Highlight continue with our cause and this video, we will do the multiple linear regression model.
So multiple linear regression is a straightforward generalisation of single predictive models.
In a multiple linear regression model, the dependent variable is related to two or more independent
variables to perform on multiple linear regression analysis.
So the cycle library where we use so from the scale not linear scale model, feel the linear regression
clasper, formerly an ordinary at least square linear regression.
So as usual, we will load the library.
By typing in here.
And from Escalon Dot.
The new model.
Import linear regression and then.
Now we lose you to our model.
So is it right now moral equal?
The.
Near recession.
And then to fit the millennium model where you would fit function.
So, Al.
Modahl.
Don't fit.
We actually underscore a train come, why underscore train?
And then it this guy in the training phase with you, the data instructed fodder for this fire and at
this point we can build the model to make that prediction.
So it's very easy to predict my thoughts.
So in here, I ask you, why are those Brad Alam?
Aguer, al.
Model.
Don't predict.
And that's test.
And they told us how so I hope I don't make any mistake.
So usually, I scatterplot is you to determine whether or not there is a relationship between data,
however, as catalog can be used to analyze the performance of a linear model by reporting the actual
and predictive value on the two assets.
Is it possible to check how the data is arranged to help with the analysis?
Is it possible to trace the bisect the Cuarón?
That is the line of equation Wilko X theoretically on observations to rest on this slide, but we can
be satisfied that the data is closer to this line about how the data must be below the line and the
other half must be about the data.
So the boys that move away significantly from the life of these libraries and a possible outliners so
that Blossom's scatterplot using the library and to do so will pay out a lot.
Figure one.
Be out, they don't.
Subplot.
One, two, one.
Partido Scotter.
Why underscore?
That's why underscore bread and then potato.
Label with a.
Actual.
Values.
And then out the door.
Why?
Label.
With a.
That.
Values, and the last one is a dog title.
Is a.
New role network model.
And yet you hear the dog subplot.
One to one, I actually went to two and pay out a door, Scotter.
They lie under is called test.
Why underscore, right?
Be out the door, I.
Label is.
After.
Well, you played on.
Why label is.
Predict that.
Values.
They are they don't.
Title with a české.
Lord.
Lydia.
Gray chant model.
And then data show.
Very simple.
Now, that plot.
We got a result what?
In India, of course, the.
We got the neural network and ascalon.
I did not see the lie.
So let me pause here and I will come back in a moment.
So I did run, I was a secretary and as you say, there is a small line here, so and this one is smaller,
it's basically nearly the same.
So now that.
Some call for the so before that, we need to analyze a dirham, so analyzing the diagram is clear that
the current model shot across neural network model returned a better result.
The poor are better suited to the lie.
So the line here, they are divided equally in equal parts above and below and isolated by.
I feel so isolated.
If you're looking for this, we need to calculate our MSA for the linear regression model to do this
without you Escalon matrix where our function.
So let's try not.
So I think the before I did.
Go.
So is it important for Escalon, Dot, Matrix?
Import.
Main square, main square, not absolute.
And then we get you a Akko mean.
Square.
Mean squarer.
Why?
Has.
Come, why?
Brett.
And then bring.
Lenie.
Regression model.
And then pray.
And as.
Run the sale and we got zero point zero one five.
So zero point zero one five one five.
And we got before for a.
Where is it going?
The.
Here, zero point zero.
I'm sorry, I need you to copy this one so zero point zero six.
So zero point zero six compared with zero point zero one five.
So this one had a lower Mosquera.
This one does mean darker than your network model is better than the linear regression model.
So to get a better result, you train a neural network and you get a better result.
And I is in this video.
I hope you enjoy it and I hope you enjoyed our project.
And I hope you learn a lot from it.
So I will see you in another course with another project.
Thank you.