https://github.com/lesiaukr/goit-algo2-hw-06

Master's | Design & Analysis of Algorithms | Fundamentals of Parallel Computing and the MapReduce Model
https://github.com/lesiaukr/goit-algo2-hw-06

goit-algo2-hw-06 mapreduce-python matplotlib python threadpoolexecutor

Last synced: 4 days ago
JSON representation

Master's | Design & Analysis of Algorithms | Fundamentals of Parallel Computing and the MapReduce Model

• Host: GitHub
• URL: https://github.com/lesiaukr/goit-algo2-hw-06
• Owner: LesiaUKR
• Created: 2025-02-18T20:50:58.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2025-02-19T20:27:42.000Z (2 months ago)
• Last Synced: 2025-04-24T00:11:36.857Z (4 days ago)
• Topics: goit-algo2-hw-06, mapreduce-python, matplotlib, python, threadpoolexecutor
• Language: Python
• Homepage:
• Size: 98.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# HW-6 | Fundamentals of Parallel Computing and the MapReduce Model

**Introduction**:

Fundamentals of Parallel Computing and the MapReduce Model"

Welcome! 🧠

In today's world of modern information technology, processing large 

volumes of data requires efficient and fast methods. Your task is to 

write a Python script that applies the MapReduce paradigm to analyze

word frequency in a text and visualize the results.

Completing this homework will help you develop skills in efficiently 

handling large-scale files. It will also provide hands-on experience 

in leveraging Python’s parallel computing capabilities to accelerate 

code execution.

## **Task Descriptions**

---

Write a Python script that downloads text from a given URL, analyzes word

frequency using the MapReduce paradigm, and visualizes the most frequently

used words.

### **Step-by-Step Instructions**

1. **Import necessary modules** (e.g., `matplotlib` and others).

2. **Use the provided MapReduce implementation** from the reference notes.

3. Create a function `visualize_top_words` to visualize the results.

4. In the main code block:

   - Fetch text from the URL.

   - Apply the MapReduce paradigm.

   - Visualize the results.

👉 **For example**, for the top 10 most frequently used words, the graph 

could look like this:

![Example Visualization](./assets/task_1_1.png)

### **Acceptance Criteria**

- Code fetches text from the given URL.

- Code performs word frequency analysis using MapReduce.

- Visualization displays the top words by frequency.

- Code uses multithreading for parallel processing.

- Code is readable and adheres to PEP 8 standards.

# TASK RESULTS

![results](assets/task_1_2.jpg)

#### Visualized results

![results](assets/task_1_1.png)