Аннотация
The underrepresentation of women in STEM fields is a global problem that demands urgent attention, and Kazakhstan is no exception. Despite efforts to close the gender gap in STEM education, only 32% of undergraduate students in Kazakhstan's STEM programs are female, indicating that more needs to be done to address this issue. To tackle this problem, a groundbreaking study was conducted using machine learning techniques to analyze a dataset of female high school students in Kazakhstan. The innovative use of machine learning allowed the researchers to extract patterns and identify the most influential factors that shape Kazakh female students' interest in STEM careers. This is a step forward in the field, as the use of machine learning to address this issue is rare. The study's findings highlighted the importance of self-efficacy, parental support, and the availability of role models in STEM fields as key factors that influence Kazakh female high school students' interest in STEM careers. Based on these findings, programs that focus on increasing self-efficacy, parental involvement and support, and exposure to successful STEM professionals and role models can be implemented to encourage more female students to pursue STEM careers.