CauldWatch

Inspiration

I wanted to combine my love for data and fantasy. The idea of tracking potions using real-world analytics sounded fun and challenging, which led to CauldronWatch.

What it does

CauldronWatch is a real-time potion monitoring dashboard. It simulates potion data, detects unusual patterns like leaks or missing tickets, explains the cause, and suggests courier reassignment.

How we built it

Built in Python using Streamlit, Pandas, and Scikit-learn. A simulator generates potion data, anomaly detection flags issues, and the dashboard visualizes alerts with short explanations.

Challenges we ran into

Keeping the dashboard live while streaming data and making alerts easy to understand were the main challenges.

Accomplishments that we're proud of

Built a working end-to-end system solo within hours, combining storytelling with data science.

What we learned

Real-time data handling, quick dashboard creation in Streamlit, and designing explainable AI outputs.

What's next for Solo Registration

Add map visuals, forecasting for potion levels, and deploy the app for others to try.

Built With

• languages:-python-frameworks/libraries:-streamlit
• numpy
• pandas
• plotly
• scikit-learn