# Non-Attendance-Based Events Notebooks

Non-Attended Events are events with a start and end date, but are more fluid in impact, such as [observances](https://www.predicthq.com/intelligence/data-enrichment/event-categories/observances), [public holidays](https://www.predicthq.com/intelligence/data-enrichment/event-categories/public-holidays) and [school holidays](https://www.predicthq.com/intelligence/data-enrichment/event-categories/school-holidays).

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**](https://github.com/predicthq/phq-data-science-docs/blob/master/unattended-events/part_1_data_engineering.ipynb) shows you how to call the Events API to extract the data to a DataFrame.
* [**Part 2: Data Exploration**](https://github.com/predicthq/phq-data-science-docs/blob/master/unattended-events/part_2_data_exploration.ipynb) is a guide to exploring PredictHQ's Non-Attended Events data for Data Science teams.
* [**Part 3: Feature Engineering**](https://github.com/predicthq/phq-data-science-docs/blob/master/unattended-events/part_3_feature_engineering.ipynb) provides examples of how to aggregate and extract features from the data.


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