Career Guidance System Using Decision Tree, Random Forest, and Naïve Bayes Algorithm
Chidi Ukamaka Betrand, Obinna Aliche, Chinwe Gilean Onukwugha, Christopher Ofoegbu, Douglas Allswell Kelechi · International Journal of Science Technology and Society · 2025
Students often struggle with identifying the right options that align with their interests, abilities, and aspirations. Most students lack the required knowledge to make the right decisions. After receiving a degree, the path to career specialization always seems unclear for most students. But, if a student can manage to get it right by choosing the right path for their career, they will experience significant economic and psychological benefits. Choosing the right career path is a critical decision that can significantly impact an individual's future. Providing effective career guidance is therefore essential, especially for students who often face challenges in aligning their interests, skills, and aspirations with suitable career options. This study addresses this need by developing and evaluating a comprehensive Career Guidance System utilizing three machine learning algorithms: Decision Tree, Random Forest, and Naive Bayes. The system was built using an iterative approach, incorporating a user-friendly web page and an interactive chatbot to enhance the career guidance experience. Developed and deployed using Python and the powerful Django framework, the system leverages cutting-edge technologies to deliver personalized recommendations tailored to each student's unique profile. To evaluate the system's performance, key metrics such as accuracy, precision, recall, and F1 score were employed. Notably, the Random Forest classifier outperformed the other algorithms, achieving the highest accuracy. This superior performance highlights the algorithm's ability to capture complex relationships between student interests, passions, and career choices, making it an ideal choice for career guidance applications. The Career Guidance System developed in this study holds significant potential for revolutionizing the career counseling process. The choice of algorithms used in this study was chosen given the specific needs of the project, especially considering specific concerns of scalability and accuracy. in the advancement of computer science and its applications in career counseling. The findings demonstrate the system's overall efficiency and effectiveness, paving the way for its wider adoption and further refinement to support students in making informed and fulfilling career choices.