Try to implement stop words removal in your feature engineering code. Specifically, load the file stopwords. txt, remove the words in the file from your dictionary, and use features constructed from the new dictionary to train your model and make predictions. Compare your result in the testing data on Pegasos algorithm using T=25 and L=0.01 when you remove the words in stopwords. txt from your dictionary. Hint: Instead of replacing the feature matrix with zero columns on stop words, you can modify the bag_of_words function to prevent adding stopwords to the dictionary Accuracy on the test set using the original dictionary: 0.8020 Accuracy on the test set using the dictionary with stop words removed: