Store Item Demand

Create a Regression model on Demo Store Item Demand dataset

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 Store Item Demand dataset to create Regression analysis on sales across store locations 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 Settings tab. Click Columns tab to view 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 list of available models. Click on "New Model" to create new one.

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

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

9. Name your new model. We will call it "Regression on Demo-Store-Item-Demand".

10. Write a description of the model and select tag. If you want to, 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 Regression Model, firstly, you will need to define "Target Feature". That is the numeric column from your dataset that you'd like to make predictions about. In case of Regression on Store Item Demand dataset target feature is "Sales" column.

13. Click "Next" to get the list of model features that will be included in model scenario. The model relies on each column (feature) to make accurate predictions. When training a model, we will calculate which of the features are most important and behave as the 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 Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.

16. Explore Regression model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how model is trained and set up.

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 target column. In our case that is "Sales". 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 the outcomes. The more you use and retrain your model, the smarter it becomes!

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