How to Normalize a feature I have a feature that income of individual. It ranges from 10k to 116 Million. I have about 300k+ records.

Clearly, I cannot use this feature as is as it will distort the model output and there are outliers..

I was thinking of normalizing all values from 0 to 1. And also eleminating outliers. Like if most values from from 30k to 200k, then people with income of 200k would get a score around 1.

I have 2 questions?
1) Is this approach reasonable or should I think about it differently.
2) and suggestions on how to normalize and/or scale values to avoid outliers?
Normalization is rather common. I think you might want to consider the distribution of your data and whether you want to do anything with your outliers. Some popular normalization techniques: min-max and z-score. You will see tons of resources by googling them. From the limited amount of time I work with income data, sometimes you might want to test with a log scale as well as the top 1%-ers tend to skew the normalization as well.