that is just text file that has comma-delimited values of two columnsand they're just two columns ofmore or less randomlylinearly correlated data it can represent whatever you want let' imagine that it represents heights and weightsfor example sothe first column might represent heightsthe second column might represent weights in the lingo of machine learningwe talk about labels and featureswhere labels are usually the thing that you're trying to predictand features are set of known attributes of the data that you use to make prediction from in this examplemaybe heights are the labels and the features are the weights maybe we're trying to predict heights based on your weight it can be anythingit doesn' matter this is all normalized down to data between - and there' no real meaning to the scale of the data anywhereyou can pretend it means anything you wantreally to use this with mllibwe need to transform our data into the format it expectsebub joqvu-joftnbq mbnceby tqmju  fdupstefotf gmpbu nbq mbnceby gmpbu the first thing we're going to do is split that data up with this nbq function that just splits each line into two distinct values in listand then we're going to map that to the format that mllib expects that' going to be floating point labeland then dense vector of the feature data in this casewe only have one bit of feature datathe weightso we have vector that just has one thing in itbut even if it' just one thingthe mllib linear regression model requires dense vector there this is like mbcfmfe pjou in the older apibut we have to do it the hard way here nextwe need to actually assign names to those columns here' the syntax for doing thatdpm/bnft eg ebubup%dpm/bnft we're going to tell mllib that these two columns in the resulting rdd actually correspond to the label and the featuresand then can convert that rdd to dataframe object at this pointi have an actual dataframe orif you willa dataset that contains two columnslabel and featureswhere the label is floating point heightand the features column is dense vector of floating point weights that is the format required by mlliband mllib can be pretty picky about this stuffso it' important that you pay attention to these formats