Promotion Classification Using DecisionTree and Principal Component Analysis

The goal of human resource management is to support the employee to higher position promotion and improve their work performance. The key process for performance evaluation is promotion classification. This paper proposes the machine learning based promotion classification using decision tree and principle Component Analysis (PCA). This paper uses Human Resource Analysis Case Study dataset from Kaggle for comparing the promotion classification. The comparison of classification performance for all features, personal features, and performance features are conducted. The classification results show that the classification with PCA provides the highest accuracy at 91.25%.