Tools for Consumer Preference Analysis Based in Machine Learning

Document Type : Research Paper

Authors

1 Professor, Head of Computer Systems Department, Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine; Daugavpils University, Daugavpils, LV – 5401, Latvia.

2 Assistant Professor, Computer Systems Department, Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine.

3 Associate professor, Head of the Department of Law, Management & Economics, Daugavpils University, Daugavpils, LV-5401, Latvia.

4 Associate professor, Department of Law, Management & Economics, Daugavpils University, Daugavpils, LV-5401, Latvia.

5 Professor, Dr. Sci. (habil.) in Economics, Department of Economic Cybernetics and Economic Security Management, Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine.

6 Professor, Dr. Sci. (habil.) in Economics, Department of Business Economics and Іnternational Economic Relations, National Technical University "Kharkiv Polytechnic Institute", 61002, Kharkiv, Ukraine.

7 Associate professor, Department of Business Economics and International Economic Relations National Technical University "Kharkiv Polytechnic Institute", 61002, Kharkiv, Ukraine.

10.22059/jitm.2024.99048

Abstract

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized machine learning libraries.

Keywords

References

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Journal of Information Technology Management
Volume 16, Issue 4
2024
Pages 1-17

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