alrightnow for the fun partwe're going to use the vmujopnjbm/scikit-learn to actually perform naive bayes on the data that we have function from wfdupsj[fs $pvou fdupsj[fs dpvout wfdupsj[fsgju@usbotgpsn ebubwbmvft dmbttjgjfs vmujopnjbm/ubshfut ebubwbmvft dmbttjgjfsgju dpvoutubshfut this is what your output should now look likeonce we build vmujopnjbm/classifierit needs two inputs it needs the actual data that we're training on (dpvout)and the targets for each thing (ubshfutso dpvout is basically list of all the words in each -mail and the number of times that word occurs so this is what $pvou fdupsj[fs doesit takes the nfttbhf column from the dataframe and takes all the values from it ' going to call wfdupsj[fsgju@usbotgpsn which basically tokenizes or converts all the individual words seen in my data into numbersinto values it then counts up how many times each word occurs this is more compact way of representing how many times each word occurs in an -mail instead of actually preserving the words themselvesi' representing those words as different values in sparse matrixwhich is basically saying that ' treating each word as numberas numerical indexinto an array what that does isjust in plain englishit split each message up into list of words that are in itand counts how many times each word occurs so we're calling that dpvout it' basically that information of how many times each word occurs in each individual message mean while ubshfut is the actual classification data for each -mail that 've encountered so can call dmbttjgjfsgju using my vmujopnjbm/function to actually create model using naive bayeswhich will predict whether new -mails are spam or not based on the information we've given it