Machine learning algorithm for a dynamic set of attributes I'm new to machine learning programming and am working on an application which will calculate weights of the attributes to calculate the ETAT (Estimated Turn Around Time) of some tasks in workflow. The set of attributes is not fixed. 

For example: ETAT will depend on-


Language   
No of properties   
Experience level of resources and many
more.


For each workflow entity there can by a set of these attributes, i.e. a single workflow may be like:


Language - English 
No. of properties - 1200 
Experience level of
resources - Expert 


and


Language - Russian
No. of properties - 21000
Experience level of resources - Basic


Now for the above set of attributes, how can I design my machine learning algorithm? Any pointers will be highly useful.
I would train a gamma-distributed GLM; one for each language. Now, I assume the set of parameters is fixed within each language. You can incorporate ordinal variables (basic -> intermediate -> expert) using dummy coding (i.e., treat them as categorical variables). If there is no intermediate level one bit will do (0 = basic, 1 = expert). And so on for the other properties.

You can find free software to implement GLMs in various language, including python and R.