Smart Predict is an Augmented Analytics feature in SAP Analytics Cloud that helps you generate predictions about future events, values, and trends.
The predictive experience in SAP Analytics Cloud is simple. Smart Predict guides you step by step to create a predictive model based on historical data. The resulting model can be used to make trusted future predictions, providing you with advanced insights to guide decision making.
Smart Predict accelerates the prediction and recommendation creation process by focusing on business outcomes.
Before using Smart Predict for the first time, it really helps to understand a few basic concepts of predictive modeling. So, here they are!
The different types of predictive scenarios
There are currently 3 types of predictive scenarios available in Smart Predict:
Defining the business problem or business question you want to address will help you choose the right type of predictive scenario.
Classification Scenario: If you’re trying to determine the likelihood of whether something will happen, you’re dealing with a classification scenario.
Ex: You want to predict membership of categories such as Yes/NO, Customer is likely churn or not, replacing intervals within short or longer for manufacturing process, Binary (0 or 1).
Regression Scenario: If you’re trying to predict a numerical value and explore the key drivers behind it, you’re dealing with a regression scenario.
Ex: Predict the price of an imported product based on projected transport charges and tax duties.
Time Series Scenario: If you’re trying to forecast a future numerical value based on fluctuations over time, seasons, and other internal and external variables, you’re dealing with a time series scenario.
Predictive Scenario based on Regression:
Step 1. Loading the data
• Before we load the dataset into SAC, we cut out a few records from the original dataset. We will use this to apply our model later. I cut out 20 records of each wines from the dataset for red and white wines, each to use later. Now I have 4 datasets as follows.
A. 1580 red wines for training
B. 20 red wines for prediction
C. 4879 white wines for training
D. 20 white wines for prediction
At first create a folder in your files to save all the files at one place.
Inside your newly created folder, create a new dataset by clicking ‘+’ icon → Dataset .
Now you will be asked how you would like to begin – load data from a local file or from a data source. Since we have data in csv files, click on local data source. Select your source file. Load all 4 datasets. Your folder should look like the last screenshot.
Step 2. Training the model
In the Predictive Scenarios page click on ‘+’ to add a scenario.
Select a scenario that best suits to your analytic dataset.
Fill the details and click on OK.
Select the Input Dataset as show below.
SAC will take a while to train with the data set, and identify the best model for this problem statement. Then you now have results of your model as shown below.
Step 3. Understanding the results
Step 4. Applying the model
Repeat the same steps to know the quality of the Red wine.
Create a model based on the Output Dataset of the Predictive Scenario.
Click on Menu → Create→ Model→ Click on Get data from datasource and in the Acquire Data select Dataset.
Or You Can directly create a story on Dataset.
Go to Create → Click on Story