Say you have a binary classification problem, and a dataset with 20,000 observations and 20 columns. The target variable is very imbalanced, there are are missing values, skewed distributions, outliers, etc.My question is, in a general sense, what order should these data preprocessing steps be performed?Fill in missing values,Normalize/standardize data,Deal with skewness,Deal with outliers,Balance target variable classes
Correct sequence of data prep steps?
machine learning;data cleaning