- hacker.py:
	This file is used to call the HackerNews API (https://github.com/HackerNews/API)
	and to grab 2 types of data: stories and users. It creates json files in a folder called "data".
- datahacker.py:
	This file is used to parse the json result we got from the HackerNews API, and extract features from each story.
	In this file, we also grab features from the diffbot API that parses our URLS.
	Eventually, we create the final CSV file with the new data and all the features.
- testExtractor.py:
	This file is the one that calls the diffbot API and return usefull information that will 
	be used by the caller: datahacker.py
- tfidf.py:
	This file provides the functions that allow us to compute tf-idf weighting.
	It's parsing data, making bag of word vectors, and then training our tfidf model and extracting those tifidf weights
- learner.py:
	This is where we implemented the regression algorithms.
	First, we have a function that reads the data from a given CSV file and create an array of tupple (each tuple being information regarding one example).
	We then have functions for closed-form regression, k-fold cross validation, and gradient descent.
- project1.py:
	This is the file to run in order to test and see our algorithms in action.