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|Does subsume=Demo Day Page Parser, Demo Day Page Google Classifier
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==ProjectIntroduction==
This project that utilizes Selenium and Machine Learning to get good candidate web pages and classify webpages as a demo day page containing a list of cohort companies, currently using scikit learn's random forest model and a bag of words approach
==Code Location==
The source code and relevant files for the project can be found here:
E:\McNair\Projects\Accelerator Demo Day\
==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.
==The Crawler Functionality==
To be updated
==The Classifier==
===Input (Features)===
The input (features) right now is the frequency of X_NUMBER of words appearing in each documents. The word choice is hand selected. This is the naive bag-of-word approach.
