Original subtitles

I have to run.

And welcome in this new video.

In this video, we're going to see how to do some surprise prediction using a linear regression.

First, we're going to concatenate or extend and use tests to create a new variable in our dataset,

which is a prediction.

So.

We need to give a tipple of aura and the axis in which we want to do the concatenation, which is zero,

because we want to get by the role.

So for example, if a prince.

I printed in that a frame to her really better visualization, so we can see that we have had extreme

here and each test you now we need to create a new variable prediction in order to frame.

And in this venerable.

We put the prediction of each day for the.

It is not event, but to have a dataframe.

We know missing value.

It's better to add the train prediction, also to don't do some interference or issue in our data.

So as we can see, we have some prediction, and now we just need to print it to see if

we don't have, for example, only positive value or negative value.

And if their predictions are correct.

As we can see, we have some projection around minus two percent and plus two percent.

So.

We cannot say already that this prediction are true, but at least this prediction are realistic.