Authors : Kumarisravel S.; Dr. J. Lysa Eben

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/ycxwxwb6

Scribd : https://tinyurl.com/u7bjdvxy

DOI : https://doi.org/10.38124/ijisrt/26apr374

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The manual process in recruitment is too hard when the data is huge and complex. This increase in data can bring inaccuracy in selecting a candidate based on the job description. This project presents the involvement of Machine learning techniques in the recruitment process such as ranking of candidate’s resumes, recommendation of JD (Job description) to candidates and recommendation about resumes to recruiters and recommendation about candidate’s resumes to recruiters. This system increases accuracy, consistency, and speed in the recruitment process. Most of the common processes in recruitment got automated in this system. Techniques like TF-IDF, Cosine similarity and Classification models are used to achieve the features of classification, recommendation and ranking of resumes. TF-IDF vectorization usually converts textual data like resumes or JD (Job description) into numerical vectors, then Cosine similarity is used for providing ranking and recommendation to recruiter and candidates. Models that have higher performance in accuracy, precision, recall and F1-Score are used for resume classification features. This project carries a Dataset from Kaggle, which has suitable and required data for the process like resumes with candidate skills, communication address and experience. Later, using the taken Dataset, we train models like Logistic regression, Support vector machine, Random forest and Naive Bayes to find out which mode fits best in the classification feature.

Keywords : Machine Learning, Job Description, Resumes, Kaggle, Random Forest, TF-IDF, Cosine-Similarity, Recommendation, Ranking and Classification.

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