Introduction

In recent years, the use of Artificial Intelligence (AI) and Predictive Analytics has gained significant attention in various industries, including Human Resource Management (HRM). AI-based Predictive Analytics has the potential to revolutionize the way organizations manage their human resources by providing data-driven insights and predictions to make better decisions. This thesis aims to explore the application of AI-based Predictive Analytics in HRM and its impact on organizational performance.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Overview of AI and Predictive Analytics
2.2 AI-based Predictive Analytics in HRM
2.3 Benefits of AI-based Predictive Analytics in HRM
2.4 Challenges of implementing AI-based Predictive Analytics in HRM
2.5 Current trends in AI-based Predictive Analytics for HRM
2.6 Case studies of organizations using AI-based Predictive Analytics in HRM
2.7 Ethical considerations of AI-based Predictive Analytics in HRM
2.8 Theoretical frameworks for AI-based Predictive Analytics in HRM
2.9 Future directions of AI-based Predictive Analytics in HRM

Chapter Three: System Design and Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 AI algorithms used in Predictive Analytics for HRM
3.5 Implementation of AI-based Predictive Analytics system
3.6 Evaluation metrics for the AI-based Predictive Analytics system
3.7 Integration with existing HRM systems
3.8 Training and upskilling requirements for HR professionals

Chapter Four: System Implementation
4.1 Data preparation and preprocessing
4.2 Model development and validation
4.3 Deployment of the AI-based Predictive Analytics system
4.4 Testing and evaluation of the system
4.5 Performance optimization
4.6 Integration with HRM processes
4.7 User training and support
4.8 Maintenance and updates of the system

Chapter Five: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field of HRM
5.3 Practical implications for organizations
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

AI-based Predictive Analytics has emerged as a powerful tool for transforming HR processes and decision-making in organizations. This thesis aims to explore the integration of AI-based Predictive Analytics in Human Resource Management (HRM) and its potential impact on organizational performance. The study will investigate the benefits, challenges, and implications of implementing AI-based Predictive Analytics in HRM, as well as provide recommendations for organizations looking to adopt this technology.

Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a comprehensive literature review on AI and Predictive Analytics, its application in HRM, benefits, challenges, trends, case studies, ethical considerations, theoretical frameworks, and future directions.

Chapter Three outlines the system design and methodology, including research design, data collection methods, analysis techniques, AI algorithms, implementation, evaluation metrics, and training requirements. Chapter Four details the system implementation process, covering data preparation, model development, deployment, testing, optimization, integration, user training, and maintenance.

Chapter Five concludes the thesis with a summary of findings, contributions to HRM, practical implications for organizations, recommendations for future research, and a final conclusion. Overall, this thesis aims to contribute to the growing body of knowledge on AI-based Predictive Analytics in HRM and provide valuable insights for organizations seeking to leverage this technology for competitive advantage.

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