PlotSenseAI Hackathon Demo Projects 🚀

Welcome to the PlotSenseAI Hackathon Demo Projects repository! This collection contains three comprehensive demo projects designed to help participants learn and explore the capabilities of PlotSenseAI for data visualization, machine learning explainability, and anomaly detection.

🎯 Purpose

These demo projects serve as:

• Learning Resources for hackathon participants
• Reference Implementations showcasing PlotSenseAI best practices
• Starter Templates for building your own AI-powered data visualization applications
• Open Source Examples for the community to contribute to and extend

📂 Project Structure

PlotSenseAI-Hackathon-Demo-Projects/
├── project_one/                    # ML Explainability Demo
│   ├── ml_explainability_demo.ipynb
│   └── README.md
├── project_two/                    # Anomaly Detection Plugin
│   ├── plotsense_anomaly/
│   ├── examples/
│   ├── tests/
│   └── README.md
├── project_three/                  # Data Storytelling Web App
│   ├── app.py
│   ├── data/
│   └── README.md
├── docs/                          # Documentation
│   ├── CONTRIBUTING.md
│   ├── SETUP.md
│   └── TUTORIALS.md
└── README.md                      # This file

📋 Demo Projects Overview

🔍 Project One: ML Explainability with UCI Dataset

Focus: Machine Learning model interpretation and visualization

What you'll learn:

• Loading and preprocessing real-world datasets (UCI Breast Cancer Recurrence)
• Training machine learning models (Random Forest)
• Using PlotSenseAI for automated visualization recommendations
• Generating AI-driven explanations for model results
• Advanced explainability techniques (partial dependence, counterfactuals)

Tech Stack: Jupyter Notebook, scikit-learn, pandas, PlotSenseAI

📖 View Project One Documentation

🚨 Project Two: Anomaly Detection Plugin

Focus: Extending PlotSenseAI with custom functionality

What you'll learn:

• Building plugins for PlotSenseAI
• Implementing Z-score based anomaly detection
• Creating custom visualization components
• Writing unit tests for data science code
• Package structure and distribution

Tech Stack: Python, NumPy, pandas, pytest, PlotSenseAI

📖 View Project Two Documentation

📊 Project Three: Interactive Data Storytelling App

Focus: Building production-ready web applications

What you'll learn:

• Creating interactive web applications with Streamlit
• Integrating PlotSenseAI with web frameworks
• Building user-friendly data exploration interfaces
• Handling API keys and environment configuration
• Real-time data filtering and visualization

Tech Stack: Streamlit, pandas, PlotSenseAI, Groq API

📖 View Project Three Documentation

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager
• Git

Installation

1. Clone the repository:

git clone https://github.com/HavilahAcademy/PlotSenseAI-Hackathon-Demo-Projects.git
cd PlotSenseAI-Hackathon-Demo-Projects

2. Create a virtual environment (recommended):

python -m venv plotsense-env
source plotsense-env/bin/activate  # On Windows: plotsense-env\Scripts\activate

3. Install PlotSenseAI:

pip install plotsense

4. Choose your project and follow its specific setup guide:
   • Project One Setup
   • Project Two Setup
   • Project Three Setup

📚 Documentation

• Setup Guide - Detailed installation and configuration instructions
• Tutorials - Step-by-step tutorials for each project
• Contributing Guide - How to contribute to this project
• API Reference - PlotSenseAI official documentation

🎯 Learning Path

We recommend exploring the projects in this order:

1. Start with Project One to understand PlotSenseAI basics and ML explainability
2. Move to Project Three to see how PlotSenseAI integrates with web applications
3. Explore Project Two to learn about extending PlotSenseAI with custom plugins

🛠 Tools and Technologies

Technology	Purpose	Projects
PlotSenseAI	AI-powered data visualization	All projects
Jupyter Notebook	Interactive data analysis	Project One
Streamlit	Web application framework	Project Three
scikit-learn	Machine learning	Project One
pandas	Data manipulation	All projects
pytest	Unit testing	Project Two
Groq API	AI explanations	Project Three

🤝 Contributing

We welcome contributions from the community! Whether you're:

• 🐛 Fixing bugs
• ✨ Adding new features
• 📝 Improving documentation
• 🧪 Adding tests
• 💡 Suggesting improvements

Please read our Contributing Guide to get started.

🏆 Hackathon Guidelines

For Participants

• Use these projects as starting points for your hackathon submissions
• Feel free to modify, extend, or combine elements from different projects
• Focus on creativity and innovation while leveraging PlotSenseAI's capabilities
• Document your modifications and new features

For Organizers

• These projects provide comprehensive examples for workshops and tutorials
• Each project includes varying complexity levels suitable for different skill levels
• Use the documentation to guide participants through the learning process

📄 License

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

🙋‍♀️ Support

Need help? We've got you covered:

• 📖 Documentation: Check the individual project READMEs and documentation
• 🐛 Issues: Report bugs or request features in our Issues
• 💬 Discussions: Join the conversation in our Discussions
• 📧 Email: Contact us at support@havilahacademy.org

🌟 Acknowledgments

• Havilah Academy Team for developing these comprehensive demo projects
• PlotSenseAI (plotsenseai.org) for the amazing visualization platform
• UCI Machine Learning Repository for providing quality datasets
• The open-source community for inspiration and tools
• All hackathon participants and contributors

🔗 Useful Links

• PlotSenseAI Official Website
• PlotSenseAI Documentation
• Havilah Academy
• Hackathon Information

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Happy Hacking! 🎉

Built with ❤️ by the Havilah Academy Team for PlotSenseAI