Transforming Employee Performance Prediction: Harnessing Machine Learning and Analytics for Business Intelligence Advancement

EasyChair Preprint 12697

Abstract

This paper explores the transformative potential of leveraging machine learning and analytics for enhancing employee performance prediction within organizations. In today's competitive business landscape, understanding and predicting employee performance is crucial for effective human capital management and organizational success. Traditional methods often prove inadequate in handling the complexities of modern data-rich environments. However, by harnessing advanced technologies such as machine learning and analytics, organizations can gain deeper insights into employee behavior and performance dynamics. Through predictive modeling, data analysis, and algorithmic approaches, businesses can identify patterns, anticipate future outcomes, and make data-driven decisions to optimize talent management strategies. This paper reviews existing literature and presents case studies to illustrate the benefits, methodologies, challenges, and best practices associated with utilizing machine learning and analytics for employee performance prediction. By embracing these innovative tools, organizations can enhance their ability to recruit, retain, and develop talent, ultimately driving improved business performance and competitive advantage.Top of Form

Keyphrases: Analytics, Business Intelligence, Employee Performance Prediction, human capital management, machine learning, predictive modeling

@booklet{EasyChair:12697,
  author    = {Deep Himmatbhai Ajabani},
  title     = {Transforming Employee Performance Prediction: Harnessing Machine Learning and Analytics for Business Intelligence Advancement},
  howpublished = {EasyChair Preprint 12697},
  year      = {EasyChair, 2024}}