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All right.

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Great.

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So now they're all copies.

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Therefore we can modify them.

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Let me just show you once again how I
modified the original classification code.

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We made before to these new ones,
you know, more simplified ones,

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which will get you the accuracy
quickly and efficiently.

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So the data preprocessing phase
I kept exactly the same, including,

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you know, the feature scaling
applied to x ray, the next test.

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But here I put a template name
and I highlighted

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that you have to enter
the name of your data set here.

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So that's what we'll do I'll show you.

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But that's the only thing
that was changed.

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Indeed.

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We don't have to do much else
because this will automatically select

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all the features
and not your dependent variable.

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And this will automatically select
your dependent variable.

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And that's provided
of course you have in your data set.

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First
the features you know in the first columns

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and last the dependent variable
in the last column.

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Right. Make sure of this.

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What we're going to do now
works for any data sets, regardless

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of the number of features.

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As long as they have the features
in the first columns

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and the dependent variable
in the last column.

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Make sure to remember this.

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All right. And then all good here.

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If you want, you can change that from
oh point 25 to 0.2.

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But that's fine.

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Both values work
well then feature scaling.

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All right then
for each of the classification model,

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I kept, you know, the code to implement
and train it.

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And finally what I did in the last
cells is simply I removed,

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you know, the prints that displayed
the vector of predictions and the vector

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real results next to each other, because,

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you know, we don't really need it
for our model selection process.

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However, what I did
is that I kept this, of course,

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but in order to compute the confusion
matrix and the accuracy,

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I had to create that y print vector
containing all the predictions

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by calling to predict method apply
to x test from our classifier.

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And that's all I did.

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And I did the same
in all the different files.

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Right?

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K-nearest neighbors data preprocessing,
phase training and confusion matrix.

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Same support vector machine.

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Data preprocessing training and confusion
matrix.

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Then kernel SVM, same data preprocessing,
phase training and confusion matrix.

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Naive Bayes
data preprocessing, phase training

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and confusion
matrix and decision tree classification.

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Data preprocessing.

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Phase training, confusion matrix
and finally random forest

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data preprocessing,
phase training and confusion matrix C.

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So you have the exact same code templates
for each of the classification models

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we built together.

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The only thing that changed
is actually this cell,

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because this cell actually builds
and train the classification model.

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You want to try through this model
selection process.

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All right. Perfect.

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So now
we're getting very close to the demo.

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And so just to recap this demo works
for any data set.

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Regard list of the number of features.

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And as long as you have your features
in the first columns

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and your dependent variable
in the last column,

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and also as long as you don't have

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some special data preprocessing tools
to use on your data set.

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If you have any categorical variables
you know in strings

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or categorical variables
where you have to perform one hot encoding

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well, don't forget to use your data
preprocessing toolkit to preprocess

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the right way your data set,

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and then you can just deploy all your
classification code templates here.

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And that, my friends, is exactly
what I'm about to show you right now.

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So now the demo is going to start.

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Are you ready?

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Three. Two one go. All right.

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So I'm going to do this

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as efficiently as I can in order
to show you the power of code templates.

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So first step the first step is to upload
the data set inside the notebook.

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Right now it is connecting to runtime
to enable file browsing.

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Actually I'm going to do this for

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each of the models here because you know
it always takes a few seconds.

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So let's do it
this way to be efficient. Right.

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So I'm just
you know, loading all the files here.

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All right. Perfect.

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And everything.

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You know
every file is now connecting to a runtime.

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Now be careful.

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If you don't see the sample data here
you have to refresh.

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Otherwise you will have issues
uploading your data set.

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Good. Now it's good.

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So the next step we upload the data set.

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All right.

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So this is the model selection folder and
more precisely the classification folder.

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00:04:10,100 --> 00:04:11,866
But let me show you the path again

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I put this machine learning
model selection folder into my desktop.

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But make sure to find it on your machine
wherever it is.

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If you have not downloaded that already,

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make sure to download it
right before this tutorial.

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In the article you will find the link
at the bottom of the article.

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Then together we're going to go inside,
then inside classification

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and there we go.

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We select data dot csv,
then we click open.

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Then we press okay.

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And then what
we simply need to do inside this code

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template is just to put here
the name of the data set.

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So you just double click
this and then enter data that CSV

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or you know
the name of your future data set.

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All right. And that's it.

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That's all we have to do in each code
template.

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Only one thing to change so that we can
really call it a curved template.

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All right. Great.

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So now we're going to do the same
in each other implementation.

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So now k nearest neighbors.

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00:05:04,066 --> 00:05:06,900
Let's refresh this
because we need to see this. There we go.

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Then upload then data dot CSV then open.

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All right. Perfect okay.

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And then we replace here
the name by data dot CSV.

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Perfect.

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Then next one support
vector machine refresh upload.

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Then data dot CSV then open and perfect.

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We have the data set.

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Now we replace this by data
dot CSV and all good SVM is ready now.

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Kernel SVM refresh

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upload data

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dot CSV open okay

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replacing this by data dot CSV
or the name of your future data set.

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And there we go. Kernel SVM is ready.

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All right then Naive Bayes refresh

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upload data dot CSV open okay.

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And then replacing this by data dot CSV.