Sandbox Protocol_Active

Groundwater Analytics / BATCH_003

Protocol_Brief

Challenge

Environmental data is messy. I wanted to see if I could extract meaningful predictions from complex rainfall and usage patterns.

Solution

Combined robust data preprocessing with a Random Forest model and a visual 'What-If' simulator.

“I've always been interested in how data can help solve environmental issues. This project was my first deep dive into the 'why' behind ML predictions. I learned how to engineer domain-specific features like 'Recharge Efficiency' and used Plotly to make the data tell a story. It taught me that a model is only as good as the features you create for it.”

Learned the importance of feature engineering by creating custom environmental metrics.
Used Scikit-Learn's Random Forest to model complex, non-linear water stress patterns.
Built an interactive dashboard with Streamlit to make ML insights accessible.
Explored Explainable AI (XAI) to understand which factors most influence water stress.

Key_Modules

MapsTrendsNature

Loadout_Specs

PythonStreamlitPlotlyScikit-LearnPandasJoblib

Sys_Diagnostics

Node_Count:4

Fidelity:ML Feature Engineering

Matrix_State:STABLE

sm-pred.ais-cloud.dev

Viewport: Matrix_Simulated

Step 01

Explore Analytics

Interact with the Plotly maps—I built these to visualize spatial trends in groundwater.