Development of a Machine Learning-Based Intelligent Career Guidance System for High School Students in Rwanda Using Random Forest Algorithm

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Scope and Limitations 1.4 Structure of the Work 2. Literature Review 2.1 Career Guidance Systems 2.2 Machine Learning in Education 2.3 Random Forest Algorithm 2.4 Educational Context in Rwanda 3. Methodology 3.1 Research Design 3.2 Data Collection Techniques 3.3 Data Preprocessing Steps 3.4 Implementation of Random Forest 4. System Design and Architecture 4.1 Overview of System Components 4.2 User Interface Design 4.3 Database Management 4.4 Integration of Machine Learning Model 5. Algorithm Selection and Optimization 5.1 Justification for Random Forest 5.2 Hyperparameter Tuning 5.3 Performance Metrics 5.4 Comparison with Other Algorithms 6. Results and Analysis 6.1 System Evaluation Criteria 6.2 Interpretations of Results 6.3 Statistical Analysis Methods 6.4 Limitations and Challenges 7. Discussion 7.1 Implications for Stakeholders 7.2 Educational Impact in Rwanda 7.3 Comparison with Existing Methods 7.4 Recommendations for Improvement 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Contributions to the Field 8.3 Suggestions for Future Research 8.4 Final Remarks

1. How does the implementation of a Random Forest-based intelligent career guidance system impact the career decision-making process of high school students in Rwanda? 2. What are the key challenges and limitations faced in developing and integrating a machine learning model for career guidance in the specific educational context of Rwanda?