A Comprehensive Study of Sorting Algorithm Performance Using Real-World Dataset Metrics

Sorting algorithms play a fundamental role in computer science and are extensively applied in data processing tasks. This study provides a detailed experimental analysis of four classical algorithms: Bubble Sort, Quick Sort, Merge Sort, and Heap Sort, using real world datasets obtained from the UCI Machine Learning Repository. We evaluated the algorithms based on multiple performance metrics, including execution time, memory usage, stability, and the number of comparisons or swaps, across multiple runs to ensure reliability. We further examined algorithmic behavior on different input cases, highlighting best, worst, and random scenarios. Results show that Quick Sort achieves the fastest execution time, while Merge Sort maintains stability with moderate memory consumption. Bubble Sort, though stable, demonstrates high computational effort, and Heap Sort offers a trade-off between efficiency and stability. Visualizations such as bar charts, box plots, scatter plots, and heatmaps are employed to provide a clear comparative understanding. This research provides valuable insights for selecting appropriate sorting algorithms based on performance, stability, and computational requirements in practical applications.