While academic metrics such as transcripts and GPA are commonly used to
evaluate students' knowledge acquisition, there is a lack of comprehensive
metrics to measure their preparedness for the challenges of post-graduation
life. This research paper explores the impact of various factors on university
students' readiness for change and transition, with a focus on their
preparedness for careers. The methodology employed in this study involves
designing a survey based on Paul J. Mayer's "The Balance Wheel" to capture
students' sentiments on various life aspects, including satisfaction with the
educational process and expectations of salary. The collected data from a KBTU
student survey (n=47) were processed through machine learning models: Linear
Regression, Support Vector Regression (SVR), Random Forest Regression.
Subsequently, an intelligent system was built using these models and fuzzy
sets. The system is capable of evaluating graduates' readiness for their future
careers and demonstrates a high predictive power. The findings of this research
have practical implications for educational institutions. Such an intelligent
system can serve as a valuable tool for universities to assess and enhance
students' preparedness for post-graduation challenges. By recognizing the
factors contributing to students' readiness for change, universities can refine
curricula and processes to better prepare students for their career journeys.

探讨大学生对职业就业的准备情况，研究使用学术评估指标和智能系统以提高学生的准备性。