Enhancing Online Job E-Recruitment Selection Process Using Data Mining Techniques

Abstract

Job recommended in recruitment systems often face significant challenges with limited filtering mechanisms and lack of personalized recommendations which leads to inaccurate in candidate job matching. And it fails to capture the complexities of candidate qualification, experiences, and potential fit for the job. This study evaluates an innovative online web application designed to streamline the recruitment process by leveraging data driven techniques for precise candidate-job matching. This paper presents a comprehensive analysis of an employee recruitment dataset using advanced classification techniques and integrated with the R programming environment. We evaluate and determine the most suitable approach for filtering and identifying the best fit employees for specific job roles with graphical representations. These visual analytics aid in making data driven decisions, ultimately streamlining the selection process and ensuring that the best candidates are chosen for each role. Our findings provide a robust framework for enhancing recruitment systems through effective data analysis and visualization in an online platform enhancing the accuracy and effectiveness of the e-recruitment process.