Predicting Employee Promotion using Machine Learning

Abstract

Training and development are key components of professional development for people to improve their capacity. Professional development programs are typically organized around personal information like as background, personal goals, and work experience, as well as corporate objectives and job requirements. Individual employee classification is required to promote tailored training in the professional development process. As a result, this study provides a classification approach for employee classification in order to facilitate tailored training in enterprises. Machine learning methods such as Decision Tree, Random Forest, and Support Vector Machine are investigated. To cope with imbalance data, the Synthetic Minority Oversampling Technique (SMOTE) approach is applied. In this work, the open data form kaggle is used. The training and testing data are combined to generate the data for technique validation. There are three gropes: 80:20, 70:30, and 60:40. According to the classification results, the SMOTE can increase classification performance for all classifiers. Furthermore, random forest has the highest categorization accuracy.