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Now we have got rid of punctuation and also we have tokenized our data its time to Get rid of some redundant words, which dont add too much of meaning to our words Those words are called stop words. And in this video, we will see how we can get rid of those words So for example, there may be lots of words like am, is, the and many other such words Which if we remove also the the meaning of the sentence is same. So by removing those Extra stop words, we are giving very less words to our Python algorithm to work with and that will be much faster. So lets begin by writing code in the notebook So this was the state of the notebook when we tokenized our dataset So the second column represented text free of punctuation and in the third column We or tokenized them into list of tokens or words So here you can see that there are many stop words like so, you, in Here also I, he and these words dont add too much meaning to it. So lets get rid of them so first we need to import the nltk libr
