building more complex classifiers more complex dataset and more complex classifier learning about the seeds dataset features and feature engineering nearest neighbor classification binary and multiclass classification summary clustering finding related posts measuring the relatedness of posts how not to do it how to do it preprocessing similarity measured as similar number of common words converting raw text into bag-of-words counting words normalizing the word count vectors removing less important words stemming installing and using nltk extending the vectorizer with nltk' stemmer stop words on steroids our achievements and goals clustering kmeans getting test data to evaluate our ideas on clustering posts solving our initial challenge another look at noise tweaking the parameters summary topic modeling classification detecting poor answers latent dirichlet allocation (ldabuilding topic model comparing similarity in topic space modeling the whole of wikipedia choosing the number of topics summary sketching our roadmap learning to classify classy answers ii