An Intelligent Machine Learning Framework for Personalized Career Path Recommendation Among Engineering Students

Abstract

Engineering students frequently struggle to find the best job choices that fit their academic achievement, interests, and skill set in the present academic environment. In order to help engineering students make well-informed career decisions, this study proposes a machine learning-based Career Path Recommendation System. Through the analysis of student data, including academic scores, technical skills, preferred courses, project domains, and internship experiences, it predicts appropriate professional roles such as network administrator, software developer, data analyst, or embedded systems engineer. The model makes use of machine learning algorithms like K-Nearest Neighbors (KNN) and cosine similarity for classification and recommendation. In addition to assisting students in locating jobs that complement their objectives and strong points, the proposed approach assists educational institutions in offering tailored career guidance.This technology bridges the gap between education and industry requirements and improves career planning decision-making through visual insights and pattern analysis.