Building a regression model in SAP Analytics Cloud Smart Predict

After completing this lesson, you will be able to:

After completing this lesson, you will be able to:

  • Identify the key steps required to build a regression model

Datasets for a regression model

Input dataset recap for building regression scenarios

The training data source contains the past observations that will be used to generate the regression model. In this dataset, the values of the target variable, which is the variable corresponding to your business issue, are known.

By analyzing the training dataset Smart Predict generates a regression model that explains and predicts the target variable, based on the variables identified as influencers.

Once the regression model is trained it can be applied on an application dataset. This will generate the predicted values of the target in the output dataset.

A regression model screen with the Settings pane open on the right side of the screen. Key settings: Training data source, target, and exclude as influencers.

Build and train a regression model


One of the most critical aspects for a bank to offer a home loan is to have a home appraisal to confirm the validity of the sales price of the property for the bank.

You have been asked to build and train a regression model that will estimate the price of a home based on several factors including: square feet of home, square feet of lot, number of bedrooms, number of bathrooms, location, etc. You have been provided data for the following variables:

Variables for which you have data for the regression model

What skills will you develop in this practice exercise?

In this practice exercise, you will be able to perform the following tasks in SAP Analytics Cloud:

  1. Build a regression model
  2. Train the regression model
  3. Verify the output from the regression model

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