from the above outputwe can see that the first data instance is malignant tumor the radius of which is + step organizing data into sets in this stepwe will divide our data into two parts namely training set and test set splitting the data into these sets is very important because we have to test our model on the unseen data to split the data into setssklearn has function called the train_test_split(function with the help of the following commandswe can split the data in these setsfrom sklearn model_selection import train_test_split the above command will import the train_test_split function from sklearn and the command below will split the data into training and test data in the example given belowwe are using of the data for testing and the remaining data would be used for training the model traintesttrain_labelstest_labels train_test_split(features,labels,test_size random_state step building the model in this stepwe will be building our model we are going to use naive bayes algorithm for building the model following commands can be used to build the modelfrom sklearn naive_bayes import gaussiannb the above command will import the gaussiannb module nowthe following command will help you initialize the model gnb gaussiannb(we will train the model by fitting it to the data by using gnb fit(model gnb fit(traintrain_labelsstep evaluating the model and its accuracy in this stepwe are going to evaluate the model by making predictions on our test data then we will find out its accuracy also for making predictionswe will use the predict(function the following command will help you do thispreds gnb predict(testprint(preds[