Machine Learning Based CV Analyzer

Computer Science

In today's competitive job market, the efficiency of the recruitment process is crucial. This paper details the creation and deployment of a machine learning-driven CV analyzer, designed to automate the early phases of candidate selection. By utilizing Computational Linguistics. Techniques and machine learning algorithms, the system examines and assesses resumes to pinpoint top applicants for designated positions. The proposed system employs a multi-step process beginning with data preprocessing, where resumes are converted into a standardized format. Key attributes like education, experience, skills, and certifications are Identified through named entity extraction and part-of-speech (POS) tagging. These attributes are subsequently employed to train a classifier model, which ranks candidates according to their suitability for the job description. The performance of the model is evaluated using a dataset of resumes and corresponding job descriptions. Performance metrics such as precision, recall, and F1-score are utilized to evaluate the precision and reliability of the system. This study highlights the potential of machine learning to enhance the recruitment process by making it quicker, more objective, and scalable. Future improvements could involve integrating the system with applicant tracking systems (ATS) and incorporating feedback mechanisms to continuously refine model performance.

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