The get_feature_names() method returns our feature names for this dataset, which is the set of words that make up our vocabulary for 'documents'.
Instructions: Create a matrix with each row representing one of the 4 documents, and each column representing a word (feature name). Each value in the matrix will represent the frequency of the word in that column occurring in the particular document in that row. You can do this using the transform() method of CountVectorizer, passing in the document data set as the argument. The transform() method returns a matrix of NumPy integers, which you can convert to an array using toarray(). Call the array 'doc_array'.

Now we have a clean representation of the documents in terms of the frequency distribution of the words in them. To make it easier to understand our next step is to convert this array into a dataframe and name the columns appropriately.

Instructions: Convert the 'doc_array' we created into a dataframe, with the column names as the words (feature names). Call the dataframe 'frequency_matrix'.

Congratulations! We have successfully implemented a Bag of Words problem for a document dataset that we created. https://t.me/epythonlab/689

One potential issue that can arise from using this method is that if our dataset of text is extremely large (say if we have a large collection of news articles or email data), there will be certain values that are more common than others simply due to the structure of the language itself. For example, words like 'is', 'the', 'an', pronouns, grammatical constructs, etc., could skew our matrix and affect our analysis.

There are a couple of ways to mitigate this. One way is to use the stop_words parameter and set its value to english. This will automatically ignore all the words in our input text that are found in a built-in list of English stop words in scikit-learn.

Another way of mitigating this is by using the tfidf(Term frequency index document frequency) method.

Question: We have implemented bag of words with and without scikit-learn. Let you understand each solutions of the problem posted here https://t.me/epythonlab/689 and write short summary which method(without scikit-learn or using python code and with using scikit-learn library) do you think is best option to implement bag of words?
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