1
00:00:00,366 --> 00:00:01,833
All right, so there we go.

2
00:00:01,833 --> 00:00:03,066
Let's go inside.

3
00:00:03,066 --> 00:00:06,533
And here is the name of the module SVM,
of course.

4
00:00:06,533 --> 00:00:12,000
And the class that we must now import
in order to build our SVM model.

5
00:00:12,466 --> 00:00:14,366
All right.
So let's start with this as usual.

6
00:00:14,366 --> 00:00:20,100
Let's get you know this path and let's
adapt it inside our code cell.

7
00:00:20,200 --> 00:00:21,166
You know how to do this.

8
00:00:21,166 --> 00:00:25,266
We need to start with from
so from the scikit learn library.

9
00:00:25,266 --> 00:00:28,266
And then the SVM module of the scikit
learn library,

10
00:00:28,366 --> 00:00:33,200
we will import the SVC class okay.

11
00:00:33,566 --> 00:00:34,633
So that's the first step.

12
00:00:34,633 --> 00:00:37,333
And then the next natural step
you know it by heart.

13
00:00:37,333 --> 00:00:40,100
It is of course
to create an object of this class.

14
00:00:40,100 --> 00:00:43,100
And we need to keep here
the name classifier.

15
00:00:43,266 --> 00:00:47,400
Because in order to not have anything
to change afterwards, the classifier

16
00:00:47,400 --> 00:00:50,533
which will be nothing else,
then the SVM model itself.

17
00:00:50,900 --> 00:00:54,033
And so to create such an object
we need to call this class.

18
00:00:54,033 --> 00:00:57,900
Well I can just type it as SVC
and then add some parenthesis.

19
00:00:58,200 --> 00:01:02,866
And now the question is what do we need
to input here as parameters.

20
00:01:03,300 --> 00:01:05,800
Well I hope you have the reflex to think.

21
00:01:05,800 --> 00:01:10,266
Of course we need to input the kernel
because as I've just explained, this

22
00:01:10,466 --> 00:01:15,000
SVC class allows us to build kernel
SVM models with either a linear kernel

23
00:01:15,000 --> 00:01:18,400
which is the classic SVM model,
or a nonlinear kernel.

24
00:01:18,400 --> 00:01:21,400
So here in our parameters
we'll have to specify that we want

25
00:01:21,533 --> 00:01:25,300
a linear kernel because we're starting
with the classic SVM model.

26
00:01:25,300 --> 00:01:26,733
And then in the next section you know

27
00:01:26,733 --> 00:01:30,900
on kernel SVM we'll choose a nonlinear
kernel like RBF or polynomial.

28
00:01:30,966 --> 00:01:31,800
We'll see.

29
00:01:31,800 --> 00:01:34,200
But there you go.
That's our first argument.

30
00:01:34,200 --> 00:01:37,533
And indeed when we have a look
at the documentation, we can see

31
00:01:37,533 --> 00:01:40,733
that indeed
the second parameter here is kernel.

32
00:01:40,966 --> 00:01:42,300
And the default value is RBF.

33
00:01:42,300 --> 00:01:45,800
So we'll have to specify
that we want linear to start with.

34
00:01:46,300 --> 00:01:50,433
And then as you can see you have many many
other arguments you know parameters.

35
00:01:50,700 --> 00:01:53,366
And you can check out the description
for each of them.

36
00:01:53,366 --> 00:01:56,666
But no worries
we won't change their default value.

37
00:01:56,666 --> 00:01:58,033
We'll just keep their default value.

38
00:01:58,033 --> 00:02:00,133
Like for example,
the regularization parameter

39
00:02:00,133 --> 00:02:02,133
for which we'll keep the default
value of one.

40
00:02:02,133 --> 00:02:02,666
That's fine.

41
00:02:02,666 --> 00:02:05,100
You'll see in the visualization
that there is not much

42
00:02:05,100 --> 00:02:08,100
we can do in order to improve the model,
or to avoid overfitting.

43
00:02:08,366 --> 00:02:09,300
So there we go.

44
00:02:09,300 --> 00:02:15,466
The only parameter that we'll take is this
one kernel equals not RBF but linear.

45
00:02:15,633 --> 00:02:17,366
All right. So let's do this.

46
00:02:17,366 --> 00:02:20,333
Kernel equals in quotes

47
00:02:21,400 --> 00:02:22,666
linear.

48
00:02:22,666 --> 00:02:23,566
All right.

49
00:02:23,566 --> 00:02:27,800
And then I know I've just said that we
won't have to input any other parameters.

50
00:02:27,800 --> 00:02:29,933
But let's add anyway you know this one.

51
00:02:29,933 --> 00:02:33,900
The random state parameter
and set it equal to zero.

52
00:02:34,033 --> 00:02:38,433
Just to make sure that we get
the same result displayed on our notebook.

53
00:02:38,433 --> 00:02:42,200
You know, because there are some random
factors when we built this SVM model.

54
00:02:42,200 --> 00:02:44,133
And therefore for teaching purposes, it's

55
00:02:44,133 --> 00:02:47,900
better if we all have the same results
displayed in our notebook.

56
00:02:48,066 --> 00:02:48,800
All right.

57
00:02:48,800 --> 00:02:50,666
And that's it. Congratulations.

58
00:02:50,666 --> 00:02:54,966
That builds the SVM model,
the classic one with a linear kernel.

