Implemented various ML models on hacker news for ranking

Scraped al the articles of Hacker News at 1:00am 28th April,2015 and used top 100 as training set In testing phase, performed k fold cross validation, where k was selected by iterating over k from 1 to 10 and taking the best score among them Scraped al the articles of Hacker News at 9:30am 28th April,2015 and used top 100 as training set

With each implemented model the evaluation metrics are present in the corresponding output file

• Time(in hours) is an important feature
• Points(i.e. number of upvotes) is intutively very important
• Number of comments: Although didn't find any significant correlation, but included it
• Karma score of user who posted the news: Didn't find any correlation, so finally removed it
• As hacker news is only link aggreator, and doesn't have the articles, uesd no NLP feature.

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Used the scikit python library for various models, namely

• Linear Regression
• Logistic Regression
• Lasso
• Perceptron
• SGD Regressor
• SVR
• Bayesian Redge

Output file for each model is also present in the directory. The evaluation metrics used are:

• Mean Absolute Error of rankings
• Mean Squared Error of rankings
• R^2 Score for expected and predicted output
• Residual Sum of Squares
• Variance
• Explained Variance Score