Changes

==Development Notes==

Right now I am working on two different classifier: Kyran's old Random Forest model - optimizing it by tweaking parameters and different combination of features - and my RNN text classifier.

The RF model has a ~92% accuracy on the training data and ~70% accuracy on the test data.

The RNN currently has a ~50% accuracy on both train and est data, which is rather concerning.

Test : train ration is 1:3 (25/75)

Both model is currently using the Bag-of-word approach to preprocess data, but I will try to use Yang's code in the industry classifier to preprocess using word2vec. I'm not familiar with this approach, but I will try to learn this.