Now, let's revisit our observations from your duty.
We have already corrected missing values.
We have already corrected old Naiades.
Now, let's change and transform the crime rate variable.
Before starting, let's block the scatterplot of prime rib and thighs.
So we will write.
On this joint.
Lord.
And then record will, right?
Exequatur to crime rate.
I'd like to place.
And data is the.
If you are reopening the existing non book, you have to first import the libraries.
Let's import seabourne number and find pundits.
We'll run this again.
Now, let's be executed.
This.
Now, imagine the relations seems to be like this.
The good looks like logarithmic curve.
And we need to transform this.
Good to have a linear relationship between X and Y.
The one way I can think of is to take all of this.
If we take Lolo forward expedition, which is primary.
We can get the kind of linear relationship of Bambrick with what wavery but.
Since most of the values are near zero.
And logoff zero is not defined.
And then Staats minus infinity.
So to remove this, we will add a value of one to our crime rate.
So let us transform this video.
Will it be if the same day?
Equate it to.
And we don't know, since Lock is a function from the library.
Right.
And we don't log and record.
We'll mention one less thing if I remember to put this one.
Since low, both zero tends towards minus infinity and no love one is zero.
So it's better to start from low of one.
If we execute this.
Now, let's again loto joint law between crime data and price.
Copy this.
Come on.
Next year, this.
You can see now the relationship is looking more linear.
Earlier we were getting a girl like Lord.
But now we are getting the linear, somewhat linear plot.
I suggest you to.
Try some other transformation on your own.
If you find some better transformation, go ahead with that transformation.
But always tried to make relationship between X and Y very well, a linear relationship.
And one more thing to note, serious.
You can see there are no visible outliers or here.
There is no point, which seems like all office space or although this graph.
So as I mentioned earlier, this is another way to treat outlets.
We have just transform a logo with the relationship between our X and Y video, but now the outlets
are already treating.
Esther discussed in our theory lecture.
We have four variables for distances.
This one is to be street best for.
All these variables are conveying the same information.
Which is the distance from the employment hub.
So let's just create a average variable of this for distances to convey the same information in a single
variable.
OK, we're done you very well.
All right.
The year and then speed records.
We'll mention of you.
But even then, it is the best.
And we wanted this really want to be evidence values of this one, this will be seen this fall.
Right.
This one.
It's Boobies three.
Does this fall and winter divided by four?
Again, the average really good this.
No.
Let's run the duty of this.
We want to f.
B.
But despite.
We executed this.
You can see them being well, wouldn't you, a table, which is evidence distance.
Now, the FTC on average spends.
We can also take a minimum of this four or maximum of this for the census to represent the same information.
But for now, we have taken on the average variable, you can try those variations on your own.
So maybe people in the city looked for the nearest employments and that instead of that evidence, the
sense of the employment center.
So depending on your business and depending on your business knowledge, try to evaluate all such variations
and choose the appropriate dummy variable.
Or more, we are on the losing average of this, for instance.
We will be using this evidence this distance very.
But.
And we will be moving this forward with EBOs because average distance is, in a sense, representing
all of this for the variables.
So let's build this photo every once distance, one distance built since three.
And this is for the late.
Come on, it's dealt with, right?
Ben?
And we'll mention.
But call them names, so be it.
And screw that record.
Right.
But it's done this now.
That's done that.
You do again.
Whether we actually going the Liberty Bell or not, you can say you dispense.
One is not here.
We have been ordered to do so.
Well ahead in the nation's history, this fourth as well.
That's the video game.
You can see a little distance with able on one.
And be on the bridge distance, no data.
Now, if you remember, we had four observations from our unity.
And one of them was still believe the bus terminal variable since it was undertaking one single value.
And it was not providing any useful information or mobile.
Let's dilute that variable, Esman.
Let's look at the first five goals of our data.
You can see, but Steadman is not a part of or does it?