"MultinomialNBclassifier", "BernoulliNB", "LogisticRegression_classifier",
"SGDClassifier_classifier ", "SVC_classifier", "LinearSVC_classifier",
"NaiveBayesClassifier"
]
MultinomialNBclassifier = SklearnClassifier(MultinomialNB())
MultinomialNBclassifier.train(train_data)
print("\nMultinomialNB Accuracy is:",
(classify.accuracy(MultinomialNBclassifier, test_data)) * 100)
# GaussianNBclassifier = SklearnClassifier(GaussianNB())
# GaussianNBclassifier.train(train_data)
# print("\nGaussianNB Accuracy is:", classify.accuracy(GaussianNBclassifier, test_data))
BernoulliNB = SklearnClassifier(BernoulliNB())
BernoulliNB.train(train_data)
print("BernoulliNB Algo Accuracy: ",
(nltk.classify.accuracy(BernoulliNB, test_data)) * 100)
LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(train_data)
print("LogisticRegression Algo Accuracy: ",
(nltk.classify.accuracy(LogisticRegression_classifier, test_data)) * 100)
SGDClassifier_classifier = SklearnClassifier(SGDClassifier())
SGDClassifier_classifier.train(train_data)
print("SGDClassifier Algo Accuracy: ",
(nltk.classify.accuracy(SGDClassifier_classifier, test_data)) * 100)
SVC_classifier = SklearnClassifier(SVC())
SVC_classifier.train(train_data)
pickle.dump(LinearSVC, save_classifier)
save_classifier.close()
### Multinomial Naive Bayes
MNB = SklearnClassifier(MultinomialNB())
MNB.train(training_set)
print("Multinomial naive bayes accuracy percentage:",
(nltk.classify.accuracy(MNB, testing_set)) * 100)
save_classifier = open("pickled_algorithms/MNB_classifier.pickle", "wb")
pickle.dump(MNB, save_classifier)
save_classifier.close()
### Bernoulli naive Bayes
BernoulliNB = SklearnClassifier(BernoulliNB())
BernoulliNB.train(training_set)
print("Bernoulli naive bayes accuracy percentage:",
(nltk.classify.accuracy(BernoulliNB, testing_set)) * 100)
save_classifier = open("pickled_algorithms/BernoulliNB_classifier.pickle",
"wb")
pickle.dump(BernoulliNB, save_classifier)
save_classifier.close()
### Logistic Regression
LogisticRegression = SklearnClassifier(LogisticRegression())
LogisticRegression.train(training_set)
print("LogisticRegression accuracy percentage:",
(nltk.classify.accuracy(LogisticRegression, testing_set)) * 100)
save_classifier = open(
print("Original Naive Bayes Accuracy Percentage: ",
(nltk.classify.accuracy(classifier, test_set)) * 100)
classifier.show_most_informative_features(30)
#save_classifier = open("naivebayes.pickle", "wb")
#pickle.dump(classifier, save_classifier)
#save_classifier.close()
MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(train_set)
print("MNB_classifier Accuracy Percentage: ",
(nltk.classify.accuracy(MNB_classifier, test_set)) * 100)
BernoulliNB = SklearnClassifier(BernoulliNB())
BernoulliNB.train(train_set)
print("BernoulliNB Accuracy Percentage: ",
(nltk.classify.accuracy(BernoulliNB, test_set)) * 100)
LogisticRegression = SklearnClassifier(LogisticRegression())
LogisticRegression.train(train_set)
print("LogisticRegression Accuracy Percentage: ",
(nltk.classify.accuracy(LogisticRegression, test_set)) * 100)
SGDClassifier = SklearnClassifier(SGDClassifier())
SGDClassifier.train(train_set)
print("SGDClassifier Accuracy Percentage: ",
(nltk.classify.accuracy(SGDClassifier, test_set)) * 100)
SVC = SklearnClassifier(SVC())
SVC.train(train_set)
