Python Feature Selection: Remove Multicollinearity from Machine Learning Model in Python

Is this method better than variance inflation factor?

I have been following you for feature selection, covered forward, backward, exhaustive, variance threshold, chi2, etc.
You have not shared the dataset, in them.
for us to follow along you, why don't you share dataset?

when i enter the code line =====> corrmatrix = X_train.corr()
it gives the error of =========> AttributeError: 'numpy.ndarray' object has no attribute 'corr'

what about a scenario where the order of the columns change? since we're checking for adjacent columns and their correlations to be more than the threshold and then remove the first out of the two in case the threshold is matched or passed, if I change the order of columns, the result received will be different. is that going to a correct list of features as well?

How are diagonal elements being handled in the user defined function correlation(df, threshold) ?

Nice ! Would it be the same if we use PCA to avoid multicollinearity ?

This wasn't helpful at all. You just picked one of the correlated variables randomly without additional criteria. Anyways, correlation matrix can't do much. It's much more reliable to use VIF or hierarchical clustering for feature selection.

Thank you for the video! very well explained! keep it up!

how to find collinearity for categorical features

nice video, what about checking from where the high threshold is coming and comparing the correlation with the target column and only dropping the one with the less correlation

Hi thanks for the video!
Would'nt that remove all high correlated columns instead of just leaving one column for every relationship?

hi, how to get this dataset?

This video was really helpful, thanks a ton.

Excellent, thank you very much.

Very nice