59
00:02:55,233 --> 00:02:57,666
And now you know how to finish this.

60
00:02:57,666 --> 00:03:01,166
Of course, our last step here
is to take our classify data,

61
00:03:01,500 --> 00:03:04,500
from which
we're going to call the fit method

62
00:03:04,766 --> 00:03:09,000
to train our SVM classifier
on the training set.

63
00:03:09,000 --> 00:03:11,366
And we have to input that here
in two parts.

64
00:03:11,366 --> 00:03:15,600
First, the matrix of features
of the training set, which is X train,

65
00:03:15,800 --> 00:03:20,166
and then the dependent variable vector
of the training set, which is why train.

66
00:03:20,700 --> 00:03:22,333
All right.
So you know this perfectly well.

67
00:03:22,333 --> 00:03:24,966
Now it's like your native language right.

68
00:03:24,966 --> 00:03:26,133
Okay I hope I'm right.

69
00:03:26,133 --> 00:03:27,933
But there you go. Congratulations.

70
00:03:27,933 --> 00:03:29,733
This implementation is now over.

71
00:03:29,733 --> 00:03:33,033
Thanks to this very efficient code
template.

72
00:03:33,333 --> 00:03:38,500
And so now well we'll just run everything
and observe the final results Indian.

73
00:03:38,800 --> 00:03:39,166
All right.

74
00:03:39,166 --> 00:03:39,900
So let's do this.

75
00:03:39,900 --> 00:03:44,033
Let's not forget to
you know import the data set here.

76
00:03:44,033 --> 00:03:45,800
You know upload it in the notebook.

77
00:03:45,800 --> 00:03:47,966
So click this folder here.

78
00:03:47,966 --> 00:03:51,000
Then you will have to wait a few seconds
because your notebook

79
00:03:51,300 --> 00:03:55,666
will be connecting
to a runtime to enable file browsing.

80
00:03:56,166 --> 00:04:00,133
And in a second we should see
there we go the upload button.

81
00:04:00,400 --> 00:04:02,033
So we're going to click that.

82
00:04:02,033 --> 00:04:03,600
And well you know that's the data set.

83
00:04:03,600 --> 00:04:05,833
But I'll show you the whole path again.

84
00:04:05,833 --> 00:04:07,466
So you find your machine learning

85
00:04:07,466 --> 00:04:08,466
is that folder codes

86
00:04:08,466 --> 00:04:11,566
and data sets which you could download
in the previous tutorial in the article.

87
00:04:11,833 --> 00:04:15,633
Then you're going to go to part
three classification, then to section

88
00:04:15,633 --> 00:04:19,633
16 support Vector Machine
and Python and Social Network.

89
00:04:19,633 --> 00:04:24,966
At dot csv you click open and this will
upload the data set inside the notebook.

90
00:04:24,966 --> 00:04:25,633
There we go.

91
00:04:25,633 --> 00:04:29,666
And now now you can run all the cells
by clicking runtime here.

92
00:04:29,666 --> 00:04:32,433
And then are you ready. Run.

93
00:04:32,433 --> 00:04:36,766
and this will run all the cell
and perfect.

94
00:04:36,800 --> 00:04:41,166
Now we have our SVM model
with a linear kernel with all the default

95
00:04:41,166 --> 00:04:44,533
values of the parameters here
except this one linear kernel.

96
00:04:45,000 --> 00:04:45,733
And so there we go.

97
00:04:45,733 --> 00:04:48,300
Then we have the rest of the cells.

98
00:04:48,300 --> 00:04:51,233
When we predict the new result. Indeed
we get the right prediction.

99
00:04:51,233 --> 00:04:54,633
Remember at zero for that particular
first customer of the test

100
00:04:54,633 --> 00:04:58,500
set of age
30 and estimated salary $87,000.

101
00:04:58,800 --> 00:05:02,966
Indeed, it is predicted not to buy the SUV
as it is the case in reality.

102
00:05:03,166 --> 00:05:06,666
Okay, then when we predict
the test results once again,

103
00:05:06,666 --> 00:05:09,900
we see that we have many correct
predictions, except some of them.

104
00:05:09,900 --> 00:05:11,766
This one, this one.

105
00:05:11,766 --> 00:05:14,533
And you know this one.

106
00:05:14,533 --> 00:05:15,933
Okay, but it looks very good.

107
00:05:15,933 --> 00:05:19,533
However, what we're mostly interested in
and we're about to get it right now

108
00:05:19,800 --> 00:05:23,366
is to see the accuracy on the test set,
you know, the number

109
00:05:23,366 --> 00:05:26,366
of correct predictions or if you want
the number of incorrect predictions.

110
00:05:26,666 --> 00:05:28,900
Are you ready?
We're about to get it right now.

111
00:05:28,900 --> 00:05:33,133
Write that code cell, print the confusion
matrix and display the accuracy.

112
00:05:33,133 --> 00:05:35,166
And wow okay interesting.

113
00:05:35,166 --> 00:05:38,766
So it beat actually
the logistic regression model right.

114
00:05:38,766 --> 00:05:42,600
Remember the logistic regression
model had an accuracy of 89%.

115
00:05:43,000 --> 00:05:46,366
And here the SVM slightly beat it by 1%.

116
00:05:46,566 --> 00:05:49,433
However
it doesn't beat the turkey in and model

117
00:05:49,433 --> 00:05:53,133
which remember had an accuracy of 93%.