Abstract:
The primary goal of this research is to forecast the success of candidates using the
Naive Bayes method. What are the factors that influence job performance, and how does
the Naive Bayes method work to predict the outcome. The selection of applications for
employment is an important procedure in the realm of recruitment to identify the quality
and suitability of candidates for open vacancies. As a result, it is critical to establish
effective ways for predicting job application success in order to make better selections
during the recruitment process. The goal of this project is to analyze and improve the
Naive Bayes method to predict applicants' performance based on available data and to
identify significant factors in the prediction. This study uses the Naive Bayes method as a
framework for predicting job performance. Data that is used includes personal
information about employees, work hours, and other hiring criteria. In conclusion, the
Naive Bayes method can be used as an effective approach in assisting the process of
predicting the success of job applicants, and important factors can be identified to
improve the effectiveness of recruitment and selection.
