Upsell

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select dataset you want to use to create your machine learning model. In this case we will select Upsell dataset to create binary classification analysis on additional purchases by customer data.

1. After selection, the demo dataset will automatically load into your account and the dataset view will open immediately.

4. Adjust your dataset options on the Settings tab. Click Columns tab to view the list of available columns with their corresponding data types. Explore dataset details on Summary tab.

5. To create a new model in the Graphite Note main menu, click on "Models"

6. You will get a list of available models. Click on "New Model" to create a new one.

7. Select the model type from our templates. In our case we will select "Binary Classification" by double clicking on its name.

8. Select the dataset you want to use to produce model. We will use "Demo-Upsell.csv".

9. Name your new model. We will call it "Binary Classification on Demo-Upsell".

10. Write a description of the model and select a tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.

11. Click "Create" to create your demo model environment.

12. To set up a Binary Classification model, firstly, you need to define "Target Feature". That is binary column from your dataset that you'd like to make predictions about. In case of Binary Classification on Upsell dataset target feature will be "Applied" column.

13. Click "Next" to get the list of model features that will be included in the model scenario. Your model relies upon each column (feature) to make accurate predictions. When training the model we will calculate which of the features are most important and behave as Key Drivers.

14. To start training your model click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.

15. Wait for few moments and Voilà! Your Binary Classification model is trained. Click on "Performance" tab to get model insights and view the Key Drivers.

16. Explore Binary Classification model by clicking on Impact Analysis and Training Results to get more insights on how model is trained.

17. If you want to take your model into action click on "Predict" tab in the main model menu.

18. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on the target column. In our case that is "Applied". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.

19. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!