Abstract
Many factors affect the success of prediction using Machine-Learning on given task. The quality of provided data is one of the key factors which influence accuracy of Machine-Learning (ML) and Artificial Intelligence (AI) algorithms. The main goal of this research is to explore data, choose the right parameters and remove noisy items before usage of ML or AI. This research provides results of exploratory data analysis of software requirements collected from Software Company. Presented results help identify general patterns in the dataset of software requirements for future prediction purposes.
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Acknowledgements
Work is partially supported by Grant of SP2016/100 - Knowledge modeling and its applications in software engineering II, VŠB - Technical University of Ostrava, Czech Republic and also by research project of the Ministry of Education, Youth and Sport of the Czech Republic: The National Programme for Sustainability LO1404 – TUCENET.
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Štrba, R., Štrbová, K., Vondrák, I., Ježek, D., Štolfa, S. (2018). Exploratory Data Analysis of Software Requirements Using Statistics and Kohonen’s Self-Organizing Map. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_9
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DOI: https://doi.org/10.1007/978-3-319-60834-1_9
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