Today’s Exploration: Deep Dives into Random Forest and Geographic Heat Maps

Today was a day of deep dives. I got my hands dirty with implementing the Random Forest regression model on our U.S. counties’ health metrics data. The objective was clear: to make sense of the complex relationships between various health indicators like diabetes rates, obesity levels, and physical inactivity. But the day didn’t end there. I also brought these numbers to life through geographic heat maps. Let’s dig into the details of what I did and the fascinating insights that emerged.

The Random Forest Journey

After prepping the data, I implemented the Random Forest model. Random Forest, with its ensemble of decision trees, seemed like the perfect tool for untangling the intricate relationships in our dataset.

Insights from Random Forest

1. Feature Importance: One of the standout insights was the feature importance scores, which indicated that diabetes rates were a more critical health indicator than obesity and physical inactivity.
2. Predictive Accuracy: The model achieved an R2 Score of X and a Mean Squared Error of Y, affirming its reliability for making predictions.

The Heat Map Saga

Then came the part where numbers morph into colors and shapes: geographic heat maps. Using Plotly, I created a choropleth map that visualizes the average percentage of diabetes by state.

Insights from Heat Maps

1. Geographic Disparities: The map revealed significant geographic disparities, with states in the X region displaying higher rates of diabetes compared to the Y region.
2. Hotspots and Coldspots: Identifying hotspots where the health metrics are particularly concerning can be invaluable for healthcare providers and policy-makers.