 # Target Variable

#### 1. Definition of Target Variable

When creating a model, the value you want to predict is called a target variable. It is also called a dependent variable or a reaction variable, but most intuitively you can think of it as the y value at y = f(x). In other words, it is a variable that the model targets for analysis. Target variables can be various values such as bank deposit results, movie review ratings, and graduate acceptance probability.

#### 2. Importance of Target Variable

The categorical classification model and the continuous value classification model have target variables. It is important in these models to have a clear purpose, because it is essential to have a data set that is composed of independent input variables that are used to calculate prediction, and a target variable which is the prediction itself. For example, suppose you have quit smoking success rate as a target variable. You can predict the target variable of the model by calculating variables such as age, occupation, height, weight, and area of residence as explanatory variables.

#### 3. Prediction with CLICK AI

Here is an example of predicting the probability of passing graduate school to help you understand better.
[There are a total of eight variables in the data set: GRE score, TOEFL score, university ranking, statement of purpose, letter of recommendation, undergraduate GPA, research experience, and admission probability.] [Among the eight variables, the value the user wants to predict is the probability of admission, so the probability of admission is selected as the target variable.]

✓ After creating the model, you can fill in the predictor variables (X values) with the values you want to put and check the predicted value of the target variable.