Unsupervised machine learning dimensionality reduction (Kernel PCA, TSNE) and clustering models (K-means, DBSCAN, agglomerative clustering) for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)
- Jupyter notebook UML_forged_banknotes.ipynb with all the python code, visualizations and results.
- Data file Banknote-authentication-dataset.csv.
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Available at https://www.openml.org/search?type=data&sort=runs&id=1462&status=active.
The project is licensed under the MIT license. See the LICENSE file for license rights and limitations.
SML_forged_banknotes - Comparison of numerous supervised machine learning classifier models (Logistic Regression, K-Nearest Neighbors, Support Vector Machines and Decision Trees) predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes (https://github.com/BeataWereszczynska/SML_forged_banknotes).