Usually I see example where we have taken the data for a particular stock say 'GE' one company and predicting the prices for GE. Now if i have data for let say for 20 companies for the time period. So what should be the approach. My model should have the capability to predict stock prices for any of the 20 companies.See More

I have 10 features and all of them are numeric.Does polynomial features only can be used on continuous variables and not on discrete variables?? Out of 10 features which I should pick for creating polynomial features??Choosing criteria..I should take independent features for creating polynomial features or I should take features which are highly correlated with the dependent Y variable?? See More

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Suppose I have 20 independent vsariables and I am thinking to go for PCA, Do we need to do the scaling of all these 20 independent variable, or PCA will handle it... And I hope the output of PCA will be scaled features...See More

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