AiLab-workbench

📖 Description

AiLab-workbench is a comprehensive collection of AI and data science laboratory assignments implemented in Python. This repository contains practical implementations of fundamental algorithms including path-finding algorithms (BFS, DFS, UCS), data processing, machine learning concepts, and algorithmic problem-solving techniques. Perfect for students and practitioners looking to understand core AI concepts through hands-on coding exercises.

🔗 Quick Links

• 📚 Documentation
• 🐛 Report Issues
• 🤝 Contributing Guidelines
• 🛡️ Security Policy
• 📏 Code of Conduct

📑 Table of Contents

• Description
• Quick Links
• Features
• Installation
• Usage
• Folder Structure
• Tech Stack
• Dependencies & Packages
• Contributing
• License
• Security
• Code of Conduct

✨ Features

• 🧭 Smart Campus Path Finder - Implementation of BFS, DFS, and UCS algorithms for optimal path finding
• 📊 Data Analysis & Visualization - Practical examples using NumPy, Pandas, and Matplotlib
• 🧮 Algorithm Implementations - Queue operations, heap structures, and graph traversal algorithms
• 📈 Performance Comparison - Detailed analysis and comparison of different algorithmic approaches
• 🔄 Graph Operations - Comprehensive graph manipulation including add, remove, and modify operations
• 📝 Well-Documented Code - Clear comments and explanations for educational purposes

🚀 Installation

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.7+ - Download Python
• pip - Python package installer (usually comes with Python)

Setup Steps

1. Clone the repository

git clone https://github.com/H0NEYP0T-466/AiLab-workbench.git
cd AiLab-workbench

2. Install required dependencies

pip install numpy pandas matplotlib

3. Verify installation

python --version
pip show numpy pandas matplotlib

⚡ Usage

Running Lab Assignments

Each lab file can be executed independently. Here are some examples:

Smart Campus Path Finder (Lab 466 OEL)

python Fall-23-BSCS-466-OEL.py

This will run the campus path-finding system with interactive features including:

• Add/remove campus locations
• Find paths using BFS, DFS, and UCS
• Compare algorithm performance
• Visualize traversal history

Data Analysis Labs

# Run data processing lab
python Lab12.py

# Run pandas operations
python Lab13.py

# Run visualization lab
python LAB-Paper.py

Algorithm Implementation Labs

# Queue operations
python lab6.py

# Heap operations
python lab7.py

# NumPy operations
python Lab11.py

Example Code Snippet

from collections import deque

# Simple BFS implementation example
def bfs(graph, start):
    visited = set()
    queue = deque([start])
    
    while queue:
        node = queue.popleft()
        if node not in visited:
            visited.add(node)
            queue.extend(graph[node])
    
    return visited

📂 Folder Structure

AiLab-workbench/
├── Fall-23-BSCS-466-OEL.py      # Smart Campus Path Finder (BFS, DFS, UCS)
├── Fall-23-BSCS-628-OEL.py      # Advanced algorithm implementations
├── LAB-Paper.py                  # Data visualization and plotting exercises
├── Lab11.py                      # NumPy operations and arrays
├── Lab12.py                      # NumPy advanced operations
├── Lab12.1.py                    # Pandas and NumPy integration
├── Lab13.py                      # Pandas data processing
├── lab#3.py                      # Basic algorithm exercises
├── lab#3(TASK#1).py             # Specific task implementations
├── lab#4.py                      # Intermediate algorithms
├── lab#5.py                      # Data structures
├── lab#5_Task#2.py              # Advanced data structure tasks
├── lab6.py                       # Queue implementations
├── lab7.py                       # Heap and priority queue operations
├── processed_sensor_data.csv    # Sample data file for exercises
├── student_practice_data.csv    # Practice dataset
├── traversal_history.txt        # Graph traversal logs
├── README.md                     # This file
├── LICENSE                       # MIT License
├── CONTRIBUTING.md              # Contribution guidelines
├── SECURITY.md                  # Security policy
├── CODE_OF_CONDUCT.md          # Code of conduct
└── .github/                     # GitHub-specific files
    ├── ISSUE_TEMPLATE/          # Issue templates
    │   ├── bug_report.yml
    │   ├── feature_request.yml
    │   └── config.yml
    └── pull_request_template.md # PR template

🛠 Tech Stack

Languages

Frameworks & Libraries

Development Tools

📦 Dependencies & Packages

This project uses Python's standard library along with essential data science packages.

Runtime Dependencies

• Fast numerical computing library for array operations and mathematical functions

• Powerful data manipulation and analysis library for structured data

• Comprehensive library for creating static, animated, and interactive visualizations

Standard Library Dependencies

This project also uses Python standard library modules (included with Python):

• collections - Container datatypes (deque, defaultdict)
• heapq - Heap queue algorithm (priority queue)
• queue - Queue implementations
• time - Time access and conversions
• datetime - Date and time manipulation

Installation

Install all runtime dependencies with:

pip install numpy pandas matplotlib

Or install specific versions for compatibility:

pip install numpy>=1.20.0 pandas>=1.3.0 matplotlib>=3.4.0

🤝 Contributing

We welcome contributions from the community! Please read our Contributing Guidelines before submitting pull requests.

Quick contribution steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🛡 Security

For security concerns, please review our Security Policy and report vulnerabilities responsibly.

📏 Code of Conduct

This project adheres to the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code.

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