RESUME SORTING AND RANKING USING MACHINE LEARNING MODELS

Abstract

In today's digital recruitment landscape, organisations routinely encounter a substantial volume of applications for each vacancy. Manual assessment of these submissions is not only labour-intensive but also prone to subjective bias. This automated resume screening system uses Natural Language Processing (NLP) to extract critical information (skills, experience, etc.) from resumes and match it against job descriptions to calculate an alignment score.A comparative analysis of machine learning models found that XGBoost outperformed Logistic Regression, Random Forest, and SVM, primarily due to its superior handling of complex and imbalanced data. The system also integrates word embedding to capture semantic meaning, moving beyond simple keyword matching to provide a more robust, accurate, and objective solution for modern recruitment.