Profession inclination identification using machine learning
| dc.contributor.author | Talasbek A.L. | |
| dc.date.accessioned | 2024-11-27T10:58:40Z | |
| dc.date.available | 2024-11-27T10:58:40Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | General characteristics of research. The given work is devoted to the research and development of an application that suggests recommendations for future profession selection based on the personal characteristics of a person by identifying professional inclinations. Relevance. Currently, the Kazakhstan market has virtually no systems for profession inclination identification. Modern society makes new demands on performance and professionalism. However, high levels of professionalism suggest a full disclosure of the potential of the individual, which is impossible without taking into account the personal characteristics of an individual. Many of the questionnaires conducted by organizations do not sufficiently define and describe the type of person for appointment, selection of personnel for certain special programs, and do not give a reliable result about the person in question whether the person will cope with certain official duties. | |
| dc.identifier.citation | Talasbek Assem / Profession inclination identification using machine learning / 6D070400 – «Computer Systems and Software» / 2021 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1555 | |
| dc.language.iso | en | |
| dc.publisher | Suleyman Demirel University | |
| dc.subject | machine learning techniques | |
| dc.subject | machine learning algorithms | |
| dc.subject | Myers-Briggs Type Indicator (MBTI) | |
| dc.subject | dataset | |
| dc.subject | PHD dissertation | |
| dc.title | Profession inclination identification using machine learning | |
| dc.type | Thesis |