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AI- Resume Analyzer
Subject area: Science,Engineering and Technology · Area of research: Human-AI Collaboration Model
Abstract
The aim of this project is to design and develop a tool that results into an easy and helpful solution for applicants as well as recruiters. The rapid growth of job applications across industries has created a significant challenge for recruiters who must manually evaluate large volumes of resumes. This process is often time-consuming, inconsistent, and prone to human bias. The AI Resume Analyzer aims to address these limitations by leveraging Artificial Intelligence and Natural Language Processing (NLP) to automate the process of resume screening and shortlisting. The system extracts key information such as skills, education, and work experience from resumes, and compares them with job descriptions to generate relevance scores. Using machine learning algorithms, it ranks candidates based on their suitability for the role, thereby improving efficiency, accuracy, and fairness in the recruitment process. The proposed solution provides a user-friendly interface, ensures faster decision-making, and supports organizations in streamlining their hiring workflow. This project demonstrates how AI-powered automation can significantly enhance modern recruitment practices.
References
[1] Ghousia College of Engineering, Dept. of CS & Engineering, "AI- Resume Analyzer” Mini Project Report, 2025.
[2] Streamlit Documentation, “Streamlit — The fastest way to build data apps in Python.” Available: https://docs.streamlit.io/
[3] IJITEE, “A Study on Resume Parsing Using Natural Language Processing,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 7, 2020. Available: https://www.ijitee.org/wp-content/uploads/papers/v9i7/F4078049620.pdf
[4] Academia.edu, “Resume Parser with Natural Language Processing.” Available: https://www.academia.edu/32543544/Resume_Parser_with_Natural_Language_Processing
[5] RChilli, “Resume Parsing 101 — Everything You Need to Know About Resume Parsing.” Available: https://www.rchilli.com/blog/resume-parsing-101/
[6] Wikipedia, “Résumé Parsing.” Available: https://en.wikipedia.org/wiki/R%C3%A9sum%C3%A9_parsing
[7] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[8] Matthew Honnibal and Ines Montani, “spaCy 2: Natural Language Understanding with Bloom Embeddings,” Explosion AI, 2017.
[9] Ricardo Campos et al., “YAKE! Keyword extraction from single documents using multiple local features,” Information Sciences, vol. 509, 2020.
[10] pdfplumber Documentation, “PDF text extraction for Python.” Available: https://github.com/jsvine/pdfplumber
How to cite this paper
@article{1712708,
author = {Zoya Kauser V, Aamna Noorain, Panika N},
title = {AI- Resume Analyzer},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {660-663},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712708.pdf},
abstract = {The aim of this project is to design and develop a tool that results into an easy and helpful solution for applicants as well as recruiters. The rapid growth of job applications across industries has created a significant challenge for recruiters who must manually evaluate large volumes of resumes. This process is often time-consuming, inconsistent, and prone to human bias. The AI Resume Analyzer aims to address these limitations by leveraging Artificial Intelligence and Natural Language Processing (NLP) to automate the process of resume screening and shortlisting. The system extracts key information such as skills, education, and work experience from resumes, and compares them with job descriptions to generate relevance scores. Using machine learning algorithms, it ranks candidates based on their suitability for the role, thereby improving efficiency, accuracy, and fairness in the recruitment process. The proposed solution provides a user-friendly interface, ensures faster decision-making, and supports organizations in streamlining their hiring workflow. This project demonstrates how AI-powered automation can significantly enhance modern recruitment practices.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712708}
}