models.py

# we can use sklearn algorithms in NLTK
from nltk.classify.scikitlearn import SklearnClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix

# Define models to train
names = ["SVM","K Nearest Neighbors", "Decision Tree", "Random Forest", "Logistic Regression", "SGD Classifier",
         "Naive Bayes", "SVM Linear"]

classifier = [ SVC(kernel = 'linear'),KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), LogisticRegression(),  SGDClassifier(max_iter = 100), MultinomialNB(), SVC(kernel = 'linear')]
models = zip(names, classifier)

for name, model in models:
    nltk_model = SklearnClassifier(model)
    nltk_model.train(training)
    accuracy = nltk.classify.accuracy(nltk_model, testing)*100
    print("{} Accuracy: {}".format(name, accuracy))