AI-Powered Resume Screening System Using NLP and Machine Learning

Resume Screening is a fundamental step in the recruitment process, often burdened by high applicant volume and manual filtering. This study presents a Machine Learning based system that automates resume classification using Natural Language Processing (NLP) Techniques. A dataset of categorized resumes was preprocessed using tokenization, stopword removal, and lemmatization. Feature vectors were generated via Term Frequency-Inverse Document Frequency (TF-IDF), and a Support Vector Machine (SVM) classifier was trained to categorize resumes into predefined job roles. The model achieved an accuracy of 91.3%, demonstrating strong generalization performance. A web-based API interface was also developed, enabling recruiters to upload resumes and receive real-time classification results. The system offers a scalable, accurate, and automated solution for modern hiring challenges.