Overview / Usage

Machine learning has revolutionized the way we approach problems and has opened up new possibilities for solving complex issues. One such problem is the detection of fake notes, which has been an ongoing challenge for financial institutions and businesses. It is possible to train models that can precisely predict the veracity of banknotes using machine learning. These algorithms can learn to recognise patterns and features that separate authentic notes from fakes by training them on vast datasets of both real and counterfeit notes. The way we approach challenges has been transformed by machine learning, which has also created new opportunities for resolving challenging problems. The detection of counterfeit notes is one such issue, which has been a persistent difficulty for businesses and financial institutions.

Methodology / Approach

This project aims to develop a machine learning model to predict the authenticity of banknotes. The model uses features such as variance, skewness, kurtosis, and entropy of wavelet-transformed images of the banknotes. The target value is 0 for real banknotes and 1 for fake banknotes.

Building application using intel oneDAL:The Intel oneAPI Data Analytics Library (oneDAL) contributes to the acceleration of big data analysis by providing highly optimised algorithmic building blocks for all phases of data analytics (preprocessing, transformation, analysis, modelling, validation, and decision making) in batch, online, and distributed processing modes of computation.The library optimizes data ingestion along with algorithmic computation to increase throughput and scalability.

Technologies Used

✅ Python 3.7 or higher

✅ Scikit-learn

✅ XGBoost

✅ Pandas

✅ NumPy

✅ Matplotlib

✅ Seaborn

Repository

https://github.com/arungeekay/Fake-Currency-Detection-Model-using-OneDAL