Explain Multicollinearity in Logistic Regression and how to handle it

Multicollinearity occurs when independent variables are highly correlated, making coefficient estimation unstable and difficult to interpret.

Multicollinearity is one of the most important statistical issues in Logistic Regression modeling. It occurs when two or more predictor variables contain highly overlapping information.

For example:

When features are highly correlated, the model struggles to determine which variable is actually responsible for influencing the prediction.

Why Multicollinearity is problematic:

1.…