SKILL SYNC SELECTOR USING MACHINE LEARNING TECHNIQUES

Abstract

Our study introduces an Automatic Resume Scanner Application that makes an advantage of advanced natural language processing (NLP). Together with performing thorough linguistic analysis and effectively standardizing resumes, it leverages state-of-the-art natural language processing (NLP) algorithms for sentiment analysis, context interpretation, and profile ranking. Using machine learning algorithms, it matches candidate qualifications with job demands through an intuitive interface that allows criteria to be customized. The application additionally manages bias detection and reduction in order to advance equity and inclusivity in the hiring process. When everything is said and done, it provides a fast, accurate, and neutral way to screen candidates at a preliminary stage, saving time and fostering a diversity of ability. Key Words: Resume screening, machine learning, Natural Language Processing(NLP), Talent acquisition,Text mining, KNeighbors Classifier, OnevsRest Classifier, TF-IDF vectorization, Label encoding, Feature engineering.