Data Science Notebooks
Explore our Jupyter Notebooks to making getting started with PredictHQ data easier.
Demand intelligence is a new and evolving field. We’ve created a number of Data Science Jupyter Notebooks to help you get started with PredictHQ’s intelligent event data. You’ll find guides to using our API with common Data Science tools, libraries in Python, and code samples.
Demand Forecasting Notebook
Step-by-step guide on how to use intelligent event data in demand forecasting models.
Attendance-Based Events Notebooks
Attended Events are scheduled to occur at a specific location and usually depend on attendance, such as conferences, expos, concerts, festivals, performing-arts, sports and community.
Non-Attendance-Based Events Notebooks
Non-Attendance-Based Events are events with a start and end date, but are more fluid and distributed in impact, such as observances or school holidays.
Severe Weather Event Notebooks
Severe weather is any dangerous meteorological phenomenon with the potential to cause damage, serious social disruption, or loss of human life.
Academic Events Notebooks
Academic Events are captured from an individual higher education institute’s academic calendar. They outline the general undergraduate activities, for example instruction period, break, exams, graduation, social, etc.
Live TV Events Notebooks
Live TV Events covers live broadcast sports games with a large number of people watching at a particular time across different counties across the United States.
Feature Engineering Notebook
A how-to guide with details on how to create machine learning features using PredictHQ's Features API.
Beam Notebook
Designed to provide you with the context you need to get started with the Beam API and use it effectively.
Working with Venues Notebook
Guide to exploring PredictHQ’s venue information.
All our Data Science Notebooks can be found in our GitHub repo:
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