Resume Analysis Through Machine Learning

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Abstract
The conventional technique of recruiting is not efficient and suffers from drawbacks such as time wastage and biasness in evaluation. Pre-employment resume screening is subjective and takes a lot of time; currently available solutions are keyword based. In order to address such issues, this article presents a resume analysis method, implemented with Python, which incorporates usage of Artificial Intelligence and Natural Language Processing in analyses of resumes. The proposed method makes use of machine learning to sort applicants in relation to the job for which they will be applying for based on the required skills. The major tasks involved in the development of this project include data cleaning, converting the data to TF-IDF vectors, performing classification and the development of an interactive front-end using the Streamlit module. The proposed method scans resume, provides likely job types, and suggestions, while guaranteeing that candidates don't go by looks and can be effectively evaluated based on their abilities. The purpose of this research is to increase the accuracy of prediction, and, consequently, the fairness and efficiency of the hiring processes.

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