This tutorial provides you with the prerequisites and assets required for all other Adobe Experience Platform Data Science Workspace tutorials. Once complete, the following schemas and datasets will be available to you and your organization.
The following tutorial uses a custom Luma purchase propensity model. Before proceeding, download the required assets zip folder. This folder contains:
You can use your own schema and data for any of the tutorials. However, the demo model provided in the assets does not work unless it’s provided the proper configuration files and requirements file. This demo propensity model was designed to work with Luma web data.
In order to create a model, you must have a dataset in Platform which is used to train and score your model. The following video tutorial from the Data Science Workspace course walks you through creating the Luma schema and ingesting the data used by the purchase propensity model.
In order to run the recipe builder notebook or use the API to train and score a model, you need to specify the dataset(s) and schema(s) that are used for training/scoring. The following video tutorial walks you through setting up the training, scoring, and scoring results datasets, as well as, the scoring results schema used in the Luma purchase propensity model.
By following this tutorial, you have successfully created the required schemas and datasets for the Luma propensity model. You’re now ready to continue to the next tutorial and create the model using the recipe builder notebook tutorial.
Additionally, you can explore the data using the provided Exploratory Data Analysis (EDA) notebook. This notebook can be used to help understand patterns in the Luma data, check data sanity, and summarizes the relevant data for the predictive propensity model. To learn more about Exploratory Data Analysis, visit the EDA documenation.