TechTrack: An Internship Matching Platform for Career Recommendation Using Machine Learning

TechTrack is a predictive career pathway and internship matching platform designed to assist incoming college students in computing and technology programs in making informed career decisions. Traditional academic advising often provides generalized guidance that may not fully account for students’ diverse skills, experiences, and career interests. To address this limitation, TechTrack integrates machine learning with a centralized digital platform that offers personalized career recommendations and internship opportunities. The system utilizes an Extreme Gradient Boosting (XGBoost) model to predict the most suitable career tracks by analyzing multiple factors, including technical skills, certifications, project experience, academic performance, soft skills, and personal interests. In addition to career prediction, the platform enables students to build a verifiable digital portfolio that functions as a dynamic resume, allowing them to document competencies, track academic and skill development, and present their achievements to potential employers. The internship matching module further enhances the platform by filtering available opportunities according to student competencies, preferred location, and compensation expectations, ensuring better alignment between student profiles and industry requirements. Experimental evaluation demonstrates that the XGBoost model achieved 92% accuracy, with strong precision, recall, and F1-score, outperforming traditional models such as logistic regression and decision trees. These results validate the system’s effectiveness in providing scalable, data-driven career guidance while improving internship alignment and supporting students’ career readiness in computing and technology fields.