Updated environment and README

.devcontainer/devcontainer.json

@@ -1,17 +1,22 @@
 // For format details, see https://aka.ms/devcontainer.json. For config options, see the
 // README at: https://github.com/devcontainers/templates/tree/main/src/python
 {
-	"name": "Python 3",
+	// Container definition for a Python 3.11 development environment
+	"name": "Python 3.11",
 	"image": "mcr.microsoft.com/devcontainers/python:0-3.11",
-	"onCreateCommand": "sudo apt update && sudo apt upgrade -y && pip3 install --upgrade pip && pip3 install --user -r requirements.txt",
+
+	// Custom configuration options
 	"customizations": {
 	  "vscode": {
+
+		// Use 'settings' to set default VS code values on container create
 		"settings": {
 			"jupyter.kernels.excludePythonEnvironments": ["/usr/bin/python3"],
-			"remote.portsAttributes": {
-				"ipykernel_launcher": {"onAutoForward": "ignore"}
-			}
+			"remote.autoForwardPorts": false,
+			"remote.restoreForwardedPorts": false
 		},
+
+		// Add the IDs of VS code extensions you want to install here
 		"extensions": [
 		  "-dbaeumer.vscode-eslint",
 		  "ms-python.python",
@@ -20,5 +25,10 @@
 		]
 	  }
 	},
+
+	// Use 'onCreateCommand' to run commands once when the container is created
+	"onCreateCommand": "sudo apt update && sudo apt upgrade -y && pip3 install --upgrade pip && pip3 install --user -r requirements.txt",
+
+	// Use 'postAttachCommand' to run commands each time a user connects to the container
 	"postAttachCommand": "htop"
 }

.gitignore

@@ -1,2 +1,4 @@
-.vscode
+__pycache__
 .ipynb_checkpoints
+.vscode
+.venv

README.md

@@ -1,69 +1,131 @@
-# Algorithm Optimization Project
+# Algorithm Optimization Project - Machine Learning
 
-![Preview](assets/preview.png)
+[![Codespaces Prebuilds](https://github.com/4GeeksAcademy/gperdrizet-algorithm-optimization-project-machine-learning/actions/workflows/codespaces/create_codespaces_prebuilds/badge.svg)](https://github.com/4GeeksAcademy/gperdrizet-algorithm-optimization-project-machine-learning/actions/workflows/codespaces/create_codespaces_prebuilds)
 
-This repository contains exercises designed to help you practice optimizing Python algorithms for better performance and readability.
+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.
 
-## What You'll Learn
+![Project Preview](assets/preview.png)
 
-- Text processing optimization techniques
-- Efficient list operations and filtering
-- Using Python's built-in functions and data structures
-- Code modularity and best practices
-- Performance analysis and improvement strategies
 
-## Assignment Overview
+## Project Overview
 
-The `problems.ipynb` notebook contains two main exercises:
+This project focuses on algorithm optimization through two main exercises that teach fundamental performance improvement techniques:
 
-1. **Text Processing Optimization** - Improve code that processes text by converting to lowercase, removing punctuation, counting word frequencies, and finding the most common words.
+**Exercise 1: Text Processing Optimization**
+- Convert text to lowercase
+- Remove punctuation marks efficiently
+- Count word frequencies
+- Extract most common words
 
-2. **List Processing Optimization** - Enhance code that filters even numbers, duplicates values, sums results, and checks for prime numbers.
+**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
 
-Each exercise includes working but inefficient code that you'll optimize using better algorithms, data structures, and Python idioms.
 
 ## Getting Started
 
 ### Option 1: GitHub Codespaces (Recommended)
-1. Fork this repository to your GitHub account
-2. Click the green "Code" button on your forked repository
-3. Select "Codespaces" tab
-4. Click "Create codespace on main"
-5. Wait for the environment to load (this may take a few minutes)
-6. Open `problems.ipynb` and start working!
+
+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. Fork and clone this repository
-2. Create a virtual environment: `python -m venv venv`
-3. Activate the virtual environment:
-   - On Windows: `venv\Scripts\activate`
-   - On macOS/Linux: `source venv/bin/activate`
-4. Install Jupyter: `pip install jupyter`
-5. Install dependencies: `pip install -r requirements.txt`
-6. Launch Jupyter: `jupyter notebook`
-7. Open `problems.ipynb`
-
-## Working with the Notebook
-
-- Each exercise contains the original inefficient code followed by optimization points
-- Review the provided solutions as reference implementations
-- Try implementing your own optimizations before checking the solutions
-- Run each cell to test your code and compare performance
-
-## Learning Goals
-
-By completing this assignment, you will:
-- Understand common performance bottlenecks in Python code
-- Learn to use appropriate data structures for different problems
-- Practice writing clean, modular, and efficient code
-- Gain experience with Python's built-in optimization tools
-
-## Assessment
-
-Focus on:
-- **Correctness**: Does your optimized code produce the same results?
-- **Efficiency**: Is your solution faster and more memory-efficient?
-- **Readability**: Is your code clean and well-structured?
-- **Best Practices**: Are you using appropriate Python idioms?
-
-Happy coding!
+
+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>`
+
+   ```bash
+   git clone <LINK>
+   cd <REPO_NAME>
+   ```
+
+4. **Set Up Environment**
+
+   ```bash
+   python -m venv venv
+   source venv/bin/activate
+   pip install -r requirements.txt
+   ```
+
+5. **Launch Jupyter & start the notebook**
+   ```bash
+   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