The research is aimed at developing a test survey for the effective selection of specialists in the IT field, based on the use of modern machine learning methods, particularly cluster analysis using the k- means method. Given the limited access to existing testing platforms, which are typically available only to large companies on a paid basis, the decision was made to create an alternative web application. This application will become an accessible tool for a wider range of users and will allow automating the process of evaluating candidates' skills. A key feature of the research is the application of cluster analysis for grouping users based on their professional skills, cognitive abilities, and psychological characteristics. This enables a more precise assessment of candidates' suitability for employers' requirements and facili- tates better data organization for further analysis. The research also highlights the importance of cluster analysis in cases where there is no prior hypothesis about the data structure, making this approach a universal tool for data classification. In addition to technical aspects, the study covers the prospects of using adaptive tests that can adjust the level of difficulty in real time depending on the user's responses.
This improves the accuracy of the evaluation and reduces the influence of subjective factors during selec- tion. Additionally, the possibilities of analyzing candidates' behavioral and emotional characteristics, such as stress resilience and communication skills, are considered, as they are important for successful team- work.
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