Demo Day Page Google Classifier
| Demo Day Page Google Classifier | |
|---|---|
| Project Information | |
| Project Title | Demo Day Page Google Classifier |
| Owner | Kyran Adams |
| Start Date | 2/5/2018 |
| Deadline | |
| Keywords | Accelerator, Demo Day, Google Result, Word2vec, Tensorflow |
| Primary Billing | |
| Notes | |
| Has project status | Active |
| Is dependent on | Accelerator Seed List (Data), Demo Day Page Parser |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Project
This is a tensorflow project that classifies webpages as a demo day page containing a list of cohort companies, currently using scikit learn's random forest model. The classifier itself takes:
A: The number of times each word in words.txt occurs in a webpage. This is calculated by web_demo_features.py in the same directory.
B: A set of webpages hand-classified as to whether they contain a list of cohort companies. This is stored in classification.txt, which is a tsv equivalent of Demo Day URLS.xlsx. Keep in mind that this txt file must be utf-8 encoded. In textpad, one can convert a file to utf-8 by pressing save-as, and changing the encoding at the bottom.
A demo day page is an advertisement page for a "demo day," which is a day that cohorts graduating from accelerators can pitch their ideas to investors. These demo days give us a good idea of when these cohorts graduated from their accelerator.
Project location:
E:\McNair\Projects\Accelerators\Spring 2018\google_classifier\
Training data:
E:\McNair\Projects\Accelerators\Spring 2018\demo_day_classifier\DemoDayHTMLFull\Demo Day URLs.xlsx
Possibly useful programs
Google bindings for python
E:\McNair\Projects\Accelerators\Spring 2017\Google_SiteSearch
PDF to text converter
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data\Utilities\PDF_Ripper
HTML to text converted
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data
Resources
- https://www.tensorflow.org/tutorials/word2vec
- https://machinelearnings.co/tensorflow-text-classification-615198df9231
- http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
- https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
