CAREER COACHING AND RECOMMENDATION HUB

In a generation in which employment opportunities are changing at lightning speeds and career job functions are being supplanted or reinvented, students themselves become bewildered in choosing a career direction that is appropriate to their interests, abilities, and level of educational attainment. Manual advisory services are costly, arbitrary, and not available globally, particularly in remote or underprivileged regions. This article suggests an intelligent career suggestion system with a web interface (created using HTML, CSS, and JavaScript) coupled with a Python and machine learning algorithm-based backend. The system, upon analyzing users' self-provided information like interests, skills, and education, proposes a ranked list of appropriate career fields using a Decision Tree classifier. The model was found to be 87% accurate on the test set. The paper also discusses system architecture, preprocessing practices, and user interface design practices. Findings show that the system has the potential to supplement current career counseling services and easily scale across educational institutions.