This is a project for my machine learning class.
The goal was to implement a logistic regression classifer to classify spam.

We found best results using batch gradient descent to determine the weight vector, beta and used cross validation to determine
the optimal parameters.
Running on a test set of about 1500 emails, we achieved an error rate of 6.5%.

Here's how to run the code!

First run "p2.m" to load and pre-process the training data.
Run "p2_batch.m" to determine beta using batch gradient descent (change the indicated variable to use different preprocessing methods,
i.e. if you want method 2 change the variable to "newMat2").
Run "p2_stochastic.m" to determine beta using stochastic gradient descent (same deal with the variable).  If you would like to train
with a variable learning rate, uncomment the line under "Variable learning rate".
To perform 10-fold cross validation with a grid search for the optimal learning rate and regularization parameter, run 
"cross_val_grid_search.m".
Finally, to classify the test set, run "tester_new.m".