AI-Based Resume Screening: A Machine Learning Approach to Modern Recruitment

Abstract

Abstract: The recruitment landscape is rapidly evolving with the growing demand for efficient, unbiased, and intelligent hiring solutions. Traditional resume screening methods are time-consuming and prone to human error, often leading to suboptimal hiring decisions. This research paper presents a machine learning-driven approach to automate resume screening using advanced Natural Language Processing (NLP) techniques. Leveraging tools such as spaCy, NLTK, and transformer-based models like BERT, the system extracts and analyzes key resume features in relation to specific job descriptions. Machine learning models including Scikit-learn classifiers and XGBoost are employed to evaluate and rank candidates based on relevance and fit. The system architecture supports document parsing through PyMuPDF and python-docx, and stores structured data using SQLite/CSV for prototype implementation. An optional Flask or Streamlit-based interface enhances usability for recruiters. The proposed solution significantly reduces manual workload, improves shortlisting accuracy, and enables faster decision-making. Experimental results demonstrate the effectiveness of this AI-driven framework in modern recruitment, while ethical considerations surrounding bias, fairness, and data privacy are also discussed. This research underlines the potential of intelligent resume screening systems to reshape the hiring process through automation and data-driven insights.. Keywords: AI-based resume screening, Natural Language Processing (NLP), Machine Learning, BERT, Scikit-learn, XGBoost, Automation, Recruitment technology, Flask, Streamlit, Resume parsing, Talent acquisition.

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