# General question on EDA, correlations, classification, ML

I am looking for a general best practices regarding classification and correlations. I created a new predictor feature call it B, based on a certain threshold in a feature A. Now I started to do EDA and I am not sure which feature to include in my EDA, A or B. When I do correlations plots, nothing correlates with feature B, but some features do correlate with feature A. Which one should I take into account then, A or B correlations? Also, how can I make use of those correlations and scatterplots and pairplots anyway and are they important? If I am using random forest or NN, do I even need to bother with all of the pairplots and correlations to extract features from? I have around 150 features and not sure how to approach the problem of which features to use. I haven't found a source saying how to make a proper use of all of this in a real world scenarios. Any help is appreciated.

Did you check the correlation between B and the target variable and also A and target variable? if it's negative drop it, If it's significantly high .i.e greater 0.7, use that as your feature.