Classification data mining base on naive bayes Algorithm for predicting employee tardiness
Employee tardiness is a major problem for many companies because it prevents
employees to become fully productive at work. Hence, preventing or reducing employees
tardiness is a challenging task in the human resource management field. At this point,
employee’s tardiness prediction would probably play an important role in providing
insights for tardy employees that would enable the companies to take precautions
against this issue. In this project, I worked on this problem to predict employees’
tardiness. The prediction model is built with a data mining method it makes use of detailed
employee information like age, years of experience, gender, marital status, department,
and tenure. The datasets that is used in this study are employees’ datasets that contains
40 rows and 23 columns and attendance datasets that contains 1450 rows and 6 columns
from a private school in Davao City. Both datasets contain an id that we can use to connect
them with each other. This project applied the Naive Bayes
Algorithm to discover patterns in the dataset to forecast employees’ tardiness.