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All right, my friends.

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Are you ready for the demo?

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I remind that this demo works
for any data set, you know,

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regardless of the number of features.

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And as long as they have,

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you know,
the features in the first columns

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and then the dependent variable
in the last column.

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And also assuming that any missing data
or categorical data

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was already taken care of
thanks to your data preprocessing toolkit.

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All right.

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So this is going to be very exciting
because it's really now that I'm going

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to show you the power of code templates
and how you can quickly

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and efficiently select
the best regression model.

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All right.

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So let's do this.

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Enough talking I'm
going to proceed to the demo.

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Now just resetting everything
because we're going to do something fun.

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We will actually use this run

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all option of the runtime
which will run all our cells at once.

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So that, you know,
we can really optimize the efficiency.

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But let's not forget to upload the data
set in each of the implementations.

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Otherwise,
this cell won't be able to execute.

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So we're going to upload it.

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Now it is connecting to runtime.

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And then second we should be able
to see the up the button.

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There we go.

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So let's click this upload button.

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And now on your machine
you're going to find the folder.

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You know the model selection folder.

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That's the whole machinery.
It is it folder.

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And that's

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this new model selection folder containing
you know that regression folder

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with all the good templates for regression
and the classification with all the good

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templates for classification.

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If you missed that folder
somehow, don't worry, it's

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worth given right before this tutorial.

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You know, in the article at the bottom
you had a zip folder attached

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which you could download on your machine

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and which contains exactly
the same as what I have here.

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All right.

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So now we're going to go to the regression
folder which contains

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all the implementations,
meaning all the code templates

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for each of your regression models,
both in ipynb format, which you can open

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with either Google Colab
or Jupyter Notebook, and in py format,

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which you can open with a classic Python
terminal or Spyder in Anaconda.

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So you have everything

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and you also have the data set,
you know, containing these four features.

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The temperature of the vacuum,
the ambient pressure and the humidity.

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And we predict the energy output.

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All right.

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So that's a very classic data set.

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Once again very generic.

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Trying to represent the other future
data sets you'll be working on.

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And well speaking of this data set that's
exactly what we have to select here.

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So we're going to click open
to upload the data set in our notebook.

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And there it is.

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And now as I told you
here in the implementation you only have

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to enter the name of your data set.

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And as far as we concerned
here for our demo.

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Well this data set is called data dot CSV.

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All right.

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So now we're going to quickly do exactly
the same for other implementations.

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Upload

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then data dot CSV then open

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and loading it's uploading it
and we'll have it in a second.

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And then we'll just replace the name of
the data set here by data CSV.

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So that's for polynomial regression.

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Now for support
vector regression will same upload data

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dot CSV open and loading it uploading it
in a second we should have it.

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There we go.

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Now we only replace this by data dot csv.

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Then for decision tree regression.

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There we go.

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Upload then data dot csv open
and we will have it in a second.

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Upload it in the notebook.

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And now replacing this by data dot CSV.

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And finally for random forest regression

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upload data CSV.

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Open.

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And now replacing this by data that is V.

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And now, my friends, we are finally ready

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to test each of our regression models

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and figure out in flashlight
which one is the best.

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Remember, the closer the R-squared
coefficient is to one, the better.

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Is your regression model.

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So in order to figure out
which is going to be the best

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model, we'll just take the one
with the highest R-squared.

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You know, with R-squared
that is the closest to one.

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All right.

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Are you ready.

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Let's do this
starting with multiple linear regression.

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So now we're simply
going to go to run time and then run.

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Oh and all the cells are now executing.

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And there you go.

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We end up with an R-squared coefficient of
oh point 93.

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Very good.

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Very good. One.

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As we can clearly see
you know the predictions are amazing.

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They're very close to the real results.

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So remember this
first column is the vector of predictions.

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And this second one
is the vector of real results.

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And that's why here we have an amazing
R-squared coefficient.