HR Analytics: Resume Parsing Using NER and Candidate Hiring Prediction Using Machine Learning Model

Abstract

HR analytics is a leveraging technology in which the process of HR business is automated and to develop data driven insights to inform talent decisions. In this project, the main objective is to eliminate or to reduce the HRs job and make the selection process automated. In order to achieve this, the project was divided into two modules. One module processes the resumes and parses them to extract the required data. Firstly, the resume parsing is done using the Named entity recognition (NER) method using the spacy model. The resumes are annotated using NER annotator which labels the required text/ data in the resumes. The second module wants to make the candidate hiring predictions automated. The candidates will be checked on different aspects like experience, their stand towards one company i.e. stability which tells how stable the candidates are in staying with one company. The tenure with the current company they are working for, which tells how many months the candidate is working in the current company when they are applying for the job. The first module gave results that were accurately obtained after using the Named entity recognition method to parse the resumes and extract the skills from the resumes. After successfully extracting the correct skills from resumes with high accuracy, it was matched with the Job ID which matches the skills with the required data in the job description. The second module of the project gave the results based on a decision tree which was plotted and made decisions whether the candidate profile is fitted or not. The prediction is based on training the decision tree ML model which gave an accuracy of 70 %. The precision, recall and f1 score for the data is obtained and checked how well the data is trained. Overall project will make the HR job easier and automate the process which will help to reduce the complete time of recruitment.