I am new to ML, NB etc. I am looking at a way to classify names as spam or ham in a particular context. Suppose I have a training set consisting of names classified as spam or ham. I heard that I can make n-grams out of each training example and then feed it to a GaussianNB() classifier. However I notice that the n-grams will be of different lengths depending on the length of the name so how can I normalize the training data so that I can train a naive bayes classifier? Sorry if my question is vague as I am myself struggling to understand some of these concepts better.
Using Naive Bayes to classify spam names
machine learning;python;scikit learn;naive bayes classifier