damar Zaky damzaky

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	X = data['text']
	y = data['label']

	vectorizer = CountVectorizer()
	vectorizer.fit(X)

	X = vectorizer.transform(X).toarray()

	import pandas as pd
	from sklearn.feature_extraction.text import CountVectorizer
	from sklearn import naive_bayes

	data = pd.read_csv("imdb2k.csv")

	var q = document.createElement('a');
	q.href = document.querySelector('source').src;
	q.download = document.title;
	q.click();

	prediction = classifier.predict(vectorizer.transform([input('enter input: ')]).toarray())
	print('positive' if prediction[0] == 1 else 'negative')

	classifier = naive_bayes.MultinomialNB()
	classifier.fit(X,y)