1
00:00:00,300 --> 00:00:00,700
All right.

2
00:00:00,700 --> 00:00:03,500
So for us we
don't have a training set of tested here.

3
00:00:03,500 --> 00:00:06,200
So I'm going to remove this line of code.

4
00:00:06,200 --> 00:00:09,166
I'm going to you know remove that as well.

5
00:00:09,166 --> 00:00:13,066
All the index selections
and here as well.

6
00:00:13,066 --> 00:00:15,133
And I'm just keeping x.

7
00:00:15,133 --> 00:00:16,500
And then let's see what we have to do.

8
00:00:16,500 --> 00:00:16,766
All right.

9
00:00:16,766 --> 00:00:19,766
So first that's already more clean.

10
00:00:19,800 --> 00:00:21,766
Now with what we have here.

11
00:00:21,766 --> 00:00:25,500
You know these three lines of code,
we are indeed applying feature

12
00:00:25,500 --> 00:00:29,700
scaling to the matrix of features X,
which is one of the things we have to do.

13
00:00:29,700 --> 00:00:31,033
Indeed. That's good.

14
00:00:31,033 --> 00:00:34,800
However, as we've just explained,
we also have to scale

15
00:00:34,866 --> 00:00:38,700
the dependent variable vector,
the salaries, and of course,

16
00:00:38,700 --> 00:00:41,700
and that's the important thing
you had to understand and figure out.

17
00:00:41,800 --> 00:00:45,633
We're not going to use the same
standard scaler object

18
00:00:45,800 --> 00:00:49,833
on both the matrix of features X
and the dependent variable vector y.

19
00:00:50,066 --> 00:00:50,866
Why is that?

20
00:00:50,866 --> 00:00:54,800
That's because,
you know, when you fit your object and C

21
00:00:54,966 --> 00:00:57,866
on your data, well,
it is going to compute

22
00:00:57,866 --> 00:01:01,100
the mean and the standard deviation
of that same variable.

23
00:01:01,266 --> 00:01:04,333
And therefore since of course
we don't have the same mean and same

24
00:01:04,333 --> 00:01:08,666
standard deviation for our levels here
and our salaries, well, obviously

25
00:01:08,700 --> 00:01:13,766
we have to create two standard scaler
object, one that will be fitted to X

26
00:01:13,766 --> 00:01:16,833
in order to compute the mean and standard
deviation of the position levels,

27
00:01:17,033 --> 00:01:19,833
and one that will be fitted to Y to indeed

28
00:01:19,833 --> 00:01:22,866
compute the mean and the standard
deviation of the salaries.

29
00:01:23,033 --> 00:01:23,366
All right.

30
00:01:23,366 --> 00:01:26,400
So that was the only important thing
to understand.

31
00:01:26,500 --> 00:01:31,200
And therefore here I'm
actually going to call this subject c x

32
00:01:31,533 --> 00:01:34,900
in order to say that it's the scalar
of the matrix of features x.

33
00:01:35,233 --> 00:01:38,233
And here's x as well.

34
00:01:38,333 --> 00:01:41,766
And now I'm going to create a new standard
scalar object,

35
00:01:42,000 --> 00:01:45,900
the one that will be used on our dependent
variable vector y.

36
00:01:46,166 --> 00:01:52,233
And therefore here I'm replacing x
by we're going to call it's c

37
00:01:52,233 --> 00:01:55,500
y so that it's perfectly clear
that this is a scalar of x.

38
00:01:55,500 --> 00:01:57,033
And this is a scalar of y.

39
00:01:57,033 --> 00:02:02,566
And now we're going to copy this paste
that right below.

40
00:02:02,833 --> 00:02:06,266
And here we'll just have
three little replacements to do

41
00:02:06,333 --> 00:02:09,333
which are first replacing x here by y.

42
00:02:09,433 --> 00:02:15,133
Then x here by c y and x here by y.

43
00:02:15,433 --> 00:02:18,433
So that now we are ready to scale both

44
00:02:18,433 --> 00:02:22,333
our matrix of features x
and our dependent variable vector y.

45
00:02:22,466 --> 00:02:25,466
Let's check it out. Let's run this cell.

46
00:02:25,500 --> 00:02:28,900
And now we're going to add
two new code cells

47
00:02:29,066 --> 00:02:32,866
to print the new x and the new y
and see what they have become.

48
00:02:33,000 --> 00:02:35,533
All right so two new code cells.

49
00:02:35,533 --> 00:02:39,300
And let's start here with a print of X.

50
00:02:39,800 --> 00:02:40,600
Good.

51
00:02:40,600 --> 00:02:43,600
And then a print of Y.

52
00:02:43,866 --> 00:02:48,000
And now let's run these two cells here
starting with print x.

53
00:02:48,533 --> 00:02:51,833
And indeed
well we have some perfectly scaled values

54
00:02:51,833 --> 00:02:55,233
of the position levels going from -1.5.

55
00:02:55,233 --> 00:02:57,600
And that corresponds
of course to position level number

56
00:02:57,600 --> 00:03:01,933
one and 1.56 which corresponds to position
level number ten.

57
00:03:02,466 --> 00:03:02,800
All right.

58
00:03:02,800 --> 00:03:04,800
So now let's print y.

59
00:03:04,800 --> 00:03:07,033
And this will be interesting.

60
00:03:07,033 --> 00:03:11,000
Here the values will go from minus 0.7
which corresponds

61
00:03:11,000 --> 00:03:15,433
of course
to the salary of 45,000 for year and 2.64

62
00:03:15,433 --> 00:03:17,833
which corresponds
to the $1 million salary.

63
00:03:17,833 --> 00:03:19,166
And as you can see here, this time

64
00:03:19,166 --> 00:03:23,000
the values are in the range from minus one
to plus three.

65
00:03:23,000 --> 00:03:27,400
That's why in the data preprocessing part
I told you that usually standardization

66
00:03:27,600 --> 00:03:30,800
transform your values
between minus three and plus three.