How to do feature engineering on anonymous dataset?
There are many approaches to feature engineering, which could be also used on anonymized data:

transformation of continuous variables: log, power, normalization;
aggregations: `df.groupby(['cat_column']).agg({'continuous_column': ['min', 'max', 'mean', 'std'])
interactions of continuous variables: addition, subtraction, multiplications, division
And so on.
I have a task to perform classification on a data set in which the feature names are anonymous like var1,var2 etc. There is mixed continuous and one hot encoded features. I am bit stuck how to make new features out of the existing features and perform EDA on it.