1
00:00:00,566 --> 00:00:01,000
All right.

2
00:00:01,000 --> 00:00:04,533
And now let's do the same
for our dependent variable vector.

3
00:00:04,866 --> 00:00:06,900
And you know this will be exactly
the same.

4
00:00:06,900 --> 00:00:08,666
We'll just have to change
one little thing.

5
00:00:08,666 --> 00:00:12,733
So I'm going to copy this
and paste it right here.

6
00:00:13,100 --> 00:00:16,166
And now according to you based on
what I've just explained,

7
00:00:16,666 --> 00:00:21,233
what do we have to change here in order
to get the dependent variable vector,

8
00:00:21,433 --> 00:00:24,566
which is most of the time
in in our data set.

9
00:00:24,566 --> 00:00:27,133
Indeed, the last column.

10
00:00:27,133 --> 00:00:27,433
Okay.

11
00:00:27,433 --> 00:00:30,000
So let's see first okay.

12
00:00:30,000 --> 00:00:31,200
We have to take our data set

13
00:00:31,200 --> 00:00:34,200
because we want to extract
this text column from our data set.

14
00:00:34,500 --> 00:00:38,933
Then we have to use I look to collect
indexes of the rows and columns we want.

15
00:00:39,300 --> 00:00:42,766
Then we indeed
want to take all the rows of the data set,

16
00:00:42,766 --> 00:00:47,100
because we want to take all the purchase
decisions of these customers,

17
00:00:47,100 --> 00:00:50,566
whether or not they decided to purchase
yes or no the product.

18
00:00:50,733 --> 00:00:52,500
So okay, we want all the rows.

19
00:00:52,500 --> 00:00:55,633
But then which comes
do we want to get here?

20
00:00:55,633 --> 00:00:58,266
Well we only want to get the last column.

21
00:00:58,266 --> 00:01:01,166
So according to you what is index.

22
00:01:01,166 --> 00:01:04,166
We need to input here in order
to get only the last column.

23
00:01:04,700 --> 00:01:08,033
Well this time
since we only want to get one column,

24
00:01:08,033 --> 00:01:09,900
we definitely don't want to get a range.

25
00:01:09,900 --> 00:01:12,866
And therefore
I'm going to remove the range here.

26
00:01:12,866 --> 00:01:14,700
And then what are we left here?

27
00:01:14,700 --> 00:01:16,600
We're left here with minus one.

28
00:01:16,600 --> 00:01:20,800
And as I've told you, minus one is exactly
the index of the last column.

29
00:01:21,000 --> 00:01:22,000
So there we go.

30
00:01:22,000 --> 00:01:26,166
That's exactly what we need to create this
dependent variable vector.

31
00:01:26,666 --> 00:01:28,700
And thus this line of code is done.

32
00:01:28,700 --> 00:01:30,000
Congratulations.

33
00:01:30,000 --> 00:01:32,433
Now you know how to import data set.

34
00:01:32,433 --> 00:01:36,066
Create a matrix of features
and create a dependent variable vector.

35
00:01:36,300 --> 00:01:37,500
And the cherry on the cake

36
00:01:37,500 --> 00:01:40,766
is that any time we want to create these
for your data sets,

37
00:01:40,966 --> 00:01:44,500
you won't have anything to change,
because this will automatically take

38
00:01:44,700 --> 00:01:47,033
all the first columns
for the matrix of features

39
00:01:47,033 --> 00:01:49,900
and the last column
for the dependent variable vector.

40
00:01:49,900 --> 00:01:50,266
All right.

41
00:01:50,266 --> 00:01:54,066
So now I'm going to show you indeed that x
and y will be well created.

42
00:01:54,200 --> 00:01:57,700
And in order to do this
we're going to add a new code cell here

43
00:01:57,900 --> 00:02:00,433
inside to which we're just going to print.

44
00:02:00,433 --> 00:02:03,933
So that's the famous print function
which allows you to print anything,

45
00:02:03,933 --> 00:02:08,700
whether it is a text or, you know,
an array like x or vector like y.

46
00:02:08,966 --> 00:02:11,066
So we're going to first print x

47
00:02:11,066 --> 00:02:15,633
and then I'm going to add a new code
cell here where we're going to print y.

48
00:02:15,633 --> 00:02:19,000
And this is just to show you that indeed x
and y will

49
00:02:19,000 --> 00:02:22,200
well be created with this code okay.

50
00:02:22,266 --> 00:02:24,800
So let's do this.
Now time for the fun part.

51
00:02:24,800 --> 00:02:27,000
We're going to execute all the cells here.

52
00:02:27,000 --> 00:02:30,000
Because you know so far
we've just written the implementations.

53
00:02:30,000 --> 00:02:33,500
But we have to run the cells
in order to build all this.

54
00:02:33,700 --> 00:02:37,566
So let's first run this code
cell importing the libraries.

55
00:02:38,300 --> 00:02:39,600
All right. So import it.

56
00:02:39,600 --> 00:02:40,800
As you can see if I click here.

57
00:02:40,800 --> 00:02:43,800
Yes this one here means it is executed.

58
00:02:44,133 --> 00:02:45,600
Now time to run the second one.

59
00:02:45,600 --> 00:02:48,666
But before running this
we have to do something very important.

