What machine learning model should be used to predict coincidence factor I have coincidence  factor for different sizes of groups and the associated attributes (e.g. building usage type and floor area) for each consumer in the group. I want to predict coincidence  factor for new groups using the attribute for each consumer in the new group and the number of consumers in the group. What machine learning method can solve the problem? :)

Defintions:
Coincidence factor is the peak of a system divided by the sum of peak loads of its individual components
From what you are saying I guess you have both categorical and continuous variables, One suggestion would be to check the distributions of your features to see if, more or less, the criteria for a linear regression are met. 
If you have too many variables, which I still haven't picked up, you could even apply a PCA and then a regression.