Should variables be dropped according to its skewness values? I am creating a classification model to predict the credit score of a person based on lots of factors. I got the dataset from kaggle. When I started doing the EDA part, I noticed that the skewness values of the numerical variables are too high such as 11, 20, etc. So is it ideal that I should drop those variables or is there any other option by which I can reduce the skewness and make it a normal distribution.
I tried checking the outliers of those numerical variables and each one of them contained outliers. The data contains 100000 samples with 28 variables.

Typically, when using a parametric model (e.g., regression), researchers use transformation to reduce skewness. These different transformations may include 1) log transform 2) square root transform and 3) box-cox transform. The purpose of these transformations is to make the dataset follow the Gaussian distribution - allowing the transformed dataset to be analyzed with the largest set of statistical tools (e.g., ANOVA, t-test, f-test, etc.).
Source:
https://www.kaggle.com/getting-started/110134
