SDSC/open-pulse-hackathon-data-analysis huggingface dataset:
Repository: MVPee/LauzHack-2024
Description: Hackathon LauzHack 2024 (24h)
Primary language: Python
Topics: 2024, 24hour, challenge, datascience, hackathon, lauzhack, ubs

<h1 align="center">
	💻 Lauzhack 2024
</h1>

<p align="center">
	<img alt="GitHub code size in bytes" src="https://img.shields.io/github/languages/code-size/MVPee/LauzHack-2024?color=lightblue" />
	<img alt="Code language count" src="https://img.shields.io/github/languages/count/MVPee/LauzHack-2024?color=yellow" />
	<img alt="GitHub top language" src="https://img.shields.io/github/languages/top/MVPee/LauzHack-2024?color=blue" />
	<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/MVPee/LauzHack-2024?color=green" />
</p>

# 💡 About the project
> UBS LauzHack Hackathon Challenge (24 hours)

This project, developed during a 24-hour UBS hackathon, addresses the challenge of identifying identical users within a massive set of banking transaction data. By leveraging advanced data processing and matching algorithms, our solution efficiently detects and connects user profiles across multiple transactions, ensuring accuracy and scalability.

# 📜 Features:

- High-performance data parsing and analysis for large-scale datasets.
- Intelligent matching algorithms to identify duplicate users based on transaction patterns, metadata, and identifiers.
- Scalable and adaptable for integration into banking systems.

# ⚙️ Performance Stats:

- Transactions Processed: 1,450,000
- Time Taken: 90 seconds
- Matching Accuracy: 66%

# 👥 Team

This hackathon project was collaboratively developed by
- [Yonyc (Arnaud)](https://github.com/Yonyc)
- [jdecorte (John)](https://github.com/jdecorte-be)
- [dspilleb (Dan)](https://github.com/dspilleb)
- [MVPee (Marius)](https://github.com/MVPee)
Repository: MakeGreatBox/frontend
Primary language: TypeScript

# SmartBox Control System
Welcome to MakeGreatBox, a solution developed during LauzHack to tackle the challenge of controlling a production machine in a cardboard box factory. This system streamlines the process of managing box production by introducing a centralized, user-friendly interface for monitoring, configuring, and controlling the production line in real time.

# Features
Real-time Machine Monitoring: Track the current status of the production machine (e.g., speed, temperature, and errors).

Production Configuration: Customize parameters such as box dimensions, material thickness, and batch size.

Error Management: Receive alerts and troubleshoot machine errors efficiently.

Performance Analytics: View production metrics such as throughput and machine utilization.

Historical Data: View what happened on specific dates to better understand how it's going.

# Tech Stack

## Frontend:

## Backend:

## Database:

## Hardware Integration:

Raspberry Pi 5 for machine communication.

# How to use