MultinomialNB.txt

x=df.iloc[:,1]
y= df.iloc[:,0]

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(analyzer = 'word',lowercase = True)
x_poly = cv.fit_transform(x).toarray()

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x_poly,y,train_size =0.80,random_state=42)

from sklearn.naive_bayes import MultinomialNB
clf1 = MultinomialNB()
clf1=clf1.fit(x_train, y_train)

y_pred1 = clf1.predict(x_test)

from sklearn.metrics import confusion_matrix 
cm = confusion_matrix(y_test, y_pred1)

print(cm)

from sklearn.metrics import accuracy_score, classification_report
print(accuracy_score(y_test, y_pred1))

print(classification_report(y_pred1, y_test))

# print(y_pred.mean())

# from collections import Counter

# Counter(y_test)