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Naive Bayes is ready.

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So now let's move on to decision tree
classification.

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And there you go.

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So that happens when you have too many
sessions opened on the Google Colab.

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I left it on purpose because I'm sure
you will also encounter this situation

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and what to do in this situation. Well,
no worries at all.

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That's very simple since Google

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Colab actually allows only maximum
five sessions to run at the same time.

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Well, what we'll just do here
is that for decision tree

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classification and random forest,
because here that's the same.

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Well we'll just close them for now.

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All right.

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And we will reopen them
after we get the best accuracy

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from these five models okay.

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So we'll get the best from these five.

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Then we'll run the last two ones.

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Decision tree classification
and random forest classification.

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And we'll see which one wins.

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Which one is the big winner.

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All right.

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So now all are implementations already.

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So of course
next natural step now is to run

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all these cells to get all the accuracies
of these five models.

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All right
so let's start with logistic regression.

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Are you ready.

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Let's click run time and then run
and all the cells will be running.

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And we shouldn't get any error. Indeed.

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And we get wow.

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We start with a very good accuracy
because we get an accuracy

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close to 95% for logistic regression.

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All right.

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And indeed

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we only have four plus five equals
nine errors nine incorrect predictions.

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Well pretty good.

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So let's see what we get
with the next ones. You know.

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And it's really reassuring
that we get these high accuracies

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because we were doing predictions
for breast cancer.

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So we really want to,
you know, be accurate on predicting

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if patients have a benign
or malignant tumor.

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Okay.

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So let's hope that we can do
even better than this.

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All right. So I'm going to scroll back up.

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Oh no actually I'm going to leave
that here in case we forget.

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So 0.947.

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Now let's move on to k nearest neighbors.

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Let's click run time here then run all.

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And there we go my friends.

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We're about to get the next accuracy
which is exactly this same one.

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I just check that,
you know I put the right model here.

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But we have exactly the same one actually,
which you know, can totally happen

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because you just have to make nine
incorrect predictions.

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You know, two classification models
can make the same number of incorrect

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predictions, and therefore you will end up
with the exact same accuracy.

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So that's very interesting.

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Actually,
this is the first time I observe this.

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All right.

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So well let's still hope we can beat this
with our next classification models.

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So now with Support Vector
Machine we're going to click run time.

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And we're going to click Run All
to see that next accuracy we're getting.

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And all right interesting.

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This time we get a lower accuracy
but still a very very good one.

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And you know that makes me very excited
to see what kernel SVM is going to do.

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You know with a nonlinear kernel.

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Because indeed here we get ten incorrect
predictions as opposed to nine

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incorrect predictions before with logistic
regression and K-nearest neighbors.

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But here with SVM, it's still very,
very good.

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We get 94% accuracy.

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All right. And now let's try kernel SVM.

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I look forward to seeing what
we're going to get.

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So let's click run time.

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And let's click run all.

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And the accuracy is yes we beat it

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95% 95.3%.

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That's excellent.

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And that was actually expected kernel SVM.

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You know is really really good.

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You will get good results with this
because you know,

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we get flexibility on the curve
to catch the correct predictions.

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All right. So very very good.

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But we still have three
other classification models.

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Let's see what we're going to get
with them starting with Naive Bayes.

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All right.

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So let's click run time and then run all

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and and the next accuracy is okay.

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So like SVM ten incorrect predictions
resulting in an accuracy of 94%.

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All right.

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That's okay.

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And now
well we still have two more chances.

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One was decision tree classification

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and the other one
with random forest classification.

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So now what we're going to do
is we're going to click runtime

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here then manage sessions.

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Then we're going to terminate
all these sessions here.

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Because you know we're only allowed to run
maximum five sessions at the same time.

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So I terminated all of them.

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You can close it
now. And we still keep the accuracy.

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So that's totally fine right.

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We keep the accuracy everywhere here.

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So we can totally compare
with our last two.

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So let's do this.

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Let's open first you know
random forest classification because

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you know it gives them in that order.

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Well actually
that doesn't really matter here.

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But anyway let's open
decision tree classification now.

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All right.

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And here we go.

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We have our last two models
I can't wait to try them

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because I can't wait to see
who is going to be the big winner.

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And if we can beat even more
that best accuracy of 95.3%.

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All right.

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So next step is not to click runtime here.

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Because remember we haven't uploaded yet
the data set into the notebook.

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So no need for refresh here
I'll good upload

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then data dot CSV open.

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Then let's do quickly
the same for random forest classification.

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But first let's not forget to replace this
by data dot

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CSV are good now
random forest classification

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little folder here, then upload,

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then data dot csv open, then okay.

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And then let's replace this
by data dot CSV.

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All right.

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And now my friends
we're about to reveal the final podium.

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You know the three best models
with the three highest accuracies.

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So let's do this starting with decision
tree classification.

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So let's click run time here. Run all.

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And now there we go.

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Wow that's incredible.

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We actually beat the accuracy
I didn't I really didn't expect this.

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Usually decision tree
classification is not the winner.

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But here we have
a beautiful exception to the rule.

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Indeed we get a beautiful
accuracy of almost 96% 95.9.