Fake Currency Notes Detection using Supervised Learning Methods

This paper discusses the story of to identify the type of money that if a sample is given money fraud. Traditional techniques are different as well methods available for the detection of counterfeit money that on its color, widths, along with unique currency identification number on it. Today, the time of present-day of advance computational age-progressed computation techniques, different AI calculations have been created for picture handling that gives close to $100 \%$ precision of phony money. Strategies for obtaining and acknowledgment over calculations incorporate associations, for example, shading, shape, paper width, picture sifting note. This paper proposes a fake money acknowledgment technique utilizing K-Nearest Neighbors were trailed by picture handling and further refinement of boundaries. KNN has a high precision of little informational collections making it alluring utilized for PC discovery work. The banknote picture properties dataset has been made with the development of computational and numerical methodologies, which results in the right information and data in regards to the substances and elements identified with the money. Information handling and information Extraction is finished by utilizing AI and picture calculations handling to get the end-product and exactness.