Data Science Tools Application for Business Processes Modelling in Aviation

Maryna Nehrey (National University of Life and Environmental Sciences of Ukraine, Ukraine) and Taras Hnot (National University of Life and Environmental Science of Ukraine, Ukraine)
Copyright: © 2019 |Pages: 190
EISBN13: 9781522595878|DOI: 10.4018/978-1-5225-7588-7.ch006
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Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.
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