Prediction

Step 1

1 Prediction

Select the prediction dataset on this screen. Let’s review the prediction data used in this tutorial.

Step 2

2 Prediction

Using the prediction model that we created, we predict the withdrawal probability for each customer (by subscription route). In this tutorial, you will use sample data that you have prepared in advance as prediction data. Unlike the data for creating a prediction model (training), the variable of whether to withdraw the membership is not used.

Step 3

3 Prediction

Specify the prediction data 2_Churn(For Prediction).csv here.

Click [Select from Uploaded Data] and select the sample data from the data list on the [Samples] tab.

Step 4

4 Prediction

The prediction dataset preview is displayed. Predict “Withdraw or not (target)” displayed as “?” from information such as “Number of Views in the last three months”, and “Subscription Route”.

Click [Run Prediction]. Wait a while until the preview screen of the prediction result is displayed.

Step 5

5 Prediction

Please specify “Row Num” in [Add the following variables to the first column]. Click [Save Prediction Results], enter “File name” and click [Save].

Step 6

When the prediction is complete, the following screen is displayed and the prediction results are saved in the specified file.

6 Prediction

Predicted results are output in the following format (this format may vary depending on the option settings). For each customer, the predicted probability of continuation or withdrawal is calculated.

7 Prediction