Project Chooser

A sophisticated, AI-enhanced recommendation engine designed for academic project allocation in higher education institutions. This system employs advanced algorithmic approaches including Bayesian inference and niche discovery to optimise student-project matching.

Features

• 🧠 Bayesian Inference Engine: Smart question selection to minimize cognitive load during preference elicitation
• 🔍 Niche Discovery Algorithm: Identifies uncommon yet potentially suitable projects through cross-domain analysis
• ⚡ Adaptive Questioning: Dynamically adjusts strategy based on user responses to maximize information gain
• 📊 Multi-dimensional Scoring: Combines preference matching, novelty detection, and practical constraints
• 📈 Statistical Analysis: Comprehensive analytical reports on project distribution and allocation patterns
• ⚙️ Configurable Parameters: Fine-tune scoring weights and algorithmic parameters for your institution

Quick Start

Installation

Install from PyPI using pip:

pip install project-chooser

Or using Poetry:

poetry add project-chooser

Basic Usage

1. Interactive Recommendation (recommended for new users):

   project-chooser recommend --interactive
   

2. Batch Processing with a JSON file:

   project-chooser recommend --input preferences.json --output recommendations.json
   

3. Data Validation:

   project-chooser validate projects.json
   

4. Generate Analysis Report:

   project-chooser analyse projects.json --output analysis_report.json
   

Project Data Format

Create a projects.json file with your project data:

{
  "projects": [
    {
      "id": "PROJ001",
      "title": "Machine Learning in Climate Modeling",
      "description": "Apply ML techniques to climate data analysis",
      "degree": "Computer Science",
      "year": 4,
      "supervisor": "Dr. Smith",
      "topics": ["machine learning", "climate science", "data analysis"],
      "skills_required": ["Python", "TensorFlow", "Statistics"],
      "difficulty": "Hard",
      "group_size": "Individual"
    }
  ]
}

Algorithm Details

Bayesian Inference Engine

The system uses information-theoretic question selection to minimize the number of questions needed to understand user preferences, reducing cognitive load while maximizing information gain.

Niche Discovery

Advanced algorithms identify projects that may not be obvious matches but could be excellent fits based on:

• Cross-domain skill transfer potential
• Novelty scoring mechanisms
• Hidden preference pattern recognition

Scoring System

Multi-dimensional scoring considers:

• Direct preference matching
• Skill requirement alignment
• Supervisor preferences
• Workload and difficulty matching
• Diversity and exploration factors

Development Setup

For contributors and advanced users:

# Clone the repository
git clone https://github.com/unkokaeru/project-chooser.git
cd project-chooser

# Install with Poetry (recommended)
poetry install --with dev
poetry shell

# Or install with pip
pip install -e ".[dev]"

Configuration

The system can be configured through environment variables or config files:

export PROJECT_CHOOSER_DEBUG=true
export PROJECT_CHOOSER_MAX_QUESTIONS=10

Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for your changes
5. Run the test suite (poetry run pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

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

Support

• 📚 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

Changelog

See CHANGELOG.md for a detailed history of changes.

Made with ❤️ for academic institutions worldwide