So I have this dataset with about 750 variables (columns) and 50,000 rows of entries. I would like to reduce the dimensionality of the dataset to say 25-50-100 dimensions and then compute a correlation matrix between these dimensions. I have continuous and discrete columns. I would also like to be able to know which dimensions represent which bundle of single variables in the original dataset. I should add that I have no output variable, I am just trying to find correlation in the data. Is there a way to do this in Python?
Yes, in scikit-learn, you can find the correlation between the elements using LedoitWolf Estimator. For dimensionality reduction, I assume you will use PCA but then, you want to backtrack the reduced to the original data, for that I don't have a solution, since, PCA transforms your data and computes the largest variance direction. But, yes both of these won't require any labels.