Resume screener system

Abstract

This research paper introduces a state-of-the-art "Resume Screener System" aimed at revolutionizing and automating the labor-intensive task of resume analysis for recruitment purposes. Developed using Python, the system integrates Artificial Intelligence and Natural Language Processing techniques to streamline the hiring process. Utilizing a dataset sourced from Kaggle, comprising a thousand resumes converted into textual data, the system undergoes comprehensive model training and evaluation. Employing advanced machine learning methodologies such as the Support Vector Classifier (SVC) and Neighbours Classifier, the system rigorously tests and analyzes these models to determine the most effective approach. By evaluating each model's performance against predefined criteria, the system identifies the optimal model for resume screening. The primary objective of this work is to provide recruiters and HR professionals with an innovative tool that efficiently matches job requirements with candidates' skill sets as presented in their resumes. By automating the initial screening phase, the system not only saves time and effort but also ensures a more objective and consistent evaluation of applicants. This research contributes to the advancement of machine learning applications in the field of human resources, illustrating the transformative impact of technology on traditional hiring practices.