Conclusion¶

The results confirm that the implemented algorithms behaved as predicted both theoretically and empirically.

Runtime measurements showed that:

Merge Sort and Quick Sort achieved the fastest execution times
due to their O(n log n) complexity.
Selection Sort and Insertion Sort performed significantly slower,
consistent with their O(n²) time complexity.

The close alignment between theoretical analysis and measured results demonstrates correctness of implementation and validity of benchmarking.

Lessons Learned¶

Simplicity vs. Efficiency¶

Simple algorithms such as Selection Sort and Insertion Sort are easier to implement and understand but do not scale well.
Scalability Matters¶

Advanced algorithms like Merge Sort and Quick Sort, while more complex, provide significantly better performance for large datasets.
Theory Meets Practice¶

This project reinforced how theoretical complexity directly impacts measurable runtime in real-world scenarios.

Real-Time Integration¶

Process live registration data directly from a university database.
Visualization Interface¶

Implement a graphical interface to visualize demand scores and sorted results dynamically.
Advanced Validation¶

Add automated data validation and stronger input error detection.
Broader Algorithm Comparison¶

Extend analysis to include algorithms such as Heap Sort and Counting Sort.

This project successfully demonstrated the relationship between data structures, algorithm design, and runtime efficiency.

It highlights how thoughtful algorithm selection directly influences system scalability and performance in practical software development.