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Hello and welcome to this art tutorial.

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So in this tutorial, we're not going
to make any machine learning model.

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We're just going to make a template.

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And this will be our regression template.

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So it's a template
that is going to be very useful

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for the next non-linear regression models
we are going to make.

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And also you can use this template
to create a regression

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model for your specific problem.

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Because as you can see,
you will only have a few things to change

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so that you will build your machine
learning model very efficiently.

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So let's make this template
and to make it we're going to go

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from what we did in polynomial regression
and generalized the whole thing.

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So I'm going to take everything from here
up to the end copy.

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And I'm going to paste that
in the template.

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And now let's make this template okay.

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So first of all let's start
with the beginning.

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So the beginning
was about pre-processing the data.

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So actually
there is nothing to change here.

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We will keep of course these two lines
because we will work on the position

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salary CSV file and build our non-linear
regression models on it.

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And for the same reason as before,
we will need to split the data

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set into a training set and a test set,
because this is a small data set

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and we will leave
feature scaling as comments here, since

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most of the libraries don't require us
to apply feature scaling manually.

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So all good for the pre-processing phase.

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And this will be the first part
of our regression template.

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Great. Now let's move on to the next part.

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So the next part
is about creating the model,

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creating the model,
and fitting it to the data set.

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So first of all, in the previous tutorial
we built two regression models

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the linear regression model
and the polynomial regression model.

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And that was just

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to compare the two results and to show you
that the polynomial regression model

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was much more appropriate to our problem
because it was non-linear regression

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model as opposed to the linear
regression model, which is a linear model.

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So we only did that
so that you can understand very clearly

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the distinction between linear models
and non-linear models.

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But now that it's
getting pretty clear in our mind,

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we are going to get rid of this section
here about linear regression.

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Because now we get the idea
and we will only focus

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on the new non-linear regression
models themselves

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without comparing them to the linear
regression model.

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But to keep the graphic results
of the linear regression model in mind,

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keep in mind that it was a straight line,
not fitting very well to data set,

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and therefore was not much appropriate
to our data set.

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Where
the data is non-linearly distributed.

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Okay,
so we're going to get rid of this done.

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Now we only have this code section here
that creates the non-linear regression

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model itself.

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So we're going to get rid of this

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because this corresponds
to the polynomial regression.

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And to generalize
we will just add a come here

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to specify
that this is where we have to create our

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regressor right here okay.

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And here we can also replace
polynomial regression

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by the regression model.

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And we are all good with this section
okay.

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Now let's move on to the next one.

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The next one is about
visualizing the results.

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So for the same reason
as what we did in this section,

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to fit the regression model
to the data set,

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we will get rid of the graphic results
of the linear regression model

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because we have them in mind,

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and we only want to focus on our future
non-linear regression models.

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So let's get rid of this.

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And now we only have the graphic results

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of our future non-linear regression
model okay.

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And the great thing about this template

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is that in this section
we won't need to change anything.

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Well, almost anything
because we're going to replace

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polynomial regression
by regression model.

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And we are going to change

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this poly rag regressor here by regressor.

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Because you know before we called it
poly rag because we also had our linear

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regressor linear.

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And so we needed to make the distinction.

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But now since we got rid of the linear
regression model,

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we will call our future non-linear
regression model regressor here.

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And that's why we input two regressor here
and the predict function.

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So basically when we get to this section,
when we build our future

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non-linear regression model,

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the only thing that we will need to change
is actually the title here.

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Basically
we don't have anything to change here.

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And that's the great thing about this
template.

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However, of course,
if you're using this template on your data

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set in your problem, well, the only thing
that you will need to change

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are the names here.

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The independent variable, and also
the name of the dependent variable here.

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That is, you'll have to replace salary
by the name of your dependent variable.

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But that's all okay.

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And we are finally getting
to the last section of this code template,

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which is about predicting the final result
to get our final verdict.

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Okay, so same reason as before.

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We're going to get rid of the linear
regression part for this.

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So here we go.

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Good bye.

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And now we are only left
with the polynomial regression prediction

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okay great.

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And so now we just need
to change something here.

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Well first it's the name of the regressor.

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Because before the name of our polynomial
regression regressor was called poly rag.

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And now
since we won't make the distinction

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between a linear regression model
and our non-linear regression model,

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well,
our regressor will be called regressor.

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So here regressor and here you know
for polynomial regression we need to input

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the polynomial features of our data frame
that we created in our data frame.

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Well we won't need to do this
for the next non-linear models

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because this is strictly specific
to the polynomial regression model

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where you know
we need to add those polynomial features.

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And so we will get rid of this
and we'll get something very simple.

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It's actually this line
that will perfectly predict

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the salary of this 6.5 position level.