So here is the data which we are going to use to build our regression tree.
Let me tell you something about this data.
In the columns, we have all the variables.
So for this data, we have 18 columns, which means we have 18 different variables.
The last column of this data is collection.
This is a dependent, but even that is this is the variable that we want to predict values for.
And we'll be using the other 17 variables, which we will also call as predictive variables.
To the value of collection.
So as you can guess, from looking at the variable names, this is a date of movies.
This is a simulated data.
That is this is not true.
They dolf movies.
And this dataset, we have variables like how much?
Was the marketing expense during making of the movie?
How much was the production expenses?
How many multiplexes were covered?
What does the budget of the movie and so on?
And we have this data for five hundred six different movies.
So in this data table, we have 506 observations.
The observations are in the rules.
So if you look at the number of rows, we have 507, which includes de ADIRU.
So using this date of 506 movies in which we already have the data of these predictors, 17 variables
and the data of how much those movies actually collected, we will be creating a model that will help
us predictive value of collection, given the values of other 17 variables.
That is, if you are creating a new movie and you have the values of all these 17 variables, you can
predict how much will your movie collect at the box office?
Most of the variables in the database are quantitative, but there are two variables which have quantitatively
dolto.
This 3D available column has only.
Yes, no time values.
And this Johna column has four categories.
That is thriller, drama, comedy and action.
So these two are categorical variables and order.
All of that are quantitative variables.
In the next video, we will see how to import this data into our software so that we can use it to create
our model.