ABSTRACT
Engineering graduates frequently confront uncertainty when plotting their post-graduation career paths. Conventional career counseling approaches often lack the depth required to offer personalized and impartial guidance, leaving students inundated with numerous options. To combat this challenge, we propose an innovative solution: a machine learning-driven Career Guidance System specifically tailored for engineering students. The system harnesses advanced algorithms like Support Vector Machine (SVM), XG Boost, and Decision Tree to analyze a broad range of datasets encompassing students‘ academic records, technical proficiencies, personal interests, and psychometric assessments. By amalgamating these variables, the system provides a comprehensive understanding of each student's profile. The primary advantage lies in its capacity to deliver personalized career recommendations devoid of subjective biases. By taking into account each individual's distinct combination of skills and aspirations, our system generates customized career pathways, thereby diminishing the ambiguity inherent in generic guidance. Additionally, our digital platform ensures broad accessibility at a fraction of the cost associated with traditional counseling services, thereby democratizing access to high-quality career advice. In summary, our machine learning-powered Career Guidance System represents a noteworthy advancement in the field of career counseling for engineering students. By leveraging the capabilities of data analysis, our system empowers students to make well- informed decisions about their future career paths with confidence and clarity.
