Image credit: UnsplashAbstract
Using various dimensionality reduction algorithms;PCA, PFA and a custom PCA L1-Norm, to reduce a largedataset to perform unsupervised clustering and compare theperformance of various clustering methods, including analysis ofthe optimal number of clusters and scoring of clustering. Thenclassifying the dataset using SVM as well as using the Auto-Sklearn library to efficiently evaluate the hyperparameters forthe problem including creating an MLP extension and testing a zeroconf approach.
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Kieran Molloy
Data Science Researcher
A highly competent data scientist involved in the Python and R open-source community - passionate about unsupervised learning techniques.