SMART RESUME SCREENING SYSTEM USING NLP & MACHINE LEARNING

Abstract

The rapid growth in job applications has made manual resume screening a timeconsuming and inefficient process for recruiters. To address this challenge, the Smart Resume Screening System using Natural Language Processing (NLP) and Machine Learning (ML) is designed to automate and enhance the candidate shortlisting process. This system leverages advanced NLP techniques to extract, analyze, and interpret relevant information from resumes, such as skills, education, experience, and keywords, in an intelligent and structured manner. The proposed system utilizes machine learning algorithms to classify and rank resumes based on their relevance to specific job descriptions. By comparing candidate profiles with predefined requirements, the system ensures accurate matching and reduces human bias in recruitment. Techniques such as text preprocessing, tokenization, named entity recognition, and semantic similarity are employed to improve the understanding of resume content. Additionally, the system can learn from past hiring decisions, enabling continuous improvement in prediction accuracy. The integration of NLP allows the system to handle unstructured resume data effectively, while ML models enhance decision-making by identifying patterns and correlations in candidate information. This leads to faster screening, improved hiring quality, and significant reduction in recruitment time and cost. Furthermore, the system provides a scalable solution that can process large volumes of resumes efficiently, making it suitable for organizations of all sizes.