Okay in Python to create a testing set in the training set we can do something like this. So I will say we can do something like x equal the data pylon okay and n minus three k W's and y equal the one I love okay then two dot values okay so we These are x&y then I can print the s and the y I'm praying a lie KK s and I have the why. So I want to see the original data. So this is the original data here. So in original data are all five variables one variable two variable, three variables variable by variable. So for these are the ones I look, here, i minus three means that I'm going to take from minus one minus two minus three, so I only be taking this variable and this second variable here.

Then for these are a lot to be taken into one to this variable. So for the y, so I print s and y have something like this S and then I have y here. So, if I say I want to spray the training say to spray the data into training saying testing set, I can do something IDs s train, an S test. Why train and why test equal to equal to this train test, train test spread and x y, the test size I say to 0.2 and then the ram density is zero. So, what this means is that, for this data set I'm going to To take 0.2 or 20% for testing set and then I'm and then I'm going to take a deeper sample the training set Okay, so for these are SSI you're 80% for training and 20% for testing set. So, to use this train test function here, I will need to import this function from this our SK learn model selection library okay so a test say I want to create a linear model, I will need to import linear model from the SK learn library.

So we go into the linear model in our next