High-Scale ATS and Semantic Resume Filtering using Django, NLP, and LLaMA3 - Groq Models

Abstract: An intelligent system that uses Natural Language Processing (NLP) and Machine Learning (ML) to automate resume classification is presented in this paper. Key resume features, such as education, skills, and job titles, are extracted and used to train models like Logistic Regression, SVM, Random Forest, and BERT. NLP preprocessing techniques, such as tokenization, stop word removal, and vectorization, prepare the text for analysis. The results of experiments show that these models improve classification accuracy and decrease the time needed for hiring. The system assists recruiters by ranking qualified candidates and eliminating applications that are not relevant, making the hiring process quicker and more efficient.