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Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

Experimenting with manual and automated data mining pipelines on the FedCSIS 2024 Data Science Challenge

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DOI: http://dx.doi.org/10.15439/2024F6884

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 751–754 ()

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Abstract. This paper reviews the 5th-best solution and results of the FedCSIS 2024 Data Science Challenge, which aimed to predict stock trends using financial indicators. It details the preprocessing, modelling, and tuning approaches and demonstrates, as well as the methods and techniques used to address the prediction problem effectively. Subsequently, the results of different experiments, including hyperparameter optimization on preprocessing steps and switching between different prediction targets, could be compared to manual experiments. Overall, a manually experienced model could be found to outperform hyperparameter-tuned pipelines.

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