Predicting Co-Ordinate Data This is on a prediction model we were trying out among a bunch of us trying out ML for the first time. Basically I have a training data set of network user ID's with their location (latitude and longitude). Also tracked are a couple of features such as the time of their first post, second post etc and a few details on their friends. My goal is to use these features to predict the location (latitude/ longitude) for a test set of data. Any ideas on what kind of model to use here and what ML technique to try out? At the moment we are using basic multiple regression to predict the latitude and longitude separately using independent models.  But I was hoping to see if it is possible to approach the problem differently.
Any thoughts?
 A: Its hard to give you a precise set of algorithms to treat this question, but many classical algorithms can be tweaked to accomodate for multidimensional responses. You may want to look into multivariate regression.
That being said, using two separate models to predict the coordinates is not ideal. Indeed, you will be ditributing variance accross both models, and combining them will result in higher variance. It is a better idea to use models which can handle both variables simultaneously, and hopefully exploit correlations between both coordinates.
For example, rather than running linear regression on each coordinate separately, you could formulate your regression differently : treat your vector $(y_1,y_2)$of coordinates as a vector response $Y$, and if your variables are a vector $X$, your parameters, instead of being a vector, could now be a matrix $A$ such that 
$$Y = AX + \epsilon$$
Where $\epsilon$ follows a hypothesis of your choice. If you make both components of $\epsilon$ independent, you'll fall back to independent regressions. But you may also choose the components to be indentical. You could also impose relations on $A$, forcing to look for correlations between both coordinates. 
A detailed account can be found here.
