Detection of Fake Currency Using KNN Algorithm

Abstract

Abstract: Bank currency is our country's most valuable asset, and to cause inconsistencies in money, criminals use phony notes that seem identical to the real one on the stock exchange. During demonetization time it is seen that so much fake currency is floating in the market. In general, for a human being, it is very difficult to identify forged notes from the genuine not instead of various parameters designed for identification as many features of forged notes are similar to the original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or ATMs. To design such an automated system there is a need to design an efficient algorithm that can predict whether the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper, six supervised machine learning algorithms are applied to the dataset available on the UCI machine learning repository for the detection of Bank currency authentication. To implement this we have applied Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, Decision Tree, K- Nearest Neighbor by considering three train test ratios 80:20, 70:30, and 60:40 and measured their performance based on various quantitative analysis parameters like Precision, Accuracy, Recall, MCC, F1-Score and others. And some SML algorithms are giving 100 % accuracy for a particular train test ratio. Keywords: Support Vector Machine, Bank currency, Supervised Machine Learning