Kasymov Alexey A. (Postgraduate student, Department of Automated and Computer Systems, Voronezh State Technical University)
Lysenko Alexey (PhD student
Department of Applied Informatics and Mathematics, Belgorod State Agrarian University named after V.Ya. Gorin
)
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Classification of big data in information systems has a critical rule in understanding the organization aims to make development in the organizations commerce. Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. this will enhance the organization activities, which will lead to more effective information system. Four classification algorithms were tested. Social Network Analysis (SNA) features are also extracted and used in classifications to enhance the information systems. The use of SNA enhanced the performance of the model of information system. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree “GBM” and Extreme Gradient Boosting “XGBOOST”. However, the best results were obtained by applying XGBOOST algorithm. This algorithm was to get two classes as accurate classification as information system to be understood and informative.
Keywords:Big data classification, Customer churn, Machine learning algorithms, Social Network Analysis (SNA), Information systems.
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Citation link: Kasymov A. A., Lysenko A. A SYSTEMATIC APPROACH TO THE CLASSIFICATION OF BIG DATA IN CORPORATE INFORMATION SYSTEMS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№06/2. -С. 129-133 DOI 10.37882/2223-2966.2024.6-2.22 |
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