Resume Analysis Using NLP and ATS Algorithm

Abstract: In today’s competitive job market, efficient and accurate resume screening is crucial for recruiters and hiring managers. Traditional manual resume review processes are time-consuming and prone to human error, which can lead to overlooking qualified candidates. This project aims to develop an automated system for resume analysis using Python, Natural Language Processing (NLP), and Applicant Tracking System (ATS) algorithms. The proposed solution leverages NLP techniques to extract key information from resumes, such as personal details, educational background, work experience, and skills. Additionally, ATS algorithms are employed to score and rank resumes based on their relevance to specific job descriptions, facilitating a more streamlined and objective hiring process. The system is designed to enhance the efficiency of resume screening by reducing the time and effort required for initial resume screening while improving the accuracy of selection. This report details the development, implementation, and evaluation of the proposed resume analysis system, highlighting its potential benefits and limitations.

Resume Screening, NLP, ATS, Resume Parsing, Machine Learning, Skills Extraction, Keyword Matching, Automated Resume Evaluation, Deep Learning, Job Fit Analysis, Text Mining, Candidate Profile Analysis, Recruitment Automation, AI in Resume Analysis, Job Description Matching

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