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Introduction

Machine Learning (ML) is a rapidly evolving field that has shown immense potential in various applications, including human resource management. Predictive Human Resource Management (HRM) is an emerging area where ML algorithms are utilized to analyze and predict employee behavior, performance, and retention. By leveraging historical data, ML algorithms can provide valuable insights that can help organizations make informed decisions and optimize their HR strategies.

This thesis aims to explore the potential of ML in the context of Predictive HRM and how it can revolutionize the way organizations manage their workforce. The study will delve into the various ML algorithms and techniques that can be applied to HR data, as well as the challenges and limitations that may arise in implementing such systems. By understanding the capabilities and limitations of ML in HRM, organizations can harness the power of data-driven insights to improve their HR practices and ultimately drive business success.

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 Evolution of HRM
2.2 Role of Data Analytics in HRM
2.3 Introduction to Machine Learning
2.4 Applications of ML in HRM
2.5 Challenges in Implementing ML in HRM
2.6 Best Practices in Predictive HRM
2.7 Case Studies of ML in HRM
2.8 Future Trends in Predictive HRM
2.9 Summary of Literature Review
2.10 Gaps in Existing Literature

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Preprocessing
3.5 Feature Selection
3.6 Model Selection
3.7 Evaluation Metrics
3.8 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Data Analysis
4.2 Model Performance
4.3 Key Findings
4.4 Implications for HRM
4.5 Recommendations for Implementation
4.6 Comparison with Existing Literature
4.7 Limitations of the Study
4.8 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions of the Study
5.4 Practical Implications
5.5 Suggestions for Future Research

Thesis Overview

Machine Learning (ML) has emerged as a powerful tool in the field of Predictive Human Resource Management (HRM), offering organizations the ability to analyze vast amounts of HR data and make informed decisions about their workforce. This thesis explores the potential of ML in HRM and its implications for organizational success.

Chapter One provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two reviews the existing literature on HRM, data analytics, ML, applications of ML in HRM, challenges, best practices, case studies, and future trends.

Chapter Three details the research methodology, including research design, data collection methods, sampling techniques, data preprocessing, feature selection, model selection, evaluation metrics, and ethical considerations. Chapter Four presents a discussion of the findings, including data analysis, model performance, key findings, implications for HRM, recommendations, comparison with existing literature, limitations, and future research directions.

Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, contributions, practical implications, and suggestions for future research in the field of ML for Predictive HRM. By leveraging ML algorithms, organizations can gain valuable insights into their workforce and optimize their HR strategies for enhanced performance and productivity.

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