60
00:02:48,900 --> 00:02:52,266
It is to upload this data set here in CSV

61
00:02:52,266 --> 00:02:55,400
format inside our Google Colab notebook.

62
00:02:55,400 --> 00:02:57,933
And to do this
you just need to click files here.

63
00:02:57,933 --> 00:03:00,933
You know this little folder, then upload.

64
00:03:01,333 --> 00:03:05,066
Then you're going to go to
this whole machine learning

65
00:03:05,066 --> 00:03:09,366
is that folder containing all the codes
and data set and which was provided to you

66
00:03:09,366 --> 00:03:10,500
in the first section.

67
00:03:10,500 --> 00:03:12,866
And I will give it to you again
in every sections

68
00:03:12,866 --> 00:03:15,066
that you make sure not to miss it.

69
00:03:15,066 --> 00:03:19,066
And inside this folder you're going to go
now to import one data preprocessing.

70
00:03:19,200 --> 00:03:22,900
In order to get indeed that data CSV

71
00:03:22,900 --> 00:03:26,700
file containing the data set
we are importing right now.

72
00:03:26,700 --> 00:03:31,733
So open and now the data set will be
indeed in Google Colab your notebook.

73
00:03:31,933 --> 00:03:35,200
And so now we can run this cell
to import it.

74
00:03:35,200 --> 00:03:37,200
And there we go. It is already import it.

75
00:03:37,200 --> 00:03:38,833
You know it is executed.

76
00:03:38,833 --> 00:03:41,666
So now we're going to execute this cell
in order

77
00:03:41,666 --> 00:03:45,333
to print the matrix of features X,
just to check that indeed

78
00:03:45,333 --> 00:03:48,633
we get all the first columns
inside this matrix.

79
00:03:49,133 --> 00:03:52,833
And indeed
well let's check the data set once again.

80
00:03:53,100 --> 00:03:56,766
Remember the first column is meaning
the features we wanted to get into.

81
00:03:56,766 --> 00:04:02,233
This matrix X are first country,
second age, and third to salary.

82
00:04:02,233 --> 00:04:03,500
These are the three columns.

83
00:04:03,500 --> 00:04:07,166
And indeed inside X
we have first the country column

84
00:04:07,166 --> 00:04:10,166
with all the countries of these customers
their age,

85
00:04:10,366 --> 00:04:14,133
and in the third column their salary
or their estimated salary.

86
00:04:14,666 --> 00:04:15,666
So that's perfect.

87
00:04:15,666 --> 00:04:19,366
We get indeed the matrix of features
X containing all the features

88
00:04:19,366 --> 00:04:22,366
or also called the independent variables.

89
00:04:22,466 --> 00:04:22,833
All right.

90
00:04:22,833 --> 00:04:26,300
And now let's run this cell to print y
the dependent variable vector.

91
00:04:26,300 --> 00:04:30,666
And indeed it gets the dependent variable
vector containing all the decisions

92
00:04:30,800 --> 00:04:33,966
whether or not the customers
purchased the product.

93
00:04:33,966 --> 00:04:35,866
Right. We can check. No. Yes. No no

94
00:04:37,300 --> 00:04:38,333
no. Yes.

95
00:04:38,333 --> 00:04:40,633
No no okay. So that's in the same order.

96
00:04:40,633 --> 00:04:41,433
That's perfect.

97
00:04:41,433 --> 00:04:42,866
We now have our data

98
00:04:42,866 --> 00:04:46,333
set our matrix of features X
and our dependent variable vector y.

99
00:04:46,633 --> 00:04:50,300
And lastly let me remind
why we had to create these two entities.

100
00:04:50,633 --> 00:04:53,766
That's because the way
we're going to build our future machine

101
00:04:53,766 --> 00:04:58,900
learning models expects exactly
these two entities in their inputs.

102
00:04:59,266 --> 00:05:01,866
You know, we will use some classes
to build these models.

103
00:05:01,866 --> 00:05:05,200
And these classes
don't expect the data set as a whole,

104
00:05:05,300 --> 00:05:08,233
but these two separate entities,
and that's

105
00:05:08,233 --> 00:05:11,533
the only reason why we had to create
these two separate entities.

106
00:05:11,533 --> 00:05:15,600
So now you know, and therefore
congratulations, not only you improve

107
00:05:15,600 --> 00:05:18,800
your knowledge of machine learning,
but also you now know how to import

108
00:05:18,800 --> 00:05:22,500
a data set and create a matrix,
a feature and a dependent variable vector.

109
00:05:23,033 --> 00:05:26,133
So now we're going to proceed
to the next step which is a new tool

110
00:05:26,166 --> 00:05:27,566
which I'm going to teach you.

111
00:05:27,566 --> 00:05:30,133
And that is taking care of missing data.

112
00:05:30,133 --> 00:05:33,033
Indeed, as you can see the data
set contains some missing data.

113
00:05:33,033 --> 00:05:35,966
Right here. You can see
this is an empty cell.

114
00:05:35,966 --> 00:05:39,066
So I will teach you
exactly how to handle that case,

115
00:05:39,300 --> 00:05:42,100
which happens
most of the time in data sets.

116
00:05:42,100 --> 00:05:43,900
So let's do this in a next tutorial.

117
00:05:43,900 --> 00:05:45,833
And until then enjoy machine learning.