Predicts age and gender (combined) of a author based on textual data.
To run, navigate to project directory, and run
$ python pymallet.py
This will generate topic model formatted into a usable format to be used in prediction
To generate cross validation data, call
$ python CrossValidationMaker.py
This will create a "folds" subdirectory where your original model was and place in it k folds. The first 90% of each fold to be used for training, the last 10% for testing.
To test the model's use in prediction, we have included a test script. The script will perform SVM classification with a linear kernel. To run the classifier, call
$ python SVMTest.py
This will iterate through all of the cross validation folds and output the accuracy of predictions