Efficient Resume Categorization Using NLP and KNN for Automated Hiring Processes

In the current competitive job market, a well-crafted resume is essential for landing employment opportunities. Resumes provide a concise overview of an individual's professional background, highlighting skills, experience, and qualifications to potential employers. Resume categorization is needed for selecting the resumes for various jobs when a company feel it tedious to find appropriate candidate for the job. This paper is focused on classification of resumes according to given attributes for various job skills. Extraction of required attributes from CV is performed using NLP. KNN classifiers are used to classify the resumes according to trained model. The system is trained with various resumes’ attributes which are suitable to different job roles. The system performance is analyzed for accuracy, score and F-Measure. It is found that system is performed best due to combination of NLP and KNN technique. The system is implemented using Kaggle data set and PANDAS libraries. Analysis of entire system is done after comparing with other existing work of resume categorization.