Abstract: The high volume of applications in modern recruitment processes presents a significant challenge to hiring managers, making manual resume screening inefficient and prone to bias. Traditional Applicant Tracking Systems (ATS) often rely on basic keyword matching, which lacks the nuance to accurately classify candidates into appropriate job domains. This paper introduces an intelligent ATS designed to automate and enhance the initial screening phase using a classic, yet effective, machine learning architecture. The system leverages a TF-IDF vectorizer to convert resume text into numerical features and a Multinomial Naive Bayes classifier for high-speed job domain categorization. By processing PDF and CSV resumes, the system cleans and standardizes textual data before feeding it into the ML pipeline for prediction. This paper provides a complete blueprint for the development and deployment of this application, from the data preprocessing and model training stages to the design of its user-facing frontend and backend API. Future work will focus on migrating from lexical-based models to modern contextual embeddings to improve semantic understanding and predictive accuracy.

Keywords: Applicant Tracking System (ATS), Natural Language Processing (NLP), Resume Classification, TF-IDF, Multinomial Naive Bayes, HR Technology, Machine Learning, Text Classification.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131111