Paper Title
Enhancing Career Readiness: A Machine Learning Approach to Resume Optimization
Article Identifiers
: Keywords
Resume screening, Machine learning, Hiring, Real time data, Support Vector Classifier, K-Nearest Neighbour, Natural Language Processing, Cosine similarity, Jaccard Index
Abstract
In an ever-evolving employment landscape, people often stumble upon bold hurdles in tailoring resumes to precisely align with task prerequisites. This research endeavours to present an automatic framework for resume categorization and alignment with special process specifications. This framework harnesses superior techniques such as Cosine Similarity, Jaccard Index, textual divergence metrics, and sophisticated herbal language processing methodologies for harmonizing resumes with task delineations. furthermore, it consists of a numerous array of gadget studying paradigms including help Vector Classifier, Random forest Classifier, amongst others, to appropriately categorize resumes. The requisite data for education these gadget learning fashions is pretty sensitive due to the scarcity of complete skill set statistics pertinent to specific process roles. to bypass this assignment, the proposed machine advocates for actual time information acquisition thru scraping LinkedIn profiles. subsequently, leveraging this records, the machine learning model undergoes meticulous training, observed through a meticulous comparative assessment of numerous algorithms based totally on metrics such as accuracy, precision, and don’t forget, F1 score.
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How To Cite (APA)
Chanchal Wadhwa, Palash Wani, Tejas Ambekar, Balaji Vaste, & Hrushikesh Joshi (March-2024). Enhancing Career Readiness: A Machine Learning Approach to Resume Optimization. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), f68-f72. https://ijnrd.org/papers/IJNRD2403508.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : f68-f72
Other Publication Details
Paper Reg. ID: IJNRD_215395
Published Paper Id: IJNRD2403508
Downloads: 000122256
Research Area: Information TechnologyÂ
Author Type: Indian Author
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403508.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403508
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