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Hello my friends.

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Congratulations again
for having implemented

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your first classification
model, logistic regression.

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And now,
before we move on to the next model,

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I would like to make a classification
template that will allow us

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to build much more efficiently the next
classification models of this part.

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And not only that, it is a template

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that you will also be able to use
for your personal data set.

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All right.

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So let's do this.

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At the top of our studio we're going to
click file and then click New File.

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And then our script. All right.

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And we're going to save this
as classification template.

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All right then we click save and you will
see it appear in your file here.

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That's the new file we just created.

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All right then we're going to take
our logistic regression implementation.

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Again.

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We're going to select everything

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copy it and then paste it
in our classification template.

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And then let's change the title.

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We're going to replace this
by classification template.

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And then in the data
preprocessing sections here we're going

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to keep everything as is except one thing.

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We're going to add one line of code
that will make sure the classes of

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our dependent variable are indeed classes
and, you know, not continuous numbers.

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So for most of the models
like logistic regression k

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and then naive Bayes, SVM and kernel SVM,
the package that we'll use to build

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those models will recognize the classes
of our data sets as classes.

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But there will be two models
that will build, which are decision tree

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and random forest
that will not recognize them as classes.

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And therefore, to make this template
applicable to all the models

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we're going to build in this part three,
we just need to add this little code

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section here that I'm going to call, 
and coding the target feature as factor.

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And that will make sure that the purchased

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dependent variable of our data

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set will be included as factors.

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And to do so we use the factor
function inside which we enter

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first the dependent variable column
which we access from our data set.

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Then adding a dollar again.

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And then there we go purchased.

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So that's the first argument
of this factor function.

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And then we just need to specify
the two classes here with this argument

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levels that will be equal
to our two classes zero and one,

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which we have to enter in this C function
which will take as input zero and one.

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All right. So let me recap this.

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This line of code is optional
for the logistic regression model.

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The k nearest neighbor model
which will implement next.

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Then the SVM model the kernel SVM model
and the Naive Bayes model.

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And it is compulsory for the decision tree
classification model

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and for the random forest
classification model.

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All right.

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So I just want to include it here
so that we can apply this template

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to all the classification models
of this part tree.

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And for that
let's make the other required changes.

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So here when splitting the data

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set into training set and a test set,
we don't have to change anything.

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Same we can keep feature scaling.

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And then that's
where we will change some things.

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So first we will remove all this

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because that's
where we'll build our next classifier.

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And so we can add a and saying
create your classifier here okay.

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Then we can replace logistic regression
here by classifier.

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All right.

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To make it generic
then here very important.

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The logistic regression
model is the only model of this part three

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that returns first its predictions
in the form of probabilities.

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All the other models will return
directly 0 or 1.

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And therefore we're going to remove this
line.

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We're going to replace here
prop pred by y bread.

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And here in the predict function
we're going to keep classifier.

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Of course we're going to remove type
equals response.

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And we're going to keep new data equals
to set minus three.

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All right.

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And this line of code

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will be applicable to all the remaining
classification models of part three.

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We won't have to change anything here
okay.

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And then same for the confusion matrix.

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This is only for the logistic regression
model because it returns probabilities.

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And so to adapt this to a model
that returns only zero one,

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well of course
we need to remove larger than 0.5.

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Right.

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Because the widespread of our future
classification models will only contain

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zero one exactly as the dependent
variable column of the test set.

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Okay, great.

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And then finally in the last two sections
here visualizing the training set

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and test results.

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Same.

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We're going to remove the probabilities
here.

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Then cut this way grid.

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And then replace this proposed variable
by y grid.

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And then remove the type
equals response parameter so that.

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Now we don't have our predictions

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returned in the form of probabilities
but zeros and ones.

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And then of course we're going to make it
generic again by replacing logistic

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regression here by classifier okay.

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Excellent.

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So now let's copy this because
we're going to do this same down there

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for the visualization of the test result.

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So I'm going to replace these two lines
by what we just copied.

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There we go.

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And then replace logistic regression again
by classifier.

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And now congratulations
we have a generic template

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that we will be able to use
to build very efficiently.

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Our next classification
rules of board three.

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So let's save this.

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And now look forward
to building those next models with you.

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And until then enjoy machine learning.