Development of a Comprehensive Technology for Analyzing Data on the Used Car Market
Data mining; K-means; Power BI; Processing technology; Region analysis
In today's information society, organizations face a huge amount of data that requires analysis and intelligent technologies to make informed decisions. In this paper, the authors consider the problem of analyzing the used car market using big and open data technologies. The used car market has characteristics characterized by heterogeneity and dynamic demand depending on the region. This problem is relevant and important not only for companies involved in producing and selling cars but also for potential buyers. The authors developed a comprehensive data analysis technique based on the Python programming language and the K-means clustering algorithm in the research process. In the article, the authors described a comprehensive technology for analyzing the used car market, including various analysis methods, such as prices, offers, and competition. The proposed comprehensive technology includes various tools and programs for collecting, processing, and analyzing data. These methods can be combined into a single system, providing a more complete picture of the market and making more informed decisions. The structure of the study reflects an independent approach to the topic under study based on open data and research by Russian and foreign scientists. It should be noted that the study is based on a large amount of analytical data obtained from reliable sources and tools that confirm the conclusions formulated in this study. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Lapina, Maria; Kulikova, Svetlana; Kireenkov, Vadim; Kumar, Sandeep
Lecture Notes in Networks and Systems;Volume;1122;pp.503-514
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-97-7426-5_38 https://www.scopus.com/pages/publications/85218197731?origin=resultslist
Lapina M., Department of Information Security of Automated Systems, North-Caucasus Federal University, Stavropol, Russian Federation; Kulikova S., Department of Applied Informatics, Moscow Polytechnic University, Moscow, Russian Federation; Kireenkov V., Department of Applied Informatics, Moscow Polytechnic University, Moscow, Russian Federation; Kumar S., CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, 560074, India