Algorithm Optimization Project - Machine Learning

A comprehensive programming optimization project focused on improving algorithm efficiency and code performance. This project demonstrates essential optimization techniques through practical exercises involving text processing and list operations.

Project Overview

This project focuses on algorithm optimization through two main exercises that teach fundamental performance improvement techniques:

Exercise 1: Text Processing Optimization

• Convert text to lowercase
• Remove punctuation marks efficiently
• Count word frequencies
• Extract most common words

Exercise 2: List Processing Optimization

• Filter even numbers from lists
• Duplicate list elements
• Sum numerical values
• Prime number detection

The project provides hands-on experience with:

• Code refactoring and optimization
• Efficient data structure usage
• Python built-in function utilization
• Modular programming practices
• Performance analysis and improvement

Getting Started

Option 1: GitHub Codespaces (Recommended)

1. Fork the Repository

   • Click the "Fork" button on the top right of the GitHub repository page
   • 4Geeks students: set 4GeeksAcademy as the owner - 4Geeks pays for your codespace usage. All others, set yourself as the owner
   • Give the fork a descriptive name. 4Geeks students: I recommend including your GitHub username to help in finding the fork if you loose the link
   • Click "Create fork"
   • 4Geeks students: bookmark or otherwise save the link to your fork

2. Create a GitHub Codespace

   • On your forked repository, click the "Code" button
   • Select "Create codespace on main"
   • If the "Create codespace on main" option is grayed out - go to your codespaces list from the three-bar menu at the upper left and delete an old codespace
   • Wait for the environment to load (dependencies are pre-installed)

3. Start Working

   • Open notebooks/assignment.ipynb in the Jupyter interface
   • Follow the step-by-step instructions in the notebook

Option 2: Local Development

1. Prerequisites

   • Git
   • Python >= 3.10

2. Fork the repository

   • Click the "Fork" button on the top right of the GitHub repository page
   • Optional: give the fork a new name and/or description
   • Click "Create fork"

3. Clone the repository

   • From your fork of the repository, click the green "Code" button at the upper right
   • From the "Local" tab, select HTTPS and copy the link
   • Run the following commands on your machine, replacing <LINK> and <REPO_NAME>

   git clone <LINK>
   cd <REPO_NAME>

4. Set Up Environment

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

5. Launch Jupyter & start the notebook

   jupyter notebook notebooks/assignment.ipynb

Project Structure

├── .devcontainer/        # Development container configuration
├── assets/               # Files and resources directory
│
├── notebooks/            # Jupyter notebook directory
│   ├── assignment.ipynb  # Assignment notebook with exercises
│   └── solution.ipynb    # Solution notebook with optimized code
│
├── .gitignore            # Files/directories not tracked by git
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

Learning Objectives

1. Algorithm Analysis: Identify performance bottlenecks in existing code
2. Data Structure Optimization: Use appropriate Python data structures for efficiency
3. Built-in Functions: Leverage Python's optimized built-in functions
4. List Comprehensions: Replace loops with more efficient comprehensions
5. Modular Design: Break code into focused, reusable functions
6. Performance Comparison: Understand the impact of different approaches

Technologies Used

• Python 3.11: Core programming language
• Collections: Counter for efficient frequency counting
• String: Built-in string processing utilities
• Math: Mathematical operations and functions
• Jupyter: Interactive development environment

Contributing

This is an educational project. Contributions for improving the optimization examples or adding new exercises are welcome:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request