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Now, once our data is ready, the first step that we take is to split the data into two parts.

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The first part will be called the training set.

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This part will be used to train the model and then that will be a second set.

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That second set will be Kordic tested and that test set will be used to test the performance of the

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created model.

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So in business and adio, if you have a lot of data, it is always recommended that you train the model

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on one set of observations and you keep one set of observations which are not shown into your model.

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You test your performance or your offer model on this previously unseen data.

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Usually we do a split of 80 20.

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That is, we use 80 percent of the data available to train the model and 20 percent of the remaining

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data to test the performance.

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So here I am showing you how to split the available data into two parts to split the data in are we

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use this package called See It tools.

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If you have this package in stolen art, it will be visible here in the packages part so you can scroll

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and see if see it is available with you or not.

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If it is not your undies, come on in.

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Stalled out packages.

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See Édouard.

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No doubt see it tools is written in single quotation marks.

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So we'll run this command.

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And it has installed seer tools in the packages.

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You can see that see a tool is available here.

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Now it is installed, but it is not active.

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That is, it cannot be used.

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As of now to make it active.

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We run this library command.

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You can also take this checkbox to will run this library command.

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And you can see that a tick comes here.

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And this year, tools package is now active.

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The next step is setting seed.

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We set seed so that we have reproducible results.

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What I mean by this is when we are doing a test transplant, we'll be randomly putting 80 percent of

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the data into printed and 20 percent of the data to test it.

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If we do not succeed, my randomly selected 80 percent of the data will be different than your randomly

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selected 80 percent of the data.

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But if we set seed to zero.

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If you also said the seed to zero and if I have said the seed to zero here, both of us will be getting

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these same split of data.

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And if we both get same speed of test and train data, we both will get the same model and that same

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model will be giving us these same results.

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So that is why we are setting the seed to zero hit.

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Next will create a variable called split in the split variable.

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We will have value.

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True and false.

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And we are giving a split ratio.

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Point eight, which means that 80 percent of the values will be true and 20 percent will be false.

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So if I run this command, you can see that a split variable is created here, which contains value

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through brute rule force and so on.

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And randomly, nearly 80 percent of these values will be true and nearly 20 percent will be false.

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Now, in my training said, I'll be taking all the movie data set values which have split value through.

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And indeed, they said, I'll have all the movie data set values which have split value falls.

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I've named this variable as Trinity because for now we are doing classification.

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That is, we are trying to predict a very well with this categorical later on in the course.

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We'll also see how to do regression in swim.

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So here I have created two variables Rainsy and D.C. Green.

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He will get values from what we dataset, which has split value through.

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So at underscore my.

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And you can see nearly 80 percent, which is nearly 400 observations are getting stored in the trains

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very well.

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And I also found this one, which is DWC.

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And I have one hundred eight observations in my test dataset.

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This is how we split the data in two days and Drame will be using the brain to do does it to rain as

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a model.

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And later on, we will compare its performance on the test see dataset.