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Okay my friends.

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So we just covered the data
preprocessing part and we covered it

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in a flash light
thanks to our data preprocessing template.

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And now we're about to really build
and train

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the simple linear regression
model on the training set of course.

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Right.

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So remember that we splitted that data set
into the training set and the test set.

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The training set will be used to train
R simple linear regression model.

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And the test set
will be used to evaluate it.

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So now we have to start with the training.

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And so there we go.

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Let's create a new code cell.

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And let's implement
the very simple linear regression model.

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All right.

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So the first thing we'll have to do
is to import the right class

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with which we're going to build this
simple linear regression model.

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Because indeed we could either implement
it from scratch or we could use libraries.

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And of course we're going to use libraries
because I want to provide

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a very clear coat template
which allows you to build

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any simple linear
regression models in a flashlight.

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And this library that we're going to use
is scikit learn,

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from which we're going to get access
to a certain module called linear model.

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And from this module we're going to call
a certain class called linear regression.

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And our simple linear regression model

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which we're going to build
will be exactly an instance of this class.

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Right. It will be an object of this class.

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All right. So let's do this.

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Let's start by importing from,
you know, from the scikit

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learn library
which has the code name sklearn.

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So from sklearn then as we said we're
going to get access to a certain module.

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So we have to add a dot here
and which is linear model.

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And from this linear module of the scikit

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learn library
well we're going to import the linear

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regress session class exactly
this one linear regression.

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And then as we said this simple linear
regression model which we're going to

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build will be an instance or an object
of this linear regression class.

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And therefore here
we have to create a new variable

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which will be exactly this instance
of the linear regression class.

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And we're going to call this object.

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We can call it by any name
but we're going to call it regressor

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because indeed we are doing right now
regression.

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Right.

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I remind a big difference
between regression and classification.

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Regression is when you have to predict

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a continuous real value, like a salary,
as we're about to do.

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And classification is when you have to
predict a category or, you know, a class,

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which we will do in part
three classification or all right.

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So regressor that's a new variable
which at the same time

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will become the object of the linear
regression class.

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And you can exactly see this object
as the linear regression model itself.

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You know, I remind that
a class allows you to implement

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a couple of instructions
to build something.

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And well, this linear

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regression class built exactly
this simple linear regression model.

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All right.

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So you have to see this regressor object
as exactly this model.

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Right. So regressor.

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And then to create an object of a class.

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Well there's nothing more simple.

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You just have to call the class itself
linear regression.

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And then add some parentheses
and that's it.

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Usually there are some parameters inside
that we can implement.

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But here you don't have to enter anything.

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This will just create a simple linear
regression model.

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And it is so simple that usually

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we don't have to play too much
with the parameters.

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All right.

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And that line of code directly creates
the simple linear regression model.

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And that's only the building part.

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You know we actually get a model.

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But now of course
we have to train it on the training set.

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And therefore we have to connect it
in some way to the training set

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and the action or,
you know, the function that connects it

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in order to train
it is called the fit function.