# train model using our results from earlier with words
# that appear atleast more than 5 times. This also helps
# reduce the amount of points found.
word_to_vec_model = Word2Vec(sentence_with_search_term, min_count=5)

# save model for later use
word_to_vec_model.save('model.bin')

# load model
word_to_vec_model = Word2Vec.load('model.bin')

# Store all the words in the model
vector_words = list(word_to_vec_model.wv.vocab)

# Access all of the vectors from a trained model
vector_of_trained_model = word_to_vec_model[word_to_vec_model.wv.vocab]

# Initilize PCA
pca = PCA(n_components=2)

# Transform the data to 2D representation
vector_2D_results = pca.fit_transform(vector_of_trained_model)

# Display results
vector_2D_results