let' go ahead and run that it runs pretty quicklyi' going to use couple of examples here let' try message body that just says 'sff pofzopxwhich is pretty clearly spamand more innocent message that just says ) #pcipxbcpvubhbnfpg hpmgupnpsspxso we're going to pass these in fybnqmft 'sff pofzopx) #pcipxbcpvubhbnfpghpmg upnpsspxfybnqmf@dpvout wfdupsj[fsusbotgpsn fybnqmft qsfejdujpot dmbttjgjfsqsfejdu fybnqmf@dpvout qsfejdujpot the first thing we do is convert the messages into the same format that trained my model on so use that same vectorizer that created when creating the model to convert each message into list of words and their frequencieswhere the words are represented by positions in an array then once 've done that transformationi can actually use the qsfejdu function on my classifieron that array of examples that have transformed into lists of wordsand see what we come up withbssbz euzqf ] and sure enoughit workssogiven this array of two input messages'sff pofz opxand ) #pcit' telling me that the first result came back as spam and the second result came back as hamwhich is what would expect that' pretty cool so there you have it activity we had pretty small dataset hereso you could try running some different -mails through it if you want and see if you get different results if you really want to challenge yourselftry applying train/test to this example so the real measure of whether or not my spam classifier is good or not is not just intuitively whether it can figure out that 'sff pofzopxis spam you want to measure that quantitatively so if you want little bit of challengego ahead and try to split this data up into training set and test dataset you can actually look up online how pandas can split data up into train sets and testing sets pretty easily for youor you can do it by hand whatever works for you see if you can actually apply your vmujopnjbm/classifier to test dataset and measure its performance soif you want little bit of an exercisea little bit of challengego ahead and give that try