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So now let's move on to the next code
template.

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Polynomial regression.

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Well here that's the same,
you know the same data preprocessing phase

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with first importing the libraries,
then importing the data sets

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where you only have to enter
the name of your data set here,

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and then splitting the data
set into the training set and test set.

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Then of course, we train the polynomial
regression model on the training set.

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So that's exactly
like what we did in this part two.

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You know when we built it
you recognize degree equals four.

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You know that's exactly the same code.

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Then we predict some test results
just to compare our predictions

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and the real results.

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And finally
we will evaluate the model performance.

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And I will reveal very soon
how to do that.

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Okay.

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So that's for polynomial regression.

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Once again very generic.

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You just have to enter here
the name of your data set.

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And then this code
template is ready to be deployed.

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All right then support vector regression.

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So here that's the same.

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First the data preprocessing phase
where we import the libraries.

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Then we import the data set.

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But then remember we have to reshape

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our dependent variable vector y
because we have two features.

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Kill it. Right.
Because we were doing regression.

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So the dependent variable vector
has continuous numerical values.

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And therefore for SVR we need to scale
the dependent variable vector.

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That's exactly the same as what we saw
together when building the SVR model.

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Then I added this.

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Of course, in order to split the data
set into the training set and test set

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so that we can indeed evaluate

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the performance of SVR
and compare it to the other models,

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then of course, we have feature
scaling compulsory for the SVR

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with remember our two scalars, one

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for the matrix of features
and one for the dependent variable vector.

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Then we train, of course
the SVR model on the training set.

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You know this very well.
We did it together.

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Then we predicted test results
just to compare and have an idea

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of how good are the predictions
of new observations.

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And finally we will evaluate
the model performance with r squared.

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No worries.

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We'll get to that very very soon.

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So that's for the SVR
then for decision tree regression.

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Well exactly the same.

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You know the data preprocessing phase
first with no feature scaling right.

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Remember we don't need feature
scaling for decision trees.

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So once again we only have to change
the name of the data set here.

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Then we split the data
set into the training set and test it.

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Then we train the decision tree regression
model on the training set,

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exactly the same as we did
in our implementation.

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When we built it together.

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Then we predict the test result

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in order to compare our predictions
to the real result in Y test.

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And that's in order to have a first idea
of the performance.

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And then of course, we will evaluate
the model performance with R squared.

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And finally we have the exact same data
preprocessing

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phase where you only have to enter
the name of your data set here.

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And then we train the random forest
regression model on the training

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set with the exact same implementation
as how we did it together.

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Then we predict the test result in order
to get a first idea of the performance.

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And finally
we evaluate the model performance.

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All right, so as I told you,
you have purely generic code templates

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which you can deploy
for any of your future data sets

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as long as they have first of features
and last, the dependent variable.

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And as long as they don't
have missing data or categorical data,

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in which case it's still fine.

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You can use your data
preprocessing toolkit, but there you go.

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You have this code template, and now I'm
going to show you how to evaluate

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your regression
models using the R-squared coefficient.

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All right. So let's start with r squared.

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You know that

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final sale in each of the implementations
evaluating the model performance.

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Let's see how we're going to do this.

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Well as I also want to train you on
how to be independent in machine learning.

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We're going to pretend once again
that I actually have no idea on how

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to evaluate the model performance
of regression models, and therefore that

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I have to go to the documentation online
to figure out how to do it.

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All right.

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I'm just training you to be independent
and quickly find an information

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whenever you need it.