Mall Customers
Create General Segmentation on Demo Mall Customers dataset
1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".
2. Select the dataset you want to use to create your machine learning model. In this case, we will select "Mall Customers dataset", to create General Segmentation analysis on customer engagement data.
3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns, with their corresponding data types. Explore the dataset details on Summary tab.
5. To create a new model in the Graphite Note main menu, click on "Models"
6. You will get list of available models. Click on the "New Model" to create a new one.
7. Select the model type from our templates. In our case, we will select "General Segmentation" by double clicking on its name.
8. Select the dataset you want to use to produce model. We will use "Demo-Mall-Customers.csv"
9. Name your new model. We will call it "General Segmentation on Demo-Mall-Customers".
10. Write a description of the model and select a tag. If you want to, you can also create new tag from the pop-up "Tags" window that will appear on the screen.
11. Click "Create" to create your demo model environment.
12. To set up your General Segmentation model, firstly, you need to define "Feature" columns. That is numeric column (or columns) from your dataset on which segmentation would be based. In the case of General Segmentation on Mall Customers dataset, the numeric feature will be "Age", Annual Income" and "Spending Score" columns.
13. To start training the model click "Run Scenario". This will train your model based on the uploaded dataset.
14. Wait for few moments and Voilà! Your General Segmentation model is trained. Click on "Results" tab to get model insights and explore segmentation clusters.
15. Navigate over different tabs to get insights from high level "Cluster Summary" to "By Cluster" charts and tables.
16. Use "Cluster Visualisations" to view the scatter plot visualisations of cluster members.
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