E-RECRUITMENT USING MACHINE LEARNING ALGORITHM

ABSTRACT

Traditional recruitment of staff for a particular job requirement poses a lot of inefficiency and inappropriate allocation of job duties to the wrong employees. In recent years, e-recruitment technologies have grown rapidly, enabling Human Resources (HR) firms to target a huge audience at a low price. The necessity to commit human resources for manually reviewing resumés and determining if applicants are qualified for the open positions may be onerous for HR departments. Efficiency could be improved by automating the analysis of candidate profiles to identify those that match the requirements of the post. The need for e-recruitment arises to eliminate some of his challenges using some machine learning techniques for developing an online job candidate screening system and a system that matches candidate to job and display result. This study also facilitates the development of an automated applicant’s job ranking system using supervised machine learning algorithm. The study created a system for online job candidate screening that connects candidates with jobs and displays the results. A system for automatically ranking job applicants was also constructed as part of the study using supervised machine learning algorithms, and the suggested method was put into use using the Python programming language and Word Pad for candidates’ details to be uploaded on the spider-python working environment. The adopted methodology for the design and analysis of this study is the Object-Oriented System Analysis and Design.