[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-260700-105":53,"doc-detail-260700-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","ai-based-resume-screening-a-machine-learning-approach-to-modern-recruitment","AI-Based Resume Screening - A Machine Learning Approach to Modern Recruitment","","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. 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Kameshwar Rao3  \n1 P.G. Scholar , Department of Computer Science and Engineering , Shri Rawatpura Sarkar University, Raipur, Chhattisgarh , India([abhinashsingh4430@gmail.com](abhinashsingh4430@gmail.com))  \n2 Assistant Professor, Department of Computer Science and Engineering , Shri Rawatpura Sarkar University, Raipur, Chhattisgarh , India([nikita.rawat@sruraipur.ac.in](nikita.rawat@sruraipur.ac.in))  \n3 Assistant Professor, Department of Computer Science and Engineering , Shri Rawatpura Sarkar University, Raipur, Chhattisgarh , India([kameshwar.rao@sruraipur.ac.in](kameshwar.rao@sruraipur.ac.in))  \nAbstract: 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..  \nKeywords:AI-based resume screening, Natural Language Processing (NLP), Machine Learning, BERT, Scikit-learn, XGBoost, Automation, Recruitment technology, Flask, Streamlit, Resume parsing, Talent acquisition.  \nI.INTRODUCTION  \nThe hiring process plays a critical role in shaping the workforce of any organization. However, traditional recruitment methods—particularly resume screening—are often manual, time-consuming, and prone to human bias and inconsistency. Recruiters may need to sift through hundreds  \nor even thousands of resumes to identify suitable candidates, which can result in fatigue, oversight, and suboptimal decision-making. In an increasingly competitive job market, there is a growing need for efficient, intelligent, and unbiased recruitment solutions that can streamline the initial stages of  \nhiring. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into recruitment processes has opened new avenues for automation and accuracy. One of the most impactful applications of AI in this domain is automated resume screening, where algorithms assess resumes and match them with job descriptions based on relevance and required qualifications. By leveraging Natural Language Processing (NLP) techniques, machines can interpret and analyze textual data in resumes, extracting meaningful features such as education, skills, experience, and certifications.  \nThis paper proposes an AI-driven framework for automated resume screening that employs Python-based NLP libraries like spaCy, NLTK, and transformer models such as BERT for textual analysis. Machine learning models including Scikit-learn classifiers and XGBoost are used to classify and rank candidates. Resumes in formats such as PDF and DOCX are processed using PyMuPDF and python-docx, and structured data is","cbCaid105LJJBoti","https://ap.wps.com/l/cbCaid105LJJBoti","pdf",483371,8,"English","# Introduction\n# Literature Survey","[{\"question\":\"What problem does the research address in recruitment?\",\"answer\":\"The research targets manual, time-consuming resume screening that can introduce human bias and inconsistencies, which may lead to suboptimal hiring decisions.\"},{\"question\":\"Which NLP and machine learning techniques are used for screening?\",\"answer\":\"The approach uses spaCy, NLTK, and BERT for textual analysis, along with machine learning classifiers from Scikit-learn and XGBoost to rank and classify candidates.\"},{\"question\":\"How does the system handle different resume formats and store results?\",\"answer\":\"PDF and DOCX resumes are processed using PyMuPDF and python-docx, while structured data is stored using SQLite or CSV for prototype implementation.\"}]","AI-Based Resume Screening - A Machine Learning Approach to Modern Recruitment | PDF",1789345107]