1
00:00:00,600 --> 00:00:04,600
So this is the dataset that we are going to use to build that classification.

2
00:00:06,050 --> 00:00:11,550
The data set is exactly similar to the one that we used for building out regression tree.

3
00:00:12,510 --> 00:00:13,830
These are the same variables.

4
00:00:14,370 --> 00:00:17,400
Only thing is that in the end.

5
00:00:19,020 --> 00:00:25,800
Last thing we were predicting the value of collection to, we had the information about all these variables

6
00:00:25,890 --> 00:00:30,250
and we were trying to predict the value of collection in this dataset.

7
00:00:31,020 --> 00:00:32,220
We already know that.

8
00:00:32,220 --> 00:00:32,760
How much?

9
00:00:34,080 --> 00:00:37,020
A movie has collected at the box office.

10
00:00:38,350 --> 00:00:45,070
Now we want to predict whether that particular movie is going to win a star like Oscar or not.

11
00:00:46,020 --> 00:00:53,310
So this particular award, will any of these movies win or Nortman besides the data?

12
00:00:53,350 --> 00:00:54,640
And this lady was.

13
00:00:55,890 --> 00:01:01,200
Again, this data is for, I wondered, six movies for these 506 movies.

14
00:01:01,320 --> 00:01:09,510
We have the data whether they've won or whether they did not win the static Oscar using this data.

15
00:01:10,470 --> 00:01:12,090
We will train our model.

16
00:01:14,100 --> 00:01:15,800
We will spread the data into two parts.

17
00:01:16,080 --> 00:01:17,690
One part will be used to pay non-moral.

18
00:01:18,090 --> 00:01:21,030
The other part will be used to check its performance.

19
00:01:23,280 --> 00:01:30,120
And once we have created this model, we can use that model to predict whether any particular movie

20
00:01:30,210 --> 00:01:37,530
is going to win a static Oscar or not by putting the value of all these other variables into our model.