u/avinsio

I’ve been working on an ML project and this is my first time dealing with a dataset with a lot of features (around 60).

I’m kinda stuck on how to approach the EDA. With smaller datasets, I usually check the features one by one, look at distributions, missing values, outliers, relationships, etc. But with 50–60 features, I’m finding it really difficult to figure out what to focus on and what I can skip.

Do you guys actually do EDA on every feature, or is there some better way to approach this?

Would love to know how you handle this when working on real ML projects.

Hey everyone,

I am learning ML and am kinda stuck at the EDA part.

I know the basics like missing values, outliers, distributions, correlations, etc. But whenever I get a new dataset, I get confused about what I should actually look for. I don't want to just run describe(), make some plots and be like okay, EDA done lol. I actually want to get better at looking at a dataset and knowing what questions to ask.

For people who got good at EDA, how did you practice it?

Did you follow any particular approach, datasets, courses or just kept doing projects?

Would really appreciate some advice.