Subhajit Das
Saswati Naskar
Swapna Halder
- Student Greater Kolkata College of Engineering and Management West Bengal India
- Student Greater Kolkata College of Engineering and Management West Bengal India
- Student Greater Kolkata College of Engineering and Management West Bengal India
Abstract
Finding the right person for an open position can be an unnerving task, especially when there are many applicants, and if the recruiter or the Human Resources department must sort and further categorize all those resumes then it will be a labor-intensive, time-consuming, and tiresome task. Another thing is that human review of resumes can be prone to bias and errors. Manually screening the proper candidate’s resume from the pool is not practicable; instead, an automated method using natural language processing may assist in selecting the appropriate candidate’s resume. The proposed web application is built so that job applicants and recruiters can use it easily for applying for job opportunities and screening, respectively. This study aims to revolutionize resume classification through the lens of machine learning. Unlike previous approaches, we provide a unique framework that combines advanced machine learning algorithms with complex feature engineering techniques. So, this can hamper the decision-making process and lead to delays in the hiring process. We examine the model architectures, assessment measures, feature extraction strategies, and underlying processes that went into creating and developing these systems. We also look at case studies and real-world applications to show the difficulties and benefits of using machine learning for resume screening. As a result, the company’s overall growth will be hindered. An efficient resume classification process will ease the hiring process. Machine learning algorithms like Regular Expression, Multinomial Naïve Bayes, Logistic Regression, Random Forest, SVM, etc. have been used for Resume Classification.
Keywords: Artificial Intelligence, Machine Learning, Logistic Regression, SVM, Naïve Bayes
[This article belongs to International Journal of Electronics Automation(ijea)]
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| Volume | 01 |
| Issue | 02 |
| Received | March 30, 2024 |
| Accepted | April 15, 2024 |
| Published | May 10, 2024 |