Binary Classification Model on Demo Lead Scoring dataset.
Get an overview of Lead Scoring demo dataset and how it can be used to create your new Graphite Note model in this video:
Or follow instructions below to get step by step guidance on how to use Lead Scoring demo 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 the machine learning model. In this case, we will select "Lead Scoring dataset" to create binary classification analysis on potential customer interactions 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 Columns tab to view the list of available columns with their corresponding data types. Then explore the dataset details on Summary tab.
5. Click "Models"
6. You will get 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 dataset you want to use to produce the model. We will use "Demo-Lead-Scoring.csv."
9. Name your new model. We will call it "Binary Classification on Demo-Lead-Scoring".
10. Write the description of the model and select a tag. If you want to, you can also create a 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 the "Target Feature". That is a binary column from your dataset that you'd like to make predictions about. In the case of Binary Classification on a Lead Scoring dataset, the target feature will be the "Converted" column.
13. Click "Next" to get the list of model features that will be included in the model scenario. The model relies upon each column (feature) to make accurate predictions. When training the model, it will calculate which of the features are most important and behave as Key Drivers.
14. To start training 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 the "Performance" tab to get model insights and to view the Key Drivers.
16. Explore the "Binary Classification" model by clicking on the Impact Analysis and Training Results to get more insights on how the model is trained.
17. If you want to turn 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 "Converted". Keep in mind, dataset you are uploading needs to contain same feature columns as your model.
19. Use your model often to predict future behaviour, and to learn which key drivers are impacting outcomes. The more you use and retrain your model, the smarter it becomes!
