Answer / Omdutt

Multi-collinearity occurs when independent variables in a regression model are highly correlated with each other. To solve this issue, you can:n
1. Remove one of the collinear variables from the model.
2. Combine collinear variables to create a single new variable (principal component analysis or factor analysis).
3. Apply regularization techniques like Lasso or Ridge regression that penalize large coefficients and help prevent overfitting due to multi-collinearity.
4. Orthogonalize the data using techniques such as Cholesky decomposition.

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