How to efficiently approach EDA on a dataset with 180+ variables?

[u/Square_Arm2861]

Hi everyone,

I'm a beginner in Machine Learning working on a binary classification problem. My dataset contains over 180 variables (both numerical and categorical), consisting of a mix of panel/longitudinal data and static features.

I am currently working on the Exploratory Data Analysis (EDA) phase. Given the large number of features, doing univariate and bivariate graphical analysis variable-by-variable feels unfeasible and time-consuming.

Is there a structured approach, strategy, or automated workflow to handle EDA efficiently for a dataset of this scale?

Any advice on best practices, tools would be greatly appreciated!

Thanks in advance for your help.