There are multiple methods to deal with outliers but before treating outlier values, you must know the reason of outliers. It could be due to data processing error, data capturing error, sampling error, measurement error or could be natural outlier.
If it is due to an error, we should delete or impute the outlier values with relevant values like average, mode, median or train a model to impute outlier values based on non-outlier observation.
If it is natural outlier, we can perform below operations:
- Develop two different model for outlier or non-outlier observations
- Use log, square root, square to deal with outliers.
For more detail on this, you can refer this [article].