Feature Selection in dataset containing both string and numerical values?
There are a set of operators you could use in the Attribute Weighting group within RapidMiner. For example, Weight By Correlation or Weight By Information Gain.
These will assess how much weight to give an attribute based on its relevance to the label (in this case the download flag). The resulting weights can then be used with the Select by Weights operator to eliminate those that are not needed. This approach considers attributes by themselves.
You could also build a classification model and use the forward selection operators to add more and more attributes and monitor performance. This approach will consider the relationships between attributes.
Hi I have big dataset which has both strings and numerical values
ex.
User name (str) ,  handset(str), number of requests(int), number of downloads(int) ,.......
I have around 200 such columns. 
Is there a way/algorithm which can handle both strings and integers during feature selection ?
Or how should I approach this issue.
thanks