Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

Abstract— In the world of work the presence of the best
employees becomes a benchmark of progress of the company
itself. In the determination usually by looking at the performance
of the employee e.g. from craft, discipline and also other
achievements. The goal is to optimize in decision making to the
best employees. Models obtained for employee predictions tested
on real data sets provided by IBM analytics, which includes 29
features and about 22005 samples. In this paper we try to build
system that predicts employee attribution based on A collection
of employee data from kaggle website. We have used four
different machines learning algorithms such as KNN (Neighbor
K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two
ensemble technique namely stacking and bagging. Results are
expressed in terms of classic metrics and algorithms that produce
the best result for the available data sets is the Random Forest
classifier. It reveals the best withdrawals (0,88) as good as the
stacking and bagging method with the same value.
Keywords—random forest; machine learning; best
employees; key performance index