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This example takes a dataset of weather details to train the model and predicts the possibility of playing golf. It uses
C45 algorithm for predictions.
The flow has three steps:
- Input data
In this example, we input the sample observed data as a CSV file. It contains the temperature, humidity and the possibility of playing golf. Using this dataset, the model will be created and trained to make predictions.
Run the Prediction Sample
1. Once you open Golf-DecisionTree.xml, click on Run to run the project
2. Click OK on the Data Feed Provider window (there is no need to change options in this example)
3. The prediction will be shown in the Parameters window
Open sample project
The “PredictiveAnalytics” project can be found in the attachment.
Then open Golf-DecisionTree.xml
1. Once you open Golf-DecisionTree.xml, click on Debug to start running the project
2. Click on Next Step to go through the flow step by step.
3. Once it reaches the DataSource node,
you can see the properties; data source type, data file path, etc.
4. Click Next Step again and when you reach the train node,
5. Click on data in the Parameters window
and then click on Data viewer on the right side of Parameters window
and you will see the input data of the model.
6. Click Next Step and when you reach the predict node,
you can see the properties
7. Click on Prediction Input in the properties
to see the set of data from which we want to elicit the prediction.
8. Click Next Step to end the flow and you will see the prediction as ‘no’ in the Parameters window.
Download the project
Use the attachment at the end of the page to download the sample project.