all_stopwords.remove(sw_list[j])
review = [
ps.stem(word) for word in review if not word in set(all_stopwords)
]
review = ' '.join(review)
corpus.append(review)
# Creating bag of words
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, -1].values
# Spliting of dataset
X_train, X_test, y_train, y_test = Preprocessing.datasplit(X,
y,
test_size=0.1,
random_state=0)
# Classifiers
classifierRFC = Classifier.RFC(X_train,
y_train,
n_estimators=13,
criterion='entropy')
classifierkNN = Classifier.kNN(X_train,
y_train,
n_neighbors=8,
metric='minkowski')
classifierLR = Classifier.LR(X_train, y_train)
classifierGaussNB = Classifier.GaussNB(X_train, y_train)
classifierDTC = Classifier.DTC(X_train, y_train, criterion='entropy')
classifierSVM = Classifier.SuppVM(X_train, y_train, kernel='rbf')
