Abstract:

Analysis of employee attendance is one of the important factors in productivity optimization. This research integrates statistical analysis with machine learning to study attendance behavior and develop predictive models. Using T-tests, Pearson correlation coefficient and chi-squares tests, we explore gender, employment type and age differences in overtime work. The logistic regression is used to forecast how many the late arrival while total day working hours were estimated using linear regression. The results show that statistical modelling combined with machine learning provides valuable insights into employee behavior and supports informed decision-making for workforce planning and management.