# Sequence to carry out data analysis?

I have a dataset with 4700 records and it's a classification problem. Proportion of classes is 33 and 67%

few questions

1) does this proportion qualify dataset as imbalanced ?

2) should I do cross validation and then apply (over/under or SMOTE sampling) or I should first balance my sample through these sampling techniques and then do cross validation?

3) Why is propensity score matching used only in healthcare related studies and not much in other applications?

4) How is Propensity score matching different from other ML classification algorithms?

• What do you mean by train and apply them to both train and test in below sentence only to train set and apply them to both train and test respectively.  Dec 9, 2019 at 12:09
• On the contrary, first you split, fit transformers on train set and then apply them on both train and test. That's basically why you need to use pipeline for imblearn instead of sklearn one. Dec 9, 2019 at 12:11