Mohd Anas Hussain

I've noticed that the actual modeling often gets more attention than the boring part beforehand: figuring out what the columns mean, missing values, duplicates, weird categorical values, outliers, leakage, domain aware data cleaning etc.

Curious what other people here actually spend the most time fixing before they can start modeling.

I ll go first.

I m building DataCleanist, an AI data quality platform for people working with datasets for ML. The idea came from realizing how often the same annoying stuff gets repeated whenever a new dataset shows up checking missing values, finding duplicates, dealing with outliers, figuring out column types, encoding data, balancing classes, etc.