Improve robustness of numeric data distribution visualization

[adhit-r]

Summary

The numeric data distribution helper (plot_numeric_distributions) introduced in PR #145 works well for basic use cases, but there are several opportunities to make it more robust and user-friendly for ML analysis workflows.

Tasks

• Add explicit validation for the columns argument:
  • Ensure all requested columns exist in df.columns.
  • Optionally enforce or warn when non-numeric columns are requested.
  • Raise clear ValueError messages for invalid columns.
• Improve subplot layout behavior:
  • Avoid creating unused extra axes for small numbers of columns (e.g. 1 column resulting in a 1x2 grid).
  • Keep the layout readable for larger numbers of numeric features.
• Document headless usage considerations:
  • Clarify in the docstring how to use the function safely in CI or non-GUI environments (e.g. using a non-interactive matplotlib backend).
• Consider re-exporting plot_numeric_distributions from the apps/ml/visualizations/__init__.py module if this is intended as the public API surface.

Acceptance Criteria

• Calling plot_numeric_distributions with invalid or non-existent column names produces clear, actionable error messages.
• Subplot layouts are sensible for 1, 2, and many numeric columns.
• The docstring documents headless/CI usage expectations.
• Existing tests continue to pass, and additional tests are added where helpful (e.g. column validation).

[adhit-r]

@Tanishkapundhir78 Would you like to take this follow-up? It builds on your visualization helper by adding column validation, better layouts, and headless guidance. Feel free to self-assign if you’re up for it.