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In logistic regression model in Python, how to treat multicollinearity in Python in logistic regression model. What do we do when there are high correlated variables from a correlation matrix. Suggest me python steps to reduce multicollinearity

Saugat Nayak
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  1. You can try regularization. It will add an additional constraint on the weight vector and you can handle multicollinearity. This limit can be set in different ways, but, as a rule, you don't need something more complicated, than L1 or L2 regularization.
  2. Try to reduce dimensions of your df with Factor Analysis. As a result you will get a dataframe of not-correlated factors, wich generally describe the full set of your features.
HsergeyF
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