Use of Standardizer to handle outliers? I have a dataset with 60 columns and 5K records. There are few columns which has outliers. I understand that there are multiple approach to handle outliers.

Actually I don't wish to drop the data as it is an outlier because am not sure whether it is really an outlier or a meaningful value (like how income could be skewed).

So what I did is just standardize the columns using standard scaler.

Is it okay to do this? Am assuming that by doing this standard scaling, I have retained the data as well as got rid of outliers.

I did refer couple of posts (post1, post2) but couldn't get an idea

Is this what standardization does? Can anyone suggest me is there anyother way to handle outliers without dropping the records?

Can you help?
Standardising may not be the best option.

Because they will still not be bounded (like when normalised) between -1 and 1 but be distribution dependent. What I mean is if they are outliers their standard deviation will be big for these values. In any case its not that you should rescale the values to combat these outliers. Outliers are not in the first place problematic because they are big in values, but mostly because these values lie in the cluster thats not possible/representative of future data and you will learn on it.

One solution for without dropping them is for example binning, where you say all outliers that have standard deviation bigger than two should have value "X" where X is some 99,5% quantile for example.