You are given a train data set having 1000 columns and 1 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.)(https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/)
Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?(https://www.quora.com/Is-rotation-necessary-in-PCA-If-yes-why-What-will-happen-if-you-don%E2%80%99t-rotate-the-components)
You are given a data set. The data set contains many variables, some of which are highly correlated and you know about it. Your manager has asked you to run PCA. Would you remove correlated variables first? Why?(https://www.linkedin.com/pulse/questions-machine-learning-statistics-can-you-answer-saraswat/)
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