All right, everyone.
So the next step in this spam message classification project is to separate out your data into two different
buckets that will be training sets of data and texting sets of data.
Now, why we need to separate out this data because this machine learning system is kind of training
plus testing kind of system.
So modern building machine learning happens when you apply some subset of data from your audience.
I say that is called as a training dataset.
And create a model out of it and evaluate your model that how good your model is.
What is the accuracy of running behind your model?
You can apply all those remaining testing datasets on your model to get to know about how good your
model is.
So the basic rule behind is that you just segregate your data into two different buckets, like a training
data set and testing the CSA and know what it was.
So basically to say who your model really never, ever use your testing data set for the model training,
because once you learn the model, the model got created.
We will apply those testing dataset to know about how good or accurate our model is.
So the first task we will do from this data datastream object.
Let me display it.
We will divide it into two different buckets, like a training dataset and testing that a.
All right.
So we have a fourteen hundred ninety four records that I really want.
So let us keep some 30 was in charge of the guy to pasting buckets and 70 percent of the time to cleaning
buckets and thought that we are going to use this sacred land library.
Now, this sacred land is also pre Mendon as a part of this CoLab environment.
So no need to worry about any kind of installation from Skillern.
Let's go with the model selection and let me put.
Thirteen days split.
All right.
So this train is flic function will give us two different buckets.
So the first argument we need to supply for which data you want to do it.
So the first data we want to do it for.
Data message, because that is what our training input data and we are not going to give this length
and punctuation mark column for about model billing, because as we have seen, that is not very much
useful, not creating any kind of differentiating factors.
So what we can do the feature vector first, we can possibly hope it's not only if it's the data and
message column.
So data will be able to input data.
And our objective is to predict the label that is nothing but our output because the data.
In now, one more argument.
You can pass it like a test size or it will be a brain size, so test size.
So here you can view the numbers between zero to one.
Let's see if I give zero point three.
That means you are looking at a total percentage of daytime to testing.
It's.
Let's just make it.
No state.
Zero.
Now, this zero indicates here, whenever you want to recreate exactly the same reason why you are so
insecure.
Same thing on your site.
So zero here indicates it's the same reason.
So suppose if I keep some other numbers, you also have to keep the same number to recreate exactly
the same reason.
And another one is you want to shuffle.
It felt like just make it through.
All right.
Now this whole function will return for retirement.
So that will be x rayed.
Then we have our X, X, Y and escort rain in Reinders could test.
So you have X that refers to a feature input, whereas Y refers to the label as an output.
So corresponding to this particular message, it will be split into two different variables huckstering
exodus and corresponding to this particular variable.
It will be split it into Whiteley and Vytas.
So this is the standard way of writing this for variable.
Generally, everyone in a community use that.
So let me.
Executing an.
So the best way to observe the result is to apply the shape matter on the top of.
So what we can do out of one, I mean, 1494 records that are relevant and it's the total percentage
of value will be 448.
So let's us try to observe how many records containing, say, this train dog --.
Whoops.
It.
And we have a 1045 a court has been look at raining buckets safely.
And let's do this for.
X this like ship.
So it's a 449 cost because obviously you just cannot take slow to lose.
So 449 Lacourt has been assigned to testing it, put it simply.
If you just try to observe how many records are available in a training output limit, our testing output
level.
So that will be respectively 1045 and it will be 449.
So you can verify your sense of light.
Solar database has been created into two different buckets, training and testing.
So in the next video, we'll create our first model and do the training on what training data set.
So see you in the next video.
We'll get started with creating our first random forest model, applying on our spam classification
project.