Slightly better than random

It doesn't look like there's a ton of info in the features in this dataset.  I'd be interested to know how Grouper's best algorithmn does with these features.  I tried lots of different things (feature engineering, various linear models, SVM's, random forests, etc.)  Most were around 56% accurate on cross-validation sets.  The best random forest model was about 57% accurate and finds, probably unsurprisingly, that the Facebook activity variables are most predictive of whether the users become Facebook friends.

My code has an ipython notebook for exploratory plotting, model building, and variable importance.  It uses grid search and cross-validation to find the best type of model.  You can run the regular python code to train that model:

/bin/bash cleanDataSets.sh 
virtualenv .
pip install -r requirements.txt
python grouper_model.py

Final predictions are in test_data_withpreds.csv