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<speak> Let's save our outcome into the Y variable.<break strength="x-strong"/> So we are selecting only the outcome column from the diabetes_df _ copy data frame. <break strength="x-strong"/>We are saving it in Y. <break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/> So let's check output Y also.<break strength="x-strong"/> In y, we have 1, 0, 1, 0. <break strength="x-strong"/> One means yes, and 0 means no.<break strength="x-strong"/> Let's splits the data set into a train set and test set. <break strength="x-strong"/>And here we are using the train_test_split method.<break strength="x-strong"/> We are giving input as X and Y. <break strength="x-strong"/> X is our features, and Y is our labels.<break strength="x-strong"/> We are taking test size as 1/3, which is 33.33 percent.<break strength="x-strong"/> So we are using random state 42.<break strength="x-strong"/> The important thing is that every time you use 42, <break strength="strong"/>you will always get the same output the first time you make the splitting.<break strength="x-strong"/> We are storing train data in train x and train y.<break strength="x-strong"/> We are storing test data in test x and test y.<break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/>  <break strength="x-strong"/> </speak>