🍳 LAiSER Cookbook

A collection of interactive Jupyter notebooks showcasing practical applications of the LAiSER package for AI-powered skill analysis and extraction.

📖 About

The LAiSER Cookbook provides ready-to-run notebooks that demonstrate real-world applications of AI-driven skill analysis across different domains. Each "recipe" is a complete, self-contained notebook that solves a specific problem using the LAiSER package.

🚀 Quick Start

1. Prerequisites: You'll need a Hugging Face account and a User Access Token with read permissions
2. Choose a Recipe: Browse the recipes below and select one that matches your use case
3. Run the Notebook: Open the Jupyter notebook and follow the step-by-step instructions
4. Customize: Adapt the code to work with your own data

All notebooks are designed to run on standard CPU hardware and will automatically install required dependencies.

📚 Available Recipes

🔍 Job Skill Analysis for Job Seekers

Perfect for: Job seekers, career counselors, recruitment consultants

Extract and visualize the most in-demand skills from job postings to help job seekers focus their learning and tailor their resumes effectively.

Key Features:

• Automatic skill extraction from job descriptions
• Frequency analysis and visualization
• Word cloud generation for easy interpretation
• Uses sample job posting dataset (easily adaptable to your own data)

💰 Pay Equity Analysis Based on Skills

Perfect for: HR teams, compensation analysts, organizational leaders

Analyze the relationship between required skills and compensation to identify potential pay equity issues and build fairer compensation structures.

Key Features:

• Skill-based compensation analysis
• Visual correlation between skill complexity and salary
• Identifies potential pay inequities beyond job titles
• Sample HR dataset included for demonstration

🎓 University Program Skill Analysis

Perfect for: Academic administrators, curriculum designers, program evaluators

Extract and compare skills taught across different university programs to ensure curriculum alignment with industry demands.

Key Features:

• Automated skill extraction from program descriptions
• Cross-program skill comparison and visualization
• Gap analysis capabilities
• Optimized for academic text processing

📈 Recent Updates (Latest Changes)

• README Standardization: Updated all recipe README files to use consistent formatting (removed "README:" prefix from titles)
• Enhanced Documentation: Improved narrative descriptions and setup instructions for all recipes
• Better Organization: Streamlined file structure and naming conventions

🛠️ Requirements

• Python 3.7+
• Jupyter Notebook environment (local installation or cloud service like Google Colab)
• Hugging Face account and API token
• Internet connection for package installation and model access

💡 Usage Tips

• Start Simple: Begin with the sample datasets provided in each notebook
• API Limits: Be mindful of Hugging Face API rate limits when processing large datasets
• Customization: Each notebook includes clear guidance on how to adapt the code for your specific data
• Model Selection: Different Hugging Face models may produce varying results - experiment to find what works best for your use case

🤝 Contributing

We welcome contributions! Whether it's new recipes, improvements to existing ones, or bug fixes, your input helps make this resource better for everyone.

📄 License

This project is licensed under the terms specified in the LICENSE file.

🔗 Related Projects

• LAiSER Package - The core Python package powering these recipes