Kieran Molloy is a postgraduate student of Data Science at Lancaster University UK. His research interests include unsupervised deep learning, decision trees and extreme value theory. He authored several open-source packages in his field which solve common problems faced by researchers. He is currently finishing his MSc by conducting his dissertation with Gousto and examining their factory throughput.
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MSc Data Science, 2021
Lancaster University
BSc Mathematics, 2020
Manchester Metropolitan University
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whitebeam is a python module integrating decision tree classifying algorithms for small to medium scale supervised binary problems. This package focuses on bringing machine learning to non-specialists wishing to implement their own variants of trees using a general-purpose high-level language. Emphasis is put on ease of use and API consistency with established package scikit-learn. Additionally a Particle Swarm Optimisation metaheuristic is used to determine if accuracy can be increased then compared against library implementations from scikit-learn. whitebeam performs well, and achieves similar accuracy scores in less time for some datasets.
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Clustering swiss weather data using various library techniques from sklearn, AutoML, as well as some custom attempts
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