Suggestions on Feature selection techniques?
You are already doing a lot of preprocessing. The only additional step I recommend is to normalize the values after PCA. Then your data should be ready to be fed into your learning algorithm.
Or do you want to avoid PCA? If the correlation between your features is not too strong, this might be ok. Then skip PCA and just normalize the values.
Blockquote

I am a student and  beginner in Machine Learning. I want to do feature
selection  of columns.  My dataset is 50000 X 370 and it is a  binary 
classification problem.
First i removed the columns with std.deviation = 0, then i removed duplicate columns, After that i checked out top 20 features with highest ROC curve area. What should be the next step apart doing PCA? Can anybody give a sequence of steps to be followed for feature selection?