Resume Screening using Machine Learning

Abstract

This study explores the utilization of Machine Learning (ML) and Natural Language Processing (NLP) in automating the resume screening process. Traditional methods, often manual and subjective, fail to efficiently manage the volume and variety of resumes. By employing NLP techniques like named entity recognition and part-of-speech tagging, coupled with ML classifiers such as K-Nearest Neighbors and Support Vector Machines, we propose a system that enhances the precision of candidate selection while significantly reducing time and effort.