5 Things I Wish I Knew Before Doing EDA on 33Feature Dataset

I started this project thinking EDA would be the quick part. Open the dataset, run some plots, move on to modeling. That was the plan. Three days later I was still in EDA, had dropped 11 features, rewritten my preprocessing pipeline twice, and genuinely questioned every decision I'd made. So here's what I learned — the things nobody really warns you about before you sit down with a wide dataset.

More features does not mean more insight :
33 columns felt exciting at first. By hour two of EDA it felt like a problem. A lot of those features were either redundant (two columns measuring basically the same thing), too sparse to be useful, or just not relevant to what I was actually trying to analyze. I spent more time deciding what to drop than I did on any visualization. Before you start plotting everything, ask: what question am I actually trying to answer? That filters half your columns immediately.
Your first correlation heatmap will lie to you :
Not literally. But the first time you run a heatmap on 33 features, you get this massive grid that technically shows everything and practically shows nothing. I had to rebuild mine three times — subset it, adjust the color scale, remove low-variance features — before it actually communicated something. A chart that's technically correct but visually overwhelming is still a bad chart.
Missing value decisions matter more than you think:
I had a few columns with 15-20% missing data. My first instinct was to just drop them. But some of those columns had the most interesting signal. So I had to actually think: is this missing randomly, or is there a pattern to what's missing? Sometimes the missingness itself is information. I ended up imputing some, dropping others, and flagging one column entirely because I couldn't confidently explain why values were missing. That kind of decision doesn't show up in tutorials but it comes up in every real dataset.
Synthetic data has its own weirdness :
This dataset is synthetic — 10,000 generated travel records. I disclosed that upfront in the repo and I'd do it again, but synthetic data has quirks that real data doesn't. Distributions are sometimes too clean. Correlations can feel slightly artificial. You don't get the weird outliers that real-world data throws at you. It's still useful for building a pipeline and practicing analysis. Just don't expect it to surprise you the same way real data does.
EDA is not a phase you finish — it's something you keep going back to :
I thought I was done with EDA after day two. Then I started feature selection and realized I'd missed something. Then I started building visualizations and caught an encoding error I'd introduced during preprocessing. EDA doesn't end. It just gets more targeted. The sooner you accept that, the less frustrating the whole process becomes.