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So here is the data which we are going to use to build our regression tree.

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Let me tell you something about this data.

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In the columns, we have all the variables.

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So for this data, we have 18 columns, which means we have 18 different variables.

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The last column of this data is collection.

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This is a dependent, but even that is this is the variable that we want to predict values for.

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And we'll be using the other 17 variables, which we will also call as predictive variables.

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To the value of collection.

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So as you can guess, from looking at the variable names, this is a date of movies.

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This is a simulated data.

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That is this is not true.

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They dolf movies.

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And this dataset, we have variables like how much?

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Was the marketing expense during making of the movie?

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How much was the production expenses?

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How many multiplexes were covered?

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What does the budget of the movie and so on?

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And we have this data for five hundred six different movies.

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So in this data table, we have 506 observations.

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The observations are in the rules.

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So if you look at the number of rows, we have 507, which includes de ADIRU.

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So using this date of 506 movies in which we already have the data of these predictors, 17 variables

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and the data of how much those movies actually collected, we will be creating a model that will help

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us predictive value of collection, given the values of other 17 variables.

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That is, if you are creating a new movie and you have the values of all these 17 variables, you can

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predict how much will your movie collect at the box office?

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Most of the variables in the database are quantitative, but there are two variables which have quantitatively

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dolto.

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This 3D available column has only.

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Yes, no time values.

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And this Johna column has four categories.

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That is thriller, drama, comedy and action.

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So these two are categorical variables and order.

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All of that are quantitative variables.

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In the next video, we will see how to import this data into our software so that we can use it to create

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our model.