• Y Santhi Kiran
• S Srinadh Raju
• Dr. K V Satyanarayana

Because of the consistent development in online enrolment, work entryways are beginning to get a great many resumes in different styles and configurations from work searchers who have distinctive fields of aptitude and represent considerable authority in different spaces. Appropriately, consequently extricating organized data from such continues is required not exclusively to help the programmed coordinating between applicant resumes and their relating propositions for employment, yet in addition to efficiently course them to their suitable word related classifications to limit the exertion needed for overseeing and putting together them. Thus, rather than looking around the world in the whole space of resumes and occupation posts, continues that fall under a specific word related class are just those that will be coordinated to their significant occupation post. In this examination work, we present a half and half methodology that utilizes calculated based classification of resumes and occupation postings and consequently positions applicant resumes (that fall under every classification) to their comparing bids for employment. In this unique situation, we abuse a coordinated information base for completing the classification task and tentatively illustrate utilizing a genuine enrolment dataset accomplishing promising accuracy results contrasted with ordinary AI based resume classification draws near.

Resume Ranking, Bayesian Classifier, HMM Classifier, SVM Classifier, NLP

"Resume Ranking Using a Bayesian Classifier Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.e803-e807, July-2021, Available :http://www.jetir.org/papers/JETIR2107605.pdf

"Resume Ranking Using a Bayesian Classifier Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppe803-e807, July-2021, Available at : http://www.jetir.org/papers/JETIR2107605.pdf