Abstract
In the contemporary job market, students face significant challenges in selecting career paths that align with their skills and aspirations. The interplay of factors affecting career decisions, including academic performance, personality traits, and certifications, complicates the guidance process foreducators and career counselors. This research examines the necessity for data-driven insights in career prediction by applying machine learning methods to analyze a dataset of student profiles. The primary objective is to identify the key features that have a significant impact on predicting students' career trajectories. Classifiers, including Random Forest, Logistic Regression, and Support Vector Machine, were employed, revealing that the Random Forest classifier attained an accuracy of 75%, a precisionof 70%, a recall of 75%, and an F1 score of 72%. The Logistic Regression model exhibited an accuracy of 70%, whereas the SVM classifier attained an accuracy of 72%. Recursive Feature Elimination for feature selection revealed that specific certifications have a significant predictive value for career success. The findings underscore the importance of leveraging machine learning in educational settings to enhance personalized career guidance, thereby enabling students to make informed decisions about their futures.