Fill Missing Improvement

[ItsRikan]

Problem

If a dataset contains a column full of null values, or an empty column, or a column of the same values, though they do not affect training models, they remain there; the fill_missing function does not drop these columns.

Solution

Check if any column contains all the same values or all null values; if found, then drop them.

[ItsRikan]

Permit me to implement this solution. Also, I have one question: if we implement it there should we edit the pre-existing fill_missing function inside cleaning.py or should we create a new function (advanced_fill_missing) to implement that?

[Kritika11052005]

I’d like to take this up.

I’ll review the identified gaps, improve API consistency, handle missing edge cases, and add tests where required. Refactoring will be done carefully to maintain backward compatibility while improving extensibility and usability.

Happy to discuss scope or priorities before proceeding.