Used TF-IDF Vectorizer to vectorize the words and the features were formed accordingly (the features are 5000 in number).
The features were then trained on a Voting Classifier Model which was made using Classification algorithms like Random Forest, Naive Bayes and Logistic Regression.
The confusion matrix plot
