Predictive Salary Increment Models Using R

Abstract

Predictive salary increment models offer a quantitative basis for determining salary adjustments, promoting transparency and fairness in human resource management. This study presents strategies for developing such models using R, a versatile programming language for statistical computing and machine learning. The paper emphasizes data preprocessing, feature engineering, and algorithm selection to predict salary increments accurately. The methodology includes data analysis, regression modeling, and machine learning techniques like decision trees and ensemble methods. Results from sample datasets demonstrate the feasibility and accuracy of the proposed approach. The study concludes with recommendations for practical implementation in organizational settings.