Sorting Algorithms Analysis (ADA Lab - Unit 1)

This project implements and analyzes three sorting algorithms:

• Bubble Sort
• Selection Sort
• Insertion Sort

The performance of each algorithm is analyzed using the Step-Count Method.

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Objective

• Sort numbers in ascending and descending order.
• Count key operations (comparisons and swaps).
• Analyze Best, Average, and Worst cases.
• Compare performance for input sizes: 10, 20, 30, 40.
• Plot graphs for Input Size vs Step Count.

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Step-Count Method

We count:

• 1 comparison = 1 step
• 1 swap = 3 steps (assignment operations)

This helps measure time complexity practically.

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Cases Analyzed

1. Best Case → Already sorted input
2. Average Case → Randomized input
3. Worst Case → Reverse sorted input

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How to Run

1. Install matplotlib:

   pip install matplotlib

2. Run the program:

   python sorting_analysis.py

3. Graphs will be automatically saved inside:

   screenshots_of_graphs/

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Files Included

• sorting_analysis.py → Main implementation
• sample_output.txt → Example output
• screenshots_of_graphs/ → Generated graphs

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Expected Learning

• Understanding sorting algorithm behavior
• Comparing time complexities
• Practical analysis using step counting
• Graphical performance comparison