Which stage should the correlation analysis be done?

I was thinking about it, but I couldn't find a logical explanation.

Mostly im following below steps after data become ready:

  • Correlation analysis and elimination
  • Apply dummy if categorical variables exist
  • Balance the data if data is unbalanced
  • Scale data
  • Feature selection (Backward, Stepwise etc.)
  • Train model

Where would the correlation analysis be applied for this path I followed would make more sense? After the data is balanced? After scaling? Or at first?

Topic correlation feature-selection

Category Data Science

1
answered at

Correlation is a bivariate feature analysis technique.

Typically, that is done after univariate features analysis. But before any feature engineering.

Most machine learning is iterative so correlation can be revisited at any stage.

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