Non-Attended Events - Data Science Guides
Welcome to Non-Attended Events' Data Science documentation. This content is provided as a resource for Data Science teams to help get them up and running quickly.
We provide guides to using our API and data with common Data Science tools and libraries in Python. The articles include a link to Jupyter Notebooks that you can download and run. The guides include code samples and instructions for performing common tasks.
This How to Series consist of the following three Jupyter notebooks, allows you to quickly extract the data (Part 1), explore the data (Part 2) and experiment with different aggregations (Part 3):
Part 1: Data Engineering shows you how to call the Events API to extract the data to a DataFrame.
Part 2: Data Exploration is a guide to exploring PredictHQ's Non-Attended Events data for Data Science teams.
Part 3: Feature Engineering provides examples of how to aggregate and extract features from the data.