HARRY-WORD2VEC

Harry potter's 7 books are used to create corpus

Then gensim framework is used to create word2vec out of corpus

Model itself learns similarities between characters

model.most_similar(positive=['ron'], topn=7)

o/p->

[('ginny', 0.8942017555236816), ('neville', 0.8824698328971863), ('hagrid', 0.8696408867835999), ('hermione', 0.8595108985900879), ('quickly', 0.7818440198898315), ('harry', 0.7702754735946655), ('luna', 0.7642109394073486)]

###Then using tsne visualiztion of vectors is done