How to handle columns with large/infinte values in dataset for ML classification Computed a column using a formula (formula does't involve any log functions, just a group by with .sum()), but as expected this column would result in infinite/exponential values like below:

-inf
 nan
 inf
-3.000e+32
 7.3297+23 ...etc (similar data)


My doubt is how should I handle this column as I could also not avoid this column for prediction for a binary classification problem. What are the techniques to use before using this column in model classifier.

Thanks!
Depends a bit on the model you are going to run. I will explain a bit for Linear Models and for Decision trees ensembling (gradient boosting and random forest)

Decision tree

Not much to do, when the tree is built each branch will choose a split. If there is any information gain with a large value, it will choose it and make a split.

Some implementations, as catboost have a quantization in it. The split could be done eventually in something like 95% quantile and that will work with large values.

Generalized Linear Models

You will need to change this. My suggestion is creating a feature based in the values of the column (if df.col1> 9999: 1 else 0).
And then Winsorizing by a certain threshold.