CSAL - Preliminary (Brute Force)

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Real-Life Situation/Scenerio: VIDEO EDITOR JOB HIRING

You are the hiring manager for a video production company looking to fill a video editing position. You've received applications from 50 talented individuals, but their names are all mixed up, making it challenging to review them efficiently. To streamline the process, you decide to implement the Bubble Sort algorithm to sort the applicants' names alphabetically by surname, to be followed by their first name.

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Functionality Dscription

SOURCE CODE AVAILABLE

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BUBBLE SORTING USAGE

Bubble Sort is a straightforward sorting algorithm that compares adjacent elements and swaps them if they are in the wrong order. While not the most efficient for large datasets, it is suitable for smaller lists like sorting job applicants' names.

Why Bubble Sort

Bubble Sort is chosen for sorting the list of job applicants' names alphabetically because of its simplicity and ease of implementation.

Small Dataset: With only 50 applicants, the dataset is relatively small. Bubble Sort's time complexity of O(n^2) is less of a concern for smaller datasets compared to larger ones.
Ease of Implementation: Bubble Sort is straightforward to understand and implement, making it a suitable choice for this scenario where simplicity and clarity are prioritized over performance optimization.
Stable Sorting: Bubble Sort is a stable sorting algorithm, meaning it preserves the relative order of equal elements. In the context of hiring, maintaining the original order of applicants with the same name is essential to ensure fairness in the evaluation process.
No Additional Memory Requirement: Bubble Sort operates in-place, meaning it doesn't require additional memory beyond the initial list of applicants' names. This is advantageous in situations where memory resources are limited.

SOURCE CODE AVAILABLE

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PSEUDOCODE

function bubbleSort(applicants):

n = length of applicants

for i from 0 to n-1:

for j from 0 to n-i-1:

if applicants[j] > applicants[j+1]:

swap applicants[j] and applicants[j+1]

SOURCE CODE AVAILABLE

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MAIN CODE

SOURCE CODE AVAILABLE

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MAIN CODE OUTPUT

SOURCE CODE AVAILABLE

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CODE BREAKDOWN

def bubble_sort(applicants): Defines a function bubble_sort that takes a list of applicant names as input.
n = len(applicants): Gets the length of the input list.
Nested loops iterate over the list to compare adjacent elements and swap them if they're in the wrong order.
if applicants[j] > applicants[j+1]: compares adjacent elements.
applicants[j], applicants[j+1] = applicants[j+1], applicants[j]: Swaps elements if they are out of order.

CSAL REFLECTION

REFLECTION

GAINED KNOWLEDGE

Implementing the Bubble Sort algorithm in this project provided a practical understanding of how sorting algorithms work, especially in the context of organizing data for real-world applications. I gained insights into the mechanics of comparing and swapping elements to achieve a sorted list, as well as considerations such as time complexity and algorithm choice based on dataset size.

SKILL APPLICATION

This project allowed me to apply my Python programming skills to solve a real-life problem. I utilized my knowledge of Python syntax, loops, conditional statements, and list manipulation to implement the Bubble Sort algorithm effectively. Additionally, I practiced writing clean and readable code to ensure that the sorting process was transparent and easily understandable.

SKILL & KNOWLEDGE DISTRIBUTION

The project involved the distribution of both programming skills and algorithmic knowledge. On the programming side, I applied Python programming concepts to implement the sorting algorithm. On the algorithmic side, I gained knowledge about sorting techniques and their suitability for different scenarios, such as the trade-offs between simplicity and efficiency.

DIFFICULTIES

While implementing Bubble Sort was relatively straightforward, I encountered challenges in optimizing the sorting process for larger datasets. Bubble Sort's time complexity of O(n^2) can lead to performance issues with larger lists of applicants' names. Balancing simplicity and efficiency posed a difficulty, as optimizing the algorithm without sacrificing readability required careful consideration.

ADAPTABILITY

Through this project, I learned the importance of adaptability in problem-solving. While Bubble Sort served as a suitable solution for sorting a small list of job applicants' names, I also recognized the need to adapt to different scenarios by considering alternative sorting algorithms for larger datasets. This flexibility in approach ensures that the chosen solution aligns with the specific requirements and constraints of each situation.

IMPROVEMENTS

To enhance the project further, I could explore alternative sorting algorithms, such as Merge Sort or Quick Sort, to improve efficiency for larger datasets while maintaining clarity and readability in the code. Additionally, incorporating error handling mechanisms to handle unexpected input or edge cases would enhance the robustness of the sorting process. Continuous learning and experimentation with different techniques would contribute to refining my problem-solving skills in future projects.