The analysis of skill gaps and prediction of employability have become a key to closing the gap between the ability of students, industrial needs, and the government programmes and schemes of training the skills of the population. The present paper is a detailed data-basedframework,which combines the viewpoints of students, industry, and government to assess and forecast the student employability. The model uses synthetic datasets of all three entities and uses machine learning algorithms on Google Colab to test various academic, technical and behavioral parameters. More than 30 visualizations were created to investigate the correlation between these variables, which gave an answer to the multidimensionality of employability. A predictive model calculates an Employability Score and produces an individualized feedback to steer students on specific skill improvement. The suggested system shows how the educational establishment and policymakers can take advantage of such analytics to become more career-ready and prepare educational results and outcomes in accordance with national labor needs.

Keywords: Employability Prediction, Skill Gap Analysis, Machine Learning, Student–Industry–Government Alignment, Data Visualization