Abstract
The aim of the work is to create matrix of feature objects for machine learning problems. The task is to search for data sources, develop a preprocessing algorithm, process statistical community data and form a matrix of feature objects for its further use in the clustering problem. Methods used: data analysis, finding descriptive statistics, methods of Python libraries: Pandas, NumPy. The novelty of this study lies in solving the problem of searching for relevant data of social network communities and developing an algorithm that forms a matrix of feature objects for its further use by researchers. Result: the analysis of services providing statistics of social networks was carried out, a program code was developed that implements the algorithm for generating a matrix of feature objects. The practical significance lies in the processing relevant statistical data and using a matrix of feature objects in machine learning problems.
The work published in this paper has been partly supported by Saint-Petersburg State University.
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Fursov, D., Krylatov, A., Svirkin, M., Prokhorenko, F. (2023). Problems of Data Processing in the Problem of Modeling Advertising Campaigns in Social Networks Using Python Libraries. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_85
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