial transactions.