Churn data based on multi filters
This visualization explores customer churn data from Kaggle, showing the relationship between estimated salary and credit score. An interactive brush on a binned salary histogram filters the main scatter plot, while dropdown and radio menus let users select specific countries and genders. The visualization uses vega-lite-api with D3.js to fetch CSV data, and employs interactive selections (interval brushing and single-point binding) to link the two views and adjust point opacity based on selection state.
AI-generated descriptionA visualization constructed using the vega-lite-api.
The data shown here comes from the Kaggle, via Gist.
I took help from https://observablehq.com/@uwdata/interaction?collection=@uwdata/visualization-curriculum.
You can select a range of salaries on the top graph to view that in the below graph. I have binned the salaries into 50 bins.
Additionaly, you can select which gender you want to focus on and the country the customers are from.
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