Organization-wide GitHub configuration and profile
This repository contains organization-wide GitHub configuration files, templates, and documentation for the Random Iceberg organization.
- Organization Profile: README displayed on the organization page
- Issue Templates: Standardized issue reporting templates
- Pull Request Templates: Consistent PR submission guidelines
- GitHub Actions Workflows: Shared CI/CD workflows
- Community Guidelines: Code of conduct and contribution guidelines
Tip
Ready to explore? Visit our main project repository for a full-stack AI web application!
A production-ready web application that predicts Titanic passenger survival using machine learning models.
Key Features:
- 🤖 5 ML Algorithms (Random Forest, SVM, Decision Tree, KNN, Logistic Regression)
- 🔐 JWT Authentication with role-based access control
- 📱 Mobile-First Design with responsive UI
- 🐳 One-Command Deployment using Docker Compose
- ⚡ Real-time Predictions with <150ms latency
Quick Start:
git clone --recurse-submodules https://github.com/random-iceberg/docker-compose.git
cd docker-compose
docker compose up --build -d
open http://localhost:8080| Repository | Description | Tech Stack |
|---|---|---|
| docker-compose | 🏠 Main orchestration & documentation | Docker, Compose |
| web-frontend | 🎨 React TypeScript frontend | React 19, TypeScript, Tailwind |
| web-backend | ⚙️ FastAPI web backend | FastAPI, PostgreSQL, JWT |
| model-backend | 🧠 ML inference service | FastAPI, scikit-learn |
| docker-compose.wiki | 📚 Project documentation | Markdown, Git |
- Python: Ruff formatting, type hints, pytest
- TypeScript: ESLint + Prettier, strict TypeScript
- Documentation: Comprehensive README files, inline comments
- Testing: >80% test coverage across all services
- Branching: Feature branches with descriptive names
- Commits: Conventional commit messages
- Pull Requests: Required reviews, automated testing
- Releases: Semantic versioning with automated releases
- Build: Docker multi-stage builds
- Test: Automated unit, integration, and E2E tests
- Deploy: GitHub Container Registry
- Quality: Code coverage, security scanning
Note
University Project: Developed as part of Software Engineering coursework at Deggendorf Institute of Technology (DIT) under Prof. Dr. Christoph Schober.
This project demonstrates:
- Enterprise-level architecture with microservices
- Modern development practices with containerization
- AI/ML integration in web applications
- Full-stack development with React and FastAPI
- Production deployment with Docker Compose
Software Engineering students specializing in full-stack development and machine learning applications.
| Specialization | Focus Areas |
|---|---|
| Full-Stack Development | React, TypeScript, Python, FastAPI |
| Machine Learning | scikit-learn, model training, inference optimization |
| DevOps & Infrastructure | Docker, CI/CD, containerization |
| UI/UX Design | Responsive design, accessibility, user experience |
- 12,000+ lines of code
- 4 containerized microservices
- 20+ RESTful API endpoints
- 5 machine learning algorithms
- 80%+ test coverage
- 3 supported browsers (Chrome, Firefox, Safari)
- 🏠 Main Project - Complete application
- 📚 Documentation - Technical docs
- 🚀 Quick Start Guide - Get started in minutes
- 🛠️ Development Setup - Development environment
Projects under this organization are licensed under the MIT License unless otherwise specified.
Showcasing modern software engineering practices
From concept to production deployment